Patent Description:
Wireless communication systems are widely deployed to provide various telecommunication services such as telephony, video, data, messaging, broadcasts, or other similar types of services. These wireless communication systems may employ multiple-access technologies capable of supporting communication with multiple users by sharing available system resources with those users (e.g., bandwidth, transmit power, or other resources). Multiple-access technologies can rely on any of code division, time division, frequency division, orthogonal frequency division, single-carrier frequency division, or time division synchronous code division, to name a few. These and other multiple access technologies have been adopted in various telecommunication standards to provide a common protocol that enables different wireless devices to communicate on a municipal, national, regional, and even global level.

Although wireless communication systems have made great technological advancements over many years, challenges still exist. For example, complex and dynamic environments can still attenuate or block signals between wireless transmitters and wireless receivers, undermining various established wireless channel measuring and reporting mechanisms, which are used to manage and optimize the use of finite wireless channel resources. Consequently, there exists a need for further improvements in wireless communications systems to overcome various challenges.

<CIT> discusses a method in which a network entity forms a network-entity deep neural network (DNN) for processing broadcast or multicast communications transmitted over a wireless communication system. <NPL> discusses applications in various data-driven domains where AI is useful for wireless network design and optimization.

Certain aspects can be implemented in a method for wireless communication performed by at least one of a base station (BS) or other network entity. The method includes the steps according to appended claim <NUM>.

Certain aspects can be implemented in an apparatus for wireless communication. The apparatus includes means according to appended claim <NUM>.

Certain aspects can be implemented in a method for wireless communication performed by a user equipment (UE). The method includes the steps according to appended claim <NUM>.

By way of example, an apparatus may comprise a processing system, a device with a processing system, or processing systems cooperating over one or more networks.

The following description and the appended figures set forth certain features for purposes of illustration. The scope of protection is solely defined by the appended claims <NUM>-<NUM>.

The appended figures depict certain features of the various aspects described herein and are not to be considered limiting of the scope of this disclosure.

Aspects of the present disclosure provide apparatuses, methods, processing systems, and computer-readable media for executing neural network functions (NNFs) by configuring and using corresponding machine learning (ML) models to perform one or more ML-based wireless communications management procedures.

For example, machine learning may be used by a wireless communications device to generate a predictive ML model, which may, in turn, be used to form inferences about input data. In some cases, one or more ML models may be used to execute an NNF for performing one or more ML-based wireless communications management procedures. In some cases, it may be advantageous for a base station of a wireless communication network to dynamically configure a UE with at least one NNF and/or one or more corresponding ML models. This dynamic configuration may provide flexibility within the wireless communication network. For example, this dynamic configuration may allow the UE to not have to store all possible ML models for a particular NNF, saving storage space on the UE. Instead, the UE may separately download a particular ML model (e.g., model structure and/or model parameters) when indicated to use that particular ML model. Additionally, dynamic configuration may provide the base station with flexibility to selectively choose, at any given time and for a particular scenario, which NNF(s) and/or corresponding model(s) to use for performing one or more ML-based wireless communications management procedures. Moreover, dynamic configuration may allow the base station to dynamically update ML models for NNFs.

However, there currently does not exist a way for a base station to configure a UE to use a particular NNF and/or ML model (e.g., to perform certain ML-based wireless communications management procedures). Thus, aspects of the present disclosure provide techniques to facilitate the configuration and use of NNFs and corresponding ML models within a wireless communication network.

<FIG> depicts an example of a wireless communications network <NUM>, in which aspects described herein may be implemented.

Generally, wireless communications network <NUM> includes base stations (BSs) <NUM>, user equipments (UEs) <NUM>, one or more core networks, such as an Evolved Packet Core (EPC) <NUM> and fifth generation (<NUM>) Core (5GC) network <NUM>, which interoperate to provide wireless communications services.

Base stations <NUM> may provide an access point to the EPC <NUM> and/or 5GC <NUM> for a user equipment <NUM>, and may perform one or more of the following functions: transfer of user data, radio channel ciphering and deciphering, integrity protection, header compression, mobility control functions (e.g., handover, dual connectivity), inter-cell interference coordination, connection setup and release, load balancing, distribution for non-access stratum (NAS) messages, NAS node selection, synchronization, radio access network (RAN) sharing, multimedia broadcast multicast service (MBMS), subscriber and equipment trace, RAN information management (RIM), paging, positioning, and delivery of warning messages, among other functions. Base stations may include and/or be referred to as a NodeB, evolved NodeB (eNB), next generation NodeB (gNB), next generation eNB (ng-eNB) (e.g., an eNB that has been enhanced to provide connection to both EPC <NUM> and 5GC <NUM>), an access point, a base transceiver station, a radio base station, a radio transceiver, a transceiver function, or a transmission reception point in various contexts.

Base stations <NUM> wirelessly communicate with UEs <NUM> via communications links <NUM>. Each of base stations <NUM> may provide communication coverage for a respective geographic coverage area <NUM>, which may overlap in some cases. For example, small cell <NUM>' (e.g., a low-power base station) may have a coverage area <NUM>' that overlaps a portion of the coverage area <NUM> of one or more macrocells (e.g., high-power base stations).

The communication links <NUM> between base stations <NUM> and UEs <NUM> may include uplink (UL) (also referred to as "reverse link") transmissions from a user equipment <NUM> to a base station <NUM> and/or downlink (DL) (also referred to as "forward link") transmissions from a base station <NUM> to a user equipment <NUM>. The communication links <NUM> may use multiple-input, multiple-output (MIMO) antenna technology, including spatial multiplexing, beamforming, and/or transmit diversity in various aspects.

Examples of UEs <NUM> include a cellular phone, a smartphone, a session initiation protocol (SIP) phone, a laptop, a personal digital assistant (PDA), a satellite radio, a global positioning system (GPS) device, a multimedia device, a video device, a digital audio player, a camera, a game console, a tablet, a smart device, a wearable device, a vehicle, an electric meter, a gas pump, a large or small kitchen appliance, a healthcare device, an implant, a sensor/actuator, a display, or other wireless devices. Some of the UEs <NUM> may be Internet of things (IoT) devices (e.g., parking meter, gas pump, toaster, vehicles, heart monitor, or other IoT devices), always on (AON) devices, or edge processing devices. A UE <NUM> may also be referred to more generally as a station, a mobile station, a subscriber station, a mobile unit, a subscriber unit, a wireless unit, a remote unit, a mobile device, a wireless device, a wireless communications device, a remote device, a mobile subscriber station, an access terminal, a mobile terminal, a wireless terminal, a remote terminal, a handset, a user agent, a mobile client, or a client.

Communications using higher frequency bands may have higher path loss and a shorter range compared to lower frequency communications. Accordingly, certain base stations (e.g., base station <NUM> in <FIG>) may utilize beamforming <NUM> with a UE <NUM> to improve path loss and range. For example, base station <NUM> and UE <NUM> may each include a plurality of antennas, such as antenna elements, antenna panels, and/or antenna arrays to facilitate beamforming.

In some cases, base station <NUM> may transmit a beamformed signal to UE <NUM> in one or more transmit directions <NUM>'. UE <NUM> may receive the beamformed signal from the base station <NUM> in one or more receive directions <NUM>". UE <NUM> may also transmit a beamformed signal to the base station <NUM> in one or more transmit directions <NUM>". Base station <NUM> may also receive the beamformed signal from UE <NUM> in one or more receive directions <NUM>'. Base station <NUM> and UE <NUM> may then perform beam training to determine the best receive and transmit directions for each of base station <NUM> and UE <NUM>. Notably, the transmit and receive directions for base station <NUM> may or may not be the same. Similarly, the transmit and receive directions for UE <NUM> may or may not be the same.

Wireless communications network <NUM> (and more particularly, a base station <NUM>) includes ML model configuration component <NUM>, which may be configured to perform one or more operations illustrated in <FIG> and <NUM>, as well as other operations described herein for determining NNFs and configuring and using corresponding ML models to perform one or more ML-based wireless communications management procedures. Wireless communications network <NUM> further (and more particularly, a UE <NUM>) includes ML model configuration component <NUM>, which may be configured to perform one or more operations illustrated in <FIG> and <FIG>, as well as other operations described herein for determining NNFs and configuring and using corresponding ML models to perform one or more ML-based wireless communications management procedures.

