Functionality-based double-

Abstract

Apparatus, methods, and computer-readable media for performing wireless communication are disclosed. For example, a process for wireless communication is provided. The process may include receiving a first set of operations supported by one or more machine learning models of a network entity; receiving a first set of parameters associated with the first set of operations, wherein the first set of parameters is supported by the one or more machine learning models of the network entity; selecting a machine learning model for performing a first operation in the first operation set based on the first parameter set; detecting a change in at least one of the first operation or a parameter associated with the first operation; and sending an indication to change the first operation based on the detected change.

Description

Technical Field

[0001] The present disclosure generally relates to artificial intelligence (AI) and / or machine learning (ML) systems for wireless communications. For example, aspects of the present disclosure relate to systems and techniques for providing dual-sided ML / AI operation based functionality for wireless communication systems. Background Art

[0002] Wireless communication systems are deployed to provide a variety of telecommunication and data services, including telephony, video, data, messaging, and broadcasts. Broadband wireless communication systems have evolved over several generations, including first-generation analog wireless telephone service (1G), second-generation (2G) digital wireless telephone service (including transitional 2.5G networks), third-generation (3G) high-speed data wireless devices with internet capabilities, and fourth-generation (4G) services (e.g., Long Term Evolution (LTE), WiMax). Examples of wireless communication systems include code division multiple access (CDMA) systems, time division multiple access (TDMA) systems, frequency division multiple access (FDMA) systems, orthogonal frequency division multiple access (OFDMA) systems, and Global System for Mobile Communications (GSM) systems. Other wireless communication technologies include 802.11 Wi-Fi, Bluetooth, and others.

[0003] The fifth-generation (5G) mobile standard calls for higher data transfer speeds, a greater number of connections, and better coverage, among other improvements. According to the Next Generation Mobile Networks Alliance, the 5G standard (also known as "New Radio" or "NR") is designed to deliver data rates of tens of megabits per second to tens of thousands of users and 1 gigabit per second to dozens of employees across an office floor. To support large-scale sensor deployments, hundreds of thousands of simultaneous connections should be supported. Artificial intelligence (AI) and machine learning-based algorithms can be incorporated into 5G and future standards to improve telecommunications and data services. Summary of the Invention

[0004] The following presents a simplified summary of one or more aspects disclosed herein. Therefore, the following summary should not be considered an exhaustive overview of all contemplated aspects, nor should it be considered to identify key or critical elements related to all contemplated aspects or to delineate the scope associated with any particular aspect. Accordingly, the following summary presents certain concepts related to one or more aspects of the mechanisms disclosed herein in a simplified form prior to the detailed description presented below.

[0005] In some cases, devices (e.g., user equipment (UE) and network devices) may use multiple ML models to implement functionality that can be used to communicate with other devices (e.g., UE to network device, network device to UE, etc.). ML models may perform operations based on different parameters. In situations where both the UE and a network entity are using ML models to perform corresponding operations (e.g., one or more ML models of the UE are used to generate channel state information (CSI) information, one or more ML models of the network device are used to decode CSI information, and / or other operations), the UE and network entity should use compatible ML models. Techniques for coordinating ML model operations between parties (e.g., between two networked devices, such as a UE and a network device) may be useful.

[0006] Described herein are systems and techniques for providing functionality-based, two-sided ML / AI operations for wireless communication systems. For example, the systems and techniques may provide functionality-based assistance information and / or indications between wireless devices, which can be used to coordinate ML model usage for compatibility while avoiding disclosing the identity of the ML model for one or more of the wireless devices. In some aspects, the systems and techniques may provide information associated with operations performed by machine learning techniques for a UE and / or network device. For example, a UE (or network device) may provide an indication of an operation that may be performed by the ML model to the network device (or UE). In some cases, parameters associated with the operation may also be provided. Based on the provided indication of the operation provided by the UE, the network device may detect changes to the operation performed by the ML model or changes to parameters used by the ML model, and the network may indicate to the UE, such as via an activation / deactivation message or assistance information, that adjustments to the ML model used by the UE to perform the corresponding operation may be changed. Similarly, the UE may detect a change in an operation performed by the ML model, or a change in a parameter used by the ML model, and the UE may indicate to the network device, such as via an activation / deactivation message, that adjustments to the ML model used by the network device to perform the corresponding operation may be changed.

