Apparatus, method and computer program

Abstract

There is provided an apparatus comprising means for receiving, from a network, a configuration comprising at least one parameter associated with at least one condition, determining, based at least in part on the at least one parameter associated with at least one condition, at least one parameter to apply for selecting measurement samples for a machine learning model from a number of time domain samples and providing an indication of the determined at least one parameter to the network.

Description

APPARATUS, METHOD AND COMPUTER PROGRAMTECHNICAL FIELD

[0001] Various embodiments of this disclosure relate generally to methods, apparatus and computer programs, and in particular, but not exclusively, to a method to configure sample measurements for AI / ML-based use cases.BACKGROUND

[0002] A communication system can be seen as a facility that enables communication sessions between two or more communication devices, or provides communication devices access to a network. A mobile or wireless communication network is one example of a communication network. A communication device may be provided with a service by an application server.

[0003] A mobile or wireless communication network may operate in accordance with standard(s), such as those provided by 3GPP (Third Generation Partnership Project) or ETSI (European Telecommunications Standards Institute). Examples of mobile or wireless communication network that operate in accordance with 3GPP standards are generally referred to as 4G (4th Generation) networks, 5G (5th Generation) network, 5G-Advanced networks and 6G networks.SUMMARY

[0004] Some embodiments of this disclosure will be described with respect to certain aspects. These aspects are not intended to indicate key or essential features of the various example embodiments of this disclosure, nor are they intended to be used to limit the scope of thereof. Other features, aspects, and elements will be readily apparent to a person skilled in the art in view of this disclosure. For example, it should be appreciated that further aspects may be provided by the combination of any two or more of the various aspects described herein.

[0005] In a first aspect there is provided a method comprising receiving, from a network, a configuration comprising at least one parameter associated with at least one condition, determining, based at least in part on the at least one parameter associated with at least one condition, at least one parameter to apply for selecting measurement samples for a machine learning model from a number of time domain samples and providing an indication of the determined at least one parameter to the network.

[0006] The at least one condition may comprise at least one of: channel conditions, user equipment capability, power level, user equipment location, quality of service requirements or user equipment mobility.

[0007] The at least one parameter may comprise at least one of: a number of measurement samples, whether the selected samples are consecutive or non-consec- utive, or reporting granularity.

[0008] The method may comprise providing the determined at least one parameter with a timestamp.

[0009] The method may comprise performing measurement of samples, selecting measurement samples according to the at least one determined parameter and providing the selected measurement samples to the network.

[0010] The method may comprise providing the indication of the determined at least one parameter together with the selected measurement samples.

[0011] The measurement samples may be used as input for machine learning model inference, training or monitoring.

[0012] The machine learning model may be a machine learning positioning model.

[0013] The method may comprise providing a flag indicating whether the measurement samples are to be used for inference, training or monitoring.

[0014] The method may comprise receiving the configuration from a network node of the network.

[0015] The network node may comprise a location management function, an access and mobility management function, a network data and analytics function or a radio access node.

[0016] The configuration may comprise an identifier.

[0017] The method may comprise determining the at least one parameter further based on the identifier.

[0018] The method may comprise observing at least one condition at the apparatus; and means for determining the at least one parameter further based on the observed at least one condition.

[0019] The method may be performed at a user equipment or a radio access node.

[0020] In a second aspect there is provided a method comprising, at an apparatus, providing, to at least one second apparatus, a configuration comprising at least one parameter associated with at least one condition for determining at least one parameter for selecting measurement samples for a machine learning model from a number oftime domain samples and receiving an indication of at least one determined parameter from the at least one second apparatus.

[0021] The at least one second apparatus may comprise at least one user equipment or at least one radio access node.

[0022] The at least one user equipment may comprise a plurality of user equipment.

[0023] The method may comprise providing the configuration to the plurality of user equipment in dedicated signalling or broadcast signalling.

[0024] The at least one condition may comprise at least one of: channel conditions, user equipment capability, power level, user equipment location, quality of service requirements or user equipment mobility.

[0025] The at least one parameter may comprise at least one of: a number of measurement samples, whether the selected samples are consecutive or non-consec- utive, or reporting granularity.

[0026] The configuration may have a validity timer.

[0027] The method may comprise receiving the determined at least one parameter with a timestamp.

[0028] The method may comprise receiving the indication of the determined at least one parameter together with selected measurement samples.

[0029] The measurement samples may be used as input for machine learning model inference, training or monitoring.

[0030] The machine learning model may be a machine learning positioning model.

[0031] The method may comprise receiving a flag indicating whether the measurement samples are to be used for inference, training or monitoring.

[0032] The method may be performed at a network node of the network.

[0033] The network node may comprise a location management function, an access and mobility management function, a network data and analytics function or a radio access node.

[0034] The configuration may comprise an identifier.

[0035]

[0036] In a fourth aspect there is provided an apparatus comprising at least one processor, and at least one memory storing instructions which, when executed by the at least one processor, cause the apparatus at least to perform a method according to the first or second aspect.

[0037] In a fifth aspect there is provided a non-transitory computer readablemedium comprising instructions wherein the instructions when executed by at least one processor of an apparatus cause the apparatus to perform the method according to the first or second aspect.

