Monitoring and updating machine learning models

Abstract

Methods, systems, and devices for wireless communication are described. The method may include a user equipment (UE) may receive a control signal indicating an event trigger for reporting performance parameters associated with a machine learning model. Further, the UE may receive one or more signals indicative of input data for monitoring performance of the machine learning model by the UE. Upon detecting the event trigger, the UE may send a report including the performance parameter.

Description

[0001] Cross-references

[0002] This patent application claims the benefit of U.S. patent application No. 17 / 956,200, entitled “MONITORING AND UPDATING MACHINE LEARNING MODELS,” filed by KUMAR et al. on September 29, 2022, which is assigned to the assignee of this application and is expressly incorporated herein by reference. Technical Field

[0003] The following involves wireless communications, including monitoring and updating machine learning models. Background Art

[0004] Wireless communication systems are widely deployed to provide various types of communication content, such as voice, video, packet data, messaging, broadcasting, and the like. These systems may be able to support communication with multiple users by sharing available system resources (e.g., time, frequency, and power). Examples of such multiple access systems include fourth generation (4G) systems (such as long term evolution (LTE) systems, advanced LTE (LTE-A) systems, or LTE-A Pro systems) and fifth generation (5G) systems (which may be referred to as new radio (NR) systems). These systems may employ techniques such as code division multiple access (CDMA), time division multiple access (TDMA), frequency division multiple access (FDMA), orthogonal FDMA (OFDMA), or discrete Fourier transform spread orthogonal frequency division multiplexing (DFT-S-OFDM). A wireless multiple access communication system may include one or more base stations, each of which supports wireless communications for communication devices, which may be referred to as user equipment (UE).

[0005] In some examples, the wireless communication system may implement a machine learning model. Machine learning can be described as a branch of artificial intelligence that provides systems with the ability to improve and learn from experience. To implement machine learning, a device (e.g., a network entity or a UE) of a wireless communication system may use a machine learning model for wireless communication. Summary of the invention

[0006] The described techniques relate to improved methods, systems, devices, and apparatuses that support monitoring and updating machine learning models. For example, the described techniques enable devices of a network to monitor the performance of machine learning models implemented at these devices. In some examples, a user equipment (UE) may receive a control signal from a network entity or server. The control signal may indicate an event trigger for reporting a performance parameter associated with the machine learning model. Upon receiving the control signal, the UE may receive one or more signals indicating input data for monitoring the performance of the machine learning model (e.g., a true value for the machine learning model) by the UE. When the UE detects an event trigger, the UE may send a report including the performance parameters. The UE may determine the performance parameters based on a comparison of the input data with the data output from the machine learning model. Upon receiving the report, the network entity or server may exchange signaling with the UE to update the machine learning model. Such techniques may enable efficient monitoring and reporting of the performance of the machine learning model.

[0007] A method for wireless communication at a UE is described. The method may include: receiving a control signal indicating an event trigger for reporting a performance parameter associated with a machine learning model; receiving one or more signals indicating input data for monitoring performance of the machine learning model by the UE; and sending a report including the performance parameter based on monitoring the performance of the machine learning model and based on a comparison between the input data and output data of the machine learning model based on detecting the event trigger.

[0008] An apparatus for wireless communication at a UE is described. The apparatus may include: a processor; a memory coupled to the processor; and instructions stored in the memory. The instructions may be executed by the processor to cause the apparatus to: receive a control signal indicating an event trigger for reporting a performance parameter associated with a machine learning model; receive one or more signals indicating input data for monitoring the performance of the machine learning model by the UE; and send a report including the performance parameter based on detecting the event trigger, wherein the performance parameter is based on monitoring the performance of the machine learning model and based on a comparison between the input data and the output data of the machine learning model.

[0009] Another apparatus for wireless communication at a UE is described. The apparatus may include: a component for receiving a control signal indicating an event trigger for reporting a performance parameter associated with a machine learning model; a component for receiving one or more signals indicating input data for monitoring performance of the machine learning model by the UE; and a component for sending a report including the performance parameter based on detecting the event trigger, wherein the performance parameter is based on monitoring the performance of the machine learning model and based on a comparison between the input data and output data of the machine learning model.

[0010] A non-transitory computer-readable medium storing code for wireless communication at a UE is described. The code may include instructions executable by a processor to: receive a control signal indicating an event trigger for reporting a performance parameter associated with a machine learning model; receive one or more signals indicating input data for monitoring performance of the machine learning model by the UE; and send a report including the performance parameter based on monitoring the performance of the machine learning model and based on a comparison between the input data and output data of the machine learning model based on detecting the event trigger.

[0011] A method for wireless communication at a node is described. The method may include: sending a control signal indicating an event trigger for reporting a performance parameter associated with a machine learning model; sending one or more signals indicating input data for monitoring performance of the machine learning model by a UE; and receiving a report including the performance parameter, wherein the performance parameter is based on a comparison between the input data and output data of the machine learning model.

[0012] An apparatus for wireless communication at a node is described. The apparatus may include: a processor; a memory coupled to the processor; and instructions stored in the memory. The instructions are executable by the processor to cause the apparatus to: send a control signal indicating an event trigger for reporting a performance parameter associated with a machine learning model; send one or more signals indicating input data for monitoring performance of the machine learning model by a UE; and receive a report including the performance parameter, wherein the performance parameter is based on a comparison between the input data and output data of the machine learning model.

[0013] Another apparatus for wireless communication at a node is described. The apparatus may include: means for sending a control signal indicating an event trigger for reporting a performance parameter associated with a machine learning model; means for sending one or more signals indicating input data for monitoring performance of the machine learning model by a UE; and means for receiving a report including the performance parameter, wherein the performance parameter is based on a comparison between the input data and output data of the machine learning model.

[0014] A non-transitory computer-readable medium storing code for wireless communication at a node is described. The code may include instructions executable by a processor to: send a control signal indicating an event trigger for reporting a performance parameter associated with a machine learning model; send one or more signals indicating input data for monitoring performance of the machine learning model by a UE; and receive a report including the performance parameter, wherein the performance parameter is based on a comparison between the input data and output data of the machine learning model.

[0015] A method for wireless communication at a node is described. The method may include: receiving a first control signal indicating input data for monitoring performance of a machine learning model by the node; generating a performance parameter associated with the machine learning model based on detecting an event trigger, wherein the performance parameter is based on a comparison between the input data and output data of the machine learning model; and sending a second control signal to configure a second machine learning model to a UE or activate the second machine learning model based on the performance parameter.

[0016] An apparatus for wireless communication at a node is described. The apparatus may include: a processor; a memory coupled to the processor; and instructions stored in the memory. The instructions are executable by the processor to cause the apparatus to: receive a first control signal indicating input data for monitoring performance of a machine learning model by the node; generate a performance parameter associated with the machine learning model based on detecting an event trigger, wherein the performance parameter is based on a comparison between the input data and output data of the machine learning model; and send a second control signal to configure a second machine learning model to a UE or activate the second machine learning model based on the performance parameter.

[0017] Another apparatus for wireless communication at a node is described. The apparatus may include: a component for receiving a first control signal indicating input data for monitoring performance of a machine learning model by the node; a component for generating a performance parameter associated with the machine learning model based on detecting an event trigger, wherein the performance parameter is based on a comparison between the input data and output data of the machine learning model; and a component for sending a second control signal to configure a second machine learning model to a UE or activate the second machine learning model based on the performance parameter.

[0018] A non-transitory computer-readable medium storing code for wireless communication at a node is described. The code may include instructions executable by a processor to: receive a first control signal indicating input data for monitoring performance of a machine learning model by the node; generate a performance parameter associated with the machine learning model based on detecting an event trigger, wherein the performance parameter is based on a comparison between the input data and output data of the machine learning model; and send a second control signal to configure a second machine learning model to a UE or activate the second machine learning model based on the performance parameter. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 An example of a wireless communication system that supports monitoring and updating a machine learning model according to one or more aspects of the present disclosure is illustrated.

[0020] Figure 2An example of a network architecture that supports monitoring and updating a machine learning model according to one or more aspects of the present disclosure is illustrated.

[0021] Figure 3 An example of a wireless communication system that supports monitoring and updating a machine learning model according to one or more aspects of the present disclosure is illustrated.

[0022] Figures 4 to 9 An example of a process flow that supports monitoring and updating a machine learning model in accordance with one or more aspects of the present disclosure is illustrated.

[0023] Fig.10 and Fig.11 A block diagram of a device that supports monitoring and updating a machine learning model according to one or more aspects of the present disclosure is shown.

[0024] Fig.12 A block diagram of a communication manager that supports monitoring and updating a machine learning model in accordance with one or more aspects of the present disclosure is shown.

[0025] Fig.13 A diagram of a system including a device that supports monitoring and updating a machine learning model according to one or more aspects of the present disclosure is shown.

[0026] Fig.14 and Fig.15 A block diagram of a device that supports monitoring and updating a machine learning model according to one or more aspects of the present disclosure is shown.

[0027] Fig.16 A block diagram of a communication manager that supports monitoring and updating a machine learning model in accordance with one or more aspects of the present disclosure is shown.

[0028] Fig.17 A diagram of a system including a device that supports monitoring and updating a machine learning model according to one or more aspects of the present disclosure is shown.

[0029] Figures 18 to 23 A flowchart illustrating a method for supporting monitoring and updating a machine learning model according to one or more aspects of the present disclosure is shown. DETAILED DESCRIPTION

[0030] In some examples, a device (e.g., user equipment (UE)) may utilize machine learning to conduct one or more wireless communication processes. When the UE performs one or more wireless communication processes, changes or updates may be made to the machine learning model to enhance the performance of the machine learning model. For example, a new machine learning model may be selected or the machine learning model may be updated. In some examples, if the machine learning model or system performance degrades, the device may change or update the model. In order to determine whether the model or system performance has degraded, the device may monitor the performance and key performance indicators (KPIs) corresponding to the machine learning model. A signaling scheme for reporting KPIs (e.g., to a network entity) has not yet been implemented.

[0031] As described herein, a device (e.g., a UE) may report performance parameters (e.g., KPIs) to a node (e.g., a network entity or a third-party server) based on an event trigger. In some examples, the UE may receive a control signal indicating an event trigger for reporting performance parameters associated with a machine learning model. The event trigger may include a setting change at the UE or node, a location or environment change, or a service change. In addition, the UE may receive a signal indicating monitoring input data associated with the machine learning model. The monitoring input data may include metadata (e.g., a threshold associated with a performance parameter) or a true value for the machine learning model. The UE may monitor the performance parameters using the output of the machine learning model and the monitoring input data, and report the performance parameters to the node when an event trigger is detected. The node may receive the report and send a signal to the UE indicating that the UE activates a new machine model or updates one or more parameters associated with the machine learning model based on the performance parameters. In addition, the node may forward the report to a second node (e.g., for handover preparation). A similar process may be implemented for network-side machine learning model monitoring.

[0032] Aspects of the present disclosure are first described in the context of a wireless communication system. Additional aspects are described in the context of a process flow. Aspects of the present disclosure are further illustrated by and described with reference to apparatus diagrams, system diagrams, and flow diagrams related to monitoring and updating a machine learning model.

[0033] Figure 1 An example of a wireless communication system 100 that supports monitoring and updating a machine learning model according to one or more aspects of the present disclosure is illustrated. The wireless communication system 100 may include one or more network entities 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, a New Radio (NR) network, or a network operating according to other systems and radio technologies, including future systems and radio technologies not explicitly mentioned herein.

[0034] The network entities 105 may be dispersed throughout a geographic area to form the wireless communication system 100, and may include devices in different forms or with different capabilities. In various examples, the network entities 105 may be referred to as network elements, mobility elements, radio access network (RAN) nodes, or network equipment, among other nomenclature. In some examples, the network entities 105 and the UE 115 may communicate wirelessly via one or more communication links 125 (e.g., radio frequency (RF) access links). For example, the network entity 105 may support a coverage area 110 (e.g., a geographic coverage area) within which the UE 115 and the network entity 105 may establish one or more communication links 125. The coverage area 110 may be an example of a geographic area within which the network entity 105 and the UE 115 may support signal communications according to one or more radio access technologies (RATs).

[0035] The UEs 115 may be dispersed throughout the coverage area 110 of the wireless communication system 100, and each UE 115 may be stationary or mobile or both stationary and mobile at different times. The UEs 115 may be devices in different forms or with different capabilities. Figure 1 Some example UEs 115 are illustrated in FIG. 1. The UEs 115 described herein may be capable of supporting communication with various types of devices such as Figure 1 Other UEs 115 or network entities 105 are shown communicating.

[0036] As described herein, a node of the wireless communication system 100 (which may be referred to as a network node or a wireless node) may be a network entity 105 (e.g., any network entity described herein), a UE 115 (e.g., any UE described herein), a network controller, an apparatus, a device, a computing system, one or more components, or another suitable processing entity configured to perform any of the techniques described herein. For example, the node may be a UE 115. As another example, the node may be a network entity 105. As another example, a first node may be configured to communicate with a second node or a third node. In one aspect of this example, the first node may be a UE 115, the second node may be a network entity 105, and the third node may be a UE 115. In another aspect of this example, the first node may be a UE 115, the second node may be a network entity 105, and the third node may be a network entity 105. In other aspects of this example, the first node, the second node, and the third node may be different relative to these examples. Similarly, references to UE 115, network entity 105, apparatus, device, computing system, etc. may include disclosure of UE 115, network entity 105, apparatus, device, computing system, etc. as nodes. For example, a disclosure that UE 115 is configured to receive information from network entity 105 also discloses that the first node is configured to receive information from a second node.

[0037] In some examples, the network entities 105 may communicate with the core network 130, or with each other, or both. For example, the network entities 105 may communicate with the core network 130 via one or more backhaul communication links 120 (e.g., according to S1, N2, N3, or other interface protocols). In some examples, the network entities 105 may communicate with each other via the backhaul communication links 120 (e.g., according to X2, Xn, or other interface protocols) directly (e.g., directly between the network entities 105) or indirectly (e.g., via the core network 130). In some examples, the network entities 105 may communicate with each other via the midhaul communication links 162 (e.g., according to the midhaul interface protocol) or the fronthaul communication links 168 (e.g., according to the fronthaul interface protocol) or any combination thereof. The backhaul communication links 120, the midhaul communication links 162, or the fronthaul communication links 168 may be or include one or more wired links (e.g., electrical links, optical fiber links), one or more wireless links (e.g., radio links, wireless optical links), etc. or various combinations thereof. UE 115 may communicate with core network 130 via communication link 155 .

[0038] One or more of the network entities 105 described herein may include or may be referred to as a base station 140 (e.g., a transceiver base station, a radio base station, an NR base station, an access point, a radio transceiver, a Node B, an evolved Node B (eNB), a next generation Node B, or a Gigabit Node B (any of which may be referred to as a gNB), a 5G NB, a next generation eNB (ng-eNB), a home Node B, a home evolved Node B, or other suitable terms). In some examples, the network entity 105 (e.g., a base station 140) may be implemented in an aggregated (e.g., monolithic, stand-alone) base station architecture, which may be configured to utilize a protocol stack physically or logically integrated within a single network entity 105 (e.g., a single RAN node, such as a base station 140).

[0039] In some examples, the network entity 105 may be implemented in a disaggregated architecture (e.g., a disaggregated base station architecture, a disaggregated RAN architecture) that may be configured to utilize a protocol stack that is physically or logically distributed between two or more network entities 105, such as an integrated access backhaul (IAB) network, an open RAN (O-RAN) (e.g., a network configuration sponsored by the O-RAN Alliance), or a virtualized RAN (vRAN) (e.g., a cloud RAN (C-RAN)). For example, the network entity 105 may include one or more of the following: a central unit (CU) 160, a distributed unit (DU) 165, a radio unit (RU) 170, a RAN intelligent controller (RIC) 175 (e.g., a near real-time RIC (near RT RIC), a non-real-time RIC (non-RT RIC)), a service management and orchestration (SMO) 180 system, or any combination thereof. The RU 170 may also be referred to as a radio head, a smart radio head, a remote radio head (RRH), a remote radio unit (RRU), or a transmit receive point (TRP). One or more components of the network entity 105 in the decomposed RAN architecture may be co-located, or one or more components of the network entity 105 may be located in distributed locations (e.g., separate physical locations). In some examples, one or more network entities 105 of the decomposed RAN architecture may be implemented as virtual units (e.g., virtual CU (VCU), virtual DU (VDU), virtual RU (VRU)).

[0040] The functional split between CU 160, DU 165, and RU 170 is flexible and may support different functionalities depending on which functions (e.g., network layer functions, protocol layer functions, baseband functions, RF functions, and any combination thereof) are performed at CU 160, DU 165, or RU 170. For example, a functional split of a protocol stack may be employed between CU 160 and DU 165 such that CU 160 may support one or more layers of a protocol stack and DU 165 may support one or more different layers of a protocol stack. In some examples, CU 160 may host higher protocol layer (e.g., layer 3 (L3), layer 2 (L2)) functionality and signaling (e.g., radio resource control (RRC), service data adaptation protocol (SDAP), packet data convergence protocol (PDCP)). The CU 160 may be connected to one or more DUs 165 or RUs 170, and the one or more DUs 165 or RUs 170 may host lower protocol layers, such as layer 1 (L1) (e.g., physical (PHY) layer) or L2 (e.g., radio link control (RLC) layer, medium access control (MAC) layer) functionality and signaling, and may each be at least partially controlled by the CU 160. Additionally or alternatively, a functional split of the protocol stack may be employed between the DUs 165 and RUs 170, such that the DUs 165 may support one or more layers of the protocol stack, and the RUs 170 may support one or more different layers of the protocol stack. The DUs 165 may support one or more different cells (e.g., via one or more RUs 170). In some cases, the functional split between CU 160 and DU 165 or between DU 165 and RU 170 may be within the protocol layer (e.g., some functions of the protocol layer may be performed by one of CU 160, DU 165, or RU 170, while other functions of the protocol layer are performed by different ones of CU 160, DU 165, or RU 170). CU 160 may be further functionally split into CU control plane (CU-CP) and CU user plane (CU-UP) functions. CU 160 may be connected to one or more DUs 165 via midhaul communication links 162 (e.g., F1, F1-c, F1-u), and DU 165 may be connected to one or more RUs 170 via fronthaul communication links 168 (e.g., open fronthaul (FH) interface). In some examples, midhaul communication link 162 or fronthaul communication link 168 may be implemented based on an interface (e.g., a channel) between layers of a protocol stack supported by respective network entities 105 that communicate via such communication links.

