Artificial intelligence radio function model management in communication network

Abstract

The wireless user equipment transmits learning model information of a learning model corresponding to facilitating the radio function to the network node. In response, the node transmits a model management indication configuration corresponding to the learning model information to the user equipment. The user equipment monitors a learning model parameter metric indicative of learning model performance. The monitored metrics may be used to determine control actions or operations for execution based on analysis of the metrics relative to model performance metrics. The model management indication may be transmitted to the user device to indicate the determined control action. Examples of control operations may include deactivating the learning model of the current operation or retraining the learning model.

Description

[0001] Cross - Reference to Related Applications

[0002] This application claims priority to U.S. Non - Provisional Patent Application No. 18 / 072,347, filed on November 30, 2022, and entitled "ARTIFICIAL INTELLIGENCE RADIO FUNCTION MODEL MANAGEMENT IN A COMMUNICATION NETWORK", the entire content of which is incorporated herein by reference. BACKGROUND OF THE INVENTION

[0003] The term "New Radio" (NR) associated with the fifth - generation mobile wireless communication system ("5G") refers to aspects of the technology used in a radio access network ("RAN") that includes several quality - of - service classes (QoS), including ultra - reliable and low - latency communication ("URLLC"), enhanced mobile broadband ("eMBB"), and massive machine - type communication ("mMTC"). The URLLC QoS class is associated with strict latency requirements (e.g., low latency or low signal / message latency) and high reliability of radio performance, while conventional eMBB usage can be associated with high - capacity wireless communication, which may permit less - strict latency requirements (e.g., higher latency than URLLC) and less - reliable radio performance compared to URLLC. The performance requirements for mMTC can be lower than those for eMBB use cases. Some use - case applications involving mobile devices or mobile user equipment such as smart phones, wireless tablets, smart watches, etc. may impose variations on a given RAN resource load or demand. SUMMARY OF THE INVENTION

[0004] The following presents a simplified summary of the disclosed subject matter in order to provide a basic understanding of some of the embodiments. This summary is not an extensive overview of all embodiments. It is neither intended to identify key or critical elements of all embodiments nor to delineate the scope of any or all embodiments. Its sole purpose is to present some concepts of the disclosure in a concise form as a prelude to the more detailed description that is presented later.

[0005] In an example embodiment, a method may include a user equipment including a processor receiving, from a radio access network node, a model management indication configuration corresponding to a first radio function of the user equipment. The user equipment may monitor model performance parameters, which may be indicated for monitoring in a first radio function model configuration of a first radio function learning model corresponding to implementing a radio function on the user equipment, to obtain a monitored model performance metric. The monitored model performance metric may be analyzed relative to a model performance metric criterion to obtain an analyzed monitored performance metric. Based on the analyzed monitored performance metric being determined to not meet the model performance metric criterion, such as a threshold corresponding to learning model parameters corresponding to the monitored metric, the user equipment may transmit a model management indication to the radio access network node according to the model management indication configuration. The example method may further include: the user equipment receiving a control instruction corresponding to an operation of the radio function learning model and based on the model management indication, and implementing at least one control operation according to the control instruction.

[0006] The at least one control operation may include deactivating the first radio function learning model and activating a configured default radio function to perform the radio function, wherein the at least one control operation is determined by the radio access network node.

[0007] The first radio function model configuration may include at least one configured radio function model parameter value, and wherein the at least one control operation includes using the at least one configured radio function model parameter value to retrain the first radio function learning model to obtain a retrained first radio function learning model. The at least one configured radio function model parameter value may be part of a retraining data set.

[0008] The first radio function model configuration may include at least one configured radio function model parameter value, and the method may further include: receiving an updated first radio function model configuration, the updated first radio function model configuration including at least one configured updated radio function model parameter value, replacing the at least one configured radio function model parameter value with the at least one configured updated radio function model parameter value, and implementing the radio function by using the first radio function learning model with the at least one configured updated radio function model parameter value, the at least one configured updated radio function model parameter value may be part of a retraining data set.

[0009] The model management indication may include a control operation request for requesting an update of the first radio function model configuration, and the method may further include: receiving, from a radio access network node in response to the model management indication, an updated first radio function model configuration; in response to receiving the updated first radio function model configuration, deactivating the use of the first radio function learning model according to the first radio function model configuration; and activating the use of the first radio function learning model according to the updated first radio function model configuration. In another embodiment, the control operation may include an instruction to deactivate the use of the first radio function learning model according to the first radio function model configuration and to activate the implementation of the radio function according to a previously configured default model, which may be a configured probability learning model or may be a configured deterministic model or function.

[0010] The at least one control operation may include retraining the first radio function learning model using at least one configured radio function model parameter value to obtain a retrained first radio function learning model. The at least one configured radio function model parameter value may correspond to a data set corresponding to an index. The index may be transmitted in the model management indication.

[0011] The model performance parameter includes at least one of the following: mean squared error value, root mean squared error value, normalized mean squared error value, mean absolute error value, R-squared value, generalized cosine similarity value, squared generalized cosine similarity value, accuracy value, number of true negative values, number of true positive values, number of false negative values, number of false positive values, precision, recall value, or F1 score value.

[0012] In an embodiment, the example method may further include transmitting, by a user equipment, an indication of radio function learning model information including learning model information corresponding to a first radio function to a radio access network node, wherein the indication of radio function learning model information may include a first indication, and wherein the learning model information includes at least one of the following: at least one learning model type indication, at least one radio function indication indicating at least one corresponding radio function, a list of at least one metric parameter corresponding to at least one learning model corresponding to the at least one learning model type indication being estimated or reported, a value representing the number of learning models stored by the user equipment for implementing at least one corresponding radio function, or a second indication of at least one learning model category corresponding to at least one data set used by the radio access network node to determine the monitored performance metric being analyzed.

[0013] The first radio function learning model may be one of a plurality of different radio function learning models capable of implementing a radio function. The model performance parameter includes a mathematical function, such as the example shown in Table 1.

[0014] The control instruction may include an instruction to perform an operation to deactivate the use of the first radio function learning model, wherein the control instruction includes an instruction to activate a second radio function learning model in a different radio function learning model to implement a radio function, wherein the first radio function model configuration defines the first radio function learning model and the second radio function model configuration defines the second radio function learning model, and wherein the first radio function model configuration is different from the second radio function model configuration.

[0015] In another exemplary embodiment, a method may include receiving, by a radio access network node including a processor, learning model information corresponding to a current radio function learning model of a user equipment. In this embodiment, the method may further include: the radio access network node generating a model management indication configuration based on the learning model information; transmitting the model management indication configuration to the user equipment; receiving a performance parameter metric corresponding to a learning model performance metric parameter and associated with the performance of the current radio function learning model; and transmitting, based on the performance parameter metric, a control instruction corresponding to the operation of the current radio function learning model by the user equipment.

[0016] The performance parameter metric corresponds to a learning model performance metric parameter. The learning model performance metric parameter may include at least one of the following: mean squared error value, root mean squared error value, normalized mean squared error value, mean absolute error value, R-squared value, generalized cosine similarity value, squared generalized cosine similarity value, accuracy value, number of true negative values, number of true positive values, number of false negative values, number of false positive values, precision, recall value, or F1 score value.

[0017] The learning model information may include at least one of the following: at least one learning model type indication, at least one radio function indication indicating a corresponding at least one radio function, at least one metric corresponding to at least one learning model and corresponding to the at least one learning model type indication that is estimated or reported, the number of learning models that the user equipment can store for the at least one radio function, or an indication of at least one learning model category corresponding to the use of at least one data set for which the radio access network node determines the performance parameter metric.

[0018] The model management indication can include a data size based at least on a performance parameter metric. Thus, depending on the metrics being monitored and reported, the amount of data (e.g., number of bytes) used to transmit the metrics can vary. For example, using a quantization indication of the monitored metric can cause the transmitted data size of the indicated metric to use fewer bytes or a configured, uniform number of bytes. Accordingly, the performance parameter metric can be indicated by a configured quantization performance parameter metric value or quantization index. The quantization value or index can correspond to the range into which the monitored metric value falls. In an embodiment, the model management indication configuration includes a quantization performance parameter metric value. In an embodiment, the performance parameter metric can be a preamble corresponding to a user equipment.

[0019] The control instruction includes an instruction to deactivate the current radio function learning model and activate a default radio function. The control instruction includes an instruction to train the current radio function learning model to obtain an updated radio function learning model. The control instruction can include an indication of a configured training data set recommended, which indication can be configured at the user equipment to be the configured training data set used to train the current radio function learning model.

[0020] Example method embodiments can further include: analyzing a performance parameter metric relative to a performance parameter metric criterion to obtain an analyzed performance parameter metric; determining that the analyzed performance parameter metric fails to meet the performance parameter metric criterion; and determining a transmission control instruction based on the performance parameter metric being determined to not meet the performance parameter metric criterion.

[0021] In an embodiment, the model management indication includes a request from the user equipment to transmit a control instruction.

[0022] In another embodiment, a radio access network node operably communicating with a plurality of user devices may be configured to receive a second radio function learning model information indication from a second user device, the second radio function learning model information indication including learning model information corresponding to a second radio function learning model of the second user device. The radio access network node may generate a second model management indication configuration corresponding to the second radio function learning model, transmit the second model management indication configuration to the second user device, and receive a second performance parameter metric associated with the performance of the second radio function learning model from the second user device. The second performance parameter metric may correspond to a second learning model performance metric parameter; and the radio access network node may transmit a second control instruction corresponding to the operation of the second radio function learning model by the second user device based on the second performance parameter metric, wherein the first radio function learning model corresponds to a first learning model type, wherein the second radio function learning model corresponds to a second learning model type, wherein the first learning model type and the second learning model type are of the same type, wherein the first learning model type operates according to a first learning model configuration, the second learning model type operates according to a second learning model configuration, and wherein the first learning model configuration is different from the second learning model configuration. The second control instruction corresponding to the operation of the second radio function learning model by the second user device may be based on analyzing the second performance parameter metric relative to a standard through the user device.

[0023] In an embodiment, the analysis may be performed by the radio access network node. In an embodiment, the analysis may be based on current or historical performance data of the learning model at the user device, which may include a plurality of data metrics. The learning model performance data may be combined with network operating conditions, background information (e.g., environment or weather related). The data for performance analysis may be directly generated by the network device or may be provided by a third-party application. Thus, based on the radio access network node having a larger view of the network aspects made by the user device, the data for analysis (e.g., determining the standard to which the learning model parameter metric is compared) may facilitate the user device's ability to determine the learning model performance that can be used to facilitate radio functions. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 Illustrates a wireless communication system environment.

[0025] Figure 2 Illustrates an example environment with radio functions implemented in conjunction with corresponding learning models.

[0026] Figure 3A 、 Figure 3B 、 Figure 3C and Figure 3DIllustrates an embodiment of a learning model metric report.

[0027] Figure 4 Illustrates a timing diagram of an example embodiment method for configuring a user equipment to manage a radio function learning model.

[0028] Figure 5 Illustrates an example embodiment of a radio function learning model retraining configuration.

[0029] Figure 6 Illustrates a timing diagram of an example embodiment method for configuring a user equipment to retrain a radio function learning model.

[0030] Figure 7 Illustrates a flowchart of an example method for managing a learning model that facilitates radio functions at a user equipment.

[0031] Figure 8 Illustrates a block diagram of an example method.

[0032] Figure 9 Illustrates a block diagram of an example user equipment.

[0033] Figure 10 Illustrates a block diagram of an example non-transitory machine-readable medium.

[0034] Figure 11 Illustrates a block diagram of an example method.

[0035] Figure 12 Illustrates a block diagram of an example user equipment.

[0036] Figure 13 Illustrates a block diagram of an example non-transitory machine-readable medium.

[0037] Figure 14 Illustrates an example computer environment.

[0038] Figure 15 Illustrates a block diagram of an example wireless UE. Detailed Description

[0039] As a preliminary matter, those skilled in the art will readily appreciate that the present embodiments admit of wide utilization and application. In light of the substance or scope of the various embodiments of the present application, numerous methods, embodiments, and modifications, as well as numerous variations, modifications, and equivalent arrangements, will be apparent or reasonably suggested.

[0040] Accordingly, while the present application has been described in detail herein with respect to various embodiments, it is to be understood that the present disclosure is illustrative of one or more concepts expressed by the various example embodiments and is made only for the purpose of providing a comprehensive and enabling disclosure. The disclosure below is not intended to and should not be construed as limiting the present application or otherwise excluding any such other embodiments, modifications, variations, alterations, and equivalent arrangements, which are defined only by the appended claims and their equivalents in the embodiments described herein.

[0041] As used in the present disclosure, in some embodiments, the terms “component,” “system,” etc. are intended to refer to or include a computer-related entity or an entity related to an operating device having one or more specific functions, where the entity can be hardware, a combination of hardware and software, software, or software in execution. By way of example, a component can be, but is not limited to, a process running on a processor, a processor, an object, an executable program, an execution thread, computer-executable instructions, a program, and / or a computer. By way of illustration and not limitation, both an application running on a server and the server can be a component.

