Apparatus and method for identification of an ai / ML model of a wireless communication system

Abstract

Various aspects of the present disclosure relate to methods, apparatuses, and devices for wireless communication. A user equipment (UE) may receive (1302) a downlink message from a first network entity, wherein the downlink message comprises a first identifier. The UE may determine (1304), in response to receiving the downlink message, a set of second identifiers, wherein the set of second identifiers is determined based on a supported artificial intelligence (AI) functionality associated with the first identifier. The UE may also transmit (1306) the set of second identifiers to the first network entity.

Description

APPARATUS AND METHOD FOR IDENTIFICATION OF A MODEL OF A WIRELESS COMMUNICATION SYSTEM TECHNICAL 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 UE may receive a downlink message from a first network entity, wherein the downlink message comprises a first identifier. The UE may determine, in response to receiving the downlink message, a set of second identifiers, wherein the set of second identifiers is determined based on a supported AI functionality associated with the first identifier. The UE may transmit the set of second identifiers to the first network entity. 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 block diagram of a node A-side model in accordance with aspects of the present disclosure.

[0009] Figure 5 illustrates an example of a block diagram of a node B-side model in accordance with aspects of the present disclosure.

[0010] Figures 6A through 6D illustrate an example of an information element (IE) in accordance with aspects of the present disclosure.

[0011] Figure 7 illustrates an example of communications in accordance with aspects of the present disclosure.

[0012] Figure 8 illustrates an example of handover communications in accordance with aspects of the present disclosure.

[0013] Figure 9 illustrated an example functional framework in accordance with aspects of the present disclosure.

[0014] Figure 10 illustrates an example of a UE in accordance with aspects of the present disclosure.

[0015] Figure 11 illustrates an example of a processor in accordance with aspects of the present disclosure.

[0016] Figure 12 illustrates an example of a network equipment (NE) in accordance with aspects of the present disclosure.

[0017] Figure 13 illustrates a flowchart of a method performed by a UE in accordance with aspects of the present disclosure.

[0018] Figure 14 illustrates a flowchart of a method performed by a NE in accordance with aspects of the present disclosure. DETAILED DESCRIPTION

[0019] 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.

[0020] 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.

[0021] Aspects of the present disclosure are described in the context of a wireless communications system.

[0022] 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 access technologies. 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.

[0023] 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.

[0024] 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 maybe 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.

[0025] 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 referred to 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 Internet-of-Things (IoT) device, an Internet-of-Everything (IoE) device, or machine-type communication (MTC) device, among other examples.

[0026] 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).

[0027] 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., S1, 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).

[0028] The CN 106 may support user authentication, access authorization, tracking, connectivity, and other access, routing, or mobility functions. The CN 106 may be anevolved 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.

[0029] The CN 106 may communicate with a packet data network over one or more backhaul links (e.g., via an S1, 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).

[0030] 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.

[0031] 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., ^=0) may be associated with a first subcarrierspacing (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., ^=3) may be associated with a fourth subcarrier spacing (e.g., 120 kHz) and a normal cyclic prefix. A fifth numerology (e.g., ^=4) may be associated with a fifth subcarrier spacing (e.g., 240 kHz) and a normal cyclic prefix.

[0032] 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.

[0033] 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., ^=0, ^=1, ^=2, ^=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 thatreference to a first numerology (e.g., ^=0) associated with a first subcarrier spacing (e.g., 15 kHz) may be used interchangeably between subframes and slots.

[0034] 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.

[0035] 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., ^=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., ^=2), which includes 60 kHz subcarrier spacing; and a fourth numerology (e.g., ^=3), which includes 120 kHz subcarrier spacing.

[0036] 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, UE1), 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).

[0037] Specifically, Figure 2 shows one example of the wireless network 200 with one NE 102-a (e.g., may be represented by node ^^and may be equipped with ^antennas) and ^ UEs 104-a, 104-b, 104-c (e.g., be denoted by ^^, ^^ , ⋯ , ^^ thateach have ^ antennas). ^^^ ^^^ may denote a channel at time ^ over frequency band ^, ^ ∈{1,2, … , ^} , between ^^ and ^^ which may form a matrix of size ^ × ^ with complexentries, i.e., ^^^ ^^^ ∈ ℂ^×^.

[0038] At time ^ and frequency band ^, it may be assumed that the NE 102-a wants to transmit message ^^^ ^^^ to user ^ ^^ where = {1,2, ⋯ , ^} while it uses "^ ^^^ ∈ℂ^×^as a precoding vector. The received signal at ^^, #^^^^^, may be written as:

[0039] #^^ ^^^ = ^^^^^^"^^^^^^^^ ^^^ + %^^^^^,

[0040] where %^^^^^ represents a noise vector at a receiver.

[0041] Toan achievable rate of the link, in one example, the NE 102-a selects "^^^^^ that maximizes a received signal-to-interference and noise ratio (SINR). Several different configurations may be used for selection of "^^^^^ where some of the configurations may have some knowledge about ^^^^^^.

[0042] The NE 102-a may get knowledge of ^^^^^^ 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 ^^^^^^. This may require a large amount of data if there are a large number oflarge frequency bands.

[0043] 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, ^^^^^^may be denoted using ^^^.

[0044] ^^^^^ may be defined as a matrix of size ^ × ^ × ^ which is formed bystacking ^^^ for all frequency bands (e.g., the entries at ^^&', (, ^)^^^ is equal to^^^ &', ()^^^^. In total, each UE 104-a, 104-b, and 104-c may provide feedbackinformation about most recent ^ × ^ × ^ complex numbers to the NE 102-a.