[0041] In some wireless communication systems (e.g., wireless communication system 100), infrastructure and spectrum resources for radio access may support wireless backhaul link capabilities to supplement wired backhaul connections, thereby providing an IAB network architecture (e.g., to core network 130). In some cases, in an IAB network, one or more network entities 105 (e.g., IAB nodes 104) may be partially controlled by each other. One or more IAB nodes 104 may be referred to as donor entities or IAB donors. One or more DUs 165 or one or more RUs 170 may be controlled in part by one or more CUs 160 associated with a donor network entity 105 (e.g., donor base station 140). One or more donor network entities 105 (e.g., IAB donors) may communicate with one or more additional network entities 105 (e.g., IAB nodes 104) via supported access and backhaul links (e.g., backhaul communication links 120). The IAB node 104 may include an IAB mobile terminal (IAB-MT) controlled (e.g., scheduled) by a coupled IAB donor's DU 165. The IAB-MT may include an independent set of antennas for relaying communications with the UE 115, or may share the same antennas of the IAB node 104 (e.g., of the RU 170) for access via the DU 165 of the IAB node 104 (e.g., referred to as a virtual IAB-MT (vIAB-MT)). In some examples, the IAB node 104 may include a DU 165 that supports communication links with additional entities (e.g., IAB node 104, UE 115) within a relay chain or configuration (e.g., downstream) of the access network. In such cases, one or more components of the decomposed RAN architecture (e.g., one or more IAB nodes 104 or components of the IAB node 104) may be configured to operate according to the techniques described herein.

[0042] For example, an access network (AN) or RAN may include an access node (e.g., an IAB donor), communications between an IAB node 104, and one or more UEs 115. The IAB donor may facilitate a connection between the core network 130 and the AN (e.g., via a wired or wireless connection to the core network 130). That is, the IAB donor may refer to a RAN node having a wired or wireless connection to the core network 130. The IAB donor may include a CU 160 and at least one DU 165 (e.g., and RU 170), wherein the CU 160 may communicate with the core network 130 via an interface (e.g., a backhaul link). The IAB donor and the IAB node 104 may communicate via an F1 interface according to a protocol defining a signaling message (e.g., an F1 AP protocol). Additionally or alternatively, CU 160 may communicate with the core network via an interface (which may be an example of a portion of a backhaul link) and may communicate with other CUs 160 (e.g., CU 160 associated with an alternative IAB donor) via an Xn-C interface (which may be an example of a portion of a backhaul link).

[0043] An IAB node 104 may refer to a RAN node that provides IAB functionality (e.g., access for UE 115, wireless self-backhaul capabilities, etc.). DU 165 may act as a distributed scheduling node toward child nodes associated with IAB node 104, and IAB-MT may act as a scheduled node toward a parent node associated with IAB node 104. That is, an IAB donor may be referred to as a parent node that communicates with one or more child nodes (e.g., an IAB donor may relay for transmissions to UEs through one or more other IAB nodes 104). Additionally or alternatively, depending on the relay chain or configuration of the AN, an IAB node 104 may also be referred to as a parent node or child node of other IAB nodes 104. Thus, the IAB-MT entity of the IAB node 104 may provide a Uu interface for the child IAB node 104 to receive signaling from the parent IAB node 104 , and a DU interface (eg, DU 165 ) may provide a Uu interface for the parent IAB node 104 to signal to the child IAB node 104 or the UE 115 .

[0044] For example, the IAB node 104 may be referred to as a parent node supporting communications for a child IAB node or as a child IAB node associated with an IAB donor, or both. The IAB donor may include a CU 160 having a wired or wireless connection (e.g., backhaul communication link 120) to the core network 130, and may act as a parent node of the IAB node 104. For example, the DU 165 of the IAB donor may relay the transmission to the UE 115 through the IAB node 104, or may directly signal the transmission to the UE 115, or both. The CU 160 of the IAB donor may signal the establishment of a communication link to the IAB node 104 via the F1 interface, and the IAB node 104 may schedule the transmission (e.g., the transmission relayed from the IAB donor to the UE 115) via the DU 165. That is, data may be relayed to and from the IAB node 104 via signaling via the NR Uu interface of the MT to the IAB node 104. Communications with the IAB node 104 may be scheduled by the DU 165 of the IAB donor, and communications with the IAB node 104 may be scheduled by the DU 165 of the IAB node 104 .

[0045] Where the techniques described herein are applied in the context of a disaggregated RAN architecture, one or more components of the disaggregated RAN architecture may be configured to support monitoring and updating of machine learning models as described herein. For example, some operations described as being performed by a UE 115 or a network entity 105 (e.g., a base station 140) may additionally or alternatively be performed by one or more components of the disaggregated RAN architecture (e.g., an IAB node 104, a DU 165, a CU 160, a RU 170, a RIC 175, a SMO 180).

[0046] UE 115 may include or may be referred to as a mobile device, a wireless device, a remote device, a handheld device, or a subscriber device, or some other suitable term, where a "device" may also be referred to as a unit, a station, a terminal, or a client, etc. UE 115 may also include or may be referred to as a personal electronic device, such as a cellular phone, a personal digital assistant (PDA), a tablet computer, a laptop computer, or a personal computer. In some examples, UE 115 may include or may be referred to as a wireless local loop (WLL) station, an Internet of Things (IoT) device, an Internet of Everything (IoE) device, or a machine type communication (MTC) device, etc., which may be implemented in various objects such as appliances or vehicles, meters, etc.

[0047] The UE 115 described herein may be capable of communicating with various types of devices, such as other UEs 115, which may sometimes act as relays, as well as network entities 105 and network equipment including macro eNBs or gNBs, small cell eNBs or gNBs, or relay base stations, etc. Figure 1shown.

[0048] The UE 115 and the network entity 105 may wirelessly communicate with each other via one or more communication links 125 (e.g., access links) using resources associated with one or more carriers. The term "carrier" may refer to a collection of RF spectrum resources having a physical layer structure defined for supporting the communication link 125. For example, a carrier for the communication link 125 may include a portion of an RF spectrum band (e.g., a bandwidth portion (BWP)) that operates according to one or more 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 operations, user data, or other signaling. The wireless communication system 100 may support communications with the UE 115 using carrier aggregation or multi-carrier operation. Depending on the carrier aggregation configuration, the UE 115 may be configured with multiple downlink component carriers and one or more uplink component carriers. Carrier aggregation may be used for both frequency division duplex (FDD) and time division duplex (TDD) component carriers. Communication between the network entity 105 and other devices may refer to communication between these devices and any part (e.g., entity, sub-entity) of the network entity 105. For example, the terms "send", "receive", or "communicate" when referring to the network entity 105 may refer to any part of the network entity 105 (e.g., base station 140, CU 160, DU 165, RU 170) of the RAN communicating with another device (e.g., directly or via one or more other network entities 105).

[0049] In some examples, such as in a carrier aggregation configuration, a carrier may also have acquisition signaling or control signaling to coordinate the operation of 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 RF Channel Number (EARFCN)) and may be identified according to a channel raster for discovery by UE 115. A carrier may operate in a standalone mode, in which case initial acquisition and connection may be made by UE 115 via the carrier, or a carrier may operate in a non-standalone mode, in which case a different carrier (e.g., of the same or different radio access technology) is used to anchor the connection.

[0050] The communication link 125 shown in the wireless communication system 100 may include downlink transmissions (e.g., forward link transmissions) from the network entity 105 to the UE 115, uplink transmissions (e.g., return link transmissions) from the UE 115 to the network entity 105, or both, as well as other transmission configurations. A carrier may carry downlink communications or uplink communications (e.g., in FDD mode), or may be configured to carry downlink communications and uplink communications (e.g., in TDD mode).

[0051] A carrier may be associated with a particular bandwidth of the RF spectrum, and in some examples, the carrier bandwidth may be referred to as the "system bandwidth" of the carrier or wireless communication system 100. For example, the carrier bandwidth may be one of a set of bandwidths of carriers for a particular radio access technology (e.g., 1.4 megahertz (MHz), 3 MHz, 5 MHz, 10 MHz, 15 MHz, 20 MHz, 40 MHz, or 80 MHz). Devices of the wireless communication system 100 (e.g., the network entity 105, the UE 115, or both) may have a hardware configuration that supports communications using a particular carrier bandwidth, or may be configured to support communications using one of the carrier bandwidths in the set of carrier bandwidths. In some examples, the wireless communication system 100 may include a network entity 105 or a UE 115 that supports concurrent communications using carriers associated with multiple carrier bandwidths. In some examples, each served UE 115 may be configured to operate using a portion (e.g., a subband, a BWP) or all of the carrier bandwidth.

[0052] The signal waveform transmitted via the carrier may include 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 employing MCM techniques, a resource element may refer to a resource of one symbol period (e.g., the duration of one modulation symbol) and one subcarrier, in which case the symbol period and the subcarrier spacing may be inversely related. The amount of bits carried by each resource element may depend on the modulation scheme (e.g., the order of the modulation scheme, the coding rate of the modulation scheme, or both), so that a relatively higher amount of resource elements (e.g., in the transmission duration) and a relatively higher order of the modulation scheme may correspond to a relatively higher communication rate. Wireless communication resources may refer to a combination of RF spectrum resources, time resources, and spatial resources (e.g., spatial layers or beams), and the use of multiple spatial resources may increase the data rate or data integrity used for communication with UE 115.

[0053] The time interval for the network entity 105 or the UE 115 may be expressed in multiples of a basic time unit, which may be, for example, a sampling period T s =1 / (Δf max·N f ) seconds, where Δf max It can represent the supported subcarrier spacing, and N f The supported discrete Fourier transform (DFT) size may be indicated. Time intervals of the communication resources may be organized according to radio frames, each having a specified duration (e.g., 10 milliseconds (ms)). Each radio frame may be identified by a system frame number (SFN) (e.g., ranging from 0 to 1023).

[0054] Each frame may include multiple consecutively numbered subframes or time slots, and each subframe or time slot may have the same duration. In some examples, the frame may be divided into subframes (e.g., in the time domain), and each subframe may be further divided into a certain number of time slots. Alternatively, each frame may include a variable number of time slots, and the number of time slots may depend on the subcarrier spacing. Each time slot may include a certain number of symbol periods (e.g., depending on the length of the cyclic prefix appended in front of each symbol period). In some wireless communication systems 100, the time slot may be further divided into a plurality of micro-time slots associated with one or more symbols. Excluding the cyclic prefix, each symbol period may be associated with one or more (e.g., N f The duration of a symbol period may depend on the subcarrier spacing or the operating frequency band.

[0055] A subframe, a time slot, a mini-time slot, or a 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 amount 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 a shortened TTI (sTTI)).

[0056] According to various techniques, physical channels may be multiplexed using carriers for communication. Physical control channels and physical data channels may be multiplexed for signaling via downlink carriers, for example, using one or more of a time division multiplexing (TDM) technique, a frequency division multiplexing (FDM) technique, or a hybrid TDM-FDM technique. A control region (e.g., a control resource set (CORESET)) of a physical control channel may be defined by a set of symbol periods and may extend across a system bandwidth of a carrier or a subset of that system bandwidth. One or more control regions (e.g., CORESETs) may be configured for a set of UEs 115. For example, one or more UEs in UE 115 may monitor or search a control region 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 of a control channel candidate may refer to the amount of control channel resources (e.g., control channel elements (CCEs)) associated with coded information for a control information format having a given payload size. The search space sets may include a common search space set configured for transmitting control information to multiple UEs 115 , and a UE-specific search space set for transmitting control information to a specific UE 115 .

[0057] In some examples, the network entities 105 (e.g., base stations 140, RUs 170) may be mobile and thus provide communication coverage for mobile coverage areas 110. In some examples, different coverage areas 110 associated with different technologies may overlap, but the different coverage areas 110 may be supported by the same network entity 105. In some other examples, overlapping coverage areas 110 associated with different technologies may be supported by different network entities 105. The wireless communication system 100 may include, for example, a heterogeneous network in which different types of network entities 105 provide coverage for various coverage areas 110 using the same or different radio access technologies.

[0058] The wireless communication system 100 may be configured to support ultra-reliable communication or low-latency communication or various combinations thereof. For example, the wireless communication system 100 may be configured to support ultra-reliable low-latency communication (URLLC). UE 115 may be designed to support ultra-reliable or low-latency or critical functions. Ultra-reliable communication may include private communication or group communication, and may be supported by one or more services (such as push-to-talk, video, or data). Support for ultra-reliable, low-latency functions may include prioritization of services, and such services may be used for public safety or general commercial applications. The terms "ultra-reliable", "low latency", and "ultra-reliable low latency" are used interchangeably herein.

[0059] In some examples, a UE 115 may be configured to support communication directly with other UEs 115 via a device-to-device (D2D) communication link 135 (e.g., according to a peer-to-peer (P2P), D2D, or sidelink protocol). In some examples, one or more UEs 115 in a group that are performing D2D communication may be within a coverage area 110 of a network entity 105 (e.g., a base station 140, a RU 170), which may support aspects of such D2D communication configured by the network entity 105 (e.g., scheduled by the network entity). In some examples, one or more UEs 115 in such a group may be outside the coverage area 110 of the network entity 105, or may otherwise be unable or not configured to receive transmissions from the network entity 105. In some examples, a group of UEs 115 communicating via D2D communication may support a one-to-many (1:M) system, in which each UE 115 transmits to each of the other UEs 115 in the group. In some examples, network entity 105 may facilitate scheduling of resources for D2D communications. In some other examples, D2D communications may be performed between UEs 115 without involving network entity 105.

[0060] The core network 130 may provide user authentication, access authorization, tracking, Internet Protocol (IP) connectivity, and other access, routing, or mobility functions. The core network 130 may be an evolved packet core (EPC) or a 5G core (5GC), which may include at least one control plane entity (e.g., a mobility management entity (MME), an access and mobility management function (AMF)) for managing access and mobility and at least one user plane entity (e.g., a serving gateway (S-GW), a packet data network (PDN) gateway (P-GW), or a user plane function (UPF)) for routing packets or interconnecting to an external network. The control plane entity may manage non-access layer (NAS) functions, such as mobility, authentication, and bearer management of UE 115 served by a network entity 105 (e.g., a base station 140) associated with the core network 130. User IP packets may be delivered through the user plane entity, which may provide IP address allocation and other functions. The user plane entity may be connected to the IP service 150 of one or more network operators. IP services 150 may include access to the Internet, an intranet, an IP Multimedia Subsystem (IMS), or packet-switched streaming services.

[0061] The wireless communication system 100 may operate using one or more frequency bands that may be in the range of 300 megahertz (MHz) to 300 gigahertz (GHz). Generally speaking, the region from 300 MHz to 3 GHz is referred to as the ultra-high frequency (UHF) region or decimeter band because the wavelength ranges from about one decimeter to one meter in length. UHF waves may be blocked or redirected by buildings and environmental features (which may be referred to as clusters), but these waves may be sufficient to penetrate structures so that macro cells provide services to UEs 115 located indoors. Communications using UHF waves may be associated with smaller antennas and shorter ranges (e.g., less than 100 kilometers) than communications using lower frequencies and longer waves in the high frequency (HF) or very high frequency (VHF) portions of the spectrum below 300 MHz.

[0062] The wireless communication system 100 can utilize licensed and unlicensed RF spectrum bands. For example, the wireless communication system 100 can use unlicensed bands (such as the 5 GHz Industrial, Scientific, and Medical (ISM) band) to employ license assisted access (LAA), LTE unlicensed (LTE-U) radio access technology, or NR technology. When operating using unlicensed RF spectrum bands, devices such as network entity 105 and UE 115 can employ carrier sensing for conflict detection and avoidance. In some examples, operations using unlicensed bands can be based on carrier aggregation configuration (e.g., LAA) in combination with component carriers operating using licensed bands. Operations using unlicensed spectrum may include downlink transmissions, uplink transmissions, P2P transmissions, or D2D transmissions, and the like.

[0063] The network entity 105 (e.g., base station 140, RU 170) or UE 115 may be equipped with multiple antennas that can be used to employ technologies such as transmit diversity, receive diversity, multiple-input multiple-output (MIMO) communication, or beamforming. The antennas of the network entity 105 or UE 115 may be located in one or more antenna arrays or antenna panels, which may support MIMO operations 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, antennas or antenna arrays associated with the network entity 105 may be located at different geographical locations. The network entity 105 may include an antenna array having a collection of multiple rows and columns of antenna ports that the network entity 105 can use to support beamforming for communications with the UE 115. Similarly, the UE 115 may include one or more antenna arrays that may support various MIMO or beamforming operations. Additionally or alternatively, the antenna panel may support RF beamforming for signals sent via the antenna ports.

