Apparatus and method for identification of a model of a wireless communication system

Abstract

Various aspects of the present disclosure relate to methods, apparatuses, and devices for wireless communication. A first apparatus may determine (702) a first set of information based on measurement data and a first index associated with the measurement data, wherein the measurement data is measured by the first apparatus or received at the first apparatus, and wherein the first index is associated with at least one of a first set of one or more parameters for the first apparatus and a second set of one or more parameters for a second apparatus. The first apparatus may generate (704) one or more datasets, wherein each dataset of the one or more datasets is a subset of the first set of information. The first apparatus may also transmit (706) at least one dataset of the one or more datasets.

Description

APPARATUS AND METHOD FOR IDENTIFICATION OF A MODEL OF AWIRELESS COMMUNICATION SYSTEMTECHNICAL FIELD

[0001] The present disclosure relates to wireless communications, and more specifically to identification of a model of a wireless communication system.BACKGROUND

[0002] A wireless communications system may include one or multiple network communication devices, such as base stations, which may support wireless communications for one or multiple user communication devices, which may be otherwise known as user equipment (UE), or other suitable terminology. The wireless communications system may support wireless communications with one or multiple user communication devices by utilizing resources of the wireless communication system (e.g., time resources (e.g., symbols, slots, subframes, frames, or the like) or frequency resources (e.g., subcarriers, carriers, or the like). Additionally, the wireless communications system may support wireless communications across various radio access technologies including third generation (3G) radio access technology, fourth generation (4G) radio access technology, fifth generation (5G) radio access technology, among other suitable radio access technologies beyond 5G (e.g., sixth generation (6G)).SUMMARY

[0003] An article “a” before an element is unrestricted and understood to refer to “at least one” of those elements or “one or more” of those elements. The terms “a,” “at least one,” “one or more,” and “at least one of one or more” may be interchangeable. As used herein, including in the claims, “or” as used in a list of items (e.g., a list of items prefaced by a phrase such as “at least one of’ or “one or more of’ or “one or both of’) indicates an inclusive list such that, for example, a list of at least one of A, B, or C means A or B or C or AB or AC or BC or ABC (i.e., A and B and C). Also, as used herein, the phrase “based on” shall not be construed as a reference to a closed set of conditions. For example, an example step that is described as “based on condition A” may be based on both a condition A and a condition B without departing from the scope of the present disclosure. In other words, as used herein, the phrase “based on” shall beconstrued in the same manner as the phrase “based at least in part on.” Further, as used herein, including in the claims, a “set” may include one or more elements.

[0004] Various aspects of the present disclosure relate to wireless communications, including improved methods and apparatuses that support identifying a model (e.g., encoder and decoder) of a wireless communication system. A first apparatus (e.g., a base station, a UE, a node of the base station, or a node of the UE, etc.) may determine a first set of information based on measurement data and a first index associated with the measurement data. The first apparatus may perform one or more measurements to obtain the measurement data. Additionally, or alternatively, the first apparatus may receive the measurement data, for example, from another apparatus (e.g., a base station, a UE, a node of the base station, or a node of the UE, etc.). The first index may be associated with at least one of a first set of one or more parameters for the first apparatus and a second set of one or more parameters for a second apparatus. The first apparatus may generate one or more datasets, wherein each dataset of the one or more datasets is a subset of the first set of information. The first apparatus may also transmit at least one dataset of the one or more datasets.BRIEF DESCRIPTION OF THE DRAWINGS

[0005] Figure 1 illustrates an example of a wireless communications system in accordance with aspects of the present disclosure.

[0006] Figure 2 illustrates an example of a wireless network in accordance with aspects of the present disclosure.

[0007] Figure 3 illustrates an example of a block diagram of a two-sided model in accordance with aspects of the present disclosure.

[0008] Figure 4 illustrates an example of a UE in accordance with aspects of the present disclosure.

[0009] Figure 5 illustrates an example of a processor in accordance with aspects of the present disclosure.

[0010] Figure 6 illustrates an example of a network equipment (NE) in accordance with aspects of the present disclosure.

[0011] Figure 7 illustrates a flowchart of a method performed by a first apparatus in accordance with aspects of the present disclosure.

[0012] Figure 8 illustrates a flowchart of a method performed by a second apparatus in accordance with aspects of the present disclosure.DETAILED DESCRIPTION

[0013] Various aspects of the present disclosure relate to supporting (e.g., configuring, enabling) a two-sided model of a wireless communication system. The two-sided model may be associated with an encoder of a first wireless device which may be referred to as an encoder device) and a decoder of the first wireless device or a second wireless device (which may be referred to as a decoder device). Some functions (e.g., operations, behaviors, features, constraints) of the model may be performed by the first wireless device (e.g., the encoder device) and other functions (e.g., operations, behaviors, features, constraints) of the model may be performed by the first wireless device or the second wireless device (e.g., the decoder device). In some implementations, one or more of the first wireless device (e.g., the encoder device) or the second wireless device (e.g., the decoder device) may manage (e.g., update, adjust, modify) a set of one or more parameters of the two-sided model at a frequency (e.g., rate, pattern, interval). However, in some cases, excessive data may be used, for example, based on a quantity (e.g., amount) of data transferred to update the two-sided model and a frequency (e.g., rate) of the updates.

[0014] By reducing one or more of the transfers including the quantity (e.g., amount) of data for updating the two-sided model or the frequency (e.g., rate) of updating the two-sided model, apparatuses (e.g., wireless devices) performing one or more of encoding or decoding may reduce power consumption, reduce processor usage, reduce data usage, and increase overall system performance.

[0015] Aspects of the present disclosure are described in the context of a wireless communications system.

[0016] Figure 1 illustrates an example of a wireless communications system 100 in accordance with aspects of the present disclosure. The wireless communications system 100 may include one or more NE 102, one or more UE 104, and a core network (CN) 106. The wireless communications system 100 may support various radio accesstechnologies. In some implementations, the wireless communications system 100 may be a 4G network, such as an LTE network or an LTE-Advanced (LTE-A) network. In some other implementations, the wireless communications system 100 may be a new radio (NR) network, such as a 5G network, a 5G-Advanced (5G-A) network, or a 5G ultrawideband (5G-UWB) network. In other implementations, the wireless communications system 100 may be a combination of a 4G network and a 5G network, or other suitable radio access technology including Institute of Electrical and Electronics Engineers (IEEE) 802.11 (Wi-Fi), IEEE 802.16 (WiMAX), IEEE 802.20. The wireless communications system 100 may support radio access technologies beyond 5G, for example, 6G. Additionally, the wireless communications system 100 may support technologies, such as time division multiple access (TDMA), frequency division multiple access (FDMA), or code division multiple access (CDMA), etc.

[0017] The one or more NE 102 may be dispersed throughout a geographic region to form the wireless communications system 100. One or more of the NE 102 described herein may be or include or may be referred to as a network node, a base station, a network element, a network function, a network entity, a radio access network (RAN), a NodeB, an eNodeB (eNB), a next-generation NodeB (gNB), or other suitable terminology. An NE 102 and a UE 104 may communicate via a communication link, which may be a wireless or wired connection. For example, an NE 102 and a UE 104 may perform wireless communication (e.g., receive signaling, transmit signaling) over a Uu interface.

[0018] An NE 102 may provide a geographic coverage area for which the NE 102 may support services for one or more UEs 104 within the geographic coverage area. For example, an NE 102 and a UE 104 may support wireless communication of signals related to services (e.g., voice, video, packet data, messaging, broadcast, etc.) according to one or multiple radio access technologies. In some implementations, an NE 102 may be moveable, for example, a satellite associated with an NTN. In some implementations, different geographic coverage areas associated with the same or different radio access technologies may overlap, but the different geographic coverage areas may be associated with different NE 102.