[0042] One or more components can reside within a process and / or an execution thread, and a component can be located on one computer and / or distributed between two or more computers. Further, these components can execute from various computer-readable media having various data structures stored thereon. These components can communicate via local and / or remote processes, such as in accordance with signals having one or more data packets (e.g., data from one component that interacts with another component in a local system, a distributed system, and / or across a network such as the Internet with other systems). As another example, a component can be a device having specific functionality provided by mechanical parts operated by an electrical or electronic circuit, which is operated by a software application or a firmware application executed by a processor, where the processor can be inside or outside the device and executes at least a portion of the software application or the firmware application. As yet another example, a component can be a device having specific functionality provided by an electronic component without mechanical parts, where the electronic component can include a processor to execute software or firmware that at least partially imparts the functionality to the electronic component. Although the various components are illustrated as separate components, it should be understood that, without departing from the example embodiments, multiple components can be implemented as a single component, or a single component can be implemented as multiple components.

[0043] As used herein, the term "facilitate" is considered in the context of a system, device, or component "facilitating" one or more actions or operations, given the nature of complex computing environments in which multiple components and / or multiple devices may be involved in some computing operations. Non-limiting examples of actions that may or may not involve multiple components and / or multiple devices include transmitting or receiving data, establishing a connection between devices, determining intermediate results towards obtaining a result, etc. In this regard, a computing device or component can facilitate an operation by playing any role in the implementation of the operation. When the components described herein operate, it should thus be understood that in cases where an operation is described as being facilitated by a component, the operation can optionally be completed through the collaboration of one or more other computing devices or components, such as but not limited to sensors, antennas, audio and / or visual output devices, other devices, etc.

[0044] Further, various embodiments can be implemented as a method, apparatus, or article of manufacture using standard programming and / or engineering techniques to produce software, firmware, hardware, or any combination thereof to control a computer to implement the disclosed subject matter. As used herein, the term "article of manufacture" is intended to cover a computer program that can be accessed from any computer-readable (or machine-readable) device or computer-readable (or machine-readable) storage / communication medium. For example, computer-readable storage media can include, but are not limited to, magnetic storage devices (such as hard disks, floppy disks, magnetic strips), optical disks (such as compact discs (CDs), digital versatile discs (DVDs)), smart cards, and flash memory devices (such as cards, sticks, key drives). Of course, those skilled in the art will recognize that numerous modifications can be made to this configuration without departing from the scope or spirit of the various embodiments.

[0045] Artificial intelligence ("AI") and machine learning ("ML") models can facilitate performance and operational functions and improvements in 5G implementations, such as network automation, optimizing signaling overhead, energy savings at the device, and maximizing service capacity. AI / ML capabilities can be implemented and structured in multiple different forms and in different vendor-proprietary designs. A 5G radio network may only know the AI / ML capabilities and capacities of user equipment, including radio functions performed or enhanced by learning models. To facilitate consistent behavior of user equipment devices, the radio access network node ("RAN") of the network to which a user equipment device may attach or register manages or controls the real-time AI / ML model performance at different user equipment devices for various radio functions, even if the RAN does not know the actual AI / ML model(s) implementation at each user equipment device. Such management or control can facilitate minimizing the propagation of learning model errors corresponding to poorly performing AI / ML models at the device over multiple execution instances of the corresponding radio function execution, which can degrade the performance of the radio function.

[0046] As disclosed herein, several embodiments facilitate the dynamic management of various AI / ML models deployed at different user equipment devices. The network RAN can rely on the monitoring and analysis of defined real-time performance metrics corresponding to the learning models to dynamically control the activation, deactivation, and triggering of model retraining (which can be radio function specific) of the learning models. Thus, it allows for the detection of poorly performing AI / ML models in near real-time. It should be understood that in some embodiments disclosed herein, even though the learning model may implement a specific radio function, the metrics being monitored or analyzed can be learning model metrics and not necessarily radio function metrics (e.g., mathematical / statistical metrics and not necessarily radio function metrics such as, for example, signal strength).

[0047] Conventional rule-based models can be implemented in user equipment to perform various radio frequency (“RF”) functions or signal processing functions, such as beamforming, channel estimation, demodulation, and decoding, and can be based on well-established system models. As long as such models closely follow the actual behavior of the radio network system in which the user equipment is operating, such models can achieve satisfactory performance. However, the performance of traditional conventional models can provide less than optimal performance. AI / ML-based models generally outperform their conventional counterparts; unlike conventional rule-based models, AI / ML-based models can be based on data rather than the rules of a predetermined conventional model. Thus, the output or result of a conventional rule-based model can be considered “deterministic” because the input is applied to static rules that cause a “determined” output, while the output or result of an AI / ML model can be considered probabilistic because the learning model typically infers possible outputs based on coefficients, factors, functions, or other variables that may have arrived based on previous inputs to the model.

[0048] Although AI / ML-based models trained with data from actual, real-world operations may potentially outperform traditional rule-based models, in cases where the radio system / environment may have undergone changes that may not have been experienced or "seen" during the training of the learning model, the learning model may be less robust and thus provide less desirable results, and thus in such cases where the learning model is "unknown", the learning model may infer outputs that are less desirable than static rule-based models. Such problematic situations can be caused by, for example, specific network / user equipment conditions or configurations, or by the architecture of the AI / ML learning model, or a combination thereof. Accordingly, it is desirable to implement a process such that the network RAN can monitor the AI / ML learning model performance of the user equipment via UE feedback / signaling and react to metrics corresponding to such monitoring, which may enable a fallback mechanism, or initiate, trigger, or otherwise cause retraining of a poorly performing learning model at the user equipment (e.g., poor performance as determined by the monitored learning model metrics).

[0049] Unlike conventional rule-based solutions, the implementation of AI / ML learning models for radio functions is inherently probabilistic (e.g., determining an uncertain output for certain inputs), and the output will depend on the number and quality of samples seen during training for a given model architecture.

[0050] For example, for the implementation of an AI / ML learning model for radio functions at the user equipment, the user equipment or gNB / RAN may predict a modulation and coding scheme ("MCS") and may use a given amount of channel state information reporting instances for this purpose. However, channel conditions or interference conditions that did not exist during training may systematically cause poor MCS selection, which can correspondingly lead to violation of minimum device performance objectives. Additionally, due to the probabilistic nature of the AI / ML learning model, variable conditions make user equipment device performance testing problematic. Accordingly, the network RAN, which knows the AI / ML learning model performance in near real-time, can facilitate the dynamic preemption, management, or calibration of the AI / ML learning model. Such control allows the network to activate or deactivate a detected poorly performing AI / ML learning model, or support / accelerate the retraining of the learning model by triggering adaptive model retraining or dataset distribution and correspondingly, thereby helping the user equipment to recover a satisfactory performance of the AI / ML model performance, thus potentially assisting the user equipment.

[0051] The embodiments disclosed herein may facilitate a RAN (e.g., via devices that are part of the RAN) to dynamically manage and control the performance of various AI / ML learning models, which may facilitate different radio functions at multiple user equipment devices. Uncontrolled AI / ML learning model performance may cause or permit degradation of radio functions and, correspondingly, may result in violation of minimum performance requirements for a given user equipment. Embodiments of a RAN for dynamically managing different AI / ML proprietary learning models at various user equipment facilitate detection and possible recovery of poorly performing models via additional auxiliary inference retraining signaling or data sets.

[0052] The embodiments disclosed herein may include a network RAN node dynamically managing AI / ML models at different user equipment devices and may include defining and compiling model-specific performance metrics, dynamic reporting of AI / ML learning model performance metrics, and adaptive inference retraining assistance, which may be tuned to a given radio function facilitated by a corresponding AI / ML learning model.

[0053] Turning now to the figures, Figure 1 An example of a wireless communication system 100 that supports blind decoding of PDCCH candidates or search spaces in accordance with aspects of the present disclosure is illustrated. The wireless communication system 100 may include one or more base stations 105, one or more UEs 115, and a core network 130. In some examples, the wireless communication system 100 may be a Long Term Evolution (LTE) network, an Advanced LTE (LTE-A) network, an LTE-A Pro network, or a New Radio (NR) network. In some examples, the wireless communication system 100 may support enhanced broadband communications, ultra-reliable (e.g., mission-critical) communications, low-latency communications, communications with low-cost and low-complexity devices, or any combination thereof. As shown, examples of UEs 115 may include smart phones, automobiles or other vehicles, or drones or other aircraft. Another example of a UE may be a virtual reality device 117, such as smart glasses, virtual reality headsets, augmented reality headsets, and other similar devices that may provide images, video, audio, tactile, gustatory, or olfactory sensations to a wearer. A UE such as a VR application 117 having a RAN base station 105 may transmit or receive wireless signals via a long-range wireless link 125, or the UE / VR application may receive or transmit wireless signals via a short-range wireless link 137, which may include a wireless link with the UE device 115, such as a Bluetooth link, a Wi-Fi link, etc. A UE such as application 117 may communicate simultaneously via multiple wireless links, such as via link 125 with the base station 105 and via the short-range wireless link. The VR application 117 may also communicate with the wireless UE via a cable or other wired connection. The RAN or its components may be referred to by Figure 12implemented by one or more of the described computer components.

[0054] Continuing the discussion Figure 1 , the base stations 105 can be distributed throughout a geographic area to form the wireless communication system 100, and can be devices of different forms or with different capabilities. The base stations 105 and the UEs 115 can communicate wirelessly via one or more communication links 125. Each base station 105 can provide a coverage area 110 over which the UEs 115 and the base station 105 can establish one or more communication links 125. The coverage area 110 can be an example of a geographic area over which the base stations 105 and the UEs 115 can support signal communication according to one or more radio access technologies.

[0055] The UEs 115 can be distributed throughout the coverage area 110 of the wireless communication system 100, and each UE 115 can be fixed, or mobile, or both fixed and mobile at different times. The UEs 115 can be devices of different forms or with different capabilities. Figure 1 Some example UEs 115 are illustrated in. The UEs 115 described herein can be capable of communicating with various types of devices, such as other UEs 115, base stations 105, or network devices (e.g., core network nodes, relay devices, integrated access and backhaul (IAB) nodes, or other network devices), as Figure 1 shown.

[0056] The base stations 105 can communicate with the core network 130, or with each other, or both. For example, the base stations 105 can interface with the core network 130 via one or more backhaul links 120 (e.g., via S1, N2, N3, or other interfaces). The base stations 105 can communicate with each other directly (e.g., directly between base stations 105) or indirectly (e.g., via the core network 130) or both via the backhaul links 120 (e.g., via X2, Xn, or other interfaces). In some examples, the backhaul links 120 can include one or more wireless links.

[0057] One or more of the base stations 105 described herein can include or can be referred to by those of ordinary skill in the art as base transceiver stations, radio base stations, access points, radio transceivers, Node Bs, eNodeBs (eNBs), next-generation Node Bs, or giga-NodeBs (any of which can be referred to as bNodeBs or gNBs), home Node Bs, home eNodeBs, or other suitable terms.

[0058] The UE 115 may include or may be referred to as a mobile device, wireless device, remote device, handheld device, or subscriber device, or some other suitable term, where "device" may also be referred to as a unit, station, terminal, or client, etc. The UE 115 may also include or may be referred to as a personal electronic device, such as a cellular phone, personal digital assistant (PDA), tablet computer, laptop computer, personal computer, or router. In some examples, the UE 115 may include or be referred to as a wireless local loop (WLL) station, Internet of Things (IoT) device, all Internet of Things (IoE) devices, or machine type communication (MTC) devices, etc., which may be implemented in various objects such as applications, vehicles, or smart meters.

[0059] The UE 115 may be capable of communicating with various types of devices, such as other UE 115s that may sometimes act as relays, as well as the base station 105 and network devices, which include macro eNBs or gNBs, small cell eNBs or gNBs, or relay base stations, and other examples, as Figure 1 shown.

[0060] The UE 115 and the base station 105 may communicate wirelessly with each other via one or more communication links 125 over one or more carriers. The term "carrier" may refer to a set of radio frequency spectrum resources that have a defined physical layer structure for supporting the communication link 125. For example, a carrier for the communication link 125 may include a portion (e.g., bandwidth part (BWP)) of the radio frequency spectrum band that operates according to the physical layer channels for a given radio access technology (e.g., LTE, LTE-A, LTE-A Pro, NR). Each physical layer channel may carry acquisition signaling (e.g., synchronization signals, system information), control signaling for coordinating carrier operation, user data, or other signaling. The wireless communication system 100 may support communicating with the UE 115 using carrier aggregation or multi-carrier operation. The UE 115 may be configured to have multiple downlink component carriers and one or more uplink component carriers according to a carrier aggregation configuration. Carrier aggregation may be used with both frequency division duplexing (FDD) and time division duplexing (TDD) component carriers.