[0007] In an illustrative example, an apparatus for wireless communication is provided. The apparatus includes at least one memory and at least one processor coupled to the at least one memory. The at least one processor is configured to: receive a first set of operations supported by one or more machine learning models of a network entity; receive a first set of parameters associated with the first set of operations, wherein the first set of parameters is supported by the one or more machine learning models of the network entity; select a machine learning model for performing a first operation in the first set of operations based on the first set of parameters; detect a change in at least one of the following: the first operation; or a parameter associated with the first operation; and send an indication to change the first operation based on the detected change.

[0008] As another example, a method for wireless communication is provided. The method includes receiving a first set of operations supported by one or more machine learning models of a network entity; receiving a first set of parameters associated with the first set of operations, wherein the first set of parameters is supported by the one or more machine learning models of the network entity; selecting a machine learning model for performing a first operation in the first set of operations based on the first set of parameters; detecting a change in at least one of: the first operation; or a parameter associated with the first operation; and sending an indication to change the first operation based on the detected change.

[0009] In another example, a non-transitory computer-readable medium having instructions stored thereon is provided. The instructions, when executed by at least one processor, cause the at least one processor to: receive a first set of operations supported by one or more machine learning models of a network entity; receive a first set of parameters associated with the first set of operations, wherein the first set of parameters is supported by the one or more machine learning models of the network entity; select a machine learning model for performing a first operation in the first set of operations based on the first set of parameters; detect a change in at least one of: the first operation; or a parameter associated with the first operation; and send an indication to change the first operation based on the detected change.

[0010] As another example, an apparatus for wireless communication is provided. The apparatus includes: means for receiving a first set of operations supported by one or more machine learning models of a network entity; means for receiving a first set of parameters associated with the first set of operations, wherein the first set of parameters is supported by the one or more machine learning models of the network entity; means for selecting a machine learning model for performing a first operation in the first set of operations based on the first set of parameters; means for detecting a change in at least one of: the first operation; or a parameter associated with the first operation; and means for sending an indication of a change to the first operation based on the detected change.

[0011] In another example, an apparatus for wireless communication is provided. The apparatus includes at least one memory and at least one processor coupled to the at least one memory. The at least one processor is configured to: receive an indication of a first set of operations supported by one or more machine learning models of a network entity; select a machine learning model for performing a first operation in the first set of operations; detect a change in at least one of: the first operation; or a parameter associated with the first operation; and send an indication to change the first operation based on the detected change.

[0012] As another example, a method for wireless communication is provided. The method includes receiving an indication of a first set of operations supported by one or more machine learning models of a network entity; selecting a machine learning model for performing a first operation in the first set of operations; detecting a change in at least one of: the first operation; or a parameter associated with the first operation; and sending an indication to change the first operation based on the detected change.

[0013] In another example, a non-transitory computer-readable medium having instructions stored thereon is provided. The instructions, when executed by at least one processor, cause the at least one processor to: receive an indication of a first set of operations supported by one or more machine learning models of a network entity; select a machine learning model for performing a first operation in the first set of operations; detect a change in at least one of: the first operation; or a parameter associated with the first operation; and send an indication to change the first operation based on the detected change.

[0014] As another example, an apparatus for wireless communication is provided. The apparatus includes: means for receiving an indication of a first set of operations supported by one or more machine learning models of a network entity; means for selecting a machine learning model for performing a first operation in the first set of operations; means for detecting a change in at least one of: the first operation; or a parameter associated with the first operation; and means for sending an indication to change the first operation based on the detected change.

[0015] In another example, an apparatus for wireless communication is provided. The apparatus includes at least one memory and at least one processor coupled to the at least one memory. The at least one processor is configured to: determine a first set of operations supported by one or more machine learning models of the apparatus; determine a first set of parameters associated with the first set of operations, wherein the first set of parameters is supported by the one or more machine learning models of the apparatus; send the first set of operations and the first set of parameters to a network entity; detect a change in at least one of: a first operation in the first set of operations; or a parameter associated with the first operation; and send an indication of a change from the first operation based on the detected change.