[0038] In a sixth aspect there is provided a computer program comprising instructions which, when executed by an apparatus, cause the apparatus to perform at least the following a method according to the first or second aspect.

[0039] Some embodiments of the invention are defined in the dependent claims.

[0040] In the above, many different aspects have been described. As previously noted, it should be appreciated that further aspects may be provided by the combination of any two or more of the aspects described above (or otherwise in this disclosure).

[0041] Various other aspects are also described in the following detailed description and in the claims.BRIEF DESCRIPTION OF THE FIGURES

[0042] Some embodiments will be described, by way of non-limiting and illustrative example only, with reference to the figures, in which:

[0043] Fig. 1 shows an example of a communication network to which examples disclosed herein may be applied;

[0044] Fig. 2 shows a flowchart of an example method;

[0045] Fig. 3 shows a flowchart of an example method;

[0046] Fig. 4 shows a flowchart of an example method;

[0047] Fig. 5 shows a flowchart of an example method;

[0048] Fig. 6 shows an example signalling flow;

[0049] Fig. 7 shows an example of an apparatus.DETAILED DESCRIPTION

[0050] The following embodiments are provided by way of non-limiting and illustrative example. Although the specification may refer to “an”, “one”, or “some” embodiments) in several locations of the text, this does not necessarily mean that each reference is made to the same embodiment(s), or that a particular feature only applies to a single embodiment. Single features of different embodiments may also be combined to provide other embodiments. Further, when a particular feature, structure, or characteristic is described in connection of an embodiment, it intended such feature, structure, or characteristic may be applied in connection with other embodiments (whether or notexplicitly described).

[0051] It shall be understood that although the terms “first,” “second” and the like may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another.

[0052] For the purposes of this disclosure, the phrases “at least one of A or B”, “at least one of A and B”, and “A and / or B” means (A), (B), or (A and B). For the purposes of this disclosure, the phrase “A, B, and / or C” means (A), (B), (C), (A and B), (A and C), (B and C), or (A, B, and C).

[0053] As used herein, the term “or” refers to a non-exclusive “or” unless otherwise indicated (e.g., use of “or else” or “or in the alternative”).

[0054] As used herein, unless stated explicitly, performing a respective feature, step, or functionality “in response to A” does not indicate that the respective feature, step, or functionality is performed immediately after “A” occurs as one or more intervening features, steps, or functionalities may be performed (at least in part) between an occurrence of the respective feature, step, or function and “A”. Analogously, performing a respective feature, step, or functionality “based on A” does not indicate that the respective feature, step, or functionality is performed solely based on “A” as the respective feature, step, or functionality may be further based on one or more other features, steps, or functionalities in addition to “A”.

[0055] Embodiments described herein may be implemented in a communication network, such as any of the following radio access technologies (RATs): Worldwide Interoperability for Micro-wave Access (WiMAX), Global System for Mobile communications (GSM, 2G), GSM EDGE radio access Network (GERAN), General Packet Radio Service (GRPS), Universal Mobile Telecommunication System (UMTS, 3G) based on basic wideband-code division multiple access (W-CDMA), high-speed packet access (HSPA), Long Term Evolution (LTE), LTE-Advanced, and enhanced LTE (eLTE), 5G (also called NR), or any future RAT such as 6G. Moreover, communication within the communication network may utilize any proper wireless communication technology, comprising but not limited to: Code Division Multiple Access (CDMA), Frequency Division Multiple Access (FDMA), Time Division Multiple Access (TDMA), Frequency Division Duplex (FDD), Time Division Duplex (TDD), Multiple-Input Multiple-Output (MIMO), Orthogonal Frequency Division Multiple (OFDM), and / or Discrete Fourier Transform spread OFDM (DFT-s-OFDM).

[0056] As used herein, the term “network device” or “network node” refers to a node in a communication network via which user equipment may access the networkand / or which is configured to control radio communication and managing radio resources within a cell. The network node or network device may be referred to as a base station (BS), an access point (AP) or an access node. The network device may be, depending on the applied technology, for example, a node B (NodeB or NB), an evolved NodeB (eNodeB or eNB), an NR NB (also referred to as a gNB), a Remote Radio Unit (RRU), a radio head (RH), a remote radio head (RRH), a relay, an Integrated Access and Backhaul (IAB) node, a low power node, a non-terrestrial network (NTN) or nonground network device, such as a satellite network device, a low earth orbit (LEO) satellite and a geosynchronous earth orbit (GEO) satellite, or an aircraft network device.

[0057] Moreover, in connection of split radio access network (RAN), the network device may refer to a centralised unit (CU) of a base station and / or a distributed unit (DU) of a base station. An interface between CU and DU may be referred to as an F1 interface in NR. In the split RAN architecture, node operations may be carried out, at least partly, in the central / centralized unit, CU, (e.g. server, host or node) operationally coupled to the DU, (e.g. a radio head / node). One CU may control one or more DUs, acting at least as transmit / receive (Tx / Rx) nodes. In some embodiments, the DUs may comprise e.g. a radio link control (RLC), medium access control (MAC) layer and a physical (PHY) layer, whereas the CU may comprise the layers above RLC layer, such as a packet data convergence protocol (PDCP) layer, a radio resource control (RRC) and an internet protocol (IP) layers. Other functional splits are possible too. In practice, any processing task may be performed in either the CU or the DU and the boundary where the responsibility is shifted between the CU and the DU may depend on the applied implementation.