[0019] The one or more UE 104 may be dispersed throughout a geographic region of the wireless communications system 100. A UE 104 may include or may be referredto as a remote unit, a mobile device, a wireless device, a remote device, a subscriber device, a transmitter device, a receiver device, or some other suitable terminology. In some implementations, the UE 104 may be referred to as a unit, a station, a terminal, or a client, among other examples. Additionally, or alternatively, the UE 104 may be referred to as an Intemet-of-Things (loT) device, an Intemet-of-Everything (loE) device, or machine-type communication (MTC) device, among other examples.

[0020] A UE 104 may be able to support wireless communication directly with other UEs 104 over a communication link. For example, a UE 104 may support wireless communication directly with another UE 104 over a device-to-device (D2D) communication link. In some implementations, such as vehicle-to-vehicle (V2V) deployments, vehicle-to-everything (V2X) deployments, or cellular-V2X deployments, the communication link may be referred to as a sidelink. For example, a UE 104 may support wireless communication directly with another UE 104 over a UE-to-UE interface (PC5 interface).

[0021] An NE 102 may support communications with the CN 106, or with another NE 102, or both. For example, an NE 102 may interface with other NE 102 or the CN 106 through one or more backhaul links (e.g., SI, N2, N2, or network interface). In some implementations, the NE 102 may communicate with each other directly. In some other implementations, the NE 102 may communicate with each other or indirectly (e.g., via the CN 106. In some implementations, one or more NE 102 may include subcomponents, such as an access network entity, which may be an example of an access node controller (ANC). An ANC may communicate with the one or more UEs 104 through one or more other access network transmission entities, which may be referred to as a radio heads, smart radio heads, or transmission -reception points (TRPs).

[0022] The CN 106 may support user authentication, access authorization, tracking, connectivity, and other access, routing, or mobility functions. The CN 106 may be an evolved packet core (EPC), or a 5G core (5GC), which may include a control plane entity that manages access and mobility (e.g., a mobility management entity (MME), an access and mobility management functions (AMF)) and a user plane entity that routes packets or interconnects to external networks (e.g., a serving gateway (S-GW), a Packet Data Network (PDN) gateway (P-GW), or a user plane function (UPF)). In some implementations, the control plane entity may manage non-access stratum (NAS)functions, such as mobility, authentication, and bearer management (e.g., data bearers, signal bearers, etc.) for the one or more UEs 104 served by the one or more NE 102 associated with the CN 106.

[0023] The CN 106 may communicate with a packet data network over one or more backhaul links (e.g., via an SI, N2, N2, or another network interface). The packet data network may include an application server. In some implementations, one or more UEs 104 may communicate with the application server. A UE 104 may establish a session (e.g., a protocol data unit (PDU) session, or the like) with the CN 106 via an NE 102. The CN 106 may route traffic (e.g., control information, data, and the like) between the UE 104 and the application server using the established session (e.g., the established PDU session). The PDU session may be an example of a logical connection between the UE 104 and the CN 106 (e.g., one or more network functions of the CN 106).

[0024] In the wireless communications system 100, the NEs 102 and the UEs 104 may use resources of the wireless communications system 100 (e.g., time resources (e.g., symbols, slots, subframes, frames, or the like) or frequency resources (e.g., subcarriers, carriers)) to perform various operations (e.g., wireless communications). In some implementations, the NEs 102 and the UEs 104 may support different resource structures. For example, the NEs 102 and the UEs 104 may support different frame structures. In some implementations, such as in 4G, the NEs 102 and the UEs 104 may support a single frame structure. In some other implementations, such as in 5G and among other suitable radio access technologies, the NEs 102 and the UEs 104 may support various frame structures (i.e., multiple frame structures). The NEs 102 and the UEs 104 may support various frame structures based on one or more numerologies.

[0025] One or more numerologies may be supported in the wireless communications system 100, and a numerology may include a subcarrier spacing and a cyclic prefix. A first numerology (e.g., jU=O) may be associated with a first subcarrier spacing (e.g., 15 kHz) and a normal cyclic prefix. In some implementations, the first numerology (e.g., ^=0) associated with the first subcarrier spacing (e.g., 15 kHz) may utilize one slot per subframe. A second numerology (e.g., ^=1) may be associated with a second subcarrier spacing (e.g., 30 kHz) and a normal cyclic prefix. A third numerology (e.g., ^=2) may be associated with a third subcarrier spacing (e.g., 60 kHz) and a normal cyclic prefix or an extended cyclic prefix. A fourth numerology (e.g., ju=3)may be associated with a fourth subcarrier spacing (e.g., 120 kHz) and a normal cyclic prefix. A fifth numerology (e.g., jU=4) may be associated with a fifth subcarrier spacing (e.g., 240 kHz) and a normal cyclic prefix.

[0026] A time interval of a resource (e.g., a communication resource) may be organized according to frames (also referred to as radio frames). Each frame may have a duration, for example, a 10 millisecond (ms) duration. In some implementations, each frame may include multiple subframes. For example, each frame may include 10 subframes, and each subframe may have a duration, for example, a 1 ms duration. In some implementations, each frame may have the same duration. In some implementations, each subframe of a frame may have the same duration.

[0027] Additionally or alternatively, a time interval of a resource (e.g., a communication resource) may be organized according to slots. For example, a subframe may include a number (e.g., quantity) of slots. The number of slots in each subframe may also depend on the one or more numerologies supported in the wireless communications system 100. For instance, the first, second, third, fourth, and fifth numerologies (i.e., / r=0, ju=l, ,11=2. [1=3, =4) associated with respective subcarrier spacings of 15 kHz, 30 kHz, 60 kHz, 120 kHz, and 240 kHz may utilize a single slot per subframe, two slots per subframe, four slots per subframe, eight slots per subframe, and 16 slots per subframe, respectively. Each slot may include a number (e.g., quantity) of symbols (e.g., orthogonal frequency division multiplexing (OFDM) symbols). In some implementations, the number (e.g., quantity) of slots for a subframe may depend on a numerology. For a normal cyclic prefix, a slot may include 14 symbols. For an extended cyclic prefix (e.g., applicable for 60 kHz subcarrier spacing), a slot may include 12 symbols. The relationship between the number of symbols per slot, the number of slots per subframe, and the number of slots per frame for a normal cyclic prefix and an extended cyclic prefix may depend on a numerology. It should be understood that reference to a first numerology (e.g., i=0) associated with a first subcarrier spacing (e.g., 15 kHz) may be used interchangeably between subframes and slots.

[0028] In the wireless communications system 100, an electromagnetic (EM) spectrum may be split, based on frequency or wavelength, into various classes, frequency bands, frequency channels, etc. By way of example, the wireless communications system 100 may support one or multiple operating frequency bands,such as frequency range designations FR1 (410 MHz - 7.125 GHz), FR2 (24.25 GHz - 52.6 GHz), FR3 (7.125 GHz - 24.25 GHz), FR4 (52.6 GHz - 114.25 GHz), FR4a or FR4-1 (52.6 GHz - 71 GHz), and FR5 (114.25 GHz - 300 GHz). In some implementations, the NEs 102 and the UEs 104 may perform wireless communications over one or more of the operating frequency bands. In some implementations, FR1 may be used by the NEs 102 and the UEs 104, among other equipment or devices for cellular communications traffic (e.g., control information, data). In some implementations, FR2 may be used by the NEs 102 and the UEs 104, among other equipment or devices for short-range, high data rate capabilities.

[0029] FR1 may be associated with one or multiple numerologies (e.g., at least three numerologies). For example, FR1 may be associated with a first numerology (e.g., ^=0), which includes 15 kHz subcarrier spacing; a second numerology (e.g., ^=1), which includes 30 kHz subcarrier spacing; and a third numerology (e.g., jU=2), which includes 60 kHz subcarrier spacing. FR2 may be associated with one or multiple numerologies (e.g., at least 2 numerologies). For example, FR2 may be associated with a third numerology (e.g., jU=2), which includes 60 kHz subcarrier spacing; and a fourth numerology (e.g., jU=3), which includes 120 kHz subcarrier spacing.