[0061] In some examples (e.g., in a carrier aggregation configuration), a carrier may also have acquisition signaling or control signaling that coordinates the operation for other carriers. A carrier may be associated with a frequency channel (e.g., an evolved universal mobile telecommunications system terrestrial radio access (E-UTRA) absolute radio frequency channel number (EARFCN)) and may be positioned according to a channel raster used for UE 115 discovery. A carrier may operate in an independent mode, where initial acquisition and connection may be performed by UE 115 via the carrier, or a carrier may operate in a non-independent mode, where a different carrier (e.g., having the same or different radio access technology) is used to anchor the connection.

[0062] The communication link 125 shown in the wireless communication system 100 may include an uplink transmission from UE 115 to the base station 105, or a downlink transmission from the base station 105 to UE 115. A carrier may carry downlink or uplink communication (e.g., in an FDD mode), or may be configured to carry both downlink and uplink communication (e.g., in a TDD mode).

[0063] A carrier may be associated with a specific bandwidth of the radio frequency spectrum, and in some examples, the carrier bandwidth may be referred to as the "system bandwidth" of the carrier or the wireless communication system 100. For example, the carrier bandwidth may be one of a plurality of determined bandwidths for a carrier of a particular radio access technology (e.g., 1.4, 3, 5, 10, 15, 20, 40, or 80 megahertz (MHz)). Devices of the wireless communication system 100 (e.g., the base station 105, UE 115, or both) may have a hardware configuration that supports communication on a specific carrier bandwidth or may be configured to support communication on one of a set of carrier bandwidths. In some examples, the wireless communication system 100 may include a base station 105 or UE 115 that supports simultaneous communication via carriers associated with multiple carrier bandwidths. In some examples, each served UE 115 may be configured to operate on a portion (e.g., a sub-band, BWP) or all of the carrier bandwidth.

[0064] The signal waveform transmitted on a carrier can be composed of multiple subcarriers (e.g., using multi-carrier modulation (MCM) techniques such as orthogonal frequency division multiplexing (OFDM) or discrete Fourier transform spread OFDM (DFT-S-OFDM)). In a system adopting MCM techniques, a resource element can be composed of a symbol period (e.g., the duration of a modulation symbol) and a subcarrier, where the symbol period and the subcarrier spacing are inversely related. The number of bits carried by each resource element can depend on the modulation scheme (e.g., the order of the modulation scheme, the coding rate of the modulation scheme, or both). Therefore, the more resource elements received by the UE 115 and the higher the order of the modulation scheme, the higher the data rate for the UE. Wireless communication resources can refer to a combination of radio frequency spectrum resources, time resources (e.g., search spaces), or space resources (e.g., spatial layers or beams), and the use of multiple spatial layers can further increase the data rate or data integrity for communicating with the UE 115.

[0065] One or more numerologies for a carrier can be supported, where a numerology can include a subcarrier spacing (Δf) and a cyclic prefix. A carrier can be divided into one or more BWPs with the same or different numerology schemes. In some examples, the UE 115 can be configured with multiple BWPs. In some examples, a single BWP for a carrier can be active at a given time and the communication for the UE 115 can be restricted to one or more active BWPs.

[0066] The time interval for the base station 105 or the UE 115 can be expressed as a multiple of a basic time unit, which can refer to, for example, T s = 1 / (Δf max ·N f ) seconds of sampling period, where Δf max can represent the maximum supported subcarrier spacing, and N f can represent the maximum supported discrete Fourier transform (DFT) size. The time intervals of communication resources can be organized according to radio frames each having a specific duration (e.g., 10 milliseconds (ms)). Each radio frame can be identified by a system frame number (SFN) (e.g., ranging from 0 to 1023).

[0067] Each frame may include a plurality of consecutively numbered sub - frames or time slots, and each sub - frame or time slot may have the same duration. In some examples, a frame may be divided (e.g., in the time domain) into sub - frames, and each sub - frame may be further divided into a plurality of time slots. Alternatively, each frame may include a variable number of time slots, and the number of time slots may depend on the sub - carrier spacing. Each time slot may include a plurality of symbol periods (e.g., depending on the length of the cyclic prefix prepended to each symbol period). In some wireless communication systems 100, a time slot may be further divided into a plurality of mini - time slots each containing one or more symbols. In addition to the cyclic prefix, each symbol period may contain one or more (e.g., N f ) sampling periods. The duration of a symbol period may depend on the sub - carrier spacing or the operating frequency band.

[0068] A sub - frame, time slot, mini - time slot, or symbol may be the smallest scheduling unit (e.g., in the time domain) of the wireless communication system 100 and may be referred to as a transmission time interval (TTI). In some examples, the TTI duration (e.g., the number of symbol periods in a TTI) may be variable. Additionally, or alternatively, the smallest scheduling unit of the wireless communication system 100 may be dynamically selected (e.g., in a burst of shortened TTIs (sTTIs)).

[0069] Physical channels may be multiplexed on a carrier according to various techniques. Physical control channels and physical data channels may be multiplexed on a downlink carrier using, for example, one or more of time - division multiplexing (TDM) techniques, frequency - division multiplexing (FDM) techniques, or hybrid TDM - FDM techniques. A control region (e.g., a control resource set (CORESET)) for a physical control channel may be defined by a plurality of symbol periods and may extend across the system bandwidth of the carrier or a subset of the system bandwidth. One or more control regions (e.g., CORESETs) may be configured for a set of UEs 115. For example, one or more of the UEs 115 may monitor or search a control region or space for control information according to one or more search space sets, and each search space set may include one or more control channel candidates in one or more aggregation levels arranged in a cascaded manner. The aggregation level for a control channel candidate may refer to the number of control channel resources (e.g., control channel elements (CCEs)) associated with the encoded information of a control information format having a given payload size. The search space set may include a common search space set configured to send control information to a plurality of UEs 115 and a UE - specific search space set for sending control information to a specific UE 115. Novel and unconventional other search spaces and configurations for monitoring and decoding thereof are disclosed herein.

[0070] Base station 105 may provide communication coverage via one or more cells, such as macro cells, small cells, hotspots, or other types of cells, or any combination thereof. The term "cell" may refer to a logical communication entity for communicating with base station 105 (e.g., via a carrier) and may be associated with an identifier (e.g., physical cell identifier (PCID), virtual cell identifier (VCID), or others) for differentiating neighboring cells. In some examples, a cell may also refer to the geographical coverage area 110 or a portion (e.g., a sector) of the geographical coverage area 110 on which the logical communication entity operates. Depending on various factors such as the capabilities of base station 105, the scope of such a cell may range from a relatively small area (e.g., a structure, a subset of a structure) to a relatively large area. For example, a cell may be or include a building, a subset of a building, or an external space between or overlapping with geographical coverage area 110, etc.

[0071] Macro cells typically cover a relatively large geographical area (e.g., with a radius of several kilometers) and may allow UEs 115 with service subscriptions to access the network provider supporting the macro cell without restriction. Compared with macro cells, small cells may be associated with low-power base stations 105, and small cells may operate in the same or different (e.g., licensed, unlicensed) frequency bands as macro cells. Small cells may provide unrestricted access to UEs 115 with service subscriptions to the network provider, or may provide restricted access to UEs 115 associated with the small cell (e.g., UEs 115 in a closed subscriber group (CSG), UEs 115 associated with users in a home or office). Base station 105 may support one or more cells and may also support communication on one or more cells using one or more component carriers.

[0072] In some examples, a carrier may support multiple cells, and different cells may be configured according to different protocol types (e.g., MTC, narrowband Internet of Things (NB-IoT), enhanced mobile broadband (eMBB)) that may provide access for different types of devices.

[0073] In some examples, base station 105 may be movable, thus providing communication coverage for a mobile geographical coverage area 110. In some examples, different geographical coverage areas 110 associated with different technologies may overlap, but different geographical coverage areas 110 may be supported by the same base station 105. In other examples, overlapping geographical coverage areas 110 associated with different technologies may be supported by different base stations 105. The wireless communication system 100 may include, for example, a heterogeneous network in which different types of base stations 105 provide coverage for various geographical coverage areas 110 using the same or different radio access technologies.

[0074] The wireless communication system 100 may support synchronous or asynchronous operations. For synchronous operations, the base stations 105 may have similar frame timings, and transmissions from different base stations 105 may be approximately aligned in time. For asynchronous operations, the base stations 105 may have different frame timings, and in some examples, transmissions from different base stations 105 may not be aligned in time. The techniques described herein may be used for synchronous or asynchronous operations.

[0075] Some UEs 115 (e.g., MTC or IoT devices) may be low-cost or low-complexity devices and may provide automated communication between machines (e.g., via machine-to-machine (M2M) communication). M2M communication or MTC may refer to data communication technologies that allow devices to communicate with each other or with the base station 105 without human intervention. In some examples, M2M communication or MTC may include communication from devices that integrate sensors or meters to measure or capture information and relay such information to a central server or application that utilizes the information or presents the information to a human who interacts with the application. Some UEs 115 may be designed to collect information or implement automated behavior of machines or other devices. Examples of applications for MTC devices include smart metering, inventory monitoring, water level monitoring, device monitoring, healthcare monitoring, wildlife monitoring, weather and geological event monitoring, fleet management and tracking, remote security sensing, physical access control, and transaction-based commercial charging.

[0076] Some UEs 115 may be configured to operate in power-saving modes, such as half-duplex communication (e.g., a mode that supports one-way communication via transmission or reception but does not support transmission and reception simultaneously). In some examples, half-duplex communication may be performed at a reduced peak rate. Other power-saving techniques for UEs 115 include entering a power-saving deep sleep mode when not participating in active communication, operating on a limited bandwidth (e.g., according to narrowband communication), or a combination of these techniques. For example, some UEs 115 may be configured to operate using a narrowband protocol type associated with a defined portion or range within a carrier, within a guard band of the carrier, or outside the carrier (e.g., a set of subcarriers or resource blocks (RBs)).

[0077] The wireless communication system 100 can be configured to support ultra-reliable communication or low-latency communication or various combinations thereof. For example, the wireless communication system 100 can be configured to support ultra-reliable low-latency communication (URLLC) or mission-critical communication. The UE 115 can be designed to support ultra-reliability, low latency, or critical functions (e.g., mission-critical functions). Ultra-reliable communication can include private communication or group communication and can be supported by one or more mission-critical services, such as mission-critical push-to-talk (MCPTT), mission-critical video (MCVideo), or mission-critical data (MCData). Support for mission-critical functions can include prioritization of services, and mission-critical services can be used for public safety or general commercial applications. The terms ultra-reliable, low-latency, mission-critical, and ultra-reliable low-latency can be used interchangeably herein.

[0078] In some examples, the UE 115 can also be capable of directly communicating with other UEs 115 via a device-to-device (D2D) communication link 135 (e.g., using a peer-to-peer (P2P) or D2D protocol). The communication link 135 can include a sidelink communication link. One or more UEs 115 utilizing D2D communication can be within the geographical coverage area 110 of the base station 105. Other UEs 115 in such a group can be outside the geographical coverage area 110 of the base station 105 or cannot receive transmissions from the base station 105 for other reasons. In some examples, a group of UEs 115 communicating via D2D communication can utilize a one-to-many (1:M) system, where a UE transmits to each other UE in the group. In some examples, the base station 105 facilitates resource scheduling for D2D communication. In other cases, D2D communication is performed between UEs 115 without involving the base station 105.

[0079] In some systems, the D2D communication link 135 can be an example of a communication channel (e.g., a sidelink communication channel) between vehicles (e.g., the UE 115). In some examples, vehicles can communicate using vehicle-to-everything (V2X) communication, vehicle-to-vehicle (V2V) communication, or some combination thereof. Vehicles can signal information related to traffic conditions, signal scheduling, weather, safety, emergencies, or any other information related to the V2X system. In some examples, vehicles in a V2X system can communicate with roadside infrastructure such as roadside units, or with the network, or with both, using vehicle-to-network (V2N) communication via one or more RAN network nodes (e.g., the base station 105).

[0080] The core network 130 can provide user authentication, access authorization, tracking, Internet Protocol (IP) connectivity, and other access, routing, or mobility functions. The core network 130 can be an evolved packet core (EPC) or a 5G core (5GC), which can include at least one control plane entity that manages access and mobility (e.g., a mobility management entity (MME), an access and mobility management function (AMF)) and at least one user plane entity that routes packets or interconnects to an external network (e.g., a serving gateway (S-GW), a packet data network (PDN) gateway (P-GW), or a user plane function (UPF)). The control plane entity can manage non-access stratum (NAS) functions, such as mobility, authentication, and bearer management for the UE 115 served by the base station 105 associated with the core network 130. User IP packets can be transmitted through the user plane entity, which can provide IP address allocation and other functions. The user plane entity can be connected to IP services 150 for one or more network operators. It can include access to the Internet, an (intra)net, an IP multimedia subsystem (IMS), or packet-switched streaming services.

[0081] Some network devices, such as the base station 105, can include subcomponents, such as an access network entity 140, which can be an example of an access node controller (ANC). Each access network entity 140 can communicate with the UE 115 through one or more other access network transmission entities 145, which can be referred to as radio heads, intelligent radio heads, or transmission / reception points (TRPs). Each access network transmission entity 145 can include one or more antenna panels. In some configurations, the various functions of each access network entity 140 or the base station 105 can be distributed across various network devices (e.g., radio heads and ANCs) or consolidated into a single network device (e.g., the base station 105).