[0058] The term “terminal device” refers to any end device that may be configured to perform wireless communication. By way of example, a terminal device may be referred to as a communication device, user equipment (UE), a Subscriber Station (SS), or a Mobile Station (MS). The terminal device may include a mobile phone, a cellular phone, a smart phone, voice over IP (VoIP) phones, wireless local loop phones a tablet, a wearable terminal device, a personal digital assistant (PDA), portable computers, desktop computer, image capture terminal devices such as digital cameras, gaming terminal devices, music storage and playback appliances, vehicle-mounted wireless terminal devices, USB dongles, an Internet of Things (loT) device, a watch or other wearable, a head-mounted display (HMD), a vehicle, a drone, a medical device and applications (e.g., remote surgery), an industrial device and applications (e.g., a robot and / or other wireless devices operating in an industrial and / or an automated processingchain contexts), a consumer electronics device, a device operating on commercial and / or industrial wireless networks, and the like.

[0059] A term “resource”, as used herein, may refer to radio resources in time domain, in frequency domain, in space domain, and / or in code domain. Some examples of resources may include, e.g., a physical resource block (PRB), a radio frame, a subframe, a time slot, a subband, a frequency region, a sub-carrier, a beam, etc. The term “transmission” and / or “reception” may refer to wirelessly transmitting and / or receiving via a wireless propagation channel on radio resources.

[0060] Fig. 1 illustrates an example of a communication network to which examples disclosed herein may be applied. The communication network or a cellular communication network may comprise a network node 110 configured to provide one or more cells, such as cell 100, and a network node 112 configured to provide one or more other cells, such as cell 102. Each cell may, for example, be a macro cell, a micro cell, femto, or a pico cell. The cell may define a coverage area or a service area of the corresponding access node.

[0061] The network node (110, 112) may be configured to provide a user equipment (UE) 120 (one or more UEs) with wireless access to the communication network. The wireless access may comprise downlink (DL) communication from the network node (1 10, 1 12) to the UE 120 and uplink (UL) communication from the UE 120 to the network node (1 10, 112). Examples of uplink channels may comprise physical uplink control channel (PUCCH) for transmitting control information and physical uplink shared channel (PUSCH) for transmitting data towards the network. Examples of downlink channels may comprise physical downlink control channel (PDCCH) for transmitting control information and physical downlink shared channel (PDSCH) for transmitting data towards the user equipment.

[0062] There may be a plurality of UEs (120, 122) in the system. Each of the plurality of UEs may be served by the same or by different network nodes (110, 1 12). UE may be configured with dual connectivity (DC), wherein the UE, for example UE 120, may be connected to multiple network nodes (110, 112). The UEs (120, 122) may communicate with each other, in case device-to-device (D2D) communication interface is established between them via a so-called sidelink (SL). Such D2D communications may be referred to as machine-to-machine, peer-to-peer (P2P) communications, or ve- hicle-to-vehicle (V2V), for example.

[0063] In the case of multiple network nodes in the communication network, the network nodes may be connected to each other via an interface. LTE specifications, forexample, refer to such an interface as an X2 interface. An interface between an LTE node and a 5G node, or between two 5G nodes may be called an Xn interface.

[0064] The network nodes 110 and 112 may be further connected via another interface to a core network 116 of the communication network. The LTE specifications specify the core network as an evolved packet core (EPC), and the core network may comprise a plurality of entities (e.g. a mobility management entity (MME) and a gateway node). The MME may handle mobility of terminal devices in a tracking area encompassing a plurality of cells and handle signalling connections between the terminal devices and the core network. The gateway node may handle data routing in the core network and to / from the terminal devices. The 5G specifications specify the core network as a 5G core (5GC). The 5GC may, for example, comprise an access and mobility management function (AMF) and a user plane function / gateway (UPF) and other functions. The AMF may handle termination of non-access stratum (NAS) signalling, NAS ciphering & integrity protection, registration management, connection management, mobility management, access authentication and authorization, security context management. The UPF node may, for example, support packet routing and forwarding, packet inspection and quality of service (QoS) handling.

[0065] The location management function (LMF) manages the overall co-ordination and scheduling of resources required for the location of a UE that is registered with or accessing 5GCN. It also calculates or verifies a final location and any velocity estimate and may estimate the achieved accuracy. The LMF receives location requests for a target UE from the serving AMF using the Nlmf interface. The LMF interacts with the UE in order to exchange location information applicable to UE assisted and UE based position methods and interacts with the NG-RAN, N3IWF or TNAN in order to obtain location information.

[0066] The network data and analytics function (NWDAF) represents operator managed network analytics logical function. The NWDAF includes the following functionality:- Support data collection from NFs and AFs;- Support data collection from GAM;- NWDAF service registration and metadata exposure to NFs / AFs;- Support analytics information provisioning to NFs, AF.

[0067] The following is related to Artificial Intelligence (AI)ZMachine Learning (ML)-based use cases.