[0045] A two-sided model is one example described herein to reduce required feedback information where an encoding part (e.g., at the UE) computes a quantized latent representation of the input data, and the decoding part (e.g., at the gNB) gets this latent representation and uses that to reconstruct the desired output. The input data maybe data which is based on channel measurements. For example, may be raw channel inputs of ^^or ^^^, or, for example, the precoders that are computed from the channel matrix (e.g., the eigenvector associated with the largest eigen-vector of ^^for each subband).

[0046] 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.

[0047] In some systems, ^*and ^+may be used for a logical model. Meaning that node A 302 may chose not to use ^*directly and instead develop and use several internal models like ^*^^^, ^*^^^,…, ^*^^^based on some private parameters and decisions. In these cases,may be that all these internal models should meet at least the performance of pairing original ^*with ^+. 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 ^+and some internal models at the node B 304.

[0048] 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 , 302 (e.g., UE) and the node ^ 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 ^ 304 may not need to be aware of the internal structure of the NN module of the other side.

[0049] In certain examples, such as for CSI prediction, beam management, and / or positioning, the model is only at one side (e.g., node A-Side, or node B-Side). Figure 4 and Figure 5 illustrate embodiments of such examples.

[0050] Figure 4 illustrates an example of a block diagram of a node A-side model 400 in accordance with aspects of the present disclosure. The node A-side model 400 includes a node A 402 (e.g., encoder, encoding model, MA) and a node B 404 (e.g., no model). Input data 406 is provided to the node A 402, then there is a result 408 in communications from the node A 402 to the node B 404. In some configurations, the node A 402 is located at the UE 104, and the node B 404 is located at the NE 102, but in other configurations the node A 402 and the node B 404 may be located in other devices.

[0051] Figure 5 illustrates an example of a block diagram of a node B-side model 500 in accordance with aspects of the present disclosure. The node A-side model 500 includes a node A 502 (e.g., no model) and a node B 504 (e.g., decoder, decoder model, MB). Terminated data 506 is provided from the node A 502 to the node B 504, and the node B 504 outputs data 508. In some configurations, the node A 502 is located at the UE 104, and the node B 504 is located at the NE 102, but in other configurations the node A 502 and the node B 504 may be located in other devices.

[0052] Returning to Figure 3, the node A 302 and the node B 304 may decide not to use a single encoder-decoder 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 the samples 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 assumedthat it may be beneficial to design ^*&-) − ^+&-), for - = {1,2, … , ^} where the - / 0pair 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.

[0053] Having multiple models for different states of the node A 302 and the node B 304 during an inference phase, the node A / node B may decide on which of these models should be activated based on the current node A / node B states. Examples herein may provide solutions for this problem and, for simplicity, it may be assumed that there is only one model per node A and node B.

[0054] It should be noted that one node A may be connected to different node Bs, and vice versa (e.g., one node B may be connected to different node As). For example, if there is CSI feedback, the node A may be a UE equipped with a Qualcomm chipset, and the node B could a gNB manufactured by Ericsson or Nokia. In these scenarios, especially if there are two-sided models and separate training schemes are used (e.g., where the encoder and decoder parts are trained respectively on two nodes owned by the node A and node B sides), it may be assumed that node A develops a model in collaboration with each of the node Bs. In one example, Qualcomm and Ericsson collaborate to generate ^1^ * (e.g., Q1 represents the first encoder model of Qualcomm chipset) and ^+2^(e.g., E1 represents the first decoder model of the Ericsson gNB). In addition, Qualcomm and Nokia collaborate to generate ^1^ * (e.g., Q2 represents the second encoder model of Qualcomm chipset) and ^+^^(e.g., N1 represents the first decoder model of the Nokia gNB).

[0055] After training of the models, there may be multiple encoder models (e.g., at node A-side) associated with different node Bs and also there may be multiple decoder models (e.g., at node B-side) associated with different node As. Having different models, there may be a mechanism for node A / node B to select the appropriate model during an inference phase. The main challenge here is that, in some scenarios, during the inference phase node A and node B may not want to reveal their identity to the other side. In the present example, it may mean that during the inference time the Qualcomm chipset may select between ^1^ 1 * and ^ ^ * , but the gNB side does not want to tell the Qualcomm chipset if is a gNB made by Ericsson or Nokia.

[0056] As described herein, there are several solutions to allow node A and node B to determine / select an appropriate artificial intelligence (AI) / machine learning (ML) model that may be used during the inference phase in a transparent manner. Moreover, there may be an assumption that proxy-IDs are assigned to a node A and / or a node B. Considering that the statistics of training data samples depend on the type of the twonodes (e.g., node A and node B) which are involved during data collection, it may be desirable to train and use different models for different pairs (e.g., type of node A, type of node B). By using proxy-IDs it may be possible to perform data collection / model training such that the data of different pairs (e.g., type of node A, type of node B) may be separated without each node (e.g., side) knowing the exact type of the other side. Hence, transparency between the involved nodes (e.g., node A (UE) / node B(gNB)) may be preserved. The proxy-ID assigned / allocated to node A (e.g., UE) is referred to as 3456, and correspondingly the proxy-ID for node B (e.g., gNB) is referred to as 3756. Signalling procedures are described herein to provide support for the exchange of the assigned proxy-IDs among different involved nodes to allow an appropriate AI / ML model or AI / ML functionality selection during mobility events such as handover.