[0030] Figure 2 illustrates an example of a wireless network 200 in accordance with aspects of the present disclosure. The wireless network 200 may include a NE 102-a (e.g., one embodiment of a NE 102, a gNB), a first UE 104-a (e.g., one embodiment of a UE 104, UEi), a second UE 104-b (e.g., one embodiment of a UE 104, UE2), and a third UE 104-c (e.g., one embodiment of a UE 104, UEK, the third UE 104-c represents any number of additional UEs).

[0031] Specifically, Figure 2 shows one example of the wireless network 200 with one NE 102-a (e.g., may be represented by nodeand may be equipped with M antennas) and K UEs 104-a, 104-b, 104-c (e.g., may be denoted byU2, --- , UKthat each have N antennas). H;k(t) may denote a channel at time t over frequency band I, I G {1,2, ... , L] , between B1and Ukwhich may form a matrix of size N X M with complex entries, i.e., H'c(t) G CNxM.

[0032] At time t and frequency band I, it may be assumed that the NE 102-a wants to transmit message %;k(t) to user Ukwhere k = {1,2, ••• , K} while it uses w;k(t) G CMxlas a precoding vector. The received signal at Uk, yi (t), may be written as:

[0034] where n^t) represents a noise vector at a receiver.

[0035] To improve an achievable rate of the link, in one example, the NE 102-a selects W;k(t) that maximizes a received signal-to-interference and noise ratio (SINR). Several different configurations may be used for selection of wk(t) where some of the configurations may have some knowledge about H;k(t).

[0036] The NE 102-a may get knowledge of H;k(t) by direct measurement (e.g., in a time duplex division (TDD) mode and assuming reciprocity of a channel), or indirectly using information that one of the UEs 104-a, 104-b, and 104-c sends to the NE 102-a (e.g., in a frequency division duplex (FDD) mode). In the indirect method, a large amount of feedback may be used to send accurate information about H;k(t). This may require a large amount of data if there are a large number of antennas or / and large frequency bands.

[0037] In one example, a single time slot is analyzed, but the example may be extended to the examples with more than a single time slot. Without loss of generality, H;k(t) may be denoted using H;k.

[0038] Hk(t) may be defined as a matrix of size N X M X L which is formed by stacking H;kfor all frequency bands (e.g., the entries at Hk[n, m, l](t) is equal to H;k[n, m](t)). In total, each UE 104-a, 104-b, and 104-c may provide feedback information about most recent N X M X L complex numbers to the NE 102-a.

[0039] Various configurations may be used to reduce a rate of required feedback. Two-sided models may include two parts where a first part is deployed at a UE side (e.g., UE 104) and the second part is deployed at a gNB side (e.g., NE 102). The UE and gNB sides may include one or a few neural network (NN) blocks which are trained using data driven approaches. The UE side may be responsible for computing a latent representation of input data (e.g., what needs to be transferred to the gNB) with as low of a number of bits as possible. Receiving what has been transmitted by the UE side, the gNB side reconstructs the information intended to be transmitted to the gNB. An encoding part of the two-sided model (at Node A, e.g., UE) may compute a quantized latent representation of the input data, and a decoding part of the two-sided model (atNode B, e.g., the gNB) may get this latent representation and use it to reconstruct the desired output.

[0040] Figure 3 illustrates an example of a block diagram of a two-sided model 300 (e.g., NN-based model) in accordance with aspects of the present disclosure. The two- sided model 300 includes a node A 302 (e.g., encoder, encoding model, Me) and a node B 304 (e.g., decoder, decoding model, Md). Figure 3 illustrates only one example of a two-sided model 300, while in other examples the location of the encoder and decoder may be swapped. Input data 306 is provided to the node A 302, then there is a latent representation 308 in communications from the node A 302 to the node B 304, and the node B 304 outputs data 310. In some configurations, the node A 302 is located at the UE 104, and the node B 304 is located at the NE 102, but in other configurations the node A 302 and the node B 304 may be located in other devices.

[0041] In some systems, Meand Mdmay be used for a logical model. Meaning that node A 302 may chose not to use Medirectly and instead develop and use several internal models like Me^,Me^ based on some private parameters and decisions. In these cases, one assumption may be that all these internal models should meet at least the performance of pairing original Mewith Md. With this explanation, the node B 304 may not need to be concerned about what is the actual model used at the node A 302. The same may be correct for Mdand some internal models at the node B 304.

[0042] There may be several methods to train neural network (NN) modules of a two-sided model, including, centralized training, simultaneous training, and separate training. Similarly, updating a two-sided model may be carried out centrally on one entity, on different entities but simultaneously, or separately. In separate training and / or model update, the NN modules of the node A 302 (e.g., UE) and the node B 304 (e.g., gNB) may be trained in different training sessions (e.g., no forward or backpropagation path between the two parts). One advantage of separate training may be that the node A 302 and the node B 304 may not need to be aware of the internal structure of the NN module of the other side.

[0043] The node A 302 and the node B 304 may decide not to use a single encoderdecoder pair, and instead may construct different encode-decoder pairs which are applicable to different conditions of the node A 302 and / or the node B 304 since thesamples of different states may have different statistics compared to other settings, and, therefore, it may be reasonable to construct two different encoder-decoder models for these two cases instead of trying to train a single model that can generalize well to different settings. For example, for channel state information (CSI) feedback systems, it may be beneficial to have separate encoder-decoder pairs for unlicensed mobile access (UMA) environment and indoor environment compared to systems that build a single encoder-decoder pair covering both environments. So, it may be assumed that it may be beneficial to design Me[t] —for i = {1,2, ..., K} where the Ithpair is trained based on a training data collected when the node A 302 and the node B 304 are in one condition or a set of conditions. Condition may refer to when the parameters of the node A 302 and the node B 304 are in a certain state.

[0044] If there are multiple models, it may be difficult to differentiate between them and / or activate / deactivate them. Moreover, the node A 302 and the node B 304 may not be willing to reveal some part of their parameters which still affect their conditions.

[0045] In some embodiments described herein, there may be a scheme in which the nodes involved in training and inference can exchange information using which consistency may be achieved between the conditions under which the model has been trained and will be used for inference, while not sending sensitive information of one- side to another side. For example, a UE may create separate models in situations in which it has a low-battery and is outdoors, a low-battery and is indoor, a high-battery and is outdoors, and high-battery and indoor, but it may not be willing to tell the other side that if it has low or high battery level in none of the life cycle management (LCM) stages (e.g., data collection, model training, model inference).

[0046] One way to differentiate different models developed under different conditions is that when a UE and / or a gNB develop a model they decide on an identifier (ID) for that model. This ID may be used for activation and deactivation of models. The shortcoming of this scheme may be a number of different models that may need to be developed for different modes of operations and their combinations (since some models may be developed to support more than one mode of operations). There may also be a complexity concerning how to exchange information between the nodes and associate them with the model ID such that the internal conditions of the nodes remain transparent to the other side.

[0047] Some systems may use a dataset ID. This may mean that an ID is assigned to each dataset of samples that has been used collected for a model training and then the model ID is constructed based on the ID of the dataset. In such systems, it may not completely be determined how conditions of a node A and node B can be considered in assignment of dataset IDs. Additionally, one dataset may contain samples from different UE and / or gNB conditions, therefore, if a single ID is assigned to a dataset, then creation of different models for different UE and / or gNB conditions based on their associated samples may not be straightforward.

[0048] In certain systems, for training two-sided models M7, j = {1,2, ... , K}, each of the models may be designed for one or a set of conditions of node A and node B. Each model may include an encoder model Me!and a decoder model Mdresiding at a node A and a Node B, respectively. In some cases, the model may be at a single sided node (e.g., there is no Me}or Mdpart). Also, there may be cases in which the model depends only on the conditions of one side (e.g., only node A or node B).