[0068] AI / ML general framework for one-sided AI / ML models within the scope of3GPP Release 19 on AI / ML for NR Air Interface includes signalling and protocol aspects of Life Cycle Management (LCM) enabling functionality and model (if justified) selection, activation, deactivation, switching, fallback, identification related signalling is part of the above objective, necessary signalling / mechanism(s) for LCM to facilitate model training, inference, performance monitoring, data collection (except for the purpose of CN / OAM / OTT collection of UE-sided model training data) for both UE-sided and NW- sided models and signalling mechanism of applicable functionalities / models.

[0069] One AI / ML use case is AI / ML-based positioning. Positioning accuracy enhancements, encompassing [RAN1 / RAN2 / RAN3] includeCase 1 : UE-based positioning with UE-side model, direct AI / ML or AI / ML assisted positioningCase 2a: UE-assisted / LMF-based positioning with UE-side model, AI / ML assisted positioningCase 2b: UE-assisted / LMF-based positioning with LMF-side model, direct AI / ML positioningCase 3a: NG-RAN node assisted positioning with gNB-side model, AI / ML assisted positioningCase 3b: NG-RAN node assisted positioning with LMF-side model, direct AI / ML positioning

[0070] Necessary measurements, signalling / mechanism(s) to facilitate Life Cycle Management (LCM) operations specific to the Positioning accuracy enhancements use cases, if any are to be specified. The necessary signalling of measurement enhancements (if any) is to be investigated and specified. Method(s) to ensure consistency between training and inference regarding NW-side additional conditions (if identified) for inference at UE for relevant positioning sub use cases should be enabled.

[0071] For the positioning use case, there are two approach in AIML based positioning 1 ) Direct positioning 2) Assisted positioning. In the direct approach network entity (UE / LMF) estimates the location based on the channel measurements (e.g., Channel Impulse Response (CIR), Power Delay Profile (PDP), Delay Profile (DP), Reference Signal Received Power (RSRP), Reference Signal Received Path Power (RSRPP), Reference Signal Time Difference (RSTD) etc.). In the second approach, network entity (UE / gNB) estimates the intermediate features such as Time of Arrival (ToA), Line-of-Sight (LOS)ZNon-Line-of-Sight (NLOS), path phase based on the channel measurements.

[0072] In Rel-19 AI / ML based positioning, regarding the time domain channelmeasurements, the measurements may be sample-based measurements, where the timing information is an integer multiple of sampling periods or path-based measurements, where the timing information is according to the detected path timing and may not be an integer multiple of sampling periods.

[0073] The issues to be studied include, but are not limited to, the following: a trade-off of positioning accuracy and signaling overhead, impact and necessary details of gNB / UE implementation to obtain the channel measurement values, whether the same Alternative(s) applies to all cases or not, the applicability and necessity of specifying the Alternative(s) to different cases. Different use-cases may have different issues.

[0074] In addition to timing information, the components for the channel measurement for model input may also include power and potentially phase.

[0075] The input dimension for the measurements is NTRP * Nport * Nt, where NTRP is the number of Transmission-Reception Points (TRP), Nport is the number of transmit / receive antenna port pairs, Nt is the number of consecutive time domain samples. If Nt’ (Nt’ < Nt) samples with the strongest power are selected as model input, with remaining (Nt - Nt’) time domain samples set to zero, it is also assumed that timing info for the Nt’ samples need to be provided as model input.

[0076] Sample-based measurement is defined as a measurement composed of Nt' samples of the estimated channel response in time domain. The timing information for the Nt' samples are reported with a timing granularity T, where T=2kxTc. k represents the timing reporting granularity factor. Tc is the basic time unit for NR. The parameter k is configurable by LMF, and 0<=k<=5. Thus, the timing information is configured to be reported with granularity in the range of Tcto 32xTc. The corresponding measurement (e.g., power if reported) corresponds to the measurement for the reported Nt' samples. Nt' and k can be signalled to the UE from the network.

[0077] Reporting resolution may be compared with the sampling period. A sampling period may be defined as 1 / (NfxAf). For FR1 , sampling period = 1 / (4096x30)=8.14 (ns), where Nf=4096 according to 38.211 , and Af =30 kHz is the subcarrier spacing." That is, sampling period = 16xTcwas used for SCS=30 kHz.

[0078] This shows that even with the existing resolution of reporting timing information, per-path power RSRP can be requested with finer timing granularity than the sampling period. Finer timing granularity may achieve tighter positioning accuracy target, for example.

[0079] Different types of measurements as well as associated configurations have been considered for AI / ML-based positioning. However, when configuring these at UE (or gNB), e.g., when LMF requests sample measurements, Nt’ and k should be configured appropriately to ensure a balanced trade-off between positioning accuracy and signaling overhead.

[0080] For example, Nt’ time domain samples with the strongest power are selected as model input. For model input Nt = 256 using different values Nt’. Reducing Nt’ from 256 to 64 does not appreciably degrade the positioning accuracy while overhead shrinks by1 / 4. The effects of reducing Nt’ on accuracy and overhead reduction are summarized in Table 1 .

[0081] RSRP reporting may limit the performance of positioning because of the limited number of taps (9 path delays). When time domain samples are used as model input and sub-sampling is applied, the selection of Nt’ measurements may be based on the strongest power, unless explicitly stated otherwise. The number and resolution of time domain samples directly affect the granularity of the channel estimation. As addressed above, for inference and data collection, selection of sub sampling and which samples needs to be collected and reported affects the accuracy of positioning information. Moreover, legacy specification does not clearly state how the samples are collected e.g., whether consecutive or non-consecutive, which is an important factor to determine the size of the input and report. The overhead increases as it requires transfer of at least channel tap powers for a potentially large number of time instances, even if the tap powers are small or negligible.