<FIG> depicts aspects of an example base station (BS) <NUM> and a user equipment (UE) <NUM>.

Generally, base station <NUM> includes various processors (e.g., <NUM>, <NUM>, <NUM>, and <NUM>), antennas 234a-t (collectively <NUM>), transceivers 232a-t (collectively <NUM>), which include modulators and demodulators (modems), and other aspects, which enable wireless transmission of data (e.g., from data source <NUM>) and wireless reception of data (e.g., to data sink <NUM>). For example, base station <NUM> may send and receive data between itself and user equipment <NUM>.

Base station <NUM> includes controller/processor <NUM>, which may be configured to implement various functions related to wireless communications. In the depicted example, controller/processor <NUM> includes ML model configuration component <NUM>, which may be representative of ML model configuration component <NUM> of <FIG>. Notably, while depicted as an aspect of controller/processor <NUM>, ML model configuration component <NUM> may be implemented additionally or alternatively in various other aspects of base station <NUM> in other implementations.

Generally, user equipment <NUM> includes various processors (e.g., <NUM>, <NUM>, <NUM>, and <NUM>), antennas 252a-r (collectively <NUM>), transceivers 254a-r (collectively <NUM>), which include modulators and demodulators, and other aspects, which enable wireless transmission of data (e.g., from data source <NUM>) and wireless reception of data (e.g., to data sink <NUM>).

User equipment <NUM> includes controller/processor <NUM>, which may be configured to implement various functions related to wireless communications. In the depicted example, controller/processor <NUM> includes ML model configuration component <NUM>, which may be representative of ML model configuration component <NUM> of <FIG>. Notably, while depicted as an aspect of controller/processor <NUM>, ML model configuration component <NUM> may be implemented additionally or alternatively in various other aspects of user equipment <NUM> in other implementations.

<FIG> depict aspects of data structures for a wireless communication network, such as wireless communications network <NUM> of <FIG>. In particular, <FIG> is a diagram <NUM> illustrating an example of a first subframe within a <NUM> (e.g., <NUM> NR) frame structure, <FIG> is a diagram <NUM> illustrating an example of DL channels within a <NUM> subframe, <FIG> is a diagram <NUM> illustrating an example of a second subframe within a <NUM> frame structure, and <FIG> is a diagram <NUM> illustrating an example of UL channels within a <NUM> subframe.

Further discussions regarding <FIG>, <FIG>, and <FIG>-3D are provided later in this disclosure.

Machine learning (ML) is generally the process of producing a predictive ML model (e.g., an artificial neural network, a tree, or other structures), which represents a generalized fit to a set of training data that is known a priori. Applying the trained model to new data produces inferences, which may be used to gain insights into the new data.

<FIG> illustrates an example network environment <NUM> in which a node <NUM> employs a predictive ML model <NUM>. As shown in <FIG>, network environment <NUM> includes node <NUM>, a training system <NUM>, and a training repository <NUM>, communicatively connected via network <NUM>. Node <NUM> may be a UE (e.g., such as the UE <NUM> in the wireless communications network <NUM>) or a BS (e.g., such as the BS <NUM> in the wireless communications network <NUM>). Network <NUM> may include a wireless network such as wireless communications network <NUM>, which may be a <NUM> NR network, a WiFi network, an long term evolution (LTE) network, and/or another type of network. While training system <NUM>, node <NUM>, and training repository <NUM> are illustrated as separate components in <FIG>, training system <NUM>, node <NUM>, and training repository <NUM> may be implemented on any number of computing systems, either as one or more standalone systems or in a distributed environment. Additionally, while training system <NUM>, node <NUM>, and training repository <NUM> are illustrated as communicating via network <NUM>, the training system <NUM>, node <NUM>, and training repository <NUM> may also communicate via a hardwired connection (or multiple hardwired connections) between the training system <NUM>, node <NUM>, and/or training repository <NUM>.

Training system <NUM> generally includes a predictive model training manager <NUM> that uses training data to generate the predictive ML model <NUM> for predicting output data based on input data. Predictive ML model <NUM> may be generated based, at least in part, on the information in training repository <NUM>. Training repository <NUM> may include training data obtained before and/or after deployment of node <NUM>. Node <NUM> may be trained in a simulated communication environment (e.g., in field testing or drive testing) prior to deployment of node <NUM>. After deployment, training repository <NUM> can be updated to include feedback associated with packet buffering durations used by node <NUM>. The training repository can also be updated with information from other BSs and/or other UEs, for example, based on learned experience by those BSs and/or UEs.

Predictive model training manager <NUM> may use the information in training repository <NUM> to determine predictive ML model <NUM> (e.g., algorithm). Predictive model training manager <NUM> may use various different types of machine learning algorithms to form predictive ML model <NUM>. Training system <NUM> may be located in or on node <NUM>, a BS in the network <NUM>, or a different entity that determines predictive ML model <NUM>. If located on a different entity, then predictive ML model <NUM> is provided to node <NUM>. Training repository <NUM> may be a storage device, such as a memory. Training repository <NUM> may be located in or on node <NUM>, training system <NUM>, or another entity in network <NUM>. Training repository <NUM> may be in cloud storage, for example. Training repository <NUM> may receive training information from node <NUM>, entities in network <NUM> (e.g., BSs and/or UEs in network <NUM>), the cloud, or other sources.

Predictive model training manager <NUM> may use any appropriate machine learning algorithm to form the predictive ML model <NUM>. In some non-limiting examples, the machine learning algorithm is a supervised learning algorithm, a deep learning algorithm, an artificial neural network algorithm, or other type of machine learning algorithm.

In some examples, the machine learning (e.g., used by the predictive model training manager <NUM> in the training system <NUM>) is performed using a deep convolutional network (DCN). DCNs are networks of convolutional networks, configured with additional pooling and normalization layers. DCNs have achieved state-of-the-art performance on many tasks. DCNs can be trained using supervised learning in which both the input and output targets are known for many exemplars and are used to modify the weights of the network by use of gradient descent methods. DCNs may be feed-forward networks. In addition, as described above, the connections from a neuron in a first layer of a DCN to a group of neurons in the next higher layer are shared across the neurons in the first layer. The feed-forward and shared connections of DCNs may be exploited for fast processing. The computational burden of a DCN may be much less, for example, than that of a similarly sized neural network that comprises recurrent or feedback connections.

In some examples, the machine learning (e.g., used by the predictive model training manager <NUM> in the training system <NUM>) is performed using a neural network. Neural networks may be designed with a variety of connectivity patterns. In feed-forward networks, information is passed from lower to higher layers, with each neuron in a given layer communicating to neurons in higher layers. A hierarchical representation may be built up in successive layers of a feed-forward network. Neural networks may also have recurrent or feedback (also called top-down) connections. In a recurrent connection, the output from a neuron in a given layer may be communicated to another neuron in the same layer. A recurrent architecture may be helpful in recognizing patterns that span more than one of the input data chunks that are delivered to the neural network in a sequence. A connection from a neuron in a given layer to a neuron in a lower layer is called a feedback (or top-down) connection. A network with many feedback connections may be helpful when the recognition of a high-level concept may aid in discriminating the particular low-level features of an input.

An artificial neural network, which may be composed of an interconnected group of artificial neurons (e.g., neuron models), is a computational device or represents a method performed by a computational device. These neural networks may be used for various applications and/or devices, such as Internet protocol (IP) cameras, Internet of things (IoT) devices, autonomous vehicles, and/or service robots. Individual nodes in the artificial neural network may emulate biological neurons by taking input data and performing simple operations on the data. The results of the simple operations performed on the input data are selectively passed on to other neurons. Weight values are associated with each vector and node in the network, and these values constrain how input data is related to output data. For example, the input data of each node may be multiplied by a corresponding weight value, and the products may be summed. The sum of the products may be adjusted by an optional bias, and an activation function may be applied to the result, yielding the node's output signal or "output activation. " The weight values may initially be determined by an iterative flow of training data through the network (e.g., weight values are established during a training phase in which the network learns how to identify particular classes by their typical input data characteristics).