[0064] Beamforming (which may also be referred to as spatial filtering, directional transmission, or directional reception) is a signal processing technique that may be used at a sender device or a receiver device (e.g., network entity 105, UE 115) to shape or direct an antenna beam (e.g., a transmit beam, a receive beam) along a spatial path between the sender device and the receiver device. Beamforming may be achieved by combining signals conveyed via antenna elements of an antenna array so that some signals propagating along a particular orientation relative to the antenna array experience constructive interference, while other signals experience destructive interference. Adjustments to signals conveyed via antenna elements may include the sender device or the receiver device applying an amplitude offset, a phase offset, or both to signals carried via antenna elements associated with the device. Adjustments associated with each of these antenna elements may be defined by a set of beamforming weights associated with a particular orientation (e.g., relative to the antenna array of the sender device or the receiver device or relative to some other orientation).

[0065] The wireless communication system 100 may be a packet-based network operating according to a layered protocol stack. In the user plane, communications at the bearer or PDCP layer may be IP-based. The RLC layer may perform packet segmentation and reassembly to communicate via logical channels. The MAC layer may perform priority handling and multiplexing of logical channels into transport channels. The MAC layer may also implement error detection techniques, error correction techniques, or both to support retransmission to improve link efficiency. In the control plane, the RRC layer may provide the establishment, configuration, and maintenance of an RRC connection for a radio bearer supporting user plane data between the UE 115 and the network entity 105 or the core network 130. The PHY layer may map transport channels to physical channels.

[0066] As described herein, a device (e.g., UE 115 or network entity 105) may monitor the performance of a machine learning model implemented at the device. In some examples, UE 115 may receive a control signal from network entity 105 or a server. The control signal may indicate an event trigger for reporting a performance parameter associated with the machine learning model. Upon receiving the control signal, UE 115 may receive one or more signals indicating input data for monitoring the performance of the machine learning model by UE 115 (e.g., a true value for the machine learning model). When UE 115 detects an event trigger, UE 115 may send a report including the performance parameter. UE 115 may determine the performance parameter based on a comparison of the input data with the data output from the machine learning model. Upon receiving the report, network entity 105 or the server may exchange signaling with UE 115 to update the machine learning model.

[0067] Figure 2An example of a network architecture 200 (e.g., a disaggregated base station architecture, a disaggregated RAN architecture) that supports monitoring and updating a machine learning model according to one or more aspects of the present disclosure is illustrated. The network architecture 200 may illustrate an example for implementing one or more aspects of the wireless communication system 100. The network architecture 200 may include one or more CUs 160-a, which may communicate directly with a core network 130-a via a backhaul communication link 120-a, or indirectly communicate with the core network 130-a through one or more disaggregated network entities 105 (e.g., a near RT RIC 175-b via an E2 link or a non-RT RIC 175-a associated with an SMO 180-a (e.g., an SMO framework), or both. The CU 160-a may communicate with one or more DUs 165-a via a corresponding midhaul communication link 162-a (e.g., an F1 interface). The DU 165-a may communicate with one or more RUs 170-a via a corresponding fronthaul communication link 168-a. The RUs 170-a can be associated with respective coverage areas 110-a and can communicate with the UEs 215 via one or more communication links 125-a. In some implementations, the UEs 215 can be served by multiple RUs 170-a simultaneously.

[0068] Each of the network entities 105 of the network architecture 200 (e.g., CU 160-a, DU 165-a, RU 170-a, non-RT RIC 175-a, near-RT RIC 175-b, SMO 180-a, open cloud (O-Cloud) 205, open eNB (O-eNB) 210) may include one or more interfaces or may be coupled to one or more interfaces configured to receive or send signals (e.g., data, information) via a wired or wireless transmission medium. Each network entity 105 or an associated processor (e.g., a controller) that provides instructions to an interface of the network entity 105 may be configured to communicate with one or more of the other network entities 105 via a transmission medium. For example, these network entities 105 may include a wired interface that is configured to receive signals on a wired transmission medium or to send signals to one or more of the other network entities 105 on a wired transmission medium. Additionally or alternatively, the network entity 105 may include a wireless interface, which may include a receiver, transmitter, or transceiver (e.g., an RF transceiver) configured to receive signals over a wireless transmission medium, or to send signals over a wireless transmission medium to one or more of the other network entities 105, or both.

[0069] In some examples, CU 160-a may host one or more higher layer control functions. Such control functions may include RRC, PDCP, or SDAP, etc. Each control function may be implemented using an interface that is configured to communicate signals with other control functions hosted by CU 160-a. CU 160-a may be configured to handle user plane functionality (e.g., CU-UP), control plane functionality (e.g., CU-CP), or a combination thereof. In some examples, CU 160-a may be logically split into one or more CU-UP units and one or more CU-CP units. When implemented in an O-RAN configuration, the CU-UP unit may communicate bidirectionally with the CU-CP unit via an interface (such as an E1 interface). As needed, CU 160-a may be implemented to communicate with DU 165-a for network control and signaling.

[0070] DU 165-a may correspond to a logical unit that includes one or more functions (e.g., base station functions, RAN functions) for controlling the operation of one or more RUs 170-a. In some examples, DU 165-a may at least partially host one or more aspects of a PHY layer (e.g., a high PHY layer, such as modules or components for FEC encoding and decoding, scrambling, modulation and demodulation, etc.), one or more of an RLC layer and a MAC layer, depending at least in part on functional splits, such as those defined by the Third Generation Partnership Project (3GPP). In some examples, DU 165-a may also host one or more low PHY layers. Each layer may be implemented using an interface that is configured to communicate signals with other layers hosted by DU 165-a or with control functions hosted by CU 160-a.

[0071] In some examples, lower layer functionality may be implemented by one or more RUs 170-a. For example, a RU 170-a controlled by a DU 165-a may correspond to a logical node that hosts RF processing functions or low PHY layer functions (e.g., performing fast Fourier transform (FFT), inverse FFT (iFFT), digital beamforming, physical random access channel (PRACH) extraction and filtering, etc.) or both based at least in part on functional splitting (such as lower layer functional splitting). In this architecture, RU 170-a may be implemented to handle over-the-air (OTA) communications with one or more UEs 215. In some specific implementations, real-time and non-real-time aspects of control plane and user plane communications with RU 170-a may be controlled by corresponding DU 165-a. In some examples, this configuration may enable DU 165-a and CU 160-a to be implemented in a cloud-based RAN architecture (such as a vRAN architecture).

[0072] The SMO 180-a may be configured to support RAN deployment and provisioning of non-virtualized and virtualized network entities 105. For non-virtualized network entities 105, the SMO 180-a may be configured to support the deployment of dedicated physical resources for RAN coverage requirements, which may be managed via an operation and maintenance interface (e.g., an O1 interface). For virtualized network entities 105, the SMO 180-a may be configured to interact with a cloud computing platform (e.g., O-Cloud 205) via a cloud computing platform interface (e.g., an O2 interface) to perform network entity lifecycle management (e.g., to instantiate virtualized network entities 105). Such virtualized network entities 105 may include, but are not limited to, CU 160-a, DU 165-a, RU 170-a, and near-RT RIC 175-b. In some specific implementations, the SMO 180-a may communicate with components configured according to a 4G RAN (e.g., via an O1 interface). Additionally or alternatively, in some implementations, the SMO 180-a may communicate directly with one or more RUs 170-a via an O1 interface.The SMO 180-a may also include a non-RT RIC 175-a configured to support the functionality of the SMO 180-a.

[0073] The non-RT RIC 175-a may be configured to include logic functions that implement non-real-time control and optimization of RAN elements and resources, artificial intelligence (AI) or machine learning (ML) workflows (including model training and updating, or policy-based guidance of applications / features in the near-RT RIC 175-b). The non-RT RIC 175-a may be coupled to or communicate with the near-RT RIC 175-b (e.g., via an A1 interface). The near-RT RIC 175-b may be configured to include logic functions that implement near real-time control and optimization of RAN elements and resources via data collection and actions on an interface connecting one or more CUs 160-a, one or more DUs 165-a, or both, and the O-eNB 210 to the near-RT RIC 175-b (e.g., via an E2 interface).

[0074] In some examples, in order to generate an AI / ML model to be deployed in the near-RT RIC 175-b, the non-RT RIC 175-a may receive parameters or external enrichment information from an external server. Such information may be utilized by the near-RT RIC 175-b and may be received from a non-network data source or from a network function at the SMO 180-a or the non-RT RIC 175. In some examples, the non-RT RIC 175-a or the near-RT RIC 175-b may be configured to tune RAN behavior or performance. For example, the non-RT RIC 175-a may monitor long-term trends and patterns of performance and employ an AI model or ML model to perform corrective actions through the SMO 180-a (e.g., via reconfiguration of O1) or via the generation of a RAN management policy (such as an A1 policy).

[0075] As described herein, a device (e.g., UE 215) may monitor the performance of a machine learning model implemented at the device. In some examples, UE 215 may receive a control signal from a network entity (e.g., CU 160 or DU 165) or a server. The control signal may indicate an event trigger for reporting a performance parameter associated with the machine learning model. Upon receiving the control signal, UE 215 may receive one or more signals indicating input data for monitoring the performance of the machine learning model (e.g., a true value for the machine learning model) by UE 215. When UE 215 detects an event trigger, UE 215 may send a report including the performance parameter. UE 215 may determine the performance parameter based on a comparison of the input data with the data output from the machine learning model. Upon receiving the report, the network entity (e.g., CU 160 or DU 165) or the server may exchange signaling with UE 215 to update the machine learning model.

[0076] Figure 3 An example of a wireless communication system 300 that supports monitoring and updating a machine learning model according to one or more aspects of the present disclosure is illustrated. In some examples, the wireless communication system 300 can implement aspects of the wireless communication system 100 and the network architecture 200. For example, the wireless communication system 300 may include a UE 315, which may be as described in reference Figure 1 The UE 115 or as referenced Figure 2 In addition, the wireless communication system 300 may include a node 305-a, which may be as described in reference Figure 1 The network entity 105 or as referenced Figure 2 In addition, the wireless communication system 300 may include a node 305-b, which may be as described in reference Figure 1 The network entity 105 or as referenced Figure 2 An example of the DU 165 described.

[0077] In some examples, the UE 315 may implement machine learning to perform one or more wireless communication processes. Machine learning may be described as a series of algorithms that are automatically improved through experience. The UE 315 may use machine learning to detect delays associated with line-of-sight (LOS) signals, perform beam management, perform cell selection, perform cell reselection, etc. In order to implement machine learning, the UE 315 may obtain a machine learning model and a machine learning function. The machine learning function may be defined as a function supported by one or more machine learning models and may be specific to the task being performed, and the machine learning model may be defined by a model structure (e.g., the number of nodes or layers of the machine learning model) and a parameter set (e.g., the weights and other configuration parameters of the machine learning model). In some examples, a machine learning feature name (MLFN) may be used to classify machine learning models based on functionality. A machine learning model ID or a model structure ID (MS ID) may be used to identify a machine learning model associated with an MLFN. The machine learning ID may indicate a machine learning model, and the MS ID may indicate a machine learning model and a corresponding parameter set via a parameter set ID (PS ID). In some examples, the model ID or MS ID may be unique for each MLFN (e.g., identifying a separate model for each third-party vendor).

[0078] In some examples, the machine learning model implemented at the UE 315 can change over time. For example, the UE 315 can receive a new machine learning model or refine a current machine learning model. In some examples, changes to the machine learning model can occur if the performance of the model degrades, the performance of the system degrades, or if the machine learning model criteria (e.g., model structure or parameter set) are invalid. Therefore, in order to determine whether changes to the machine learning model are beneficial, the UE 315 can monitor the machine learning model.

[0079] In some examples, the UE 315 may monitor the performance of the machine learning model. In such an example, the UE 315 may receive a monitoring configuration message 320 including an indication of an event trigger for reporting a performance parameter. In one example, the UE 315 may receive the monitoring configuration message 320 from a node 305-a, which may be an example of a network entity as described herein. Alternatively, in some examples, the node 305-a may receive the monitoring configuration message 320 from a server 310 and relay the monitoring configuration message 320 to the UE 315. Alternatively, the UE 315 may receive the monitoring configuration message 320 from the server 310. In some examples, the server 310 may be an example of an edge cloud, a UE-model repository (UE-MR), a third-party server, etc.

[0080] The event trigger may include a setting change at the UE 315. For example, the event trigger may include a change in the number of antennas used by the UE 315 to communicate with the node 305-a or a change in the active component carrier used by the UE 315 to communicate with the node 305-a. Alternatively or additionally, the event trigger may include a change in the environment of the UE 315. For example, the event trigger may include the UE 315 moving from an indoor environment to an outdoor environment, the UE 315 moving from an outdoor environment to an indoor environment, a change in the orientation of the UE 315, or a change in the speed of the UE 315. Alternatively or additionally, the event trigger may include a service change at the UE 315. For example, the event trigger may include a change to a network slice, a change to a quality of service (QoS) flow, or a change in a session. Alternatively or additionally, the event trigger may include a performance parameter meeting a threshold. For example, the event trigger may include a KPI falling below a threshold.

[0081] In addition, UE 315 may receive monitoring input data 325 from node 305-a. In some examples, UE 315 may receive monitoring input data 325 according to periodicity. That is, UE 315 may periodically receive monitoring input data 325 from node 305-a. In some examples, monitoring input data 325 may include true values ​​or metadata for a machine learning model. An example of metadata may be a threshold value of a performance parameter. Upon receiving monitoring input data 325, UE 315 may monitor the performance of the machine learning model. In some examples, UE 315 may apply model input data to the machine learning model and generate model output data. UE 315 may perform monitoring on model output data using monitoring input data 325 and generate performance parameters. In one example, UE 315 may generate one or more KPIs, such as system KPIs. System KPIs may include network load, uplink and downlink throughput, delay packet loss, or radio link failure (RLF). In another example, UE 315 may generate one or more performance KPIs. Performance KPIs may include MMSE error thresholds compared to true values ​​or interference delays (e.g., how long it takes a machine learning model to generate model output data).

[0082] In some examples, when monitoring the machine learning model, the UE 315 may detect an event trigger. As an example, the UE 315 may determine that the KPI falls below a threshold. In response to detecting the event trigger, the UE 315 may send a monitoring report 330 including performance parameters. In one example, the UE 315 may send a monitoring report to the node 305-a. In some examples, in response to receiving the monitoring report 330 from the UE 315, the node 305-a may forward the monitoring report to one or both of the node 305-b or the server 310. In some examples, the node 305-b may be an example of a network entity (e.g., a network entity adjacent to the node 305-a). In another example, the node 305-a and the node 305-b may be examples of distributed base stations, wherein the node 305-a is a CU of the distributed base station and the node 305-b is a DU of the distributed base station. In another example, the UE 315 may send the monitoring report directly to the server 310.

[0083] Upon receiving the monitoring report 330, the server 310 or the node 305-a may determine whether to update the machine learning model at the UE 315 using the performance parameters included in the monitoring report 330. If the server 310 or the node 305-a determines to update the machine learning model at the UE 315, the server 310 or the node 305-a may exchange signaling with the UE 315 to update the machine learning model. In a certain example, updating the machine learning model may include configuring or activating a second machine learning model or deactivating the machine learning model for the UE 315. In another example, updating the machine learning model may include refining the machine learning model (e.g., by providing a new set of parameters to the UE 315).

[0084] In another example, the node 305-a may monitor the performance of the machine learning model. In this example, in some cases, the node 305-a may send a monitoring configuration message 320, which includes an indication of the UE 315 sending monitoring input data 325 to the node 305-a. Alternatively, the node 305-a may receive the monitoring configuration message 320 from one of the UE 315 or the server 310, wherein the monitoring configuration message 320 includes an indication of an event trigger for generating a performance parameter.

[0085] The event trigger may include a setting change at the UE 315 or node 305-a. For example, the event trigger may include a change in the number of antennas used by the UE 315 to communicate with the node 305-a or a change in the active component carrier used by the UE 315 to communicate with the node 305-a. Additionally or alternatively, the event trigger may include a change in the environment of the UE 315. For example, the event trigger may include the UE 315 moving from an indoor environment to an outdoor environment, the UE 315 moving from an outdoor environment to an indoor environment, a change in the orientation of the UE 315, or a change in the speed of the UE 315. Alternatively or additionally, the event trigger may include a service change at the UE 315. For example, the event trigger may include a change to a network slice, a change to a QoS flow, or a change in a session. Alternatively or additionally, the event trigger may include a performance parameter meeting a threshold. For example, the event trigger may include a KPI falling below a threshold.

[0086] In addition, the node 305-a may receive monitoring input data 325 from the UE 315. In some examples, the node 305-a may receive the monitoring input data 325 according to a periodicity. That is, the node 305-a may periodically receive the monitoring input data 325 from the UE 315. In some examples, the monitoring input data 325 may include a true value or metadata for machine learning. An example of metadata may be a threshold value of a performance parameter. Upon receiving the monitoring input data 325, the node 305-a may monitor the performance of the machine learning model. In some examples, the node 305-a may apply the model input data to the machine learning model and generate model output data. The node 305-a may use the monitoring input data 325 to perform monitoring on the model output data and generate performance parameters. In one example, the node 305-a may generate one or more system KPIs. The system KPI may include network load, uplink and downlink throughput, delay packet loss or RLF. In another example, the node 305-a may generate one or more performance KPIs. Performance KPIs may include MMSE error thresholds compared to true values ​​or interference delays (e.g., how long it takes a machine learning model to generate model output data).