[0082] In addition to efficient sample selection, current positioning procedures may not fulfill the AIML data collection requirements for different positioning scenarios. Different scenarios (e.g., static / dynamic) may require specific methodology for samples collection (e.g., consecutive or non-consecutive samples). For model inputs how to determine selection of the samples while reducing the signaling overhead? Differentvalues of Nt’ may show different trade-off between positioning accuracy and signaling overhead. Continuous sampling can lead to increased power consumption, which might be a concern for limited UE-capability. The number and resolution of time domain samples directly affect the granularity of the channel estimation but not necessarily for LOS channel. As such, NW-assistance for the UE for AIML operations when performing inference and data collection process would be desirable, such that appropriate positioning measurement configuration can be adapted based on the scenario and channel conditions, thereby inducing a fair trade-off between accuracy, and signaling overhead (i.e., reporting granularity) and / or power consumption.

[0083] Fig. 2 shows a flowchart of a method according to an example embodiment. The method may be performed at an apparatus. The apparatus may comprise or be comprised in a UE or a radio access node (e.g., gNB).

[0084] At 201 , the method comprises receiving, from a network, a configuration comprising at least one rule for determining at least one parameter for selecting measurement samples for a machine learning model from a number of time domain samples.

[0085] At 202, the method comprises determining, based on the at least one rule, the at least one parameter.

[0086] At 203, the method comprises providing an indication of the determined at least one parameter to the network.

[0087] Fig. 3 shows a flowchart of a method according to an example embodiment. The method may be performed at an apparatus. The apparatus may comprise or be comprised in a network node. The network node may comprise a LMF, an AMF, a NWDAF or a radio access node (e.g., a gNB).

[0088] At 301 the method comprises providing, to at least one second apparatus, a configuration comprising at least one rule for determining at least one parameter for selecting measurement samples for a machine learning model from a number of time domain samples.

[0089] At 302, the method comprises receiving an indication of at least one determined parameter from the at least one second apparatus.

[0090] The at least one rule may comprise a number of measurement samples, a minimum number of measurement samples, a value range of number of measurement samples or a maximum number of measurement samples, an indication whether measurement samples are consecutive or non-consecutive, a measurement threshold, or a maximum granularity for reporting measurement samples, a minimum granularity forreporting measurement samples or a granularity range for reporting measurement samples.

[0091] For example, the rule may comprise a specific number or a minimum and / or maximum number of samples, Nt’ and / or whether samples should be consecutive vs non-consecutive or reporting granularity.

[0092] Where the at least one rule comprises measurement thresholds, the measurement may comprise a minimum reference signal received power value, a maximum reference signal received power value or a range of reference signal received power values, for example min / max / range of RSRP value(s) that needs to be satisfied for a sample.

[0093] The measurement threshold may comprise a minimum delay value, a maximum delay value or a range of delay values between measurement samples, e.g., min / max / range of delay value(s) that needs to be satisfied between two consecutive samples, etc.

[0094] For reporting granularity, the criteria may be e.g., min and / or max value of k.

[0095] Fig. 4 shows a flowchart of a method according to an example embodiment. The method may be performed at an apparatus. The apparatus may comprise or be comprised in a UE or a radio access node (e.g., gNB).

[0096] At 401 , the method comprises receiving, from a network, a configuration comprising at least one parameter associated with at least one condition.

[0097] At 402, the method comprises determining, based at least in part on the at least one parameter associated with at least one condition, at least one parameter to apply for selecting measurement samples for a machine learning model from a number of time domain samples; and

[0098] At 403, the method comprises providing an indication of the determined at least one parameter to the network.

[0099] Fig. 5 shows a flowchart of a method according to an example embodiment. The method may be performed at an apparatus. The apparatus may comprise or be comprised in a network node. The network node may comprise a LMF, an AMF, a NWDAF or a radio access node (e.g., a gNB).

[0100] At 501 , the method comprises providing, to at least one second apparatus, a configuration comprising at least one parameter associated with at least one condition for determining at least one parameter for selecting measurement samples for a machine learning model from a number of time domain samples.

[0101] At 502, the method comprises receiving an indication of at least one determined parameter from the at least one second apparatus.

[0102] The at least one condition may comprise at least one of channel conditions, user equipment capability, power level, user equipment location, quality of ser- vice requirements, e.g., positioning accuracy, latency requirements, or user equipment mobility. The at least one parameter may be a rule as described with reference to Figs. 2 and 3 or selection criteria.