[0057] In one example, during the inference phase, node A (e.g., or node B) tells node B (e.g., or node A) about its identity and then each side determines the correct model to use based on the identity of the other side. Although simple, this may not work if, during the inference phase, node A / node B do not want to tell / reveal their identity to the other side.

[0058] In another example, when a model is trained, data is gathered from different node As and node Bs and a single model that works with all pairs is generated. In a previous example described herein, it may mean that data is collected from both Ericsson and Nokia gNBs and a model that works for both is trained. Then, during the inference phase, the UE does not decide which model to use (e.g., as it only has one model) and, therefore, it does not need to know the identity of the other side. The main drawback of this example is that based on the use-case and the model structure, the performance of one single model which is applicable to all different node A-node B pairs may be inferior to having separate models, each of them for a group of node A- node B pairs, so it may be better to train different models and then find a way for correct model selection during the inference phase. Another difficulty of this approach may be that collaboration between different node As and node Bs (e.g., during the training phase) to train a single model may not be very practical. In one example, it may not be very practical to ask different chipset vendors and gNB vendors to collaborate together to determine a model that works for all pairing situations.

[0059] As described herein, there may be an assumption that proxy-IDs are assigned to a node A and / or a node B. Considering that the statistics of training data samples depends on the type of the two nodes (e.g., node A and node B) which are involved during data collection, it may be desirable to train and use different models for different pairs (e.g., type of node A, type of node B). By using proxy-IDs it may be possible to perform data collection / model training such that the data of different pairs (e.g., type of node A, type of node B) may be separated without each node (e.g., side) knowing the exact type of the other side. Hence transparency between the involved nodes (e.g. node A (UE) / node B (gNB)) may be preserved. The proxy-ID assigned / allocated to node A (e.g., UE) may be referred to as 3456, and correspondingly the proxy-ID for node B may be referred to as 3756.

[0060] In certain examples, a NW may request a UE to provide supported AI / ML models. It may be assumed that different models, each associated with a particular 3564 , 3756, have been already trained and also a) the encoder / decoder part of the model has been transferred back to node A / node B respectively, and b) the encoder / decoder part of the model has been stored on a server / node owned by that particular node A / node B type respectively.

[0061] In one embodiment, a first node requests a second node to provide a set of identifiers based on which the first node is aware of the AI / ML models supported by the first node for the second node. According to one implementation of the embodiment, the first node (e.g., gNB), explicitly requests a second node (e.g., UE) to provide some indication of the AI-ML models for a specific AI / ML functionality (e.g., CSI compression / decompression, beam prediction, that the second node (e.g., UE) supports the first node (e.g., gNB)). To become aware of the supported AI / ML models (e.g., encoder models) supported by the UE for a specific gNB (e.g., vendor), the gNB may explicitly request that the UE provide such information. To ensure the transparency between the involved nodes (e.g., node A (UE) / node B(gNB)) and not reveal the exact type device (e.g., detailed chipset information, detailed gNB vendor information), some identifiers (e.g., proxy-IDs) are assigned to the involved nodes (e.g., either pre-assigned or allocated during LCM phases (e.g., during the model training phase)). In one example, the gNB requests that the UE provide the supported proxy-IDs for the proxy- ID associated with the gNB (e.g., 3756^. According to another implementation of the embodiment, the NW request contains 3756for which the NW requests the UE to providethe supported models. In response to the reception of this NW request, the UE determines the pairs of 3564 , 3756with the 3756set to the value signalled within the NW request. To give an example, a gNB (e.g., Ericsson gNB in a DT cell) may want to understand UE supported models for an Ericsson gNB (e.g., 3756=2). The gNB sends an explicit request to the UE to provide the models (e.g., model for encoder / decoder for a 3756=2). The UE (e.g., Lenovo UE with QC chipset) provides the 3456s for which it hasa trained model associated with ^e. g. , 3564 , 3567 = 2^. Based on the received information(e.g., 3456s), the gNB may select a model and activate it.

[0062] In one example, the request message is sent via a radio resource control (RRC) message (e.g., UEAIEnquiry message) which contains an identifier, e.g. gNB proxy-ID, for which the UE should provide the supported models / UE proxy-ID(s). The UE responds to this request by sending a message (e.g., UEAIInformation message) which includes the corresponding UE proxy-IDs / model IDs. Using RRC signaling for the request and response message may ensure that the messages are protected (e.g., ciphered). According to some implementations, the request is sent via a medium access control (MAC) control element (CE) which contains the proxy-ID of the gNB for which the UE may provide the supported models / UE proxy-IDs. In another example, a downlink control information (DCI) is used to send the request to the UE.

[0063] According to one implementation, the response message (e.g., UEAIInformation message) including the supported proxy-IDs or supported models / model ID(s) for a specific gNB (e.g., 3756) is sent in an RRC message. Alternatively, a MAC CE is used which carries the IDs.

[0064] It should be noted that a node (e.g., UE / gNB) may be assigned / allocated with one or more proxy-IDs. For example, if a UE is assigned multiple proxy-IDs, each proxy-ID may refer to a specific model supported by the type of UE. In one example,3564 = 4 <'= 3564 = 5 may refer to a Lenovo UE with a Qualcomm chipset supporting afirst and a second encoder model. Those models may be trained with a Nokia gNB with 3756=2 (3564 = 4, 3567 = 2^ <'= ^3564 = 5, 3756=2) are the supported pairs of IDs. In another example, a node (UE / gNB) may be assigned with only one single proxy-ID referring to the type of node (e.g., Lenovo UE with Qualcomm chipset). In addition to the proxy-ID, a node may be assigned / allocated with additional identifiers referring to the supported models without revealing the details of the models itself. Such additionalidentifiers may be assigned / allocated during a LCM phase (e.g., during the model training phase) similar to the proxy-ID.