[0087] In some examples, while monitoring the machine learning model, the node 305-a may detect an event trigger. As an example, the node 305-a may determine that the KPI falls below a threshold. In response to detecting the event trigger, the node 305-a may potentially send a monitoring report 330 including performance parameters to the node 305-b or the server 310. Additionally or alternatively, upon detecting the event trigger, the node 305-a may determine whether to use the generated performance parameters to update the machine learning model at the UE 315. If the node 305-a determines to update the machine learning model at the UE 315, the node 305-a may exchange signaling with the UE 315 to update the machine learning model. In a certain example, updating the machine learning model may include activating or configuring a second machine learning model to the UE 315 or deactivating the machine learning model.

[0088] Figure 4 An example of a process flow 400 for supporting monitoring and updating a machine learning model according to one or more aspects of the present disclosure is illustrated. In some examples, the process flow 400 may be implemented by a UE 415 and a node 405. The UE 415 may be a node 405, as described in detail in conjunction with FIG. Figures 1 to 3 The node 405 may be an example of a UE 115, a UE 215 or a UE 315. Figures 1 to 3 The examples of network entity 105, CU 160 or node 305-a described above. The following alternative examples may be implemented, where the steps are performed in an order different from the order described or not performed at all. In some cases, the steps may include additional features not mentioned below, or additional features may be added.

[0089] At 410, UE 415 may receive a control signal including a monitoring configuration from node 405. In some examples, the monitoring configuration may indicate an event trigger for reporting one or more performance parameters associated with the machine learning model. The event trigger may include a setting change (e.g., a change in the number of antennas used by UE 415 to communicate with node 405), a location change (e.g., UE 415 moves from an indoor environment to an outdoor environment), a service change (e.g., a change in the QoS flow utilized by UE 415), or one or more performance parameters associated with the machine learning model meet a threshold (e.g., fall below a threshold). In addition, the monitoring configuration may include a list of one or more performance parameters to be reported. For example, the monitoring configuration may include a list of one or more types of performance KPIs (e.g., interference delay) or system KPIs (e.g., network load). In some examples, the control signaling may be an example of an RRC configuration message. In addition, in some examples, upon receiving the control signal, UE 415 may send a signal to node 405 indicating that UE 415 successfully received and implemented the control signal (e.g., an RRC configuration completion message).

[0090] At 420, UE 415 may receive one or more signals including monitoring input data from node 405. In some examples, the monitoring input data may include one or both of a true value for evaluating one or more performance parameters (e.g., performance or system KPI) or metadata for evaluating a model switching condition (e.g., a threshold value of one or more performance parameters). In some examples, UE 415 may receive one or more signals including monitoring input data according to a periodicity so that UE 415 has the latest monitoring input data for monitoring the machine learning model. Node 405 may send one or more signals to UE 415 via unicast or broadcast.

[0091] At 425, UE 415 may detect an event trigger for reporting one or more performance parameters. In some examples, UE 415 may detect an event trigger while monitoring a machine learning model. Monitoring the machine learning model may include UE 415 inputting machine learning data into the machine learning model to generate machine learning output data and comparing the machine learning output data with monitoring input data (e.g., true values) to generate one or more performance parameters. In some examples, the type of performance parameter generated by UE 415 may be based on the control signal received at 410. In some examples, UE 415 may perform monitoring on the machine learning model after receiving each of the one or more signals including monitoring input data until UE 415 detects an event trigger. That is, UE 415 may generate one or more performance parameters for each received monitoring input data.

[0092] At 430, upon detecting an event trigger, UE 415 may send a monitoring report including one or more performance parameters to node 405. In some examples, the one or more performance parameters may be one or more performance parameters most recently generated by UE 415 prior to detecting the event trigger.

[0093] At 435, node 405 may determine to update or switch the machine learning model at UE 415 and perform a model switching process with UE 415. In some examples, if one or more performance parameters fall below a threshold, node 405 may determine to update or switch the machine learning model at UE 415. In some examples, UE 415 may be configured with multiple machine learning models. For example, UE 415 may be configured with a machine learning model and a second machine learning model (e.g., associated with the same MLFN). In such an example, node 405 may send a signal (e.g., an L2 signal or a MAC control element (MAC-CE)) to activate the second machine learning model. In another example, UE 415 may be configured with a single machine learning model (e.g., associated with an MLFN). For example, UE 415 may be configured with a machine learning model. In such an example, node 405 may send a signal (e.g., an L3 signal or an RRC reconfiguration message) to configure the second machine learning model to UE 415 and send a signal (e.g., an L2 signal or a MAC-CE) to activate the second machine learning model.

[0094] Alternatively, at 440, node 405 may determine to deactivate the machine learning model at UE 415. In such an example, UE 415 may receive a signal (e.g., an L2 signal or MAC-CE) to deactivate the machine learning model.

[0095] Figure 5 An example of a process flow 500 for supporting monitoring and updating a machine learning model according to one or more aspects of the present disclosure is illustrated. In some examples, the process flow 500 may be implemented by a UE 515 and a node 505. The UE 515 may be a node 505, as described in detail in conjunction with FIG. Figures 1 to 4 The node 505 may be an example of a UE 115, a UE 215, a UE 315 or a UE 415, respectively. Figures 1 to 4 The examples of network entity 105, CU 160, node 305-a or node 405 described above. The following alternative examples may be implemented, in which the steps are performed in an order different from the order described or not performed at all. In some cases, the steps may include additional features not mentioned below, or additional features may be added.

[0096] At 510, UE 515 may receive a control signal including a monitoring configuration from node 505. In some examples, the monitoring configuration may include a list of types of monitoring input data to be provided to node 505. In one example, node 505 may request UE 515 to provide a true value for a machine learning model or metadata associated with the machine learning model (e.g., a threshold value) so that node 505 can evaluate one or more performance parameters associated with the machine learning model. One or more performance parameters may include a performance KPI (e.g., interference delay) or a system KPI (e.g., network load). In some examples, the control signal may be an example of an RRC configuration message. In addition, in some examples, upon receiving the control signal, UE 515 may send a signal to node 505 indicating that UE 515 successfully received and implemented the control signal (e.g., an RRC configuration completion message).

[0097] At 520, upon receiving the control signal, UE 515 may send one or more signals including monitoring input data to node 505. In some examples, UE 515 may determine the type of monitoring input data to be included in the one or more signals based on the control signal received at 510. In some examples, node 505 may receive one or more signals including monitoring input data according to a periodicity so that node 505 has the latest monitoring input data for monitoring the machine learning model. In some examples, UE 515 may send one or more signals to node 505 via unicast.

[0098] At 525, the node 505 may detect an event trigger for generating one or more performance parameters. The event trigger may include a setting change (e.g., the node 505 detects a change in the number of antennas used by the UE 515 to communicate with the node 505), a location change (e.g., the node 505 detects that the UE 515 moves from an indoor environment to an outdoor environment), a service change (e.g., the node detects a change in the QoS flow utilized by the UE 515), or the value of one or more performance parameters meets a threshold (e.g., falls below a threshold). In some examples, the node 505 may detect the event trigger when monitoring the machine learning model. Monitoring the machine learning model may include the node 505 inputting machine learning data into the machine learning model to generate machine learning output data and comparing the machine learning output data with the monitoring input data (e.g., a true value) to generate one or more performance parameters (e.g., performance or system KPIs). In some examples, the node 505 may perform monitoring on the machine learning model after receiving each of the one or more control signals including the monitoring input data until the node 505 detects the event trigger. That is, the node 505 may generate one or more performance parameters for each received monitoring input data.

[0099] At 530, upon detecting an event trigger, the node 505 may determine to update or switch the machine learning model at the UE 515 and perform a model switching process with the UE 515. In some examples, if one or more performance parameters fall below a threshold, the node 505 may determine to update or switch the machine learning model at the UE 515. In some examples, the UE 515 may be configured with multiple machine learning models. For example, the UE 515 may be configured with a machine learning model and a second machine learning model (e.g., associated with the same MLFN). In such an example, the node 505 may send a signal (e.g., an L2 signal or a MAC-CE) to activate the second machine learning model. In another example, the UE 515 may be configured with a single machine learning model (e.g., associated with the MLFN). For example, the UE 515 may be configured with a machine learning model. In such an example, the node 505 may send a signal (e.g., an L3 signal or an RRC reconfiguration message) to configure the second machine learning model to the UE 515 and send a signal (e.g., an L2 signal or a MAC-CE) to activate the second machine learning model.

[0100] Alternatively, at 535, the node 505 may determine to deactivate the machine learning model at the UE 515. In such an example, the UE 515 may receive a signal (e.g., an L2 signal or a MAC-CE) to deactivate the machine learning model.

[0101] Figure 6 An example of a process flow 600 for supporting monitoring and updating a machine learning model according to one or more aspects of the present disclosure is illustrated. In some examples, the process flow 600 may be implemented by a UE 615 and a node 605. The UE 615 may be as described in reference to Figures 1 to 5 The node 605 may be an example of a UE 115, a UE 215, a UE 315, a UE 415 or a UE 515. Figures 1 to 5 The examples of network entity 105, CU 160, node 305-a, node 405 or node 505 described above. The following alternative examples may be implemented, in which the steps are performed in an order different from the order described or not performed at all. In some cases, the steps may include additional features not mentioned below, or additional features may be added.

[0102] At 610, node 605 may receive a control signal including a monitoring configuration from UE 615. In some examples, the monitoring configuration may indicate an event trigger (or event trigger list) for reporting one or more performance parameters associated with the machine learning model. That is, UE 615 may request or decide an event trigger. Event triggers may include setting changes (e.g., node 605 detects a change in the number of antennas used by UE 615 to communicate with node 605), location changes (e.g., node 605 detects that UE 615 moves from an indoor environment to an outdoor environment), service changes (e.g., node 605 detects a change in the QoS flow utilized by UE 615 and node 605), or the value of one or more performance parameters meets a threshold (e.g., drops below a threshold). In addition, one or more performance parameters may include a performance KPI (e.g., interference delay) or a system KPI (e.g., network load). In some examples, the control signal may be included in UE assistance information (UAI).

[0103] At 620, node 605 may receive one or more signals including monitoring input data from UE 615. In some examples, the monitoring input data may include one or both of a true value for evaluating one or more performance parameters (e.g., performance or system KPIs) or metadata for evaluating a model switching condition (e.g., a threshold corresponding to an event trigger). In some examples, node 605 may receive one or more signals including monitoring input data according to a periodicity so that node 605 has the latest monitoring input data for monitoring the machine learning model. UE 615 may send one or more signals to node 605 via unicast.

[0104] At 625, node 605 may detect an event trigger for generating one or more performance parameters. In some examples, node 605 may detect an event trigger when monitoring a machine learning model. Monitoring the machine learning model may include node 605 inputting machine learning data into the machine learning model to generate machine learning output data and comparing the machine learning output data with monitoring input data (e.g., true values) to generate one or more performance parameters (e.g., performance or system KPIs). In some examples, node 605 may perform monitoring on the machine learning model after receiving each of the one or more control signals including monitoring input data until node 605 detects an event trigger. That is, node 605 may generate one or more performance parameters for each received monitoring input data.

[0105] At 630, upon detecting an event trigger, the node 605 may determine to update or switch the machine learning model at the UE 615 and perform a model switching process with the UE 615. In some examples, if one or more performance parameters fall below a threshold, the node 605 may determine to update or switch the machine learning model at the UE 615. In some examples, the UE 615 may be configured with multiple machine learning models. For example, the UE 615 may be configured with a machine learning model and a second machine learning model (e.g., associated with the same MLFN). In such an example, the node 605 may send a signal (e.g., an L2 signal or a MAC-CE) to activate the second machine learning model. In another example, the UE 615 may be configured with a single machine learning model (e.g., associated with the MLFN). For example, the UE 615 may be configured with a machine learning model. In such an example, the node 605 may send a signal (e.g., an L3 signal or an RRC reconfiguration message) to configure the second machine learning model to the UE 615 and send a signal (e.g., an L2 signal or a MAC-CE) to activate the second machine learning model.

[0106] Alternatively, at 635, the node 605 may determine to deactivate the machine learning model at the UE 615. In such an example, the UE 615 may receive a signal (e.g., an L2 signal or a MAC-CE) to deactivate the machine learning model.

[0107] Figure 7 An example of a process flow 700 for supporting monitoring and updating a machine learning model according to one or more aspects of the present disclosure is illustrated. In some examples, the process flow 700 may be implemented by a node 705-a and a node 705-b. Node 705-a may be as shown in FIG. Figures 1 to 6 The network entity 105, CU 160, node 305-a, node 405, node 505 or node 605 are examples. Node 705-b may be as respectively referred to in Figures 1 to 3 The examples of network entity 105, DU 165 or node 305-b described below. The following alternative examples may be implemented, where the steps are performed in an order different from the order described or not performed at all. In some cases, the steps may include additional features not mentioned below, or additional features may be added.

[0108] At 710, node 705-a may obtain a monitoring report. In some examples, node 705-a may obtain a monitoring report from a UE. The UE may generate a monitoring report upon detecting an event trigger. In another example, node 705-a may generate a monitoring report upon detecting an event trigger. Event triggers may include setting changes (e.g., the UE or node 705-a detects a change in the number of antennas used by the UE to communicate with node 705-a), location changes (e.g., the UE or node 705-a detects that the UE moves from an indoor environment to an outdoor environment), service changes (e.g., the UE or node 705-a detects a change in the QoS flow utilized by the UE), or one or more performance parameters associated with the machine learning model meet a threshold (e.g., fall below a threshold). In some examples, the monitoring report may include one or more performance parameters associated with the machine learning model. Examples of one or more performance parameters may be performance KPIs (e.g., interference delay) or system KPIs (e.g., network load).

[0109] At 715, node 705-a may send a monitoring report to node 705-b. In some examples, node 705-a may be an example of a first network entity, and node 705-b may be an example of a second network entity (e.g., an adjacent network entity). In this example, node 705-a may send a monitoring report to node 705-b via an Xn interface. In another example, node 705-a may be an example of a CU, and node 705-b may be an example of a DU. In this example, node 705-a may send a monitoring report to node 705-b via an F1 interface. In some examples, the monitoring report may be included in one or more messages (e.g., measurement reports, handover requests, or mobility control information) exchanged between node 705-a and node 705-b during the handover process. Alternatively, the monitoring report may be included in a resource status update message or other existing Xn or F1 message. In some examples, node 705-b (e.g., a target node) may utilize the monitoring report to optimize machine learning configuration parameters (e.g., parameter sets for each MS ID).

[0110] Figure 8 An example of a process flow 800 for supporting monitoring and updating a machine learning model according to one or more aspects of the present disclosure is illustrated. In some examples, the process flow 800 may be implemented by a node 805, a UE 815, and a server 810. The node 805 may be as described in reference to Figures 1 to 7 The network entity 105, CU 160, node 305-a, node 405, node 505, node 605, or node 705-a described above are examples. UE 815 may be as described in reference Figures 1 to 6The example of UE 115, UE 215, UE 315, UE 415, UE 515 or UE 615 described above. The server 810 may be as shown in FIG. Figure 3 The example of the server 310 described above. In addition, the server can be an example of an operation, administration and maintenance (OAM) server, an edge cloud, a UE-MR, a third-party server, a network data analysis function (NWDAF) server, or a RIC. The following alternative examples can be implemented, in which the steps are performed in an order different from the order described or not performed at all. In some cases, the steps may include additional features not mentioned below, or additional features may be added.

[0111] At 820, the node 805 may receive a control signal including a monitoring configuration from the server 810. In some examples, the monitoring configuration may indicate an event trigger (or a list of event triggers) for reporting one or more performance parameters associated with the machine learning model. The event trigger may include a setting change (e.g., the node 805 or UE 815 detects a change in the number of antennas used by the UE 815 to communicate with the node 805), a location change (e.g., the node 805 or UE 815 detects that the UE 815 moves from an indoor environment to an outdoor environment), a service change (e.g., the node 805 or UE 815 detects a change in the QoS flow utilized by the UE 815 and the node 805), or the value of one or more performance parameters associated with the machine learning model meets a threshold (e.g., falls below a threshold). The one or more performance parameters may include a performance KPI (e.g., interference delay) or a system KPI (e.g., network load). In addition, in some examples, the control signal may include a request for the node 805 to report one or more performance parameters generated via UE-side monitoring or node-side monitoring of the machine learning model. In some examples, the control signal may be an example of an HTTP message. Additionally, at 825 , the node 805 may send (or forward) a control signal including a monitoring configuration to the UE 815 .

[0112] In some examples, upon receiving a control signal including a monitoring configuration, UE 815 and node 805 may exchange one or more signals including monitoring input data. In some examples, the monitoring input data may include one or both of true values ​​for evaluating one or more performance parameters (e.g., performance or system KPIs) or metadata for evaluating a model switching condition (e.g., a threshold corresponding to an event trigger).

[0113] At 830, one or both of the UE 815 or the node 805 may detect an event trigger for generating one or more performance parameters. In some examples, the node 805 or the UE 815 may detect an event trigger when monitoring the machine learning model. Monitoring the machine learning model may include the node 805 or the UE 815 inputting machine learning data into the machine learning model to generate machine learning output data and comparing the machine learning output data with the monitoring input data (e.g., a true value) to generate one or more performance parameters (e.g., performance or system KPIs). In some examples, the node 805 or the UE 815 may perform monitoring on the machine learning model after receiving each of the one or more control signals including the monitoring input data until the node 805 or the UE 815 detects an event trigger. That is, the node 805 or the UE 815 may generate one or more performance parameters for each received monitoring input data.

