First device, second device, first network node, second network node and methods performed thereby, for handling data pertaining to reference signals

Abstract

A method performed by a first device (131). The method is for handling data pertaining to Reference Signals (RSs). The first device (131) operates in a communications system (100). The first device (131) obtains (902), from a first network node (111) operating in the communications system (100), one or more configurations indicating one or more patterns of collection of data pertaining to overlaid RSs. The first device (131) then performs (904) an action to support the collection of the data based on the obtained one or more configurations.

Description

[0001] FIRST DEVICE, SECOND DEVICE, FIRST NETWORK NODE, SECOND NETWORK NODE AND METHODS PERFORMED THEREBY, FOR HANDLING DATA PERTAINING TO REFERENCE SIGNALS

[0002] TECHNICAL FIELD

[0003] The present disclosure relates generally to a first device and methods performed thereby for handling data pertaining to Reference Signals (RSs). The present disclosure also relates generally to a second device and methods performed thereby for handling data pertaining to the RSs. The present disclosure further relates generally to a first network node and methods performed thereby for handling data pertaining to the RSs. The present disclosure also relates generally to a second network node and methods performed thereby for handling data pertaining to the RSs. The present disclosure also relates generally to a computer program and computer-readable storage medium, having stored thereon the computer program to carry out these methods.

[0004] BACKGROUND

[0005] Wireless devices within a wireless communications network may be e.g., User Equipments (UEs), stations (STAs), mobile terminals, wireless terminals, terminals, and / or Mobile Stations (MS). Wireless devices are enabled to communicate wirelessly in a cellular communications network or wireless communication network, sometimes also referred to as a cellular radio system, cellular system, or cellular network. The communication may be performed e.g., between two wireless devices, between a wireless device and a regular telephone and / or between a wireless device and a server via a Radio Access Network (RAN) and possibly one or more core networks, comprised within the wireless communications network. Wireless devices may further be referred to as mobile telephones, cellular telephones, laptops, or tablets with wireless capability, just to mention some further examples. The wireless devices in the present context may be, for example, portable, pocket-storable, hand-held, computer-comprised, or vehicle-mounted mobile devices, enabled to communicate voice and / or data, via the RAN, with another entity, such as another terminal or a server.

[0006] The wireless communications network covers a geographical area which may be divided into cell areas, each cell area being served by a network node, which may be an access node such as a radio network node, radio node or a base station (BS), e.g., a Radio Base Station (RBS), which sometimes may be referred to as e.g., gNB, evolved Node B (“eNB”), “eNodeB”, “NodeB”, “B node”, Transmission Point (TP), or BTS (Base Transceiver Station), depending on the technology and terminology used. The base stations may be of different classes such as e.g., Wide Area Base Stations, Medium Range Base Stations, Local Area Base Stations, HomeBase Stations, pico base stations, etc..., based on transmission power and thereby also cell size. A cell is the geographical area where radio coverage is provided by the base station or radio node at a base station site, or radio node site, respectively. One base station, situated on the base station site, may serve one or several cells. Further, each base station may support one or several communication technologies. The base stations may communicate over the air interface operating on radio frequencies with the terminals within range of the base stations. The wireless communications network may also comprise network nodes which may serve receiving nodes, such as wireless devices, with serving beams. In 3rd Generation Partnership Project (3GPP) Long Term Evolution (LTE), base stations, which may be referred to as eNodeBs or even eNBs, may be directly connected to one or more core networks. In the context of this disclosure, the expression Downlink (DL) may be used for the transmission path from the base station to the wireless device. The expression Uplink (UL) may be used for the transmission path in the opposite direction i.e., from the wireless device to the base station.

[0007] The standardization organization 3GPP is currently in the process of specifying a New Radio Interface called NR or 5G-UTRA, as well as a Fifth Generation (5G) Packet Core Network (CN), which may be referred to as Next Generation (NG) Core Network, abbreviated as NG-CN, NGC, 5G CN or 5G Core (5GC). NG may be understood to refer to the interface / reference point between the Radio Access Network (RAN) and the CN in 5G / NR. In a 5G System (5GS), a radio base station in NR may be referred to as a gNB or 5G Node B. An NR UE may be referred to as an nUE.

[0008] Beam management

[0009] Beam management procedure

[0010] In high frequency range (FR2), multiple Radio Frequency (RF) beams may be used to transmit and receive signals at a gNB and a UE. For each DL beam from a gNB, there may be typically an associated best UE Receive (Rx) beam for receiving signals from the DL beam. The DL beam and the associated UE Rx beam may be understood to form a beam pair. The beam pair may be identified through a so-called beam management process in NR.

[0011] A DL beam may be, typically, identified by an associated DL reference signal (RS) transmitted in the beam, either periodically, semi-persistently, or aperiodically. The DL RS for the purpose may be a Synchronization Signal (SS) and Physical Broadcast Channel (PBCH) block (SSB) or a Channel State Information RS (CSI-RS). By measuring all the DL RSs, the UE may determine and report to the gNB the best DL beam to use for DL transmissions. The gNB may then transmit a burst of DL-RS using the reported best DL beam to let the UE evaluate candidate UE RX beams.

[0012] Although not explicitly stated in the NR specification, beam management may be understood to have been divided into three procedures, schematically illustrated in Figure 1, which is a schematic diagram illustrating a non-limiting example of a beam managementprocedure:

[0013] P-1 : The purpose may be understood to be to find a coarse direction for the UE using wide gNB TX beam covering the whole angular sector.

[0014] P-2: The purpose may be understood to be to refine the gNB TX beam by doing a new beam search around the coarse direction found in P1.

[0015] P-3: May be used for UE that may have analog beamforming to let the UE find a suitable UE RX beam.

[0016] P-1 may be expected to utilize beams with rather large beamwidths and where the beam reference signals may be transmitted periodically and may be shared between all UEs of the cell. Typically, reference signals to use for P-1 may be periodic CSI-RS or SSB. The UE may then report the N best beams to the gNB and their corresponding Reference Signal Received Power (RSRP) values.

[0017] P-2 may be expected to use aperiodic / or semi-persistent CSI-RS transmitted in narrow beams around the coarse direction found in P-1.

[0018] P-3 may be expected to use aperiodic or semi-persistent CSI-RSs repeatedly transmitted in one narrow gNB beam. One alternative way may be to let the UE determine a suitable UE RX beam based on the periodic SSB transmission. Since each SSB may be understood to consist of four Orthogonal frequency division multiplexing (OFDM) symbols, a maximum of four UE RX beams may be evaluated during each SSB burst transmission. One benefit with using SSB instead of CSI-RS may be understood to be that no extra overhead of CSI-RS transmission may be needed.

[0019] Codebook-based precoding

[0020] Multi-antenna techniques may significantly increase the data rates and reliability of a wireless communication system. The performance may be improved if both the transmitter and the receiver are equipped with multiple antennas, which may result in a multiple-input multipleoutput (MIMO) communication channel. Such systems and / or related techniques may be commonly referred to as MIMO systems.

[0021] A core component of NR may be understood to be the support of MIMO related techniques. NR may be understood to support up to 8-layer spatial multiplexing for up to 32 transmit antenna ports at the gNB with channel dependent precoding. Figure 2 is a schematic diagram illustrating a non-limiting example of data transmission with spatial multiplexing, where the information carrying symbol vectors = [s1,s2, ...,sr]rmay be first multiplied, or precoded, by a precoding matrix W G cNrXrbefore being sent over / VTantenna ports. Each symbol in s may be associated to a data layer and r may be understood to be the number of data layers or rank, which may be understood to be a property of the wireless channel between the transmitter and the receiver. W may be understood to serve to beamform each data layer towards the UE such that signal to interference plus noise ratio (SINR) may be maximized and cross layerinterference may be minimized at the UE receiver. Spatial multiplexing may be achieved since multiple symbols may be transmitted simultaneously in a same time and frequency resource element (RE).

[0022] The received Afe x 1 signal vector x at the UE equipped with Afe receive antennas may be expressed as:

[0023] x = HWs + e

[0024] where H G CNRXNTmay be understood to be the MIMO channel between the transmit and receive antennas, e may be understood to be a noise plus interference vector due to receiver noise and interference.

[0025] The precoder matrix W may be chosen to match the characteristics of the A / RxA / TMIMO channel matrix H resulting in so-called channel dependent precoding. The precoder W may be a wideband precoder, e.g., the same over a whole bandwidth, or a subband precoder, e.g., optimized per subband. W may be typically selected from a codebook of precoding matrices by the UE and reported to the gNB in terms of a precoding matrix indicator (PMI).

[0026] One example method for a UE to select a precoder matrix W may be to select the Wkfrom a codebook that may maximize the Frobenius norm of the hypothesized equivalent channel:

[0027]

[0028] Where

[0029] H may be understood to be a channel estimate

[0030] Wkmay be understood to be a hypothesized precoder matrix with index k.

[0031] In addition to W feedback, a UE may typically also feedback a rank indicator (Rl) and channel quality indicator(s) (CQI) as part of channel state information (CSI) feedback. Given the CSI feedback from the UE, the gNB may determine the transmission parameters to use for data transmissions to the UE.

[0032] For channel estimation purposes, a so-called channel state information reference signal (CSI-RS) may be typically transmitted to the UE.

[0033] Two dimension (2D) Antenna arrays

[0034] The antennas with A / Tantenna ports discussed above may be either a linear antenna array or 2D plenary antenna array. A linear antenna array may be understood to be a special case of a 2D antenna array. A 2D antenna array may be described by Nhcolumns, corresponding to the horizontal dimension, Nvrows, corresponding to the vertical dimension, and Nppolarizations. The total number of antenna ports may thus be N = NhNvNp. An example of a cross polarized, e.g., Np= 2, antenna array with Nh,Nv) = (4,4) is illustrated in Figure 3.Figure 3 is a schematic diagram illustrating a non-limiting example of a two-dimensional antenna array of cross-polarized antenna elements (NP= 2), with Nh= 4 horizontal antenna elements and Nv= 4 vertical antenna elements.

[0035] It may be noted that the 2D antenna array may be rotated at any angle. In this case, the row and columns may no longer correspond to vertical and horizontal directions. To reflect this more general case in NR, a 2D antenna array may be simply defined by a number of antenna ports in each of two dimensions, e.g., N1and N2, and Npmay be understood to always be 2. Thus, the total number of antenna ports may be understood to be N = 2N1N2.

[0036] The concept of an antenna port may be understood to be non-limiting in the sense that it may refer to any virtualization, e.g., linear mapping, of the physical antenna elements. For example, pairs of physical sub-elements may be fed the same signal, and hence share the same virtualized antenna port.

[0037] Reference signal

[0038] Reference signal configurations

[0039] CSI-RS:

[0040] A CSI-RS may be transmitted over each transmit (Tx) antenna port at the network node and for different antenna ports. The CSI-RS may be multiplexed in time, frequency, and code domain such that the channel between each Tx antenna port at the network node and each receive antenna port at a UE may be measured by the UE. The time-frequency resource used for transmitting CSI-RS may be referred to as a CSI-RS resource.

[0041] In NR, the CSI-RS for beam management may be defined as a 1- or 2-port CSI-RS resource in a CSI-RS resource set, where the field repetition may be present. The following three types of CSI-RS transmissions may be supported. A type of CSI-transmissions may be periodic CSI-RS, according to which CSI-RS may be transmitted periodically in certain slots. This CSI-RS transmission may be semi-statically configured using Radio Resource Control (RRC) signaling with parameters such as CSI-RS resource, periodicity, and slot offset. Another type of CSI-transmissions may be semi-persistent CSI-RS. Similar to periodic CSI-RS, resources for semi-persistent CSI-RS transmissions may be semi-statically configured using RRC signaling with parameters such as periodicity and slot offset. However, unlike periodic CSI-RS, dynamic signaling may be needed to activate and deactivate the CSI-RS transmission. Yet another type of CSI-transmissions may be aperiodic CSI-RS. This may be understood to be a one-shot CSI-RS transmission that may happen in any slot. Here, one-shot may be understood to mean that CSI-RS transmission may only happen once per trigger. The CSI-RS resources, e.g., the Resource Element (RE) locations which may consist of subcarrier locations and OFDM symbol locations, for aperiodic CSI-RS may be semi-statically configured. The transmission of aperiodic CSI-RS may be triggered by dynamic signaling through Physical Downlink Control CHannel (PDCCH) using the CSI request field in UL Downlink Control Information (DCI), in the same DCIwhere the UL resources for the measurement report may be scheduled. Multiple aperiodic CSI-RS resources may be included in a CSI-RS resource set and the triggering of aperiodic CSI-RS may be on a resource set basis.

[0042] SSB:

[0043] In NR, an SSB may consist of a pair of synchronization signals (SSs), physical broadcast channel (PBCH), and DeModulation Reference Signal (DMRS) for PBCH. An SSB may be mapped to 4 consecutive OFDM symbols in the time domain and 240 contiguous subcarriers, 20 Resource Blocks (RBs) in the frequency domain.

[0044] To support beamforming and beam-sweeping for SSB transmission, in NR, a cell may transmit multiple SSBs in different narrow-beams in a time multiplexed fashion. The transmission of these SSBs may be confined to a half frame time interval, 5 ms. It may also be possible to configure a cell to transmit multiple SSBs in a single wide-beam with multiple repetitions. The design of beamforming parameters for each of the SSBs within a half frame may be up to network implementation. The SSBs within a half frame may be broadcasted periodically from each cell. The periodicity of the half frames with SS / PBCH blocks may be referred to as SSB periodicity, which may be indicated by System Information Block 1 (SIB1).

[0045] The maximum number of SSBs within a half frame, denoted by L, may be understood to depend on the frequency band, and the time locations for these L candidate SSBs within a half frame may be understood to depend on the Subcarrier Spacing (SCS) of the SSBs. The L candidate SSBs within a half frame may be indexed in an ascending order in time from 0 to L-1. By successfully detecting PBCH and its associated DMRS, a UE may know the SSB index. A cell may not necessarily transmit SS / PBCH blocks in all L candidate locations in a half frame, and the resource of the un-used candidate positions may be used for the transmission of data or control signaling instead. It may be up to network implementation to decide which candidate time locations to select for SSB transmission within a half frame, and which beam to use for each SSB transmission.

[0046] Measurement resource configurations

[0047] In NR, a UE may be configured with N>1 CSI reporting settings, e.g., alternatively referred to as CSI-ReportConfig, M>1 resource settings, e.g., alternatively referred to as CSI-ResourceConfig, where each CSI reporting setting may be linked to one or more resource setting for channel and / or interference measurement. The CSI framework may be modular, meaning that several CSI reporting settings may be associated with the same Resource Setting.

[0048] The measurement resource configurations for beam management may be provided to the UE by RRC Information Elements (lEs) CSI-ResourceConfigs. One CSI-ResourceConfig may contain several Non Zero Power (NZP)-CSI-RS-ResourceSets and / or CSI-SSB-ResourceSets.

[0049] A UE may be configured to perform measurement on CSI-RSs. Here the RRC information element (IE) NZP-CSI-RS-ResourceSet may be used. A NZP CSI-RS resource set may containthe configuration of Ks >1 CSI-RS resources, where the configuration of each CSI-RS resource may include at least: mapping to REs, the number of antenna ports, time-domain behavior, etc. Up to 64 CSI-RS resources may be grouped to an NZP-CSI-RS-ResourceSet. A UE may also be configured to perform measurements on SSBs. Here, the RRC IE CSI-SSB-ResourceSet may be used. Resource sets comprising SSB resources may be defined in a similar manner.

[0050] In the case of aperiodic CSI-RS and / or aperiodic CSI reporting, the network node may configure the UE with ScCSI triggering states. Each triggering state may contain the aperiodic CSI report setting to be triggered along with the associated aperiodic CSI-RS resource sets.

[0051] Periodic and semi-persistent Resource Settings may only comprise a single resource set, e.g., S=1 , while S>=1 for aperiodic Resource Settings. This may be understood to be because in the aperiodic case, one out of the S resource sets comprised in the Resource Setting may be indicated by the aperiodic triggering state that may trigger a CSI report.

[0052] The RRC lEs described above may be defined in 3GPP 38.331 v. 18.0.0.

[0053] Measurement Reporting

[0054] Three types of CSI reporting may be supported in NR as follows:

[0055] A type of CSI reporting may be periodic CSI reporting on Physical Uplink Control Channel (PUCCH), according to which CSI may be reported periodically by a UE. Parameters such as periodicity and slot offset may be configured semi-statically by higher layer RRC signaling from the network node to the UE.

[0056] Another type of CSI reporting may be semi-persistent CSI reporting on Physical Uplink Shared Channel (PUSCH) or PUCCH. Similar to periodic CSI reporting, semi-persistent CSI reporting may be understood to have a periodicity and slot offset which may be semi-statically configured. However, a dynamic trigger from network node to UE may be needed to allow the UE to begin semi-persistent CSI reporting. A dynamic trigger from network node to UE may be needed to request the UE to stop the semi-persistent CSI reporting.

[0057] Yet another type of CSI reporting may be aperiodic CSI reporting on PUSCH. This type of CSI reporting may involve a single-shot, that is, one time, CSI report by a UE which may be dynamically triggered by the network node using DCI. Some of the parameters related to the configuration of the aperiodic CSI report may be semi-statically configured by RRC but the triggering may be done dynamically via DCI.

[0058] Machine Learning

[0059] Machine learning (ML) may be understood as the study of computer algorithms that may improve automatically through experience. It is seen as a part of Artificial Intelligence (Al). ML algorithms may build a model based on sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to do so. ML algorithms may be used in a wide variety of applications, such as email filtering and computer vision, where it may be difficult or unfeasible to develop conventional algorithms to perform the needed tasks.There may be basically three types of ML Algorithms: Supervised Learning, Unsupervised Learning, and Reinforcement Learning (RL).

[0060] Supervised Learning algorithms may comprise a target / outcome variable, or dependent variable, which may have to be predicted from a given set of predictors, that is, independent variables. Using this set of variables, a function may be generated that may map inputs to desired outputs. The training process may continue until the model may achieve a desired level of accuracy on the training data. Once an ML model may have been trained, an inference process may begin, whereby new data may be run through the ML model to calculate an output. Examples of Supervised Learning may be Regression, Decision Tree, Random Forest, KNN, Logistic Regression etc.

[0061] In Unsupervised Learning algorithms, there may be no target or outcome variable to predict / estimate. It may be used for clustering a population into different groups, which may be widely used for segmenting customers in different groups for specific intervention. Examples of Unsupervised Learning may be K-means, mean-shift clustering, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Expectation-Maximization (EM) Clustering using Gaussian Mixture Models (GMM), Agglomerative Hierarchical Clustering, etc....

[0062] Cluster analysis or clustering may be understood as an ML technique which may comprise grouping a set of objects in such a way that objects in the same group, which may be called a cluster, may be understood to be more similar, in some sense, to each other than to those in other groups, that is, other clusters. It may be understood as a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including pattern recognition, image analysis, information retrieval, bioinformatics, data compression, computer graphics and ML.

[0063] Reinforcement learning (RL) may be understood to be a type of ML where an agent may learn to make decisions by taking actions in an environment to achieve some goal. The agent may receive feedback in the form of rewards, which it may use to learn the best strategy, or policy, to accumulate the most reward overtime.

[0064] Reinforcement learning may involve the following. An agent may be understood to be a learner or decision maker that may interact with the environment. The environment may be understood to refer to the world that the agent may interact with and learn from. A state (s) may be understood to refer to a representation of the current situation that the agent may be in. It may be understood to be the context within which the agent may make decisions. An action (a) may be understood to refer to a choice made by the agent that may affect the state. A reward (r) : may be understood to refer to feedback from the environment in response to the actions taken by the agent. It may be a scalar signal that may indicate how well the agent is doing at a given moment. A Policy may be understood to be a strategy used by the agent, which may map states to actions. The policy may be deterministic, that is, always the sameaction for a given state, or stochastic, that is, probabilistic actions for a given state. A value function may be understood to refer to a function that may estimate how good it may be for the agent to be in a given state, or how good it may be to perform a certain action in a given state. The "goodness" may be typically measured as the expected return, e.g., the cumulative reward, that may be achieved. A Q-function, an Action-Value Function, may be understood to refer to a function that may estimate the value of taking a certain action in a given state, and then following the current policy thereafter. A model of the environment may be understood to refer to the fact that some RL approaches may involve learning a model that may predict the next state (s’) and the reward for the current state and action. This may be understood to allow for planning and reasoning about the future without needing to actually take the action.

[0065] RL may involve making decisions in regard to exploration vs. exploitation. In reinforcement learning, the agent may need to balance exploration, that is, trying new things to discover better rewards, with exploitation, that is, using known information to maximize rewards. This may be understood to be a trade-off in RL.

[0066] In the reinforcement learning problem, the state may change every time the agent may apply a new action. The problem may be represented in the following way: The agent may receive the state of the environment at a certain time (s). Then the agent may select on action (a) and apply it in the environment. When this action is applied, the environment may provide a reward (r) and change to a new state (s’), the reward and state may be provided finally by an interpreter. In reinforcement learning, the term "interpreter" may be used to describe a component of a reinforcement learning system that may interpret the state of the environment and the actions of an agent. It may be understood to be the part of the system that may bridge the agent with the environment it may be trying to learn from.

[0067] This cyclic procedure may be understood to bring a sequence of states, actions and rewards: s1, a1,r1;...;sT,aT,rT. The agent may use different learning algorithms to learn the most appropriate action to take on every different state of the NW, e.g., policy-learning based, such as actor-critic approaches, or value-based learning, such as deep-q networks.

[0068] Rel-18 / Rel-19 AI / ML for CSI feedback enhancements

[0069] Artificial Intelligence (Al) and Machine Learning (ML) have been investigated, both in academia and industry, as promising tools to optimize the design of the air-interface in wireless communication networks. Example use cases may include using autoencoders for CSI compression to reduce the feedback overhead and improve channel prediction accuracy; using deep neural networks for classifying Line-of-Sight (LOS) and Non-LOS (NLOS) conditions to enhance the positioning accuracy; using reinforcement learning for beam selection at the network side and / or the User Equipment (UE) side to reduce the signaling overhead and beam alignment latency; and using deep reinforcement learning to learn an optimal precoding policy for complex Multiple Input Multiple Output (MIMO) precoding problems.In 3rd Generation Partnership Project (3GPP) New Radio (NR) standardization work, a release 18 study item on AI / ML for the NR air interface started in May 2022 and completed in December 2023. This study item explored the benefits of augmenting the air-interface with features enabling improved support of AI / ML based algorithms for enhanced performance and / or reduced complexity and / or overhead. Through studying a few selected use cases, CSI feedback, beam management, and positioning, this study item may be understood to aim at laying the foundation for future air-interface use cases leveraging AI / ML techniques.

[0070] Terminologies such as AI / ML model, AI / ML model inference, which may be henceforth referred to as inference, AI / ML model training, which may be henceforth referred to as training, data collection, and model monitoring may be as defined in Section 3.1 of 3GPP TR38.843 v.

[0071] 18.0.0.

[0072] Two Al CSI use cases were studied in 3GPP Rel-18, and they are under continued study in 3GPP Rel-19:

[0073] The CSI prediction use case using one or more one-sided UE-sided models, where the model inference may be performed entirely at the UE. One or more AI / ML models may be trained and deployed at a UE for the Al-based CSI-prediction feature. During model inference, a UE may beconfigured by the gNB to measure a set of historical CSI-RSs and then report a predicted CSI for one or multiple future time instances using its AI / ML model(s). Figure 4 provides a non-limiting example for the inference procedure for CSI prediction. Figure 4 is a schematic diagram illustrating a non-limiting example of the CSI prediction using UE-sided Al model(s). For generating the input of a CSI prediction model, the UE may need some further pre-processing on the measured channel; for the output of the CSI prediction model, some further post-processing may also be applied.

[0074] The CSI compressing use case using one or more two-sided AI / ML models. A two-sided AI / ML model may be understood to refer to a paired AI / ML Model(s) over which joint inference may be performed across the UE and the network (NW), e.g., the first part of the inference may be firstly performed by UE and then the remaining part may be performed by gNB, or vice versa. As an example, Figure 5 shows a non-limiting example of the autoencoder (AE)-based CSI compression, where an encoder, a UE-part of the two-sided AE model, may be operated at a UE to compress the estimated wireless channel, and the output of the encoder, the compressed wireless channel information estimates, may be reported from the UE to a gNB. Figure 5 is a schematic diagram illustrating a non-limiting example of an autoencoder (AE)-based CSI compression using two-sided AI / ML model use case. The gNB may use a decoder, the NW-part of the two-sided AE model, to reconstruct the estimated wireless channel information. Here, the two-sided AI / ML model may be composed of the encoder at the UE side and the decoder at the base station, e.g., a gNB, side. Note that in the case of a two-sided model, the code may be generated by the encoder and only interpretable by a jointly trained decoder. The situation maybe understood to be different from running an AI / ML model in the UE, reporting the output over the air in a fully standardized format, and running a separate AI / ML model at the base station.

[0075] DMRS-based channel estimation

[0076] A broad framework for DMRS provision may be established in NR where DMRS symbols may be multiplexed with Physical Uplink or Downlink Shared Channel (PxSCH) REs in the Time / Frequency (T / F) grid. Examples of some DMRS framework aspects are depicted in Figure 6.

[0077] Single-symbol and double-symbol based DMRS may be used.

[0078] Frequency mapping may be Type 1, that is, comb based with 2 Code Division Multiplexing (CDM) groups, or Type 2, that is, non-comb based with 3 CDM groups. Type 1 and Type 2 single-symbol and double-symbol based DMRS examples are depicted in Figure 6.

[0079] OFDM Symbol mapping may be Type A, for slot-based scheduling, where DMRS may start in symbol 2 or 3 from slot boundary, and counting may start at 0, or Type B, non-slot based scheduling, where DMRS may start in PxSCH symbol 0, and counting may start at 0. The four different illustrations for Type A and the four illustrations for Type B in Figure 6, from left to right, represent different reserved symbol pattens. In type A, the first two symbols may always be reserved for PDCCH and DMRS cannot be there. In type B, the scheduled DL transmission may use any subset of symbols in a slot and DMRS location may not be constrained this way.

[0080] The UE may use the DMRS symbols on the DMRS REs to perform channel estimation, the channel estimates may then be used for demodulating data symbols in data REs.

[0081] Overlaid DMRS

[0082] DMRS that may be embedded in Physical Downlink Shared Channel (PDSCH) transmissions according to existing NR specifications may take up a non-negligible fraction of the T / F resources, typically between 5-20%. Embedded as used here may be understood to refer to dedicated DMRS REs in the allocated PDSCH time-frequency resources, REs that may not be used for PDSCH data symbol transmission. As a consequence, maximum user throughput and maximum cell capacity may be reduced by the same fraction. Using overlaid DMRS+data symbol allocation in PxSCH resources has been proposed as a way to retain or improve channel estimation performance while reducing the effective resource overhead for DMRS provision. The overlay approach is illustrated in Figure 7.

[0083] Additional benefits from overlaid DMRS may include enhanced channel tracking capabilities, simplified allocation of pilot resources in the time and frequency domains, and simplified receiver processing vs. joint AI / ML-based receiver.

[0084] SUMMARY

[0085] It is an object of embodiments herein to improve the handling of data pertaining toReference Signals (RSs) in a communications system.

[0086] According to a first aspect of embodiments herein, the object is achieved by a computer-implemented method, performed by a first device. The method is for handling data pertaining to Reference Signals (RSs). The first device operates in a communications system. The first device obtains one or more configurations. The one or more configurations indicate one or more patterns of collection of data pertaining to overlaid RSs. The obtaining is from a first network node operating in the communications system. The first device also performs an action. The action is to support the collection of the data. The collection of the data is based on the obtained one or more configurations.

[0087] According to a second aspect of embodiments herein, the object is achieved by a computer-implemented method, performed by a second device. The method is for handling the data pertaining to the RSs. The second device operates in the communications system. The second device obtains a first configuration. The first configuration is of the one or more configurations. The one or more configurations indicate a first pattern. The first pattern is of the one or more patterns. The one or more patterns are of collection of the data pertaining to overlaid RSs. The obtaining is from the first network node operating in the communications system. The second device predicts demodulation of a first channel with the overlaid RSs. The predicting is using collected fourth data as input to a trained MLM. The second device then initiates outputting a fifth indication of the predicted demodulation.

[0088] According to a third aspect of embodiments herein, the object is achieved by a computer-implemented method, performed by a first network node. The method is for handling the data pertaining to the RSs. The first network node operates in a communications system. The first network node sends the one or more configurations. The one or more configurations indicate the one or more patterns. The one or more patterns are of collection of the data pertaining to the overlaid RSs.