[0103] Where the at least one condition comprises channel conditions, a NW may associate different selection criteria (e.g., at least one parameter) to different channel conditions between UE and TRP(s) (e.g., LOS / NLOS, etc.), for example, by providing a mapping between them as in Table 1 .Table 1

[0104] For UE mobility, a NW may associate different selection criteria to differentUE mobility status, e.g., as in Table 2.Table 2

[0105] For positioning requirements: NW associates different selection criteria with different positioning Quality of Service (QoS) requirements. For example, NW provides a mapping between different levels or ranges of required QoS (e.g., in terms of positioning accuracy and / or latency) and above selection criteria, e.g., as in Table 3:Table 3

[0106] A condition may be UE location, e.g., UE coarse location (e.g., cell ID) or area / cell / Transmission-Reception Point (TRP) / beam / Positioning Reference Signal (PRS). A NW may associate different selection criteria to UE coarse location or TRP(s) / beam(s) that UE measures, e.g., with a mapping as in Table 4.Table 4

[0107] The timing reporting granularity factor k may be configured by the LMF via LTE Positioning Protocol (LPP) for the RSTD measurement. The value of Nt’ may be linked with the value of k. For example, in case of NLOS channel and high positioning requirements, higher value of Nt’ requires low value of k.

[0108] In an example embodiment, a network, e.g., LMF for a positioning use case, provides a set of configurations to UE for measurement configuration used for data collection corresponding to inference input or / and training data. As described above, the configuration may comprise at least one rule for determining at least one parameter for selecting measurement samples for a machine learning model from a number of time domain samples or at least one parameter for selecting measurement samples for a machine learning model from a number of time domain samples, the at least one parameter associated with at least one condition.

[0109] The measurement samples may be used as input for ML model inference, training or monitoring. A method as described with reference to Figs. 2 and 4 may comprise providing a flag to the network indicating whether the measurement samples are to be used for inference, training or monitoring. In an example, a configuration stated in may be used with additional flag to indicate to the UE whether the collected sample(s)corresponding to the specific configuration can be used for inference as well as for data collection (DC) related to other purposes such as training and / or monitoring.

[0110] For example, while the configuration may be utilized for inference, the same could be used for DC purposes, e.g., collecting the configured measurements for training AI / ML.

[0111] The ML model may be a positioning model. Other use cases include but are not limited to beam management (e.g., beam prediction in time, and / or spatial domain for overhead and latency reduction, beam selection accuracy improvement), CSI feedback enhancement (e.g., beam prediction in time, and / or spatial domain for overhead and latency reduction, beam selection accuracy improvement), etc.

[0112] In an example embodiment, LMF determines a configuration for selection of measurement samples which consists of a sample selection criteria. Sample selection criteria is an example of a rule for determining the at least one parameter, e.g., whether samples should be consecutive or not. The configuration may be provided to a UE for inference purposes, as well as for any other data collection purposes (e.g., training, monitoring) that may use the configured measurements.

[0113] Based on the given channel characteristics and provided selection criterion, UE selects a value for the Nt’ (number of samples), flag f’ (indicative of whether samples are consecutive or non-consecutive) and k (time-granularity of samples). Nt’, k and f’ are examples of the at least parameter. Nt’ may be Nt if all measurement samples are to be reported.

[0114] The at least one parameter may be provided from the UE to the NW with a timestamp. UE may inform the LMF about the selected parameter settings for its measurements (Nt’, f’, k, etc.) together with a timestamp which can be used later by the LMF for the purpose of DC.

[0115] The above aims to enable measurement collection for assisting AI / ML inference input at UE. Although UE-side models i.e., inference is performed by the UE, are considered above, the same idea can be also applicable for gNB-side models.

[0116] The second apparatus as described with reference to Figs. 3 and 5 may comprise at least one UE or a plurality of UEs, A method as described with reference to Figs. 3 and 5 may comprise providing the configuration to the plurality of UEs in dedicated signalling or broadcast signalling. For example, the configuration may be targeted for a single UE (e.g., target UE) or a group of UEs (e.g., only PRUs, or non-PRUs, all UEs in a cell, etc.), and can be provided via dedicated signaling or a broad- cast / groupcast (e.g., via SIB), respectively.

[0117] The configuration may comprise a validity timer e.g., indicating how long / of- ten UE should utilize the provided configuration.

[0118] The configuration may comprise an identifier (ID). The identifier may be an identifier of a positioning related configuration, for example, PRS configuration, measurement configuration, or positioning reporting configuration. The identifier may be associated with PRS parameters, TRP information (e.g., TRP IDs, TRP locations, antenna parameters), as well as proprietary information at the network side such as parameters or configurations related to gNB power amplifier, gNB antenna. The identifier may be the identifier of the network node from which the configuration is received or of the model for which DC is being performed). In an embodiment, a configuration (e.g., criteria for sample selection, including values for minimum and / or maximum number of samples Nt’) may be mapped to an associated ID. In other words, for each (or a set of) associated ID, the configuration may be different for data collection. This allows to maintain consistency between the training and inference data samples.

[0119] Methods as described with reference to Figs. 2 and 4 may comprise performing measurement samples, selecting measurement samples according to the at least one determined parameter and providing the selected measurement samples to the network. The indication of the determined at least one parameter may be provided together with the selected measurement samples.

[0120] Fig. 6 shows an example signaling flow between a UE and LMF. At step 1 , the LMF determines a configuration for selection of measurement samples from a number of time domain samples. The configuration may consist of a sample selection criteria (e.g., a rule or a condition associated with at least one parameter). The LMF may determine a list of configurations for selection of measurement samples from a number of time domain samples. The LMF provides the list of configurations for DC with an associated ID as described above.

[0121] At step 2, the UE determines the number of samples Nt’, flag f’ and k based on the received configuration, and further based on at least one of capability, associ- atedlD, observed channel conditions or another condition.