[0114] At 835, upon detecting an event trigger, UE 815 may generate a monitoring report including one or more performance parameters and send the monitoring report to node 805. Additionally, at 835, node 805 may generate a monitoring report including one or more performance parameters upon detecting an event trigger. In some examples, the one or more performance parameters may be one or more performance parameters most recently generated by UE 815 or node 805 before detecting the event trigger.

[0115] At 840 , node 805 may compile the monitoring reports into a single compiled monitoring report. The single compiled monitoring report may include one or more performance parameters included in the monitoring report received from UE 815 and one or more performance parameters included in the monitoring report generated by node 805 .

[0116] At 845, the node 805 may send the compiled monitoring report to the server 810. In some examples, the node 805 may send the compiled monitoring report to the server upon detecting an event trigger (e.g., an event trigger that is the same as or different from the event trigger indicated in the control signal), or the node 805 may send the compiled monitoring report according to a periodicity. That is, the node 805 may compile the monitoring report and send the compiled monitoring report to the server 810 during the next periodic occasion. In some examples, the server 810 may utilize the compiled report to perform parameter re-tuning (e.g., refine the machine learning model at the UE 815 or node 805) or perform machine learning model training (e.g., offline federated learning). In some examples, if one or more of the performance parameters included in the monitoring report falls below a threshold, the server 810 may perform parameter re-tuning or machine learning model training.

[0117] Fig. 9An example of a process flow 900 for supporting monitoring and updating a machine learning model according to one or more aspects of the present disclosure is illustrated. In some examples, the process flow 900 may be implemented by a UE 915 and a server 910. The UE 915 may be a server such as the one described in reference Figures 1 to 6 as well as Figure 8 The server 910 may be an example of UE 115, UE 215, UE 315, UE 415, UE 515, UE 615 or UE 815. Figure 3 and Figure 8 Examples of server 310 and server 810 described above. In addition, server 910 may be an example of an OAM server, an edge cloud, a UE-MR, a third-party server, an NWDAF server, or a RIC. The following alternative examples may be implemented, in which the steps are performed in an order different from the order described or not performed at all. In some cases, the steps may include additional features not mentioned below, or additional features may be added.

[0118] At 905, the UE 915 may receive a control signal including a monitoring configuration from the server 910. In some examples, the monitoring configuration may indicate an event trigger (or a list of triggering events) for reporting one or more performance parameters associated with the machine learning model. The event trigger may include a setting change (e.g., the UE 915 detects a change in the number of antennas used by the UE 915 to communicate with the node), a location change (e.g., the UE 915 detects that the UE 915 moves from an indoor environment to an outdoor environment), a service change (e.g., the UE 915 detects a change in the QoS flow utilized by the UE 915 and the node), or the value of one or more performance parameters associated with the machine learning model meets a threshold (e.g., falls below a threshold). The one or more performance parameters may include a performance KPI (e.g., interference delay) or a system KPI (e.g., network load). In addition, in some examples, the control signal may include a request for the UE 915 to report one or more performance parameters generated via UE-side monitoring of the machine learning model. In some examples, the control signal may be an example of an HTTP message.

[0119] In some examples, upon receiving a control signal including a monitoring configuration, the UE 915 may receive one or more signals including monitoring input data from the node. In some examples, the monitoring input data may include one or both of the true values ​​for evaluating one or more performance parameters (e.g., performance or system KPIs) or metadata for evaluating model switching conditions (e.g., thresholds corresponding to event triggers). In some examples, the UE 915 may receive one or more signals including monitoring input data according to a periodicity so that the UE 915 has the latest monitoring input data for monitoring the machine learning model. The node may send one or more signals to the UE 915 via unicast or broadcast.

[0120] At 920, the UE 915 may detect an event trigger for generating one or more performance parameters. In some examples, the UE 915 may detect an event trigger while monitoring the machine learning model. Monitoring the machine learning model may include the UE 915 inputting machine learning data into the machine learning model to generate machine learning output data and comparing the machine learning output data with monitoring input data (e.g., true values) to generate one or more performance parameters (e.g., performance or system KPIs). In some examples, the UE 915 may perform monitoring on the machine learning model after receiving each of the one or more signals including monitoring input data until the UE 915 detects an event trigger. That is, the UE 915 may generate one or more performance parameters for each received monitoring input data.

[0121] At 925, upon detecting an event trigger, the UE 915 may generate a monitoring report including one or more performance parameters and send the monitoring report to the server 910. In some examples, the one or more performance parameters may be one or more performance parameters most recently generated by the UE 915 before the event trigger is detected. In addition, the monitoring report may include machine learning input data and machine learning output data (e.g., used during interference). In some examples, the server 910 may utilize the monitoring report to perform parameter retuning (e.g., refine the machine learning model at the UE 915) or perform machine learning model training (e.g., offline federated learning). In some examples, if one or more of the performance parameters included in the monitoring report falls below a threshold, the server 910 may perform parameter retuning or machine learning model training.

[0122] Fig.10A block diagram 1000 of a device 1005 that supports monitoring and updating a machine learning model according to one or more aspects of the present disclosure is shown. The device 1005 can be an example of aspects of a UE 115 as described herein. The device 1005 can include a receiver 1010, a transmitter 1015, and a communication manager 1020. The device 1005 can also include a processor. Each of these components can communicate with each other (e.g., via one or more buses).

[0123] Receiver 1010 may provide means for receiving information (such as packets, user data, control information, or any combination thereof) associated with various information channels (e.g., control channels, data channels, information channels related to monitoring and updating machine learning models). The information may be communicated to other components of device 1005. Receiver 1010 may utilize a single antenna or a collection of multiple antennas.

[0124] Transmitter 1015 may provide means for transmitting signals generated by other components of device 1005. For example, transmitter 1015 may transmit information (such as packets, user data, control information, or any combination thereof) associated with various information channels (e.g., control channels, data channels, information channels related to monitoring and updating machine learning models). In some examples, transmitter 1015 may be co-located with receiver 1010 in a transceiver. Transmitter 1015 may utilize a single antenna or a collection of multiple antennas.

[0125] The communication manager 1020, the receiver 1010, the transmitter 1015, or various combinations thereof or various components thereof may be examples of means for performing various aspects of monitoring and updating machine learning models as described herein. For example, the communication manager 1020, the receiver 1010, the transmitter 1015, or various combinations thereof or components thereof may support methods for performing one or more of the functions described herein.

[0126] In some examples, the communication manager 1020, the receiver 1010, the transmitter 1015, or various combinations or components thereof may be implemented in hardware (e.g., in a communication management circuit). The hardware may include a processor, a digital signal processor (DSP), a central processing unit (CPU), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic device, a microcontroller, discrete gate or transistor logic components, discrete hardware components, or any combination thereof that is configured to or otherwise supports components for performing the functions described herein. In some examples, the processor and a memory coupled to the processor may be configured to perform one or more of the functions described herein (e.g., by executing instructions stored in the memory by the processor).

[0127] Additionally or alternatively, in some examples, the communication manager 1020, the receiver 1010, the transmitter 1015, or various combinations or components thereof may be implemented in code executed by a processor (e.g., as communication management software or firmware). If implemented in code executed by a processor, the functionality of the communication manager 1020, the receiver 1010, the transmitter 1015, or various combinations or components thereof may be performed by a general purpose processor (e.g., configured as or otherwise supporting components for performing the functions described in the present disclosure), a DSP, a CPU, an ASIC, an FPGA, a microcontroller, or any combination of these or other programmable logic devices.

[0128] In some examples, communication manager 1020 may be configured to perform various operations (e.g., receive, obtain, monitor, output, send) using or otherwise cooperating with receiver 1010, transmitter 1015, or both. For example, communication manager 1020 may receive information from receiver 1010, transmit information to transmitter 1015, or be integrated in conjunction with receiver 1010, transmitter 1015, or both to obtain information, output information, or perform various other operations as described herein.

[0129] According to examples as disclosed herein, the communication manager 1020 may support wireless communications at a UE. For example, the communication manager 1020 may be configured to or otherwise support a component for receiving a control signal indicating an event trigger for reporting a performance parameter associated with a machine learning model. The communication manager 1020 may be configured to or otherwise support a component for receiving one or more signals indicating input data for monitoring the performance of the machine learning model by the UE. The communication manager 1020 may be configured to or otherwise support a component for sending a report including a performance parameter based on detecting an event trigger, wherein the performance parameter is based on monitoring the performance of the machine learning model and based on a comparison between the input data and the output data of the machine learning model.

[0130] By including or configuring a communications manager 1020 according to the examples described herein, a device 1005 (e.g., a processor controlling a receiver 1010, a transmitter 1015, a communications manager 1020, or a combination thereof or otherwise coupled thereto) may support techniques for reducing processing, lowering power consumption, and more efficiently utilizing communications resources.

[0131] Fig.11A block diagram 1100 of a device 1105 that supports monitoring and updating a machine learning model according to one or more aspects of the present disclosure is shown. The device 1105 can be an example of aspects of the device 1005 or UE 115 as described herein. The device 1105 may include a receiver 1110, a transmitter 1115, and a communication manager 1120. The device 1105 may also include a processor. Each of these components may communicate with each other (e.g., via one or more buses).

[0132] The receiver 1110 may provide means for receiving information (such as packets, user data, control information, or any combination thereof) associated with various information channels (e.g., control channels, data channels, information channels related to monitoring and updating machine learning models). The information may be communicated to other components of the device 1105. The receiver 1110 may utilize a single antenna or a collection of multiple antennas.

[0133] The transmitter 1115 may provide means for transmitting signals generated by other components of the device 1105. For example, the transmitter 1115 may transmit information (such as packets, user data, control information, or any combination thereof) associated with various information channels (e.g., control channels, data channels, information channels related to monitoring and updating machine learning models). In some examples, the transmitter 1115 may be co-located with the receiver 1110 in a transceiver. The transmitter 1115 may utilize a single antenna or a collection of multiple antennas.

[0134] Device 1105 or its various components may be examples of components for performing various aspects of monitoring and updating machine learning models as described herein. For example, communication manager 1120 may include UE monitoring configuration component 1125, UE monitoring input component 1130, UE monitoring report transmitter 1135, or any combination thereof. Communication manager 1120 may be an example of various aspects of communication manager 1020 as described herein. In some examples, communication manager 1120 or its various components may be configured to use or otherwise cooperate with receiver 1110, transmitter 1115, or both to perform various operations (e.g., receive, obtain, monitor, output, send). For example, communication manager 1120 may receive information from receiver 1110, transmit information to transmitter 1115, or integrate with receiver 1110, transmitter 1115, or both to obtain information, output information, or perform various other operations as described herein.

[0135] According to examples as disclosed herein, the communication manager 1120 may support wireless communications at a UE. The UE monitoring configuration component 1125 may be configured or otherwise support a component for receiving a control signal indicating an event trigger for reporting a performance parameter associated with a machine learning model. The UE monitoring input component 1130 may be configured or otherwise support a component for receiving one or more signals indicating input data for monitoring the performance of the machine learning model by the UE. The UE monitoring report transmitter 1135 may be configured or otherwise support a component for sending a report including a performance parameter based on detecting an event trigger, wherein the performance parameter is based on monitoring the performance of the machine learning model and based on a comparison between the input data and the output data of the machine learning model.

[0136] Fig.12 A block diagram 1200 of a communication manager 1220 that supports monitoring and updating a machine learning model in accordance with one or more aspects of the present disclosure is shown. The communication manager 1220 may be an example of aspects of the communication manager 1020, the communication manager 1120, or both as described herein. The communication manager 1220 or its various components may be examples of components for performing various aspects of monitoring and updating a machine learning model as described herein. For example, the communication manager 1220 may include a UE monitoring configuration component 1225, a UE monitoring input component 1230, a UE monitoring report sender 1235, a UE event triggering component 1240, a UE model updating component 1245, or any combination thereof. Each of these components may communicate directly or indirectly with each other (e.g., via one or more buses).

[0137] According to examples as disclosed herein, the communication manager 1220 may support wireless communications at a UE. The UE monitoring configuration component 1225 may be configured as or otherwise support a component for receiving a control signal indicating an event trigger for reporting a performance parameter associated with a machine learning model. The UE monitoring input component 1230 may be configured as or otherwise support a component for receiving one or more signals indicating input data for monitoring the performance of the machine learning model by the UE. The UE monitoring report transmitter 1235 may be configured as or otherwise support a component for sending a report including a performance parameter based on detecting an event trigger, wherein the performance parameter is based on monitoring the performance of the machine learning model and based on a comparison between the input data and the output data of the machine learning model.

[0138] In some examples, to support detection event triggering, the UE event triggering component 1240 may be configured as or otherwise support components for detecting changes in one or more communication parameters associated with the UE, wherein the one or more communication parameters include the number of antennas used for communication between the UE and the node, the active component carriers used for communication between the UE and the node, the position of the UE relative to the node, the orientation of the UE, the speed of the UE, the network slice, the QoS flow, the session, or a combination thereof.

[0139] In some examples, to support detection event triggering, UE event triggering component 1240 may be configured as or otherwise support components for detecting that a performance parameter satisfies a threshold.

[0140] In some examples, UE model update component 1245 may be configured or otherwise support components for receiving a second control signal indicating one or more parameters associated with the machine learning model based on sending the report. In some examples, UE model update component 1245 may be configured or otherwise support components for updating the machine learning model based on one or more parameters associated with the machine learning model.

[0141] In some examples, UE model update component 1245 may be configured or otherwise support components for receiving a second control signal to configure a second machine learning model to the UE or activate the second machine learning model based on sending the report. In some examples, UE model update component 1245 may be configured or otherwise support components for implementing the second machine learning model to perform one or more wireless communication processes based in part on the second control signal.

[0142] In some examples, UE monitoring configuration component 1225 may be configured to or otherwise support components for sending a second control signal indicating that the UE successfully received and implemented the control signal, wherein receiving the one or more signals is based on sending the second control signal.

[0143] In some examples, to support receiving one or more signals, UE monitoring input component 1230 may be configured or otherwise support components for receiving a set of multiple signals according to a periodicity.

[0144] In some examples, the input data includes metadata corresponding to evaluating the performance of the machine learning model, a true value for the machine learning model, one or more thresholds associated with a performance parameter, or a combination thereof.

[0145] In some examples, the performance parameters include system key performance indicators or interference key performance indicators. In some examples, the report also includes input data and output data of the machine learning model.

[0146] Fig.13 A diagram of a system 1300 including a device 1305 that supports monitoring and updating a machine learning model according to one or more aspects of the present disclosure is shown. The device 1305 may be an example of a device 1005, a device 1105, or a UE 115 as described herein, or include components thereof. The device 1305 may communicate (e.g., wirelessly) with one or more network entities 105, one or more UEs 115, or any combination thereof. The device 1305 may include components for two-way voice and data communications, including components for sending and receiving communications, such as a communication manager 1320, an input / output (I / O) controller 1310, a transceiver 1315, an antenna 1325, a memory 1330, a code 1335, and a processor 1340. These components may be electronically communicated or otherwise coupled (e.g., operatively, communicatively, functionally, electronically, electrically) via one or more buses (e.g., a bus 1345).

[0147] I / O controller 1310 can manage input signals and output signals of device 1305. I / O controller 1310 can also manage peripheral devices that are not integrated into device 1305. In some cases, I / O controller 1310 can represent a physical connection or port to an external peripheral device. In some cases, I / O controller 1310 can utilize an operating system, such as or another known operating system. Additionally or alternatively, I / O controller 1310 may represent or interact with a modem, keyboard, mouse, touch screen, or similar device. In some cases, I / O controller 1310 may be implemented as part of a processor, such as processor 1340. In some cases, a user may interact with device 1305 via I / O controller 1310 or via hardware components controlled by I / O controller 1310.

[0148] In some cases, the device 1305 may include a single antenna 1325. However, in some other cases, the device 1305 may have more than one antenna 1325, which may be capable of concurrently transmitting or receiving multiple wireless transmissions. The transceiver 1315 may communicate bidirectionally via one or more antennas 1325, a wired or wireless link, as described herein. For example, the transceiver 1315 may represent a wireless transceiver and may communicate bidirectionally with another wireless transceiver. The transceiver 1315 may also include a modem for: modulating packets; providing the modulated packets to one or more antennas 1325 for transmission; and demodulating packets received from one or more antennas 1325. The transceiver 1315 or the transceiver 1315 and one or more antennas 1325 may be examples of transmitters 1015, transmitters 1115, receivers 1010, receivers 1110, or any combination thereof or components thereof as described herein.

[0149] Memory 1330 may include random access memory (RAM) and read-only memory (ROM). Memory 1330 may store computer-readable, computer-executable code 1335 including instructions that, when executed by processor 1340, cause device 1305 to perform various functions described herein. Code 1335 may be stored in a non-transitory computer-readable medium such as system memory or another type of memory. In some cases, code 1335 may not be directly executable by processor 1340, but may (e.g., when compiled and executed) cause a computer to perform the functions described herein. In some cases, memory 1330 may also include, among other things, a basic I / O system (BIOS) that may control basic hardware or software operations, such as interaction with peripheral components or devices.