[0065] It should be noted that embodiments found herein are not limited to the exchange / signaling of the proxy-IDs between different nodes, but may be equally applicable to the signaling / exchange of any additional assigned IDs identifying a supported model (e.g., supported encoder / decoder model) or AI / ML functionality.

[0066] In some embodiments, AI related identifiers allow a node (e.g., UE / gNB) to select / activate an AI model and are reported as part of UE assistance information (UAI).

[0067] A UAI procedure may be a specific RRC mechanism by which a UE can inform various internal statuses to the network so that the gNB can better adapt UEs’ configuration / parameters for a specific period of time. For example, UAI may be useful for the network to enable UE power consumption reduction on radio resource management (RRM) measurements. Another example of UAI is OverheatingAssistance which is used to notify a NW of the UE’s preference for a reduced configuration (e.g., number of bandwidth parts (BWPs) or common carriers (CCs)) to solve an overheating issue. According to one implementation, a UE reports its assigned proxy-IDs / Model ID(s) as part of the UAI. According to another implementation, an AIMLAssistanceInformation IE is used within UEAssistanceInformation (e.g., AIMLAssistanceInformation is reported as part of a UAI procedure). Using a UAI procedure to provide a gNB with AI / ML related information (e.g., assigned / allocated identifiers such as proxy-IDs or supported model IDs) may have the benefit that the information can be signaled to the NW whenever the UE sees the need to provide such information (e.g., change of the supported models).

[0068] Figures 6A through 6D illustrate an example of an IE 600 in accordance with aspects of the present disclosure. The IE 600 may be part of a UAI message including AIML related Information (e.g., proxy-IDs, model IDs, functionality IDs).

[0069] UAI may be sent to the gNB to allow the gNB to configure parameters which best fit a current situation of the UE. In one example, the UE triggers the transmission of the UAI for cases that the list of IDs (e.g., proxy-IDs or IDs of supported models) has changed.

[0070] According to one embodiment, the UE provides AI related identifiers allowing a node (e.g., UE / gNB) to select / activate an AI model as part of the UE capability information. In one implementation, a UE provides the proxy-IDs and / or IDs of supported models within a UE capability information message. The UE capability procedure may be an RRC signaling mechanism by which the UE can inform its capabilities to the gNB to help the gNB not to configure anything that is beyond the supported capabilities of the UE. The gNB may request the UE to provide information about its capabilities by sending a UE capability inquiry message and the UE may respond to this request by sending a UE capability information message. Within the UE capability information message, the UE reports similar information like RF / PHY information and supported feature sets. In one example, new AIML related information is reported as part of the UE capability information. In one example, the UE indicates to the gNB the change of its capabilities. Based on this indication the gNB may request the UE to provide its capabilities.

[0071] In one implementation, the UE provides the AIML related assistance information (e.g., identifiers) allowing a node (e.g., UE / gNB) to select / activate an AI model during an RRC connection setup procedure (e.g., when transitioning from RRC_IDLE to RRC_CONNECTED). In one example, the UE provides the proxy-IDs and / or IDs of supported models / functionalities within a UE capability information message during RRC connection setup. During a connection setup procedure, the RRC layer identifies the UE and configures it with the basic (e.g., initial) RRC configuration allowing it to start basic information exchange between the UE and the gNB / NW. In response to having setup the RRC connection, the UE RRC acknowledgment message also delivers the non-access stratum (NAS) information to the network to enable setting up remaining user plane details (e.g., services and their configuration details). For this purpose, the basic RRC connection setup includes a configuration that ensures that the UE capabilities can be queried and that further reconfiguration can be done. This may allow the network to perform the first RRC reconfiguration that finally initiates the user plane communication using a desired bandwidth based on the UE capabilities. Once the user plane (UP) configuration is ready and the UE is configured according to its capabilities, the connection establishment is fully completed. As part of the UE capability enquire / reporting procedure, the UE provides the AIML related identifiers tothe NW which allows the network to select / activate a specific AIML model for a certain functionality supported by the UE.

[0072] Figure 7 illustrates an example of communications 700 in accordance with aspects of the present disclosure. The communications 700 may be used to transmit and / or receive information such as UAI and / or UE capability information. Moreover, the communications 700 may be between a UE 702 and a network 704.

[0073] At 706, there may be reception of system information (e.g., cell reselection and initial access parameters). At 708, there maybe ETWS / CMAS notification. Moreover, at 710, there may be a connection establishment request sent.

[0074] At 712, there may be a connection setup. Then, at 714, there may be an NAS information transfer. At 716, there may be initial security activation. Next, at 718, there may be a UE capability enquiry.

[0075] At 720, there may be UE capability reporting. Then, at 722, there may be a connection reconfiguration (e.g., user plane and DRB configuration). At 724, there may be a connection reconfiguration (e.g., lower layer parameters).

[0076] At 726, there may be a connection reconfiguration (e.g., measurement and gap configuration). Next, at 728, there may be a connection reconfiguration (e.g., serving cell management for CA / DC).

[0077] At 730, there may be a connection reconfiguration (e.g., RRC context transfer plus handover with security key change). Then, at 732, there may be a connection release.