[0049] The condition of node A and node B may be related to some states and / or parameters of the nodes. In one example, considering a model that is trained for CSI, the conditions may include: feedback when the UE has 2 antennas, linearly polarized with the antenna spacing is 1 cm, and the gNB has 16 antennas, a cell ID of the gNB, and / or cross-polarized antennas with antenna spacing of 0.5 m.

[0050] In certain systems, some parameters are already communicated between the UE and gNB (e.g., using a UE capability report), another group of parameters are not part of the UE capability report but it is okay for the gNB and / or UE to share this information (e.g., polarization information), and some parameters that are important to determine a condition of a node, but the UE and / or gNB does not want to share it with the other side (e.g., antenna spacing).

[0051] In one example, y = f for i = {1,2, ••• , m is used to refer parameters to a node A and node A is okay to reveal the actual parameter y and its actual value It . In this example, y may be ftnumber of UE antenna = 2, time the data is collected = 12.02.2024.

[0052] Furthermore, a = for i = {1,2, ••• , n} is used as a dummy / local name and dummy / local value for a parameter / set of parameters that node A is not okay toshare the parameter or its value, e.g., instead of UE antenna spacing = 1cm -=Pt . It should be noted that node A knows an actual mapping between actual parameters and the dummy names and also the value of the parameters and the dummy value it used. In another example, a local condition index / ID is associated with a set of local parameters (and values) at node B, and the local condition ID is shared / communicated to another node (e.g., node B) to indicate the local condition at node A without sharing the set of local parameters.

[0053] It should be noted that each node, like node A, may always do consistent mapping between actual and dummy parameters / values, e.g., during data collection, model training, model inference, and so forth.

[0054] Based on these notations, certain methods may include the following during a data collection and training phase:

[0055] A. One of the nodes that is responsible for data collection, e.g., node A, measures and / or collects training samples from the environment;

[0056] B. Each sample / group of samples is associated with a condition parameter which indicates the condition of node A and / or node B. The condition parameter may be considered as the set ofand aL= pt for node A and / or node B, or may be considered as an ID referring to a set of Ytfor node A and / or node B;

[0057] C. Different datasets may be constructed as the union of some sample s / group of samples. Each dataset may be associated with a metadata representing the condition parameter of the samples / group of samples included in that set; and / or

[0058] D. Train different models based on one or a few of the datasets or subsets of datasets. Each model may be associated with a metadata representing the condition parameter of the samples / group of samples included in the dataset or subsets of the datasets that are used for training of the dataset.

[0059] The following factors may be associated with different examples: whether the model is a one-sided or a two-sided model, where the data is collected, where the training is happening, where different models are stored after training (it may be determined which of them is appropriate for inference), and where the model resides during inference.

[0060] In one example, a one-sided model, data collection, training, model storage, and inference occur at a UE or a NE. In some examples, node A collects data, a node at the node A-side trains the model, a node at the node A-side stores the models, and node A is for inference. In another example, node B collects data, a node at the node B-side trains the model, a node at the node B-side stores the models, and node B is for inference.

[0061] It should be noted that a node at a node A-side may mean a node that node A can freely expose more of its parameters to. Moreover, a node at the node A-side may be a node-A itself.

[0062] In one example, as a training node and node A are in a same side, a condition parameter of samples / group of samples may include any parameters of node A and associated values (or a node A-specific condition ID indicating the set of node A parameters and associated values). Additionally, node A may be aware of some y = It and a = ptparameters of node B which may be included in the condition parameter. In some examples, a node B-specific condition ID indicating the set of y parameters and a node-B local condition ID representing the set of af = ptlocal parameters at node B may be included in the condition parameter. In other examples, the node B- specific condition ID may jointly indicate the set of y parameters and the local condition ID.

[0063] In one implementation, node A may use a mapping table to associate an ID to possible combinations of the parameters (of itself or what it has received from node B), and then use that ID as a condition parameter.

[0064] Multiple models may be trained, each of them may be trained using datasets having different sets of samples with one / a few condition parameters. The metadata of the model may show the condition parameters of the samples that the model is trained for. Each dataset may also have a metadata that shows the condition parameters of the samples included in that dataset. The trained models may then be stored at a node A- side node.

[0065] During an inference phase, node A knows its own parameters and may receive the set of y = l and / or af = pt(or the node B-specific condition ID and / or node B-specific local condition ID) from node B. These information elements, andpossibly the mapping table if it exists, may be used to determine a current condition parameter.

[0066] Comparing the current condition parameter with the metadata of the available model, node A (e.g., a node at node A-side) may determine the model that may be used for inference. If the model is not at node A, it may be transferred to that node.

[0067] In a first implementation, when a one-sided model training resides at a UE, the set of y may include at least one of the following: a transmission configuration indicator (TCI) state ID corresponding to a joint / downlink (DL) TCI state, a CSI reporting configuration ID, a codebook configuration, a cell ID, a non-zero power (NZP) CSI reference signal (RS) (CSI-RS) resource ID, and / or n NZP CSI-RS resource set ID.

[0068] In another example, a one-sided model, data collection, model inference at one side, training, and model storage may be at another side. In one example, node A collects data and the model may be used at node A during inference, node A may send data to a node B-side for training, and trained models are stored at the node B-side.

[0069] In some examples, after collecting data, node A associates a condition parameter to each samples / group of samples. Node A may include some of the y = It and a =that it wants (or a node A-specific condition ID indicating the set of node A parameters and associated values) and also it may be able to include y = It andai=Pi parameters of node B (or a Node B-specific condition ID and / or a Node-B local condition ID) that it may have received from node B.

[0070] Moreover, a mapping table may be used to associate an ID to possible combination of the parameters (of itself or what it has received from node B), and then use that ID as a condition parameter. The mapping table may be communicated with the other side so both sides have the same understanding.

[0071] Some examples may create a dataset having different sets of samples with one / a few condition parameters. This dataset may also have a metadata that shows the condition parameters of the samples included in that dataset. The dataset may be transferred to another node at the node B-side (could be node B itself). The samples of the dataset at the node B-side node may be associated with some more parameters of thenode B, e.g., some more yf = lj and af = pj. These may include the parameters of the node B (or a node-B local condition ID) that node B does not want to send to node A but it is okay to share them with a node at the node B side. A mapping table may be used here. It should be noted that this node at the node B-side may receive datasets from multiple node A-side nodes.

[0072] In one example there may be a method to see what parameters should be assigned to each sample and the Node B may store its parameters at different time slots / time ranges. After receiving a dataset, the samples may have a parameter representing a sample measurement time. Node B (or another node at the node B-side) may use this information to determine appropriate parameters for that sample and then associate it with the sample.

[0073] In certain examples, a training node trains multiple models based on sets of datasets or subsets of a dataset. Metadata of the model then shows the condition parameters of the samples that the model is trained for. It should be noted that these condition parameters may include the condition parameters received from node A and also a part that is added by node B after a data set transfer. The training node may be node B itself. The trained models may then be stored at a node B-side node.

[0074] In one implementation, during an interference phase, node B sends a set of parameters values yf = lj and af = pj (the ones which has not been communicated with node A but has been communicated with the training node) (or a node-B local condition ID) to the node B-side node and also set of yf = It and a = pt (or a node B- specific condition ID) that has been shared with node A and also the current parameters of node A, yf = l and af = pt(or a node A-specific condition ID indicating the node- A parameters and / or node A-specific local condition ID indicating the node-A local parameters). The node B-side node may select the appropriate model based on the metadata of the model and send it to node A for inference.

[0075] In another implementation, node B sends a set of parameters values yf = lj and af = pj (the ones which have not been communicated with node A but have been communicated with the training node) and yf = It and af = ptparameters (the ones which have been communicated with node A) to the node B-side node. The node B-side node may select all appropriate models based on the metadata of the model. Sending allthese models to node A, node A may then use the metadata of those models and its current condition based on its y = It and a =to select the model that should be used for inference.