[0150] Processor 1340 may include an intelligent hardware device (e.g., a general-purpose processor, a DSP, a CPU, a microcontroller, an ASIC, an FPGA, a programmable logic device, a discrete gate or transistor logic component, a discrete hardware component, or any combination thereof). In some cases, processor 1340 may be configured to operate a memory array using a memory controller. In some other cases, the memory controller may be integrated into processor 1340. Processor 1340 may be configured to execute computer-readable instructions stored in a memory (e.g., memory 1330) to enable device 1305 to perform various functions (e.g., functions or tasks that support monitoring and updating machine learning models). For example, device 1305 or a component of device 1305 may include processor 1340 and memory 1330 coupled to or coupled to processor 1340, and processor 1340 and memory 1330 are configured to perform various functions described herein.

[0151] According to examples as disclosed herein, the communication manager 1320 may support wireless communications at a UE. For example, the communication manager 1320 may be configured to or otherwise support a component for receiving a control signal indicating an event trigger for reporting a performance parameter associated with a machine learning model. The communication manager 1320 may be configured to or otherwise support a component for receiving one or more signals indicating input data for monitoring the performance of the machine learning model by the UE. The communication manager 1320 may be configured to or otherwise support a component for sending a report including a performance parameter based on detecting an event trigger, wherein the performance parameter is based on monitoring the performance of the machine learning model and based on a comparison between the input data and the output data of the machine learning model.

[0152] By including or configuring a communication manager 1320 according to examples as described herein, the device 1305 can support techniques for improving user experience related to reduced processing, lowering power consumption, more efficiently utilizing communication resources, and improving coordination between devices.

[0153] In some examples, the communication manager 1320 may be configured to perform various operations (e.g., receive, monitor, transmit) using or otherwise cooperating with the transceiver 1315, one or more antennas 1325, or any combination thereof. Although the communication manager 1320 is illustrated as a separate component, in some examples, one or more functions described with reference to the communication manager 1320 may be supported or performed by the processor 1340, the memory 1330, the code 1335, or any combination thereof. For example, the code 1335 may include instructions that are executable by the processor 1340 to cause the device 1305 to perform various aspects of monitoring and updating the machine learning model as described herein, or the processor 1340 and the memory 1330 may be otherwise configured to perform or support such operations.

[0154] Fig.14 A block diagram 1400 of a device 1405 that supports monitoring and updating a machine learning model according to one or more aspects of the present disclosure is shown. The device 1405 can be an example of aspects of the network entity 105 as described herein. The device 1405 may include a receiver 1410, a transmitter 1415, and a communication manager 1420. The device 1405 may also include a processor. Each of these components may communicate with each other (e.g., via one or more buses).

[0155] Receiver 1410 may provide means for obtaining (e.g., receiving, determining, identifying) information associated with various channels (e.g., control channels, data channels, information channels, channels associated with a protocol stack), such as user data, control information, or any combination thereof (e.g., I / Q samples, symbols, packets, protocol data units, service data units). The information may be passed to other components of device 1405. In some examples, receiver 1410 may support obtaining information by receiving signals via one or more antennas. Additionally or alternatively, receiver 1410 may support obtaining information by receiving signals via one or more wired (e.g., electrical, optical) interfaces, wireless interfaces, or any combination thereof.

[0156] The transmitter 1415 may provide a means for outputting (e.g., sending, providing, conveying, transmitting) information generated by other components of the device 1405. For example, the transmitter 1415 may output information associated with various channels (e.g., control channels, data channels, information channels, channels associated with a protocol stack) such as user data, control information, or any combination thereof (e.g., I / Q samples, symbols, packets, protocol data units, service data units). In some examples, the transmitter 1415 may support outputting information by sending signals via one or more antennas. Additionally or alternatively, the transmitter 1415 may support outputting information by sending signals via one or more wired (e.g., electrical, optical) interfaces, wireless interfaces, or any combination thereof. In some examples, the transmitter 1415 and the receiver 1410 may be co-located in a transceiver, which may include a modem or be coupled to a modem.

[0157] The communication manager 1420, the receiver 1410, the transmitter 1415, or various combinations thereof or various components thereof may be examples of means for performing various aspects of monitoring and updating machine learning models as described herein. For example, the communication manager 1420, the receiver 1410, the transmitter 1415, or various combinations thereof or components thereof may support methods for performing one or more of the functions described herein.

[0158] In some examples, the communication manager 1420, the receiver 1410, the transmitter 1415, or various combinations or components thereof may be implemented in hardware (e.g., in a communication management circuit). The hardware may include a processor, a DSP, a CPU, an ASIC, an FPGA or other programmable logic device, a microcontroller, discrete gate or transistor logic components, discrete hardware components, or any combination thereof configured as or otherwise supporting components for performing the functions described in the present disclosure. In some examples, a processor and a memory coupled to the processor may be configured to perform one or more of the functions described herein (e.g., by executing instructions stored in the memory by the processor).

[0159] Additionally or alternatively, in some examples, the communication manager 1420, the receiver 1410, the transmitter 1415, or various combinations or components thereof may be implemented in code executed by a processor (e.g., as communication management software or firmware). If implemented in code executed by a processor, the functionality of the communication manager 1420, the receiver 1410, the transmitter 1415, or various combinations or components thereof may be performed by a general purpose processor (e.g., configured as or otherwise supporting components for performing the functions described in the present disclosure), a DSP, a CPU, an ASIC, an FPGA, a microcontroller, or any combination of these or other programmable logic devices.

[0160] In some examples, communication manager 1420 may be configured to perform various operations (e.g., receive, obtain, monitor, output, send) using or otherwise cooperating with receiver 1410, transmitter 1415, or both. For example, communication manager 1420 may receive information from receiver 1410, transmit information to transmitter 1415, or be integrated in conjunction with receiver 1410, transmitter 1415, or both to obtain information, output information, or perform various other operations as described herein.

[0161] According to examples as disclosed herein, the communication manager 1420 may support wireless communications at a node. For example, the communication manager 1420 may be configured to or otherwise support components for sending a control signal indicating an event trigger for reporting a performance parameter associated with a machine learning model. The communication manager 1420 may be configured to or otherwise support components for sending one or more signals indicating input data for monitoring the performance of the machine learning model by the UE. The communication manager 1420 may be configured to or otherwise support components for receiving a report including a performance parameter, wherein the performance parameter is based on a comparison between the input data and the output data of the machine learning model.

[0162] Additionally or alternatively, according to examples as disclosed herein, the communication manager 1420 may support wireless communications at the node. For example, the communication manager 1420 may be configured to or otherwise support a component for receiving a first control signal indicating input data for monitoring the performance of a machine learning model by the node. The communication manager 1420 may be configured to or otherwise support a component for generating a performance parameter associated with the machine learning model based on detecting an event trigger, wherein the performance parameter is based on a comparison between the input data and the output data of the machine learning model. The communication manager 1420 may be configured to or otherwise support a component for sending a second control signal to configure a second machine learning model to the UE or activate the second machine learning model based on the performance parameter.

[0163] By including or configuring a communications manager 1420 according to the examples described herein, a device 1405 (e.g., a processor controlling a receiver 1410, a transmitter 1415, a communications manager 1420, or a combination thereof or otherwise coupled thereto) may support techniques for reducing processing, lowering power consumption, and more efficiently utilizing communications resources.

[0164] Fig.15 A block diagram 1500 of a device 1505 that supports monitoring and updating a machine learning model according to one or more aspects of the present disclosure is shown. The device 1505 can be an example of aspects of the device 1405 or network entity 105 as described herein. The device 1505 may include a receiver 1510, a transmitter 1515, and a communication manager 1520. The device 1505 may also include a processor. Each of these components may communicate with each other (e.g., via one or more buses).

[0165] Receiver 1510 may provide means for obtaining (e.g., receiving, determining, identifying) information associated with various channels (e.g., control channels, data channels, information channels, channels associated with a protocol stack), such as user data, control information, or any combination thereof (e.g., I / Q samples, symbols, packets, protocol data units, service data units). The information may be passed to other components of device 1505. In some examples, receiver 1510 may support obtaining information by receiving signals via one or more antennas. Additionally or alternatively, receiver 1510 may support obtaining information by receiving signals via one or more wired (e.g., electrical, optical) interfaces, wireless interfaces, or any combination thereof.

[0166] The transmitter 1515 may provide a means for outputting (e.g., sending, providing, conveying, transmitting) information generated by other components of the device 1505. For example, the transmitter 1515 may output information associated with various channels (e.g., control channels, data channels, information channels, channels associated with a protocol stack) such as user data, control information, or any combination thereof (e.g., I / Q samples, symbols, packets, protocol data units, service data units). In some examples, the transmitter 1515 may support outputting information by sending signals via one or more antennas. Additionally or alternatively, the transmitter 1515 may support outputting information by sending signals via one or more wired (e.g., electrical, optical) interfaces, wireless interfaces, or any combination thereof. In some examples, the transmitter 1515 and the receiver 1510 may be co-located in a transceiver, which may include a modem or be coupled to a modem.

[0167] Device 1505 or its various components may be examples of components for performing various aspects of monitoring and updating machine learning models as described herein. For example, communication manager 1520 may include monitoring configuration component 1525, monitoring input component 1530, monitoring report manager 1535, model performance component 1540, model update component 1545, or any combination thereof. Communication manager 1520 may be an example of various aspects of communication manager 1420 as described herein. In some examples, communication manager 1520 or its various components may be configured to use or otherwise cooperate with receiver 1510, transmitter 1515, or both to perform various operations (e.g., receive, obtain, monitor, output, send). For example, communication manager 1520 may receive information from receiver 1510, transmit information to transmitter 1515, or integrate with receiver 1510, transmitter 1515, or both to obtain information, output information, or perform various other operations as described herein.

[0168] According to examples as disclosed herein, the communication manager 1520 may support wireless communications at a node. The monitoring configuration component 1525 may be configured or otherwise support components for sending a control signal indicating an event trigger for reporting a performance parameter associated with a machine learning model. The monitoring input component 1530 may be configured or otherwise support components for sending one or more signals indicating input data for monitoring the performance of the machine learning model by the UE. The monitoring report manager 1535 may be configured or otherwise support components for receiving a report including a performance parameter, wherein the performance parameter is based on a comparison between the input data and the output data of the machine learning model.

[0169] Additionally or alternatively, according to examples as disclosed herein, the communication manager 1520 may support wireless communications at the node. The monitoring input component 1530 may be configured or otherwise support a component for receiving a first control signal indicating input data for monitoring the performance of a machine learning model by the node. The model performance component 1540 may be configured or otherwise support a component for generating a performance parameter associated with the machine learning model based on detecting an event trigger, wherein the performance parameter is based on a comparison between the input data and the output data of the machine learning model. The model update component 1545 may be configured or otherwise support a component for sending a second control signal to configure a second machine learning model to the UE or activate the second machine learning model based on the performance parameter.

[0170] Fig.16 A block diagram 1600 of a communication manager 1620 supporting monitoring and updating a machine learning model according to one or more aspects of the present disclosure is shown. The communication manager 1620 may be an example of aspects of the communication manager 1420, the communication manager 1520, or both as described herein. The communication manager 1620 or its various components may be examples of components for performing various aspects of monitoring and updating a machine learning model as described herein. For example, the communication manager 1620 may include a monitoring configuration component 1625, a monitoring input component 1630, a monitoring report manager 1635, a model performance component 1640, a model update component 1645, an event trigger component 1650, or any combination thereof. Each of these components may communicate with each other directly or indirectly (e.g., via one or more buses), which communication may include communication within a protocol layer of a protocol stack, communication associated with a logical channel of a protocol stack (e.g., between protocol layers of a protocol stack, within a device, component, or virtualized component associated with a network entity 105, between devices, components, or virtualized components associated with a network entity 105), or any combination thereof.

[0171] According to examples as disclosed herein, the communication manager 1620 may support wireless communications at a node. The monitoring configuration component 1625 may be configured or otherwise support components for sending a control signal indicating an event trigger for reporting a performance parameter associated with a machine learning model. The monitoring input component 1630 may be configured or otherwise support components for sending one or more signals indicating input data for monitoring the performance of the machine learning model by the UE. The monitoring report manager 1635 may be configured or otherwise support components for receiving a report including a performance parameter, wherein the performance parameter is based on a comparison between the input data and the output data of the machine learning model.

[0172] In some examples, the event trigger includes a change in one or more of one or more communication parameters associated with the node. In some examples, the one or more communication parameters include the number of antennas used for communication between the UE and the node, the active component carriers used for communication between the UE and the node, the position of the UE relative to the node, the orientation of the UE, the speed of the UE, the network slice, the QoS flow, the session, or a combination thereof.

[0173] In some examples, an event trigger includes a performance parameter satisfying a threshold.

[0174] In some examples, monitoring report manager 1635 may be configured or otherwise support components for sending the report to the second node based on receiving the report.

[0175] In some examples, model performance component 1640 can be configured or otherwise support means for generating a second performance parameter based on detecting the event trigger, wherein the report also includes the second performance parameter.

[0176] In some examples, monitoring configuration component 1625 can be configured or otherwise support components for receiving a control signal from the second node, where sending the control signal is based on receiving the control signal from the second node.

[0177] In some examples, model update component 1645 may be configured or otherwise support components for sending a second control signal indicating one or more parameters associated with the machine learning model based on receiving the report.

[0178] In some examples, model update component 1645 may be configured as or otherwise support components for sending a second control signal to configure a second machine learning model or activate a second machine learning model to the UE based on receiving the report.

[0179] In some examples, to support sending one or more signals, monitoring input component 1630 may be configured or otherwise support components for sending a collection of multiple signals according to a periodicity.

[0180] In some examples, the input data includes metadata corresponding to evaluating the performance of the machine learning model, a true value for the machine learning model, one or more thresholds associated with a performance parameter, or a combination thereof.

[0181] In some examples, the node includes a network entity, a CU included in the network entity, a distributed unit associated with the network entity, an OAM server, a third-party server, a machine learning MR, a NWDAF server, or a RIC.

[0182] Additionally or alternatively, the communication manager 1620 may support wireless communications at the node according to examples as disclosed herein. In some examples, the monitoring input component 1630 may be configured or otherwise support a component for receiving a first control signal indicating input data for monitoring the performance of a machine learning model by the node. The model performance component 1640 may be configured or otherwise support a component for generating a performance parameter associated with the machine learning model based on detecting an event trigger, wherein the performance parameter is based on a comparison between the input data and the output data of the machine learning model. The model update component 1645 may be configured or otherwise support a component for sending a second control signal to configure a second machine learning model to the UE or activate the second machine learning model based on the performance parameter.

[0183] In some examples, to support detecting event triggering, event triggering component 1650 may be configured as or otherwise support components for detecting changes in one or more communication parameters associated with a node, wherein the one or more communication parameters include the number of antennas used for communication between the UE and the node, the active component carriers used for communication between the UE and the node, the position of the UE relative to the node, the orientation of the UE, the speed of the UE, a network slice, a QoS flow, a session, or a combination thereof.

[0184] In some examples, to support detecting event triggering, event triggering component 1650 may be configured or otherwise support components for detecting that a performance parameter satisfies a threshold.

[0185] In some examples, monitoring configuration component 1625 can be configured or otherwise support components for receiving a third control signal indicating an event trigger.

[0186] In some examples, monitoring configuration component 1625 can be configured or otherwise support components for sending a third control signal indicating an event trigger.

[0187] Fig.17A diagram of a system 1700 including a device 1705 that supports monitoring and updating a machine learning model according to one or more aspects of the present disclosure is shown. The device 1705 may be an example of a device 1405, a device 1505, or a network entity 105 as described herein, or include components thereof. The device 1705 may communicate with one or more network entities 105, one or more UEs 115, or any combination thereof, which may include communication through one or more wired interfaces, through one or more wireless interfaces, or any combination thereof. The device 1705 may include components that support output and acquisition of communications, such as a communication manager 1720, a transceiver 1710, an antenna 1715, a memory 1725, a code 1730, and a processor 1735. These components may be electronically communicated or otherwise coupled (e.g., operatively, communicatively, functionally, electronically, electrically) via one or more buses (e.g., bus 1740).

[0188] As described herein, the transceiver 1710 may support bidirectional communication via a wired link, a wireless link, or both. In some examples, the transceiver 1710 may include a wired transceiver and may communicate bidirectionally with another wired transceiver. Additionally or alternatively, in some examples, the transceiver 1710 may include a wireless transceiver and may communicate bidirectionally with another wireless transceiver. In some examples, the device 1705 may include one or more antennas 1715, which may be capable of sending or receiving wireless transmissions (e.g., concurrently). The transceiver 1710 may also include a modem for: modulating a signal; providing the modulated signal for transmission (e.g., via one or more antennas 1715, via a wired transmitter); receiving the modulated signal (e.g., from one or more antennas 1715, from a wired receiver); and demodulating the signal. In some implementations, the transceiver 1710 may include one or more interfaces, such as one or more interfaces coupled to one or more antennas 1715 configured to support various receiving or obtaining operations, or one or more interfaces coupled to one or more antennas 1715 configured to support various sending or outputting operations, or a combination thereof. In some implementations, the transceiver 1710 may include or be configured to be coupled to one or more processors or memory components that are operable to: perform or support operations based on received or obtained information or signals; or generate information or other signals for transmission or other output, or any combination thereof. In some implementations, the transceiver 1710, or the transceiver 1710 and one or more antennas 1715, or the transceiver 1710 and one or more antennas 1715 and one or more processors or memory components (e.g., processor 1735, or memory 1725, or both) may be included in a chip or chip assembly installed in the device 1705. In some examples, the transceiver may be operable to support communications via one or more communication links (eg, communication link 125, backhaul communication link 120, midhaul communication link 162, fronthaul communication link 168).