[0082] The wireless communication system 100 can operate using one or more frequency bands typically in the range of 300 megahertz (MHz) to 300 gigahertz (GHz). Generally, the region from 300 MHz to 3 GHz is known as the ultra-high frequency (UHF) region or the decimeter band because the wavelength ranges from approximately 1 decimeter to 1 meter in length. UHF waves can be blocked or redirected by buildings and environmental features, but the waves can penetrate building structures sufficiently to enable a macro cell to serve a UE 115 located indoors. Compared to transmissions at smaller frequencies and longer wavelengths using the high frequency (HF) or very high frequency (VHF) portions of the spectrum below 300 MHz, UHF wave transmissions can be associated with smaller antennas and shorter ranges (e.g., less than 100 kilometers).

[0083] The wireless communication system 100 may also operate in the super high frequency (SHF) region using a frequency band from 3 GHz to 30 GHz (also known as the centimeter band) or in the extremely high frequency (EHF) region of the spectrum (e.g., from 30 GHz to 300 GHz) (also known as the millimeter band). In some examples, the wireless communication system 100 may support millimeter wave (mmW) communication between the UE 115 and the base station 105, and the EHF antennas of the corresponding devices may be smaller and more closely spaced than UHF antennas. In some examples, it may facilitate the use of antenna arrays within the device. However, the propagation of EHF transmissions may be subject to greater atmospheric attenuation and shorter range than SHF or UHF transmissions. The techniques disclosed herein may be employed across transmissions using one or more different frequency regions, and the specified use of frequency bands across these frequency regions may vary due to national or regulatory authorities.

[0084] The wireless communication system 100 may use both licensed and unlicensed radio frequency spectrum bands. For example, the wireless communication system 100 may employ licensed-assisted access (LAA), LTE-unlicensed (LTE-U) radio access technology, or NR technology in an unlicensed band such as the 5 GHz industrial, scientific, and medical (ISM) band. When operating in an unlicensed radio frequency spectrum band, devices such as the base station 105 and the UE 115 may employ carrier sensing to achieve collision detection and avoidance. In some examples, operation in the unlicensed band may be combined with component carriers operating in a licensed band (e.g., LAA) based on a carrier aggregation configuration. Operation in the unlicensed spectrum may include downlink transmissions, uplink transmissions, peer-to-peer (P2P) transmissions, or device-to-device (D2D) transmissions, etc.

[0085] The base station 105 or the UE 115 may be equipped with multiple antennas, which may be used to employ techniques such as transmit diversity, receive diversity, multiple-input multiple-output (MIMO) communication, or beamforming. The antennas of the base station 105 or the UE 115 may be located within one or more antenna arrays or antenna panels that may support MIMO operation or transmit or receive beamforming. For example, one or more base station antennas or antenna arrays may be co-located at an antenna assembly such as an antenna tower. In some examples, the antennas or antenna arrays associated with the base station 105 may be located at different geographical locations. The base station 105 may have an antenna array having multiple rows and columns of antenna ports for beamforming that the base station 105 may use these antenna ports to support communication with the UE 115. Similarly, the UE 115 may have one or more antenna arrays that may support various MIMO or beamforming operations. Additionally or alternatively, the antenna panel may support radio frequency beamforming for signals transmitted via the antenna ports.

[0086] Base station 105 or UE 115 can use MIMO communication to utilize multipath signal propagation and increase spectral efficiency by transmitting or receiving multiple signals via different spatial layers. This technique can be referred to as spatial multiplexing. For example, multiple signals can be transmitted by a transmitting device via different antennas or different combinations of antennas. Similarly, a receiving device can receive multiple signals via different antennas or different combinations of antennas. Each of the multiple signals can be referred to as a separate spatial stream and can carry bits associated with the same data stream (e.g., the same codeword) or different data streams (e.g., different codewords). Different spatial layers can be associated with different antenna ports for channel measurement and reporting. MIMO techniques include single-user MIMO (SU-MIMO), where multiple spatial layers are transmitted to the same receiving device; and multi-user MIMO (MU-MIMO), where multiple spatial layers are transmitted to multiple devices.

[0087] Beamforming (which can also be referred to as spatial filtering, directional transmission, or directional reception) is a signal processing technique that can be used at a transmitting device or a receiving device (e.g., base station 105, UE 115) to shape or direct an antenna beam (e.g., a transmission beam, a reception beam) along a spatial path between the transmitting device and the receiving device. Beamforming can be implemented by combining signals communicated via the antenna elements of an antenna array such that some signals propagating in a particular orientation relative to the antenna array experience constructive interference while other signals experience destructive interference. The adjustment of the signals communicated via the antenna elements can include the transmitting device or the receiving device applying an amplitude offset, a phase offset, or both to the signals communicated via the antenna elements associated with the device. The adjustment associated with each antenna element can be defined by a set of beamforming weights associated with a particular orientation (e.g., relative to the antenna array of the transmitting device or the receiving device, or relative to some other orientation).

[0088] Base station 105 or UE 115 can use beam scanning techniques as part of a beamforming operation. For example, base station 105 can use multiple antennas or antenna arrays (e.g., antenna panels) to perform beamforming operations for directional communication with UE 115. Some signals (e.g., synchronization signals, reference signals, beam selection signals, or other control signals) can be transmitted by base station 105 multiple times in different directions. For example, base station 105 can transmit signals according to different sets of beamforming weights associated with different transmission directions. Transmissions in different beam directions can be used to identify (e.g., by a transmitting device such as base station 105 or by a receiving device such as UE 115) the beam direction for later transmission or reception by base station 105.

[0089] Base station 105 may transmit some signals (e.g., data signals associated with a particular receiving device) in a single beam direction (e.g., a direction associated with a receiving device such as UE 115). In some examples, the beam direction associated with the transmission along a single beam direction may be determined based on the signals transmitted in one or more beam directions. For example, UE 115 may receive one or more signals transmitted by base station 105 in different directions and may report to the base station an indication of the signal with the highest signal quality or otherwise acceptable signal quality received by UE 115.

[0090] In some examples, the transmission by a device (e.g., base station 105 or UE 115) may be performed using multiple beam directions, and the device may use a combination of digital precoding or radio frequency beamforming to generate the combined beam for transmission (e.g., from base station 105 to UE 115). UE 115 may report feedback indicating precoding weights for one or more beam directions, and the feedback may correspond to the configured number of beams across the system bandwidth or one or more subbands. Base station 105 may transmit reference signals (e.g., cell-specific reference signal (CRS), channel state information reference signal (CSI-RS)), which may be precoded or not precoded. UE 115 may provide feedback for beam selection, which may be a precoding matrix indicator (PMI) or codebook-based feedback (e.g., multi-panel type codebook, linear combination type codebook, port selection type codebook). Although these techniques are described with reference to signals transmitted by base station 105 in one or more directions, UE 115 may employ similar techniques to transmit signals multiple times in different directions (e.g., for identifying the beam directions used by UE 115 for subsequent transmission or reception) or to transmit signals in a single direction (e.g., for transmitting data to a receiving device).

[0091] A receiving device (e.g., UE 115) may attempt multiple receive configurations (e.g., directional listening) when receiving various signals (such as, synchronization signals, reference signals, beam selection signals, or other control signals) from a base station 105. For example, the receiving device may attempt multiple receive directions by receiving via different antenna sub-arrays, by processing the received signals according to different antenna sub-arrays, by receiving according to different sets of receive beamforming weights (e.g., different sets of directional listening weights) applied to the signals received at multiple antenna elements of an antenna array, or by processing the received signals according to different sets of receive beamforming weights applied to the signals received at multiple antenna elements of an antenna array, any of which may be referred to as "listening" according to different receive configurations or receive directions. In some examples, the receiving device may use a single receive configuration to receive along a single beam direction (e.g., when receiving a data signal). The single receive configuration may be aligned in a beam direction determined based on listening according to different receive configuration directions (e.g., a beam direction determined to have the highest signal strength, the highest signal-to-noise ratio (SNR), or otherwise acceptable signal quality based on listening according to multiple beam directions).

[0092] The wireless communication system 100 may be a packet-based network operating according to a layered protocol stack. In the user plane, the communication of the packet data convergence protocol (PDCP) layer may be IP-based. The radio link control (RLC) layer may perform packet segmentation and reassembly for communication over logical channels. The medium access control (MAC) layer may perform priority handling and multiplex logical channels into transport channels. The MAC layer may also use error detection techniques, error correction techniques, or both to support retransmissions at the MAC layer to improve link efficiency. In the control plane, the radio resource control (RRC) protocol layer may provide the establishment, configuration, and maintenance of an RRC connection supporting a radio bearer for user plane data between the UE 115 and the base station 105 or the core network 130. At the physical layer, transport channels may be mapped to physical channels.

[0093] UE 115 and base station 105 may support retransmission of data to increase the likelihood of successfully receiving the data. Hybrid Automatic Repeat reQuest (HARQ) feedback is a technique for increasing the likelihood of correctly receiving data over communication link 125. HARQ may include a combination of error detection (e.g., using Cyclic Redundancy Check (CRC)), Forward Error Correction (FEC), and retransmission (e.g., Automatic Repeat reQuest (ARQ)). HARQ may improve the throughput of the MAC layer under poor radio conditions (e.g., low signal-to-noise ratio conditions). In some examples, a device may support HARQ feedback for the same time slot, where the device may provide HARQ feedback for data received in previous symbols in that time slot in a particular time slot. In other cases, the device may provide HARQ feedback in a subsequent time slot or according to some other time interval.

[0094] Now turning to Figure 2 , the figure shows a system 200 including a RAN node 105 communicating with a user equipment 115 via a wireless link 125. UE 115 may perform various radio functions 205A to 205n, which may be facilitated by corresponding machine learning models 215A to machine learning model 215n, respectively. UE 115 may transmit an indication of radio function learning model information 207 to RAN 105. RAN 105 may transmit a machine learning model management indication configuration 210 corresponding to or based on learning model information 207 to UE 115. During wireless operation of UE 115 and communication with RAN 105, the UE may transmit parameter metric reports 220A to parameter metric reports 220n, which may include one or more learning model parameter metrics corresponding to 215A to 215n, respectively. In an embodiment, reports 220A to reports 220n may include one or more control action requests, e.g., requests for one or more of models 215A to 215n to be deactivated or retrained. The control action requests may be determined by UE 115 based on monitored metrics corresponding to the operation of models 215A to 215n, or the control action may be determined by RAN 115 based on the monitored metrics transmitted in one or more of reports 220A to 220n.

[0095] AI / ML model inference performance monitoring and dynamic reporting of AI / ML model performance indications.

[0096] The network RAN can monitor the UE learning model inference performance by sending a test data set to the UE. The RAN can additionally provide the UE with one or more optional minimum performance requirements associated with the test data set. The test data set can be sent periodically according to the network configuration period, or can be dynamically transmitted to the UE based on a trigger (e.g., a change in the serving node / RAN that provides radio network services to the UE). The network RAN can provide the UE with the (multiple) test data sets that can represent the current conditions experienced by the UE, and the RAN can generally "know" or determine the conditions based on information related to the UE channel and radio environment, which is determined via various UE reports or sensing information that can provide information from the UE to the RAN. The attributes of the test data set, the size or format of the data set can be configured per UE or per group of related UEs.

[0097] The AI / ML learning model deployed at the UE device 115 (such as Figure 2 the model 215 shown) can be implementation-specific (e.g., a vendor-proprietary learning model). (Examples of vendors that can provide proprietary learning models can include user equipment manufacturers or application providers for user equipment, network equipment manufacturers or application providers for network equipment, or mobile network operators or application providers for mobile network operators.) The network RAN can determine the overall performance of the learning model deployed at the UE to meet the minimum device performance requirements. As disclosed herein, a dynamic reporting procedure can facilitate the compilation and reporting of indications by the user equipment device, which can be configured or pre-configured to reflect or indicate the model performance of the corresponding learning model.

[0098] A particular user equipment device can employ several different AI / ML learning model implementations for running, executing, or otherwise facilitating different radio functions. Different learning model parameter metrics can indicate the performance of different learning models. The embodiments disclosed herein can enable the user equipment device to compile and report one or more different learning model performance indication parameter metrics or indications for each learning model. Different learning model metrics can be associated with different corresponding filtering or time resolution configurations. Thus, as Figure 1 or Figure 2As shown, this customization metric report for a given learning model can facilitate optimized tracking and reporting of each active learning model for each user equipment device 115 that can be served by RAN 105. Accordingly, network RAN 105 can obtain and use the real-time performance of each learning model active at UE 115 to facilitate the best performance of the learning model and the inferences it can generate. Additionally, several report variants can be customized to suit various AI / ML learning model implementations and purposes. For example, the absolute, relative, quantization, or temporal (e.g., historical) metric reports of precision as disclosed and described herein. Network node RAN 105 can dynamically trade off the AI / ML learning model reporting overhead with respect to obtaining accuracy in AI / ML model performance metrics.