Different types of artificial neural networks can be used to implement machine learning (e.g., used by the predictive model training manager <NUM> in the training system <NUM>), such as recurrent neural networks (RNNs), multilayer perceptron (MLP) neural networks, convolutional neural networks (CNNs), and the like. RNNs work on the principle of saving the output of a layer and feeding this output back to the input to help in predicting an outcome of the layer. In MLP neural networks, data may be fed into an input layer, and one or more hidden layers provide levels of abstraction to the data. Predictions may then be made on an output layer based on the abstracted data. MLPs may be particularly suitable for classification prediction problems where inputs are assigned a class or label.

Convolutional neural networks (CNNs) are a type of feed-forward artificial neural network. Convolutional neural networks may include collections of artificial neurons that each has a receptive field (e.g., a spatially localized region of an input space) and that collectively tile an input space. Convolutional neural networks have numerous applications. In particular, CNNs have broadly been used in the area of pattern recognition and classification. In layered neural network architectures, the output of a first layer of artificial neurons becomes an input to a second layer of artificial neurons, the output of a second layer of artificial neurons becomes an input to a third layer of artificial neurons, and so on. Convolutional neural networks may be trained to recognize a hierarchy of features. Computation in convolutional neural network architectures may be distributed over a population of processing nodes, which may be configured in one or more computational chains. These multi-layered architectures may be trained one layer at a time and may be fine-tuned using back propagation.

As noted above, machine learning may be employed to generate a predictive ML model that may be used to form inferences about input data. In other words, the ML model may be used to predict output data based on data input into the ML model. An ML model may be composed of a model structure and a set of parameters. The model structure may include, for example, a graph structure with vectors or matrices, and the set of parameters may include a set of weights with specific values for the vectors or matrices. In some cases, the set of parameters may be location or configuration specific.

In some cases, an ML model may be used to perform a neural network function (NNF) in a wireless communication network, such as the wireless communications network <NUM> illustrated in <FIG>, and may be configured to perform one or more ML-based wireless communications management procedures within the wireless communication network. In some cases, different NNFs may correspond to different ML-based wireless communications management procedures. For example, these ML-based wireless communications management procedures may include cell reselection procedures, idle or inactive mode measurement procedures, radio resource management (RRM) measurement procedures, and the like. Each different NNF may be identified by an NNF identifier (ID), which corresponds to a particular ML-based wireless communications management procedure.

In some cases, different ML models may be employed to perform the ML-based wireless communications management procedures. In other words, multiple ML models may be associated with a particular NNF and used to perform a particular ML-based wireless communications management procedure. In some cases, the particular ML model to be used to execute an NNF may be indicated via a ML model ID.

As noted above, NNFs may be used within wireless communication networks to perform certain ML-based wireless communications management procedures. In some cases, it may be advantageous for a base station of a wireless communication network to manage these NNFs and the configuration and use of corresponding ML models at user equipments within the wireless communication network. However, there currently does not exist a way for a base station to configure a UE to perform an NNF using one or more particular ML models to execute certain ML-based wireless communications management procedures. Thus, aspects of the present disclosure provide techniques to facilitate the configuration and use of ML models within a wireless communication network to perform certain NNFs.

<FIG> is a call flow diagram illustrating example operations <NUM> between a BS <NUM> and a UE <NUM> for configuring and using NNFs and corresponding ML models to perform one or more ML-based wireless communications management procedures. In some cases, the BS <NUM> may be an example of the BS <NUM> in the wireless communications network <NUM> illustrated in <FIG>. Additionally, the UE <NUM> may be an example of the UE <NUM> illustrated in <FIG>. Further, in some cases, a Universal Mobile Telecommunications System (UMTS) air interface (Uu interface) may be established to facilitate communication between the BS <NUM> and UE <NUM>; however, in other aspects, a different type of interface may be used.

As shown, the BS <NUM> may include a number of logical entities for performing one or more of the operations <NUM>, such as a centralized unit control plane (CU-CP) <NUM> for managing the radio resource control (RRC) layer and packet data convergence protocol (PDCP) layer of the BS <NUM>, a centralized unit ML plane (CU-MLP) <NUM> for managing machine learning functions, and a distributed unit (DU) <NUM> for managing the radio link control (RLC) layer, the media access control (MAC) layer, and parts of the physical (PHY) layer of the BS <NUM>. While the CU-MLP <NUM> is illustrated in <FIG> as being part of the BS <NUM>, separate from the CU-CP <NUM>, the CU-MLP <NUM> may alternatively be implemented as part of the CU-CP or as (a portion of) a network entity separate from the BS <NUM>.

Operations <NUM> begin at <NUM> with the UE <NUM> transmitting, to the BS <NUM>, UE capability information indicating at least one radio capability of the UE and at least one ML capability of the UE. In some cases, the UE <NUM> may transmit the UE capability information during a radio resource control (RRC) setup procedure in an RRC connection setup message. In some cases, the UE capability information may be received by the CU-CP <NUM> of the BS <NUM>.

In some cases, the radio capability of the UE may indicate a capability of the UE to perform one or more wireless communications management procedures, which may be ML-based. For example, the radio capability of the UE may indicate at least one of a capability to perform an (ML-based) cell reselection procedure, a capability to perform an (ML-based) idle or inactive mode measurement procedure, a capability to perform an (ML-based) radio resource management (RRM) measurement procedure, a capability to perform an (ML-based) radio link monitoring (RLM) procedure, a capability to perform an (ML-based) channel state information (CSI) measurement procedure, a capability to perform an (ML-based) precoding matrix indicator (PMI), rank indicator (RI), and channel quality indicator (CQI) feedback procedure, a capability to perform an (ML-based) radio link failure (RLF) and beam failure recovery (BFR) procedure, or a capability to perform an (ML-based) RRM relaxation procedure.

In some cases, the at least one ML capability of the UE may indicate one or more capabilities supported by the UE for performing ML. For example, the ML capability of the UE may indicate at least one of an ML training capability, an ML inference capability, a processing capability, one or more supported ML model formats, one or more supported ML libraries, or an indication of one or more locally cached ML models.

At <NUM>, the CU-CP <NUM> of the BS <NUM> determines whether to use ML functionality to perform one or more wireless communications management procedures. For example, in some cases, the CU-CP <NUM> of the BS <NUM> may select an ML-based wireless communications management procedure to be used at the UE and determine the at least one NNF for performing at least a portion of the selected ML-based wireless communications management procedure. In other words, the BS <NUM> may determine which NNFs, if any, are supported by the UE at <NUM> and may generate a requested NNF list.

Thereafter, at <NUM>, the CU-CP <NUM> sends a context setup request message to the CU-MLP <NUM> to establish a context for the UE <NUM> for using the at least one NNF. The context setup request message includes an indication of the at least one NNF determined by the CU-CP <NUM> (e.g., the requested NNF list) and a request to the CU-MLP <NUM> to support the use of the at least one NNF. While <FIG> illustrates the use of a context setup request message for requesting that the CU-MLP <NUM> support the use of the at least one NNF, it should be understood that other or additional signaling procedures may be used.

The context setup request message may include the at least one ML capability of the UE. The CU-MLP <NUM> may then select at least one ML model for use in the at least one NNF to perform at least the portion of the ML-based wireless communications management procedure. In some cases, the CU-MLP <NUM> may select the at least one ML model based, at least in part, on the at least one ML capability of the UE. Additionally, in some cases, the CU-MLP <NUM> may select the at least one ML model based on at least one of a cell ID, gNB ID, or UE context information. In some cases, the UE context information may indicate such information as a UE type, a data radio bearer (DRB) configuration, and/or an antenna switching (AS) configuration.

Thereafter, at <NUM>, the CU-MLP <NUM> sends a context setup response message to the CU-CP <NUM>. The context setup response message may provide an indication that the UE context has been successfully set up for the at least one NNF indicated in the context setup request message (e.g., an accepted NNF list). Additionally, the context setup response message may provide an indication of the at least one ML model selected for the at least one NNF to perform at least the portion of the ML-based wireless communications management procedure. In some cases, the indication of the at least one ML model may comprise an ML model ID.