[0089] According to a fourth aspect of embodiments herein, the object is achieved by a computer-implemented method, performed by the second network node. The method is for handling the data pertaining to the RSs. The second network node operates in the communications system. The second network node obtains collected data. The obtaining is from at least the first device operating in the communications system. The data has been collected by the first device. The data has been collected based on the one or more configurations. The one or more configurations indicate the one or more patterns of collection of data pertaining to overlaid RSs. The second network node initiates training of the MLM. The training of the MLM is with the collected data. The training of the MLM is to predict the demodulation of the channel with the overlaid RSs. The second network node initiates outputting a third indication of the trained MLM.According to a fifth aspect of embodiments herein, the object is achieved by the first device. The first device is for handling the data pertaining to the RSs. The first device is configured to operate in the communications system. The first device is configured to obtain, from the first network node configured to operate in the communications system, the one or more configurations configured to indicate the one or more patterns of collection of data configured to pertain to overlaid RSs. The first device is also configured to perform the action to support the collection of the data based on the one or more configurations configured to be obtained.

[0090] According to a sixth aspect of embodiments herein, the object is achieved by the second device. The second device is for handling the data pertaining to the RSs. The second device is configured to operate via the communications system. The second device is configured to obtain, from the first network node configured to operate in the communications system, the first configuration of the one or more configurations configured to indicate the first pattern of the one or more patterns of collection of data pertaining to overlaid RSs. The second device 132 is configured to predict, using the collected fourth data as input to the trained MLM, demodulation of the first channel with the overlaid RSs. The second device is also configured to initiate outputting the fifth indication of the demodulation configured to be predicted.

[0091] According to a seventh aspect of embodiments herein, the object is achieved by the first network node. The first network node is for handling the data pertaining to the RSs. The first network node is configured to operate in the communications system. The first network node is configured to send, to the first device configured to operate in the communications system, the one or more configurations configured to indicate the one or more patterns of collection of data pertaining to overlaid RSs.

[0092] According to an eighth aspect of embodiments herein, the object is achieved by the second network node. The second network node is for handling the the data pertaining to the RSs. The second network node is configured to operate in the communications system. The second network node is configured to obtain, from at least the first device configured to operate in the communications system, the collected data by the first device based on the one or more configurations. The one or more configurations are configured to indicate the one or more patterns of collection of data pertaining to overlaid RSs. The second network node is also configured to initiate training of the MLM with the collected data. The MLM is configured to predict demodulation of the channel with the overlaid RSs. The second network node is further configured to initiate outputting the third indication of the MLM configured to be trained.

[0093] By obtaining the one or more configurations, the first device may be enabled to perform the action to support the collection of the data pertaining to overlaid RSs and thereby be enabled itself or enable the first network node or the second network node to then train the MLM with data collected based on the one or more configurations. The MLM may be used to undo the couplingbetween the RSs and data signal components in, e.g., REs where overlaid data and RSs may be used, and perform related operations such as RS symbol extraction, channel estimation, and / or direct data symbol demodulation. This may in turn enable the second device to use the train MLM to predict the demodulation of the first channel with the overlaid RSs.

[0094] BRIEF DESCRIPTION OF THE DRAWINGS

[0095] Examples of embodiments herein are described in more detail with reference to the accompanying drawings, according to the following description.

[0096] Figure 1 is a schematic diagram illustrating a non-limiting example of a beam management procedure, according to existing methods.

[0097] Figure 2 is a schematic diagram illustrating a non-limiting example of data transmission with spatial multiplexing, according to existing methods.

[0098] Figure 3 is a schematic diagram illustrating a non-limiting example of a two-dimensional antenna array of cross-polarized antenna elements, according to existing methods.

[0099] Figure 4 is a schematic diagram illustrating a non-limiting example of the CSI prediction using UE-sided Al model(s), according to existing methods.

[0100] Figure 5 is a schematic diagram illustrating a non-limiting example of an autoencoder (AE)-based CSI compression using a two-sided AI / ML model use case, according to existing methods. Figure 6 is a schematic diagram illustrating non-limiting examples of some DMRS framework aspects.

[0101] Figure 7 is a schematic diagram illustrating a non-limiting example of an overlay approach. Figure 8 is a schematic diagram depicting an example of a communications system, according to embodiments herein.

[0102] Figure 9 is a flowchart depicting a method in a first device, according to embodiments herein. Figure 10 is a flowchart depicting a method in a second device, according to embodiments herein.

[0103] Figure 11 is a flowchart depicting a method in a first network node, according to embodiments herein.

[0104] Figure 12 is a flowchart depicting a method in a second network node, according to embodiments herein.

[0105] Figure 13 is a flowchart depicting a non-limiting example of methods according to embodiments herein.

[0106] Figure 14 is a flowchart depicting another non-limiting example of methods according to embodiments herein.

[0107] Figure 15 is a flowchart depicting a non-limiting example of methods according to embodiments herein.Figure 16 is a flowchart depicting another non-limiting example of methods according to embodiments herein.

[0108] Figure 17 is a schematic diagram depicting non-limiting examples of aspects of embodiments herein.

[0109] Figure 18 is a schematic diagram depicting non-limiting examples of aspects of embodiments herein.

[0110] Figure 19 is a schematic block diagram illustrating an embodiment of a first device, according to embodiments herein.

[0111] Figure 20 is a schematic block diagram illustrating an embodiment of a second device,

[0112] according to embodiments herein.

[0113] Figure 21 is a schematic block diagram illustrating an embodiment of a first network node, according to embodiments herein.

[0114] Figure 22 is a schematic block diagram illustrating an embodiment of a second network node, according to embodiments herein.

[0115] Figure 23 is a flowchart depicting a method in a first device, according to examples related to embodiments herein.

[0116] Figure 24 is a flowchart depicting a method in a second device, according to examples related to embodiments herein.

[0117] Figure 25 is a flowchart depicting a method in a first network node, according to examples related to embodiments herein.

[0118] Figure 26 is a flowchart depicting a method in a second network node, according to examples related to embodiments herein.

[0119] Figure 27 is a schematic block diagram illustrating an example of a communication system 2700 in accordance with some embodiments.

[0120] Figure 28 is a schematic block diagram illustrating another example of a communication system 2800 according to some embodiments.

[0121] Figure 29 is a schematic block diagram illustrating an example of a wireless device 2900, which may be configured to operate in communication system 2700 of Figure 27 or in communication system 2800 of Figure 28.

[0122] Figure 30 is a schematic block diagram illustrating an example of a network node 3000 in accordance with some embodiments.

[0123] Figure 31 is a schematic block diagram illustrating an example of a virtualization environment 3100 in which functions implemented by some embodiments may be virtualized.

[0124] DETAILED DESCRIPTION

[0125] As part of the development of embodiments herein, one or more challenges with the existing technology will first be identified and discussed.An AI / ML model may be used to undo the coupling between the DMRS and data signal components in REs where overlaid data and DMRS may be used, and perform related operations such as DMRS symbol extraction, channel estimation, and / or direct data symbol demodulation.

[0126] However, data collection may be understood to be needed to train such an AI / ML model to perform these tasks, or to perform other Life Cycle Management (LCM) stages, such as model verification or performance monitoring. Examples of data required to be collected for supervised learning may include overlaid symbols, e.g., AI / ML model inputs, and their associated ground truth and / or labels, e.g., AI / ML model outputs, etc.. Currently, no network signaling and signal formats and / or structures may be understood to be available for supporting such data collection. Hence, it may be understood to be a problem that AI / ML models for overlaid DMRS and data cannot be effectively developed in current 3GPP based telecommunication systems.

[0127] Certain aspects of the present disclosure and their embodiments may provide solutions to these or other challenges. Embodiments herein may be generally understood to relate to data collection for ML model-based overlaid DMRS.

[0128] To perform data collection for overlaid RS and data communications and enable for AI / ML LCM operations such as model training, model verification, model performance monitoring, etc., the transmitter may perform the communication using a special pattern comprised of overlaid REs and optionally additional non-overlaid REs. For ease of description, these special patterns may be referred to throughout this document as “data collection patterns” and their associated configuration as “data collection configurations”.

[0129] The approach may be used in the UL, wherein the UE may be understood to be the TX and the BS may be understood to be the RX, or in the DL, that is, the reverse. The UE may receive a data collection configuration message from the NW, containing one or more data collection pattern configurations and / or parameters for transmission and / or reception. The parameters may include overlaid pattern format, overlaid RS sequence and other parameters, overlaid and / or nonoverlaid RE alternation pattern, overlaid data sequence generation parameters, power ratio information, etc.

[0130] Depending on the capability of the UE and / or NW, a training pattern may be selected and transmitted by the NW and / or the UE for the UE and / or the NW to perform data collection for the overlaid RS feature. Examples of such patterns may include those where the data may be known and / or unknown or where orthogonal RSs may be transmitted or not.

[0131] In the main example, the overlaid REs may contain unknown (user) data and additional non-overlaid REs may contain orthogonal RSs. The non-overlaid REs may be placed in close proximity / interspersed / interleaved with the overlaid REs to minimize channel differences experienced by the two RE groups. These non-overlaid REs may allow the receiver to estimate the channel based on the orthogonal RS, remove the overlaid RS component, and perform useful data symbol demodulation from the overlaid REs. Subsequently, the receiver may use for LCM-related purposes, e.g., i) at least the correctly decoded signals for LCM-related purposes, e.g., as ground truth / labels for the supervised training of an AI / ML model performing direct demodulation, and / or ii) at least the channel estimated based on the non-overlaid RSs, e.g., as ground truth / labels for the supervised training of an AI / ML model performing channel estimation.

[0132] Related to the data collection procedure, additional signaling may be performed, including e.g., the NW transmitting data collection initiation and termination indications and the UE providing capability information related to data collection support for overlaid REs.

[0133] Some of the embodiments contemplated will now be described more fully hereinafter with reference to the accompanying drawings, in which examples are shown. In this section, the embodiments herein will be illustrated in more detail by a number of exemplary embodiments. Other embodiments, however, are contained within the scope of the subject matter disclosed herein. The disclosed subject matter should not be construed as limited to only the embodiments set forth herein; rather, these embodiments are provided by way of example to convey the scope of the subject matter to those skilled in the art. It should be noted that the exemplary embodiments herein are not mutually exclusive. Components from one embodiment may be tacitly assumed to be present in another embodiment and it will be obvious to a person skilled in the art how those components may be used in the other exemplary embodiments.

[0134] Figure 8 depicts two non-limiting examples, in panel a) and panel b), respectively, of a communications system 100, e.g., a wireless network or wireless communications network, sometimes also referred to as a wireless communications system, cellular radio system, or cellular network, in which embodiments herein may be implemented. The communications system 100 may be a 5G system, 5G network, or Next Gen System. In other examples, the communications system 100 may be a newer system with similar functionality, e.g., a6G network. In other examples, the communications system 100 may, e.g., alternatively or additionally, support other technologies such as, for example, Long-Term Evolution (LTE), e.g., LTE for Machines (LTE-M), LTE Frequency Division Duplex (FDD), LTE Time Division Duplex (TDD), LTE Half-Duplex Frequency Division Duplex (HD-FDD), LTE operating in an unlicensed band, such as LTE Licensed-Assisted Access (LAA), enhanced eLAA (eLAA), further enhanced LAA (feLAA) and / or MulteFire. Yet in other examples, the communications system 100 may further support other technologies such as, for example Wideband Code Division Multiple Access (WCDMA), Universal Terrestrial Radio Access (UTRA) TDD, Global System for Mobile communications (GSM) network, GSM / Enhanced Data Rates for GSM Evolution (EDGE) Radio Access Network (GERAN) network, Ultra-Mobile Broadband (UMB), EDGE network, network comprising any combination of Radio Access Technologies (RATs) such as e.g. MultiStandard Radio (MSR) base stations, multi-RAT base stations etc., any 3rdGeneration Partnership Project (3GPP) cellular network, WiFi networks, Worldwide Interoperability for Microwave Access (WiMax), or any cellular network or system. The communications system100 may support Machine Type Communication (MTC), enhanced MTC (eMTC), Internet of Things (loT) and / or NarrowBand loT (NB-loT). Thus, although terminology from 5G / NR and LTE may be used in this disclosure to exemplify embodiments herein, this should not be seen as limiting the scope of the embodiments herein to only the aforementioned system.

[0135] The communications system 100 may comprise a first network node 111 as depicted in the non-limiting examples of Figure 8. In some embodiments, the communications system 100 may comprise a second network node 112. In other embodiments, the communications system 100 may comprise a third network node 113. Any of the first network node 111, the second network node 112 and the third network node 113 may be a radio network node, radio access node or RAN node. That is, a transmission point such as a radio base station, for example a gNB, or any other network node with similar features capable of serving a user equipment, such as a wireless device or a machine type communication device, in the communications system 100. In some examples, any of the first network node 111 , the second network node 112 and the third network node 113 may be a distributed node or may manage a virtualized network function, and may partially perform its functions in collaboration with a virtual node in a cloud 115. Any of the first network node 111, the second network node 112 and the third network node 113 may be directly connected to one or more core networks, e.g., to one or more network nodes in the one or more core networks. Any of the first network node 111 , the second network node 112 and the third network node 113 may be of different classes, such as, e.g., macro base station, home base station or pico base station, based on transmission power and thereby also cell size. In some examples, any of the first network node 111 , the second network node 112 and the third network node 113 may serve receiving nodes with serving beams. Any of the first network node 111 , the second network node 112 and the third network node 113 may support one or several communication technologies, and its name may depend on the technology and terminology used.

[0136] In some examples, the third network node 113 may be a core network node, e.g., a core network access node. In some examples, as depicted in panel b) of Figure 8, the third network node 113 may be a network node in the cloud 115.

[0137] Any of the first network node 111 , the second network node 112 and the third network node 113 may be the same node, or be co-localized. In some examples, as depicted in the non-limiting example of panel a) of Figure 8, the first network node 111 , the second network node 112 and the third network node 113 may be the same network node. In some examples, as depicted in the non-limiting example of panel b) of Figure 8, the first network node 111 and the second network node 112 may be the same network node, and the third network node 113 may be a different network node. In other examples, the first network node 111 , the second network node 112 and the third network node 113 may be different network nodes.The communications system 100 may cover a geographical area, which in some embodiments may be divided into cell areas, wherein each cell area may be served by a radio network node, although, one radio network node may serve one or several cells.

[0138] In some examples, the communications system 100 may include an access network, such as a radio access network (RAN), and a core network, which may include one or more core network nodes. The access network may include one or more access network nodes, such as any of the first network node 111 , the second network node 112, the third network node 113, e.g., which may be generally referred to as network nodes, or any other similar 3rd Generation Partnership Project (3GPP) access nodes or non-3GPP access points. Moreover, as will be appreciated by those of skill in the art, a network node may not necessarily be limited to an implementation in which a radio portion and a baseband portion are supplied and integrated by a single vendor. Thus, it may be understood that network nodes may include disaggregated implementations or portions thereof. For example, in some embodiments, the communications system 100 may include one or more Open-RAN (ORAN) network nodes. Any of the first network node 111 , the second network node 112, and the third network node 113 may be ORAN network nodes. An ORAN network node may be understood to be a node in the communications system 100 that may support an ORAN specification, e.g., a specification published by the O-RAN Alliance, or any similar organization, and may operate alone or together with other nodes to implement one or more functionalities of any node in the communications system 100, including one or more network nodes and / or core network nodes.

[0139] Examples of an ORAN network node may include an open radio unit (O-RU), an open distributed unit (O-DU), an open central unit (O-CU), including an O-CU control plane (O-CU-CP) or an O-CU user plane (O-CU-UP), a RAN intelligent controller, near-real time or non-real time, hosting software or software plug-ins, such as a near-real time control application, e.g., xApp, or a non-real time control application, e.g., rApp, or any combination thereof, the adjective “open” designating support of an ORAN specification. Any of the first network node 111 , the second network node 112, and the third network node 113 may support a specification by, for example, supporting an interface defined by the ORAN specification, such as an A1 , F1 , W1 , E1 , E2, X2, Xn interface, an open fronthaul user plane interface, or an open fronthaul management plane interface. Moreover, an ORAN access node may be a logical node in a physical node. Furthermore, an ORAN network node may be implemented in a virtualization environment, in which one or more network functions may be virtualized. For example, the virtualization environment may include an O-Cloud computing platform orchestrated by a Service Management and Orchestration Framework via an O-2 interface defined by the O-RAN Alliance or comparable technologies.

[0140] O-RAN may specify intent Application Programming Interfaces (APIs) toward the Service Management and Orchestration (SMO).A plurality of devices may be located in the communication system 100, whereof a first device 131 is depicted in the non-limiting example of Figure 8. In some embodiments, as depicted in the non-limiting examples of Figure 8, the communications system 100 may comprise a second device 132. Any of the first device 131 and the second device 132 may be a wireless communication device such as a UE, a 5G UE or nUE, which may also be known as e.g., mobile terminal, wireless terminal and / or mobile station, a mobile telephone, cellular telephone, or laptop with wireless capability, just to mention some further examples. Any of the first device 131 and the second device 132 may be, for example, portable, pocket-storable, hand-held, computer-comprised, ora vehicle-mounted mobile device, enabled to communicate voice and / or data, via the RAN, with another entity, such as a server, a laptop, a Personal Digital Assistant (PDA), or a tablet, Machine-to-Machine (M2M) device, a sensor, loT device, NB-loT device, device equipped with a wireless interface, such as a printer or a file storage device, modem, or any other radio network unit capable of communicating over a radio link in a communications system. Any of the first device 131 and the second device 132 comprised in the communications system 100 may be enabled to communicate wirelessly in the communications system 100. The communication may be performed e.g., via a RAN, and possibly the one or more core networks, which may be comprised within the communications system 100.

[0141] The first device 131 may be configured to communicate within the wireless communications network 100 with the first network node 111 over a first link, e.g., a radio link, via any of the one or more beams 121, 122, 123 or one or more cells. The first device 131 may be configured to communicate within the wireless communications network 100 with the second network node 112 over a second link 142, e.g., a radio link. The second device 132 may be configured to communicate within the wireless communications network 100 with the second network node 112 over a third link 143, e.g., a radio link. The second device 132 may be configured to communicate within the wireless communications network 100 with the third network node 113 over a respective link, e.g., a radio link, such as via any of the one or more beams 121 , 122, 123 or one or more cells, or other one or more beams served by the third network node 113. The first network node 111 may be configured to communicate within the communications system 100 with the second network node 112 over a fourth link 144, e.g., a radio link or a wired link.

[0142] Generally, all terms used herein are to be interpreted according to their ordinary meaning in the relevant technical field, unless a different meaning is clearly given and / or is implied from the context in which it is used. All references to a / an / the element, apparatus, component, means, step, etc. are to be interpreted openly as referring to at least one instance of the element, apparatus, component, means, step, etc., unless explicitly stated otherwise. The steps of any methods disclosed herein do not have to be performed in the exact order disclosed, unless a stepis explicitly described as following or preceding another step and / or where it is implicit that a step must follow or precede another step. Any feature of any of the embodiments disclosed herein may be applied to any other embodiment, wherever appropriate. Likewise, any advantage of any of the embodiments may apply to any other embodiments, and vice versa. Other objectives, features and advantages of the enclosed embodiments will be apparent from the following description.

[0143] In general, the usage of “first”, “second”, “third”, “fourth”, “fifth”, and / or “sixth” herein may be understood to be an arbitrary way to denote different elements or entities, and may be understood to not confer a cumulative or chronological character to the nouns they modify, unless otherwise noted, based on context.

[0144] Several embodiments are comprised herein. It should be noted that the examples herein are not mutually exclusive. Components from one embodiment may be tacitly assumed to be present in another embodiment and it will be obvious to a person skilled in the art how those components may be used in the other exemplary embodiments.

[0145] More specifically, the following are embodiments related to a device, such as the first device 131, e.g., a first UE, a second device, such as the second device 132, e.g, a second UE, a first network node, such as the first network node 111, e.g., a first gNB, and a second network node, such as the second network node 112, e.g., a second gNB.

[0146] In the following description DMRS is taken an illustrative example. DMRS may be replaced in the following description by a / the “first reference signal” or a / the “first RS”.

[0147] Embodiments of a computer-implemented method, performed by a device, such as the first device 131 , will now be described with reference to the flowchart depicted in Figure 9. The method is for handling data pertaining to Reference Signals (RSs). The first device 131 operates in a communications system, such as the communications system 100.

[0148] In some embodiments, the communications system 100 may support New Radio (NR). Several embodiments are comprised herein. The method may comprise two or more of the following actions. In some embodiments, all the actions may be performed. In some examples, Action 902 and Action 904 are performed. One or more embodiments may be combined, where applicable. It should be noted that the examples herein are not mutually exclusive. One or more embodiments may be combined, where applicable. Components from one embodiment may be tacitly assumed to be present in another embodiment and it will be obvious to a person skilled in the art how those components may be used in the other exemplary embodiments. All possible combinations are not described to simplify the description. A non-limiting example of the method performed by the first device 131 is depicted in Figure 9. In Figure 9, optional actions in some embodiments may be represented withdashed lines. In some embodiments, the actions may be performed in a different order than that depicted Figure 9.

[0149] Action 901

[0150] In this Action 901 , the first device 131 may provide a first indication. The providing in this Action 901 may be to the first network node 111.

[0151] The first indication may indicate a capability. The capability may be to perform an action, as described in Action 904, to support collection of data based on one or more configurations, to be obtained by the first device 131. The one or more configurations may indicate one or more patterns of collection of data pertaining to overlaid RSs.

[0152] In the context of embodiments herein, a collection of data may be understood to refer to e.g., obtaining measurement data or other information to operate or manage a ML model.

[0153] For inference, a collection of data may be understood to mean performing signal reception to estimate one or more relevant quantities, e.g., channel coefficient measurements, RE contents comprising a sum of data and RS components, etc., that may be used as input to the ML model.

[0154] In training of the ML, or other LCM contexts such as model monitoring or validity assessment, a collection of data may be understood to mean obtaining similar one or more estimated quantities and additional side information such as ground truth / labels, to perform ML model training. The latter may be used to determine a loss metric or an intermediate quality metric.

[0155] A pattern of collection of data, also known as a data collection pattern, may be understood as a configuration of data and RS information transmission during a data collection process, wherein the configuration may be understood to pertain to e.g., data and RS signal component allocation in a time-frequency grid, their relative powers, etc.

[0156] Overlaid RSs may be understood to refer to transmitted RS symbols in the same REs where data symbols may be transmitted, whereby the transmitted RE contents may comprise a properly weighted sum of a data symbol and an RS symbol.

[0157] The one or more patterns of collection of data pertaining to overlaid RS operation may be different combinations of data-only, RS-only, and overlaid data+RS REs with different relative power levels.

[0158] The action may be a data collection support operation according to one of the one or more configurations. That the action is to support collection of data may be understood to mean that the action may comprise the actual collection of data or configuration, request, confirmation etc. of data collection patterns, or activation or other control signaling to control the data collection process.The one or more configurations may comprise one or more of: overlaid pattern format, overlaid RS sequence and other parameters, overlaid / non-overlaid RE alternation pattern, overlaid data sequence generation parameters, etc. Overlaid pattern format may be understood as a label or identifier that may be associated with a certain overlaid transmission configuration comprising multiple configuration parameters. Overlaid data sequence generation parameters may be understood as configuration parameters governing data and RS symbol placement in the time-frequency grid, their relative power allocations, sequence generation, etc.

[0159] In some examples, the one or more configurations may comprise an overlay power ratio parameter and / or a measurement report configuration intended to be used by a receiver to measure interference.

[0160] In some examples, in this Action 901, the first device 131 may transmit to a NW node such as the first network node 111 , the capability to perform a data collection support operation for overlaid RS transmission in the UL and / or in the DL. An RS port may be associated with a PDSCH or PUSCH layer.

[0161] In some embodiments, one or more of the following may apply. According to one option, the RSs may be used for demodulation. According to another option, the RSs may be used for phase tracking, e.g., the RSs may comprise PT-RS. In some examples, the RS may be used for demodulation, e.g., DMRS, and / or for phase tracking, e.g., Phase Tracking Reference Signal (PT-RS). According to another option, the RSs may comprise Demodulation RSs (DMRSs). According to yet another option, the data to be collected may be to be used in a life cycle management (LCM) of a machine learning model (MLM). The described data collection functionality may be used in a number of AI / ML LCM-related purposes, e.g., AI / ML model training, retraining and incremental training, AI / ML model performance monitoring, AI / ML model inference, AI / ML model / functionality selection, AI / ML model / functionality (de)activation, AI / ML model / functionality switching, and AI / ML model / functionality fallback.

[0162] According to another option, the one or more patterns of collection of data pertaining to overlaid RSs may comprise one of: a) Resource Elements (REs) comprising the RSs overlaid with data, referred to herein as “first data”, wherein the first data may be known data; in some examples, the data sequence generation, that is, e.g. generating a pseudo-random symbols sequence using a predefined generator algorithm and seed parameters, may be based on the one or more configurations, b) REs comprising the RSs overlaid with data, referred to herein as “second data”, and REs comprising non-overlaid Reference Signal REs, wherein the second data may be unknown data, and with a cyclic redundancy check, and c) REs comprising the RSs overlaid with data, referred to herein as “second data”, wherein the second data may be unknown data, and REs comprising non-overlaid Reference Signal REs lacking data, and REs comprising only data, referred to herein as “third data”. In some examples, the regular PxSCH format may be reused so that regular DMRS may be transmitted in all DMRS REs andoverlaid RS and data may be transmitted in all data REs. In some examples, the data contents in the non-overlaid REs may be based on the data contents in the overlaid REs, e.g., they may be the same or a known subset / function / mapping derived from the data in the overlaid REs. According to another option, content of the third data in the non-overlaid Reference Signal REs may be the second data comprised in the REs comprising the RSs overlaid with the second data. According to yet another option, the action may be in the downlink and the data to be collected may have to comprise information indicating one or more measurements of interference. According to another option, at least one of the one or more configurations may comprise an overlay power ratio parameter. That is, any of the one or more configurations may comprise an overlay power ratio parameter intended to be used by a receiver to measure interference. An overlay power ratio parameter may be understood as relative power allocation of the data and RS symbol components, e.g. the ratio of the data power to the RS power, or the other way around. According to another option, at least one of the one or more configurations may comprise the overlay power ratio parameter and / or a measurement report configuration intended to be used by the receiver to measure interference. That is, any of the one or more configurations may comprise an overlay power ratio parameter and / or a measurement report configuration intended to be used by the receiver to measure interference. According to another option, RSs used for overlaid RS transmissions may be reused for non-overlaid RS REs, e.g., the data / pilot overlay power ratio may be set to zero for certain configured REs. According to another option, the communications system 100 may be a wireless communications system 100.

[0163] In this document, the phrase “overlaid REs” may be used to refer to the REs containing an RS symbol component and a PxSCH data symbol component in an overlaid manner, also known as superimposed pilots (SIP). The RS may refer to DMRS, Tracking Reference Signal (TRS), PT-RS, and other types of RS. In the following, the concepts may be exemplified using DMRS as example, but they may also be applied to other RS, where applicable.

[0164] Examples of data collection pattern structures

[0165] At least two aspects may be used to differentiate the main types of data collection patterns for the overlaid RSs, e.g., DMRS, feature: known or unknown data contents, and / or presence or lack of REs with separate additional orthogonal RS, no data contents.

[0166] In a first type of data collection patterns, as e.g., depicted later in Figure 17A, known, e.g., predefined, data may be used to generate the overlaid RE contents. The entire data collection pattern may contain only overlaid REs; no non-overlaid REs may be transmitted. The known data generation information and power ratio information may be provided in a previous configuration message or in a second indication that will be described in Action 903, e.g., in a data collection sequence initiation command.In a first example of the first type data collection pattern, all overlaid REs may have the same power ratio.

[0167] In a second example, the power ratios may be different for different groups of overlaid REs, where the multiple RE group definitions and the associated multiple power ratios may be provided e.g., in the configuration messages. It may be noted that by setting the power ratio to 100% RS in selected RE groups, RS-only REs may be also achieved, which may be alternatively viewed as a third example, a data collection pattern with known data and RS-only REs.

[0168] In a second type of data collection patterns, as e.g., depicted in Figure 17B, the overlaid REs may contain unknown (user) data and additional non-overlaid REs may contain orthogonal RS. The non-overlaid REs may be placed in close proximity / interspersed / interleaved with the overlaid REs to minimize channel differences experienced by the two RE groups.

[0169] In one example of the second type of data collection pattern, an unmodified PxSCH format may be used where the regular DMRS pattern, associated with conventional data transmission, may be used as the orthogonal RS. Data REs in such PxSCH format may be used for transmitting overlaid REs. This may minimize the impact of the training or other data collection tasks on regular data transmission activities. Due to the orthogonality of DMRS, one layer in Multi-user MIMO (MU-MIMO) transmission may then be used for data collection signals while another layer may be simultaneously used for conventional data transmission.

[0170] In the third type, as e.g., depicted in Figure 17C, there may also be additional data-only REs repeating the overlaid RE data contents when user data may be used for data collection.