[0122] At step 3, the UE provided an indication of the determined Nt’, f’ and k.

[0123] Although LMF is shown in Fig. 6, another core network entity, e.g., AMF or NWDAF, or a RAN node, e.g., gNB, may take the place of LMF. For a RAN node, the communication between gNB and UE may take place via RRC or MAC protocol.

[0124] A gNB may take the palace of a UE in the signaling flow of Fig. 6.. In this case the communication between LMF and gNB can take place via NR Positioning Protocol A (NRPPa) protocol.

[0125] Methods as described with reference to Figs. 2 to 6 may provide efficient measurement configuration mechanism to reduce reporting overhead and power consumption, by include a sample selection rule while considering the UE capability, radio conditions and accuracy requirements for sample-based measurements.

[0126] Fig. 7 shows, by way of example, a block diagram of an apparatus 10. The apparatus 10 comprises, for example, at least one processor 12 and at least one memory 14 storing instructions 15 that, when executed by the at least one processor, cause the apparatus 10 at least to perform the method or methods (or portion(s) thereof) as disclosed herein, and any of the embodiments (or respective portion(s) thereof). In an example, the at least one memory and the instructions (e.g. a computer program code, software), are configured, with the at least one processor, to cause the apparatus 10 to perform the method or methods (or portion(s) thereof) as disclosed herein, and any of the embodiments (or respective portion(s) thereof).

[0127] A processor 12 may comprise circuitry, or be constituted as circuitry or circuitries, the circuitry or circuitries being configured to perform phases of methods in accordance with embodiments described herein.

[0128] As used herein, the term “circuitry” may refer to one or more or all of the following: (a) hardware-only circuit implementations, such as implementations in only analog and / or digital circuitry, and (b) combinations of hardware circuits and software, such as, as applicable: (I) a combination of analog and / or digital hardware circuit(s) with software / firmware and (ii) any portions of hardware processor(s) with software (including digital signal processor(s)), software, and memory(ies) that work together to cause an apparatus, such as a user equipment, to perform various functions) and (c) hardware circuit(s) and or processor(s), such as a microprocessor(s) or a portion of a microprocessors), that requires software (e.g., firmware) for operation, but the software may not be present when it is not needed for operation. This definition of circuitry applies to all uses of this term herein, including in any claims. As a further example, as used herein, the term circuitry also covers an implementation of merely a hardware circuit or processor (or multiple processors) or portion of a hardware circuit or processor and its (or their) accompanying software and / or firmware. The term circuitry also covers, for example and if applicable to the particular claim element, a baseband integrated circuit or processor integrated circuit for a mobile device or a similar integrated circuit in server, a cellular network device, or other computing or network device.

[0129] The memory 14 may be implemented using any suitable data storage technology. The memory may comprise a database for storing data. The memory 14may, for example, be at least in part external to apparatus 10 but accessible to apparatus 10.

[0130] The instructions 15 may be comprised in a computer readable medium or a non-transitory computer readable medium. A term non-transitory, as used herein, is a limitation of the medium itself (i.e. tangible, not a signal) as opposed to a limitation on data storage persistency (e.g. random access memory, RAM, vs. read only memory, ROM).

[0131] For example, the apparatus 10 is a terminal device, such as a UE. As another example, the apparatus is comprised in such a terminal device, e.g. as a chipset configured to control the terminal device. The apparatus 10 may be caused or configured or comprise means to perform at least the method of Figs. 2 and 4 and / or any one or more of the embodiments described herein.

[0132] As another example, the apparatus 10 is a network entity. In another embodiment, the apparatus is comprised in such a network entity, e.g. as a chipset configured to control the network entity. The apparatus 10 may be caused or configured or comprise means to perform at least the method of Figs. 3 and 5 and / or any one or more of the embodiments described herein.

[0133] The apparatus may comprise one or more entities of any of protocol layers, such as a MAC entity, an RRC entity, an RLC entity, a PDCP entity or a PHY entity. In some embodiments, the entity is configured to perform at least the method of Figs. 3 and 5, and / or any one or more of the embodiments described.

[0134] The apparatus 10 comprises a radio interface 16. The radio interface16 may provide the apparatus 10 with communication capabilities. The radio interface 16 may comprise a receiver configured to receive information in accordance with at least one cellular or non-cellular standard. The radio interface 16 may comprise a transmitter configured to transmit information in accordance with at least one cellular or non- cellular standard. The receiver may comprise more than one receiver. The transmitter may comprise more than one transmitter. The radio interface 16 may comprise a transceiver configured to receive and transmit information in accordance with at least one cellular or non-cellular standard. The transceiver may comprise more than one transceiver.

[0135] The apparatus 10 may comprise a user interface 18 comprising, for example, at least one of a keypad, a microphone, a touch display, a display, a speaker, etc. The user interface 18 may be used to control the apparatus by the user. The user interface 18 may be external to the apparatus 10. For example, the apparatus 10 maybe connected to another device, such as a computer, either via wireless or wired connection, and the apparatus 10 is controlled by the user via the computer.