Thereafter, as illustrated at <NUM>, the BS <NUM> (e.g., via the CU-CP <NUM>) transmits, to the UE <NUM> based on the UE capability information received at <NUM>, ML configuration information. In some cases, the BS <NUM> may transmit the ML configuration information in an RRC reconfiguration message. The ML configuration information may include an indication of the at least one NNF (e.g., the accepted NNF list) and the at least one ML model corresponding to the at least one NNF. In some cases, the at least one NNF is indicated by an NNF ID and the at least one ML model is indicated by an ML model ID. As noted, the at least one ML model may be associated with a model structure and one or more sets of parameters (e.g., weights, biases, and/or activation functions). In some cases, the ML model ID may indicate the model structure associated with the at least one ML model, while the one or more sets of parameters may be indicated in the ML configuration information by a parameter set ID.

In some cases, the ML model and ML model structure may be associated with multiple sets of parameters (e.g., the one or more sets of parameters include multiple sets of parameters). In such cases, each set of parameters may be valid for use with the model structure for a particular geographic area, such as a cell, or a particular configuration. In other words, depending on, for example, the particular geographic area or configuration of the UE <NUM>, different sets of parameters may be used with the model structure. This may allow the UE <NUM> to use one model structure for performing the ML-based wireless communications management procedure while adaptively changing the set of parameters used with the model structure depending on the particular geographic area or configuration of the UE <NUM>.

Thereafter, as illustrated at <NUM>, in response to receiving the RRC reconfiguration message including the ML configuration information, the UE <NUM> transmits an RRC reconfiguration complete message to the BS <NUM>, which may indicate that the UE <NUM> successfully received the ML configuration information.

As illustrated at <NUM>, the UE <NUM> may then download the at least one ML model corresponding to the at least one NNF if the at least one ML model is not locally stored at the UE <NUM> already. For example, in some cases, if the ML model is not locally stored by the UE <NUM>, the UE <NUM> may receive, from the BS <NUM> at least one of the ML model structure corresponding to the at least one ML model or the one or more sets of parameters (e.g., weights, biases, and/or activation functions) associated with the ML model structure.

Thereafter, as illustrated at <NUM>, once the UE <NUM> has received the ML structure and/or one or more parameters associated with the at least one ML model, the UE <NUM> transmits a message to the BS <NUM>, indicating that the at least one ML model is ready to be used by the UE <NUM>. In some cases, the message may be received by the CU-CP <NUM> and, thereafter, forwarded to the CU-MLP <NUM>, as illustrated at <NUM>.

Thereafter, at some point in the future, the BS <NUM> may decide that the UE <NUM> should use the at least one NNF and corresponding at least one ML model to perform at least the portion of the ML-based wireless communications management procedure. In such cases, the BS <NUM> transmits a signal to the UE <NUM>, activating use of the at least one ML model to perform the at least the portion of the ML-based wireless communications management procedure. In some cases, the signal activating the use of the at least one ML model may be transmitted by the BS <NUM> (e.g., via the CU-CP <NUM> or CU-MLP <NUM>) in a media access control-control element (MAC-CE) or in RRC signaling.

Thereafter, the UE <NUM> may perform the ML-based wireless communications management procedure using the at least one ML model based on the activation signal. In some cases, performing the ML-based wireless communications management procedure may include inputting one or more input variables to the at least one ML model and obtaining an output from the at least one ML model based on the one or more input variables.

As noted above, the at least one ML model may be associated with a model structure and one or more sets of parameters. When performing the ML-based wireless communications management procedure, the UE <NUM> may determine a particular set of parameters to use in combination with the ML structure to process the one or more input variables. This determination may be based on, for example, a particular geographic area, such as a cell, or configuration of the UE <NUM>. For example, in some cases, when the UE <NUM> is in a first cell, the UE <NUM> may select a first set of parameters for use with the model structure to perform the ML-based wireless communications management procedure. In other cases, when the UE <NUM> is in a second cell, the UE <NUM> may select a second set of parameters for use with the model structure to perform the ML-based wireless communications management procedure.

As an example, the ML-based wireless communications management procedure may include a cell reselection and idle/inactive mode measurement procedure. In such cases, to perform the cell reselection and idle/inactive mode measurement procedure, the UE <NUM> may input one or more input variables into a model structure of an ML model specifically trained for cell reselection and idle/inactive mode measurements. The UE <NUM> may also select a set of parameters to use with the model structure (e.g., weights, biases, and/or activation functions), as described above. In some cases, the input variables to the ML model may include, for example, serving cell measurements (e.g., reference signal received power (RSRP) measurements associated with the BS <NUM>), neighboring cell measurements, services specified by the UE <NUM>. The at least one ML model may take into account the serving cell measurements, the neighboring cell measurements, and the services specified by the UE <NUM>, and provide an output, indicating a target cell to reselect and/or target cells to perform the idle/inactive mode measurements.

In some cases, NNFs and corresponding ML models may be used in both the BS <NUM> and the UE <NUM>. For example, in view of the description above, the BS <NUM> may configure the UE <NUM> with a particular NNF and corresponding first ML model for performing at least a portion of one or more ML-based wireless communications management procedure. However, the BS <NUM> may also configure itself with a second ML model for performing at least a portion of the ML-based wireless communications management procedure. In some cases, the first ML model and the second ML model may comprise matched ML models whereby the output of one of the ML models is used as an input to the other ML model.

For example, returning to <FIG>, after setting up the context for the UE <NUM> associated with the at least one NNF and selecting the at least one ML model (e.g., the first ML model) corresponding to the at least one NNF, the CU-MLP <NUM> may additionally determine a second ML model for the BS <NUM> to perform at least a portion of the ML-based wireless communications management procedure. In some cases, the CU-MLP <NUM> may determine the other ML model for the BS <NUM> based on ML capability information associated with the DU <NUM>. As shown at <NUM>, the CU-MLP <NUM> may then send an ML model setup request message to the DU <NUM>, requesting that the DU <NUM> set up the second ML model for performing at least a portion of the ML-based wireless communications management procedure. Thereafter, once the second ML model has been set up, the DU <NUM> sends an ML model setup response message to the CU-MLP <NUM> at <NUM>, indicating that the setup of the second ML model is complete.

As noted above, in some cases, the first ML model configured for use by the UE <NUM> may be matched with the second ML model configured for use by the BS <NUM>. In other words, an output of the first ML model used by the UE <NUM> may be used as an input to the second ML model used by the BS <NUM>.

The BS <NUM> transmits a signal activating the use of the first ML model (e.g., the at least one ML model, described above) for performing the ML-based wireless communications management procedure. The BS <NUM> may also activate use of the second ML model at the BS <NUM>. Thereafter, the UE <NUM> may perform the ML-based wireless communications management procedure by inputting one or more input variables to the first ML model and obtaining an output from the first ML model based on the one or more input variables. The UE <NUM> may then transmit the output of the first ML model, which may be received by the BS <NUM>. The BS may then input the output received from the UE to the second ML model at the BS <NUM> and obtain an output to the second ML model at the BS.

An example use case for this matched ML model configuration between the first ML model and the second ML model may be for performing certain functions, such as data compression for reporting channel state information (CSI) feedback. For example, in cases where a wireless communication network employs a large number of antennas, such as in massive multiple-input, multiple-output (MIMO) scenarios, CSI feedback may be very large. In such cases, the first ML model may be used by the UE <NUM> to compress the CSI feedback to conserve time and frequency resources on a radio channel when the UE <NUM> transmits the CSI feedback to the BS <NUM>. Once the CSI feedback is received by the BS <NUM>, the BS <NUM> may use the second ML model to decompress the CSI feedback.