[0099] For AI / ML learning model performance, various parameters and metrics corresponding thereto can be considered, analyzed, or evaluated based on the nature of the problem being solved and the corresponding learning model functionality (e.g., regression or classification), or radio functions performed or facilitated by the learning model. For example, for radio functions such as channel estimation or channel state information (“CSI”) compression, a regression function can be used in the learning model, which can have the following parameters or corresponding metrics that can be evaluated: mean squared error (“MSE”); root mean squared error (“RMSE”); normalized mean squared error (“NMSE”); mean absolute error (“MAE”); R-squared; generalized cosine similarity (“GCS”); or squared generalized cosine similarity (“SGCS”). Table 1 shows an example function defining the corresponding learning model parameters, which can be associated with the metrics monitored and evaluated as listed above.

[0100]

[0101] Table 1

[0102] For classification problems such as beam index prediction, the accuracy parameter metric can be analyzed to determine the performance of the learning model facilitating beam index prediction. Other example learning model parameter metrics that can indicate the performance of a learning model solving a classification problem can include, but are not limited to: the absolute numbers of true negatives, true positives, false negatives, and false positives; precision and recall; or F1 score. The F1 score can include an evaluation metric that is used to represent the performance of a machine learning model or classifier and provides combined information about the precision and recall of the learning model. A higher F1 score metric generally indicates higher values for both the recall and precision metrics.

[0103] The implementation of AI / ML learning models at different devices can be supplier - proprietary as described above and can be transparent to network nodes (e.g., the RAN serving the UE may not have access to specific functions and program a given learning model that facilitates radio functions deployed in the UE). To manage and facilitate the UE device to achieve performance goals, the network RAN can be made aware of the UE device's capabilities and overall AI / ML learning model performance. Thus, when an active UE device first connects to the serving network RAN, it can transmit device - specific AI / ML capability information including the following information elements (“IEs”): types of algorithms supported by AI / ML, including supervised learning, unsupervised learning, and reinforcement learning; a list of radio functions supported by AI / ML; a list of model - specific metrics supported for estimation and reporting; the model library size for each radio function, e.g., the number of models that can be stored for each radio function; or an indication of model classification (small / medium / large), which can facilitate the network RAN to define or determine the data set to be used by the learning model. For example, for a large number of neurons (e.g., nodes of a learning model neural network), the determination of a comparable number of information samples can be used to avoid overfitting of the learning model. The AI / ML capability IE can be part of the device - capability signaling based on subsequent Radio Resource Control (“RRC”) signaling or on dynamically scheduled uplink control information (“UCI”) transmission.

[0104] Accordingly, the network RAN can configure a UE device with AI / ML learning model capabilities to monitor, estimate, and report certain model - specific parameter metrics indicating the learning model performance, and report the monitored metrics as model management indication transmissions on the uplink channel. The learning model can be identified in the model management indication by a network - assigned model identifier. As Figure 3A depicted, in one embodiment, the model management indication includes, at the UE device running the model, separately with Figure 2Independent performance metrics 310, 315, and 320 corresponding to the active AI / ML models 215A, 215B, and 215n shown. Metrics 310, 315, and 320 may correspond to model identifiers 311, 316, and 321, respectively. Enabling multiple metric configurations and reports for each model allows the device and network to coordinate on the best AI / ML performance metric that reflects the actual performance of the corresponding AI / ML model, because different models may be represented by various performance metrics with different computational complexities and reporting overheads. The RAN or UE may determine a metric configuration for reporting a learning model metric that indicates learning model performance. The varying metric reporting may cause the size of the model management indication to vary with respect to time depending on the active model and the corresponding reported metric size (e.g., various performance metrics may have different sizes each time, or be reported as exact values ​​or as quantization levels or ranges corresponding to values). Therefore, the model management indication may be dynamically scheduled on the uplink data channel (PUSCH) to facilitate scheduling efficiency and flexibility.

[0105] An example of analyzing the monitored metric value relative to the model performance metric criterion may include a criterion as a threshold and the user equipment comparing the monitored metric to the threshold (e.g., comparing the mean square error ("MSE") or comparing any other monitored metric value from Table 1 to the threshold and reporting to the radio access network if the MSE is greater than the threshold). Another example may include a criterion as a range of values ​​and the user equipment determining whether the monitored metric value is within a given range and reporting to the radio access network if the monitored metric is outside the criterion range.

[0106] exist Figure 3A In the example of FIG. 1 , the dynamic learning model management metrics report 305 can dynamically report learning model specific metrics based on the learning model identifiers corresponding to the respective learning models. The report 305 can include Figure 2 Metric reports 310, 315, and 320 corresponding to the learning models 215A, 215B, and 215n are shown.

[0107] To reduce the overhead of reporting AI / ML model management indications, quantization of model-specific performance metrics can be used, where the quantization level of the specific metric corresponding to the range can be configured by the RAN. Figure 3BAs shown, a UE device transmitting a model management indication may select a quantized representation of an estimated model specific metric, rather than an exact metric value report, resulting in a reduction in the aggregate size of the model management indication. The quantized indication may be in the form of a continuous quantization level indication, i.e. bit by bit, or a sequence / preamble representation of a determined quantization metric level. An example of quantization would be for an actual metric value determined by the UE to fall within a range where an index value may be transmitted rather than the actual metric value to reduce the amount of data used to transmit the model management indication to the serving RAN.

[0108] In another embodiment, the network RAN ​​may configure devices supporting AI / ML learning models through learning model parameter metrics, which may be referred to as model performance metrics, such as thresholds associated with active AI / ML models. Accordingly, when the learning model of the UE device fails or violates the configured learning model-specific performance metric threshold, the UE may transmit a model-specific "failure" indication to the serving RAN. Therefore, the network RAN ​​may determine whether to trigger a retraining of the data set based on the received model-failure indication. Figure 3B In the example dynamic learning model management metrics report 325, the learning model specific quantitative learning model metrics can be dynamically reported according to the learning model identifier corresponding to the corresponding learning model. The report 325 can include Figure 2 Metric reports 330, 335, and 340 corresponding to the illustrated learning models 215A, 215B, and 215n. The metric reports 330, 335, and 340 may correspond to model identifiers 331, 336, and 341, respectively.

[0109] In another embodiment, the network RAN ​​may configure the UE device to periodically report historical model-specific performance metrics over a configured time period. This may be useful for AI / ML learning models (e.g., temporal models) that predict certain radio conditions or actions at a set of future time instants. Accordingly, Figure 3C As shown, the UE device may compile a model-specific model management indication report over a configured historical or time reporting period, including an accurate, or quantized indication of a selected model-specific parameter. In another embodiment, the network RAN ​​may configure the UE device with a filtered time report of a certain model parameter metric (e.g., indicated by a corresponding model ID). It may instruct the UE device to perform filtering on the selected model-specific metric samples over a configured reporting period according to the indicated layer 1 filter type and filter coefficients, thereby further reducing the network resource overhead of the used reporting model management indication (which may be used in addition to transmit service data).

[0110] exist Figure 3CIn [the example], the example historical learning model management metric report 350 can report learning model metrics specific to a historical learning model based on a learning model identifier corresponding to the learning model. The report 350 can include metric reports 355, 360, and 365 corresponding to, for example, Figure 2 one of the learning models 215, x2, and x3 shown. The metric reports 355, 360, and 365 can correspond to metric report identifiers 356, 361, and 365, respectively.

[0111] Figure 3D An embodiment of an example historical learning model management metric report 370 is illustrated, which can report learning model metrics specific to a historical learning model based on a learning model identifier corresponding to the respective learning model. The report 370 can include metric reports 375, 380, and 385 corresponding to, for example, Figure 2 the learning models 215A, 215B, and 215n shown. The metric reports 375, 380, and 385 can correspond to model identifiers 376, 381, and 386, respectively. Thus, each of the metrics 375, 380, and 385 can include a historical report for the respective models 376, 381, and 386. The reports 375, 380, and 385 can include information or data that can be included in the historical report Figure 3A described in the reference. In other words, the reports 375, 380, and 385 can include the historical report 350, with each historical report corresponding to a different model. Thus, the report 370 can include multiple reports 350, with each report corresponding to a different learning model.

[0112] Now turning to Figure 4, The figure illustrates a timing diagram of an example method for configuring a user equipment 115 through a learning model metric report configuration from a network node / RAN 105. At action 405, a UE or wireless transmit and receive unit (WTRU) 115 (hereinafter sometimes referred to only as UE 115) transmits capability information to a serving cell RAN 105. The capability information may include an indication of radio function learning model information, which may include learning model information corresponding to one or more radio function learning models of the user equipment. The capability information may include an indication of one or more radio functions that can be implemented, operated, executed, or otherwise facilitated by UE 115. The capability information may include information indicating the type of AI / ML learning model (e.g., supervised, unsupervised, reinforcement learning, etc.) that may correspond to, support, or facilitate a radio function. At action 410, RAN 105 transmits, and UE / WTRU 115 receives one or more AI / ML learning model management configurations from RAN 105. The learning model management configuration may include format information corresponding to an AI / ML model management request that can be made by UE 115.

[0113] The format information transmitted and received at action 410 may include: information currently indicated by UE 115 for generating learning model metrics for each active learning model, which may have a model identifier assigned by RAN 105 associated therewith; information indicating the generation of aggregated metric information corresponding to multiple or all currently potentially active learning models based on the associated model identifier of the model assigned by RAN 105; information corresponding to the historical metrics of the determined or configured number of learning models by active learning model; and the corresponding RAN-assigned model identifier. The format information may include learning model-specific performance metrics to report according to the model identifier or reporting conditions of the model management request, including the reporting period or learning model degradation criteria, which may be referred to as model performance metric criteria, such as thresholds associated with model-specific configured metrics, used to trigger the transmission of a model management request from UE 115 to RAN 105 based on non-satisfaction of the model performance metric criteria.

[0114] At action 415, under conditions where the period for transmitting an AI / ML learning model management request has expired or a relevant reporting condition for the learning model has been met (e.g., a monitored metric fails to meet a standard), the UE / WTRU 115 may transmit a learning model management request based on a configured AI model management request format. The learning model management request corresponds to one or more AI / ML learning models that can actively facilitate radio functions at the UE model. The AI model management request format indicates a request for deactivating one or more active AI models.

[0115] The RAN 105 receives the learning model management request transmitted by the UE 115 at action 415. The UE 115 may have active AI / ML learning models that actively facilitate radio functions. At action 420, the RAN node 105 may transmit a model deactivation request associated with one or more indicated model identifiers of one or more learning models operating on the UE 115. The model deactivation request may instruct the UE 115 to deactivate one or more learning models that are currently facilitating radio functions, such that the radio functions revert to operating according to a previous learning model configuration, a default learning model configuration, or a default deterministic radio function model. At action 425, the RAN node 105 may abort or flush an active inference assistance signaling procedure that may correspond to the deactivated model at the UE 115, based on the learning model identifier associated with the deactivated learning model at the UE 115.

[0116] In an embodiment, the RAN 105 may transmit, at action 420, a request to retransmit the learning model management request transmitted at action 415 for the UE 115 to verify that there is a problem at the UE.

[0117] AI / ML model retraining.

[0118] Based on, for example, reference Figure 2 Figure 3 or Figure 4In the case of a poorly performing AI / ML learning model detected by the described embodiments, the serving RAN can assist the serving UE device with an additional training dataset to calibrate and recover the degraded inference performance of the poorly performing model. The additional training dataset can be dedicated to radio functions facilitated by the poor AI / ML model. Accordingly, a list of training data codebooks is defined, where each of one or more of the codebooks is associated with certain radio functions such as power control, scheduling, or beam management. The training codebook can include a training dataset that is specifically designed to train or calibrate a learning model that performs a specific radio function. Accordingly, the training codebook can indicate to the UE device various datasets, training / retraining durations, timing resolutions (e.g., the time scale or instance between retraining data transmissions according to OFDM symbols, mini-slots, slots, frames, the aggregated number of frames, etc.), or a set of resources configured dynamically or semi-statically for the model. The RAN node can configure the device with available training codebooks for various radio functions. Under the condition that the user equipment device or the network node (based on the metrics sent by the user equipment) detects or determines a poorly performing model for one or more active radio functions, a request indication indicating a request for inference retraining or model recovery can be transmitted together with a recommended inference codebook determined based on the real-time performance of the AI / ML model (based on the monitored metrics) and the tolerance / requirements of the corresponding radio function. The network RAN can dynamically or semi-statically schedule the transmission of the selected training dataset with the corresponding timing duration, resource size, and timing resolution. Multiple available datasets for each AI / ML-facilitated radio function can be transmitted to facilitate the AI / ML learning model to perform different operations for the same radio function, with training datasets customized with respect to their corresponding real-time AI / ML performance.