[0171] In some embodiments, one of the following may apply. According to one option, the action may be performed in the UL; the action may comprise performing a transmission using the one or more patterns of collection of data. According to another option, the action may be performed in the DL; and the action may comprise performing a reception using the one or more patterns of collection of data.

[0172] In some examples, the overlaid DMRS may also be applied in other channels or other RS types, e.g., DMRS in PxDCCH, or CSI-RS / TRS. Training patterns for such channels may be formed analogously to the examples provided for PxSCH.

[0173] In some examples, the legacy NR / 6G design may be partly re-used for the non-overlaid RS REs, e.g. the frequency domain allocation and / or FD_OCC code design and / or the TD-OCC code design may be re-used, and / or the time domain allocation.

[0174] In some examples, the DMRS time domain allocation of legacy NR DMRS design may be changed; this may be used such that overlaid DMRS more easily may be placed next to, in time domain, non-overlaid DMRS, such that the ground truth determined by non-overlaid DMRS may be as closely related in time as possible as the overlaid DMRS, to mitigate impact of channel aging when training the AI / ML model.Capability signaling, indicating ability to transmit and / or receive the overlaid DMRS training patterns may be provided by the first device 131, e.g., during initial connection establishment from idle.

[0175] In some embodiments, the method may further comprise one or more of the following two actions.

[0176] Action 902

[0177] In this Action 902, the first device 131 obtains the one or more configurations.

[0178] The one or more configurations indicate the one or more patterns. The one or more patterns are of the collection of data. The data pertain to, e.g., may be about / indicate / be based on, overlaid RSs.

[0179] Obtaining in this Action 904 may comprise receiving, retrieving, or fetching.

[0180] In some embodiments, the obtaining in this Action 902 is from the first network node 111 operating in the communications system 100.

[0181] In an example, the first device 131 may, in this Action 902, receive from a NW node, such as the first network node 111, one or more overlaid RS data collection pattern configurations.

[0182] As examples of configuration parameters, one or more of the following parameters may be included in the one or more configurations. In one option, the one or more configurations may comprise overlay parameters, such as RS, e.g., DMRS, format and sequence, additional RS, e.g., DMRS, design parameters, and / or RS, e.g., DMRS / data symbol relative power, that is, power ratio, in overlaid REs. In another option, the one or more configurations may comprise data collection pattern parameters, such as known data sequence or generation seed, and / or multiplexing parameters for non-overlaid and overlaid REs: time, frequency, T / F, see Figure 18, spacing of non-overlaid and overlaid REs, ... In another option, the one or more configurations may comprise data collection transmission parameters, such as data collection support type, e.g., for channel estimation or demodulation ML model, data collection data mode, e.g., known data or user data, data collection duration, e.g., frames, seconds, until termination notice, ..., and / or data collection data versus regular user data priority / alternation rules, e.g., rules for handling prioritized user data that may arrive during a training data session based on known data, whether to terminate or continue after user data may have been handled, etc.

[0183] In some examples, the first device 131 may receive from the first network node 111 a configuration for PDSCH data reception, where the configuration may include the RS, e.g., DMRS, format and overlay parameters. In an example, the first network node 111, e.g., a BS, may configure the first device 131 for PDSCH reception with overlaid RS, e.g., DMRS. The first network node 111 may then transmit PDSCH with overlaid RS, e.g., DMRS.

[0184] Training pattern configuration and configuration update may be provided by the first network node 111, e.g., via RRC or Medium Access Control (MAC) Control Element (CE)signaling. The training pattern configuration may be understood to refer to the data collection pattern used during training, or other non-inference operations,.

[0185] Data collection phase for the UL

[0186] In some examples, the first device 131 may receive from the first network node 111, e.g., a radio access network node, a core network access node or the BS, the one or more configurations that may comprise, e.g., a configuration for a data collection procedure, or a data collection signal transmission, where the configuration may include RS, e.g., DMRS, format, data collection pattern details, data collection duration, etc. Configuration details may depend on the ML algorithm type and ground truth provision choices, discussed in more detail below. In an example, the first network node 111, e.g., a BS, may configure the first device 131 for PUSCH transmission with a training pattern, that is, a pattern of collection of data, for overlaid DMRS.

[0187] The first device 131 may receive from the first network node 111, e.g., the BS, the one or more configurations that may comprise, e.g., a configuration for PUSCH data transmission, where the configuration may include the DMRS format and overlay parameters. In an example, the first network node 111, e.g., a BS, may configure the first device 131 for PUSCH transmission with overlaid DMRS.

[0188] Data collection phase for the DL

[0189] In some examples, the first device 131 may receive, from the first network node 111, e.g., a BS or any other network node such as a RAN node or a core network node, the one or more configurations that may comprise, e.g., a configuration for a data collection procedure, or a data collection signal transmission, where the configuration may include RS, e.g., DMRS format, data collection pattern details, data collection duration, etc. In an example, the first network node 111, e.g., a BS, may configure the first device 131 for PDSCH reception with a training pattern for overlaid DMRS.

[0190] Action 903

[0191] In this Action 903, the first device 131 may obtain a second indication.

[0192] The second indication may trigger performing of the action, as will be described in Action 904.

[0193] The obtaining in this Action 903 may be from the first network node 111.

[0194] In an example, the first device 131 may, in this Action 903, receive from a NW node, such as the first network node 111 , an overlaid RS data collection initiation indication.

[0195] The second indication may indicate one of: to transmit the one or more patterns of collection of data, e.g., based on the obtained one or more configurations, and to receive the one or more patterns of collection of data, e.g., based on the obtained one or more

[0196] configurations.In some examples, the operation may be in the UL, the second indication, e.g., the data collection initiation indication, may be an indication to transmit a data collection pattern based on the configuration, and the operation may be performing a transmission using the data collection pattern.

[0197] In one example, the first device 131 may use receiving the one or more configurations as a trigger to initiate data collection pattern transmission. In another example, the first device 131 may receive an explicit initiation / trigger signal to initiate the transmission, for example from the first network node 111, e.g., a RAN node such as the base station. In a particular example, the BS may initiate transmission of PUSCH transmission with the training pattern for overlaid RS, e.g., DMRS.

[0198] In some examples, the operation may be in the DL, the second indication, e.g., the data collection initiation indication may be an indication to receive a data collection pattern based on the configuration, and the operation may be performing a reception using the data collection pattern.

[0199] In one example, the first device 131 may use receiving the one or more configurations as a trigger to initiate data collection pattern reception. In another example, the first device 131 may receive an explicit initiation / trigger signal to initiate the reception. For example, a mobile network operator controlled node in the RAN network, e.g., the first network node 111, may be used to trigger the initiation and likewise disabling of data collection in the first device 131. Alternatively, this collection may be triggered by the network vendor controlled node or functionality in the network side. In a particular example, the first network node 111, e.g., a BS, may indicate start of PDSCH transmission with the training pattern for overlaid DMRS. The first network node 111, e.g., the BS, may then transmit PDSCH using the configured training pattern.

[0200] Training initiation and termination messages, that is, commands to trigger, e.g., start, and stop data collection, may be provided by the first network node 111, e.g., via Downlink Control Information (DCI) or MAC CE signaling.

[0201] Action 904

[0202] In this Action 904, the first device 131 performs the action.

[0203] The action is to support the collection of the data. The collection of the data is based on the obtained one or more configurations.

[0204] Data collection phase for the UL

[0205] In some examples, in a data collection phase for the UL, the first device 131 may then transmit the data collection pattern, based on a previously configured transmission length, or until terminated by a separate signaling, not shown. In a particular example, the first device 131 may transmit PUSCH using the configured training pattern.In some examples, for the UL operation, the first device 131 may transmit the collected data to the first network node 111, e.g., a radio access network node or a code network node.

[0206] In some examples, the first network node 111, e.g., the BS or the BS vendor, may then use the received data collection pattern transmissions from one or more UEs to e.g., perform ML model training. Ground truth may be provided via the pattern, or derived by the first network node 111 based on the configuration information, as discussed below.

[0207] Alternatively, the collected UL data may be passed to a node in RAN or in the core network, such as the second network node 112. The node that may perform the collection may be controlled by the network vendor or the mobile network operator.

[0208] Data collection phase for the DL

[0209] The first device 131 may then receive data collection pattern transmissions, based on a previously configured transmission length, or until terminated by a separate signaling.

[0210] In some examples, the operation may be in the DL, and the user plane may be used to transfer the collected data.

[0211] In some examples, the operation may be in the DL, and the collected data may comprise interference measurement information.

[0212] In some examples, RSs used for overlaid RS transmissions may be re-used for nonoverlaid RS REs, e.g., the data / pilot overlay power ratio may be set to zero for certain configured REs.

[0213] Inference phase for the UL

[0214] In some examples, in an inference phase for the UL, when receiving a grant for UL data transmission, e.g., via PDCCH / DCI, the first device 131 may transmit the PUSCH using the configured overlaid RS, e.g., DMRS, format.

[0215] Inference phase for the DL

[0216] In some examples, in an inference phase for the DL, when receiving a DL data scheduling message, e.g., via PDCCH / DCI, the first device 131 may perform PDSCH reception, assuming the configured overlaid RS, e.g., DMRS, format.

[0217] Action 905

[0218] In this Action 905, the first device 131 may initiate training ofthe MLM.

[0219] Initiating may comprise starting or triggering.

[0220] The MLM may be any of, e.g., a Neural Network, a Convolutional Neural Network, a Federated Learning MLM, an Autoencoder, Decision Tree, etc.

[0221] The training of the MLM may be with data collected based on one or more configurations, e.g., resulting from performing the action.The MLM may be to predict demodulation. The demodulation may be of a channel with the overlaid RSs. The channel may be, e.g., between the first device 131 and the first network node 111.

[0222] The first device 131 may be a device that may collect data to be used as input to the MLM, e.g., during a training phase of the MLM. In some examples, the first device 131 may train the MLM itself.

[0223] The training of the MLM may be performed by one of: the first device 131, the second device 132, the first network node 111 and the second network node 112 operating in the communications system 100.

[0224] With the proviso the training of the MLM is performed by one of: the second device 132, the first network node 111 and the second network node 112, the initiating in Action 905 of the training may comprise sending one or more indications referred to herein as one or more fourth indications. The one or more fourth indications may indicate the collected data based on the obtained one or more configurations. The sending of the one or more fourth indications may be to the one of: the second device 132, the first network node 111 and the second network node 112.

[0225] In the data collection phase for the DL, the first device 131, the second device 132, ora UE / chipset vendor may then use the received data collection patterns from one or more BSs, e.g., from the first network node 111 , to e.g., perform ML model training. For example, the first device 131 , the second device 132, or a UE / chipset vendor may train an AI / ML model for PDSCH demodulation with overlaid RS, e.g., DMRS, using ground truth from the training pattern. Providing the training data to a UE / chipset vendor development site for training may include dataset transfer via Radio Access Network (RAN) or Over-The-Top (OTT) interfaces. For example, the user plane may be used to transfer the collected data to the first network node 111, e.g., the RAN node or CN node.

[0226] To improve the model performance, information of the interference or relative interference to signal, e.g., SINR, or similar information may be useful to the ML model. Each measurement in the data collection step may thus also have a measurement of the interference using a configured interference measurement resource (IMR). This IMR may capture the inter-cell interference for example may be used to compute an SINR or Signal to Noise Ratio (SNR) value for the data sample. The estimated interference level or SINR or SNR by the first device 131 may be associated to each data sample and transferred together with the data samples to the data collection node on the NW side.

[0227] Examples of ML model input / output relations and label provision during training In one class of examples, the ML model-based algorithm may be designed for channel estimation. The input of the model during inference may be overlaid REs and output may be channel estimates for a certain RE set.For training such a model, it may be necessary to obtain channel estimate ground truth. In one example, when the first type of data collection pattern may be provided, e.g., on Figure 17A, the ground truth may be obtained from overlaid REs with known data. When the data / RS power ratio may be available, the combined known overlaid data and RS components, using their properly weighted sum, may be used as the effective RS sequence for traditional channel estimation, yielding the labels / ground truth. It may be noted that according to the third example of the first type, if the power ratio of regular DMRS RE groups is 100% RS, legacy channel estimation algorithms may be used for obtaining the ground truth.

[0228] In another example, when the second type of data collection pattern is provided, e.g., on Figure 17B, the ground truth may be obtained e.g., by using the non-overlaid REs with predefined RS sequence in some REs in the training pattern and allowing the receiver to perform conventional channel estimation. The channel estimates from those REs may then be interpolated or otherwise processed to serve as channel estimate ground truth for the overlaid REs during model training.

[0229] As explained above, the non-overlaid REs may be placed in close proximity / interspersed / interleaved with the overlaid REs to minimize channel differences experienced by the two RE groups. In one example, once the channel estimation may have been performed, the orthogonal RS REs may be filled with synthetic contents, using the newly estimated channel coefficients. The REs for e.g., a given slot may then be used for training with minimal impact to data distribution compared to the one encountered during inference. In another example, the overlaid REs from multiple symbols / slots may be collected to form a full slot, or another number of scheduled symbols, containing overlaid symbols, to be used as input to the model for training.

[0230] In another class of examples, the ML model-based algorithm may be designed for demodulation of data symbols in the overlaid REs. The input of the model during inference may be overlaid REs and the output may be soft values, e.g., log-likelihood ratio values describing the probability that a bit is 0 or 1 , from the data symbols in these REs.

[0231] For training such a model, it may be necessary to obtain data symbol labels / ground truth. In one example, when the first type of data collection pattern may be provided, e.g., on Figure 17A, the data labels may be provided by configuring the first device 131 with a data sequence generation algorithm or a seed. One or more algorithms and / or seeds may be provided in a specification document. The receiver may be provided with an indication of which algorithm or seed to use, either explicitly via BS signaling or as a pointer to a predefined option, e.g., in the specification. The receiver may thus possess the data labels for training, while the transmitted overlaid REs may contain the same data sequence, comprising a known data transmission.In one example, when the second type of data collection pattern may be provided, e.g., on Figure 17B, using user data with Cyclic Redundancy Check (CRC) protection as ground truth may be achieved by utilizing the separate orthogonal RS REs. The data labels may be obtained by performing channel estimation using the non-overlaid RS, then removing the RS component in the overlaid REs and demodulating the data symbols, which may then yield the ground truth labels if the CRC checks out. This approach may yield a slightly lower data collection rate if there are erroneously decoded transport blocks but any user data may be used fortraining and additional data repetition overhead may be avoided.

[0232] In one example, when the third type of data collection pattern may be provided, e.g., on Figure 17C, using user data with CRC protection as ground truth may be achieved by utilizing the separate orthogonal RS REs. and the data-only REs matching the data contents in overlaid REs in some. The receiver may perform conventional channel estimation and demodulation of the data-only REs to extract the soft value labels from the conventional demodulator output. For better ground truth quality, the demodulated data may be decoded and, if successful, the soft value labels may be regenerated. The approach may yield best data demodulation performance, and thus best ground truth quality, but may incur an overhead due to the separate data-only REs that may repeat the overlaid data contents. As an extension of this approach, the overlaid and non-overlaid data contents may use different Modulation and Coding Schemes (MCS). After demodulation and / or decoding of the non-overlaid user data, the resulting uncoded stream may be remapped to the MCS used in the overlaid REs.

[0233] In yet another example, the data labels may be provided via a separate signaling, e.g., via RRC.

[0234] Action 906

[0235] In this Action 906, the first device 131 may initiate outputting a third indication.

[0236] Initiating may comprise starting or triggering.

[0237] The third indication may be of the trained MLM.

[0238] In some embodiments, the method may further comprise the following action.

[0239] Action 907

[0240] In this Action 907, the first device 131 may use the trained MLM.

[0241] The using in this Action of the trained MLM may be e.g., to predict demodulation, e.g., with an overlaid first RS.

[0242] This Action 907 may be understood to correspond to an inference phase for the DL. In some examples, the first device 131 may then use the previously trained ML model for PDSCH data reception. For example, the first device 131 may perform PDSCH demodulationbased on the received PDSCH with overlaid DMRS. After that, other legacy procedures, e.g. HARQ signaling, may proceed as usual.

[0243] Embodiments of a computer-implemented method, performed by a device, such as the second device 132, will now be described with reference to the flowchart depicted in Figure 10.

[0244] The method is for handling the data pertaining to the RSs. The second device 132 operates in a system, such as the communications system 100.

[0245] In some embodiments, the communications system 100 may support New Radio (NR). Several embodiments are comprised herein. The method may comprise three or more of the following actions. In some embodiments, all the actions may be performed. In some examples, Action 1002, Action 1004, and Action 1005 are performed. One or more embodiments may be combined, where applicable. It should be noted that the examples herein are not mutually exclusive. Components from one embodiment may be tacitly assumed to be present in another embodiment and it will be obvious to a person skilled in the art how those components may be used in the other exemplary embodiments. All possible combinations are not described to simplify the description. A non-limiting example of the method performed by the second device 132 is depicted in Figure 10. In Figure 10, optional actions in some embodiments may be represented with dashed lines. In some embodiments, the actions may be performed in a different order than that depicted Figure 10.

[0246] The detailed description of some of the following corresponds to the same references provided above, in relation to the actions described for the first device 131 and will thus not be repeated here to simplify the description. For example, the MLM may be any of, e.g., a Neural Network, a Convolutional Neural Network, a Federated Learning MLM, an Autoencoder, Decision Tree, etc.

[0247] The second device 132 may be a device that may perform inference with the MLM, once trained. The second device 132 may use similar input data to the MLM as the first device 131, using new or fresh data or measurements.

[0248] Action 1001

[0249] In this Action 1001, the second device 132 may obtain the trained MLM. The MLM may have been trained to predict demodulation of a channel with the overlaid RSs.

[0250] The obtaining in this Action 1001 may be, e.g., from the first device 131 or the second network node 112 operating in the communications network 100.

[0251] Action 1002

[0252] In this Action 1002, the second device 132 obtains a first configuration.The first configuration is of the one or more configurations. The one or more configurations indicate a first pattern. The first pattern is of the one or more patterns. The one or more patterns are of collection of the data pertaining to overlaid RSs.

[0253] The obtaining in this Action 1002 is from the first network node 111 operating in the communications system 100.

[0254] Action 1003

[0255] In this Action 1003, the second device 132 performs an action, e.g., a first action.

[0256] The first action may be to support the collection of fourth data. The collection may be based on the obtained first configuration.

[0257] Performance of the first action may result in collected fourth data. The collected fourth data may comprise received fourth data or transmitted fourth data. Inference input data may comprise e.g., complex symbol estimates extracted from the individual REs, where the estimates may contain a data component and an RS component.

[0258] In some embodiments, one of the following may apply: i) the first action may be performed in the UL and the first action may comprise performing a transmission using the first pattern of collection of data, ii) the first action may be performed in the DL and the first action may comprise performing a reception using the first pattern of collection of data, and iii) the first action may be in the downlink and the collected fourth data may comprise information indicating one or more measurements of interference.

[0259] Action 1004

[0260] In this Action 1004, the second device 132 predicts demodulation.

[0261] The demodulation is of a channel, e.g., a first channel, with the overlaid RSs.

[0262] The predicting in this Action 1004 is using the collected fourth data as input to the trained MLM.

[0263] In some embodiments, one or more of the following may apply: i) the RSs may be used for demodulation, ii) the RSs may be used for phase tracking, iii) the RSs may comprise DMRSs, iv) the one or more patterns of collection of data pertaining to overlaid RSs may comprise REs comprising the RSs overlaid with data, wherein the data may be unknown data, v) content of data in the non-overlaid Reference Signal REs may be the data comprised in the REs comprising the RSs overlaid with data, vi) the RSs may be transmitted by the third network node 103 operating in the communications system 100, vii) the first channel may be between the second device 132 and the third network node 113, viii) at least of the one or more configurations may comprise an overlay power ratio parameter, ix) at least one of the one or more configurations may comprise an overlay power ratio parameter and / or a measurement report configuration intended to be used by a receiver to measure interference, x) RSs used foroverlaid RS transmissions may be re-used for non-overlaid RS REs, e.g., the data / pilot overlay power ratio may be set to zero for certain configured REs, and xi) the communications system 100 may be the wireless communications system 100.

[0264] Action 1005

[0265] In this Action 1005, the second device 132 initiates outputting a fifth indication.

[0266] Initiating may comprise starting or triggering, e.g., sending, the fifth indication.

[0267] The fifth indication is of the predicted demodulation. The fifth indication may be a result of the inference using the MLM.

[0268] Embodiments of a computer-implemented method, performed by a network node, such as the first network node 111, will now be described with reference to the flowchart depicted in Figure 11. The method is for handling the data pertaining to the RSs. The first network node 111 operates in a communications system, such as the communications system 100.

[0269] In some embodiments, the communications system 100 may support New Radio (NR). Several embodiments are comprised herein. The method may comprise one or more of the following actions. In some embodiments, all the actions may be performed. In some examples, Action 1102 is performed. One or more embodiments may be combined, where applicable. It should be noted that the examples herein are not mutually exclusive.

[0270] Components from one embodiment may be tacitly assumed to be present in another embodiment and it will be obvious to a person skilled in the art how those components may be used in the other exemplary embodiments. All possible combinations are not described to simplify the description. A non-limiting example of the method performed by the first network node 111 is depicted in Figure 11. In Figure 11 , optional actions in some embodiments may be represented with dashed lines. In some embodiments, the actions may be performed in a different order than that depicted Figure 11.

[0271] The detailed description of some of the following corresponds to the same references provided above, in relation to the actions described for the first device 131 and will thus not be repeated here to simplify the description. For example, the MLM may be any of, e.g., a Neural Network, a Convolutional Neural Network, a Federated Learning MLM, an Autoencoder, Decision Tree, etc.

[0272] The first network node 111 may be a network node that may configure the first device 131 to collect data to use as input to the MLM during a training phase of the MLM. In some examples, the first network node 111 may train the MLM itself.

[0273] Action 1101

[0274] In this Action 1101, the first network node 111 may obtain the first indication.The first indication may indicate the capability. The capability may be to perform the action to support the collection of the data based on the sent one or more configurations.

[0275] The obtaining in this Action 1101 may be from the first device 131.

[0276] Action 1102

[0277] In this Action 1102, the first network node 111 sends the one or more configurations, e.g., the first configuration.

[0278] The one or more configurations indicate the one or more patterns. The one or more patterns are of collection of the data pertaining to, e.g., be about / indicate / be based on, the overlaid RSs.

[0279] The sending in this Action 1102 is to the first device 131 operating in the communication system 100.

[0280] The first network node 111 may send, in this Action 1102, the first configuration to the second device 132.

[0281] Some aspects of the embodiments herein may have ORAN implementation impact. In one example, the data collection pattern configurations determined at a baseband functionality of the first network node 111, e.g., gNB Centralized Unit (CU) / Distributed Unit (DU), may be signalled over ORAN interfaces to the Transmission Reception Points (TRPs), Radio Units (RU), for signal generation and transmission.

[0282] Action 1103

[0283] In this Action 1103, the first network node 111 may send the second indication.

[0284] The second indication may trigger the performing of the action.

[0285] The sending in this Action 1103 may be to the first device 131.

[0286] In some embodiments, one of the following may apply: i) the action, e.g., the first action, may be performed in the UL, and the action, e.g., the first action, may comprise performing a transmission using the one or more, e.g., the first, patterns of collection of data, and ii) the action, e.g., the first action, may be performed in the DL, and the action, e.g., the first action, may comprise performing a reception using the one or more, e.g., the first, patterns of collection of data.

[0287] The second indication may indicate one of: i) to transmit the one or more patterns of collection of data based on the sent one or more configurations, and ii) to receive the one or more patterns of collection of data based on the sent one or more configurations.

[0288] The first network node 111 may transmit the overlaid RSs.

[0289] In some embodiments, the method may further comprise one or more of the following three actions.Action 1104

[0290] In this Action 1104, the first network node 111 may receive the one or more fourth indications.

[0291] The one or more fourth indications may indicate the collected data, e.g., by the first device 131, based on the sent one or more configurations. The receiving in this Action 1104 may be from at least the first device 131.

[0292] In one example, the collected data may be transferred over ORAN interfaces from the RU to the baseband functionality.

[0293] Action 1105

[0294] In this Action 1105, the first network node 111 may initiate training of the MLM.

[0295] The training of the MLM may be with the collected data, e.g., resulting from performing the action.

[0296] The MLM may be to predict demodulation. The demodulation may be of the channel with the overlaid RSs. The channel may be, e.g., between the first device 131 and the first network node 111.

[0297] In a particular example, the vendor of the first network node 111, e.g., BS / vendor may train an AI / ML model for PUSCH demodulation with overlaid DMRS.

[0298] In some embodiments, one or more of the following may apply: i) the RSs may be used for demodulation, ii) the RSs may be used for phase tracking, iii) the RSs may comprise DMRSs, iv) the data to be collected may be to be used in a life cycle management of the MLM, v) the one or more patterns of collection of data pertaining to overlaid RSs may comprise one of: a) REs comprising the RSs overlaid with data, referred to herein as “first data”, wherein the first data may be known data, b) REs comprising the RSs overlaid with data, referred to herein as “second data”, and REs comprising non-overlaid Reference Signal REs, wherein the second data may be unknown data, and with a cyclic redundancy check, and c) REs comprising the RSs overlaid with second data, wherein the second data may be unknown data, and REs comprising non-overlaid Reference Signal REs lacking data, and REs comprising only data, referred to herein as “third data”, vi) the content of the third data in the non-overlaid Reference Signal REs may be the second data comprised in the REs comprising the RSs overlaid with the second data, vii) the action may be in the downlink and the data to be collected may have to comprise information indicating one or more measurements of interference, viii) at least one of the one or more configurations may comprise an overlay power ratio parameter intended to be used by a receiver to measure interference, ix) at least one of the one or more configurations may comprise an overlay power ratio parameter and / or a measurement report configuration intended to be used by a receiver to measure interference, x) RSs used for overlaid RS transmissions may be re-used for non-overlaid RS REs, e.g., the data / pilot overlay power ratiomay be set to zero for certain configured REs, and xi) the communications system 100 may be the wireless communications system 100.

[0299] Action 1106

[0300] In this Action 1106, the first network node 111 may initiate outputting the third indication. The third indication may be of the trained MLM.

[0301] The first network node 111 may then use the previously trained ML model for PUSCH data reception. In a particular example, the first network node 111 may perform PUSCH demodulation based on the received PUSCH with overlaid DMRS. After that, other legacy procedures, e.g., Hybrid Automatic Repeat Request (HARQ) signaling, may proceed as usual. For example, the BS may perform HARQ signalling, etc.

[0302] Embodiments of a computer-implemented method, performed by a network node, such as the second network node 112, will now be described with reference to the flowchart depicted in Figure 12. The method is for handling the data pertaining to the RSs. The second network node 112 may operate in a communications system, such as the communications system 100.

[0303] In some embodiments, the communications system 100 may support New Radio (NR). Several embodiments are comprised herein. The method may comprise one or more of the following actions. In some embodiments, all the actions may be performed. In some examples, Action 1201, Action 1202 and Action 1203 are performed. One or more embodiments may be combined, where applicable. It should be noted that the examples herein are not mutually exclusive. Components from one embodiment may be tacitly assumed to be present in another embodiment and it will be obvious to a person skilled in the art how those components may be used in the other exemplary embodiments. All possible combinations are not described to simplify the description. A non-limiting example of the method performed by the second network node 112 is depicted in Figure 12. In Figure 12, optional actions in some embodiments may be represented with dashed lines. In some embodiments, the actions may be performed in a different order than that depicted Figure 12.

[0304] The detailed description of some of the following corresponds to the same references provided above, in relation to the actions described for the first device 131 and will thus not be repeated here to simplify the description. For example, the MLM may be any of, e.g., a Neural Network, a Convolutional Neural Network, a Federated Learning MLM, an Autoencoder, Decision Tree, etc.

[0305] The second network node 112 may be a network node 112 that may collect data to use as input to the MLM during a training phase of the MLM and train the MLM.Action 1201

[0306] In this Action 1201 , the second network node 112 obtains the collected data.

[0307] The obtaining in this Action 1201 is from at least the first device 131 operating in the communications system 100. That is, the second network node 112 may receive a respective third indication from every first device.

[0308] The data has been collected by the first device 131. The data has been collected based on the one or more configurations. The one or more configurations indicate the one or more patterns of collection of data pertaining to overlaid RSs.

[0309] Action 1202

[0310] In this Action 1202, the second network node 112 initiates training of the MLM.

[0311] The training of the MLM is with the collected data.

[0312] The training of the MLM is to predict the demodulation of the channel with the overlaid RSs.

[0313] In a particular example, the vendor of the second network node 112, e.g., BS / vendor may train an AI / ML model for PUSCH demodulation with overlaid DMRS.

[0314] Action 1203

[0315] In this Action 1203, the second network node 112 initiates outputting the third indication. The third indication is of the trained MLM.

[0316] Initiating may comprise starting or triggering.