[0076] In a further implementation, node B sends a set of parameters values yf = lj and a = pj (the ones which have not been communicated with node A but have been communicated with the training node) to the node B-side node. The node B-side node may select all appropriate models based on the metadata of the model. Sending all these models to node A, node A may then use the metadata of those models and its current condition based on its y = Z, and af = pt and also yf = Z, and af = pt parameters of node B that it may have received from node B to select the that model should be used for inference.

[0077] In one implementation, the one-sided model is intended for CSI prediction or beam management, where node A and node B correspond to a UE side and a NW side respectively. In this implementation, the data collection is based on a first set of DL RSs, e.g., CSI-RS received at the UE based on a configured CSI reporting setting, a CSI resource setting, or a combination thereof. The UE may feed back CSI measurements to the network side based on one or more CSI reports or dataset reports over a physical uplink channel.

[0078] The NW side may collect a plurality of datasets from a plurality of UEs, wherein the plurality of datasets corresponds to a common cell ID, a common TCI state, a common antenna configuration, a common CSI reporting setting, a common CSI resource setting, or a combination thereof. The NW side may train a model for CSI prediction or beam management based on the plurality of datasets in addition to the set of conditions and / or parameters, if applicable.

[0079] Moreover, the NW side signals a model ID or a set of parameters corresponding to the model to the UE side. In one example, all UEs on the UE side are associated with the same model ID or the same set of parameters, and, in another example, each UE is associated with a distinct model ID or a distinct set of parameters. Further, each UE in the UE side uses a second set of DL RSs for model inference based on the respective model signaled via the NW side.

[0080] Various systems may use a two-sided model with data collection at one side, and model training, model storage, and model inference at both sides (each side may manage its own part of the model). One example of this case is that node A collects data, node A and node B sides collaborate on model training, and each side stores its own part of the model and uses it for inference.

[0081] There may be several methods fortraining a two-sided model. In one example there may be a separate training approach where a node B-side starts training. In this example, after collecting data, node A associates a condition parameter to each sample / group of samples. Node A may include some of the yd = l and a = Pi parameters (or the node A-specific condition ID and / or node A-specific local condition ID) that it wants and may be able to include y =and a = pLparameters of node B (or the node B-specific condition ID and / or node B-specific local condition ID) that it may have received from node B. Further, a mapping table may be used to associate an ID to possible combinations of the parameters (of itself or what it received from node B), and then use that ID as the condition parameter. The mapping table may be communicated with the other side so both sides have the same understanding. It should be noted that a node at a node B-side may mean a node that node B can freely expose more of its parameters to that node. A node at the Node B-side can be Node-B itself.

[0082] Certain examples may create a dataset having different sets of samples with one / a few condition parameters. The dataset may then be transferred to another node at the node B-side. Moreover, the dataset may also have metadata that shows the condition parameters of the samples included in that dataset. The metadata may be in the form of an ID determined using the mapping table and the condition parameters.

[0083] The samples of the dataset at the node B-side node may be associated with some more parameters of the Node B, e.g., some more y® = lj and a = pj. These are the parameters of the node B (or a node-B local condition ID) that node B does not want to send to node A, but it is okay to share them with a node at the node B side. A mapping table may also be used. The condition parameters of the samples in each dataset may include the received condition parameters from node A and the new parameters added by the node B.

[0084] One method to see what parameters should be assigned to each sample is that the node B may store its parameters at different time slots / time ranges. After receiving a dataset, the samples may have a parameter representing the sample measurement time. Node B may use this information to determine the appropriate parameters forthat sample and then associate it with the sample. It should be noted that the training node at node B-side (which received the datasets and could be also node B itself) might receive datasets from more than one node A and then use all the datasets for training.

[0085] The training node may train one or multiple decoder models based on sets of datasets or subsets of dataset. The metadata of the model may show the condition parameters of the samples that the model is trained for. These condition parameters may include the condition parameters received from node A and also the part that is added by node B after a data set transfer. The metadata may include an ID determined using the mapping table and the condition parameters. The trained decoder models may be stored at a node B-side node (could be the node B itself). This node may be the same as the training node or another node.

[0086] For each of the trained decoder models, a dataset may be created where the samples of that dataset are based on the samples of the dataset used for training of that decoder mode. Therefore, may be possible to use the condition parameters of the samples of the datasets used for training to determine the condition parameters for the samples of the generated dataset. The generated dataset may also have metadata that shows the condition parameters of the samples included in that dataset. The generated datasets may then be transferred to a training node at the node A-side.

[0087] In one implementation, the condition parameters of the samples of the generated dataset may be equal to the part of the condition parameters of the samples of the dataset used for training which is originally received from node A. It may be due to the fact that the generated dataset will be feedback to the node A-side and there may be no parameters that node B is not willing to reveal to node A side. The metadata may be in the form of an ID determined using the mapping table and the condition parameters. It should be noted that a node at a node A-side may mean a node that node A can freely expose more of its parameters to that node. A node at the node A-side can be node-A itself.

[0088] In some examples, samples of a generated dataset, at the node A-side node may be associated with some more parameters of the node A, e.g., some more y = lj and a-]= pj . These are the parameters of the node A (or a Node-A local condition ID) that node A does not want to send to node B, but it is okay to share them with a node at the node A side. A mapping table may also be used in such examples. The condition parameters of the samples in each generated dataset may include the received condition parameters of samples received from a node B-side node and the new parameters added by the node A.

[0089] One method to see what parameters should be assigned to each sample is that the node A may store its parameters at different time slots / time ranges. After receiving a generated dataset, the samples may have a parameter representing the sample measurement time. Node A may use this information to determine the appropriate parameters for that sample and then associate it with the sample. It should be noted that the training node at the node A-side (which received the datasets and could be also node A itself) might receive datasets from more than one node B-side node and then use all the datasets for training.

[0090] The training node may train one or multiple encoder models based on sets of received generated datasets or subsets of dataset. The metadata of the model may show the condition parameters of the samples that the model is trained for. It should be noted that these condition parameters may include the condition parameters received from node B and also the part that is added by node A after a data set transfer. The metadata may be in the form of an ID determined using the mapping table and the condition parameters. The trained encoder models may be stored at a node A-side node. This node may be the same as the training node or another node.

[0091] In one implementation, during an inference phase, node A may send its current parameters of node A, y = It and a = pt (or a node A-specific condition ID and / or a node-A local condition ID) to a node-B side node and then node B also sends: a set of parameters y® = lj and a = pj (the ones which has not been communicated with node A but has been communicated with the node B-side training node) (or node-B local condition ID) to the node B-side node and a set of y = l and a = pt(or a node B-specific condition ID) that has been shared with node A to the node B-side node. Thenode B-side node may then select the appropriate decoder model based on the metadata of the models and may send it to the node B for inference.

[0092] Node B may send its current parameters of node A, y = l and a = ptto a node-A side node and then node A may send a set of parameters yd = lj and a-]= pj (the ones which has not been communicated with node B but has been communicated with the node A-side training node) to the node A-side node and a set of y = li and a = pi that has been shared with node B to the node-A side node. The node A-side node may then select the appropriate encoder model based on the metadata of the models and may send it to the node A for inference.

[0093] In one implementation, the two-sided model is intended for CSI feedback compression, where node A and node B correspond to UE side and NW side respectively. In this implementation, the data collection may be based on a first set of DL RSs, e.g., CSI-RS received at the UE based on a configured CSI reporting setting, a CSI resource setting, or a combination thereof. The UE may feed back CSI measurements to the network side based on one or more CSI reports or dataset reports over a physical uplink channel. The network side may collect a plurality of datasets from a plurality of UEs, wherein the plurality of datasets corresponds to a common cell ID, a common TCI state, a common antenna configuration, a common CSI reporting setting, a common CSI resource setting, or a combination thereof. The NW side may train a first model for CSI prediction or beam management based on the plurality of datasets in addition to the set of conditions and / or parameters, if applicable. The first model is based on a first model training at the NW side.