[0078] According to one embodiment, the AIML related info (e.g., identifiers allowing a node (e.g., UE / gNB) to select / activate an AI functionality / model), is stored as part of a UE context. Storing the AIML related capabilities / information (e.g., information on supported models / functionality or proxy-IDs), within the UE context in the AMF allows a gNB to fetch this information based on the UE identity given e.g., at the RRC connection request when the NAS connection is established between UE and the AMF. Based on AIML related information, the network may configure the UE accordingly (e.g., select / activate a specific AIML functionality / model supported by the UE). The UE context management function allows the AMF to establish, modify, and / or release a UE context in the AMF and the NG-RAN node.

[0079] According to one embodiment, a source gNB provides AI / ML related information during mobility (e.g., handover) of the UE to the target gNB. According to one implementation, the source gNB provides AI / ML related identities of the UE to the target gNB during the handover preparation phase. In one example, the source gNB provides the proxy-IDs and / or IDs of the models / functionalities supported by the UE to the target gNB. To allow the target gNB to select / activate a specific AI / ML functionality / model after the handover, the target gNB is provided with the AI / ML related identities. In one example, the source gNB requests that the UE provide the identifiers for a specific gNB proxy-ID. For example, if the target gNB has an assigned proxy-ID 3567 = 2, the source gNB requests that the UE provides the corresponding IDs(e.g., proxy-IDs and / or model IDs) which are supported by the UE for this node B proxy-ID 3567 = 2. In response to this request, the UE may provide the 356^?^ 4 and / or model IDs which the UE supports for 3567 = 2. The source gNB forwards thisinformation to the target gNB. In one example, the information is included in a HO REQUEST message. The source gNB requesting the AI / ML identities and corresponding UE response message may be sent before the handover request message.

[0080] Figure 8 illustrates an example of handover communications 800 in accordance with aspects of the present disclosure. The handover communications 800 include communications between a UE 802, a source gNB 804, a target gNB 806, an AMF 808, and UPF(s) 810.

[0081] At 812, user data is communicated. Then, at 814, mobility control information is provided by the AMF 808. At 816, measurement control and reports are communicated. Next, at 818, a handover decision is made. At 820, a handover request is communicated. Then, at 822, admission control is determined. At 824, a handover request acknowledge is communicated.

[0082] At 826, RAN handover initiation occurs. Next, at 828, there is an SN status transfer. At 830, the UE 802 detaches from an old cell and synchronizes to a new cell. At 832, the source gNB 804 delivers buffered data and new data from the UPF(s) 810. Then, at 834, user data is communicated.

[0083] At 836, the target gNB 806 buffers user data from the source gNB 804. At 838, a RAN handover completion message is communicated. Next, at 840, user data iscommunicated. At 842, a path switch request is transmitted. At 844, there is a path switch in UPF(s) 810.

[0084] Then, at 846, an end marker is communicated. At 848, user data is communicated. At 850, a path switch request acknowledge is communicated. Next, at 852, a UE context release is communicated.

[0085] In one example, the AI / ML information (e.g., proxy-ID and / or model IDs), is included in a HANDOVER REQUEST message. The source gNB 804 may issue a handover request message to the target gNB 806 passing a transparent RRC container with necessary information to prepare the handover at the target side. The information may include at least the target cell ID, KgNB*, the cell radio network temporary identifier (C-RNTI) of the UE in the source gNB 804, RRM-configuration including UE inactive time, basic AS-configuration including antenna information and a DL carrier frequency, the current QoS flow to DRB mapping rules applied to the UE, the SIB1 information from source gNB, the UE capabilities for different RATs, PDU session related information, and / or the UE reported measurement information including beam- related information, if available.

[0086] In another example, the RRCReconfiguration message initiating the HO may include some indication (e.g., from the target gNB 806) of which AI / ML functionality / model to use. The target gNB 806 may determine this based on the provided information from the source gNB 804. The RRCReconfiguration message sent to the UE may contain the information required to access the target cell: at least the target cell ID, the new C-RNTI, and / or the target gNB 806 security algorithm identifiers for the selected security algorithms. It may also include radio access channel (RACH) related information.

[0087] According to one implementation, the source gNB 804 provides, during mobility, HO information of the performance of a model in the source cell to the target cell. Monitoring the performance of AI / ML models or AI / ML functionalities may be one of the functions of a life cycle management (LCM). The monitoring of AI / ML models or AI / ML functionalities may be part of the management. If the performance monitoring is done at the network side, the source gNB 804 forwards calculated performance metrics for a AI / ML model or AI / ML functionality that was applied by the UE / source gNB 804 before the HO to the target cell. Based on the provided historyinformation of the performance target cell, a more efficient performance monitoring may be performed (e.g., if the UE uses a AI / ML model or AI / ML functionality such as CSI compression) in several neighboring cells and it may be more efficient to exchange information on the model performance (e.g., performance metrics) between the neighboring gNBs.

[0088] Figure 9 illustrated an example functional framework 900 in accordance with aspects of the present disclosure. The functional framework 900 may be for AI / ML for a NR air interface. The functional framework 900 includes data collection 902, model training 904, management 906, inference 908, and model storage 910.

[0089] According to one implementation, a UE reports the performance metrics for a AI / ML mode or AI / ML functionality to the target cell after a successful handover completion. In one example, the UE triggers the transmission of a AI / ML performance report to a new serving gNB after HO is successfully executed.