[0189] Memory 1725 may include RAM and ROM. Memory 1725 may store computer-readable, computer-executable code 1730 including instructions that, when executed by processor 1735, cause device 1705 to perform various functions described herein. Code 1730 may be stored in a non-transitory computer-readable medium such as system memory or another type of memory. In some cases, code 1730 may not be directly executable by processor 1735, but may (e.g., when compiled and executed) cause a computer to perform the functions described herein. In some cases, memory 1725 may also include, among other things, a BIOS that may control basic hardware or software operations, such as interaction with peripheral components or devices.

[0190] Processor 1735 may include an intelligent hardware device (e.g., a general-purpose processor, DSP, ASIC, CPU, FPGA, microcontroller, programmable logic device, discrete gate or transistor logic components, discrete hardware components, or any combination thereof). In some cases, processor 1735 may be configured to operate a memory array using a memory controller. In some other cases, the memory controller may be integrated into processor 1735. Processor 1735 may be configured to execute computer-readable instructions stored in a memory (e.g., memory 1725) to enable device 1705 to perform various functions (e.g., functions or tasks that support monitoring and updating machine learning models). For example, device 1705 or a component of device 1705 may include processor 1735 and memory 1725 coupled to processor 1735, and processor 1735 and memory 1725 are configured to perform various functions described herein. Processor 1735 may be an example of a cloud computing platform (e.g., one or more physical nodes and supporting software such as an operating system, a virtual machine, or a container instance), which may host functions for performing functions of device 1705 (e.g., by executing code 1730). The processor 1735 may be any one or more suitable processors capable of executing scripts or instructions of one or more software programs stored in the device 1705 (such as in the memory 1725). In some specific implementations, the processor 1735 may be a component of a processing system. A processing system may generally refer to a system or a series of machines or components that receive inputs and process these inputs to produce an output set (which may be passed to, for example, other systems or components of the device 1705). For example, the processing system of the device 1705 may refer to a system including various other components or subcomponents of the device 1705, such as the processor 1735, or the transceiver 1710, or the communication manager 1720, or other components or combinations of components of the device 1705. The processing system of the device 1705 may be docked with other components of the device 1705, and may process information (such as input or signals) received from other components or output information to other components. For example, a chip or modem of the device 1705 may include a processing system and one or more interfaces for outputting information or for obtaining information or both. The one or more interfaces may be implemented as or otherwise include a first interface configured to output information and a second interface configured to obtain information, or the same interface configured to output information and obtain information, as well as other specific implementations. In some specific implementations, the one or more interfaces may refer to an interface between a processing system of a chip or modem and a transmitter, so that the device 1705 can send information output from the chip or modem.Additionally or alternatively, in some implementations, the one or more interfaces may refer to an interface between a processing system of a chip or modem and a receiver, such that the device 1705 may obtain information or signal input, and the information may be passed to the processing system. A person of ordinary skill in the art will readily recognize that the first interface may also obtain information or signal input, and the second interface may also output information or signal output.

[0191] In some examples, bus 1740 may support communications of protocol layers (e.g., within a protocol layer) of a protocol stack. In some examples, bus 1740 may support communications associated with logical channels of a protocol stack (e.g., between protocol layers of a protocol stack), which may include communications performed within components of device 1705, or communications performed between different components of device 1705 that may be co-located or may be located in different locations (e.g., where device 1705 may refer to a system in which one or more of communication manager 1720, transceiver 1710, memory 1725, code 1730, and processor 1735 may be located in one component of different components or divided between different components).

[0192] In some examples, the communication manager 1720 may manage aspects of communications with the core network 130 (e.g., via one or more wired or wireless backhaul links). For example, the communication manager 1720 may manage the delivery of data communications for client devices, such as one or more UEs 115. In some examples, the communication manager 1720 may manage communications with other network entities 105 and may include a controller or scheduler for controlling communications with the UEs 115 in coordination with the other network entities 105. In some examples, the communication manager 1720 may support an X2 interface within an LTE / LTE-A wireless communication network technology to provide communications between network entities 105.

[0193] According to examples as disclosed herein, the communication manager 1720 may support wireless communications at a node. For example, the communication manager 1720 may be configured to or otherwise support a component for sending a control signal indicating an event trigger for reporting a performance parameter associated with a machine learning model. The communication manager 1720 may be configured to or otherwise support a component for sending one or more signals indicating input data for monitoring the performance of the machine learning model by the UE. The communication manager 1720 may be configured to or otherwise support a component for receiving a report including a performance parameter, wherein the performance parameter is based on a comparison between the input data and the output data of the machine learning model.

[0194] Additionally or alternatively, according to examples as disclosed herein, the communication manager 1720 may support wireless communications at the node. For example, the communication manager 1720 may be configured to or otherwise support a component for receiving a first control signal indicating input data for monitoring the performance of a machine learning model by the node. The communication manager 1720 may be configured to or otherwise support a component for generating a performance parameter associated with the machine learning model based on detecting an event trigger, wherein the performance parameter is based on a comparison between the input data and the output data of the machine learning model. The communication manager 1720 may be configured to or otherwise support a component for sending a second control signal to configure a second machine learning model to the UE or activate the second machine learning model based on the performance parameter.

[0195] By including or configuring an example communication manager 1720 as described herein, the device 1705 can support techniques for improving communication reliability, reducing latency, improving user experience related to reduced processing, reducing power consumption, more efficiently utilizing communication resources, and improving coordination between devices.

[0196] In some examples, the communication manager 1720 may be configured to perform various operations (e.g., receive, obtain, monitor, output, transmit) using or otherwise cooperating with the transceiver 1710, one or more antennas 1715 (e.g., where applicable), or any combination thereof. Although the communication manager 1720 is illustrated as a separate component, in some examples, one or more functions described with reference to the communication manager 1720 may be supported or performed by the transceiver 1710, the processor 1735, the memory 1725, the code 1730, or any combination thereof. For example, the code 1730 may include instructions that are executable by the processor 1735 to cause the device 1705 to perform various aspects of monitoring and updating the machine learning model as described herein, or the processor 1735 and the memory 1725 may be otherwise configured to perform or support such operations.

[0197] Fig.18 1800 is a flowchart illustrating a method 1800 for supporting monitoring and updating a machine learning model according to one or more aspects of the present disclosure. The operations of the method 1800 may be implemented by a UE or a component thereof as described herein. For example, the operations of the method 1800 may be implemented by a UE or a component thereof as described herein. Figures 1 to 13 The UE 115 described herein may be executed. In some examples, the UE may execute a set of instructions to control the functional elements of the UE to perform the described functions. Additionally or alternatively, the UE may use dedicated hardware to perform various aspects of the described functions.

[0198] At 1805, the method may include receiving a control signal indicating an event trigger for reporting a performance parameter associated with the machine learning model. The operations of 1805 may be performed according to examples as disclosed herein. In some examples, aspects of the operations of 1805 may be performed as described in reference to Fig.12 The UE monitoring configuration component 1225 is used to execute.

[0199] At 1810, the method may include receiving one or more signals indicating input data for monitoring performance of a machine learning model by a UE. The operations of 1810 may be performed according to examples as disclosed herein. In some examples, aspects of the operations of 1810 may be performed as described in reference Fig.12 The UE monitoring input component 1230 is executed.

[0200] At 1815, the method may include sending a report including a performance parameter based on detecting an event trigger, wherein the performance parameter is based on monitoring the performance of the machine learning model and based on a comparison between input data and output data of the machine learning model. The operations of 1815 may be performed according to examples disclosed herein. In some examples, aspects of the operations of 1815 may be performed as described in reference to Fig.12 The UE monitoring report sender 1235 is used to execute.

[0201] Fig.19 A flowchart illustrating a method 1900 for supporting monitoring and updating a machine learning model according to one or more aspects of the present disclosure is shown. The operations of the method 1900 may be implemented by a UE or a component thereof as described herein. For example, the operations of the method 1900 may be implemented by a UE or a component thereof as described herein. Figures 1 to 13 The UE 115 described herein may be executed. In some examples, the UE may execute a set of instructions to control the functional elements of the UE to perform the described functions. Additionally or alternatively, the UE may use dedicated hardware to perform various aspects of the described functions.

[0202] At 1905, the method may include receiving a control signal indicating an event trigger for reporting a performance parameter associated with the machine learning model. The operations of 1905 may be performed according to examples as disclosed herein. In some examples, aspects of the operations of 1905 may be performed as described in reference to Fig.12 The UE monitoring configuration component 1225 is used to execute.

[0203] At 1910, the method may include receiving one or more signals indicating input data for monitoring performance of a machine learning model by a UE. The operations of 1910 may be performed according to examples as disclosed herein. In some examples, aspects of the operations of 1910 may be performed as described in reference Fig.12 The UE monitoring input component 1230 is executed.

[0204] At 1915, the method may include sending a report including a performance parameter based on detecting an event trigger, wherein the performance parameter is based on monitoring the performance of the machine learning model and based on a comparison between input data and output data of the machine learning model. The operations of 1915 may be performed according to examples as disclosed herein. In some examples, aspects of the operations of 1915 may be performed as described in reference to Fig.12 The UE monitoring report sender 1235 is used to execute.

[0205] At 1920, the method may include receiving a second control signal indicating one or more parameters associated with the machine learning model based on sending the report. The operations of 1920 may be performed according to examples as disclosed herein. In some examples, aspects of the operations of 1920 may be performed as described in reference to Fig.12 The UE model update component 1245 is used to execute.

[0206] At 1925, the method may include updating the machine learning model based on one or more parameters associated with the machine learning model. The operations of 1925 may be performed according to examples disclosed herein. In some examples, aspects of the operations of 1925 may be performed as described in reference to Fig.12 The UE model update component 1245 is used to execute.

[0207] Fig. 20 A flowchart illustrating a method 2000 for supporting monitoring and updating a machine learning model according to one or more aspects of the present disclosure is shown. The operations of the method 2000 may be implemented by a network entity or a component thereof as described herein. For example, the operations of the method 2000 may be implemented by a network entity or a component thereof as described herein. Figures 1 to 9 as well as Figures 14 to 17 In some examples, the network entity may execute a set of instructions to control the functional elements of the network entity to perform the described functions. Additionally or alternatively, the network entity may use dedicated hardware to perform various aspects of the described functions.

[0208] At 2005, the method may include sending a control signal indicating an event trigger for reporting a performance parameter associated with the machine learning model. The operations of 2005 may be performed according to examples as disclosed herein. In some examples, aspects of the operations of 2005 may be performed as described in reference to Fig.16 The monitoring configuration component 1625 is used to execute.

[0209] At 2010, the method may include sending one or more signals indicating input data for monitoring performance of the machine learning model by the UE. The operations of 2010 may be performed according to examples as disclosed herein. In some examples, aspects of the operations of 2010 may be performed as described in reference Fig.16The monitoring input component 1630 is used to execute.

[0210] At 2015, the method may include receiving a report including a performance parameter, wherein the performance parameter is based on a comparison between input data and output data of the machine learning model. The operations of 2015 may be performed according to examples as disclosed herein. In some examples, aspects of the operations of 2015 may be performed as described in reference to Fig.16 The monitoring report manager 1635 is used to execute.

[0211] Fig.21 A flowchart illustrating a method 2100 for supporting monitoring and updating a machine learning model according to one or more aspects of the present disclosure is shown. The operations of the method 2100 may be implemented by a network entity or a component thereof as described herein. For example, the operations of the method 2100 may be implemented by a network entity or a component thereof as described herein. Figures 1 to 9 as well as Figures 14 to 17 In some examples, the network entity may execute a set of instructions to control the functional elements of the network entity to perform the described functions. Additionally or alternatively, the network entity may use dedicated hardware to perform various aspects of the described functions.

[0212] At 2105, the method may include sending a control signal indicating an event trigger for reporting a performance parameter associated with the machine learning model. The operations of 2105 may be performed according to examples as disclosed herein. In some examples, aspects of the operations of 2105 may be performed as described in reference to Fig.16 The monitoring configuration component 1625 is used to execute.

[0213] At 2110, the method may include sending one or more signals indicating input data for monitoring the performance of the machine learning model by the UE. The operations of 2110 may be performed according to examples as disclosed herein. In some examples, aspects of the operations of 2110 may be performed as described in reference Fig.16 The monitoring input component 1630 is used to execute.

[0214] At 2115, the method may include receiving a report including a performance parameter, wherein the performance parameter is based on a comparison between input data and output data of the machine learning model. The operations of 2115 may be performed according to examples disclosed herein. In some examples, aspects of the operations of 2115 may be performed as described in reference Fig.16 The monitoring report manager 1635 is used to execute.

[0215] At 2120, the method may include sending a second control signal indicating one or more parameters associated with the machine learning model based on receiving the report. The operations of 2120 may be performed according to examples as disclosed herein. In some examples, aspects of the operations of 2120 may be performed as described in reference Fig.16 The model update component 1645 is used to execute.

[0216] Fig. 22 A flowchart illustrating a method 2200 for supporting monitoring and updating a machine learning model according to one or more aspects of the present disclosure is shown. The operations of the method 2200 may be implemented by a network entity or a component thereof as described herein. For example, the operations of the method 2200 may be implemented by a network entity or a component thereof as described herein. Figures 1 to 9 as well as Figures 14 to 17 In some examples, the network entity may execute a set of instructions to control the functional elements of the network entity to perform the described functions. Additionally or alternatively, the network entity may use dedicated hardware to perform various aspects of the described functions.

[0217] At 2205, the method may include receiving a first control signal indicating input data for monitoring performance of a machine learning model by a node. The operations of 2205 may be performed according to examples disclosed herein. In some examples, aspects of the operations of 2205 may be performed as described in reference Fig.16 The monitoring input component 1630 is used to execute.

[0218] At 2210, the method may include generating a performance parameter associated with the machine learning model based on detecting the event trigger, wherein the performance parameter is based on a comparison between input data and output data of the machine learning model. The operations of 2210 may be performed according to examples disclosed herein. In some examples, aspects of the operations of 2210 may be performed as described in reference Fig.16 The model performance component 1640 is executed.

[0219] At 2215, the method may include sending a second control signal to configure a second machine learning model to the UE or activate the second machine learning model based on the performance parameter. The operations of 2215 may be performed according to examples as disclosed herein. In some examples, aspects of the operations of 2215 may be performed as described in reference Fig.16 The model update component 1645 is used to execute.

[0220] Fig.23 A flowchart illustrating a method 2300 for supporting monitoring and updating a machine learning model according to one or more aspects of the present disclosure is shown. The operations of the method 2300 may be implemented by a network entity or a component thereof as described herein. For example, the operations of the method 2300 may be implemented by a network entity or a component thereof as described herein. Figures 1 to 9 as well as Figures 14 to 17 In some examples, the network entity may execute a set of instructions to control the functional elements of the network entity to perform the described functions. Additionally or alternatively, the network entity may use dedicated hardware to perform various aspects of the described functions.

[0221] At 2305, the method may include receiving a second control signal indicating an event trigger. The operations of 2305 may be performed according to examples disclosed herein. In some examples, aspects of the operations of 2305 may be performed as described in reference to Fig.16 The monitoring configuration component 1625 is used to execute.

[0222] At 2310, the method may include receiving a first control signal indicating input data for monitoring performance of a machine learning model by a node. The operations of 2310 may be performed according to examples as disclosed herein. In some examples, aspects of the operations of 2310 may be performed as described in reference Fig.16 The monitoring input component 1630 is used to execute.

[0223] At 2315, the method may include generating a performance parameter associated with the machine learning model based on detecting the event trigger, wherein the performance parameter is based on a comparison between input data and output data of the machine learning model. The operations of 2315 may be performed according to examples disclosed herein. In some examples, aspects of the operations of 2315 may be performed as described in reference Fig.16 The model performance component 1640 is executed.

[0224] At 2320, the method may include sending a second control signal to configure a second machine learning model to the UE or activate the second machine learning model based on the performance parameter. The operations of 2320 may be performed according to examples as disclosed herein. In some examples, aspects of the operations of 2320 may be performed as described in reference Fig.16 The model update component 1645 is used to execute.

[0225] The following provides an overview of various aspects of the disclosure:

[0226] Aspect 1: A method for wireless communication at a UE, the method comprising: receiving a control signal indicating an event trigger for reporting a performance parameter associated with a machine learning model; receiving one or more signals indicating input data for monitoring the performance of the machine learning model by the UE; and sending a report including the performance parameter based at least in part on detecting the event trigger, wherein the performance parameter is based at least in part on monitoring the performance of the machine learning model and on a comparison between the input data and output data of the machine learning model.

[0227] Aspect 2: A method according to Aspect 1, wherein detecting the event trigger includes: detecting a change in one or more communication parameters associated with the UE, wherein the one or more communication parameters include the number of antennas used for communication between the UE and a node, an active component carrier used for communication between the UE and the node, a position of the UE relative to the node, an orientation of the UE, a speed of the UE, a network slice, a QoS flow, a session, or a combination thereof.

[0228] Aspect 3: The method according to any one of Aspects 1 to 2, wherein detecting the event trigger comprises: detecting that the performance parameter satisfies a threshold.

[0229] Aspect 4: According to the method described in any one of Aspects 1 to 3, the method further includes: receiving a second control signal indicating one or more parameters associated with the machine learning model based at least in part on sending the report; and updating the machine learning model based at least in part on the one or more parameters associated with the machine learning model.

[0230] Aspect 5: According to the method described in any one of Aspects 1 to 3, the method also includes: receiving a second control signal to configure a second machine learning model to the UE or activate the second machine learning model based at least in part on sending the report; and implementing the second machine learning model to perform one or more wireless communication processes based in part on the second control signal.