<FIG> is a flow diagram illustrating example operations <NUM> for wireless communication. The operations <NUM> may be performed, for example, by a BS (e.g., such as the BS <NUM> in the wireless communications network <NUM> of <FIG>) and/or another network entity (e.g., a device implementing the CU-CP <NUM> or CU-MLP <NUM>) for determining NNFs and configuring and using corresponding ML models for performing one or more ML-based wireless communications management procedures. The operations <NUM> may be implemented as software components that are executed and run on one or more processors (e.g., controller/processor <NUM> of <FIG>). Further, the transmission and reception of signals by the BS (and/or other network entity) in operations <NUM> may be enabled, for example, by one or more transceivers and antennas (e.g., transceivers <NUM> and antennas <NUM> of <FIG>). In certain aspects, the transmission and/or reception of signals by the BS (and/or other network entity) may be implemented via a bus interface of one or more processors (e.g., controller/processor <NUM>, including the ML model configuration component <NUM>) obtaining and/or outputting signals.

Operations <NUM> begin at block <NUM> with receiving, from a user equipment (UE), UE capability information indicating at least one radio capability of the UE and at least one machine learning (ML) capability of the UE.

In block <NUM>, the BS or other network entity transmits, to the UE based on the UE capability information, ML configuration information indicating at least one neural network function (NNF) and at least one ML model corresponding to the at least one NNF.

In some cases, in the ML configuration information, the at least one NNF is indicated by an NNF identifier (ID) and the at least one ML model is indicated by an ML model ID.

The at least one NNF comprises at least a portion of an ML-based wireless communications management procedure, and the ML model is configured to perform the at least the portion of the ML-based wireless communications management procedure.

In some cases, the ML-based wireless communications management procedure comprises at least one of: a cell reselection procedure, an idle or inactive mode measurement procedure, a radio resource management (RRM) measurement procedure, a radio link monitoring (RLM) procedure, a channel state information (CSI) measurement procedure, a precoding matrix indicator (PMI), rank indicator (RI), and channel quality indicator (CQI) feedback procedure, a radio link failure (RLF) and beam failure recovery (BFR) procedure, or a power saving procedure.

In some cases, operations <NUM> may further include the BS or other network entity selecting the ML-based wireless communications management procedure to be used at the UE. In some cases, operations <NUM> may further include the BS or other network entity determining the at least one NNF corresponding to the selected ML-based wireless communications management procedure. In some cases, operations <NUM> may further include the BS or other network entity selecting the at least one ML model for performing the at least the portion of the ML-based wireless communications management procedure based, at least in part, on at least one of the radio capability of the UE or the ML capability of the UE.

In some cases, operations <NUM> may further include the BS or other network entity establishing a context for the UE based, at least in part, on the at least one NNF, the at least one ML model, and the at least one ML capability of the UE.

In some cases, the at least one ML model for performing the at least the portion of the ML-based wireless communications management procedure comprises an ML model structure and one or more sets of parameters associated with the ML model structure. In some cases, the one or more sets of parameters comprise a set of weights, which may be indicated in the ML configuration information by a parameter set ID. In some cases, operations <NUM> may further include the BS or other network entity transmitting at least one of the ML model structure or the one or more sets of parameters associated with the ML model structure, to the UE.

In some cases, operations <NUM> may further include the BS or other network entity receiving, from the UE after transmitting the ML configuration information, a signal indicating that the at least one ML model is ready to be used. Operations <NUM> include the BS or other network entity transmitting, to the UE, a signal activating use of the at least one ML model to perform the at least the portion of the ML-based wireless communications management procedure. In some cases, transmitting the signal activating the use of the at least one ML model comprises transmitting the signal activating the use of the at least one ML model in a media access control control element (MAC-CE) or in radio resource control (RRC) signaling.

In some cases, operations <NUM> may further include the BS or other network entity activating use of a second ML model at the BS or other network entity, receiving a UE output of the at least one ML model from the UE, inputting the UE output to the second ML model at the BS or other network entity, and obtaining an output to the second ML model at the BS or other network entity.

In some cases, receiving the UE capability information comprises receiving the UE capability information in a radio resource control (RRC) connection setup message. Additionally, in some cases, transmitting the ML configuration information comprises transmitting the ML configuration information in a radio resource control (RRC) reconfiguration message.

In some cases, the at least one radio capability of the UE comprises at least one of: a capability to perform an ML-based cell reselection procedure, a capability to perform an ML-based idle or inactive mode measurement procedure, a capability to perform an ML-based radio resource management (RRM) measurement procedure, a capability to perform an ML-based radio link monitoring (RLM) procedure, a capability to perform an ML-based channel state information (CSI) measurement procedure, a capability to perform an ML-based precoding matrix indicator (PMI), rank indicator (RI), and channel quality indicator (CQI) feedback procedure, a capability to perform an ML-based radio link failure (RLF) and beam failure recovery (BFR) procedure, or a capability to perform an ML-based RRM relaxation procedure.

In some cases, the at least one ML capability of the UE comprises at least one of: an ML training capability, an ML inference capability, a processing capability, one or more supported ML model formats, one or more supported ML libraries, or an indication of one or more locally cached ML models.

<FIG> is a flow diagram illustrating example operations <NUM> for wireless communication, in accordance with certain aspects of the present disclosure. The operations <NUM> may be performed, for example, by a UE (e.g., such as the UE <NUM> in the wireless communications network <NUM> of <FIG>) for determining NNFs and configuring and using corresponding ML models for performing one or more ML-based wireless communications management procedures. The operations <NUM> may be complementary to the operations <NUM> performed by the BS and/or other network entity. The operations <NUM> may be implemented as software components that are executed and run on one or more processors (e.g., controller/processor <NUM> of <FIG>). Further, the transmission and reception of signals by the UE in operations <NUM> may be enabled, for example, by one or more transceivers and antennas (e.g., transceivers <NUM> and antennas <NUM> of <FIG>). In certain aspects, the transmission and/or reception of signals by the UE may be implemented via a bus interface of one or more processors (e.g., controller/processor <NUM>, including the ML model configuration component <NUM>) obtaining and/or outputting signals.

Operations <NUM> begin at block <NUM> with transmitting, to a base station (BS) and/or other network entity, UE capability information indicating at least one radio capability of the UE and at least one machine learning (ML) capability of the UE.

In block <NUM>, the UE receives, from the BS or other network entity based on the UE capability information, ML configuration information indicating at least one neural network function (NNF) and at least one ML model corresponding to the at least one NNF.

In some cases, in the ML configuration information the at least one NNF is indicated by an NNF identifier (ID) and the at least one ML model is indicated by an ML model ID. The at least one NNF comprises at least a portion of an ML-based wireless communications management procedure, and the ML model is configured to perform the at least the portion of the ML-based wireless communications management procedure.

In some cases, the at least one ML model for performing the ML-based wireless communications management procedure comprises an ML model structure and one or more sets of parameters associated with the ML model structure. In some cases, the one or more sets of parameters comprise a set of weights, which may be indicated in the ML configuration information by a parameter set ID. In some cases, operations <NUM> further include the UE receiving (indication(s) of) at least one of the ML model structure or the one or more sets of parameters associated with the ML model structure, from the BS or other network entity.

In some cases, operations <NUM> further include the UE transmitting, to the BS or other network entity after receiving the (indication(s) of) at least one of the ML model structure or the one or more sets of parameters, a signal indicating that the at least one ML model is ready to be used.

Operations <NUM> further include the UE receiving, from the BS or other network entity, a signal activating use of the at least one ML model to perform the at least the portion of the ML-based wireless communications management procedure. In some cases, receiving the signal activating the use of the at least one ML model comprises receiving the signal activating the use of the at least one ML model in a media access control control element (MAC-CE) or in radio resource control (RRC) signaling.

In some cases, operations <NUM> further include the UE performing the ML-based wireless communications management procedure using the at least one ML model based on the activation signaling. In some cases, performing the ML-based wireless communications management procedure comprises: inputting one or more input variables to the at least one ML model, obtaining an output from the at least one ML model based on the one or more input variables, and transmitting the output of the at least one ML model to the BS or other network entity.

In some cases, transmitting the UE capability information comprises transmitting the UE capability information during a radio resource control (RRC) connection setup procedure. In some cases, receiving the ML configuration information comprises receiving the ML configuration information in a radio resource control (RRC) reconfiguration message.