[0119] The performance of various AI / ML models that perform different radio functions can depend on several factors, including traffic channel conditions, traffic distribution, interference statistics, link adaptation, scheduling determination, etc. Such factors can be time-varying, where the rate of change depends on factors such as the traffic load level of the RAN or the type of traffic transmission (e.g., short and sporadic transmissions or larger payload transmissions). Therefore, the AI / ML learning model at the user equipment device can be occasionally improved to obtain or retain the desired inference performance of the learning model. In an example, for an implementation with an AI / ML learning model specifically running at the user equipment, the network RAN can provide assistance signaling and datasets for calibrating and improving the corresponding inference performance in the case where a poorly performing AI / ML learning model is detected (e.g., the learning model metrics do not meet the learning model parameter criteria or multiple criteria).

[0120] In Figure 5In the illustrated example learning model management retraining configuration 500, the inference retraining data set 520 can be dedicated to each of one or more radio functions 515, and the one or more radio functions 515 can be performed or facilitated by corresponding AI / ML models to be improved at the user equipment. The AI / ML learning models that can facilitate different radio functions can be configured or reconfigured with different inference retraining data sets. For example, an AI / ML learning model that facilitates a radio function for deriving power control at the user equipment device can use a data set that causes the user equipment to perform a number of uplink transmissions with a number of transmission power level values and power control parameters. However, a different learning model that facilitates, for example, beam management can use a data set that includes parameters used to facilitate beam scanning, beam detection, and beam recovery procedures. Thus, a user equipment device with AI / ML capabilities can be configured with various AI / ML inference retraining data sets 520, where the retraining sets can be associated with certain radio functions 515 and can be dynamically configured according to parameters 525 that can include data set length, resource size, time resolution, and coordinated network device actions. The retraining data set 520 can be delivered to the user equipment device using a system information block, RRC establishment signaling, or via direct downlink control information ("DCI"). In addition, for certain radio functions, the network RAN can provide both sampling and tagging in the data set 520. For other radio functions, the RAN can initially transmit a first data set 520 (e.g., samples) and then transmit a second data set 520 (e.g., tags) after transmitting network processing information. For example, if the network RAN does not have accurate user equipment device condition information available for use in generating tags, this kind of segmented transmission of the data set 520 can be used.

[0121] Accordingly, a user equipment device with AI / ML capabilities may request inference retraining from the serving RAN, where an indication of the recommended inference retraining data set or codebook 520 corresponds to the respective radio function 510 of a pre-configured inference retraining list and the respective available inference data set / codebook associated with the radio function. The user equipment device may select an inference retraining data set in terms of data length, resource size, and timing resolution based on the current conditions of the active AI / ML learning model and the corresponding radio function performed by the user equipment and include it in the request. In response, the network RAN may transmit an inference retraining data set to the requesting user equipment device based on the requested inference retraining data set. In an embodiment, channel resources for transmitting the inference retraining data set may be dynamically scheduled, for example, using DCI signaling according to the resource set size indicated by the user equipment device. In an embodiment, the transmission of the inference retraining data set may be semi-statically configured based on the inference retraining data set codebook, for example, using RRC signaling and a pre-reserved resource pool.

[0122] In another example embodiment, the inference training data set may be labeled or unlabeled to facilitate AI / ML learning models that utilize supervised or unsupervised learning capabilities, respectively. Accordingly, the network RAN may transmit inference data samples and the respective labels for each data sample. The data labels of the inference training data set may be dynamically configured according to usage and the radio function facilitated by the AI / ML learning model. Various labels may indicate different radio parameters or settings that may be adopted when transmitting the inference training data set.

[0123] Thus, the network RAN may transmit a set of inference training codebooks with an associated set of labels, or the network RAN may transmit a single inference data set, or a set of labels for a respective previously transmitted inference data set. The latter case may be useful in certain AI / ML learning model deployments where the user equipment device may have received inference data samples but may still be missing the associated set of labels. In such a case, the user equipment device may transmit a request for the set of AI / ML learning model labels corresponding to the previously received set of inference samples to the serving cell RAN.

[0124] Now turning to Figure 6 , the figure illustrates a timing diagram of an example method embodiment 600 for configuring a user equipment 115 with a learning model management retraining configuration from a network node / RAN 105. At action 605, the user equipment 115 transmits learning model capabilities, similar to the transmission of learning model capabilities described with reference to Figure 4 action 405 shown. Continuing Figure 6In an embodiment, at operation 605, the RAN 105 transmits an AI / ML learning model management configuration to the UE 115 based on information transmitted by the UE 115. The AI / ML learning model management configuration may include a set, list, or codebook of available inference retraining data sets, such as Figure 5 the set 520 shown in Figure 5 , where each set is associated with one or more RF functions 515 and is associated with corresponding frequency resource sizes, time resolutions, and durations represented by information elements 525, such as those in Figure 6 Continuing with the embodiment, at operation 615, the UE 115 transmits an AI / ML learning model management request for one or more AI / ML learning models active at the UE under the condition that the configured period for transmitting the AI model management request has expired, or a reporting condition associated with the triggering of the transmission of the model management request is met or satisfied. The AI / ML model management request may be generated or transmitted according to a configured AI model management request format and may include an indication of the recommended inference retraining data set. In an example, the indication of the recommended inference retraining data set may include a data set index selected from a pre-configured list of data sets, which indicates a request for immediate model retraining or inference retraining for the recommended data set.

[0125] The AI / ML learning model management configuration transmitted at operation 610 may include the format of information for generating or transmitting an AI model management request, e.g., individual and current indications for each active model associated with a model identifier, an indication for aggregating and current active model identifiers, or a predetermined or configured number of model history indications for each active model corresponding to the respective model identifier. The AI / ML learning model management configuration may include reporting conditions or criteria that, if met, complied with, or otherwise triggered, may cause the UE 115 to generate a model management request or transmit a model management request. The reporting conditions may include model parameter metrics or multiple criteria, such as a reporting period or a learning model degradation criterion, which may be referred to as a model performance metric, e.g., a threshold, used to trigger the transmission of a model management request based on the non-satisfaction of the criterion or multiple criteria.

[0126] The RAN node 105 receives, at action 615, an AI / ML learning model management request transmitted from the UE 115, and at action 620, the RAN 105 schedules and transmits a retraining data set corresponding to the model identifier indicated in the request transmitted at action 615. The retraining data set is transmitted from the RAN 105 to the UE 115 as part of the control instruction transmission. The UE 115 may perform retraining based on the received retraining data set or set or other information transmitted at action 620.

[0127] Now turning to Figure 7 , the figure illustrates a flowchart of an example method 700 for managing one or more learning models that may facilitate one or more corresponding radio functions. Method 700 begins at action 705. At action 710, the user equipment transmits information corresponding to one or more learning models used at the user equipment to a RAN node serving the user equipment to facilitate one or more corresponding radio functions. At action 715, after receiving the learning model information transmitted at action 710, the RAN generates a learning model management configuration based on the learning model information. At action 720, the user equipment monitors metrics corresponding to learning model parameters. The parameters measured by the user equipment at action 720 may include learning model parameters, such as the statistical parameters described with reference to Table 1, which are not necessarily radio function metrics. Examples of radio function metrics may include signal strength, beam identifier, etc. In an embodiment, the user equipment may not determine a control instruction but may transmit, at action 740, the metrics monitored at action 720 to the RAN.

[0128] However, at action 725, the user equipment may determine a control instruction. The user equipment may determine whether the metrics monitored at action 720 meet a learning model performance metric standard based on the metrics monitored at action 720. If the monitored metrics meet or comply with the standard, such that for example the user equipment may potentially determine a recommended control instruction that may remedy unsatisfactory performance through the learning model at the user equipment, then method 700 proceeds to action 730. At action 730, the user equipment may determine a control instruction request and transmit, at action 735, the control instruction request to the RAN. After transmitting the control instruction request at action 735, method 700 proceeds to action 745.

[0129] At action 745, the RAN may determine a control instruction. If reaching action 745 via action 735, the RAN may evaluate the control instruction request transmitted from the user equipment at action 735 and determine whether the implementation of the control action is likely to mitigate the performance degradation of one or more learning models determined by the user equipment at action 730. For example, the RAN may know or have been notified of network conditions or other conditions that may be unrelated to the user equipment and that may have caused the user equipment at action 730 to determine that a control instruction would be beneficial to the user equipment, and the RAN may determine that the control instruction requested by the UE would be beneficial and may generate a control instruction for the UE to implement the operation requested in the control operation request transmitted at action 735.

[0130] Or the RAN may determine at action 745 a control instruction that should implement a control instruction to reject the request transmitted at action 735. In other words, if the RAN determines at action 745 that the control action request generated at action 730 does not need to be implemented because it may not improve the performance of one or more learning models at the user equipment, the RAN may determine at action 745 a control instruction that does not change the operation of one or more learning models that the user equipment may implement.

[0131] If reaching action 745 via action 740, the RAN may determine a control instruction at action 745 based on analyzing the metrics monitored at action 720 and transmitted at 740 with respect to the learning model performance criteria.

[0132] After determining the control instruction at action 745, method 700 proceeds to step 750 and transmits the determined control instruction to the user equipment. At action 755, the user equipment may implement the control instruction, as described above, and the control instruction may include doing nothing. Or the control instruction may include an instruction for the user equipment to deactivate one or more learning models corresponding to the metrics monitored at action 720. The control instruction may include an instruction indicating that the user equipment resume performing radio functions under the default learning model or a default deterministic model that does not include a learning model or under an operation facilitated by the default learning model or a default deterministic model that does not include a learning model.

[0133] The RAN may also perform operations according to the control instruction. For example, the RAN may flush, clear, or delete information, data, or configuration values corresponding to one or more learning models at the user equipment, where the control instruction transmitted at action 750 includes a delete instruction. The flush at action 755 may include flushing or deleting the inference assistance signaling process for the deactivated model according to the model identifier executed by the UE. After performing the operation at action 755, method 700 proceeds to action 760 and ends.

[0134] The control instruction determined at operation 745 may include an instruction to retrain the learning model, and the metric is monitored for the learning model at operation 720. For example, at operation 715, the instruction to retrain the learning model may include a retraining data set, or an index of the retraining data set that the user equipment may use to look up in the retraining codebook that has been sent from the RAN to the user equipment. If the control instruction determined at operation 745 is for the user equipment to retrain one or more learning models, then at operation 755, the user equipment performs the retraining of one or more learning models based on the information, data set, codebook, or other data transmitted by the RAN at operation 750. After performing the operation at operation 755, method 700 proceeds to operation 760 and ends.

[0135] Now turning to Figure 8 , which illustrates an example embodiment method 800, including receiving, by a user equipment including a processor, from a radio access network node at block 810 a model management indication configuration corresponding to a first radio function learning model of the user equipment; at block 815, monitoring, by the user equipment, model performance parameters of a first radio function model configuration corresponding to the first radio function learning model implementing a radio function on the user equipment to obtain a monitored model performance metric; at block 820, analyzing the monitored model performance metric with respect to a model performance metric standard to obtain an analyzed monitored performance metric; at block 825, based on the analyzed monitored performance metric being determined not to meet the model performance metric standard, transmitting, by the user equipment, a model management indication to the radio access network node according to the model management indication configuration; at block 830, receiving, by the user equipment, a control instruction based on the model management indication corresponding to the operation of the radio function learning model; and at block 835, implementing at least one control operation according to the control instruction.

[0136] Now turning to Figure 9, the figure illustrates a user equipment 900, which includes a processor at block 905, and the processor is configured to: transmit an indication of radio function learning model information to a radio access network node, where the radio function learning model information includes learning model information corresponding to a radio function learning model; at block 910, in response to the indication of the radio function learning model information, receive a model management indication configuration corresponding to the learning model information; at block 915, monitor model performance parameters of a first radio function model configuration that configures the radio function learning model to implement a radio function via the user equipment to obtain a monitored model performance metric; at block 920, according to the model management indication configuration, transmit the monitored model performance metric to the radio access network node via a model management indication; at block 925, receive a control instruction from the radio access network node, where the control instruction is based on the monitored first model performance metric; at block 930, perform at least one control operation according to the control instruction; at block 935, where at least one control operation includes: deactivating the radio function learning model configured with the first radio function model configuration; and at block 940, activating the radio function learning model configured with a second radio function model configuration to implement a radio function.

[0137] Now turning to Figure 10 , the figure illustrates a non-transitory machine-readable medium 1000 including executable instructions at block 1005, and when the executable instructions are executed by a processor of a user equipment, it facilitates the execution of operations, including: monitoring model performance parameters of a first radio function model configuration corresponding to a radio function learning model that facilitates a radio function to be executed by the user equipment, and obtaining a monitored first model performance metric; at block 1010, analyzing the monitored first model performance metric with respect to a model performance metric standard of the first radio function model configuration, and the analysis obtains an analyzed monitored performance metric; at block 1015, determining a control operation recommendation based on the analyzed monitored performance metric being determined not to meet the model performance metric standard; at block 1020, transmitting the control operation recommendation to a radio access network node according to a model management indication configuration corresponding to the radio function learning model.