[0317] In some embodiments, the action, performed by the first device 131, to support the collection of the data based on the one or more configurations may comprise one of the following: i) the action, e.g., the first action, may be performed in the UL, and the action, e.g., the first action, may comprise performing a transmission using the one or more, e.g., the first, patterns of collection of data, and ii) the action, e.g., the first action, may be performed in the DL, and the action, e.g., the first action, may comprise performing a reception using the one or more, e.g., the first, patterns of collection of data.

[0318] In some examples, the action may be in the downlink and the collected data may comprise information indicating one or more measurements of interference.

[0319] In some embodiments, one or more of the following may apply: i) the RSs may be used for demodulation, ii) the RSs may be used for phase tracking, iii) the RSs may comprise DMRSs, iv) the one or more patterns of collection of data pertaining to overlaid RSs may comprise REs comprising the RSs overlaid with first data, wherein the data first may be unknown data, v) the third indication may be output to one or more of: the first device 131 and the second device 132 operating in the communications system 100, vi) the channel may be between one or more of: a) the first device 131 and the first network node 111 operating in thecommunications system 100, and b) the second device 132 and the third network node 113 operating in the communications system 100, vii) at least one of the one or more configurations may comprise an overlay power ratio parameter, viii) at least one of the one or more configurations may comprise an overlay power ratio parameter and / or a measurement report configuration intended to be used by a receiver to measure interference, ix) RSs used for overlaid RS transmissions may be re-used for non-overlaid RS REs, e.g., the data / pilot overlay power ratio may be set to zero for certain configured REs, and x) the communications system 100 may be the wireless communications system 100.

[0320] Some embodiments herein will now be further described with some non-limiting examples, which may be combined with the embodiments just described.

[0321] In the following description, any reference to a / the UE, may be understood to equally refer any of the first device 131 and the second device 132, based on context; any reference to a / the gNB and / or a / the Radio access node and / or a / the core network access node and / or a / the BS and / or a / the RAN node and / or a / the base station and / or a / the NW node may be understood to equally refer to any of the first network node 111 and the second network node 112, based on context.

[0322] High-level procedure steps for data collection and inference phases for UL and for DL are depicted in Figures 13-14 and Figure 15-16, respectively. In any of Figures 13-16, the first network node 111 is represented as a BS and the first device 131 is represented as a UE.

[0323] Data collection phase for the UL

[0324] Figure 13 is a signalling diagram illustrating a non-limiting example of embodiments herein during a training phase of the MLM, wherein the action is performed by the first device 131 in the UL, and the training of the MLM is performed on the network side, by the first network node 111. The first device 131 may, in accordance with Action 902 and Action 1102, receive from the first network node 111, e.g., a radio access network node, a core network access node or the BS a configuration for a data collection procedure, or a data collection signal transmission, where the configuration may include DMRS format, data collection pattern details, data collection duration, etc. Configuration details may depend on the ML algorithm type and ground truth provision choices, discussed in more detail herein. In an example, the first network node 111 may configure the first device 131 for PUSCH transmission with a training pattern for overlaid DMRS. In one example, the first device 131 may, according to Action 903, use receiving the configuration as a trigger to initiate data collection pattern transmission. In another example, the first device 131 may, according to Action 903 receive an explicit initiation / trigger signal to initiate the transmission, for example from a RAN node such as the base station. In a particular example, the first network node 111 , at 1301 , may initiate transmission of PUSCHtransmission with the training pattern for overlaid DMRS. The first device 131 may then, according to Action 904, transmit the data collection pattern, based on a previously configured transmission length, or until terminated by a separate signaling (not shown). In a particular example, the first device 131 may transmit PUSCH using the configured training pattern. The first network node 111 or the first network node 111 vendor may then use the received data collection pattern transmissions from one or more UEs to e.g., perform ML model training according to Action 1105. Ground truth may be provided via the pattern, or derived by the first network node 111 based on the configuration information, as discussed below. Alternatively, the collected UL data may be passed to a node in RAN or in the core network. The node that may perform the collection may be controlled by the network vendor or the mobile network operator. In a particular example, the first network node 111 / vendor may train an AI / ML model for PUSCH demodulation with overlaid DMRS. Actions in boxes with solid or dashed-only lines may be performed online. Actions in boxes with dashed and dotted lines may be performed offline by the node or vendor.

[0325] Inference phase for the UL

[0326] Figure 14 is a signalling diagram illustrating a non-limiting example of embodiments herein during an inference phase of the MLM, wherein the action is performed by the first device 131 in the UL, and the inference with the MLM is performed on the network side, by the first network node 111. In this example, the first device 131 is the same device as the second device 132. The first device 131 may, according to Action 902 and Action 1102, receive from the first network node 111 a configuration for PUSCH data transmission, where the configuration may include the DMRS format and overlay parameters. In an example, the network node 111 may configure the UE for PUSCH transmission with overlaid DMRS. When receiving a grant for UL data transmission, e.g., via PDCCH / DCI, in accordance with Action 903, the first device 131 may, according to Action 904 and Action 1104, transmit the PUSCH using the configured overlaid DMRS format. The first network node 111 may then use the previously trained ML model for PUSCH data reception, in accordance with Action 1104. In a particular example, the first network node 111 may perform PUSCH demodulation based on the received PUSCH with overlaid DMRS. After that, other legacy procedures, e.g., Hybrid Automatic Repeat Request (HARQ) signaling, may proceed as usual. For example, the first network node 111 may perform HARQ signalling, etc at 1401. Actions in boxes with solid or dashed-only lines may be performed online. Actions in boxes with dashed and dotted lines may be performed offline by the node or vendor.

[0327] Data collection phase for the DLFigure 15 is a signalling diagram illustrating a non-limiting example of embodiments herein during a training phase of the MLM, wherein the action is performed by the first device 131, UE-sided, in the DL, and the training of the MLM is performed by the first device 131. The first device 131 may receive, from the first network node 111 or any other network node such as a RAN node or a core network node, in accordance with Action 902 and Action 1102, a configuration for a data collection procedure, or a data collection signal transmission, where the configuration may include DMRS format, data collection pattern details, data collection duration, etc. In an example, the first network node 111 may configure the first device 131 for PDSCH reception with a training pattern for overlaid DMRS. In one example, the first device 131, in accordance with Action 903, may use receiving the configuration as a trigger to initiate data collection pattern reception. In another example, the first device 131 may receive an explicit initiation / trigger signal to initiate the reception. For example, a mobile network operator controlled node in the RAN network may be used to trigger the initiation and likewise disabling of data collection in the first device 131. Alternatively, this collection may be triggered by the network vendor controlled node or functionality in the network side. In a particular example, the first network node 111 may, in accordance with Action 1103, indicate start of PDSCH transmission with the training pattern for overlaid DMRS. The first network node 111 may then, at 1501, transmit PDSCH using the configured training pattern. The first device 131 may then receive data collection pattern transmissions, based on a previously configured transmission length, or until terminated by a separate signaling (not shown). The first device 131 or the first device 131 / chipset vendor may then use the received data collection patterns from one or more BSsto e.g., perform ML model training. For example, the first device 131 or the first device 131 / chipset vendor may train an AI / ML model for PDSCH demodulation with overlaid DMRS, using ground truth from the training pattern. Providing the training data to the first device 131 / chipset vendor development site fortraining may include dataset transfer via Radio Access Network (RAN) or Over-The-Top (OTT) interfaces. For example, the user plane may be used to transfer the collected data to the RAN node or CN node. To improve the model performance, information of the interference or relative interference to signal, e.g., SINR, or similar information may be useful to the ML model. Each measurement in the data collection step may thus also have a measurement of the interference using a configured interference measurement resource (IMR). This IMR may capture the inter-cell interference for example may be used to compute an SINR or SNR value for the data sample. The estimated interference level or SINR or Signal to Noise Ratio (SNR) by the first device 131 may be associated to each data sample and transferred together with the data samples to the data collection node on the NW side. Actions in boxes with solid or dashed-only lines may be performed online. Actions in boxes with dashed and dotted lines may be performed offline by the node or vendor.Inference phase for the DL

[0328] Figure 16 is a signalling diagram illustrating a non-limiting example of embodiments herein during an inference phase of the MLM, wherein the action is performed by the first device 131 in the DL, and the inference with the MLM is performed by the first device 131. In this example, the first device 131 is the same device as the second device 132. The first device 131, in accordance with Action 902 and Action 1102, may receive from the first network node 111 a configuration for PDSCH data reception, where the configuration may include the DMRS format and overlay parameters. In an example, the first network node 111 may configure the first device 131 for PDSCH reception with overlaid DMRS. The first network node 111 may then transmit PDSCH with overlaid DMRS at 1601. When receiving a DL data scheduling message, e.g., via PDCCH / DCI, the first device 131 may, in accordance with Action 907 perform PDSCH reception, assuming the configured overlaid DMRS format. The first device 131 may then use the previously trained ML model for PDSCH data reception. For example, the first device 131 may perform PDSCH demodulation based on the received PDSCH with overlaid DMRS. After that, other legacy procedures, e.g. HARQ signaling, may proceed as usual at 1602. Actions in boxes with solid or dashed-only lines may be performed online. Actions in boxes with dashed and dotted lines may be performed offline by the node or vendor.

[0329] Figure 17 is a schematic diagram depicting non-limiting examples of aspects of embodiments herein. Particularly, Figure 17, in panels a), b), and c), depicts, respectively, a non-limiting example of each of the first type of data collection pattern, the second type of data collection pattern and the third type of data collection pattern, as described earlier in relation to Action 901. Each of the diagrams depicted in Figure 17 represents time in the horizonal axis, and frequency in the vertical axis. The first type of data collection pattern, as schematically depicted in the example of panel a, may comprise overlaid REs with known contents, in a first example, with equal power ratio. All REs contain overlaid symbols, RS sequence and data contents may be previously defined. The second type of data collection pattern, as schematically depicted in the example of panel b, may comprise additional REs with orthogonal RS. REs may contain overlaid symbols based on user data, unknown data, and RS-only REs provided for channel estimation. The third type of data collection pattern, as schematically depicted in the example of panel c, may comprise additional REs with orthogonal RS and data. Some REs may contain overlaid symbols based on user data, unknown data, RS-only REs for channel estimation for data demodulation, and data-only REs repeating the same user data.

[0330] Figure 18 is a schematic diagram depicting non-limiting examples of aspects of embodiments herein. Particularly, Figure 18, in panels a), b), and c), depicts, respectively, anon-limiting example of each of a T / F multiplexing first example of data collection pattern, a T multiplexing second example of data collection pattern, and a Frequency (F) multiplexing third example of data collection pattern. Each of the diagrams depicted in Figure 18 represents time in the horizonal axis, and frequency in the vertical axis. In each of the examples, the multiplexing is performed with overlaid RE, depicted in horizontal stripes, and non-overlaid RE, depicted in black diamonds.

[0331] Examples of embodiments herein may include:

[0332] Example 1. Method in a UE to support data collection for overlaid RS transmissions, the method comprising:

[0333] receiving from a NW node one or more overlaid RS data collection pattern configurations,

[0334] receiving from a NW node an overlaid RS data collection initiation indication, performing a data collection support operation according to one of the one or more configurations.

[0335] Example 2. + the operation is in the UL, the data collection initiation indication is an indication to transmit a data collection pattern based on the configuration, and the operation is performing a transmission using the data collection pattern.

[0336] Example 2. + the operation is in the DL, the data collection initiation indication is an indication to receive a data collection pattern based on the configuration, and the operation is performing a reception using the data collection pattern.

[0337] Example 3. + the RS is used for demodulation (e.g., DMRS) and / or for phase tracking (e.g, PT- RS).

[0338] Example 4. + the data collected is used for AI / ML LCM-related purposes, e.g., AI / ML model (re)training, AI / ML model monitoring, AI / ML model inference, AI / ML model / functionality selection, AI / ML model / functionality (de)activation, AI / ML model / functionality switching, AI / ML model / functionality fallback.

[0339] Example 5. + the data collection pattern comprises REs with RSs overlaid with dataa. + the data contents comprise data unknown to the receiver with a cyclic redundancy check.

[0340] b. + the pattern additionally comprises REs with non-overlaid RS REs, i.e., REs without data.

[0341] i. + the regular PxSCH format is reused so that regular DMRS is transmitted in all DMRS REs and overlaid RS+data is transmitted in all data REs.

[0342] c. + the data contents in the REs comprise known data, where the data sequence generation is based on the configuration.

[0343] d. the pattern additionally comprises REs with non-overlaid data REs, where the data contents in the non-overlaid REs are based on the data contents in the overlaid REs.

[0344] Example 6. + the UE may transmit to a NW node the capability to performing a data collection support operation for overlaid RS transmission in the UL and / or in the DL.

[0345] Example 7. + an RS port is associated with a PDSCH or PUSCH layer.

[0346] Example 8. + for the DL operation, the UE may transmit the collected data to a radio access network node or a code network node.

[0347] Example 9. + the operation is in the DL, the user plane may be used to transfer the collected data.

[0348] Example 10. + the configuration may comprise one or more of: overlaid pattern format, overlaid RS sequence and other parameters, overlaid / non-overlaid RE alternation pattern, overlaid data sequence generation parameters, etc.

[0349] Example 11. + the configuration may comprise an overlay power ratio parameter and / or a measurement report configuration intended to be used by the receiver to measure interference.

[0350] Example 12. + the legacy NR / 6G design may be partly re-used for the non-overlaid RS REs, e.g. the frequency domain allocation and / or FD_OCC code design and / or the TD-OCC code design may be re-used, and / or the time domain allocation+ the DMRS time domain allocation of legacy NR DMRS design may be changed; this may be used such that overlaid DMRS more easily may be placed next to, in time domain, non-overlaid DMRS, such that the ground truth determined by nonoverlaid DMRS may be as closely related in time as possible as the overlaid DMRS, to mitigate impact of channel aging when training the AI / ML model.

[0351] Example 13. + RSs used for overlaid RS transmissions may be re-used for non-overlaid RS REs, e.g., the data / pilot overlay power ratio may be set to zero for certain configured REs.

[0352] Example 14. + the operation is in the DL, the collected data may comprise interference measurement information.

[0353] Certain embodiments disclosed herein may provide one or more of the following technical advantage(s), which may be summarized as follows.

[0354] The presented data collection procedure for overlaid DMRS+PxSCH transmissions may allow performing AI / ML LCM to implement such functionality, e.g., by enabling the acquisition of training data for supervised learning. Notably, the method may also facilitate an adjustment of the training overhead versus label quality trade-off, e.g., by enabling the data collection when transmitting useful data via the simultaneous transmission of overlaid and non-overlaid RSs with data. The label quality trade-off may be understood to refer to refer to that providing data symbol labels separately may provide higher-reliability labels but also may take up more REs, increasing the overhead.

[0355] Figure 19 depicts an example of the arrangement that the first device 131 may comprise to perform the method actions described above in relation to Figure 9 and / or any of Figures 13-18. The first device 131 is for handling data pertaining to RSs. The first device 131 is configured to operate in the communications system 100.

[0356] Several embodiments are comprised herein. It should be noted that the examples herein are not mutually exclusive. One or more embodiments may be combined, where applicable. All possible combinations are not described to simplify the description. Components from one embodiment may be tacitly assumed to be present in another embodiment and it will be obvious to a person skilled in the art how those components may be used in the other exemplary embodiments. The detailed description of some of the following corresponds to the same references provided above, in relation to the actions described for the first device 131 and will thus not be repeated here. For example, the MLM may be any of, e.g., a Neural Network, a Convolutional Neural Network, a Federated Learning MLM, an Autoencoder, Decision Tree, etc.The first device 131 is configured to perform the obtaining in Action 902, e.g. by means of a processing circuitry 1901 within the first device 131 configured to, obtain, from the first network node 111 configured to operate in the communications system 100, the one or more configurations configured to indicate the one or more patterns of collection of data configured to pertain to overlaid RSs.

[0357] The first device 131 is also configured to perform the performing in Action 904, e.g. by means of the processing circuitry 1901 within the first device 131 configured to, perform the action to support the collection of the data based on the one or more configurations configured to be obtained.

[0358] In some embodiments, one of the following may apply: a) the action may be configured to be performed in the UL, and the action may be configured to comprise performing a transmission using the one or more patterns of collection of data, and b) the action may be configured to be performed in the DL, and the action may be configured to comprise performing a reception using the one or more patterns of collection of data.

[0359] In some embodiments, the method may further comprise one or more of the following two configurations:

[0360] The first device 131 may be configured to perform the providing in Action 901, e.g. by means of the processing circuitry 1901 within the first device 131 configured to, provide, to the first network node 111 the first indication configured to indicate the capability to perform the action to support the collection of the data based on the one or more configurations configured to be obtained.

[0361] The first device 131 may be configured to perform the obtaining in Action 903, e.g. by means of the processing circuitry 1901 within the first device 131 configured to, obtain, from the first network node 111 , the second indication configured to trigger the performing of the action.

[0362] In some embodiments, the second indication may be configured to indicate one of: i) to transmit the one or more patterns of collection of data based on the one or more configurations configured to be obtained, and ii) to receive the one or more patterns of collection of data based on the one or more configurations configured to be obtained.

[0363] In some embodiments, one or more of the following may apply: i) the RSs may be configured to be used for demodulation, ii) the RSs may be configured to be used for phase tracking, iii) the RSs may be configured to comprise DMRSs, iv) the data configured to be collected may be configured to be used in a life cycle management of the MLM, v) the one or more patterns of collection of data configured to pertain to overlaid RSs may be configured to comprise one of: a) REs configured to comprise the RSs overlaid with the first data, wherein the first data may be configured to be known data, b) REs configured to comprise the RSs overlaid with the second data and REs configured to comprise non-overlaid Reference Signal REs, wherein the second data may be configured to be unknown data, and with a cyclicredundancy check, and c) REs configured to comprise the RSs overlaid with the second data, wherein the second data may be configured to be unknown data, and REs configured to comprise non-overlaid Reference Signal REs lacking data, and REs configured to comprise only the third data, vi) the content of the third data in the non-overlaid Reference Signal REs may be configured to be the second data configured to be comprised in the REs configured to comprise the RSs overlaid with the second data, vii) the action may be in the downlink and data to be collected may be configured to comprise the information configured to indicate the one or more measurements of interference, viii) at least one of the one or more configurations may be configured to comprise the overlay power ratio parameter intended to be used by the receiver to measure interference, ix) at least one of the one or more configurations may be configured to comprise the overlay power ratio parameter and / or the measurement report configuration intended to be used by the receiver to measure interference, x) RSs configured to be used for overlaid RS transmissions may be configured to be re-used for non-overlaid RS REs, and xi) the communications system 100 may be configured to be a wireless communications system 100.

[0364] In some embodiments, the method may further comprise one or more of the following two configurations:

[0365] The first device 131 may be configured to perform the initiating in Action 905, e.g. by means of the processing circuitry 1901 within the first device 131 configured to, initiate training of the MLM, with the data configured to be collected based on the one or more configurations, the MLM being configured to predict demodulation of the channel with the overlaid RSs, wherein the channel may be configured to be between the first device 131 and the first network node 111.

[0366] The first device 131 may be configured to perform the initiating in Action 906, e.g. by means of the processing circuitry 1901 within the first network node 111 configured to, initiate outputting the third indication of the trained MLM.

[0367] In some embodiments, the method may further comprise the following configuration: The first device 131 may be configured to perform the using in Action 907, e.g. by means of the processing circuitry 1901 within the first device 131 configured to, use the MLM configured to be trained to predict demodulation with the overlaid first RS.

[0368] In some embodiments, the training of the MLM may be configured to be performed by one of: the first device 131 , the second device 132, the first network node 111 and the second network node 112 configured to operate in the communications system 100. With the proviso the training of the MLM is configured to be performed by one of: the second device 132, the first network node 111 and the second network node 112, the initiating of the training may be configured to comprise sending the one or more fourth indications indicating the data configuredto be collected based on the one or more configurations configured to be obtained, to the one of: the second device 132, the first network node 111 and the second network node 112 The embodiments herein in the first device 131 may be implemented through one or more processors, such as a processing circuitry 1901 in the first device 131 depicted in Figure 19, together with computer program code for performing the functions and actions of the embodiments herein. A processor, as used herein, may be understood to be a hardware component. The program code mentioned above may also be provided as a computer program product, for instance in the form of a data carrier carrying computer program code for performing the embodiments herein when being loaded into the first device 131. One such carrier may be in the form of a CD ROM disc. It is however feasible with other data carriers such as a memory stick. The computer program code may furthermore be provided as pure program code on a server and downloaded to the first device 131.

[0369] The first device 131 may further comprise a memory 1902 comprising one or more memory units. The memory 1902 is arranged to be used to store obtained information, store data, configurations, schedulings, and applications etc. to perform the methods herein when being executed in the first device 131.

[0370] In some embodiments, the first device 131 may receive information from, e.g., the first network node 111 , the second network node 112, the third network node 113, the second device 132, or another structure in the communications system 100, through a receiving port 1903. In some embodiments, the receiving port 1903 may be, for example, connected to one or more antennas in first device 131. In other embodiments, the first device 131 may receive information from another structure in the communications system 100 through the receiving port 1903. Since the receiving port 1903 may be in communication with the processing circuitry 1901, the receiving port 1903 may then send the received information to the processing circuitry 1901. The receiving port 1903 may also be configured to receive other information.

[0371] The processing circuitry 1901 in the first device 131 may be further configured to transmit or send information to e.g., the first network node 111 , the second network node 112, the third network node 113, the second device 132, or another structure in the communications system 100, through a sending port 1904, which may be in communication with the processing circuitry 1901, and the memory 1902.

[0372] Those skilled in the art will also appreciate that the processing circuitry 1901 described above may comprise a combination of analog and digital modules, and / or one or more processors configured with software and / or firmware, e.g., stored in memory, that, when executed by the one or more processors such as the processing circuitry 1901 , perform as described above. One or more of these processors, as well as the other digital hardware, may be included in a single Application-Specific Integrated Circuit (ASIC), or several processors andvarious digital hardware may be distributed among several separate components, whether individually packaged or assembled into a System-on-a-Chip (SoC).

[0373] The processing circuitry 1901 may be configured to, or operable to, perform the method actions according to Figure 9 and / or any of Figures 13-18.

[0374] Also, in some embodiments, the first device 131 may be configured to perform the actions of Figure 9 and / or any of Figures 13-18 with respective units that may be implemented as one or more applications running on one or more processors such as the processing circuitry 1901.

[0375] Thus, the methods according to the embodiments described herein for the first device 131 may be respectively implemented by means of a computer program 1905 product, comprising instructions, i.e., software code portions, which, when executed on at least one processing circuitry 1901 , cause the at least one processing circuitry 1901 to carry out the actions described herein, as performed by the first device 131. The computer program 1905 product may be stored on a computer-readable storage medium 1906. The computer-readable storage medium 1906, having stored thereon the computer program 1905, may comprise instructions which, when executed on at least one processing circuitry 1901, cause the at least one processing circuitry 1901 to carry out the actions described herein, as performed by the first device 131. In some embodiments, the computer-readable storage medium 1906 may be a non-transitory computer-readable storage medium, such as a CD ROM disc, or a memory stick. In other embodiments, the computer program 1905 product may be stored on a carrier containing the computer program 1905 just described, wherein the carrier is one of an electronic signal, optical signal, radio signal, or the computer-readable storage medium 1906, as described above.

[0376] The first device 131 may comprise a communication interface configured to facilitate communications between the first device 131 and other nodes or devices, e.g., the first network node 111 , the second network node 112, the third network node 113, the second device 132, or another structure in the communications system 100. The interface may, for example, include a transceiver configured to transmit and receive radio signals over an air interface in accordance with a suitable standard.

[0377] In other embodiments, the first device 131 may also comprise a radio circuitry 1907, which may comprise e.g., the receiving port 1903 and the sending port 1904. The radio circuitry 1907 may be configured to set up and maintain at least a wireless connection with the first network node 111 , the second network node 112, the third network node 113, the second device 132, or another structure in the communications system 100. Circuitry may be understood herein as a hardware component.

[0378] Hence, embodiments herein also relate to the first device 131 comprising the processing circuitry 1901 and the memory 1902, said memory 1902 containing instructions executable bysaid processing circuitry 1901, whereby the first device 131 is operative to perform the actions described herein in relation to the first device 131, e.g., in Figure 9 and / or any of Figures 13-18.

[0379] Figure 20 depicts an example of the arrangement that the second device 132 may comprise to perform the method actions described above in relation to Figure 10 and / or any of Figures 13-18. The second device 132 is for handling the data pertaining to RSs. The second device 132 is configured to operate in the communications system 100.

[0380] Several embodiments are comprised herein. It should be noted that the examples herein are not mutually exclusive. One or more embodiments may be combined, where applicable. All possible combinations are not described to simplify the description. Components from one embodiment may be tacitly assumed to be present in another embodiment and it will be obvious to a person skilled in the art how those components may be used in the other exemplary embodiments. The detailed description of some of the following corresponds to the same references provided above, in relation to the actions described for the second device 132 and will thus not be repeated here. For example, the MLM may be any of, e.g., a Neural Network, a Convolutional Neural Network, a Federated Learning MLM, an Autoencoder, Decision Tree, etc.

[0381] The second device 132 is configured to perform the obtaining in Action 1002, e.g. by means of a processing circuitry 2001 within the second device 132 configured to, obtain, from the first network node 111 configured to operate in the communications system 100, the first configuration of the one or more configurations configured to indicate the first pattern of the one or more patterns of collection of data pertaining to overlaid RSs.

[0382] The second device 132 is configured to perform the predicting in Action 1004, e.g. by means of the processing circuitry 2001 within the second device 132 configured to, predict, using the collected fourth data as input to the trained MLM, demodulation of the first channel with the overlaid RSs.

[0383] The second device 132 is configured to perform the initiating in Action 1005, e.g. by means of the processing circuitry 2001 within the second device 132 configured to, initiate outputting the fifth indication of the demodulation configured to be predicted.

[0384] In some embodiments, the second network node 112 may be further configured with one or more of the following two configurations.

[0385] The second device 132 may be configured to perform the performing in Action 1003, e.g. by means of the processing circuitry 2001 within the second device 132 configured to, perform the first action to support the collection of the fourth data based on the first configuration configured to be obtained.

[0386] The second device 132 may be configured to perform the obtaining in Action 1001, e.g. by means of the processing circuitry 2001 within the second device 132 configured to, obtain thetrained MLM, from the first device 131 or the second network node 112 configured to operate in the communications system 100.

[0387] In some embodiments, one of the following may apply: i) the first action may be configured to be performed in the UL, and the first action may be configured to comprise performing a transmission using the first pattern of collection of data, ii) the first action may be configured to be performed in the DL, and the first action may be configured to comprise performing a reception using the first pattern of collection of data, and iii) the first action may be configured to be in the DL and the fourth data configured to be collected may be configured to comprise information indicating one or more measurements of interference.

[0388] In some embodiments, one or more of the following may apply: i) the RSs may be configured to be used for demodulation, ii) the RSs may be configured to be used for phase tracking, iii) the RSs may be configured to comprise DMRSs, iv) the one or more patterns of collection of data pertaining to overlaid RSs may be configured to comprise REs configured to comprise the RSs overlaid with the second data, wherein the second data may be configured to be unknown data, v) the RSs may be configured to be transmitted by the third network node 113 configured to operate in the communications system 100, vi) the first channel may be configured to be between the second device 132 and the third network node 113, vii) at least one of the one or more configurations may be configured to comprise the overlay power ratio parameter intended to be used by the receiver to measure interference, viii) at least one of the one or more configurations may be configured to comprise the overlay power ratio parameter and / or the measurement report configuration intended to be used by the receiver to measure interference, ix) RSs configured to be used for overlaid RS transmissions may be configured to be re-used for non-overlaid RS REs, and x) the communications system 100 may be configured to be a wireless communications system 100.

[0389] The embodiments herein in the second device 132 may be implemented through one or more processors, such as a processing circuitry 2001 in the second device 132 depicted in Figure 20, together with computer program code for performing the functions and actions of the embodiments herein. A processor, as used herein, may be understood to be a hardware component. The program code mentioned above may also be provided as a computer program product, for instance in the form of a data carrier carrying computer program code for performing the embodiments herein when being loaded into the second device 132. One such carrier may be in the form of a CD ROM disc. It is however feasible with other data carriers such as a memory stick. The computer program code may furthermore be provided as pure program code on a server and downloaded to the second device 132.

[0390] The second device 132 may further comprise a memory 2002 comprising one or more memory units. The memory 2002 is arranged to be used to store obtained information, storedata, configurations, schedulings, and applications etc. to perform the methods herein when being executed in the second device 132.

[0391] In some embodiments, the second device 132 may receive information from, e.g., the first network node 111 , the second network node 112, the third network node 113, the first device 131, or another structure in the communications system 100, through a receiving port 2003. In some embodiments, the receiving port 2003 may be, for example, connected to one or more antennas in second device 132. In other embodiments, the second device 132 may receive information from another structure in the communications system 100 through the receiving port 2003. Since the receiving port 2003 may be in communication with the processing circuitry 2001 , the receiving port 2003 may then send the received information to the processing circuitry 2001. The receiving port 2003 may also be configured to receive other information.