<FIG> is a flow diagram illustrating example operations <NUM> for wireless communication. The operations <NUM> may be performed, for example, by a first network entity (e.g., device implementing the CU-MLP <NUM>) for determining NNFs and configuring and using corresponding ML models for performing one or more ML-based wireless communications management procedures. The operations <NUM> may be implemented as software components that are executed and run on one or more processors. Further, the transmission and reception of signals by the network entity in operations <NUM> may be enabled, for example, by one or more transceivers and antennas. In certain aspects, the transmission and/or reception of signals by the network entity may be implemented via a bus interface of one or more processors obtaining and/or outputting signals.

Operations <NUM> begin in block <NUM> with receiving, from a second network entity, a first signal including a first indication of at least one machine learning (ML) capability of a user equipment (UE) and a second indication of at least one neural network function (NNF) corresponding to an ML-based wireless communications management procedure associated with the UE.

In block <NUM>, the first network entity selects at least one ML model for performing at least a portion of the ML-based wireless communications management procedure based, at least in part, on the at least one ML capability of the UE.

In block <NUM>, the first network entity transmits a second signal, to the second network entity, including ML configuration information indicating the at least one NNF and the at least one ML model.

In some cases, in the ML configuration information: the at least one NNF is indicated by an NNF identifier (ID), and the at least one ML model is indicated by an ML model ID.

In some cases, the at least one ML model comprises an ML model structure and one or more sets of parameters associated with the ML model structure.

In some cases, operations <NUM> further include determining the ML model structure and the one or more sets of parameters associated with the ML model structure.

In some cases, the one or more sets of parameters comprise at least a set of weights, and the one or more parameters are indicated in the ML configuration information by a parameter set ID.

In some cases, operations <NUM> further include transmitting a third signal, to a third network entity, including an indication of the at least one NNF and a second ML model corresponding to the at least one NNF for performing at least another portion of the ML-based wireless communications management procedure.

In some cases, the first network entity comprises a centralized unit machine learning plane (CU-MLP) entity, the second network entity comprises a centralized unit control plane (CU-CP) entity, and the third network entity comprises a distributed unit (DU) entity. In some cases, the CU-CP entity is a part of a base station, and the CU-MLP entity is at least a portion of an entity separate from the base station.

In some cases, operations <NUM> further include receiving a third signal, forwarded by the second network entity from the UE, indicating that the at least one ML model is ready to be used.

In some cases, operations <NUM> further include establishing a context for the UE for the at least one NNF. In such cases, the first signal comprises a context setup request message and the second signal comprises a context setup response message.

In some cases, operations <NUM> further include transmitting, to the UE, a third signal activating use of the at least one ML model to perform the at least the portion of the ML-based wireless communications management procedure. In some cases, transmitting the third signal activating the use of the at least one ML model comprises transmitting the third signal activating the use of the at least one ML model in a media access control control element (MAC-CE) or in radio resource control (RRC) signaling.

<FIG> depicts an example communications device <NUM> that includes various components operable, configured, or adapted to perform operations for the techniques disclosed herein, such as the operations depicted and described with respect to <FIG> and <NUM>. In some examples, communications device <NUM> may be a base station <NUM> as described, for example with respect to <FIG> and <FIG>, or other network entity, such as the CU-CP <NUM> or the CU-MLP <NUM> (e.g., when implemented as a standalone device).

Communications device <NUM> includes a processing system <NUM> coupled to a transceiver <NUM> (e.g., a transmitter and/or a receiver). Transceiver <NUM> is configured to transmit (or send) and receive signals for the communications device <NUM> via an antenna <NUM>, such as the various signals as described herein. Processing system <NUM> may be configured to perform processing functions for communications device <NUM>, including processing signals received and/or to be transmitted by communications device <NUM>.

Processing system <NUM> includes one or more processors <NUM> coupled to a computer-readable medium/memory <NUM> via a bus <NUM>. In certain aspects, computer-readable medium/memory <NUM> is configured to store instructions (e.g., computer-executable code) that when executed by the one or more processors <NUM>, cause the one or more processors <NUM> to perform the operations illustrated in <FIG> and <NUM>, or other operations for performing the various techniques discussed herein for determining NNFs and configuring and using corresponding ML models for performing one or more ML-based wireless communications management procedures.

In the depicted example, computer-readable medium/memory <NUM> stores code <NUM> for receiving, code <NUM> for transmitting, code <NUM> for selecting, code <NUM> for determining, code <NUM> for establishing, code <NUM> for activating, code <NUM> for inputting, and code <NUM> for obtaining.

In the depicted example, the one or more processors <NUM> include circuitry configured to implement the code stored in the computer-readable medium/memory <NUM>, including circuitry <NUM> for receiving, circuitry <NUM> for transmitting, circuitry <NUM> for selecting, circuitry <NUM> for determining, circuitry <NUM> for establishing, circuitry <NUM> for activating, circuitry <NUM> for inputting, and circuitry <NUM> for obtaining.

Various components of communications device <NUM> may provide means for performing the methods described herein, including with respect to <FIG> and <NUM>.

In some examples, means for transmitting or sending (or means for outputting for transmission) may include the transceivers <NUM> and/or antenna(s) <NUM> of the base station <NUM> illustrated in <FIG> and/or transceiver <NUM> and antenna <NUM> of the communications device <NUM> in <FIG>.

In some examples, means for receiving (or means for obtaining) may include the transceivers <NUM> and/or antenna(s) <NUM> of the base station illustrated in <FIG> and/or transceiver <NUM> and antenna <NUM> of the communications device <NUM> in <FIG>.

In some examples, means for selecting, means for determining, means for establishing, means for activating, means for inputting, and means for obtaining may include various processing system components, such as: the one or more processors <NUM> in <FIG>, or aspects of the base station <NUM> depicted in <FIG>, including receive processor <NUM>, transmit processor <NUM>, TX MIMO processor <NUM>, and/or controller/processor <NUM> (including ML model configuration component <NUM>).

Notably, <FIG> is an example, and many other examples and configurations of communications device <NUM> are possible.

<FIG> depicts an example communications device <NUM> that includes various components operable, configured, or adapted to perform operations for the techniques disclosed herein, such as the operations depicted and described with respect to <FIG> and <FIG>. In some examples, communications device <NUM> may be a user equipment <NUM> as described, for example with respect to <FIG> and <FIG>.

Processing system <NUM> includes one or more processors <NUM> coupled to a computer-readable medium/memory <NUM> via a bus <NUM>. In certain aspects, computer-readable medium/memory <NUM> is configured to store instructions (e.g., computer-executable code) that when executed by the one or more processors <NUM>, cause the one or more processors <NUM> to perform the operations illustrated in <FIG> and <FIG>, or other operations for performing the various techniques discussed herein for determining NNFs and configuring and using corresponding ML models for performing one or more ML-based wireless communications management procedures.

In the depicted example, computer-readable medium/memory <NUM> stores code <NUM> for transmitting, code <NUM> for receiving, and code <NUM> for performing.

In the depicted example, the one or more processors <NUM> include circuitry configured to implement the code stored in the computer-readable medium/memory <NUM>, including circuitry <NUM> for transmitting, circuitry <NUM> for receiving, and circuitry <NUM> for performing.

Various components of communications device <NUM> may provide means for performing the methods described herein, including with respect to <FIG> and <FIG>.

In some examples, means for transmitting or sending (or means for outputting for transmission) may include the transceivers <NUM> and/or antenna(s) <NUM> of the user equipment <NUM> illustrated in <FIG> and/or transceiver <NUM> and antenna <NUM> of the communications device <NUM> in <FIG>.

In some examples, means for receiving (or means for obtaining) may include the transceivers <NUM> and/or antenna(s) <NUM> of the user equipment <NUM> illustrated in <FIG> and/or transceiver <NUM> and antenna <NUM> of the communications device <NUM> in <FIG>.

In some examples, means for performing may include various processing system components, such as: the one or more processors <NUM> in <FIG>, or aspects of the user equipment <NUM> depicted in <FIG>, including receive processor <NUM>, transmit processor <NUM>, TX MIMO processor <NUM>, and/or controller/processor <NUM> (including ML model configuration component <NUM>).