[0138] Now turning to Figure 11, which illustrates an example embodiment method 1100, including, at block 1105, receiving, by a radio access network node including a processor, learning model information corresponding to a current radio function learning model of a user equipment from the user equipment; at block 1110, generating, by the radio access network node, a model management indication configuration based on the learning model information; at block 1115, transmitting, by the radio access network node, the model management indication configuration to the user equipment; at block 1120, receiving, by the radio access network node, a performance parameter metric associated with the performance of the current radio function learning model from the user equipment, which corresponds to a learning model performance metric parameter; at block 1125, transmitting, by the radio access network node, a control instruction corresponding to the operation of the current radio function learning model by the user equipment to the user equipment based on the performance parameter metric; and at block 1130, wherein the control instruction includes an instruction to deactivate the current radio function learning model and activate a default radio function.

[0139] Now turning to Figure 12 , which illustrates an example system 1200, which at block 1205 includes computer-executable components of a communication network node, the communication network node including a processor configured to: receive a first indication of first radio function learning model information, the first radio function learning model information including first learning model information corresponding to a first radio function learning model of a first user equipment from the first user equipment; at block 1210, generate a first model management indication configuration corresponding to the first radio function learning model; at block 1215, transmit the first model management indication configuration to the first user equipment; at block 1220, receive a first performance parameter metric associated with the performance of the first radio function learning model from the first user equipment, which corresponds to a first learning model performance metric parameter; at block 1225, transmit a first control instruction corresponding to the operation of the first radio function learning model by the first user equipment to the first user equipment based on the first performance parameter metric; and at block 1230, wherein the first control instruction includes a first retraining instruction to retrain the first radio function learning model to obtain a retrained first radio function learning model, wherein the processor is further configured to: transmit a first retraining data set for use by the first user equipment to retrain the first radio function learning model.

[0140] Now turning to Figure 13, the figure illustrates a non-transitory machine-readable medium 1300 including executable instructions at block 1305, which, when executed by a processor of a radio access network node of a communication network, facilitates the execution of operations, including: receiving, from a user equipment, a first radio function learning model information indication, the first radio function learning model information indication including learning model information corresponding to a first radio function learning model of the user equipment; at block 1310, generating a first model management indication configuration corresponding to the first radio function learning model; at block 1315, transmitting the first model management indication configuration to the user equipment; at block 1320, receiving, from the user equipment, a first model management indication according to the first model management indication configuration, the first model management indication including a first performance parameter metric corresponding to a first learning model performance metric parameter and associated with the performance of the first radio function learning model; and at block 1325, transmitting, based on the first performance parameter metric, a first control instruction corresponding to the operation of the first radio function learning model by the user equipment.

[0141] To facilitate providing additional context for the various embodiments described herein, Figure 14 and the following discussion is intended to provide a brief general description of an implementation of a suitable computing environment 1400 for the various embodiments described herein that may be implemented. Although the embodiments have been described above in the general context of computer-executable instructions that may run on one or more computers, those skilled in the art will recognize that the embodiments may also be implemented in combination with other program modules and / or as a combination of hardware and software.

[0142] In general, program modules include routines, programs, components, data structures, etc. that perform particular tasks or implement particular abstract data types. In addition, those skilled in the art will appreciate that the method may be practiced with other computer system configurations, including single-processor or multi-processor computer systems, minicomputers, mainframe computers, IoT devices, distributed computing systems, and personal computers, handheld computing devices, microprocessor-based or programmable consumer electronic devices, etc., each of which is operatively coupled to one or more associated devices.

[0143] The embodiments illustrated herein may also be practiced in a distributed computing environment where certain tasks are performed by remote processing devices linked through a communication network. In a distributed computing environment, program modules may be located in local and remote memory storage devices.

[0144] Computing devices generally include various media, which may include computer-readable storage media, machine-readable storage media, and / or communication media, the two terms being used differently from each other herein as follows. Computer-readable storage media or machine-readable storage media can be any available storage media accessible by a computer and include both volatile and non-volatile media, and both removable and non-removable media. By way of example and not limitation, computer-readable storage media or machine-readable storage media can be implemented in conjunction with any method or technology for storing information such as computer-readable or machine-readable instructions, program modules, structured data, or unstructured data.

[0145] Computer-readable storage media can include, but are not limited to, random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CDROM), digital versatile disc (DVD), Blu-ray disc (BD) or other optical disc storage, magnetic tape cartridges, magnetic tape, magnetic disk storage or other magnetic storage devices, solid state drives or other solid state storage devices, or other tangible and / or non-transitory media that can be used to store the desired information. In this regard, the terms "tangible" or "non-transitory" as applied to storage, memory, or computer-readable media herein should be understood to exclude only propagating transitory signals per se as a modifier and not to relinquish rights to all standard storage, memory, or computer-readable media that are not only propagating transitory signals per se.

[0146] Computer-readable storage media can be accessed by one or more local or remote computing devices, e.g., via an access request, query, or other data retrieval protocol, for various operations on the information stored on the media.

[0147] Communication media typically embody computer-readable instructions, data structures, program modules, or other structured or unstructured data in a data signal such as a carrier wave or other transport mechanism-modulated data signal, and include any information delivery or transmission medium. The term "modulated data signal" or signals refers to a signal that has one or more of its characteristics set or changed in such a manner as to encode information in one or more signals. By way of example and not limitation, communication media include wired media (such as a wired network or direct line connection), and wireless media (such as acoustic, RF, infrared, and other wireless media).

[0148] Refer again to Figure 14, An example environment 1400 for implementing various embodiments for aspects described herein includes a computer 1402, which includes a processing unit 1404, a system memory 1406, and a system bus 1408. The system bus 1408 couples system components including, but not limited to, the system memory 1406 to the processing unit 1404. The processing unit 1404 can be any of a variety of commercially available processors and can include a cache memory. Dual microprocessors and other multiprocessor architectures can also be used as the processing unit 1404.

[0149] The system bus 1408 can be any of several types of bus structures, which can further interconnect to a memory bus (with or without a memory controller), a peripheral bus, and a local bus using any of a variety of commercially available bus architectures. The system memory 1406 includes a ROM 1410 and a RAM 1412. The basic input / output system (BIOS) can be stored in non-volatile memory such as ROM, erasable programmable read-only memory (EPROM), EEPROM, where the BIOS contains basic routines such as those that help transfer information between elements within the computer 1402 during startup. The RAM 1412 can also include high-speed RAM, such as static RAM for caching data.

[0150] The computer 1402 also includes an internal hard disk drive (HDD) 1414 (e.g., EIDE, SATA), one or more external storage devices 1416 (e.g., a magnetic floppy disk drive (FDD) 1416, a memory stick or flash drive reader, a memory card reader, etc.), and an optical disc drive 1420 (e.g., which can read from or write to a CD-ROM disc, a DVD, a BD, etc.). Although the internal HDD 1414 is illustrated as being within the computer 1402, the internal HDD 1414 can also be configured for external use in a suitable chassis (not shown). Additionally, although not shown in the environment 1400, a solid-state drive (SSD) can be used in addition to, or instead of, the HDD 1414. The HDD 1414, the external storage device 1416, and the optical disc drive 1420 can be connected to the system bus 1408 via an HDD interface 1424, an external storage interface 1426, and an optical disc drive interface 1428, respectively. The interface 1424 for external drive implementation can include at least one or both of a universal serial bus (USB) and Institute of Electrical and Electronics Engineers (IEEE) 1394 interface technologies. Other external drive connection technologies are also contemplated within the embodiments described herein.

[0151] The drive and its associated computer-readable storage medium provide non-volatile storage of data, data structures, computer-executable instructions, etc. For computer 1402, the drive and storage medium accommodate the storage of any data in an appropriate digital format. Although the above-described embodiments of computer-readable storage media relate to corresponding types of storage devices, those skilled in the art should understand that other types of computer-readable storage media, whether currently existing or developed in the future, can also be used in the exemplary operating environment, and further, any such storage medium can contain computer-executable instructions for performing the methods described herein.

[0152] Multiple program modules can be stored in the drive and RAM 1412, including operating system 1430, one or more application programs 1432, other program modules 1434, and program data 1436. All or part of the operating system, applications, modules, and / or data can also be cached in RAM 1412. The systems and methods described herein can be implemented using various commercially available operating systems or combinations of operating systems.

[0153] Computer 1402 can optionally include emulation technology. For example, a hypervisor (not shown) or other intermediary can emulate the hardware environment for operating system 1430, and the emulated hardware can optionally be different from Figure 14 the hardware shown. In such an embodiment, operating system 1430 can include a virtual machine (VM) among multiple VMs hosted at computer 1402. Additionally, operating system 1430 can provide a runtime environment for application programs 1432, such as a Java runtime environment or a.NET framework. A runtime environment is a consistent execution environment that allows application 1432 to run on any operating system that includes the runtime environment. Similarly, operating system 1430 can support containers, and application 1432 can be in the form of a container, which is a lightweight, independent, executable software package that includes, for example, code, runtime, system tools, system libraries, and application settings.

[0154] Further, computer 1402 can include a security module, such as a Trusted Platform Module (TPM). For example, for the TPM, before loading the next boot component, the boot component hashes the next boot component in time and waits for the result to match a security value. This process can occur at any layer in the code execution stack of computer 1402, for example, at the application execution level or in an operating system (OS) kernel-level application, so that security can be achieved at any code execution level.

[0155] A user may input commands and information into computer 1402 through one or more wired / wireless input devices (such as keyboard 1438, touch screen 1440, and a pointing device such as mouse 1442). Other input devices (not shown) may include a microphone, an infrared (IR) remote control, a radio frequency (RF) remote control or other remote controls, a joystick, a virtual reality controller and / or a virtual reality headset, a game pad, a stylus, an image input device (e.g., (a) camera), a gesture sensor input device, a vision motion sensor input device, an emotion or face detection device, a biometric input device (e.g., a fingerprint or iris scanner), etc. These and other input devices are typically connected to processing unit 1404 through input device interface 1444, which may be coupled to system bus 1408, but may also be connected through other interfaces, such as a parallel port, an IEEE 1394 serial port, a game port, a USB port, an IR interface, an interface, etc.

[0156] Monitor 1446 or other types of display devices may also be connected to system bus 1408 through an interface such as video adapter 1448. In addition to monitor 1446, a computer typically includes other peripheral output devices (not shown), such as speakers, printers, etc.

[0157] Computer 1402 may operate in a networked environment using a logical connection to one or more remote computers (such as (a) remote computer(s) 1450) via wired and / or wireless communication. (A) remote computer(s) 1450 may be a workstation, a server computer, a router, a personal computer, a portable computer, a microprocessor-based entertainment device, a peer device, or other common network nodes, and typically includes many or all of the elements described with respect to computer 1402, although only memory / storage device 1452 is shown for the sake of brevity. The depicted logical connections include a wired / wireless connection to a local area network (LAN) 1454 and / or a larger network (e.g., a wide area network (WAN) 1456). Such LAN and WAN network environments are common in offices and companies and facilitate enterprise-wide computer networks such as intranets, all of which may be connected to a global communication network, such as the Internet.

[0158] When used in a LAN networking environment, computer 1402 may be connected to local network 1454 through a wired and / or wireless communication network interface or adapter 1458. Adapter 1458 may facilitate wired or wireless communication to LAN 1454, which may also include a wireless access point (AP) disposed thereon for communicating with adapter 1458 in a wireless mode.

[0159] When used in a WAN networking environment, computer 1402 can include a modem 1460 or can be connected to a communication server on WAN 1456 via other devices for establishing communications on the WAN 1456 (such as via the Internet). The modem 1460 can be internal or external and a wired or wireless device, and can be connected to the system bus 1408 via the input device interface 1444. In a networked environment, program modules depicted relative to computer 1402 or portions thereof can be stored in the remote memory / storage device 1452. It should be understood that the network connections shown are examples and other devices for establishing a communications link between computers can be used.

[0160] When used in a LAN or WAN networking environment, computer 1402 can access a cloud storage system or other network-based storage systems in addition to or instead of the external storage device 1416 described above. Generally, the connection between computer 1402 and the cloud storage system can be established on the LAN 1454 or WAN 1456 respectively through, for example, an adapter 1458 or a modem 1460. When connecting computer 1402 to an associated cloud storage system, the external storage interface 1426 can manage the storage provided by the cloud storage system with the help of the adapter 1458 and / or the modem 1460, just like other types of external storage. For example, the external storage interface 1426 can be configured to provide access to cloud storage sources as if those sources were physically connected to computer 1402.

[0161] Computer 1402 can operably communicate with any wireless device or entity operably set in wireless communication, such as printers, scanners, desktop and / or portable computers, portable data assistants, communication satellites, any device or location associated with a wirelessly detectable tag (such as kiosks, newsstands, product display stands, etc.) and telephones. This can include wireless fidelity (Wi-Fi) and wireless technologies. Thus, the communication can be of a predefined structure like a conventional network or merely an ad hoc communication between at least two devices.