[0392] The processing circuitry 2001 in the second device 132 may be further configured to transmit or send information to e.g., the network node 111 , the second network node 112, the third network node 113, the first device 131 , or another structure in the communications system 100, through a sending port 2004, which may be in communication with the processing circuitry 2001 , and the memory 2002.

[0393] Those skilled in the art will also appreciate that the processing circuitry 2001 described above may comprise a combination of analog and digital modules, and / or one or more processors configured with software and / or firmware, e.g., stored in memory, that, when executed by the one or more processors such as the processing circuitry 2001 , perform as described above. One or more of these processors, as well as the other digital hardware, may be included in a single Application-Specific Integrated Circuit (ASIC), or several processors and various digital hardware may be distributed among several separate components, whether individually packaged or assembled into a System-on-a-Chip (SoC).

[0394] The processing circuitry 2001 may be configured to, or operable to, perform the method actions according to Figure 10 and / or any of Figures 13-18.

[0395] Also, in some embodiments, the second device 132 may be configured to perform the actions of Figure 10 and / or any of Figures 13-18 with respective units that may be implemented as one or more applications running on one or more processors such as the processing circuitry 2001.

[0396] Thus, the methods according to the embodiments described herein for the second device 132 may be respectively implemented by means of a computer program 2005 product, comprising instructions, i.e., software code portions, which, when executed on at least one processing circuitry 2001 , cause the at least one processing circuitry 2001 to carry out the actions described herein, as performed by the second device 132. The computer program 2005 product may be stored on a computer-readable storage medium 2006. The computer-readable storage medium 2006, having stored thereon the computer program 2005, maycomprise instructions which, when executed on at least one processing circuitry 2001, cause the at least one processing circuitry 2001 to carry out the actions described herein, as performed by the second device 132. In some embodiments, the computer-readable storage medium 2006 may be a non-transitory computer-readable storage medium, such as a CD ROM disc, or a memory stick. In other embodiments, the computer program 2005 product may be stored on a carrier containing the computer program 2005 just described, wherein the carrier is one of an electronic signal, optical signal, radio signal, or the computer-readable storage medium 2006, as described above.

[0397] The second device 132 may comprise a communication interface configured to facilitate communications between the second device 132 and other nodes or devices, e.g., the network node 111, the second network node 112, the third network node 113, the first device 131, or another structure in the communications system 100. The interface may, for example, include a transceiver configured to transmit and receive radio signals over an air interface in accordance with a suitable standard.

[0398] In other embodiments, the second device 132 may also comprise a radio circuitry 2007, which may comprise e.g., the receiving port 2003 and the sending port 2004. The radio circuitry 2007 may be configured to set up and maintain at least a wireless connection with the network node 111, the second network node 112, the third network node 113, the first device 131, or another structure in the communications system 100. Circuitry may be understood herein as a hardware component.

[0399] Hence, embodiments herein also relate to the second device 132 comprising the processing circuitry 2001 and the memory 2002, said memory 2002 containing instructions executable by said processing circuitry 2001 , whereby the second device 132 is operative to perform the actions described herein in relation to the second device 132, e.g., in Figure 10 and / or any of Figures 13-18.

[0400] Figure 21 depicts an example of the arrangement that the first network node 111 may comprise to perform the method actions described above in relation to Figure 11 and / or any of Figures 13-18. The first network node 111 is for handling the data pertaining to RSs. The first network node 111 is configured to operate in the communications system 100.

[0401] Several embodiments are comprised herein. It should be noted that the examples herein are not mutually exclusive. One or more embodiments may be combined, where applicable. All possible combinations are not described to simplify the description. Components from one embodiment may be tacitly assumed to be present in another embodiment and it will be obvious to a person skilled in the art how those components may be used in the other exemplary embodiments. The detailed description of some of the following corresponds to the same references provided above, in relation to the actions described for the first network node 111and will thus not be repeated here. For example, the MLM may be any of, e.g., a Neural Network, a Convolutional Neural Network, a Federated Learning MLM, an Autoencoder, Decision Tree, etc.

[0402] The first network node 111 is configured to perform the sending in Action 1102, e.g. by means of a processing circuitry 2101 within the first network node 111 configured to, send, to the first device 131 configured to operate in the communications system 100, the one or more configurations configured to indicate the one or more patterns of collection of data pertaining to overlaid RSs.

[0403] In some embodiments, the first network node 111 may be further configured with one or more of the following two configurations.

[0404] The first network node 111 may be configured to perform the obtaining in Action 1101, e.g. by means of the processing circuitry 2101 within the first network node 111 configured to, obtain, from the first device 131 , the first indication configured to indicate the capability to perform the action to support the collection of the data based on the one or more configurations configured to be sent.

[0405] The first network node 111 may be configured to perform the sending in Action 1103, e.g. by means of the processing circuitry 2101 within the first network node 111 configured to, send, to the first device 131, the second indication configured to trigger the performing of the action.

[0406] In some embodiments, one of the following may apply: a) the action may be configured to be performed in the UL, and the action may be configured to comprise performing a transmission using the one or more patterns of collection of data, and b) the action may be configured to be performed in the DL, and the action may be configured to comprise performing a reception using the one or more patterns of collection of data.

[0407] In some embodiments, the second indication may be configured to indicate one of: i) to transmit the one or more patterns of collection of data based on the one or more configurations configured to be sent, and ii) to receive the one or more patterns of collection of data based on the one or more configurations configured to be sent.

[0408] In some embodiments, one or more of the following may apply: i) the RSs may be configured to be used for demodulation, ii) the RSs may be configured to be used for phase tracking, iii) the RSs may be configured to comprise DMRSs, iv) the data configured to be collected may be configured to be used in a life cycle management of the MLM, v) the one or more patterns of collection of data configured to pertain to overlaid RSs may be configured to comprise one of: a) REs configured to comprise the RSs overlaid with the first data, wherein the first data may be configured to be known data, b) REs configured to comprise the RSs overlaid with the second data and REs configured to comprise non-overlaid Reference Signal REs, wherein the second data may be configured to be unknown data, and with a cyclic redundancy check, and c) REs configured to comprise the RSs overlaid with the second data,wherein the second data may be configured to be unknown data, and REs configured to comprise non-overlaid Reference Signal REs lacking data, and REs configured to comprise only the third data, vi) the content of the third data in the non-overlaid Reference Signal REs may be configured to be the second data configured to be comprised in the REs configured to comprise the RSs overlaid with the second data, vii) at least one of the one or more configurations may be configured to comprise the overlay power ratio parameter intended to be used by the receiver to measure interference, viii) at least one of the one or more configurations may be configured to comprise the overlay power ratio parameter and / or the measurement report configuration intended to be used by the receiver to measure interference, ix) RSs configured to be used for overlaid RS transmissions may be configured to be re-used for non-overlaid RS REs, and x) the communications system 100 may be configured to be a wireless communications system 100.

[0409] In some embodiments, the third network node 113 may be further configured with one or more of the following three configurations.

[0410] The first network node 111 may be configured to perform the receiving in Action 1104, e.g. by means of the processing circuitry 2101 within the first network node 111 configured to, receive, at least from the first device 131 , the one or more fourth indications configured to indicate the collected data based on the one or more configurations configured to be sent.

[0411] The first network node 111 may be configured to perform the initiating in Action 1105, e.g. by means of the processing circuitry 2101 within the first network node 111 configured to, initiate training of the MLM with the collected data, the MLM being configured to predict demodulation of the channel with the overlaid RSs, wherein the channel may be configured to be between the first device 131 and the first network node 111.

[0412] The first network node 111 may be configured to perform the initiating in Action 1106, e.g. by means of the processing circuitry 2101 within the first network node 111 configured to, initiate outputting the third indication of the trained MLM.

[0413] The embodiments herein in the first network node 111 may be implemented through one or more processors, such as a processing circuitry 2101 in the first network node 111 depicted in Figure 21 , together with computer program code for performing the functions and actions of the embodiments herein. A processor, as used herein, may be understood to be a hardware component. The program code mentioned above may also be provided as a computer program product, for instance in the form of a data carrier carrying computer program code for performing the embodiments herein when being loaded into the first network node 111. One such carrier may be in the form of a CD ROM disc. It is however feasible with other data carriers such as a memory stick. The computer program code may furthermore be provided as pure program code on a server and downloaded to the first network node 111.The first network node 111 may further comprise a memory 2102 comprising one or more memory units. The memory 2102 is arranged to be used to store obtained information, store data, configurations, schedulings, and applications etc. to perform the methods herein when being executed in the first network node 111.

[0414] In some embodiments, the first network node 111 may receive information from, e.g., the second network node 112, the third network node 113, the first device 131, the second device 132, or another structure in the communications system 100, through a receiving port 2103. In some embodiments, the receiving port 2103 may be, for example, connected to one or more antennas in first network node 111. In other embodiments, the first network node 111 may receive information from another structure in the communications system 100 through the receiving port 2103. Since the receiving port 2103 may be in communication with the processing circuitry 2101, the receiving port 2103 may then send the received information to the processing circuitry 2101. The receiving port 2103 may also be configured to receive other information.

[0415] The processing circuitry 2101 in the first network node 111 may be further configured to transmit or send information to e.g., the second network node 112, the third network node 113, the first device 131, the second device 132, or another structure in the communications system 100, through a sending port 2104, which may be in communication with the processing circuitry 2101, and the memory 2102.

[0416] Those skilled in the art will also appreciate that the processing circuitry 2101 described above may comprise a combination of analog and digital modules, and / or one or more processors configured with software and / or firmware, e.g., stored in memory, that, when executed by the one or more processors such as the processing circuitry 2101 , perform as described above. One or more of these processors, as well as the other digital hardware, may be included in a single Application-Specific Integrated Circuit (ASIC), or several processors and various digital hardware may be distributed among several separate components, whether individually packaged or assembled into a System-on-a-Chip (SoC).

[0417] The processing circuitry 2101 may be configured to, or operable to, perform the method actions according to Figure 11 and / or any of Figures 13-18.

[0418] Also, in some embodiments, the first network node 111 may be configured to perform the actions of Figure 11 and / or any of Figures 13-18 with respective units that may be implemented as one or more applications running on one or more processors such as the processing circuitry 2101.

[0419] Thus, the methods according to the embodiments described herein for the first network node 111 may be respectively implemented by means of a computer program 2105 product, comprising instructions, i.e., software code portions, which, when executed on at least one processing circuitry 2101, cause the at least one processing circuitry 2101 to carry out the actions described herein, as performed by the first network node 111. The computer program2105 product may be stored on a computer-readable storage medium 2106. The computer-readable storage medium 2106, having stored thereon the computer program 2105, may comprise instructions which, when executed on at least one processing circuitry 2101, cause the at least one processing circuitry 2101 to carry out the actions described herein, as performed by the first network node 111. In some embodiments, the computer-readable storage medium 2106 may be a non-transitory computer-readable storage medium, such as a CD ROM disc, or a memory stick. In other embodiments, the computer program 2105 product may be stored on a carrier containing the computer program 2105 just described, wherein the carrier is one of an electronic signal, optical signal, radio signal, or the computer-readable storage medium 2106, as described above.

[0420] The first network node 111 may comprise a communication interface configured to facilitate communications between the first network node 111 and other nodes or devices, e.g., the second network node 112, the third network node 113, the first device 131, the second device 132, or another structure in the communications system 100. The interface may, for example, include a transceiver configured to transmit and receive radio signals over an air interface in accordance with a suitable standard.

[0421] In other embodiments, the first network node 111 may also comprise a radio circuitry 2107, which may comprise e.g., the receiving port 2103 and the sending port 2104. The radio circuitry 2107 may be configured to set up and maintain at least a wireless connection with the second network node 112, the third network node 113, the first device 131, the second device 132, or another structure in the communications system 100. Circuitry may be understood herein as a hardware component.

[0422] Hence, embodiments herein also relate to the first network node 111 comprising the processing circuitry 2101 and the memory 2102, said memory 2102 containing instructions executable by said processing circuitry 2101 , whereby the first network node 111 is operative to perform the actions described herein in relation to the first network node 111, e.g., in Figure 11 and / or any of Figures 13-18.

[0423] Figure 22 depicts an example of the arrangement that the second network node 112 may comprise to perform the method actions described above in relation to Figure 12 and / or any of Figures 13-18. The second network node 112 is for handling the data pertaining to RSs. The second network node 112 is configured to operate via the communications system 100.

[0424] Several embodiments are comprised herein. It should be noted that the examples herein are not mutually exclusive. One or more embodiments may be combined, where applicable. All possible combinations are not described to simplify the description. Components from one embodiment may be tacitly assumed to be present in another embodiment and it will be obvious to a person skilled in the art how those components may be used in the other exemplaryembodiments. The detailed description of some of the following corresponds to the same references provided above, in relation to the actions described for the second network node 112 and will thus not be repeated here. For example, the MLM may be any of, e.g., a Neural Network, a Convolutional Neural Network, a Federated Learning MLM, an Autoencoder, Decision Tree, etc.

[0425] The second network node 112 is configured to perform the obtaining in Action 1201 , e.g. by means of a processing circuitry 2201 within the second network node 112 configured to, obtain, from at least the first device 131 configured to operate in the communications system 100, the collected data by the first device 131 based on the one or more configurations. The one or more configurations is configured to indicate the one or more patterns of collection of data pertaining to overlaid RSs.

[0426] The second network node 112 is also configured to perform the initiating in Action 1202, e.g. by means of the processing circuitry 2201 within the second network node 112 configured to, initiate training of the MLM with the collected data. The MLM is configured to predict demodulation of the channel with the overlaid RSs.

[0427] The second network node 112 is configured to perform the initiating in Action 1205, e.g. by means of the processing circuitry 2201 within the second network node 112 configured to, initiate outputting the third indication of the MLM configured to be trained.

[0428] In some embodiments, the action, configured to be performed by the first device 131 , to support the collection of the data based on the one or more configurations may be configured to comprise one of: i) the action configured to be performed in the UL, wherein the action may be configured to comprise performing a transmission using the one or more patterns of collection of data, and ii) the action configured to be performed in the DL, wherein the action may be configured to comprise performing a reception using the one or more patterns of collection of data.

[0429] In some embodiments, one or more of the following may apply: i) the RSs may be configured to be used for demodulation, ii) the RSs may be configured to be used for phase tracking, iii) the RSs may be configured to comprise DMRSs, iv) the one or more patterns of collection of data configured to pertain to overlaid RSs may be configured to comprise REs configured to comprise the RSs overlaid with the second data, wherein the second data may be configured to be unknown data, v) the third indication may be configured to be output to one or more of: the first device 131 and the second device 132 configured to operate in the communications system 100, vi) the channel may be configured to be between one or more of: a) the first device 131 and a first network node 111 configured to operate in the communications system 100, and b) the second device 132 and the third network node 113 configured to operate in the communications system 100, vii) at least one of the one or more configurations may be configured to comprise the overlay power ratio parameter intended to be used by thereceiver to measure interference, viii) at least one of the one or more configurations may be configured to comprise the overlay power ratio parameter and / or the measurement report configuration intended to be used by the receiver to measure interference, ix) RSs configured to be used for overlaid RS transmissions may be configured to be re-used for non-overlaid RS REs, and x) the communications system 100 may be configured to be a wireless communications system 100.

[0430] The embodiments herein in the second network node 112 may be implemented through one or more processors, such as a processing circuitry 2201 in the second network node 112 depicted in Figure 22, together with computer program code for performing the functions and actions of the embodiments herein. A processor, as used herein, may be understood to be a hardware component. The program code mentioned above may also be provided as a computer program product, for instance in the form of a data carrier carrying computer program code for performing the embodiments herein when being loaded into the second network node 112. One such carrier may be in the form of a CD ROM disc. It is however feasible with other data carriers such as a memory stick. The computer program code may furthermore be provided as pure program code on a server and downloaded to the second network node 112.

[0431] The second network node 112 may further comprise a memory 2202 comprising one or more memory units. The memory 2202 is arranged to be used to store obtained information, store data, configurations, schedulings, and applications etc. to perform the methods herein when being executed in the second network node 112.

[0432] In some embodiments, the second network node 112 may receive information from, e.g., the first network node 111 , the third network node 113, the first device 131 , the second device 132, or another structure in the communications system 100, through a receiving port 2203. In some embodiments, the receiving port 2203 may be, for example, connected to one or more antennas in second network node 112. In other embodiments, the second network node 112 may receive information from another structure in the communications system 100 through the receiving port 2203. Since the receiving port 2203 may be in communication with the processing circuitry 2201 , the receiving port 2203 may then send the received information to the processing circuitry 2201. The receiving port 2203 may also be configured to receive other information.

[0433] The processing circuitry 2201 in the second network node 112 may be further configured to transmit or send information to e.g., the first network node 111, the third network node 113, the first device 131, the second device 132, or another structure in the communications system 100, through a sending port 2204, which may be in communication with the processing circuitry 2201 , and the memory 2202.

[0434] Those skilled in the art will also appreciate that the processing circuitry 2201 described above may comprise a combination of analog and digital modules, and / or one or more processors configured with software and / or firmware, e.g., stored in memory, that, whenexecuted by the one or more processors such as the processing circuitry 2201 , perform as described above. One or more of these processors, as well as the other digital hardware, may be included in a single Application-Specific Integrated Circuit (ASIC), or several processors and various digital hardware may be distributed among several separate components, whether individually packaged or assembled into a System-on-a-Chip (SoC).

[0435] The processing circuitry 2201 may be configured to, or operable to, perform the method actions according to Figure 12 and / or any of Figures 13-18.

[0436] Also, in some embodiments, the second network node 112 may be configured to perform the actions of Figure 12 and / or any of Figures 13-18 with respective units that may be implemented as one or more applications running on one or more processors such as the processing circuitry 2201.

[0437] Thus, the methods according to the embodiments described herein for the second network node 112 may be respectively implemented by means of a computer program 2205 product, comprising instructions, i.e., software code portions, which, when executed on at least one processing circuitry 2201 , cause the at least one processing circuitry 2201 to carry out the actions described herein, as performed by the second network node 112. The computer program 2205 product may be stored on a computer-readable storage medium 2206. The computer-readable storage medium 2206, having stored thereon the computer program 2205, may comprise instructions which, when executed on at least one processing circuitry 2201 , cause the at least one processing circuitry 2201 to carry out the actions described herein, as performed by the second network node 112. In some embodiments, the computer-readable storage medium 2206 may be a non-transitory computer-readable storage medium, such as a CD ROM disc, or a memory stick. In other embodiments, the computer program 2205 product may be stored on a carrier containing the computer program 2205 just described, wherein the carrier is one of an electronic signal, optical signal, radio signal, or the computer-readable storage medium 2206, as described above.

[0438] The second network node 112 may comprise a communication interface configured to facilitate communications between the second network node 112 and other nodes or devices, e.g., the first network node 111, the third network node 113, the first device 131, the second device 132, or another structure in the communications system 100. The interface may, for example, include a transceiver configured to transmit and receive radio signals over an air interface in accordance with a suitable standard.

[0439] In other embodiments, the second network node 112 may also comprise a radio circuitry 2207, which may comprise e.g., the receiving port 2203 and the sending port 2204. The radio circuitry 2207 may be configured to set up and maintain at least a wireless connection with the first network node 111 , the third network node 113, the first device 131 , the second device 132, or another structure in the communications system 100. Circuitry may be understood herein asa hardware component.

[0440] Hence, embodiments herein also relate to the second network node 112 comprising the processing circuitry 2201 and the memory 2202, said memory 2202 containing instructions executable by said processing circuitry 2201 , whereby the second network node 112 is operative to perform the actions described herein in relation to the second network node 112, e.g., in Figure 12 and / or any of Figures 13-18.

[0441] Generally, all terms used herein are to be interpreted according to their ordinary meaning in the relevant technical field, unless a different meaning is clearly given and / or is implied from the context in which it is used. All references to a / an / the element, apparatus, component, means, step, etc. are to be interpreted openly as referring to at least one instance of the element, apparatus, component, means, step, etc., unless explicitly stated otherwise. The steps of any methods disclosed herein do not have to be performed in the exact order disclosed, unless a step is explicitly described as following or preceding another step and / or where it is implicit that a step must follow or precede another step. Any feature of any of the embodiments disclosed herein may be applied to any other embodiment, wherever appropriate. Likewise, any advantage of any of the embodiments may apply to any other embodiments, and vice versa. Other objectives, features and advantages of the enclosed embodiments will be apparent from the following description.

[0442] As used herein, the expression “at least one of:” followed by a list of alternatives separated by commas, and wherein the last alternative is preceded by the “and” term, may be understood to mean that only one of the list of alternatives may apply, more than one of the list of alternatives may apply or all of the list of alternatives may apply. This expression may be understood to be equivalent to the expression “at least one of:” followed by a list of alternatives separated by commas, and wherein the last alternative is preceded by the “or” term.

[0443] EXAMPLES related to embodiments herein

[0444] The following are examples related to embodiments herein. Any of the features described in relation to Figures 23-26 may be combined with the actions of the examples related to embodiments herein, described in relation to Figures 8-18.

[0445] The first device 131 embodiments relate to Figure 23, any of Figures 13-19, and Figures 27-29.

[0446] A computer-implemented method, performed by a device, such as the first device 131 is described herein. The method may be understood to be for handling data pertaining to Reference Signals (RSs). The first device 131 may operate in a communications system, such as the communications system 100.In some embodiments, the communications system 100 may support New Radio (NR). The method may comprise one or more of the following actions. In some embodiments, all the actions may be performed. In some examples, Action 902 and Action 904 may be performed. One or more embodiments may be combined, where applicable. Components from one embodiment may be tacitly assumed to be present in another embodiment and it will be obvious to a person skilled in the art how those components may be used in the other exemplary embodiments. All possible combinations are not described to simplify the description. A non-limiting example of the method performed by the first device 131 is depicted in Figure 9. In Figure 9, optional actions in some embodiments may be represented with dashed lines. In some embodiments, the actions may be performed in a different order than that depicted Figure 9.

[0447] o Obtaining 902 one or more configurations. The first device 131 may be configured to perform the obtaining in this Action 902.

[0448] The one or more configurations may indicate one or more patterns. The one or more patterns may be of collection of data. The data may pertain to, e.g., be about / indicate / be based on, overlaid RSs.

[0449] Obtaining in this Action 904 may comprise receiving, retrieving, fetching or performing the first set of measurements.

[0450] In some embodiments, the obtaining in this Action 902 may be from the first network node 111 operating in the communications system 100.

[0451] o Performing 904 an action. The first device 131 may be configured to perform the performing in this Action 904.

[0452] The action may be to support the collection of the data. The collection of the data may be based on the obtained one or more configurations.

[0453] In some embodiments, one of the following may apply:

[0454] the action may be performed in the UL; the action may comprise performing a transmission using the one or more patterns of collection of data, and

[0455] the action may be performed in the DL; and the action may comprise performing a reception using the one or more patterns of collection of data.

[0456] In some embodiments, the method may further comprise one or more of the following two actions:

[0457] o Providing 901 a first indication. The first device 131 may be configured to perform the providing in this Action 901.

[0458] The first indication may indicate a capability. The capability may be to perform 904 the action to support the collection of the data based on the obtained one or more configurations.

[0459] The providing in this Action 901 may be to the first network node 111.o Obtaining 903 a second indication. The first device 131 may be configured to perform the obtaining in this Action 903.

[0460] The second indication may trigger the performing 904 of the action.

[0461] The obtaining in this Action 903 may be from the first network node 111.

[0462] The second indication may indicate one of:

[0463] - to transmit the one or more patterns of collection of data, e.g., based on the obtained configuration, and

[0464] - to receive the one or more patterns of collection of data, e.g., based on the obtained configuration.

[0465] In some embodiments, one or more of the following may apply:

[0466] i. the RSs may be used for demodulation,

[0467] ii. the RSs may be used for phase tracking,

[0468] Hi. the RSs may comprise Demodulation RSs, DMRSs,

[0469] iv. the data to be collected may be to be used in a life cycle management of a machine learning model (MLM),

[0470] v. the one or more patterns of collection of data pertaining to overlaid RSs may comprise one of:

[0471] a) Resource Elements (REs) comprising the RSs overlaid with data, e.g., wherein the data may be known data,

[0472] b) REs comprising the RSs overlaid with data and REs comprising non-overlaid Reference Signal REs, e.g., wherein the data may be unknown data, and e.g., with a cyclic redundancy check, and c) REs comprising the RSs overlaid with data, e.g., wherein the data may be unknown data, and REs comprising non-overlaid Reference Signal REs lacking data, and REs comprising only data,

[0473] vi. content of data in the non-overlaid Reference Signal REs may be the data comprised in the REs comprising the RSs overlaid with data,

[0474] vii. the action may be in the downlink and the collected data may comprise information indicating one or more measurements of interference, viii. any of the one or more configurations may comprise an overlay power ratio parameter and / or a measurement report configuration intended to be used by a receiver to measure interference,

[0475] ix. RSs used for overlaid RS transmissions may be re-used for non-overlaid RS REs, e.g., the data / pilot overlay power ratio may be set to zero for certain configured REs, andx. the communications system 100 may be a wireless communications system 100.

[0476] In some embodiments, the method may further comprise one or more of the following two actions:

[0477] o Initiating 905 training of the machine learning model, MLM. The first device 131 may be configured to perform the initiating in this Action 905.

[0478] Initiating may comprise starting or triggering.

[0479] The MLM may be any of, e.g., a Neural Network, a Convolutional Neural Network, a Federated Learning MLM, an Autoencoder, Decision Tree, etc.

[0480] The training of the MLM may be with the collected data, e.g., resulting from performing the action.

[0481] The MLM may be to predict demodulation. The demodulation may be of a channel with the overlaid RSs. The channel may be, e.g., between the first device 131 and the first network node 111.

[0482] o Initiating 906 outputting a third indication. The first device 131 may be configured to perform the initiating in this Action 906.

[0483] Initiating may comprise starting or triggering.

[0484] The third indication may be of the trained MLM.

[0485] In some embodiments, the method may further comprise one or more of the following two actions:

[0486] o Using 907 the trained MLM. The first device 131 may be configured to perform the using in this Action 907.

[0487] The using in this Action of the trained MLM may be e.g., to predict demodulation, e.g., with an overlaid first RSs.

[0488] The training of the MLM may be performed by one of: the first device 131, the second device 132, the first network node 111 and second network node 112 operating in the communications system 100.

[0489] With the proviso the training of the MLM is performed by one of: the second device 132, the first network node 111 and second network node 112, the initiating in Action 905 of the training may comprise sending one or more fourth indications. The one or more fourth indications may indicate the collected data based on the obtained one or more configurations. The sending of the one or more fourth indications may be to the one of: the second device 132, the first network node 111 and second network node 112.

[0490] The first device 131 may be a device that may collect data to be used as input to the MLM, e.g., during a training phase of the MLM. In some examples, the first device 131 may train the MLM itself.Figure 19, optional units are indicated with dashed boxes.

[0491] The first device 131 may comprise an arrangement as shown in Figure 19 or in Figure 29.

[0492] The second device 132 embodiments relate to Figure 24, any of Figures 13-18, Figure 20 and Figures 27-29.

[0493] A computer-implemented method, performed by a device, such as the second device 132 is described herein. The method may be understood to be for handling the data pertaining to the RSs. The second device 132 may operate in a communications system, such as the communications system 100.

[0494] In some embodiments, the communications system 100 may support New Radio (NR). The method may comprise one or more of the following actions. In some embodiments, all the actions may be performed. In some examples, Action 1002, Action 1004, and Action 1005 may be performed. One or more embodiments may be combined, where applicable. Components from one embodiment may be tacitly assumed to be present in another embodiment and it will be obvious to a person skilled in the art how those components may be used in the other exemplary embodiments. All possible combinations are not described to simplify the description. A non-limiting example of the method performed by the second device 132 is depicted in Figure 24. In Figure 24, optional actions in some embodiments may be represented with dashed lines. In some embodiments, the actions may be performed in a different order than that depicted Figure 24.

[0495] The detailed description of some of the following corresponds to the same references provided above, in relation to the actions described for the first device 131 and will thus not be repeated here to simplify the description. For example, the MLM may be any of, e.g., a Neural Network, a Convolutional Neural Network, a Federated Learning MLM, an Autoencoder, Decision Tree, etc.

[0496] The second device 132 may be a device that may perform inference with the MLM, once trained. The second device 132 may use similar input data to the MLM as the first measurements, using new or fresh data or measurements.

[0497] o Obtaining 1002 a first configuration. The second device 132 may be configured to perform the obtaining in this Action 1002.

[0498] The first configuration may be of the one or more configurations. The one or more configurations may indicate a first pattern. The first pattern may be of the one or more patterns. The one or more patterns may be of collection of the data. The data may pertain to overlaid RSs.