[0090] According to one embodiment, a UE triggers the transmission of a UEAIInformation message including the supported proxy-IDs or supported models / functionalities for a specific gNB (e.g., 3756) after the HO is successfully completed, e.g., RACH procedure in targetsuccessfully completed. The UE generates, in response to the HO completion, the new UEAIInformation (e.g., RRC message) or a new MAC CE which carries the IDs.

[0091] According to one embodiment, UE autonomously falls back to e.g. legacy (non-AI) mechanism or a default AI model for cases that an AI failure occurred, e.g. based on a predefined / specified event related to the AI model performance. In one example the performance monitoring functionality may trigger the fallback to a default (non-AI ) mechanism or default AI model. In one example NW configures the UE behavior for the case of AI failure, e.g. fallback to a default model which might work but not with a good performance rather than going to non-AI legacy mechanism. The NW may configure the default AI model UE should use for cases of AI failure. In one example UE indicates an AI failure to the NW (e.g. gNB). In one example the AI failure indication may be done be means of RRC signaling. In one example, the AI failure indication message contains information whether UE executed a fallback to a default AI model or the legacy (non-AI) mechanism.

[0092] Figure 10 illustrates an example of a UE 1000 in accordance with aspects of the present disclosure. The UE 1000 may include a processor 1002, a memory 1004, a controller 1006, and a transceiver 1008. The processor 1002, the memory 1004, the controller 1006, or the transceiver 1008, 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.

[0093] The processor 1002, the memory 1004, the controller 1006, or the transceiver 1008, 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.

[0094] The processor 1002 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 1002 may be configured to operate the memory 1004. In some other implementations, the memory 1004 may be integrated into the processor 1002. The processor 1002 may be configured to execute computer-readable instructions stored in the memory 1004 to cause the UE 1000 to perform various functions of the present disclosure.

[0095] The memory 1004 may include volatile or non-volatile memory. The memory 1004 may store computer-readable, computer-executable code including instructions when executed by the processor 1002 cause the UE 1000 to perform various functions described herein. The code may be stored in a non-transitory computer- readable medium such the memory 1004 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.

[0096] In some implementations, the processor 1002 and the memory 1004 coupled with the processor 1002 may be configured to cause the UE 1000 to perform one or more of the functions described herein (e.g., executing, by the processor 1002, instructions stored in the memory 1004). For example, the processor 1002 may support wireless communication at the UE 1000 in accordance with examples as disclosed herein. For example, the processor 1002 coupled with the memory 1004 may be configured to cause the UE 1000 to receive a downlink message from a first network entity, wherein the downlink message comprises a first identifier. The UE 1000 may also determine, in response to receiving the downlink message, a set of second identifiers, wherein the set of second identifiers is determined based on a supported AI functionality associated with the first identifier. The UE 1000 may transmit the set of second identifiers to the first network entity.

[0097] The controller 1006 may manage input and output signals for the UE 1000. The controller 1006 may also manage peripherals not integrated into the UE 1000. In some implementations, the controller 1006 may utilize an operating system such as iOS®, ANDROID®, WINDOWS®, or other operating systems. In some implementations, the controller 1006 may be implemented as part of the processor 1002.

[0098] In some implementations, the UE 1000 may include at least one transceiver 1008. In some other implementations, the UE 1000 may have more than one transceiver 1008. The transceiver 1008 may represent a wireless transceiver. The transceiver 1008 may include one or more receiver chains 1010, one or more transmitter chains 1012, or a combination thereof.

[0099] A receiver chain 1010 may be configured to receive signals (e.g., control information, data, packets) over a wireless medium. For example, the receiver chain 1010 may include one or more antennas for receive the signal over the air or wireless medium. The receiver chain 1010 may include at least one amplifier (e.g., a low-noise amplifier (LNA)) configured to amplify the received signal. The receiver chain 1010 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 1010 may include at least one decoder for decoding the processing the demodulated signal to receive the transmitted data.

[0100] A transmitter chain 1012 may be configured to generate and transmit signals (e.g., control information, data, packets). The transmitter chain 1012 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 1012 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 1012 may also include one or more antennas for transmitting the amplified signal into the air or wireless medium.

[0101] Figure 11 illustrates an example of a processor 1100 in accordance with aspects of the present disclosure. The processor 1100 may be an example of a processor configured to perform various operations in accordance with examples as described herein. The processor 1100 may include a controller 1102 configured to perform various operations in accordance with examples as described herein. The processor 1100 may optionally include at least one memory 1104, which may be, for example, an L1 / L2 / L3 cache. Additionally, or alternatively, the processor 1100 may optionally include one or more arithmetic-logic units (ALUs) 1106. 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).

[0102] The processor 1100 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 1100) 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).

[0103] The controller 1102 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 1100 to cause the processor 1100 to support various operations in accordance with examples as described herein. For example, the controller 1102 may operate as a control unit of the processor 1100, generating control signals that manage the operation of various components of the processor 1100. These control signals include enabling or disabling functional units, selecting data paths, initiating memory access, and coordinating timing of operations.

[0104] The controller 1102 may be configured to fetch (e.g., obtain, retrieve, receive) instructions from the memory 1104 and determine subsequent instruction(s) to be executed to cause the processor 1100 to support various operations in accordance with examples as described herein. The controller 1102 may be configured to track memory address of instructions associated with the memory 1104. The controller 1102 may be configured to decode instructions to determine the operation to be performed and the operands involved. For example, the controller 1102 may be configured to interpret the instruction and determine control signals to be output to other components of the processor 1100 to cause the processor 1100 to support various operations in accordance with examples as described herein. Additionally, or alternatively, the controller 1102 may be configured to manage flow of data within the processor 1100. The controller 1102 may be configured to control transfer of data between registers, arithmetic logic units (ALUs), and other functional units of the processor 1100.