The techniques and methods described herein may be used for various wireless communications networks (or wireless wide area network (WWAN)) and radio access technologies (RATs). While aspects may be described herein using terminology commonly associated with <NUM>, <NUM>, and/or <NUM> (e.g., <NUM> new radio (NR)) wireless technologies, aspects of the present disclosure may likewise be applicable to other communication systems and standards not explicitly mentioned herein.

<NUM> wireless communication networks may support various advanced wireless communication services, such as enhanced mobile broadband (eMBB), millimeter wave (mmWave), machine type communications (MTC), and/or mission critical targeting ultra-reliable, low-latency communications (URLLC). These services, and others, may include latency and reliability specifications.

Returning to <FIG>, various aspects of the present disclosure may be performed within the example wireless communications network <NUM>.

In 3GPP, the term "cell" can refer to a coverage area of a NodeB and/or a narrowband subsystem serving this coverage area, depending on the context in which the term is used. In NR systems, the term "cell" and BS, next generation NodeB (gNB or gNodeB), access point (AP), distributed unit (DU), carrier, or transmission reception point may be used interchangeably.

A macro cell may generally cover a relatively large geographic area (e.g., several kilometers in radius) and may allow unrestricted access by UEs with service subscription. A pico cell may cover a relatively small geographic area (e.g., a sports stadium) and may allow unrestricted access by UEs with service subscription. A femto cell may cover a relatively small geographic area (e.g., a home) and may allow restricted access by UEs having an association with the femto cell (e.g., UEs in a Closed Subscriber Group (CSG) and UEs for users in the home). A BS for a femto cell may be referred to as a femto BS, home BS, or a home NodeB.

Base stations <NUM> configured for <NUM> LTE (collectively referred to as Evolved Universal Mobile Telecommunications System (UMTS) Terrestrial Radio Access Network (E-UTRAN)) may interface with the EPC <NUM> through first backhaul links <NUM> (e.g., an S1 interface). Base stations <NUM> configured for <NUM> (e.g., <NUM> NR or Next Generation RAN (NG-RAN)) may interface with 5GC <NUM> through second backhaul links <NUM>. Base stations <NUM> may communicate directly or indirectly (e.g., through the EPC <NUM> or 5GC <NUM>) with each other over third backhaul links <NUM> (e.g., X2 interface). Third backhaul links <NUM> may generally be wired or wireless.

Small cell <NUM>' may operate in a licensed and/or an unlicensed frequency spectrum. Small cell <NUM>', employing NR in an unlicensed frequency spectrum, may boost coverage to and/or increase capacity of the access network.

Some base stations, such as a gNB may operate in a traditional sub-<NUM> spectrum, in millimeter wave (mmWave) frequencies, and/or near mmWave frequencies in communication with the UE <NUM>. When the gNB operates in mmWave or near mmWave frequencies, the gNB may be referred to as a mmWave base station.

The communication links <NUM> between base stations <NUM> and, for example, UEs <NUM>, may be through one or more carriers. For example, base stations <NUM> and UEs <NUM> may use spectrum up to Y MHz (e.g., <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, and other MHz) bandwidth per carrier allocated in a carrier aggregation of up to a total of Yx MHz (x component carriers) used for transmission in each direction. A primary component carrier may be referred to as a primary cell (PCell), and a secondary component carrier may be referred to as a secondary cell (SCell).

Wireless communications network <NUM> further includes a Wi-Fi access point (AP) <NUM> in communication with Wi-Fi stations (STAs) <NUM> via communication links <NUM> in, for example, a <NUM> and/or <NUM> unlicensed frequency spectrum. When communicating in an unlicensed frequency spectrum, the STAs <NUM> and AP <NUM> may perform a clear channel assessment (CCA) prior to communicating in order to determine whether the channel is available.

D2D communication may be through a variety of wireless D2D communications systems, such as for example, FlashLinQ, WiMedia, Bluetooth, ZigBee, Wi-Fi based on the IEEE <NUM> standard, <NUM> (e.g., LTE), or <NUM> (e.g., NR), to name a few options.

EPC <NUM> may include a Mobility Management Entity (MME) <NUM>, other MMEs <NUM>, a Serving Gateway <NUM>, a Multimedia Broadcast Multicast Service (MBMS) Gateway <NUM>, a Broadcast Multicast Service Center (BM-SC) <NUM>, and a Packet Data Network (PDN) Gateway <NUM>. MME <NUM> may be in communication with a Home Subscriber Server (HSS) <NUM>. MME <NUM> is the control node that processes the signaling between the UEs <NUM> and the EPC <NUM>. Generally, MME <NUM> provides bearer and connection management.

Generally, user Internet protocol (IP) packets are transferred through Serving Gateway <NUM>, which itself is connected to PDN Gateway <NUM>. PDN Gateway <NUM> provides UE IP address allocation as well as other functions. PDN Gateway <NUM> and the BM-SC <NUM> are connected to the IP Services <NUM>, which may include, for example, the Internet, an intranet, an IP Multimedia Subsystem (IMS), a PS Streaming Service, and/or other IP services.

BM-SC <NUM> may provide functions for MBMS user service provisioning and delivery. BM-SC <NUM> may serve as an entry point for content provider MBMS transmission, may be used to authorize and initiate MBMS bearer services within a public land mobile network (PLMN), and may be used to schedule MBMS transmissions. MBMS Gateway <NUM> may be used to distribute MBMS traffic to the base stations <NUM> belonging to a Multicast Broadcast Single Frequency Network (MBSFN) area broadcasting a particular service, and may be responsible for session management (start/stop) and for collecting eMBMS related charging information.

5GC <NUM> may include an Access and Mobility Management Function (AMF) <NUM>, other AMFs <NUM>, a Session Management Function (SMF) <NUM>, and a User Plane Function (UPF) <NUM>. AMF <NUM> may be in communication with a Unified Data Management function (UDM) <NUM>.

AMF <NUM> is generally the control node that processes the signaling between UEs <NUM> and 5GC <NUM>. Generally, AMF <NUM> provides QoS flow and session management.

All user Internet protocol (IP) packets are transferred through UPF <NUM>, which is connected to the IP Services <NUM>, and which provides UE IP address allocation as well as other functions for 5GC <NUM>. IP Services <NUM> may include, for example, the Internet, an intranet, an IP Multimedia Subsystem (IMS), a PS Streaming Service, and/or other IP services.

Returning to <FIG>, various example components of BS <NUM> and UE <NUM> (e.g., the wireless communications network <NUM> of <FIG>) are depicted, which may be used to implement aspects of the present disclosure.

At BS <NUM>, a transmit processor <NUM> may receive data from a data source <NUM> and control information from a controller/processor <NUM>. The control information may be for the physical broadcast channel (PBCH), physical control format indicator channel (PCFICH), physical hybrid ARQ indicator channel (PHICH), physical downlink control channel (PDCCH), group common PDCCH (GC PDCCH), and others. The data may be for the physical downlink shared channel (PDSCH), in some examples.

A medium access control (MAC)-control element (MAC-CE) is a MAC layer communication structure that may be used for control command exchange between wireless nodes. The MAC-CE may be carried in a shared channel such as a physical downlink shared channel (PDSCH), a physical uplink shared channel (PUSCH), or a physical sidelink shared channel (PSSCH).

Processor <NUM> may process (e.g., encode and symbol map) the data and control information to obtain data symbols and control symbols, respectively. Transmit processor <NUM> may also generate reference symbols, such as for the primary synchronization signal (PSS), secondary synchronization signal (SSS), PBCH demodulation reference signal (DMRS), and channel state information reference signal (CSI-RS).

Transmit (TX) multiple-input multiple-output (MIMO) processor <NUM> may perform spatial processing (e.g., precoding) on the data symbols, the control symbols, and/or the reference symbols, if applicable, and may provide output symbol streams to the modulators (MODs) in transceivers 232a-232t. Each modulator in transceivers 232a-232t may process a respective output symbol stream (e.g., for OFDM) to obtain an output sample stream. Downlink signals from the modulators in transceivers 232a-232t may be transmitted via the antennas 234a-234t, respectively.