[0499] The obtaining in this Action 1002 may be from the first network node 111 operating in the communications system 100.o Predicting 1004 demodulation. The second device 132 may be configured to perform the predicting in this Action 1004.

[0500] The demodulation may be of a channel, e.g., a first channel, with the overlaid RSs.

[0501] The predicting 1007 may be using the collected first data as input to the trained MLM. o Initiating 1005 outputting a sixth indication. The second device 132 may be configured to perform the initiating in this Action 1005.

[0502] Initiating may comprise starting or triggering, e.g., sending, the sixth indication.

[0503] The sixth indication may be of the predicted demodulation. The sixth indication may be a result of the inference using the MLM.

[0504] In some embodiments, the method may further comprise one or more of the following actions:

[0505] o Performing 1003 an action, e.g., a first action. The second device 132 may be configured to perform the performing in this Action 1003.

[0506] The action may be to support the collection of first data. The collection may be based on the obtained first configuration.

[0507] In some embodiments, one of the following may apply:

[0508] the action, e.g., the first action, may be performed in the UL; the action, e.g., the first action, may comprise performing a transmission using the first patterns of collection of data, the action, e.g., the first action, may be performed in the DL; and the action, e.g., the first action, may comprise performing a reception using the first pattern of collection of data, and the action, e.g., the first action, may be in the downlink and the collected data may comprise information indicating one or more measurements of interference.

[0509] o Obtaining 1001 the trained MLM. The second device 132 may be configured to perform the obtaining in this Action 1001.

[0510] The obtaining in this Action 1001 may be, e.g., from the first device 131 or the second network node 102 operating in the communications network 100.

[0511] In some embodiments, one or more of the following may apply:

[0512] i. the RSs may be used for demodulation,

[0513] ii. the RSs may be used for phase tracking,

[0514] Hi. the RSs may comprise Demodulation RSs, DMRSs,

[0515] iv. the one or more patterns of collection of data pertaining to overlaid RSs may comprise one of:

[0516] a) Resource Elements (REs) comprising the RSs overlaid with data, e.g., wherein the data may be known data,

[0517] b) REs comprising the RSs overlaid with data and REs comprising non-overlaid Reference Signal REs, e.g., wherein the data may be unknown data, and e.g., with a cyclic redundancy check, andc) REs comprising the RSs overlaid with data, e.g., wherein the data may be unknown data, and REs comprising non-overlaid Reference Signal REs lacking data, and REs comprising only data,

[0518] v. content of data in the non-overlaid Reference Signal REs may be the data comprised in the REs comprising the RSs overlaid with data,

[0519] vi. the RSs may be transmitted by the third network node 103 operating in the communications system 100

[0520] vii. the channel may be between the second device 132 and the third network node 103,

[0521] viii. any of the one or more configurations may comprise an overlay power ratio parameter and / or a measurement report configuration intended to be used by a receiver to measure interference,

[0522] ix. RSs used for overlaid RS transmissions may be re-used for non-overlaid RS REs, e.g., the data / pilot overlay power ratio may be set to zero for certain configured REs, and

[0523] x. the communications system 100 may be the wireless communications system 100.

[0524] In Figure 20, optional units are indicated with dashed boxes.

[0525] The second device 132 may comprise an arrangement as shown in Figure 20 or in Figure 29.

[0526] The first network node 111 embodiments relate to Figure 25, any of Figures 13-18, Figure 21, Figures 27-28, and Figures 30-31.

[0527] A computer-implemented method, performed by a network node, such as the first network node 111 is described herein. The method may be understood to be for handling the data pertaining to the RSs. The first network node 111 may operate in a communications system, such as the communications system 100.

[0528] In some embodiments, the communications system 100 may support New Radio (NR). The method may comprise one or more of the following actions. In some embodiments, all the actions may be performed. In some examples, Action 1102may be performed. One or more embodiments may be combined, where applicable. Components from one embodiment may be tacitly assumed to be present in another embodiment and it will be obvious to a person skilled in the art how those components may be used in the other exemplary embodiments. All possible combinations are not described to simplify the description. A non-limiting example of the method performed by the first network node 111 is depicted in Figure 11. In Figure 11 ,optional actions in some embodiments may be represented with dashed lines. In some embodiments, the actions may be performed in a different order than that depicted Figure 11.

[0529] The detailed description of some of the following corresponds to the same references provided above, in relation to the actions described for the first device 131 and will thus not be repeated here to simplify the description. For example, the MLM may be any of, e.g., a Neural Network, a Convolutional Neural Network, a Federated Learning MLM, an Autoencoder, Decision Tree, etc.

[0530] The first network node 111 may be a network node that may configure the first device 131 to collect data to use as input to the MLM during a training phase of the MLM. In some examples, the first network node 111 may train the MLM itself.

[0531] o Sending 1102 the one or more configurations, e.g., the first configuration. The first network node 111 may be configured to perform the sending in this Action 1102.

[0532] The one or more configurations may indicate the one or more patterns. The one or more patterns may be of collection of the data. The data may pertain to, e.g., be about / indicate / be based on, the overlaid RSs.

[0533] The sending in this Action 1102 may be to the first device 131 operating in the communication system 100.

[0534] The first network node 111 may send, in this Action 1102, the first configuration to the second device 132.

[0535] In some embodiments, the method may further comprise one or more of the following two actions:

[0536] o Obtaining 1101 the first indication. The first network node 111 may be configured to perform the obtaining in this Action 1101.

[0537] The first indication may indicate the capability. The capability may be to perform 1004 the action to support the collection of the data based on the obtained one or more configurations.

[0538] The obtaining in this Action 1101 may be from the first device 131.

[0539] o Sending 1103 the second indication. The first network node 111 may be configured to perform the sending in this Action 1103.

[0540] The second indication may trigger the performing of the action.

[0541] The sending in this Action 1103 may be to the first device 131.

[0542] In some embodiments, one of the following may apply:

[0543] the action, e.g., the first action, may be performed in the UL; the action, e.g., the first action, may comprise performing a transmission using the one or more, e.g., the first, patterns of collection of data, and

[0544] the action, e.g., the first action, may be performed in the DL; and the action, e.g., the first action, may comprise performing a reception using the one or more, e.g., the first, pattern of collection of data.The second indication may indicate one of:

[0545] - to transmit the one or more patterns of collection of data, e.g., based on the obtained configuration, and

[0546] - to receive the one or more patterns of collection of data, e.g., based on the obtained configuration.

[0547] In some embodiments, one or more of the following may apply:

[0548] i. the RSs may be used for demodulation,

[0549] ii. the RSs may be used for phase tracking,

[0550] Hi. the RSs may comprise Demodulation RSs, DMRSs,

[0551] iv. the data to be collected may be to be used in a life cycle management of the MLM,

[0552] v. the one or more patterns of collection of data pertaining to overlaid RSs may comprise one of:

[0553] a) REs comprising the RSs overlaid with data, e.g., wherein the data may be known data,

[0554] b) REs comprising the RSs overlaid with data and REs comprising non-overlaid Reference Signal REs, e.g., wherein the data may be unknown data, and e.g., with a cyclic redundancy check, and c) REs comprising the RSs overlaid with data, e.g., wherein the data may be unknown data, and REs comprising non-overlaid Reference Signal REs lacking data, and REs comprising only data,

[0555] vi. the content of data in the non-overlaid Reference Signal REs may be the data comprised in the REs comprising the RSs overlaid with data, vii. the action may be in the downlink and the collected data may comprise information indicating one or more measurements of interference, viii. any of the one or more configurations may comprise an overlay power ratio parameter and / or a measurement report configuration intended to be used by a receiver to measure interference,

[0556] ix. RSs used for overlaid RS transmissions may be re-used for non-overlaid RS REs, e.g., the data / pilot overlay power ratio may be set to zero for certain configured REs, and

[0557] x. the communications system 100 may be the wireless communications system 100.

[0558] The first network node 111 may transmit the overlaid RSs.

[0559] In some embodiments, the method may further comprise one or more of the following three actions:o Receiving 1104 the one or more fourth indications. The first network node 111 may be configured to perform the receiving in this Action 1104.

[0560] The one or more fourth indications may indicate the collected data based on the sent one or more configurations. The sending of the one or more fourth indications may be to the one of: the second device 132, the first network node 111 and second network node 112.

[0561] The receiving in this Action 1104 may be from at least the first device 131.

[0562] o Initiating 1105 training of the MLM. The first network node 111 may be configured to perform the initiating in this Action 1105.

[0563] The training of the MLM may be with the collected data, e.g., resulting from performing the action.

[0564] The MLM may be to predict demodulation. The demodulation may be of the channel with the overlaid RSs. The channel may be, e.g., between the first device 131 and the first network node 111.

[0565] o Initiating 1106 outputting the third indication. The first network node 111 may be configured to perform the initiating in this Action 1106.

[0566] The third indication may be of the trained MLM.

[0567] In Figure 21, optional units are indicated with dashed boxes.

[0568] The first network node 111 may comprise an arrangement as shown in Figure 21 or in any of Figures 30-31.

[0569] The second network node 112 embodiments relate to Figure 26, any of Figures 12-18, Figure 22, Figures 27-28, and Figures 30-31.

[0570] A computer-implemented method, performed by a network node, such as the second network node 112 is described herein. The method may be understood to be for handling the data pertaining to the RSs. The second network node 112 may operate in a communications system, such as the communications system 100.

[0571] In some embodiments, the communications system 100 may support New Radio (NR). The method may comprise one or more of the following actions. In some embodiments, all the actions may be performed. In some examples, Action 1201 , Action 1202 and Action 1203 may be performed. One or more embodiments may be combined, where applicable. Components from one embodiment may be tacitly assumed to be present in another embodiment and it will be obvious to a person skilled in the art how those components may be used in the other exemplary embodiments. All possible combinations are not described to simplify the description. A non-limiting example of the method performed by the second network node 112 is depicted in Figure 12. In Figure 12, optional actions in some embodiments may be represented with dashed lines. In some embodiments, the actions may be performed in a different order than that depicted Figure 12.The detailed description of some of the following corresponds to the same references provided above, in relation to the actions described for the first device 131 and will thus not be repeated here to simplify the description. For example, the MLM may be any of, e.g., a Neural Network, a Convolutional Neural Network, a Federated Learning MLM, an Autoencoder, Decision Tree, etc.

[0572] The second network node 112 may be a network node 112 that may collect data to use as input to the MLM during a training phase of the MLM and train the MLM.

[0573] o Obtaining 1201 the collected data. The second network node 112 may be configured to perform the obtaining in this Action 1201.

[0574] The obtaining in this Action 1201 may be at least from the first device 131 operating in the communications system 100. That is, the second network node 112 may receive a respective third indication from every first device.

[0575] The data may have been collected by the first device 131. The data may have been collected based on the one or more configurations. The one or more configurations may indicate the one or more patterns of collection of data. The data may pertain to overlaid RSs.

[0576] o Initiating 1202 training of the MLM. The second network node 112 may be configured to perform the initiating in this Action 1202.

[0577] The training of the MLM may be with the collected data.

[0578] The training of the MLM to predict the demodulation of the channel with the overlaid RSs. o Initiating 1205 outputting the third indication. The second network node 112 may be configured to perform the initiating in this Action 1205.

[0579] Initiating may comprise starting or triggering.

[0580] The third indication may be of the trained MLM.

[0581] In some embodiments, the action, performed by the first device 131, to support the collection of the data based on the one or more configurations may comprise one of the following:

[0582] the action, e.g., the first action, may be performed in the UL; the action, e.g., the first action, may comprise performing a transmission using the one or more, e.g., the first, patterns of collection of data, and

[0583] the action, e.g., the first action, may be performed in the DL; and the action, e.g., the first action, may comprise performing a reception using the one or more, e.g., the first, pattern of collection of data.

[0584] In some embodiments, one or more of the following may apply:

[0585] i. the RSs may be used for demodulation,

[0586] ii. the RSs may be used for phase tracking,

[0587] Hi. the RSs may comprise Demodulation RSs, DMRSs,iv. the one or more patterns of collection of data pertaining to overlaid RSs may comprise one of:

[0588] a) REs comprising the RSs overlaid with data, e.g., wherein the data may be known data,

[0589] b) REs comprising the RSs overlaid with data and REs comprising non-overlaid Reference Signal REs, e.g., wherein the data may be unknown data, and e.g., with a cyclic redundancy check, and c) REs comprising the RSs overlaid with data, e.g., wherein the data may be unknown data, and REs comprising non-overlaid Reference Signal REs lacking data, and REs comprising only data,

[0590] v. the content of data in the non-overlaid Reference Signal REs may be the data comprised in the REs comprising the RSs overlaid with data, vi. the action may be in the downlink and the collected data may comprise information indicating one or more measurements of interference, and vii. the third indication may be output to one or more of: the first device 131 and the second device 132 operating in the communications system 100, viii. the channel may be between one or more of:

[0591] a) the first device 131 and the first network node 111 operating in the communications system 100, and

[0592] b) the second device 132 and the third network node 113 operating in the communications system 100,

[0593] ii. any of the one or more configurations may comprise an overlay power ratio parameter and / or a measurement report configuration intended to be used by a receiver to measure interference,

[0594] ix. RSs used for overlaid RS transmissions may be re-used for non-overlaid RS REs, e.g., the data / pilot overlay power ratio may be set to zero for certain configured REs, and

[0595] x. the communications system 100 may be the wireless communications system 100.

[0596] In Figure 22, optional units are indicated with dashed boxes.

[0597] The second network node 112 may comprise an arrangement as shown in Figure 22 or in any of Figures 30-31.EXAMPLES related to embodiments herein:

[0598] EXAMPLE 1. A computer-implemented method performed by a first device (131), the method being for handling data pertaining to Reference Signals, RSs, the first device (131) operating in a communications system (100), the method comprising:

[0599] - obtaining (902), from a first network node (111) operating in the communications system (100), one or more configurations indicating one or more patterns of collection of data pertaining to overlaid RSs, and

[0600] - performing (904) an action to support the collection of the data based on the obtained one or more configurations.

[0601] EXAMPLE 2. The method according to example 1, wherein one of:

[0602] - the action is performed in the Uplink, UL, and the action comprises performing a transmission using the one or more patterns of collection of data, and

[0603] - the action is performed in the Downlink, DL, and the action comprises performing a reception using the one or more patterns of collection of data.

[0604] EXAMPLE 3. The method according to any of examples 1-2, further comprising one or more of:

[0605] - providing (901), to the first network node (111) a first indication indicating a capability to perform (904) the action to support the collection of the data based on the obtained one or more configurations, and

[0606] - obtaining (903), from the first network node (111), a second indication triggering the performing (904) of the action.

[0607] EXAMPLE 4. The method according to example 3, wherein the second indication indicates one of:

[0608] - to transmit the one or more patterns of collection of data based on the obtained configuration, and

[0609] - to receive the one or more patterns of collection of data based on the obtained configuration.

[0610] EXAMPLE 5. The method according to any of examples 1-4, wherein one or more of:

[0611] i. the RSs are used for demodulation,

[0612] ii. the RSs are used for phase tracking,

[0613] Hi. the RSs comprise Demodulation RSs, DMRSs,

[0614] iv. the data to be collected is to be used in a life cycle management of a machine learning model, MLM,v. the one or more patterns of collection of data pertaining to overlaid RSs comprise one of:

[0615] a) Resource Elements, REs, comprising the RSs overlaid with data, e.g., wherein the data is known data,

[0616] b) REs comprising the RSs overlaid with data and REs comprising non-overlaid Reference Signal REs, e.g., wherein the data is unknown data, and e.g., with a cyclic redundancy check, and c) REs comprising the RSs overlaid with data, e.g., wherein the data is unknown data, and REs comprising non-overlaid Reference Signal REs lacking data, and REs comprising only data, vi. content of data in the non-overlaid Reference Signal REs is the data comprised in the REs comprising the RSs overlaid with data, vii. the action is in the downlink and the collected data comprises information indicating one or more measurements of interference,

[0617] viii. any of the one or more configurations may comprise an overlay power ratio parameter and / or a measurement report configuration intended to be used by a receiver to measure interference,

[0618] ix. RSs used for overlaid RS transmissions may be re-used for non-overlaid RS REs, e.g., the data / pilot overlay power ratio may be set to zero for certain configured REs, and

[0619] x. the communications system (100) is a wireless communications system (100).

[0620] EXAMPLE 6. The method according to example 5, further comprising one or more of:

[0621] - initiating (905) training of the MLM with the collected data, the MLM being to predict demodulation of a channel with the overlaid RSs, e.g., wherein the channel is between the first device (131) and the first network node (111), and - initiating (906) outputting a third indication of the trained MLM.

[0622] EXAMPLE 7. The method according to example 6, further comprising:

[0623] - using (907) the trained MLM, e.g., to predict demodulation with an overlaid first RSs.

[0624] EXAMPLE 8. The method according to any of examples 6-7, wherein the training of the MLM is performed by one of: the first device (131), a second device (132), the first network node (111) and second network node (112) operating in the communications system (100) and wherein, with the proviso the training of the MLM is performed by one of: the second device (132), thefirst network node (111) and second network node (112), the initiating (905) of the training comprises sending one or more fourth indications indicating the collected data based on the obtained one or more configurations, to the one of: the second device (132), the first network node (111) and second network node (112).

[0625] EXAMPLE 9. A computer-implemented method performed by a second device (132), the method being for handling data pertaining to Reference Signals, RSs, the second device (132) operating in a communications system (100), the method comprising:

[0626] - obtaining (1002), from a first network node (111) operating in the communications system (100), a first configuration of one or more configurations indicating a first pattern of one or more patterns of collection of data pertaining to overlaid RSs, and

[0627] - predicting (1004), using the collected first data as input to a trained machine learning model, MLM, demodulation of a channel with the overlaid RSs, and - initiating (1005) outputting a sixth indication of the predicted demodulation.

[0628] EXAMPLE 10. The method according to example 9, wherein the method further comprises one or more of:

[0629] - performing (1003) an action to support the collection of first data based on the obtained first configuration,

[0630] - obtaining (1001) the trained MLM, e.g., from a first device (131) or a second network node (112) operating in the communications system (100).

[0631] EXAMPLE 11. The method according to example 10, wherein one of:

[0632] - the action is performed in the Uplink, UL, and the action comprises performing a transmission using the first pattern of collection of data,

[0633] - the action is performed in the Downlink, DL, and the action comprises performing a reception using the first pattern of collection of data,

[0634] - the action is in the downlink and the collected first data comprises information indicating one or more measurements of interference.

[0635] EXAMPLE 12. The method according to any of examples 9-11 , wherein one or more of:

[0636] i. the RSs are used for demodulation,

[0637] ii. the RSs are used for phase tracking,

[0638] Hi. the RSs comprise Demodulation RSs, DMRSs,

[0639] iv. the one or more patterns of collection of data pertaining to overlaid RSs comprise one of:a) Resource Elements, REs, comprising the RSs overlaid with data, e.g., wherein the data is known data,

[0640] b) REs comprising the RSs overlaid with data and REs comprising non-overlaid Reference Signal REs, e.g., wherein the data is unknown data, and e.g., with a cyclic redundancy check, and c) REs comprising the RSs overlaid with data, e.g., wherein the data is unknown data, and REs comprising non-overlaid Reference Signal REs lacking data, and REs comprising only data, v. content of data in the non-overlaid Reference Signal REs is the data comprised in the REs comprising the RSs overlaid with data, vi. the RSs are transmitted by a third network node (113) operating in the communications system (100)

[0641] vii. the channel is between the second device (132) and the third network node (113),

[0642] viii. any of the one or more configurations may comprise an overlay power ratio parameter and / or a measurement report configuration intended to be used by a receiver to measure interference,

[0643] ix. RSs used for overlaid RS transmissions may be re-used for non-overlaid RS REs, e.g., the data / pilot overlay power ratio may be set to zero for certain configured REs, and

[0644] x. the communications system (100) is a wireless communications system (100).

[0645] EXAMPLE 13. A computer-implemented method performed by a first network node (111), the method being for handling data pertaining to Reference Signals, RSs, the first network node (111) operating in a communications system (100), the method comprising:

[0646] - sending (1102), to a first device (131) operating in the communications system (100), one or more configurations indicating one or more patterns of collection of data pertaining to overlaid RSs.

[0647] EXAMPLE 14. The method according to example 13, further comprising one or more of:

[0648] - obtaining (1101), from the first device (131), a first indication indicating a capability to perform an action to support the collection of the data based on the obtained one or more configurations, and

[0649] - sending (1103), to the first device (131), a second indication triggering the performing of the action.EXAMPLE 15. The method according to example 14, wherein one of:

[0650] - the action is performed in the Uplink, UL, and the action comprises performing a transmission using the one or more patterns of collection of data, and

[0651] - the action is performed in the Downlink, DL, and the action comprises performing a reception using the one or more patterns of collection of data.

[0652] EXAMPLE 16. The method according to any of examples 14-15, wherein the second indication indicates one of:

[0653] - to transmit the one or more patterns of collection of data based on the obtained configuration, and

[0654] - to receive the one or more patterns of collection of data based on the obtained configuration.

[0655] EXAMPLE 17. The method according to any of examples 13-16, wherein one or more of:

[0656] i. the RSs are used for demodulation,

[0657] ii. the RSs are used for phase tracking,

[0658] Hi. the RSs comprise Demodulation RSs, DMRSs,

[0659] iv. the data to be collected is to be used in a life cycle management of a machine learning model, MLM,

[0660] v. the one or more patterns of collection of data pertaining to overlaid RSs comprise one of:

[0661] a) Resource Elements, REs, comprising the RSs overlaid with data, e.g., wherein the data is known data,

[0662] b) REs comprising the RSs overlaid with data and REs comprising non-overlaid Reference Signal REs, e.g., wherein the data is unknown data, and e.g., with a cyclic redundancy check, and c) REs comprising the RSs overlaid with data, e.g., wherein the data is unknown data, and REs comprising non-overlaid Reference Signal REs lacking data, and REs comprising only data, vi. content of data in the non-overlaid Reference Signal REs is the data comprised in the REs comprising the RSs overlaid with data, vii. the action is in the downlink and the collected data comprises information indicating one or more measurements of interference,

[0663] viii. any of the one or more configurations may comprise an overlay power ratio parameter and / or a measurement report configuration intended to be used by a receiver to measure interference,ix. RSs used for overlaid RS transmissions may be re-used for non-overlaid RS REs, e.g., the data / pilot overlay power ratio may be set to zero for certain configured REs, and

[0664] x. the communications system (100) is a wireless communications system (100).

[0665] EXAMPLE 18. The method according to example 17, further comprising one or more of:

[0666] - receiving (1104), at least from the first device (131), one or more fourth indications indicating the collected data based on the sent one or more configurations,

[0667] - initiating (1105) training of the MLM with the collected data, the MLM being to predict demodulation of a channel with the overlaid RSs, e.g., wherein the channel is between the first device (131) and the first network node (111), and - initiating (1106) outputting a third indication of the trained MLM.

[0668] EXAMPLE 19. A computer-implemented method performed by a second network node (112), the method being for handling data pertaining to Reference Signals, RSs, the second network node (112) operating in a communications system (100), the method comprising:

[0669] - obtaining (1201), from at least a first device (131) operating in the communications system (100), collected data by the first device (131) based on one or more configurations, the one or more configurations indicating one or more patterns of collection of data pertaining to overlaid RSs, and - initiating (1202) training of a machine learning model, MLM, with the collected data, the MLM being to predict demodulation of a channel with the overlaid RSs, and

[0670] - initiating (1203) outputting a third indication of the trained MLM.

[0671] EXAMPLE 20. The method according to example 19, wherein an action, performed by the first device (131), to support the collection of the data based on the one or more configurations comprises one of:

[0672] - the action performed in the Uplink, UL, and the action comprises performing a transmission using the one or more patterns of collection of data, and

[0673] - the action performed in the Downlink, DL, and the action comprises performing a reception using the one or more patterns of collection of data.

[0674] EXAMPLE 21. The method according to any of examples 19-20, wherein one or more of:

[0675] i. the RSs are used for demodulation,ii. the RSs are used for phase tracking,

[0676] iii. the RSs comprise Demodulation RSs, DMRSs,

[0677] iv. the one or more patterns of collection of data pertaining to overlaid RSs comprise one of:

[0678] a) Resource Elements, REs, comprising the RSs overlaid with data, e.g., wherein the data is known data,

[0679] b) REs comprising the RSs overlaid with data and REs comprising non-overlaid Reference Signal REs, e.g., wherein the data is unknown data, and e.g., with a cyclic redundancy check, and c) REs comprising the RSs overlaid with data, e.g., wherein the data is unknown data, and REs comprising non-overlaid Reference Signal REs lacking data, and REs comprising only data, v. content of data in the non-overlaid Reference Signal REs is the data comprised in the REs comprising the RSs overlaid with data, vi. the action is in the downlink and the collected data comprises information indicating one or more measurements of interference,

[0680] vii. the third indication is output to one or more of: the first device (131) and a second device (132) operating in the communications system (100), viii. the channel is between one or more of:

[0681] a) the first device (131) and a first network node (111) operating in the communications system (100), and

[0682] b) the second device (132) and a third network node (113) operating in the communications system (100), and

[0683] ix. any of the one or more configurations may comprise an overlay power ratio parameter and / or a measurement report configuration intended to be used by a receiver to measure interference,

[0684] x. RSs used for overlaid RS transmissions may be re-used for non-overlaid RS REs, e.g., the data / pilot overlay power ratio may be set to zero for certain configured REs, and

[0685] xi. the communications system (100) is a wireless communications system (100).

[0686] Further Extensions And Variations

[0687] Figure 27 shows an example of a communication system 2700 in accordance with some embodiments.In the example, the communication system 2700, such as the communications system 100, includes a telecommunications network 2702 that includes an access network 2704, such as a radio access network (RAN), and a core network 2706, which includes one or more core network nodes 2708, such as the second network node 112, in some examples. The access network 2704 includes one or more access network nodes or base stations of various types, such as any of the first network node 111, the second network node 112 and the third network node 113 in some examples. For example, access network nodes 2710A and 2710B are depicted (which may be collectively referred to as network nodes 2710), or any other similar 3rdGeneration Partnership Project (3GPP) access nodes or non-3GPP access points (APs). Some embodiments of the access network 2704 may include more than one access network technology. The network nodes 2710 of access network 2704 facilitate direct or indirect connection of wireless devices, also referred to as user equipments (UEs), e.g., any of the first device 131 and the second device 132, such as by connecting UEs 2712A, 2712B, 2712C, and 2712D (one or more of which may be generally referred to as UEs 2712) to the core network 2706 over one or more wireless connections. Any of the UEs 2712A, 2712B, 2712C, and 2712D are examples of any of the first device 131 and the second device 132.

[0688] Moreover, a network node is not necessarily limited to an implementation in which a radio portion and a baseband portion are supplied and integrated by a single vendor. Thus, it will be understood that network nodes include disaggregated implementations or portions thereof. For example, in some embodiments, the telecommunications network 2702 includes one or more Open-RAN (ORAN) network nodes. An ORAN network node is a network node in the telecommunications network 2702 that supports an ORAN specification (e.g., a specification published by the O-RAN Alliance, or any similar organization) and may operate alone or together with other network nodes to implement one or more functionalities of any network node in the telecommunications network 2702, including one or more access network nodes 2710 and / or core network nodes 2708.

[0689] Examples of an ORAN network node include an open radio unit (O-RU), an open distributed unit (O-DU), an open central unit (O-CU), including an O-CU control plane (O-CU-CP) or an O-CU user plane (O-CU-UP), a RAN intelligent controller (near-real time or non-real time) hosting software or software plug-ins, such as a near-real time control application (e.g., xApp) or a non-real time control application (e.g., rApp), or any combination thereof (the adjective “open” designating support of an ORAN specification). An ORAN network node may support a specification by, for example, supporting an interface defined by the ORAN specification, such as an A1, F1, W1, E1, E2, X2, Xn interface, an open fronthaul user plane interface, or an open fronthaul management plane interface. Moreover, an ORAN network node may be a logical node in a physical node. Furthermore, an ORAN network node may be implemented in a virtualization environment (described further below) in which one or more network functions are virtualized. Forexample, the virtualization environment may include an O-Cloud computing platform orchestrated by a Service Management and Orchestration Framework via an 0-2 interface defined by the O-RAN Alliance or comparable technologies.

[0690] The network nodes 2710, e.g., any of the first network node 111, the second network node 112 and the third network node 113, facilitate direct or indirect connection of one or more UEs 2712, e.g., any of the first device 131 and the second device 132, to the core network 2706 over one or more wireless connections. Example wireless communications over a wireless connection include transmitting and / or receiving wireless signals using electromagnetic waves, radio waves, infrared waves, and / or other types of signals suitable for conveying information without the use of wires, cables, or other material conductors. Moreover, in different embodiments, the communication system 2700 may include any number of wired or wireless networks, network nodes, UEs, and / or any other components or systems that may facilitate or participate in the communication of data and / or signals whether via wired or wireless connections. The communication system 2700 may include and / or interface with any type of communication, telecommunication, data, cellular, radio network, and / or other similar type of system.