[0094] The NW side signals a model ID or a set of parameters corresponding to the model to the UE side. In one example, all UEs on the UE side are associated with the same model ID or the same set of parameters, whereas in another example, each UE is associated with a distinct model ID or a distinct set of parameters. Moreover, each UE in the UE side may use a second set of DL RSs for training a second model, and the second model may be based on the first model signaled via the NW side. In one example, the second model includes an additional set of layers, and the additional set of layers is based on a second model training at the UE side.

[0095] Figure 4 illustrates an example of a UE 400 in accordance with aspects of the present disclosure. The UE 400 may include a processor 402, a memory 404, acontroller 406, and a transceiver 408. The processor 402, the memory 404, the controller 406, or the transceiver 408, or various combinations thereof or various components thereof may be examples of means for performing various aspects of the present disclosure as described herein. These components may be coupled (e.g., operatively, communicatively, functionally, electronically, electrically) via one or more interfaces.

[0096] The processor 402, the memory 404, the controller 406, or the transceiver 408, or various combinations or components thereof may be implemented in hardware (e.g., circuitry). The hardware may include a processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), or other programmable logic device, or any combination thereof configured as or otherwise supporting a means for performing the functions described in the present disclosure.

[0097] The processor 402 may include an intelligent hardware device (e.g., a general-purpose processor, a DSP, a CPU, an ASIC, a field programmable gate array (FPGA), or any combination thereof). In some implementations, the processor 402 may be configured to operate the memory 404. In some other implementations, the memory 404 may be integrated into the processor 402. The processor 402 may be configured to execute computer-readable instructions stored in the memory 404 to cause the UE 400 to perform various functions of the present disclosure.

[0098] The memory 404 may include volatile or non-volatile memory. The memory 404 may store computer-readable, computer-executable code including instructions when executed by the processor 402 cause the UE 400 to perform various functions described herein. The code may be stored in a non-transitory computer-readable medium such the memory 404 or another type of memory. Computer-readable media includes both non-transitory computer storage media and communication media including any medium that facilitates transfer of a computer program from one place to another. A non-transitory storage medium may be any available medium that may be accessed by a general-purpose or special-purpose computer.

[0099] In some implementations, the processor 402 and the memory 404 coupled with the processor 402 may be configured to cause the UE 400 to perform one or more of the functions described herein (e.g., executing, by the processor 402, instructions stored in the memory 404). For example, the processor 402 may support wirelesscommunication at the UE 400 in accordance with examples as disclosed herein. For example, the processor 402 coupled with the memory 404 may be configured to cause the UE 400 to determine a first set of information based on measurement data and a first index associated with the measurement data, wherein the measurement data is measured by the first apparatus or received at the first apparatus, and wherein the first index is associated with at least one of a first set of one or more parameters for the first apparatus and a second set of one or more parameters for a second apparatus. The UE 400 may also generate one or more datasets, wherein each dataset of the one or more datasets is a subset of the first set of information. The UE 400 may transmit at least one dataset of the one or more datasets.

[0100] The controller 406 may manage input and output signals for the UE 400. The controller 406 may also manage peripherals not integrated into the UE 400. In some implementations, the controller 406 may utilize an operating system such as iOS®, ANDROID®, WINDOWS®, or other operating systems. In some implementations, the controller 406 may be implemented as part of the processor 402.

[0101] In some implementations, the UE 400 may include at least one transceiver 408. In some other implementations, the UE 400 may have more than one transceiver 408. The transceiver 408 may represent a wireless transceiver. The transceiver 408 may include one or more receiver chains 410, one or more transmitter chains 412, or a combination thereof.

[0102] A receiver chain 410 may be configured to receive signals (e.g., control information, data, packets) over a wireless medium. For example, the receiver chain 410 may include one or more antennas for receive the signal over the air or wireless medium. The receiver chain 410 may include at least one amplifier (e.g., a low-noise amplifier (LNA)) configured to amplify the received signal. The receiver chain 410 may include at least one demodulator configured to demodulate the receive signal and obtain the transmitted data by reversing the modulation technique applied during transmission of the signal. The receiver chain 410 may include at least one decoder for decoding the processing the demodulated signal to receive the transmitted data.

[0103] A transmitter chain 412 may be configured to generate and transmit signals (e.g., control information, data, packets). The transmitter chain 412 may include at least one modulator for modulating data onto a carrier signal, preparing the signal fortransmission over a wireless medium. The at least one modulator may be configured to support one or more techniques such as amplitude modulation (AM), frequency modulation (FM), or digital modulation schemes like phase-shift keying (PSK) or quadrature amplitude modulation (QAM). The transmitter chain 412 may also include at least one power amplifier configured to amplify the modulated signal to an appropriate power level suitable for transmission over the wireless medium. The transmitter chain 412 may also include one or more antennas for transmitting the amplified signal into the air or wireless medium.

[0104] Figure 5 illustrates an example of a processor 500 in accordance with aspects of the present disclosure. The processor 500 may be an example of a processor configured to perform various operations in accordance with examples as described herein. The processor 500 may include a controller 502 configured to perform various operations in accordance with examples as described herein. The processor 500 may optionally include at least one memory 504, which may be, for example, an L1 / L2 / L3 cache. Additionally, or alternatively, the processor 500 may optionally include one or more arithmetic -logic units (ALUs) 506. One or more of these components may be in electronic communication or otherwise coupled (e.g., operatively, communicatively, functionally, electronically, electrically) via one or more interfaces (e.g., buses).

[0105] The processor 500 may be a processor chipset and include a protocol stack (e.g., a software stack) executed by the processor chipset to perform various operations (e.g., receiving, obtaining, retrieving, transmitting, outputting, forwarding, storing, determining, identifying, accessing, writing, reading) in accordance with examples as described herein. The processor chipset may include one or more cores, one or more caches (e.g., memory local to or included in the processor chipset (e.g., the processor 500) or other memory (e.g., random access memory (RAM), read-only memory (ROM), dynamic RAM (DRAM), synchronous dynamic RAM (SDRAM), static RAM (SRAM), ferroelectric RAM (FeRAM), magnetic RAM (MRAM), resistive RAM (RRAM), flash memory, phase change memory (PCM), and others).

[0106] The controller 502 may be configured to manage and coordinate various operations (e.g., signaling, receiving, obtaining, retrieving, transmitting, outputting, forwarding, storing, determining, identifying, accessing, writing, reading) of the processor 500 to cause the processor 500 to support various operations in accordancewith examples as described herein. For example, the controller 502 may operate as a control unit of the processor 500, generating control signals that manage the operation of various components of the processor 500. These control signals include enabling or disabling functional units, selecting data paths, initiating memory access, and coordinating timing of operations.

[0107] The controller 502 may be configured to fetch (e.g., obtain, retrieve, receive) instructions from the memory 504 and determine subsequent instruction(s) to be executed to cause the processor 500 to support various operations in accordance with examples as described herein. The controller 502 may be configured to track memory address of instructions associated with the memory 504. The controller 502 may be configured to decode instructions to determine the operation to be performed and the operands involved. For example, the controller 502 may be configured to interpret the instruction and determine control signals to be output to other components of the processor 500 to cause the processor 500 to support various operations in accordance with examples as described herein. Additionally, or alternatively, the controller 502 may be configured to manage flow of data within the processor 500. The controller 502 may be configured to control transfer of data between registers, arithmetic logic units (ALUs), and other functional units of the processor 500.

[0108] The memory 504 may include one or more caches (e.g., memory local to or included in the processor 500 or other memory, such RAM, ROM, DRAM, SDRAM, SRAM, MRAM, flash memory, etc. In some implementations, the memory 504 may reside within or on a processor chipset (e.g., local to the processor 500). In some other implementations, the memory 504 may reside external to the processor chipset (e.g., remote to the processor 500).