[0162] Go to Figure 15, which illustrates a block diagram of an example UE 1560. The UE 1560 can include a smart phone, a wireless tablet, a laptop computer with wireless capabilities, a wearable device, a machine device that can facilitate vehicle telematics, a tracking device, a remote sensing device, etc. The UE 1560 includes a first processor 1530, a second processor 1532, and a shared memory 1534. The UE 1560 includes radio front-end circuitry 1562, which may be referred to herein as a transceiver, but is understood to generally include transceiver circuitry, separate filters, and separate antennas for facilitating transmission and reception of signals over wireless links such as Figure 1 one or more of the wireless links 125, 135, and 137 shown. Additionally, the transceiver 1562 can include multiple circuit groups, or can be tunable to accommodate different frequency ranges, different modulation schemes, or different communication protocols to facilitate long-range wireless links (such as the link), device-to-device links (such as link 135), and short-range wireless links (such as link 137).

[0163] Continuing Figure 15 with the implementation, the UE 1560 may also include a SIM 1664 or SIM profile, which may include information stored in a memory (memory 34 or a separate memory section) for facilitating wireless communication with Figure 1 the RAN 105 or the core network 130 shown. Figure 15 The SIM 1564 is shown as a single component in the shape of a conventional SIM card, but it should be understood that the SIM 1564 can represent multiple SIM cards, multiple SIM profiles, or multiple eSIMs, some or all of which can be implemented in hardware or software. It should be understood that the SIM profile can include information such as security credentials (e.g., encryption keys, values that can be used to generate encryption keys, or shared values that can be shared between the SIM 1564 and another device, which can be Figure 1 a component of the RAN 105 or the core network 130 shown). The SIM profile 1564 can also include identification information unique to the SIM or SIM profile, such as an International Mobile Subscriber Identity (“IMSI”) or information that can form the IMSI.

[0164] SIM 1564 is shown as being coupled to both the first processor portion 1530 and the second processor portion 1532. Such an implementation can provide the following advantages: The first processor portion 1530 may not need to request or receive information or data from the SIM 1564 that the second processor 1532 may request, thus eliminating the use of the first processor as a "medium" when the second processor uses information from the SIM in performing its functions and executing applications. The first processor 1530 (which may be a modem processor or a baseband processor) is shown as being smaller than the processor 1532 (which may be a more complex application processor) to visually indicate the relative complexity (i.e., processing power and performance) between the two processor portions and the corresponding relative operating power consumption levels. Keeping the second processor portion 1532 in a sleep / inactive / low-power state when the UE 1560 does not need it for executing applications and processing application-related data provides the advantage of reducing power consumption when the UE only needs to use the first processor portion 1530 in a listening mode for monitoring regularly configured bearer management and mobility management / maintenance processes, or for monitoring search spaces that the UE has been configured to monitor while the second processor portion remains inactive / sleeping.

[0165] The UE 1560 may also include sensors 1566, such as temperature sensors, accelerometers, gyroscopes, barometers, humidity sensors, etc., that may provide signals to the first processor 1530 or the second processor 1532. The output device 1568 may include, for example, one or more visual displays (e.g., computer monitors, VR applications, etc.), acoustic transducers such as speakers or microphones, vibrating components, etc. The output device 1568 may include software that interfaces with output devices external to the UE 1560 (e.g., visual displays, speakers, microphones, tactile devices, olfactory or gustatory devices, etc.).

[0166] The following glossary of terms given in Table 2 is applicable to one or more of the embodiments disclosed herein.

[0167]

[0168]

[0169] Table 2

[0170] The above embodiments include non-limiting examples of various embodiments. Of course, it is not possible to describe every conceivable combination of components or methods for the purpose of describing the disclosed subject matter, and those skilled in the art will recognize that further combinations and permutations of the various embodiments are possible. The disclosed subject matter is intended to cover all such changes, modifications, and variations that fall within the spirit and scope of the appended claims.

[0171] Regarding the various functions performed by the components, devices, circuits, systems, etc. described above, unless otherwise indicated, the terms (including references to "means") used to describe these components are intended to also include any (multiple) structures (such as functional equivalents) that perform the specified functions of the described components, even if they are not equivalent in structure to the disclosed structures. Additionally, although a particular feature of the disclosed subject matter may be disclosed only with respect to one of several embodiments, such a feature may be combined with one or more other features of other embodiments, which may be desirable and advantageous for any given or particular application.

[0172] The terms "exemplary" and / or "illustrative" or their variants as used herein are intended to mean serving as an example, instance, or illustration. For the avoidance of doubt, the subject matter disclosed herein is not limited by these examples. Additionally, any aspect or design described herein as "exemplary" and / or "illustrative" is not necessarily to be construed as more preferred or advantageous than other aspects or designs, nor does it imply the exclusion of equivalent structures and techniques known to those skilled in the art. Further, insofar as the terms "comprising", "having", "including", and other similar words are used in the detailed description or claims, such terms are intended to be inclusive - in a manner similar to the term "including" as an open transitional word - and do not exclude any additional or other elements.

[0173] The term "or" as used herein refers to an inclusive "or" rather than an exclusive "or". For example, the phrase "A or B" is intended to include instances of A, B, and both A and B. Additionally, the articles "a" and "an" as used in this application and the appended claims should generally be construed to mean "one or more", unless otherwise indicated or clearly apparent from the context as referring to the singular form.

[0174] The term "set" as used herein does not include the empty set, i.e., a set with no elements. Thus, a "set" in this disclosure includes one or more elements or entities. Similarly, the term "group" as utilized herein refers to a collection of one or more entities.

[0175] The terms "first", "second", "third", etc. as used in the claims are used only for clarity and do not otherwise indicate or imply any temporal order, unless the context clearly dictates otherwise. For example, "a first determination", "a second determination", and "a third determination" do not indicate or imply that the first determination will be made before the second determination, or vice versa, etc.

[0176] The implementation of the illustrative embodiments of the present disclosure provided herein, including what is described in the abstract, is not intended to be exhaustive or to limit the disclosed embodiments to the precise forms disclosed. Although specific implementations and examples are described herein for illustrative purposes, those skilled in the art will recognize that various modifications can be considered within the scope of such implementations and examples. In this regard, although the subject matter has been described herein in connection with various embodiments and the corresponding drawings, it should be understood that, where applicable, other similar embodiments can be used, or modifications and additions can be made to the described embodiments to perform the same, similar, alternative, or substitute functions of the disclosed subject matter without departing from the subject matter. Accordingly, the disclosed subject matter should not be limited to any single embodiment described herein, but should be construed in accordance with the breadth and scope of the appended claims below.

Claims

1. A method, comprising: receiving, by a user equipment including a processor, a model management indication configuration corresponding to a first radio function learning model of the user equipment from a radio access network node; monitoring, by the user equipment, model performance parameters of a first radio function model configuration corresponding to the first radio function learning model for implementing a radio function on the user equipment to obtain a monitored model performance metric; analyzing the monitored model performance metric with respect to a model performance metric criterion to obtain an analyzed monitored performance metric; based on the analyzed monitored performance metric being determined not to meet the model performance metric criterion, transmitting, by the user equipment, a model management indication to the radio access network node according to the model management indication configuration; receiving, by the user equipment, a control instruction corresponding to an operation of the radio function learning model and based on the model management indication; and performing at least one control operation according to the control instruction.

2. The method according to claim 1, wherein the at least one control operation comprises: deactivating the first radio function learning model; and activating a configured default radio function to perform the radio function, wherein the at least one control operation is determined by the radio access network node.

3. The method according to claim 1, wherein the first radio function model configuration comprises at least one configured radio function model parameter value, and wherein the at least one control operation comprises retraining the first radio function learning model using the at least one configured radio function model parameter value to obtain a retrained first radio function learning model.

4. The method according to claim 1, wherein the first radio function model configuration comprises at least one configured radio function model parameter value, and the method further comprises: receiving an updated first radio function model configuration, the updated first radio function model configuration comprising at least one configured updated radio function model parameter value; replacing the at least one configured radio function model parameter value with the at least one configured updated radio function model parameter value; and implementing the radio function by the first radio function learning model using the at least one configured updated radio function model parameter value.

5. The method according to claim 1, wherein the model management indication comprises a control operation request for requesting an updated first radio function model configuration, and the method further comprises: receiving, from the radio access network node in response to the model management indication, the updated first radio function model configuration; in response to receiving the updated first radio function model configuration, deactivating the use of the first radio function learning model according to the first radio function model configuration; and activating the use of the first radio function learning model according to the updated first radio function model configuration.

6. The method according to claim 1, wherein the at least one control operation comprises retraining the first radio function learning model using at least one configured radio function model parameter value to obtain a retrained first radio function learning model.

7. The method according to claim 1, wherein the model performance parameter comprises at least one of the following: mean squared error value, root mean squared error value, normalized mean squared error value, mean absolute error value, R-squared value, generalized cosine similarity value, squared generalized cosine similarity value, accuracy value, number of true negative values, number of true positive values, number of false negative values, number of false positive values, precision, recall value, or F1 score value.

8. The method according to claim 1, further comprising: An indication of radio function learning model information transmitted by the user equipment to the radio access network node, the radio function learning model information comprising learning model information corresponding to the first radio function learning model; wherein the indication of the radio function learning model information is a first indication, and wherein the learning model information comprises at least one of the following: at least one learning model type indication, at least one radio function indication indicating at least one corresponding radio function, a list of at least one metric parameter corresponding to the at least one learning model type indication that is estimated or reported, a value representing the number of learning models stored by the user equipment for implementing the at least one corresponding radio function, or a second indication of at least one learning model category corresponding to at least one data set used by the radio access network node to determine the monitored performance metric being analyzed.

9. The method according to claim 1, wherein the first radio function learning model is selected from different radio function learning models capable of implementing the radio function.

10. The method according to claim 9, wherein the control instruction comprises an instruction to deactivate the use of the first radio function learning model, wherein the control instruction comprises an instruction to activate a second radio function learning model of the different radio function learning models to implement the radio function, wherein the first radio function model configuration defines the first radio function learning model and the second radio function model configuration defines the second radio function learning model, and wherein the first radio function model configuration is different from the second radio function model configuration.

11. A user equipment, comprising: a processor configured to: transmit an indication of radio function learning model information comprising learning model information corresponding to a radio function learning model to a radio access network node; receive a model management indication configuration corresponding to the learning model information in response to the indication of the radio function learning model information; monitor a model performance parameter of a first radio function model configuration that configures the radio function learning model to implement a radio function via the user equipment to obtain a monitored model performance metric; Configure according to the model management indication, and transmit the monitored model performance metric to the radio access network node via the model management indication; Receive a control instruction from the radio access network node, where the control instruction is based on the monitored first model performance metric; And Implement at least one control operation according to the control instruction.

12. The user equipment according to claim 11, wherein the processor is further configured to: Receive an updated radio function model configuration; and Implement the radio function by using the radio function learning model of the updated radio function model configuration.

13. The user equipment according to claim 11, wherein the processor is further configured to: Receive a training radio function model configuration, wherein the at least one control operation includes retraining the radio function learning model by using the training radio function model configuration.

14. The user equipment according to claim 11, wherein the at least one control operation includes: Deactivate the radio function learning model configured with the first radio function model configuration; And Activate the radio function learning model configured with the second radio function model configuration to implement the radio function.

15. The user equipment according to claim 11, wherein the model performance parameter includes a mathematical function.

16. A non-transitory machine-readable medium includes executable instructions that, when executed by a processor of a user equipment, facilitate the execution of operations, the operations including: Monitor the model performance parameter of the first radio function model configuration, the first radio function model configuration corresponding to a radio function learning model that facilitates a radio function to be executed by the user equipment, and the monitoring obtains the monitored first model performance metric; Analyze the monitored first model performance metric with respect to the model performance metric standard of the first radio function model configuration, and the analysis obtains the analyzed monitored performance metric; Based on the determined non-satisfaction of the analyzed monitored performance metric with respect to the model performance metric standard, determine a control operation recommendation; And Transmit the control operation recommendation to a radio access network node according to the model management indication configuration corresponding to the radio function learning model.

17. The non-transitory machine-readable medium according to claim 16, wherein the processor is further configured to execute operations, the operations including: In response to transmitting the control operation recommendation, receive a control instruction including at least one control operation; And Implement the at least one control operation according to the control instruction, wherein the control instruction is determined by the radio access network node based on the control operation recommendation.

18. The non-transitory machine-readable medium according to claim 16, wherein the control operation recommendation includes a recommendation to deactivate the use of the first radio function model configuration for facilitating the radio function and activate a second radio function model configuration, and wherein the user equipment is configured according to the first radio function model configuration and the second radio function model configuration.

19. The non-transitory machine-readable medium according to claim 18, wherein the processor is further configured to perform operations, the operations including: Transmitting a radio function learning model information indication including learning model information corresponding to the radio function learning model to the radio access network node; Receiving the first radio function model configuration and the model management indication configuration in response to the learning model information; Generating the control operation recommendation according to the model management indication configuration; And Scheduling the control operation recommendation to be transmitted via an uplink data channel corresponding to the user equipment.

20. The non-transitory machine-readable medium according to claim 19, wherein the model management indication configuration includes at least one of the following: at least one model identifier corresponding to the radio function learning model, at least one learning model filter type, at least one quantization configuration corresponding to the model performance parameter, the model performance metric standard, or one or more performance metrics monitored over the configured monitoring period.

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