[0105] The memory 1104 may include one or more caches (e.g., memory local to or included in the processor 1100 or other memory, such RAM, ROM, DRAM, SDRAM, SRAM, MRAM, flash memory, etc. In some implementations, the memory 1104 may reside within or on a processor chipset (e.g., local to the processor 1100). In some other implementations, the memory 1104 may reside external to the processor chipset (e.g., remote to the processor 1100).

[0106] The memory 1104 may store computer-readable, computer-executable code including instructions that, when executed by the processor 1100, cause the processor 1100 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 ofmemory. The controller 1102 and / or the processor 1100 may be configured to execute computer-readable instructions stored in the memory 1104 to cause the processor 1100 to perform various functions. For example, the processor 1100 and / or the controller 1102 may be coupled with or to the memory 1104, the processor 1100, the controller 1102, and the memory 1104 may be configured to perform various functions described herein. In some examples, the processor 1100 may include multiple processors and the memory 1104 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.

[0107] The one or more ALUs 1106 may be configured to support various operations in accordance with examples as described herein. In some implementations, the one or more ALUs 1106 may reside within or on a processor chipset (e.g., the processor 1100). In some other implementations, the one or more ALUs 1106 may reside external to the processor chipset (e.g., the processor 1100). One or more ALUs 1106 may perform one or more computations such as addition, subtraction, multiplication, and division on data. For example, one or more ALUs 1106 may receive input operands and an operation code, which determines an operation to be executed. One or more ALUs 1106 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 1106 may support logical operations such as AND, OR, exclusive-OR (XOR), not-OR (NOR), and not-AND (NAND), enabling the one or more ALUs 1106 to handle conditional operations, comparisons, and bitwise operations.

[0108] The processor 1100 may support wireless communication in accordance with examples as disclosed herein. The processor 1100 may be configured to or operable to support a means for: receiving a downlink message from a first network entity, wherein the downlink message comprises a first identifier, determining, in response to receiving the downlink message, a set of second identifiers, wherein the set of second identifiers is determined based on a supported AI functionality associated with the first identifier, and transmitting the set of second identifiers to the first network entity.

[0109] Figure 12 illustrates an example of a NE 1200 in accordance with aspects of the present disclosure. The NE 1200 may include a processor 1202, a memory 1204, acontroller 1206, and a transceiver 1208. The processor 1202, the memory 1204, the controller 1206, or the transceiver 1208, 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.

[0110] The processor 1202, the memory 1204, the controller 1206, or the transceiver 1208, 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.

[0111] The processor 1202 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 1202 may be configured to operate the memory 1204. In some other implementations, the memory 1204 may be integrated into the processor 1202. The processor 1202 may be configured to execute computer- readable instructions stored in the memory 1204 to cause the NE 1200 to perform various functions of the present disclosure. For example, the processor 1202 coupled with the memory 1204 may be configured to cause the NE 1200 to: transmit a downlink message to a UE, wherein the downlink message comprises a first identifier, and receive a set of second identifiers from the UE, wherein the set of second identifiers is determined based on a supported AI functionality associated with the first identifier.

[0112] The memory 1204 may include volatile or non-volatile memory. The memory 1204 may store computer-readable, computer-executable code including instructions when executed by the processor 1202 cause the NE 1200 to perform various functions described herein. The code may be stored in a non-transitory computer- readable medium such the memory 1204 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 anyavailable medium that may be accessed by a general-purpose or special-purpose computer.

[0113] In some implementations, the processor 1202 and the memory 1204 coupled with the processor 1202 may be configured to cause the NE 1200 to perform one or more of the functions described herein (e.g., executing, by the processor 1202, instructions stored in the memory 1204). For example, the processor 1202 may support wireless communication at the NE 1200 in accordance with examples as disclosed herein.

[0114] The controller 1206 may manage input and output signals for the NE 1200. The controller 1206 may also manage peripherals not integrated into the NE 1200. In some implementations, the controller 1206 may utilize an operating system such as iOS®, ANDROID®, WINDOWS®, or other operating systems. In some implementations, the controller 1206 may be implemented as part of the processor 1202.

[0115] In some implementations, the NE 1200 may include at least one transceiver 1208. In some other implementations, the NE 1200 may have more than one transceiver 1208. The transceiver 1208 may represent a wireless transceiver. The transceiver 1208 may include one or more receiver chains 1210, one or more transmitter chains 1212, or a combination thereof.

[0116] A receiver chain 1210 may be configured to receive signals (e.g., control information, data, packets) over a wireless medium. For example, the receiver chain 1210 may include one or more antennas for receive the signal over the air or wireless medium. The receiver chain 1210 may include at least one amplifier (e.g., a low-noise amplifier (LNA)) configured to amplify the received signal. The receiver chain 1210 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 1210 may include at least one decoder for decoding the processing the demodulated signal to receive the transmitted data.

[0117] A transmitter chain 1212 may be configured to generate and transmit signals (e.g., control information, data, packets). The transmitter chain 1212 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), frequencymodulation (FM), or digital modulation schemes like phase-shift keying (PSK) or quadrature amplitude modulation (QAM). The transmitter chain 1212 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 1212 may also include one or more antennas for transmitting the amplified signal into the air or wireless medium.