[0109] The memory 504 may store computer-readable, computer-executable code including instructions that, when executed by the processor 500, cause the processor 500 to perform various functions described herein. The code may be stored in a non- transitory computer-readable medium such as system memory or another type of memory. The controller 502 and / or the processor 500 may be configured to execute computer-readable instructions stored in the memory 504 to cause the processor 500 to perform various functions. For example, the processor 500 and / or the controller 502 may be coupled with or to the memory 504, the processor 500, the controller 502, andthe memory 504 may be configured to perform various functions described herein. In some examples, the processor 500 may include multiple processors and the memory 504 may include multiple memories. One or more of the multiple processors may be coupled with one or more of the multiple memories, which may, individually or collectively, be configured to perform various functions herein.

[0110] The one or more ALUs 506 may be configured to support various operations in accordance with examples as described herein. In some implementations, the one or more ALUs 506 may reside within or on a processor chipset (e.g., the processor 500). In some other implementations, the one or more ALUs 506 may reside external to the processor chipset (e.g., the processor 500). One or more ALUs 506 may perform one or more computations such as addition, subtraction, multiplication, and division on data. For example, one or more ALUs 506 may receive input operands and an operation code, which determines an operation to be executed. One or more ALUs 506 be configured with a variety of logical and arithmetic circuits, including adders, subtractors, shifters, and logic gates, to process and manipulate the data according to the operation.Additionally, or alternatively, the one or more ALUs 506 may support logical operations such as AND, OR, exclusive-OR (XOR), not-OR (NOR), and not-AND (NAND), enabling the one or more ALUs 506 to handle conditional operations, comparisons, and bitwise operations.

[0111] The processor 500 may support wireless communication in accordance with examples as disclosed herein. The processor 500 may be configured to or operable to support a means for: determining a first set of information based on measurement data and a first index associated with the measurement data, wherein the measurement data is measured by the first apparatus or received at the first apparatus, and wherein the first index is associated with at least one of a first set of one or more parameters for the first apparatus and a second set of one or more parameters for a second apparatus, generating one or more datasets, wherein each dataset of the one or more datasets is a subset of the first set of information, and transmitting at least one dataset of the one or more datasets.

[0112] Figure 6 illustrates an example of a NE 600 in accordance with aspects of the present disclosure. The NE 600 may include a processor 602, a memory 604, a controller 606, and a transceiver 608. The processor 602, the memory 604, the controller 606, or the transceiver 608, or various combinations thereof or variouscomponents thereof may be examples of means for performing various aspects of the present disclosure as described herein. These components may be coupled (e.g., operatively, communicatively, functionally, electronically, electrically) via one or more interfaces.

[0113] The processor 602, the memory 604, the controller 606, or the transceiver 608, or various combinations or components thereof may be implemented in hardware (e.g., circuitry). The hardware may include a processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), or other programmable logic device, or any combination thereof configured as or otherwise supporting a means for performing the functions described in the present disclosure.

[0114] The processor 602 may include an intelligent hardware device (e.g., a general-purpose processor, a DSP, a CPU, an ASIC, an FPGA, or any combination thereof). In some implementations, the processor 602 may be configured to operate the memory 604. In some other implementations, the memory 604 may be integrated into the processor 602. The processor 602 may be configured to execute computer-readable instructions stored in the memory 604 to cause the NE 600 to perform various functions of the present disclosure. For example, the processor 602 coupled with the memory 604 may be configured to cause the NE 600 to: receive one or more datasets from at least one of a first apparatus or another apparatus, wherein first samples of each dataset of the one or more datasets are associated with a first index, and wherein the first index is associated with at least one of a first set of one or more parameters for the first apparatus and a second set of one or more parameters for the second apparatus, develop one or more decoder models, wherein each decoder model of the one or more decoder models is based on the one or more datasets, assign a metadata to each decoder model of the one or more decoder models, wherein the metadata is based on at least one of the first index and a second index, and wherein the second index represents the first set of one or more parameters for the first apparatus, and is associated with second samples of the one or more datasets, generate one or more datasets, wherein each dataset of the one or more datasets based on one or more of: an input to an encoder model; an expected output of the encoder model; or a third index based on the first index and the second index, and transmit at least one dataset of the one or more datasets.

[0115] The memory 604 may include volatile or non-volatile memory. The memory 604 may store computer-readable, computer-executable code including instructions when executed by the processor 602 cause the NE 600 to perform various functions described herein. The code may be stored in a non-transitory computer-readable medium such the memory 604 or another type of memory. Computer-readable media includes both non-transitory computer storage media and communication media including any medium that facilitates transfer of a computer program from one place to another. A non-transitory storage medium may be any available medium that may be accessed by a general-purpose or special-purpose computer.

[0116] In some implementations, the processor 602 and the memory 604 coupled with the processor 602 may be configured to cause the NE 600 to perform one or more of the functions described herein (e.g., executing, by the processor 602, instructions stored in the memory 604). For example, the processor 602 may support wireless communication at the NE 600 in accordance with examples as disclosed herein.

[0117] The controller 606 may manage input and output signals for the NE 600. The controller 606 may also manage peripherals not integrated into the NE 600. In some implementations, the controller 606 may utilize an operating system such as iOS®, ANDROID®, WINDOWS®, or other operating systems. In some implementations, the controller 606 may be implemented as part of the processor 602.

[0118] In some implementations, the NE 600 may include at least one transceiver 608. In some other implementations, the NE 600 may have more than one transceiver 608. The transceiver 608 may represent a wireless transceiver. The transceiver 608 may include one or more receiver chains 610, one or more transmitter chains 612, or a combination thereof.

[0119] A receiver chain 610 may be configured to receive signals (e.g., control information, data, packets) over a wireless medium. For example, the receiver chain 610 may include one or more antennas for receive the signal over the air or wireless medium. The receiver chain 610 may include at least one amplifier (e.g., a low-noise amplifier (LNA)) configured to amplify the received signal. The receiver chain 610 may include at least one demodulator configured to demodulate the receive signal and obtain the transmitted data by reversing the modulation technique applied during transmissionof the signal. The receiver chain 610 may include at least one decoder for decoding the processing the demodulated signal to receive the transmitted data.

[0120] A transmitter chain 612 may be configured to generate and transmit signals (e.g., control information, data, packets). The transmitter chain 612 may include at least one modulator for modulating data onto a carrier signal, preparing the signal for transmission over a wireless medium. The at least one modulator may be configured to support one or more techniques such as amplitude modulation (AM), frequency modulation (FM), or digital modulation schemes like phase-shift keying (PSK) or quadrature amplitude modulation (QAM). The transmitter chain 612 may also include at least one power amplifier configured to amplify the modulated signal to an appropriate power level suitable for transmission over the wireless medium. The transmitter chain 612 may also include one or more antennas for transmitting the amplified signal into the air or wireless medium.

[0121] Figure 7 illustrates a flowchart of a method 700 in accordance with aspects of the present disclosure. The operations of the method 700 may be implemented by a first apparatus (e.g., UE) as described herein. In some implementations, a UE 400 may execute a set of instructions to control the function elements of a processor to perform the described functions.

[0122] At 702, the method may include determining a first set of information based on measurement data and a first index associated with the measurement data, wherein the measurement data is measured by the first apparatus or received at the first apparatus, and wherein the first index is associated with at least one of a first set of one or more parameters for the first apparatus and a second set of one or more parameters for a second apparatus. The operations of 702 may be performed in accordance with examples as described herein. In some implementations, aspects of the operations of 702 may be performed by a UE as described with reference to Figure 4.

[0123] At 704, the method may include generating one or more datasets, wherein each dataset of the one or more datasets is a subset of the first set of information. The operations of 704 may be performed in accordance with examples as described herein. In some implementations, aspects of the operations of 704 may be performed by a UE as described with reference to Figure 4.

[0124] At 706, the method may include transmitting at least one dataset of the one or more datasets. The operations of 706 may be performed in accordance with examples as described herein. In some implementations, aspects of the operations of 706 may be performed by a UE as described with reference to Figure 4.

[0125] Figure 8 illustrates a flowchart of another method 800 in accordance with aspects of the present disclosure. The operations of the method 800 may be implemented by a second apparatus (e.g., NE) as described herein. In some implementations, a NE 600 may execute a set of instructions to control the function elements of a processor to perform the described functions.