[0691] The UEs 2712, e.g., any of the first device 131 and the second device 132, may be any of a wide variety of communication devices, including wireless devices arranged, configured, and / or operable to communicate wirelessly with the network nodes 2710 and other communication devices. Similarly, the network nodes 2708, 2710, e.g., any of the first network node 111, the second network node 112 and the third network node 113, are arranged, capable, configured, and / or operable to communicate directly or indirectly (e.g., via other devices of telecommunications network 2702) with the UEs 2712 and / or with other network nodes or equipment in the telecommunications network 2702 to enable and / or provide network access, such as wireless network access, and / or to perform other functions, such as administration in the telecommunications network 2702. More specifically, UEs 2712 may send messages, data, and / or other signals to network nodes 2708, 2710 or other elements of the telecommunications network 2702 by transmitting such signals to the relevant device directly without the signals passing through any intervening devices or by transmitting such signals to the relevant device indirectly through an intervening device (or multiple intervening devices) that then transmit the signal to the relevant device. Similarly, network nodes 2708, 2710 may send messages, data, and other signals to UEs 27122, other network nodes 2708, 2710, and other devices in telecommunications network 2702 directly or indirectly. As one specific example, a core network node 108 may transmit a particular message to a UE 2712 by transmitting the message to an access network node 2710 that will then transmit the message to the intended UE2712. Similarly, a core network node 108 may receive a particular message from a UE 2712 by receiving the message from an access network node 2710 that itself received the message from the UE2712.In the depicted example, the core network 2706 connects elements of the access network 2704 (e.g., one or more of the network nodes 2710) to one or more host computing systems, such as host 2716. These connections may be direct or indirect via one or more intermediary networks or devices. In other examples, network nodes may be directly coupled to hosts. The core network 2706 includes one or more core network nodes (e.g., core network node 2708) of various types, one or more of which may be generally referred to as network nodes 2708. Network nodes 2708 are structured with hardware and software components. Features of these components may be substantially similar to those described with respect to the UEs, access network nodes, and / or hosts, such that the descriptions thereof are generally applicable to the corresponding components of the core network node 2708. Example core network nodes provide functions of one or more of a Mobile Switching Center (MSC), Mobility Management Entity (MME), Home Subscriber Server (HSS), Access and Mobility Management Function (AMF), Session Management Function (SMF), Authentication Server Function (AUSF), Subscription Identifier Deconcealing function (SIDF), Unified Data Management (UDM), Security Edge Protection Proxy (SEPP), Network Exposure Function (NEF), and / or a User Plane Function (UPF).

[0692] The host 2716 may be under the ownership or control of a service provider other than an operator or provider of the access network 2704 and / or the telecommunications network 2702. The host 2716 may be operated by the service provider or on behalf of the service provider. The host 2716 may host a variety of applications to provide one or more service. Examples of such applications include live and pre-recorded audio / video content, data collection services such as retrieving and compiling data on various ambient conditions detected by a plurality of UEs, analytics functionality, social media, functions for controlling or otherwise interacting with remote devices, functions for an alarm and surveillance center, or any other such function performed by a server.

[0693] As a whole, the communication system 2700 of Figure 27 enables connectivity between the UEs, network nodes, and hosts. In that sense, the communication system 2700 may be configured to operate according to predefined rules or procedures, such as specific standards that include, but are not limited to: Global System for Mobile Communications (GSM); Universal Mobile Telecommunications System (UMTS); Long Term Evolution (LTE), and / or other suitable 2G, 3G, 4G, 5G standards, or any applicable future generation standard (e.g., 6G); wireless local area network (WLAN) standards, such as the Institute of Electrical and Electronics Engineers (IEEE) 802.11 standards (Wi-Fi); and / or any other appropriate wireless communication standard, such as the Worldwide Interoperability for Microwave Access (Wi-Max), Bluetooth, Z-Wave, Near Field Communication (NFC) ZigBee, Li-Fi, and / or any low-power wide-area network (LPWAN) standards such as LoRa and Sigfox. Moreover, the communication system 2700 may be configured to support multiple different standards, protocols, or other rule sets, with individualcomponents supporting all of the relevant rule sets or with different components or sub-systems within the communication system 2700 supporting different standards, protocols, or rule sets.

[0694] As one example, in certain embodiments, access network 2704 may contain some access network nodes 2710 that support 3GPP radio access technologies (RAT), such as LTE or NR, while other access network nodes 2710 support (or the same access network nodes 2710 additionally support) non-3GPP RATs, such as Wi-Fi or a proprietary RAT. As another example, telecommunications network 2702 may support multiple generations of related communication standards (e.g., 4G and 5G 3GPP communication standards) and, as a result, may include an access network 104 and / or a core network 106 that supports multiple different standard generations or may include multiple access networks 104 and / or multiple core networks 106 with individual networks 104, 106 supporting different standard generations.

[0695] Telecommunications network 2702 may support network slicing to provide different logical networks to different devices that are connected to the telecommunications network 2702. For example, the telecommunications network 2702 may provide Ultra Reliable Low Latency Communication (URLLC) services to some UEs, while providing Enhanced Mobile Broadband (eMBB) services to other UEs, and / or Massive Machine Type Communication (mMTC)ZMassive loT services to yet further UEs.

[0696] In some examples, one or more of the UEs 2712 are configured to transmit and / or receive information without direct human interaction. For instance, a UE may be designed to transmit information to the access network 2704 on a predetermined schedule, when triggered by an internal or external event, or in response to requests from the access network 2704. Additionally, a UE may be configured for operating in single- or multi-RAT or multi-standard mode. For example, a UE may operate with any one or combination of Wi-Fi, NR (New Radio) and LTE, i.e. being configured for multi-radio dual connectivity (MR-DC), such as E-UTRAN (Evolved-UMTS Terrestrial Radio Access Network) New Radio - Dual Connectivity (EN-DC).

[0697] In the example, the hub 2714 communicates with the access network 2704 to facilitate indirect communication between one or more UEs (e.g., UE 2712C and / or 2712D) and network nodes (e.g., network node 2710B). In some examples, the hub 2714 may be a controller, router, content source and analytics, or any of the other communication devices described herein regarding UEs. For example, the hub 2714 may be a broadband router enabling access to the core network 2706 for the UEs. As another example, the hub 2714 may be a controller that sends commands or instructions to one or more actuators in the UEs. Commands or instructions may be received from the UEs, network nodes 2710, or by executable code, script, process, or other instructions in the hub 2714.

[0698] As another example, the hub 2714 may be a data collector that acts as temporary storage for UE data and, in some embodiments, may perform analysis or other processing of the data. As another example, the hub 2714 may be a content source. For example, for a UE that is a VRheadset, display, loudspeaker or other media delivery device, the hub 2714 may retrieve VR assets, video, audio, or other media or data related to sensory information via a network node, which the hub 2714 then provides to the UE either directly, after performing local processing, and / or after adding additional local content. In still another example, the hub 2714 acts as a proxy server or orchestrator for the UEs, in particular if one or more of the UEs are low energy loT devices.

[0699] The hub 2714 may have a constant / persistent or intermittent connection to the network node 271 OB. The hub 2714 may also allow for a different communication scheme and / or schedule between the hub 2714 and UEs (e.g., UE 2712C and / or 2712D), and between the hub 2714 and the core network 2706. In other examples, the hub 2714 is connected to the core network 2706 and / or one or more UEs via a wired connection. Moreover, the hub 2714 may be configured to connect to an M2M service provider over the access network 2704 and / or to another UE over a direct connection. In some scenarios, UEs may establish a wireless connection with the network nodes 2710 while still connected via the hub 2714 via a wired or wireless connection. In some embodiments, the hub 2714 may be a dedicated hub - that is, a hub whose primary function is to route communications to / from the UEs from / to the network node 2710B. In other embodiments, the hub 2714 may be a non-dedicated hub - that is, a device which is capable of operating to route communications between the UEs and network node 2710B, but which is additionally capable of operating as a communication start and / or end point for certain data channels.

[0700] Figure 28 is another example of a communication system 2800, such as the wireless communications network 100, according to some embodiments. As used herein, the communication system 2800 includes multiple access points (APs) 2810 (with four exemplary APs 2810A, 2810B, 2810C, and 2810D being depicted), such as any of the first network node 111 , the second network node 112 and the third network node 113, and multiple wireless devices, referred to in the context of communication system 2800 as stations (STAs) 2812 (referred to individually as STA2812A, STA2812B, STA2812C, STA2812D, and STA2812E), such as e.g., any of the first device 131 and the second device 132. STA 2812A is served by AP 2810A in a first basic service set (BSS) 2820A. STA 2810B and STA 2810C are served by AP 2810B in a second BSS, BSS 2820B. STA 2812D is served by AP 2810C in a third BSS, BSS 2820C. STA 2812E is served by AP 2810D in a fourth BSS, BSS 2820D. Stations 2812 may be non-AP STAs and correspond to various kinds of wireless devices, for example, user terminals, such as mobile or stationary computing devices like smartphones, laptop computers, desktop computers, tablet computers, gaming devices, head-mounted displays (HMDs) for Augmented Reality (AR) or Virtual Reality (VR), or the like. Further, stations 2812 could, for example, correspond to other kinds of equipment like smart home devices, printers, multimedia devices, data storage devices, or the like.Each of STAs 2812 may connect through a radio link to one of APs 2810. For example, depending on location or channel conditions experienced by a given STA 2812, the STA may select an appropriate AP and BSS for establishing the radio link. The radio link may be based on one or more orthogonal frequency-division multiplexing (OFDM) carriers from a frequency spectrum that is shared on the basis of a contention-based mechanism, e.g., an unlicensed or license exempt band like 2.4 GHz Industrial, Scientific, and Medical (ISM) band, the 5 GHz band, the 6 GHz band, or the 60 GHz band.

[0701] Each AP 2810 may provide data connectivity to STAs 2812 connected to a particular AP 2810. As illustrated, APs 2810 may be connected to a data network 2830. In this way, APs 2810 may also provide data connectivity between STAs 2812 and other entities, e.g., to one or more servers, service providers, data sources, data sinks, user terminals, or the like. Accordingly, the radio link established between a given STA 2812 and its serving AP 2810 may be used for providing various kinds of services to STA 2812, e.g., a voice service, a multimedia service, or other data service. Such services may be based on applications that are executed on STA 2812 and / or on a device linked to STA 2812. By way of example, Figure 28 illustrates an application service platform 2832 provided in data network 2830. The application(s) executed on STA 2812 and / or on one or more other devices linked to STA 2812 may use the radio link for data communication with one or more other STA 2812 and / or the application service platform 2832, thereby enabling utilization of the corresponding service(s) at STA 2812.

[0702] Figure 29 shows a wireless device 2900, such as any of the first device 131 and the second device 132, which may be configured to operate in communication system 2700 of Figure 27 or in communication system 2800 of Figure 280. The wireless device 2900 may be alternatively referred to as a UE 2900, like a UE 2712 within the context of communication system 2700, or as a station (STA) 2900 or as a non-access-point station (non-AP STA) 2900, like a STA 2812 within the context of the communication system 2800, in accordance with respective embodiments. As used herein, a wireless device refers to a device capable, configured, arranged and / or operable to communicate wirelessly with network nodes and / or other wireless devices. Examples of a wireless device include, but are not limited to, a smart phone, mobile phone, cell phone, voice over IP (VoIP) phone, wireless local loop phone, desktop computer, personal digital assistant (PDA), wireless cameras, gaming console or device, music storage device, playback appliance, wearable terminal device, wireless endpoint, mobile station, tablet, laptop, laptop-embedded equipment (LEE), laptop-mounted equipment (LME), smart device, wireless customer-premise equipment (CPE), vehicle, vehicle-mounted or vehicle embedded / integrated wireless device, and wireless terminal. Other examples include any type of UE identified by the 3rd Generation Partnership Project (3GPP), including a narrow band internet of things (NB-loT) UE, a machine type communication (MTC) UE, and / or an enhanced MTC (eMTC) UE.A wireless device 2900 may support device-to-device (D2D) communication, for example by implementing a 3GPP standard for sidelink communication, Dedicated Short-Range Communication (DSRC), vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), or vehicle-to-everything (V2X). In other examples, wireless device 2900 may not necessarily have a user in the sense of a human user who owns and / or operates the relevant device. Instead, wireless device 2900 may represent a device that is intended for sale to, or operation by, a human user but which may not, or which may not initially, be associated with a specific human user (e.g., a smart sprinkler controller). Alternatively, wireless device 2900 may represent a device that is not intended for sale to, or operation by, an end user but which may be associated with or operated for the benefit of a user (e.g., a smart power meter).

[0703] In particular embodiments, wireless device 2900 includes processing circuitry 2902 that is operatively coupled via a bus 2904 to an input / output interface 2906, a power source 2908, a memory 2910, a communication interface 2912, and / or any other component, or any combination thereof. Certain embodiments of wireless device 2900 may include all or a subset of the components shown in Figure 29. The level of integration between the components may vary from one embodiment of wireless device 2900 to another. In general, in a particular embodiment of wireless device 2900, processing circuitry 2902, input / output interface 2906, power source 2908, memory 2910, and communication interface 2912 may, in whole or in part, represent or include physical components common to or shared by one or more of the other elements of wireless device 2900. Further, certain embodiments of wireless devices 2900 may contain multiple instances of a component, such as multiple processors, memories, transceivers, transmitters, receivers, etc.

[0704] The processing circuitry 2902 is configured to process instructions and data and may be configured to implement any sequential state machine operative to execute instructions stored as machine-readable computer programs in the memory 2910. The processing circuitry 2902 may be implemented as one or more hardware-implemented state machines (e.g., in discrete logic, field-programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), etc.); programmable logic together with appropriate firmware; one or more stored computer programs, general-purpose processors, such as a microprocessor or digital signal processor (DSP), together with appropriate software; or any combination of the above. For example, the processing circuitry 2902 may include multiple central processing units (CPUs).

[0705] In the example, the input / output interface 2906 may be configured to provide an interface or interfaces to an input device, output device, or one or more input and / or output devices. Examples of an output device include a speaker, a sound card, a video card, a display, a monitor, a printer, an actuator, an emitter, a smartcard, another output device, or any combination thereof. An input device may allow a user to capture information into wireless device 2900. Examples of an input device include a touch-sensitive or presence-sensitive display, a camera (e.g., a digitalcamera, a digital video camera, a web camera, etc.), a microphone, a sensor, a mouse, a trackball, a directional pad, a trackpad, a scroll wheel, a smartcard, and the like. The presencesensitive display may include a capacitive or resistive touch sensor to sense input from a user. A sensor may be, for instance, an accelerometer, a gyroscope, a tilt sensor, a force sensor, a magnetometer, an optical sensor, a proximity sensor, a biometric sensor, etc., or any combination thereof. An output device may use the same type of interface port as an input device. For example, a Universal Serial Bus (USB) port may be used to provide an input device and an output device.

[0706] In some embodiments, the power source 2908 is structured as a battery or battery pack. Other types of power sources, such as an external power source (e.g., an electricity outlet), photovoltaic device, or power cell, may be used to supply power to circuitry or to charge an associated battery. The power source 2908 may further include power circuitry for delivering power from the power source 2908 itself, and / or an external power source, to the various parts of wireless device 2900 via input circuitry or an interface such as an electrical power cable. Power source 2908 may perform any formatting, converting, or other modification to make accessible power suitable for the respective components of the wireless device 2900 to which power is supplied.

[0707] The memory 2910 may be or be configured to include memory such as random access memory (RAM), read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), magnetic disks, optical disks, hard disks, removable cartridges, flash drives, and so forth. In one example, the memory 2910 includes one or more programs 2914, such as an operating system, web browser application, a widget, gadget engine, or other application, and corresponding data 2916. The memory 2910 may store, for use by wireless device 2900, any of a variety of various operating systems or combinations of operating systems.

[0708] The memory 2910 may be configured to include a number of physical drive units, such as redundant array of independent disks (RAID), flash memory, USB flash drive, external hard disk drive, thumb drive, pen drive, key drive, high-density digital versatile disc (HD-DVD) optical disc drive, internal hard disk drive, Blu-Ray optical disc drive, holographic digital data storage (HDDS) optical disc drive, external mini-dual in-line memory module (DIMM), synchronous dynamic random access memory (SDRAM), external micro-DIMM SDRAM, smartcard memory such as tamper resistant module in the form of a universal integrated circuit card (UICC) including one or more subscriber identity modules (SIMs), such as a USIM and / or ISIM, other memory, or any combination thereof. The UICC may for example be an embedded UICC (eUlCC), integrated UICC (iUICC) or a removable UICC commonly known as ‘SIM card.’ The memory 2910 may allow wireless device 2900 to access instructions, programs and the like, stored on transitory or non-transitory memory media, to off-load data, or to upload data. An article of manufacture, such asone utilizing a communication system may be tangibly embodied as or in the memory 2910, which may be or comprise a device-readable storage medium.

[0709] The processing circuitry 2902 may be configured to communicate with an access network or other network via or using the communication interface 2912. The communication interface 2912 may comprise one or more communication subsystems and may include or be communicatively coupled to an antenna 2922. The communication interface 2912 may include one or more transceivers used to communicate, such as by communicating with one or more remote transceivers of another device capable of wireless communication (e.g., another wireless device or a network node in an access network). Each transceiver may include a transmitter 2918 and / or a receiver 2920 appropriate to provide network communications (e.g., optical, electrical, frequency allocations, and so forth). Moreover, the transmitter 2918 and receiver 2920 may be coupled to one or more antennas (e.g., antenna 2922) and may share circuit components, software or firmware, or alternatively be implemented separately.

[0710] In the illustrated embodiment, communication functions of the communication interface 2912 may include cellular communication, Wi-Fi communication (e.g., according to an IEEE 802.11 family standard), LPWAN communication, data communication, voice communication, multimedia communication, short-range communications such as Bluetooth, near-field communication, location-based communication such as the use of the global positioning system (GPS) to determine a location, another like communication function, or any combination thereof. Communications may be implemented according to one or more communication protocols and / or standards, such as IEEE 802.11, Code Division Multiplexing Access (CDMA), Wideband Code Division Multiple Access (WCDMA), GSM, LTE, New Radio (NR), UMTS, WiMax, Ethernet, transmission control protocol / internet protocol (TCP / IP), synchronous optical networking (SONET), Asynchronous Transfer Mode (ATM), QUIC, Hypertext Transfer Protocol (HTTP), and so forth.

[0711] In particular embodiments, wireless device 2900 may provide an output of data captured via a sensor, through its communication interface 2912, via a wireless connection to a network node, and / or in any appropriate manner. Data captured by sensors of a wireless device 2900 can be communicated through a wireless connection to a network node via another wireless device 2900. In particular embodiments, such output may be periodic (e.g., once every 15 minutes if it reports the sensed temperature), random (e.g., to even out the load from reporting from several sensors), in response to a triggering event (e.g., when moisture is detected an alert is sent), in response to a request (e.g., a user initiated request), or a continuous stream (e.g., a live video feed of a patient).

[0712] As another example, wireless device 2900 comprises an actuator, a motor, or a switch, related to a communication interface configured to receive wireless input from a network node via a wireless connection. In response to the received wireless input the states of the actuator, themotor, or the switch may change. For example, wireless device 2900 may comprise a motor that adjusts the control surfaces or rotors of a drone in flight according to the received input or to a robotic arm performing a medical procedure according to the received input.

[0713] Wireless device 2900, when in the form of an Internet of Things (loT) device, may be a device for use in one or more application domains, these domains comprising, but not limited to, wearable technology, extended industrial application and healthcare. Non-limiting examples of such an loT device are a device which is or which is embedded in: a connected refrigerator or freezer, a TV, a connected lighting device, an electricity meter, a robot vacuum cleaner, a voice controlled smart speaker, a home security camera, a motion detector, a thermostat, a smoke detector, a door / window sensor, a flood / moisture sensor, an electrical door lock, a connected doorbell, an air conditioning system like a heat pump, an autonomous vehicle, a surveillance system, a weather monitoring device, a vehicle parking monitoring device, an electric vehicle charging station, a smart watch, a fitness tracker, a wearable for tactile augmentation or sensory enhancement, a water sprinkler, an animal- or item-tracking device, a sensor for monitoring a plant or animal, an industrial robot, an Unmanned Aerial Vehicle (UAV), and any kind of medical device, like a heart rate monitor or a remote controlled surgical robot. In particular embodiments, wireless device 2900 represents an loT device that comprises circuitry and / or software in dependence of the intended application of the loT device in addition to other components as described in relation to the example embodiment of wireless device 2900 shown in Figure 29.

[0714] As yet another specific example, in an loT scenario, wireless device 2900 may represent a machine or other device that performs monitoring and / or measurements, and transmits the results of such monitoring and / or measurements to another wireless device and / or a network node. Wireless device 2900 may in this case be an M2M device, which may in a 3GPP context be referred to as an MTC device. As one particular example, wireless device 2900 may implement the 3GPP NB-loT standard. In other scenarios, wireless device 2900 may represent a vehicle, such as a car, a bus, a truck, a ship and an airplane, or other equipment that is capable of monitoring and / or reporting on its operational status or other functions associated with its operation.

[0715] In practice, any number of wireless devices 2900 may be used together with respect to a single use case. For example, a first wireless device 2900 might be or be integrated in a drone and provide the drone’s speed information (obtained through a speed sensor) to a second wireless device 2900 that is a remote controller operating the drone. When a user makes changes from the remote controller, the first wireless device 2900 may adjust the throttle on the drone (e.g. by controlling an actuator) to increase or decrease the drone’s speed. The first and / or the second wireless device 2900 can also include more than one of the functionalities described above. For example, wireless device 2900 might comprise the sensor and the actuator, and handle communication of data for both the speed sensor and the actuators.Figure 30 shows a network node 3000, such as any of the first network node 111, the second network node 112 and the third network node 113, in accordance with some embodiments. As used herein, network node refers to equipment capable, configured, arranged and / or operable to communicate directly or indirectly with a UE and / or with other network nodes or equipment, in a telecommunications network. In accordance with respective embodiments, network node 3000 may be configured to operate in communication system 2700 of Figure 27, like network nodes 2708 or 2710, or in communication system 2800 of Figure 28, like an AP 2810 or a station 2812. Examples of network nodes include, but are not limited to, access points (APs) (e.g., radio access points), base stations (BSs) (e.g., radio base stations, Node Bs, evolved Node Bs (eNBs) and NR NodeBs (gNBs)), O-RAN nodes or components of an O-RAN node (e.g., O-RU, O-DU, O-CU).

[0716] Network nodes 3000 may be categorized based on the amount of coverage they provide (or, stated differently, their transmit power level) and so, depending on the provided amount of coverage, may be referred to as femto base stations, pico base stations, micro base stations, or macro base stations. Network node 3000 may be a relay node or a relay donor node controlling a relay. Network nodes 3000 may also include one or more (or all) parts of a distributed radio base station such as centralized digital units, distributed units (e.g., in an O-RAN access node) and / or remote radio units (RRUs), sometimes referred to as Remote Radio Heads (RRHs). Such remote radio units may or may not be integrated with an antenna as an antenna integrated radio. Parts of a distributed radio base station may also be referred to as nodes in a distributed antenna system (DAS).

[0717] Other examples of network nodes 3000 include multiple transmission point (multi-TRP) 5G access nodes, multi-standard radio (MSR) equipment such as MSR BSs, network controllers such as radio network controllers (RNCs) or base station controllers (BSCs), base transceiver stations (BTSs), transmission points, transmission nodes, multi-cell / multicast coordination entities (MCEs), Operation and Maintenance (O&M) nodes, Operations Support System (OSS) nodes, Self-Organizing Network (SON) nodes, positioning nodes (e.g., Evolved Serving Mobile Location Centers (E-SMLCs)), and / or Minimization of Drive Tests (MDTs).

[0718] In particular embodiments, network node 3000 includes a processing circuitry 3002, a memory 3004, a communication interface 3006, and a power source 3008. In general, in a particular embodiment of network node 3000, processing circuitry 3002, memory 3004, communication interface 3006, and power source 3008 may, in whole or in part, represent or include physical components common to or shared by one or more of the other elements of network node 3000.

[0719] The network node 3000 may be composed of multiple distinct network entities (e.g., a NodeB entity and a RNC entity, or a BTS entity and a BSC entity, etc.), which may each have or utilize their own respective physical components. In certain scenarios in which the network node3000 comprises multiple such entities (e.g., BTS and BSC), one or more of the separate entities may be shared among several network nodes. For example, a single RNC may control multiple NodeBs. In such a scenario, each unique NodeB and RNC pair, may in some instances be considered a single separate network node. In some embodiments, the network node 3000 may be configured to support multiple radio access technologies (RATs). In such embodiments, some components may be duplicated (e.g., separate memories 3004 or portions of memory 3004 for different RATs) and some components may be reused (e.g., a same antenna 3010 may be shared by different RATs). The network node 3000 may also include multiple sets of the various illustrated components for different wireless technologies integrated into network node 3000, for example GSM, WCDMA, LTE, NR, Wi-Fi (e.g., according to an IEEE 802.11 family standard), Zigbee, Z-wave, LoRaWAN, Radio Frequency Identification (RFID) or Bluetooth wireless technologies. These wireless technologies may be integrated into the same or different chip or set of chips and other components within network node 3000.

[0720] The processing circuitry 3002 may comprise a combination of one or more of a microprocessor, controller, microcontroller, central processing unit, digital signal processor, application-specific integrated circuit, field programmable gate array, or any other suitable computing device, resource, or combination of hardware, software and / or encoded logic operable to provide, either alone or in conjunction with other components, such as the memory 3004, to provide network node 3000 functionality.

[0721] In some embodiments, the processing circuitry 3002 includes a system on a chip (SOC). In some embodiments, the processing circuitry 3002 includes one or more of radio frequency (RF) transceiver circuitry 3012 and baseband processing circuitry 3014. In some embodiments, the RF transceiver circuitry 3012 and the baseband processing circuitry 3014 may be on separate chips (or sets of chips), boards, or units, such as radio units and digital units. In alternative embodiments, part or all of RF transceiver circuitry 3012 and baseband processing circuitry 3014 may be on the same chip or set of chips, boards, or units.

[0722] The memory 3004 may comprise any form of volatile or non-volatile computer-readable memory including, without limitation, persistent storage, solid-state memory, remotely mounted memory, magnetic media, optical media, random access memory (RAM), read-only memory (ROM), mass storage media (for example, a hard disk), removable storage media (for example, a flash drive, a Compact Disk (CD) or a Digital Video Disk (DVD)), and / or any other volatile or non-volatile, non-transitory device-readable and / or computer-executable memory devices that store information, data, and / or instructions that may be used by the processing circuitry 3002. The memory 3004 may store any suitable instructions, data, or information, including a computer program, software, an application including one or more of logic, rules, code, tables, and / or other instructions capable of being executed by the processing circuitry 3002 and utilized by the network node 3000. The memory 3004 may be used to store any calculations made by the processingcircuitry 3002 and / or any data received via the communication interface 3006. In some embodiments, the processing circuitry 3002 and memory 3004 is integrated.

[0723] The communication interface 3006 is used in wired or wireless communication of signaling and / or data with UEs, other network nodes, and / or any other network equipment. In the illustrated embodiment, communication interface 3006 comprises port(s) / terminal(s) 3016 to send and receive data, for example to and from a network over a wired connection. In particular embodiments, network node 2900 may be capable of wireless communication and communication interface 3006 may also include radio front-end circuitry 3018 that may be coupled to, or in certain embodiments a part of, an antenna 3010. Particular embodiments of radio frontend circuitry 3018 include filter(s) 3020 and amplifier(s) 3022. The radio front-end circuitry 3018 may be connected to an antenna 3010 and processing circuitry 3002. The radio front-end circuitry may be configured to condition signals communicated between antenna 3010 and processing circuitry 3002. The radio front-end circuitry 3018 may receive digital data that is to be sent out to other network nodes or UEs via a wireless connection. The radio front-end circuitry 3018 may convert the digital data into a radio signal(s) having the appropriate channel and bandwidth parameters using a combination of filters 3020 and / or amplifiers 3022. The radio signal(s) may then be transmitted via the antenna 3010. Similarly, when receiving data, the antenna 3010 may collect radio signals which are then converted into digital data by the radio front-end circuitry 3018. The digital data may be passed to the processing circuitry 3002. In other embodiments, the communication interface may comprise different components and / or different combinations of components.

[0724] In certain alternative embodiments, network node 3000 may be capable of wireless communication but does not include separate radio front-end circuitry 3018, instead, the processing circuitry 3002 includes radio front-end circuitry and is connected to the antenna 3010. Similarly, in some embodiments, all or some of the RF transceiver circuitry 3012 is part of the communication interface 3006. In still other embodiments, the communication interface 3006 includes one or more ports or terminals 3016, the radio front-end circuitry 3018, and the RF transceiver circuitry 3012, as part of a radio unit (not shown), and the communication interface 3006 communicates with the baseband processing circuitry 3014, which is part of a digital unit (not shown).