[0118] Figure 13 illustrates a flowchart of a method 1300 in accordance with aspects of the present disclosure. The operations of the method 1300 may be implemented by an apparatus (e.g., UE) as described herein. In some implementations, a UE 1000 may execute a set of instructions to control the function elements of a processor to perform the described functions.

[0119] At 1302, the method may include receiving a downlink message from a first network entity, wherein the downlink message comprises a first identifier. The operations of 1302 may be performed in accordance with examples as described herein. In some implementations, aspects of the operations of 1302 may be performed by a UE as described with reference to Figure 10.

[0120] At 1304, the method may include determining, in response to receiving the downlink message, a set of second identifiers, wherein the set of second identifiers is determined based on a supported AI functionality associated with the first identifier. The operations of 1304 may be performed in accordance with examples as described herein. In some implementations, aspects of the operations of 1304 may be performed by a UE as described with reference to Figure 10.

[0121] At 1306, the method may include transmitting the set of second identifiers to the first network entity. The operations of 1306 may be performed in accordance with examples as described herein. In some implementations, aspects of the operations of 1306 may be performed by a UE as described with reference to Figure 10.

[0122] Figure 14 illustrates a flowchart of another method 1400 in accordance with aspects of the present disclosure. The operations of the method 1400 may be implemented by a second apparatus (e.g., NE) as described herein. In some implementations, a NE 1200 may execute a set of instructions to control the function elements of a processor to perform the described functions.

[0123] At 1402, the method may include transmitting a downlink message to a UE, wherein the downlink message comprises a first identifier. The operations of 1402 may be performed in accordance with examples as described herein. In some implementations, aspects of the operations of 1402 may be performed by a NE as described with reference to Figure 12.

[0124] At 1404, the method may include receiving a set of second identifiers from the UE, wherein the set of second identifiers is determined based on a supported AI functionality associated with the first identifier. The operations of 1404 may be performed in accordance with examples as described herein. In some implementations, aspects of the operations of 1404 may be performed by a NE as described with reference to Figure 12.

[0125] 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.

[0126] 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

CLAIMS What is claimed is:

1. A user equipment (UE), comprising: at least one memory; and at least one processor coupled with the at least one memory and configured to cause the UE to: receive a downlink message from a first network entity, wherein the downlink message comprises a first identifier; determine, in response to receiving the downlink message, a set of second identifiers, wherein the set of second identifiers is determined based on a supported artificial intelligence (AI) functionality associated with the first identifier; and transmit the set of second identifiers to the first network entity.

2. The UE of claim 1, wherein the first identifier identifies the first network entity.

3. The UE of claim 1, wherein the set of second identifiers is determined based on a stored set of pairs, and wherein each set of pairs of the stored set of pairs comprises a second identifier and the first identifier.

4. The UE of claim 1, wherein the set of second identifiers comprises information indicating supported AI models for the first network entity identified by the first identifier.

5. The UE of claim 1, wherein the first identifier identifies a second network entity.

6. A processor for wireless communication, comprising: at least one controller coupled with at least one memory and configured to cause the processor to: receive a downlink message from a first network entity, wherein the downlink message comprises a first identifier; determine, in response to receiving the downlink message, a set of second identifiers, wherein the set of second identifiers is determined based on a supported artificial intelligence (AI) functionality associated with the first identifier; andtransmit the set of second identifiers to the first network entity.

7. The processor of claim 6, wherein the first identifier identifies the first network entity.

8. The processor of claim 6, wherein the set of second identifiers is determined based on a stored set of pairs, and wherein each set of pairs of the stored set of pairs comprises a second identifier and the first identifier.

9. The processor of claim 6, wherein the set of second identifiers comprises information indicating supported AI models for the first network entity identified by the first identifier.

10. The processor of claim 6, wherein the first identifier identifies a second network entity.

11. A method performed at a user equipment (UE), the method comprising: receiving a downlink message from a first network entity, wherein the downlink message comprises a first identifier; determining, in response to receiving the downlink message, a set of second identifiers, wherein the set of second identifiers is determined based on a supported artificial intelligence (AI) functionality associated with the first identifier; and transmitting the set of second identifiers to the first network entity.

12. The method of claim 11, wherein the first identifier identifies the first network entity.

13. The method of claim 11, wherein the set of second identifiers is determined based on a stored set of pairs, and wherein each set of pairs of the stored set of pairs comprises a second identifier and the first identifier.

14. An apparatus for performing a first network function, comprising: at least one memory; and at least one processor coupled with the at least one memory and configured to cause the apparatus to:transmit a downlink message to a user equipment (UE), wherein the downlink message comprises a first identifier; and receive a set of second identifiers from the UE, wherein the set of second identifiers is determined based on a supported artificial intelligence (AI) functionality associated with the first identifier.

15. The apparatus of claim 14, wherein the at least one processor is configured to cause the apparatus to, in response to receiving the set of second identifiers from the UE, send the set of second identifiers to a second network entity.

16. The apparatus of claim 14, wherein the first identifier identifies a first network entity associated with the first network function.

17. The apparatus of claim 14, wherein the set of second identifiers is determined based on a stored set of pairs, wherein each set of pairs of the stored set of pairs comprises a second identifier and the first identifier.

18. The apparatus of claim 14, wherein the set of second identifiers comprises information indicating supported AI models for the first network function identified by the first identifier.

19. The apparatus of claim 14, wherein the first identifier identifies a second network entity.

20. The apparatus of claim 14, wherein the at least one processor is configured to cause the apparatus to transmit the set of second identifiers to a second network entity.

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