[0231] Aspect 6: According to any one of Aspects 1 to 5, the method also includes: sending a second control signal indicating that the UE successfully received and implemented the control signal, wherein receiving the one or more signals is at least partially based on sending the second control signal.

[0232] Aspect 7: The method according to any one of aspects 1 to 6, wherein receiving the one or more signals comprises: receiving multiple signals according to a periodicity.

[0233] Aspect 8: A method according to any one of Aspects 1 to 7, wherein the input data includes metadata corresponding to evaluating the performance of the machine learning model, a true value for the machine learning model, one or more thresholds associated with the performance parameters, or a combination thereof.

[0234] Aspect 9: The method according to any one of aspects 1 to 8, wherein the performance parameter comprises a system KPI or an interference KPI.

[0235] Aspect 10: A method according to any one of Aspects 1 to 9, wherein the report also includes input data and output data of the machine learning model.

[0236] Aspect 11: A method for wireless communication at a node, the method comprising: sending a control signal indicating an event trigger for reporting a performance parameter associated with a machine learning model; sending one or more signals indicating input data for monitoring the performance of the machine learning model by a UE; and receiving a report including the performance parameter, wherein the performance parameter is based at least in part on a comparison between the input data and output data of the machine learning model.

[0237] Aspect 12: A method according to Aspect 11, wherein the event trigger includes a change in one or more of one or more communication parameters associated with the node, the one or more communication parameters including the number of antennas used for communication between the UE and the node, the active component carriers used for communication between the UE and the node, the position of the UE relative to the node, the orientation of the UE, the speed of the UE, a network slice, a QoS flow, a session, or a combination thereof.

[0238] Aspect 13: The method according to any one of Aspects 11 to 12, wherein the event trigger includes the performance parameter satisfying a threshold.

[0239] Aspect 14: The method according to any one of aspects 11 to 13, the method further comprising: sending the report to the second node based at least in part on receiving the report.

[0240] Aspect 15: The method according to aspect 14, the method further comprising: generating a second performance parameter based at least in part on detecting the event trigger, wherein the report further comprises the second performance parameter.

[0241] Aspect 16: The method according to any one of aspects 11 to 15, the method further comprising: receiving the control signal from a second node, wherein sending the control signal is at least partially based on receiving the control signal from the second node.

[0242] Aspect 17: According to the method described in any one of Aspects 11 to 16, the method also includes: sending a second control signal indicating one or more parameters associated with the machine learning model based at least in part on receiving the report.

[0243] Aspect 18: According to any one of Aspects 11 to 16, the method further includes: sending a second control signal to configure a second machine learning model or activate the second machine learning model to the UE based at least in part on receiving the report.

[0244] Aspect 19: The method according to any one of aspects 11 to 18, wherein sending the one or more signals comprises: sending multiple signals according to a periodicity.

[0245] Aspect 20: A method according to any one of Aspects 11 to 19, wherein the input data includes metadata corresponding to evaluating the performance of the machine learning model, a true value for the machine learning model, one or more thresholds associated with the performance parameters, or a combination thereof.

[0246] Aspect 21: A method according to any one of Aspects 11 to 20, wherein the node includes a network entity, a CU included in the network entity, a DU associated with the network entity, an OAM server, a third-party server, a machine learning MR, a NWDAF server or a RIC.

[0247] Aspect 22: A method for wireless communication at a node, the method comprising: receiving a first control signal indicating input data for monitoring the performance of a machine learning model by the node; generating a performance parameter associated with the machine learning model based at least in part on detecting an event trigger, wherein the performance parameter is based at least in part on a comparison between the input data and output data of the machine learning model; and sending a second control signal to configure a second machine learning model to a UE or activate the second machine learning model based at least in part on the performance parameter.

[0248] Aspect 23: A method according to Aspect 22, wherein detecting the event trigger includes: detecting a change in one or more communication parameters associated with the node, wherein the one or more communication parameters include the number of antennas used for communication between the UE and the node, the active component carriers used for communication between the UE and the node, the position of the UE relative to the node, the orientation of the UE, the speed of the UE, a network slice, a QoS flow, a session, or a combination thereof.

[0249] Aspect 24: The method according to any one of Aspects 22 to 23, wherein detecting the event trigger comprises: detecting that the performance parameter satisfies a threshold.

[0250] Aspect 25: According to the method according to any one of Aspects 22 to 24, the method further comprises: receiving a third control signal indicating the event triggering.

[0251] Aspect 26: According to the method according to any one of Aspects 22 to 25, the method further comprises: sending a third control signal indicating the event triggering.

[0252] Aspect 27: An apparatus for performing wireless communications at a UE, the apparatus comprising: a processor; a memory coupled to the processor; and instructions stored in the memory and executable by the processor to cause the apparatus to perform a method according to any one of Aspects 1 to 10.

[0253] Aspect 28: An apparatus for wireless communication at a UE, the apparatus comprising: at least one component for performing the method according to any one of aspects 1 to 10.

[0254] Aspect 29: A non-transitory computer-readable medium storing a code for wireless communication at a UE, the code comprising instructions executable by a processor to perform the method according to any one of aspects 1 to 10.

[0255] Aspect 30: An apparatus for performing wireless communications at a node, the apparatus comprising: a processor; a memory coupled to the processor; and instructions stored in the memory and executable by the processor to cause the apparatus to perform a method according to any one of Aspects 11 to 21.

[0256] Aspect 31: An apparatus for wireless communication at a node, the apparatus comprising: at least one component for performing a method according to any one of aspects 11 to 21.

[0257] Aspect 32: A non-transitory computer-readable medium storing code for wireless communication at a node, the code comprising instructions executable by a processor to perform the method according to any one of aspects 11 to 21.

[0258] Aspect 33: An apparatus for performing wireless communications at a node, the apparatus comprising: a processor; a memory coupled to the processor; and instructions stored in the memory and executable by the processor to cause the apparatus to perform a method according to any one of Aspects 22 to 26.

[0259] Aspect 34: An apparatus for wireless communication at a node, the apparatus comprising: at least one component for performing the method according to any one of aspects 22 to 26.

[0260] Aspect 35: A non-transitory computer-readable medium storing code for wireless communication at a node, the code comprising instructions executable by a processor to perform the method according to any one of aspects 22 to 26.

[0261] It should be noted that the methods described herein describe possible implementations, and that the various operations and steps may be rearranged or otherwise modified and that other implementations are possible. In addition, aspects of two or more of the methods may be combined.

[0262] Although aspects of LTE, LTE-A, LTE-A Pro, or NR systems may be described for example purposes, and LTE, LTE-A, LTE-A Pro, or NR terminology may be used in much of the description, the techniques described herein may also be applicable to networks other than LTE, LTE-A, LTE-A Pro, or NR networks. For example, the described techniques may be applicable to various other wireless communication systems, such as Ultra Mobile Broadband (UMB), Institute of Electrical and Electronics Engineers (IEEE) 802.11 (Wi-Fi), IEEE 802.16 (WiMAX), IEEE 802.20, Flash-OFDM, and other systems and radio technologies not explicitly mentioned herein.

[0263] The information and signals described herein may be represented using any of a variety of different techniques and methods. For example, data, instructions, commands, information, signals, bits, symbols, and chips mentioned throughout the specification may be represented by voltage, current, electromagnetic waves, magnetic fields or magnetic particles, optical fields or optical particles, or any combination thereof.

[0264] The various illustrative blocks and components described in conjunction with the disclosure herein may be implemented or performed using a general purpose processor, DSP, ASIC, CPU, FPGA or other programmable logic device, discrete gate or transistor logic components, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general purpose processor may be a microprocessor, but in an alternative, the processor may be any processor, controller, microcontroller, or state machine. The processor may also be implemented as a combination of computing devices (e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration).

[0265] The functions described herein may be implemented using hardware, software executed by a processor, firmware, or any combination thereof. When implemented using software executed by a processor, the functions may be stored as one or more instructions or codes of a computer-readable medium or sent using one or more instructions or codes of a computer-readable medium. Other examples and specific implementations are within the scope of the present disclosure and the appended claims. For example, due to the nature of software, the functions described herein may be implemented using software executed by a processor, hardware, firmware, hard wiring, or a combination of any of these items. Features that implement the functions may also be physically located at different locations, including being distributed so that the various parts of the functions are implemented at different physical locations.

[0266] Computer readable medium includes both non-transient computer storage medium and communication medium, and it includes any medium that promotes computer program to be transmitted from one position to another position.Non-transient storage medium can be any available medium that can be accessed by general or special computer.By way of example and not limitation, non-transient computer readable medium can include RAM, ROM, electrically erasable programmable ROM (EEPROM), flash memory, compact disc (CD) ROM or other optical disc storage device, magnetic disk storage device or other magnetic storage device, or can be used for carrying or storing desired program code parts and any other non-transient medium that can be accessed by general or special computer or general or special processor in the form of instruction or data structure.Disk and optical disc as used herein include CD, laser optical disc, optical optical disc, digital versatile disc (DVD), floppy disk and blue-ray disc.Disk can reproduce data magnetically, and optical disc can reproduce data optically using laser.The combination of the above items is also included in the scope of computer readable medium.

[0267] As used herein (including in the claims), "or" used in a list of items (e.g., a list of items followed by a phrase such as "at least one of" or "one or more of") indicates an inclusive list, so that, for example, a list of at least one of A, B, or C means A or B or C or AB or AC or BC or ABC (i.e., A and B and C). Moreover, as used herein, the phrase "based on" should not be interpreted as a reference to a closed set of conditions. For example, an example step described as "based on condition A" can be based on both condition A and condition B without departing from the scope of the present disclosure. In other words, as used herein, the phrase "based on" should be interpreted in the same manner as the phrase "based at least in part on."

[0268] The term "determining" encompasses a variety of actions, and thus, "determining" may include calculating, computing, processing, deriving, investigating, looking up (such as via looking up in a table, database or other data structure), ascertaining, and the like. Also, "determining" may include receiving (e.g., receiving information), accessing (e.g., accessing data stored in a memory), etc. Also, "determining" may include resolving, obtaining, selecting, choosing, establishing, and other such similar actions.

[0269] In the drawings, similar components or features may have the same reference label. In addition, various components of the same type may be distinguished by following the reference label with a dash and a second label to distinguish between the similar components. If only the first reference label is used in the specification, the description may apply to any of the similar components having the same first reference label, regardless of the second or other subsequent reference labels.

[0270] The descriptions set forth herein in conjunction with the accompanying drawings describe example configurations and do not represent all examples that may be implemented or within the scope of the claims. The term "example" as used herein means "used as an example, instance, or illustration," rather than "preferred" or "advantageous over other examples." The specific embodiments include specific details to provide an understanding of the described techniques. However, these techniques may be practiced without these specific details. In some cases, known structures and devices are shown in block diagram form to avoid obscuring the concepts of the described examples.

[0271] The description herein is provided to enable one of ordinary skill in the art to implement or use the present disclosure. Various modifications to the present disclosure will be apparent to one of ordinary skill in the art, and the general principles defined herein may be applied to other variations without departing from the scope of the present disclosure. Thus, the present disclosure is not limited to the examples and designs described herein, but should be granted the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for wireless communication at a user equipment (UE), the method comprising: receiving a control signal indicative of an event trigger for reporting a performance parameter associated with the machine learning model; receiving one or more signals indicative of input data for monitoring, by the UE, performance of the machine learning model; as well as Sending a report including the performance parameters based at least in part on detecting the event trigger, wherein the performance parameters are based at least in part on monitoring the performance of the machine learning model and on a comparison between the input data and output data of the machine learning model.

2. The method of claim 1, wherein detecting the event trigger comprises: Detecting a change in one or more communication parameters associated with the UE, wherein the one or more communication parameters include a number of antennas used for communication between the UE and a node, an active component carrier used for communication between the UE and the node, a position of the UE relative to the node, an orientation of the UE, a speed of the UE, a network slice, a quality of service flow, a session, or a combination thereof.

3. The method of claim 1 , wherein detecting the event trigger comprises: It is detected that the performance parameter satisfies a threshold.

4. The method according to claim 1, further comprising: receiving a second control signal indicative of one or more parameters associated with the machine learning model based at least in part on sending the report; as well as The machine learning model is updated based at least in part on the one or more parameters associated with the machine learning model.

5. The method according to claim 1, further comprising: receiving a second control signal to configure a second machine learning model to the UE or to activate the second machine learning model based at least in part on sending the report; as well as The second machine learning model is implemented to perform one or more wireless communication processes based in part on the second control signal.

6. The method according to claim 1, further comprising: A second control signal is transmitted indicating that the UE successfully received and implemented the control signal, wherein receiving the one or more signals is based at least in part on transmitting the second control signal.

7. The method of claim 1 , wherein receiving the one or more signals comprises: A plurality of signals are received according to a periodicity.

8. A method according to claim 1, wherein the input data includes metadata corresponding to evaluating the performance of the machine learning model, a true value for the machine learning model, one or more thresholds associated with the performance parameters, or a combination thereof.

9. The method of claim 1, wherein the performance parameter comprises a system key performance indicator or an interference key performance indicator.

10. The method of claim 1, wherein the report further comprises input data of the machine learning model and the output data.

11. A method for wireless communication at a node, the method comprising: sending a control signal indicative of an event trigger for reporting a performance parameter associated with the machine learning model; sending one or more signals indicative of input data for monitoring performance of the machine learning model by a user equipment (UE); as well as Receive a report including the performance parameter, wherein the performance parameter is based at least in part on a comparison between the input data and output data of the machine learning model.

12. The method of claim 11, wherein the event trigger comprises a change in one or more of one or more communication parameters associated with the node, the one or more communication parameters comprising a number of antennas used for communication between the UE and the node, an active component carrier used for communication between the UE and the node, a position of the UE relative to the node, an orientation of the UE, a speed of the UE, a network slice, a quality of service flow, a session, or a combination thereof. The method of claim 11 , wherein the event trigger comprises the performance parameter satisfying a threshold value.

14. The method according to claim 11, further comprising: The report is sent to a second node based at least in part on receiving the report.

15. The method according to claim 14, further comprising: A second performance parameter is generated based at least in part on detecting the event trigger, wherein the report also includes the second performance parameter.

16. The method according to claim 11, further comprising: The control signal is received from a second node, wherein sending the control signal is based at least in part on receiving the control signal from the second node.

17. The method according to claim 11, further comprising: Sending a second control signal indicative of one or more parameters associated with the machine learning model based at least in part on receiving the report.

18. The method according to claim 11, further comprising: Sending a second control signal to configure a second machine learning model to the UE or to activate the second machine learning model based at least in part on receiving the report.

19. The method of claim 11, wherein sending the one or more signals comprises: A plurality of signals are sent according to a periodicity.

20. A method according to claim 11, wherein the input data includes metadata corresponding to evaluating the performance of the machine learning model, a true value for the machine learning model, one or more thresholds associated with the performance parameters, or a combination thereof.

21. The method according to claim 11, wherein the node includes a network entity, a central unit included in the network entity, a distributed unit associated with the network entity, an operation management and maintenance server, a third-party server, a machine learning model repository, a network data analysis function server or a random access network intelligent controller.

22. A method for wireless communication at a node, the method comprising: receiving a first control signal indicative of input data for monitoring performance of a machine learning model by the node; generating a performance parameter associated with the machine learning model based at least in part on detecting an event trigger, wherein the performance parameter is based at least in part on a comparison between the input data and output data of the machine learning model; as well as Send a second control signal to configure a second machine learning model to a user equipment (UE) or activate the second machine learning model based at least in part on the performance parameter.

23. The method of claim 22, wherein detecting the event trigger comprises: Detecting a change in one or more communication parameters associated with the node, wherein the one or more communication parameters include a number of antennas used for communication between the UE and the node, an active component carrier used for communication between the UE and the node, a position of the UE relative to the node, an orientation of the UE, a speed of the UE, a network slice, a quality of service flow, a session, or a combination thereof.

24. The method of claim 22, wherein detecting the event trigger comprises: It is detected that the performance parameter satisfies a threshold.

25. The method according to claim 22, further comprising: A third control signal indicating the event triggering is received.

26. The method according to claim 22, further comprising: A third control signal indicating the event triggering is sent.

27. An apparatus for wireless communication at a user equipment (UE), the apparatus comprising: processor; a memory coupled to the processor; as well as instructions stored in the memory and executable by the processor to cause the apparatus to: receiving a control signal indicative of an event trigger for reporting a performance parameter associated with the machine learning model; receiving one or more signals indicative of input data for monitoring, by the UE, performance of the machine learning model; as well as Sending a report including the performance parameters based at least in part on detecting the event trigger, wherein the performance parameters are based at least in part on monitoring the performance of the machine learning model and on a comparison between the input data and output data of the machine learning model.

28. The apparatus of claim 27, wherein the instructions for detecting the event trigger are executable by the processor to cause the apparatus to: Detecting a change in one or more communication parameters associated with the UE, wherein the one or more communication parameters include a number of antennas used for communication between the UE and a node, an active component carrier used for communication between the UE and the node, a position of the UE relative to the node, an orientation of the UE, a speed of the UE, a network slice, a quality of service flow, a session, or a combination thereof.

29. The apparatus of claim 27, wherein the instructions for detecting the event trigger are executable by the processor to cause the apparatus to: It is detected that the performance parameter satisfies a threshold.

30. The apparatus of claim 27, wherein the instructions are further executable by the processor to cause the apparatus to: receiving a second control signal indicative of one or more parameters associated with the machine learning model based at least in part on sending the report; and The machine learning model is updated based at least in part on the one or more parameters associated with the machine learning model.

Images