[0126] At 802, the method may include receiving one or more datasets from at least one of a first apparatus or another apparatus, wherein first samples of each dataset of the one or more datasets are associated with a first index, and wherein the first index is associated with at least one of a first set of one or more parameters for the first apparatus and a second set of one or more parameters for the second apparatus. The operations of 802 may be performed in accordance with examples as described herein. In some implementations, aspects of the operations of 802 may be performed by a NE as described with reference to Figure 6.

[0127] At 804, the method may include developing one or more decoder models, wherein each decoder model of the one or more decoder models is based on the one or more datasets. The operations of 804 may be performed in accordance with examples as described herein. In some implementations, aspects of the operations of 804 may be performed by a NE as described with reference to Figure 6.

[0128] At 806, the method may include assigning a metadata to each decoder model of the one or more decoder models, wherein the metadata is based on at least one of the first index and a second index, and wherein the second index represents the first set of one or more parameters for the first apparatus, and is associated with second samples of the one or more datasets. The operations of 806 may be performed in accordance with examples as described herein. In some implementations, aspects of the operations of 806 may be performed by a NE as described with reference to Figure 6.

[0129] At 808, the method may include generating one or more datasets, wherein each dataset of the one or more datasets based on one or more of: an input to an encoder model; an expected output of the encoder model; or a third index based on the firstindex and the second index. The operations of 808 may be performed in accordance with examples as described herein. In some implementations, aspects of the operations of 808 may be performed by a NE as described with reference to Figure 6.

[0130] At 810, the method may include transmitting at least one dataset of the one or more datasets. The operations of 810 may be performed in accordance with examples as described herein. In some implementations, aspects of the operations of 810 may be performed by a NE as described with reference to Figure 6.

[0131] It should be noted that the methods described herein describe possible implementations, and that the operations and the steps may be rearranged or otherwise modified and that other implementations are possible.

[0132] The description herein is provided to enable a person having ordinary skill in the art to make or use the disclosure. Various modifications to the disclosure will be apparent to a person having ordinary skill in the art, and the generic principles defined herein may be applied to other variations without departing from the scope of the disclosure. Thus, the disclosure is not limited to the examples and designs described herein but is to be accorded the broadest scope consistent with the principles and novel features disclosed herein.

Claims

CLAIMSWhat is claimed is:1 . A first apparatus, comprising: at least one memory; and at least one processor coupled with the at least one memory and configured to cause the first apparatus to: determine a first set of information based on measurement data and a first index associated with the measurement data, wherein the measurement data is measured by the first apparatus or received at the first apparatus, and wherein the first index is associated with at least one of a first set of one or more parameters for the first apparatus and a second set of one or more parameters for a second apparatus; generate one or more datasets, wherein each dataset of the one or more datasets is a subset of the first set of information; and transmit at least one dataset of the one or more datasets.

2. The first apparatus of claim 1, wherein the at least one processor is configured to cause the first apparatus to: receive the one or more datasets from the second apparatus or another apparatus, wherein first samples of each dataset of the one or more datasets are associated with a second index that is based on the first index; develop one or more encoder models, wherein each encoder model of the one or more encoder models is based on the one or more datasets; and assign a metadata to each encoder model of the one or more encoder models, wherein the metadata is based on the second index and a third index, and wherein the third index is associated with second samples of the one or more datasets and the first set of one or more parameters for the first apparatus.

3. The first apparatus of claim 2, wherein the at least one processor is configured to cause the first apparatus to store the one or more encoder models and the metadata of each encoder model of the one or more encoder models.

4. The first apparatus of claim 2, wherein the at least one processor is configured to cause the first apparatus to: determine a state of the first apparatus based on one or more of: the first set of one or more parameters for the first apparatus in the first index or the third index; or the second set of one or more parameters for the second apparatus included in the first index; determine an active encoder model based on the metadata of each encoder model of the one or more encoder models and the state of the first apparatus; determine a set of input samples; and transmit an output of the active encoder model to the second apparatus in response to receiving the set of input samples as an input to the active encoder model.

5. The first apparatus of claim 4, wherein the at least one processor is configured to cause the first apparatus to generate state information associated with the first apparatus including one or more of: a value of at least one of the first set of one or more parameters; a time slot; or a time duration.

6. The first apparatus of claim 5, wherein the at least one processor is configured to cause the first apparatus to transmit the state information to another apparatus.

7. The first apparatus of claim 6, wherein the at least one processor is configured to cause the first apparatus to determine the third index based on the state of the first apparatus and a time parameter indicating a measurement time.

8. The first apparatus of claim 4, wherein the set of input samples is based on a channel data representation.

9. The first apparatus of claim 8, wherein the channel data representation is determined based on one or more reference signals received from the second apparatus.

10. The first apparatus of claim 8, wherein the channel data representation is associated with one or more different transmit (TX)-receive (RX) pairs communicated over different frequency bands or different time slots.

11. The first apparatus of claim 2, wherein the at least one processor is configured to cause the first apparatus to: receive an active encoder model from the second apparatus or another apparatus; determine a set of input samples; and transmit an output of the active encoder model to the second apparatus in response to receiving the set of input samples as an input to the active encoder model.

12. The first apparatus of claim 2, where the metadata of each encoder model of the one or more encoder models is based on a mapping table associated with different combinations of the second index and the third index.

13. The first apparatus of claim 2, wherein the second index includes a time parameter indicating a measurement time.

14. The first apparatus of claim 1, wherein the first index includes a time parameter indicating a measurement time.

15. The first apparatus of claim 1, wherein the first index is based on an identifier determined based on a mapping table associated with different combinations ofthe first set of one or more parameters for the first apparatus and the second set of one or more parameters for the second apparatus.

16. The first apparatus of claim 15, wherein the at least one processor is configured to cause the first apparatus to transmit the mapping table to the second apparatus or another apparatus.

17. A processor for wireless communication, comprising: at least one controller coupled with at least one memory and configured to cause the processor to: determine a first set of information based on measurement data and a first index associated with the measurement data, wherein the measurement data is measured by a first apparatus or received at the first apparatus, and wherein the first index is associated with at least one of a first set of one or more parameters for the first apparatus and a second set of one or more parameters for a second apparatus; generate one or more datasets, wherein each dataset of the one or more datasets is a subset of the first set of information; and transmit at least one dataset of the one or more datasets.

18. A method of a first apparatus, the method comprising: determining a first set of information based on measurement data and a first index associated with the measurement data, wherein the measurement data is measured by the first apparatus or received at the first apparatus, and wherein the first index is associated with at least one of a first set of one or more parameters for the first apparatus and a second set of one or more parameters for a second apparatus; generating one or more datasets, wherein each dataset of the one or more datasets is a subset of the first set of information; and transmitting at least one dataset of the one or more datasets.

19. A second apparatus, comprising:at least one memory; and at least one processor coupled with the at least one memory and configured to cause the second apparatus to: receive one or more datasets from at least one of a first apparatus or another apparatus, wherein first samples of each dataset of the one or more datasets are associated with a first index, and wherein the first index is associated with at least one of a first set of one or more parameters for the first apparatus and a second set of one or more parameters for the second apparatus; develop one or more decoder models, wherein each decoder model of the one or more decoder models is based on the one or more datasets; assign a metadata to each decoder model of the one or more decoder models, wherein the metadata is based on at least one of the first index and a second index, and wherein the second index represents the first set of one or more parameters for the first apparatus, and is associated with second samples of the one or more datasets; generate one or more datasets, wherein each dataset of the one or more datasets based on one or more of: an input to an encoder model; an expected output of the encoder model; or a third index based on the first index and the second index; and transmit at least one dataset of the one or more datasets.

20. The second apparatus of claim 19, wherein the at least one processor is configured to cause the second apparatus to store the one or more decoder models and the metadata of each decoder model of the one or more decoder models.

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