At UE <NUM>, antennas 252a-252r may receive the downlink signals from the BS <NUM> and may provide received signals to the demodulators (DEMODs) in transceivers 254a-254r, respectively. Each demodulator in transceivers 254a-254r may condition (e.g., filter, amplify, downconvert, and digitize) a respective received signal to obtain input samples. Each demodulator may further process the input samples (e.g., for OFDM) to obtain received symbols.

MIMO detector <NUM> may obtain received symbols from all the demodulators in transceivers 254a-254r, perform MIMO detection on the received symbols if applicable, and provide detected symbols. Receive processor <NUM> may process (e.g., demodulate, deinterleave, and decode) the detected symbols, provide decoded data for the UE <NUM> to a data sink <NUM>, and provide decoded control information to a controller/processor <NUM>.

On the uplink, at UE <NUM>, transmit processor <NUM> may receive and process data (e.g., for the physical uplink shared channel (PUSCH)) from a data source <NUM> and control information (e.g., for the physical uplink control channel (PUCCH) from the controller/processor <NUM>. Transmit processor <NUM> may also generate reference symbols for a reference signal (e.g., for the sounding reference signal (SRS)). The symbols from the transmit processor <NUM> may be precoded by a TX MIMO processor <NUM> if applicable, further processed by the modulators in transceivers 254a-254r (e.g., for SC-FDM), and transmitted to BS <NUM>.

At BS <NUM>, the uplink signals from UE <NUM> may be received by antennas 234at, processed by the demodulators in transceivers 232a-232t, detected by a MIMO detector <NUM> if applicable, and further processed by a receive processor <NUM> to obtain decoded data and control information sent by UE <NUM>. Receive processor <NUM> may provide the decoded data to a data sink <NUM> and the decoded control information to the controller/processor <NUM>.

Memories <NUM> and <NUM> may store data and program codes for BS <NUM> and UE <NUM>, respectively.

Scheduler <NUM> may schedule UEs for data transmission on the downlink and/or uplink.

<NUM> may utilize orthogonal frequency division multiplexing (OFDM) with a cyclic prefix (CP) on the uplink and downlink. <NUM> may also support half-duplex operation using time division duplexing (TDD). OFDM and single-carrier frequency division multiplexing (SC-FDM) partition the system bandwidth into multiple orthogonal subcarriers, which are also commonly referred to as tones and bins. Each subcarrier may be modulated with data. The minimum resource allocation, called a resource block (RB), may be <NUM> consecutive subcarriers in some examples. NR may support a base subcarrier spacing (SCS) of <NUM> and other SCS may be defined with respect to the base SCS (e.g., <NUM>, <NUM>, <NUM>, <NUM>, and others).

As above, <FIG> depict various example aspects of data structures for a wireless communication network, such as wireless communications network <NUM> of <FIG>.

In various aspects, the <NUM> frame structure may be frequency division duplex (FDD), in which for a particular set of subcarriers (carrier system bandwidth), subframes within the set of subcarriers are dedicated for either DL or UL. <NUM> frame structures may also be time division duplex (TDD), in which for a particular set of subcarriers (carrier system bandwidth), subframes within the set of subcarriers are dedicated for both DL and UL. In the examples provided by <FIG>, the <NUM> frame structure is assumed to be TDD, with subframe <NUM> being configured with slot format <NUM> (with mostly DL), where D is DL, U is UL, and X is flexible for use between DL/UL, and subframe <NUM> being configured with slot format <NUM> (with mostly UL). Note that the description below applies also to a <NUM> frame structure that is TDD.

In some examples, each slot may include <NUM> or <NUM> symbols, depending on the slot configuration.

For example, for slot configuration <NUM>, each slot may include <NUM> symbols, and for slot configuration <NUM>, each slot may include <NUM> symbols.

For slot configuration <NUM>, different numerologies (µ) <NUM> to <NUM> allow for <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, and <NUM> slots, respectively, per subframe. Accordingly, for slot configuration <NUM> and numerology µ, there are <NUM> symbols/slot and 2µ slots/subframe. The subcarrier spacing may be equal to <NUM>µ × <NUM>, where µ is the numerology <NUM> to <NUM>. As such, the numerology µ = <NUM> has a subcarrier spacing of <NUM> and the numerology µ = <NUM> has a subcarrier spacing of <NUM>. <FIG> provide an example of slot configuration <NUM> with <NUM> symbols per slot and numerology µ = <NUM> with <NUM> slots per subframe. The slot duration is <NUM>, the subcarrier spacing is <NUM>, and the symbol duration is approximately <NUM>.

As illustrated in <FIG>, some of the REs carry reference (pilot) signals (RS) for a UE (e.g., UE <NUM> of <FIG> and <FIG>).

The PSS is used by a UE (e.g., <NUM> of <FIG> and <FIG>) to determine subframe/symbol timing and a physical layer identity.

The preceding description provides examples of configuring and using ML models for performing one or more NNFs, such as ML-based wireless communications management procedures in communication systems. The preceding description is provided to enable any person skilled in the art to practice the various aspects described herein. The examples discussed herein are not limiting of the scope, applicability, or aspects set forth in the claims. For example, changes may be made in the function and arrangement of elements discussed without departing from the scope of the disclosure. In addition, the scope of the disclosure is intended to cover such an apparatus or method that is practiced using other structure, functionality, or structure and functionality in addition to, or other than, the various aspects of the disclosure set forth herein.

The techniques described herein may be used for various wireless communication technologies, such as <NUM> (e.g., <NUM> NR), 3GPP Long Term Evolution (LTE), LTE-Advanced (LTE-A), code division multiple access (CDMA), time division multiple access (TDMA), frequency division multiple access (FDMA), orthogonal frequency division multiple access (OFDMA), single-carrier frequency division multiple access (SC-FDMA), time division synchronous code division multiple access (TD-SCDMA), and other networks. A CDMA network may implement a radio technology such as Universal Terrestrial Radio Access (UTRA), cdma2000, and others. UTRA includes Wideband CDMA (WCDMA) and other variants of CDMA. An OFDMA network may implement a radio technology such as NR (e.g. <NUM> RA), Evolved UTRA (E-UTRA), Ultra Mobile Broadband (UMB), IEEE <NUM> (Wi-Fi), IEEE <NUM> (WiMAX), IEEE <NUM>, Flash-OFDMA, and others. UTRA and E-UTRA are part of Universal Mobile Telecommunication System (UMTS).

The various illustrative logical blocks, modules and circuits described in connection with the present disclosure may be implemented or performed with a general purpose processor, a DSP, an ASIC, a field programmable gate array (FPGA) or other programmable logic device (PLD), discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A processor may also be implemented as a combination of computing devices, e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, a system on a chip (SoC), or any other such configuration.

In the case of a user equipment (see <FIG>), a user interface (e.g., keypad, display, mouse, joystick, touchscreen, biometric sensor, proximity sensor, light emitting element, and others) may also be connected to the bus.

By way of example, the machine-readable media may include a transmission line, a carrier wave modulated by data, and/or a computer-readable storage medium with instructions stored thereon separate from the wireless node, all of which may be accessed by the processor through the bus interface. The machine-readable media may be embodied in a computer program product.

For example, "determining" may include calculating, computing, processing, deriving, investigating, looking up (e.g., looking up in a table, a database or another data structure), ascertaining, and the like. Also, "determining" may include receiving (e.g., receiving information), accessing (e.g., accessing data in a memory), and the like. Also, "determining" may include resolving, selecting, choosing, establishing, and the like.

Claim 1:
A method for wireless communication by a user equipment, UE (<NUM>; <NUM>), comprising:
transmitting (<NUM>; <NUM>), to a base station, BS, (<NUM>; <NUM>), UE capability information indicating at least one radio capability of the UE (<NUM>; <NUM>) and at least one machine learning, ML, capability of the UE (<NUM>; <NUM>); and
receiving (<NUM>; <NUM>), from the BS (<NUM>; <NUM>) based on the UE capability information, ML configuration information indicating:
at least one neural network function, NNF, comprising at least a portion of an ML-based wireless communications management procedure; and
at least one ML model configured to perform the at least the portion of the ML-based wireless communications management procedure; and
receiving (<NUM>), from the BS (<NUM>; <NUM>), a signal activating use of the at least one ML model to perform the at least the portion of the ML-based wireless communications management procedure.