[0136] In an embodiment, at least some of the processes described herein may be carried out by an apparatus comprising means for carrying out at least some of the described processes. Means for performing method steps as disclosed herein may include software and / or hardware components of the apparatus 10. For example, the at least one processor 12, the memory 14, and the computer program code form means for carrying out the method or methods (or portion(s) thereof) as disclosed herein, and any of the embodiments (or respective portion(s) thereof). As used herein the term “means” is to be construed in singular form, i.e. referring to a single element, or in plural form, i.e. referring to a combination of single elements. Therefore, terminology “means for [performing A, B, C]”, is to be interpreted to cover an apparatus in which there is only one means for performing A, B and C, or where there are separate means for performing A, B and C, or partially or fully overlapping means for performing A, B, C. Further, terminology “means for performing A, means for performing B, means for performing C” is to be interpreted to cover an apparatus in which there is only one means for performing A, B and C, or where there are separate means for performing A, B and C, or partially or fully overlapping means for performing A, B, C.

[0137] Even though this disclosure has been described above with reference to non-limiting and illustrative examples according to the accompanying figures, it is clear that the scope of this disclosure is not restricted thereto - but can be modified in many different ways. As technology advances, it will become apparent to a person skilled in art as to how the disclosure can be further implemented and / or modified in various ways. Further, it is clear to a person skilled in the art that the embodiments described herein may, but are not required to, be combined in various ways with other embodiments described herein.

Claims

1. CLAIMS1 . An apparatus comprising means for: receiving, from a network, a configuration comprising at least one parameter associated with at least one condition; determining, based at least in part on the at least one parameter associated with at least one condition, at least one parameter to apply for selecting measurement samples for a machine learning model from a number of time domain samples; and providing an indication of the determined at least one parameter to the network.

2. The apparatus according to claim 1 , wherein the at least one condition comprises at least one of: channel conditions, user equipment capability, power level, user equipment location, quality of service requirements or user equipment mobility.

3. The apparatus according to any of claims 1 to 2, wherein the at least one parameter comprises at least one of: a number of measurement samples, whether the selected samples are consecutive or non-consecutive, or reporting granularity.

4. The apparatus according to any of claims 1 to 3, comprising means for providing the determined at least one parameter with a timestamp.

5. The apparatus according to any of claims 1 to 4, comprising means for performing measurement of samples, selecting measurement samples according to the at least one determined parameter and providing the selected measurement samples to the network.

6. The apparatus according to claim 5, comprising means for providing the indication of the determined at least one parameter together with the selected measurement samples.

7. The apparatus according to any of claims 1 to 6, wherein the measurement samples are used as input for machine learning model inference, training or monitoring.

8. The apparatus according to claim 7, wherein the machine learning model is a machine learning positioning model.

9. The apparatus according to claim 7 or claim 8, comprising means for providing a flag indicating whether the measurement samples are to be used for inference, training or monitoring.

10. The apparatus according to any of claims 1 to 9, comprising means for receiving the configuration from a network node of the network.11 . The apparatus according to claim 10, wherein the network node comprises a location management function, an access and mobility management function, a network data and analytics function or a radio access node.

12. The apparatus according to claim 10 or claim 11 , wherein the configuration comprises an identifier.

13. The apparatus according to claim 12, comprising means for determining the at least one parameter further based on the identifier.

14. The apparatus according to any of claims 1 to 13, comprising means for observing at least one condition at the apparatus; and means for determining the at least one parameter further based on the observed at least one condition.

15. The apparatus according to any of claims 1 to 14, wherein the apparatus comprises a user equipment or a radio access node.

16. An apparatus comprising means for: providing, to at least one second apparatus, a configuration comprising at least one parameter associated with at least one condition for determining at least one parameter for selecting measurement samples for a machine learning model from a number of time domain samples; andreceiving an indication of at least one determined parameter from the at least one second apparatus.

17. The apparatus according to claim 16, wherein the at least one second apparatus comprises at least one user equipment or at least one radio access node.

18. The apparatus according to claim 17, wherein the at least one user equipment comprises a plurality of user equipment.

19. The apparatus according to claim 18, comprising means for providing the configuration to the plurality of user equipment in dedicated signalling or broadcast signalling.

20. A method comprising: receiving, from a network, a configuration comprising at least one parameter associated with at least one condition; determining, based at least in part on the at least one parameter associated with at least one condition, at least one parameter to apply for selecting measurement samples for a machine learning model from a number of time domain samples; and providing an indication of the determined at least one parameter to the network.21 . A method comprising, at an apparatus: providing, to at least one second apparatus, a configuration comprising at least one parameter associated with at least one condition for determining at least one parameter for selecting measurement samples for a machine learning model from a number of time domain samples; and receiving an indication of at least one determined parameter from the at least one second apparatus.

22. A computer program product comprising program instructions which, when the program is executed by an apparatus, cause the apparatus to perform:receiving, from a network, a configuration comprising at least one parameter associated with at least one condition; determining, based at least in part on the at least one parameter associated with at least one condition, at least one parameter to apply for selecting measurement samples for a machine learning model from a number of time domain samples; and providing an indication of the determined at least one parameter to the network.

23. A computer program product comprising program instructions which, when the program is executed by an apparatus, cause the apparatus to perform: providing, to at least one second apparatus, a configuration comprising at least one parameter associated with at least one condition for determining at least one parameter for selecting measurement samples for a machine learning model from a number of time domain samples; and receiving an indication of at least one determined parameter from the at least one second apparatus.

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