[0725] The antenna 3010 may include one or more antennas, or antenna arrays, configured to send and / or receive wireless signals. The antenna 3010 may be coupled to the radio front-end circuitry 3018 and may be any type of antenna capable of transmitting and receiving data and / or signals wirelessly. In certain embodiments, the antenna 3010 is separate from the network node 3000 and connectable to the network node 3000 through one or more interfaces or ports.

[0726] The antenna 3010, communication interface 3006, and / or the processing circuitry 3002 may be configured to perform some or all of the receiving operations and / or obtaining operationsdescribed herein as being performed by the network node 3000. Any information, data and / or signals may be received from a UE, another network node and / or any other network equipment. Similarly, the antenna 3010, the communication interface 3006, and / or the processing circuitry 3002 may be configured to perform some or all of the transmitting or sending operations described herein as being performed by the network node 3000. Any information, data and / or signals may be transmitted to a UE, another network node and / or any other network equipment.

[0727] The power source 3008 provides power to the various components of network node 3000 in a form suitable for the respective components (e.g., at a voltage and current level needed for each respective component). The power source 3008 may further comprise, or be coupled to, power management circuitry to supply the components of the network node 3000 with power for performing the functionality described herein. For example, the network node 3000 may be connectable to an external power source (e.g., the power grid, an electricity outlet) via an input circuitry or interface such as an electrical cable, whereby the external power source supplies power to power circuitry of the power source 3008. As a further example, the power source 3008 may comprise a source of power in the form of a battery or battery pack which is connected to, or integrated in, power circuitry. The battery may provide backup power should the external power source fail.

[0728] Embodiments of the network node 3000 may include additional components beyond those shown in Figure 30 for providing certain aspects of the network node’s functionality, including any of the functionality described herein and / or any functionality necessary to support the subject matter described herein. For example, the network node 3000 may include user interface equipment to allow input of information into the network node 3000 and to allow output of information from the network node 3000. This may allow a user to perform diagnostic, maintenance, repair, and other administrative functions for the network node 3000.

[0729] Figure 31 is a block diagram illustrating a virtualization environment 3100 in which functions implemented by some embodiments may be virtualized. In the present context, virtualizing means creating virtual versions of apparatuses or devices which may include virtualizing hardware platforms, storage devices and networking resources. As used herein, virtualization can be applied to any device described herein, or components thereof, and relates to an implementation in which at least a portion of the functionality is implemented as one or more virtual components. Some or all of the functions described herein may be implemented as virtual components executed by one or more virtual machines (VMs) implemented in one or more virtual environments 3100 hosted by one or more of hardware nodes, such as a hardware computing device that operates as an access network node, UE, core network node, or host. Further, in embodiments in which a virtual node does not require radio connectivity (e.g., a core network node or host), then the node may be entirely virtualized. In some embodiments, the virtualization environment3100 includes components defined by the O-RAN Alliance, such as an O-Cloud environment orchestrated by a Service Management and Orchestration Framework via an 0-2 interface.

[0730] Applications 3102 (which may alternatively be called software instances, virtual appliances, network functions, virtual nodes, virtual network functions, etc.) are run in the virtualization environment Q400 to implement some of the features, functions, and / or benefits of some of the embodiments disclosed herein.

[0731] Hardware 3104 includes processing circuitry, memory that stores software and / or instructions executable by hardware processing circuitry, and / or other hardware devices as described herein, such as a network interface, input / output interface, and so forth. Software may be executed by the processing circuitry to instantiate one or more virtualization layers 3106 (also referred to as hypervisors or virtual machine monitors (VMMs)), provide VM 3108A and VM 3108B (which may be collectively referred to asVMs 3108), and / or perform any of the functions, features and / or benefits described in relation with some embodiments described herein. The virtualization layer 3106 may present a virtual operating platform that appears like networking hardware to one or more of the VMs 3108.

[0732] The VMs 3108 comprise virtual processing, virtual memory, virtual networking or interface and virtual storage, and may be run by virtualization layer 3106. Different embodiments of the instance of a virtual appliance 3102 may be implemented on one or more of VMs 3108, and the implementations may be made in different ways. Virtualization of the hardware is in some contexts referred to as network function virtualization (NFV). NFV may be used to consolidate many network equipment types onto industry standard high volume server hardware, physical switches, and physical storage, which can be located in data centers, and customer premise equipment.

[0733] In the context of NFV, each of the VMs 3108 may be a software implementation of a physical machine that runs programs as if they were executing on a physical, non-virtualized machine. Each of the VMs 3108, and that part of hardware 3104 that executes that VM, be it hardware dedicated to that VM and / or hardware shared by that VM with others of the VMs, forms separate virtual network elements. Still in the context of NFV, a virtual network function is responsible for handling specific network functions that run in one or more of the VMs 3108 on top of the hardware 3104 and corresponds to an application 3102.

[0734] Hardware 3104 may be implemented in a standalone network node with generic or specific components. Hardware 3104 may implement some functions via virtualization. Alternatively, hardware 3104 may be part of a larger cluster of hardware (e.g. such as in a data center or CPE) where many hardware nodes work together and are managed via management and orchestration 3110, which, among others, oversees lifecycle management of applications 3102. In some embodiments, hardware 3104 is coupled to one or more radio units that each include one or more transmitters and one or more receivers that may be coupled to one or more antennas. Radio units may communicate directly with other hardware nodes via one or more appropriate networkinterfaces and may be used in combination with the virtual components to provide a virtual node with radio capabilities, such as a radio access node or a base station. In some embodiments, some signaling can be provided with the use of a control system 3112 which may alternatively be used for communication between hardware nodes and radio units.

[0735] The first device 131 embodiments relate to Figure 9, any of Figures 13-19, and Figures 27-29.

[0736] The first device 131 may comprise an arrangement as shown in Figure 19 or in Figure 29. The second device 132 embodiments relate to Figure 10, any of Figures 13-18, Figure 20 and Figures 27-29.

[0737] The second device 132 may comprise an arrangement as shown in Figure 20 or in Figure 29.

[0738] The first network node 111 embodiments relate to Figure 11, any of Figures 13-18, Figure 21, Figures 27-28, and Figures 30-31.

[0739] The first network node 111 may comprise an arrangement as shown in Figure 21 or in any of Figures 30-31.

[0740] The second network node 112 embodiments relate to Figure 12, any of Figures 12-18, Figure 22, Figures 27-28, and Figures 30-31.

[0741] The second network node 112 may comprise an arrangement as shown in Figure 22 or in any of Figures 30-31.

[0742] Although the computing devices described herein (e.g., UEs, network nodes, hosts) may include the illustrated combination of hardware components, other embodiments may comprise computing devices with different combinations of components. It is to be understood that these computing devices may comprise any suitable combination of hardware and / or software needed to perform the tasks, features, functions and methods disclosed herein. Determining, calculating, obtaining or similar operations described herein may be performed by processing circuitry, which may process information by, for example, converting the obtained information into other information, comparing the obtained information or converted information to information stored in the network node, and / or performing one or more operations based on the obtained information or converted information, and as a result of said processing making a determination. Moreover, while components are depicted as single boxes located within a larger box, or nested within multiple boxes, in practice, computing devices may comprise multiple different physical components that make up a single illustrated component, and functionality may be partitioned between separate components. For example, a communication interface may be configured to include any of the components described herein, and / or the functionality of the components may be partitioned between the processing circuitry and the communication interface. In another example, non-computationally intensive functions of any of such components may beimplemented in software or firmware and computationally intensive functions may be implemented in hardware.

[0743] In certain embodiments, some or all of the functionality described herein may be provided by processing circuitry executing instructions stored on in memory, which in certain embodiments may be a computer program product in the form of a non-transitory computer-readable storage medium. In alternative embodiments, some or all of the functionality may be provided by the processing circuitry without executing instructions stored on a separate or discrete device-readable storage medium, such as in a hard-wired manner. In any of those particular embodiments, whether executing instructions stored on a non-transitory computer-readable storage medium or not, the processing circuitry can be configured to perform the described functionality. The benefits provided by such functionality are not limited to the processing circuitry alone or to other components of the computing device, but are enjoyed by the computing device as a whole, and / or by end users and a wireless network generally.

Claims

CLAIMS:

1. A computer-implemented method performed by a first device (131), the method being for handling data pertaining to Reference Signals, RSs, the first device (131) operating in a communications system (100), the method comprising:- obtaining (902), from a first network node (111) operating in the communications system (100), one or more configurations indicating one or more patterns of collection of data pertaining to overlaid RSs, and- performing (904) an action to support the collection of the data based on the obtained one or more configurations.

2. The method according to claim 1 , wherein one of:- the action is performed in the Uplink, UL, and the action comprises performing a transmission using the one or more patterns of collection of data, and - the action is performed in the Downlink, DL, and the action comprises performing a reception using the one or more patterns of collection of data.

3. The method according to any of claims 1 -2, further comprising one or more of:- providing (901), to the first network node (111) a first indication indicating a capability to perform the action to support the collection of the data based on the obtained one or more configurations, and- obtaining (903), from the first network node (111), a second indication triggering the performing (904) of the action.

4. The method according to claim 3, wherein the second indication indicates one of:- to transmit the one or more patterns of collection of data based on the obtained one or more configurations, and- to receive the one or more patterns of collection of data based on the obtained one or more configurations.

5. The method according to any of claims 1 -4, wherein one or more of:i. the RSs are used for demodulation,ii. the RSs are used for phase tracking,Hi. the RSs comprise Demodulation RSs, DMRSs,iv. the data to be collected is to be used in a life cycle management of a machine learning model, MLM,v. the one or more patterns of collection of data pertaining to overlaid RSs comprise one of:a) Resource Elements, REs, comprising the RSs overlaid with first data, wherein the first data is known data, b) REs comprising the RSs overlaid with second data and REs comprising non-overlaid Reference Signal REs, wherein the second data is unknown data, and with a cyclic redundancy check, andc) REs comprising the RSs overlaid with second data, wherein the second data is unknown data, and REs comprising non-overlaid Reference Signal REs lacking data, and REs comprising only third data,vi. content of the third data in the non-overlaid Reference Signal REs is the second data comprised in the REs comprising the RSs overlaid with the second data,vii. at least one of the one or more configurations comprises an overlay power ratio parameter intended to be used by a receiver to measure interference,viii. at least one of the one or more configurations comprises the overlay power ratio parameter and / or a measurement report configuration intended to be used by the receiver to measure interference, ix. RSs used for overlaid RS transmissions are re-used for non-overlaid RS REs, andx. the communications system (100) is a wireless communications system (100).

6. The method according to claim 5, further comprising one or more of:- initiating (905) training of the MLM with data collected based on the one or more configurations, the MLM being to predict demodulation of a channel with the overlaid RSs, wherein the channel is between the first device (131) and the first network node (111), and- initiating (906) outputting a third indication of the trained MLM.

7. The method according to claim 6, further comprising:- using (907) the trained MLM to predict demodulation with an overlaid first RS.

8. The method according to any of claims 6-7, wherein the training of the MLM is performed by one of: the first device (131), a second device (132), the first network node (111) and a second network node (112) operating in the communications system (100)and wherein, with the proviso the training of the MLM is performed by one of: the second device (132), the first network node (111) and the second network node (112), the initiating (905) of the training comprises sending one or more fourth indications indicating the collected data based on the obtained one or more configurations, to the one of: the second device (132), the first network node (111) and the second network node (112).

9. A computer-implemented method performed by a second device (132), the method being for handling data pertaining to Reference Signals, RSs, the second device (132) operating in a communications system (100), the method comprising:- obtaining (1002), from a first network node (111) operating in the communications system (100), a first configuration of one or more configurations indicating a first pattern of one or more patterns of collection of data pertaining to overlaid RSs,- predicting (1004), using collected fourth data as input to a trained machine learning model, MLM, demodulation of a first channel with the overlaid RSs, and - initiating (1005) outputting a fifth indication of the predicted demodulation.

10. The method according to claim 9, wherein the method further comprises one or more of:- performing (1003) a first action to support the collection of fourth data based on the obtained first configuration,- obtaining (1001) the trained MLM, from a first device (131) or a second network node (112) operating in the communications system (100).

11. The method according to claim 10, wherein one of:- the first action is performed in the Uplink, UL, and the first action comprises performing a transmission using the first pattern of collection of data, - the first action is performed in the Downlink, DL, and the first action comprises performing a reception using the first pattern of collection of data,- the first action is in the downlink and the collected fourth data comprises information indicating one or more measurements of interference.

12. The method according to any of claims 9-11 , wherein one or more of:i. the RSs are used for demodulation,ii. the RSs are used for phase tracking,Hi. the RSs comprise Demodulation RSs, DMRSs,iv. the one or more patterns of collection of data pertaining to overlaid RSs comprise Resource Elements, REs, comprising the RSs overlaid with second data, wherein the second data is unknown data,v. the RSs are transmitted by a third network node (113) operating in the communications system (100)vi. the first channel is between the second device (132) and the third network node (113),vii. at least one of the one or more configurations comprises an overlay power ratio parameter intended to be used by a receiver to measure interference,viii. at least one of the one or more configurations comprises an overlay power ratio parameter and / or a measurement report configuration intended to be used by a receiver to measure interference,ix. RSs used for overlaid RS transmissions are re-used for non-overlaid RS REs, andx. the communications system (100) is a wireless communications system (100).

13. A computer-implemented method performed by a first network node (111), the method being for handling data pertaining to Reference Signals, RSs, the first network node (111) operating in a communications system (100), the method comprising:- sending (1102), to a first device (131) operating in the communications system (100), one or more configurations indicating one or more patterns of collection of data pertaining to overlaid RSs.

14. The method according to claim 13, further comprising one or more of:- obtaining (1101), from the first device (131), a first indication indicating a capability to perform an action to support the collection of the data based on the sent one or more configurations, and- sending (1103), to the first device (131), a second indication triggering the performing of the action.

15. The method according to claim 14, wherein one of:- the action is performed in the Uplink, UL, and the action comprises performing a transmission using the one or more patterns of collection of data, and - the action is performed in the Downlink, DL, and the action comprises performing a reception using the one or more patterns of collection of data.

16. The method according to any of claims 14-15, wherein the second indication indicates one of:- to transmit the one or more patterns of collection of data based on the sent one or more configurations, and- to receive the one or more patterns of collection of data based on the sent one or more configurations.

17. The method according to any of claims 13-16, wherein one or more of:i. the RSs are used for demodulation,ii. the RSs are used for phase tracking,Hi. the RSs comprise Demodulation RSs, DMRSs,iv. the data to be collected is to be used in a life cycle management of a machine learning model, MLM,v. the one or more patterns of collection of data pertaining to overlaid RSs comprise one of:a) Resource Elements, REs, comprising the RSs overlaid with first data, wherein the first data is known data, b) REs comprising the RSs overlaid with second data and REs comprising non-overlaid Reference Signal REs, wherein the second data is unknown data, and with a cyclic redundancy check, andc) REs comprising the RSs overlaid with second data, wherein the second data is unknown data, and REs comprising non-overlaid Reference Signal REs lacking data, and REs comprising only third data,vi. content of the third data in the non-overlaid Reference Signal REs is the second data comprised in the REs comprising the RSs overlaid with the second data,vii. at least one of the one or more configurations comprises an overlay power ratio parameter intended to be used by a receiver to measure interference,viii. at least one of the one or more configurations comprises the overlay power ratio parameter and / or a measurement report configuration intended to be used by the receiver to measure interference,ix. RSs used for overlaid RS transmissions are re-used for non-overlaid RS REs, andx. the communications system (100) is a wireless communications system18. The method according to claim 17, further comprising one or more of:- receiving (1104), at least from the first device (131), one or more fourth indications indicating collected data based on the sent one or more configurations,- initiating (1105) training of a Machine Learning Model, MLM, with the collected data, the MLM being to predict demodulation of a channel with the overlaid RSs, wherein the channel is between the first device (131) and the first network node (111), and- initiating (1106) outputting a third indication of the trained MLM.

19. A computer-implemented method performed by a second network node (112), the method being for handling data pertaining to Reference Signals, RSs, the second network node (112) operating in a communications system (100), the method comprising:- obtaining (1201), from at least a first device (131) operating in the communications system (100), collected data by the first device (131) based on one or more configurations, the one or more configurations indicating one or more patterns of collection of data pertaining to overlaid RSs,- initiating (1202) training of a machine learning model, MLM, with the collected data, the MLM being to predict demodulation of a channel with the overlaid RSs, and- initiating (1203) outputting a third indication of the trained MLM.

20. The method according to claim 19, wherein an action, performed by the first device (131), to support the collection of the data based on the one or more configurations comprises one of:- the action performed in the Uplink, UL, and the action comprises performing a transmission using the one or more patterns of collection of data, and - the action performed in the Downlink, DL, and the action comprises performing a reception using the one or more patterns of collection of data.

21. The method according to any of claims 19-20, wherein one or more of:i. the RSs are used for demodulation,ii. the RSs are used for phase tracking,Hi. the RSs comprise Demodulation RSs, DMRSs,iv. the one or more patterns of collection of data pertaining to overlaid RSs comprise Resource Elements, REs, comprising the RSs overlaid with first data, wherein the first data is unknown data,v. the third indication is output to one or more of: the first device (131) and a second device (132) operating in the communications system (100), vi. the channel is between one or more of:a) the first device (131) and a first network node (111) operating in the communications system (100), andb) the second device (132) and a third network node (113) operating in the communications system (100), andvii. at least one of the one or more configurations comprises an overlay power ratio parameter intended to be used by a receiver to measure interference,viii. at least one of the one or more configurations comprises an overlay power ratio parameter and / or a measurement report configuration intended to be used by a receiver to measure interference,ix. RSs used for overlaid RS transmissions are re-used for non-overlaid RS REs, andx. the communications system (100) is a wireless communications system (100).

22. A first device (131), for handling data pertaining to Reference Signals, RSs, the first device (131) being configured to operate in a communications system (100), the first device (131) being further configured to:- obtain, from a first network node (111) configured to operate in the communications system (100), one or more configurations configured to indicate one or more patterns of collection of data configured to pertain to overlaid RSs, and- perform an action to support the collection of the data based on the one or more configurations configured to be obtained.

23. The first device (131) according to claim 22, wherein one of:- the action is configured to be performed in the Uplink, UL, and the action is configured to comprise performing a transmission using the one or more patterns of collection of data, andthe action is configured to be performed in the Downlink, DL, and the action is configured to comprise performing a reception using the one or more patterns of collection of data.

24. The first device (131) according to any of claims 22-23, further configured to one or more of:- provide, to the first network node (111) a first indication configured to indicate a capability to perform the action to support the collection of the data based on the one or more configurations configured to be obtained, and- obtain, from the first network node (111), a second indication configured to trigger the performing of the action.

25. The first device (131) according to claim 24, wherein the second indication is configured to indicate one of:- to transmit the one or more patterns of collection of data based on the one or more configurations configured to be obtained, and- to receive the one or more patterns of collection of data based on the one or more configurations configured to be obtained.

26. The first device (131) according to any of claims 22-25, wherein one or more of:i. the RSs are configured to be used for demodulation,ii. the RSs are configured to be used for phase tracking,Hi. the RSs are configured to comprise Demodulation RSs, DMRSs, iv. the data configured to be collected is configured to be used in a life cycle management of a machine learning model, MLM,v. the one or more patterns of collection of data configured to pertain to overlaid RSs are configured to comprise one of:a) Resource Elements, REs, configured to comprise the RSs overlaid with first data, wherein the first data is configured to be known data,b) REs configured to comprise the RSs overlaid with second data and REs configured to comprise non-overlaid Reference Signal REs, wherein the second data is configured to be unknown data, and with a cyclic redundancy check, andc) REs configured to comprise the RSs overlaid with second data, wherein the second data is configured to be unknown data, andREs configured to comprise non-overlaid Reference Signal REs lacking data, and REs configured to comprise only third data, vi. content of the third data in the non-overlaid Reference Signal REs is configured to be the second data configured to be comprised in the REs configured to comprise the RSs overlaid with the second data, vii. the action is in the downlink and data to be collected is configured to comprise information configured to indicate one or more measurements of interference,viii. at least one of the one or more configurations is configured to comprise an overlay power ratio parameter intended to be used by a receiver to measure interference,ix. at least one of the one or more configurations is configured to comprise the overlay power ratio parameter and / or a measurement report configuration intended to be used by the receiver to measure interference,x. RSs configured to be used for overlaid RS transmissions are configured to be re-used for non-overlaid RS REs, andxi. the communications system (100) is configured to be a wireless communications system (100).

27. The first device (131) according to claim 26, further configured to one or more of:- initiate training of the MLM, with data configured to be collected based on the one or more configurations, the MLM being configured to predict demodulation of a channel with the overlaid RSs, wherein the channel is configured to be between the first device (131) and the first network node (111), and- initiate outputting a third indication of the trained MLM.

28. The first device (131) according to claim 27, being further configured to:- use the MLM configured to be trained to predict demodulation with an overlaid first RS.

29. The first device (131) according to any of claims 27-28, wherein the training of the MLM is configured to be performed by one of: the first device (131), a second device (132), the first network node (111) and a second network node (112) configured to operate in the communications system (100) and wherein, with the proviso the training of the MLM is configured to be performed by one of: the second device (132), the first network node (111) and the second network node (112), the initiating of the training is configured tocomprise sending one or more fourth indications indicating the data configured to be collected based on the one or more configurations configured to be obtained, to the one of: the second device (132), the first network node (111) and the second network node (112).

30. A second device (132), for handling data pertaining to Reference Signals, RSs, the second device (132) being configured to operate in a communications system (100), the second device (132) being further configured to:- obtain, from a first network node (111) configured to operate in the communications system (100), a first configuration of one or more configurations configured to indicate a first pattern of one or more patterns of collection of data pertaining to overlaid RSs,- predict, using collected fourth data as input to a trained machine learning model, MLM, demodulation of a first channel with the overlaid RSs, and- initiate outputting a fifth indication of the demodulation configured to be predicted.

31. The second device (132) according to claim 30, wherein the second device (132) is further configured to one or more of:- perform a first action to support the collection of the fourth data based on the first configuration configured to be obtained, and- obtain the trained MLM, from a first device (131) or a second network node (112) configured to operate in the communications system (100).

32. The second device (132) according to claim 31, wherein one of:- the first action is configured to be performed in the Uplink, UL, and the first action is configured to comprise performing a transmission using the first pattern of collection of data,- the first action is configured to be performed in the Downlink, DL, and the first action is configured to comprise performing a reception using the first pattern of collection of data, and- the first action is configured to be in the downlink and the fourth data configured to be collected is configured to comprise information indicating one or more measurements of interference.

33. The second device (132) according to any of claims 30-32, wherein one or more of:i. the RSs are configured to be used for demodulation,ii. the RSs are configured to be used for phase tracking,Hi. the RSs configured to comprise Demodulation RSs, DMRSs,iv. the one or more patterns of collection of data pertaining to overlaid RSs are configured to comprise Resource Elements, REs, comprising the RSs overlaid with second data, wherein the second data is configured to be unknown data,v. the RSs are configured to be transmitted by a third network node (113) configured to operate in the communications system (100)vi. the first channel is configured to be between the second device (132) and the third network node (113),vii. at least one of the one or more configurations is configured to comprise an overlay power ratio parameter intended to be used by a receiver to measure interference,viii. at least one of the one or more configurations is configured to comprise an overlay power ratio parameter and / or a measurement report configuration intended to be used by a receiver to measure interference, ix. RSs configured to be used for overlaid RS transmissions are configured to be re-used for non-overlaid RS REs, andx. the communications system (100) is configured to be a wireless communications system (100).

34. A first network node (111), for handling data pertaining to Reference Signals, RSs, the first network node (111) being configured to operate in a communications system (100), the first network node (111) being further configured to:- send, to a first device (131) configured to operate in the communications system (100), one or more configurations configured to indicate one or more patterns of collection of data pertaining to overlaid RSs.

35. The first network node (111) according to claim 34, being further configured to one or more of:- obtain, from the first device (131), a first indication configured to indicate a capability to perform an action to support the collection of the data based on the one or more configurations configured to be sent, and- send, to the first device (131), a second indication configured to trigger the performing of the action.

36. The first network node (111) according to claim 35, wherein one of:- the action is configured to be performed in the Uplink, UL, and the action is configured to comprise performing a transmission using the one or more patterns of collection of data, and- the action is configured to be performed in the Downlink, DL, and the action is configured to comprise performing a reception using the one or more patterns of collection of data.

37. The first network node (111) according to any of claims 34-36, wherein the second indication is configured to indicate one of:- to transmit the one or more patterns of collection of data based on the one or more configurations configured to be sent, and- to receive the one or more patterns of collection of data based on the one or more configurations configured to be sent.

38. The first network node (111) according to any of claims 35-37, wherein one or more of:i. the RSs are configured to be used for demodulation,ii. the RSs are configured to be used for phase tracking,Hi. the RSs are configured to comprise Demodulation RSs, DMRSs, iv. the data configured to be collected is configured to be used in a life cycle management of a Machine Learning Model, MLM,v. the one or more patterns of collection of data configured to pertain to overlaid RSs are configured to comprise one of:a) Resource Elements, REs, configured to comprise the RSs overlaid with first data, wherein the first data is configured to be known data,b) REs configured to comprise the RSs overlaid with second data and REs configured to comprise non-overlaid Reference Signal REs, wherein the second data is configured to be unknown data, and with a cyclic redundancy check, andc) REs configured to comprise the RSs overlaid with second data, wherein the second data is configured to be unknown data, and REs configured to comprise non-overlaid Reference Signal REs lacking data, and REs configured to comprise only third data, vi. content of the third data in the non-overlaid Reference Signal REs is configured to be the second data configured to be comprised in the REs configured to comprise the RSs overlaid with the second data,vii. at least one of the one or more configurations is configured to comprise an overlay power ratio parameter intended to be used by a receiver to measure interference,viii. at least one of the one or more configurations is configured to comprise the overlay power ratio parameter and / or a measurement report configuration intended to be used by the receiver to measure interference,ix. RSs configured to be used for overlaid RS transmissions are configured to be re-used for non-overlaid RS REs, andx. the communications system (100) is configured to be a wireless communications system (100).

39. The first network node (111) according to claim 38, further configured to one or more of:- receive, at least from the first device (131), one or more fourth indications configured to indicate collected data based on the one or more configurations configured to be sent,- initiate training of the MLM with the collected data, the MLM being configured to predict demodulation of a channel with the overlaid RSs, wherein the channel is configured to be between the first device (131) and the first network node (111), and- initiate outputting a third indication of the trained MLM.

40. A second network node (112), for handling data pertaining to Reference Signals, RSs, the second network node (112) being configured to operate in a communications system (100), the second network node (112) being further configured to:- obtain, from at least a first device (131) configured to operate in the communications system (100), collected data by the first device (131) based on one or more configurations, the one or more configurations being configured to indicate one or more patterns of collection of data pertaining to overlaid RSs, - initiate training of a machine learning model, MLM, with the collected data, the MLM being configured to predict demodulation of a channel with the overlaid RSs, and- initiate outputting a third indication of the MLM configured to be trained.

41. The second network node (112) according to claim 40, wherein an action, configured to be performed by the first device (131), to support the collection of the data based on the one or more configurations is configured to comprise one of:- the action configured to be performed in the Uplink, UL, wherein the action is configured to comprise performing a transmission using the one or more patterns of collection of data, and- the action configured to be performed in the Downlink, DL, wherein the action is configured to comprise performing a reception using the one or more patterns of collection of data.

42. The second network node (112) according to any of claims 40-41 , wherein one or more of:i. the RSs are configured to be used for demodulation,ii. the RSs are configured to be used for phase tracking,Hi. the RSs are configured to comprise Demodulation RSs, DMRSs, iv. the one or more patterns of collection of data configured to pertain to overlaid RSs are configured to comprise Resource Elements, REs, configured to comprise the RSs overlaid with second data, wherein the second data is configured to be unknown data,v. the third indication is configured to be output to one or more of: the first device (131) and a second device (132) configured to operate in the communications system (100),vi. the channel is configured to be between one or more of:a) the first device (131) and a first network node (111) configured to operate in the communications system (100), andb) the second device (132) and a third network node (113) configured to operate in the communications system (100), vii. at least one of the one or more configurations is configured to comprise an overlay power ratio parameter intended to be used by a receiver to measure interference,viii. at least one of the one or more configurations are configured to comprise an overlay power ratio parameter and / or a measurement report configuration intended to be used by a receiver to measure interference, ix. RSs configured to be used for overlaid RS transmissions are configured to be re-used for non-overlaid RS REs, andx. the communications system (100) is configured to be a wireless communications system (100).

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