[0016] As another example, a method for wireless communication is provided. The method includes: determining a first set of operations supported by one or more machine learning models of the device; determining a first set of parameters associated with the first set of operations, wherein the first set of parameters is supported by the one or more machine learning models of the device; sending the first set of operations and the first set of parameters to a network entity; detecting a change in at least one of the following: a first operation in the first set of operations; or a parameter associated with the first operation; and sending an indication of a change from the first operation based on the detected change.

[0017] In another example, a non-transitory computer-readable medium having instructions stored thereon is provided. The instructions, when executed by at least one processor, cause the at least one processor to: determine a first set of operations supported by one or more machine learning models of the device; determine a first set of parameters associated with the first set of operations, wherein the first set of parameters is supported by the one or more machine learning models of the device; send the first set of operations and the first set of parameters to a network entity; detect a change in at least one of: a first operation in the first set of operations; or a parameter associated with the first operation; and send an indication of a change from the first operation based on the detected change.

[0018] As another example, an apparatus for wireless communication is provided. The apparatus includes: a component for determining a first set of operations supported by one or more machine learning models of the apparatus; a component for determining a first set of parameters associated with the first set of operations, wherein the first set of parameters is supported by the one or more machine learning models of the apparatus; a component for sending the first set of operations and the first set of parameters to a network entity; a component for detecting a change in at least one of the following: a first operation in the first set of operations; or a parameter associated with the first operation; and a component for sending an indication of a change from the first operation based on the detected change.

[0019] In another example, an apparatus for wireless communication is provided. The apparatus includes at least one memory and at least one processor coupled to the at least one memory. The at least one processor is configured to: send a first set of operations supported by one or more machine learning models of the apparatus; send a first parameter set associated with the first set of operations, the first parameter set indicating parameters supported by the one or more machine learning models of the apparatus; receive a message based on an output of the machine learning model of a wireless device, wherein the output is based on the first set of operations and the first parameter set; receive an indication to change a first operation in the first set of operations based on a change detected by the wireless device; and change the first operation based on the indication of the change.

[0020] As another example, a method for wireless communication is provided. The method includes: transmitting a first set of operations supported by one or more machine learning models of the apparatus; transmitting a first parameter set associated with the first set of operations, the first parameter set indicating parameters supported by the one or more machine learning models of the apparatus; receiving a message based on an output of the machine learning model of the wireless device, wherein the output is based on the first set of operations and the first parameter set; receiving an indication to change a first operation in the first set of operations based on a change detected by the wireless device; and changing the first operation based on the indication of the change.

[0021] In another example, a non-transitory computer-readable medium having instructions stored thereon is provided. The instructions, when executed by at least one processor, cause the at least one processor to: send a first set of operations supported by one or more machine learning models of the apparatus; send a first parameter set associated with the first set of operations, the first parameter set indicating parameters supported by the one or more machine learning models of the apparatus; receive a message based on an output of a machine learning model of a wireless device, wherein the output is based on the first set of operations and the first parameter set; receive an indication to change a first operation in the first set of operations based on a change detected by the wireless device; and change the first operation based on the indication of the change.

[0022] As another example, an apparatus for wireless communication is provided. The apparatus includes: means for transmitting a first set of operations supported by one or more machine learning models of the apparatus; means for transmitting a first parameter set associated with the first set of operations, the first parameter set indicating parameters supported by the one or more machine learning models of the apparatus; means for receiving a message based on an output of a machine learning model of a wireless device, wherein the output is based on the first set of operations and the first parameter set; means for receiving an indication to change a first operation in the first set of operations based on a change detected by the wireless device; and means for changing the first operation based on the indication of the change.

[0023] In another example, an apparatus for wireless communication is provided. The apparatus includes at least one memory and at least one processor coupled to the at least one memory. The at least one processor is configured to: send an indication of a first set of operations supported by one or more machine learning models of the apparatus; receive a message based on an output of a machine learning model of a wireless device, wherein the output is based on the first set of operations; receive an indication to change a first operation in the first set of operations based on a change detected by the wireless device; and change the first operation of the apparatus based on the indication of the change.

[0024] As another example, a method for wireless communication is provided. The method includes: sending an indication of a first set of operations supported by one or more machine learning models of the apparatus; receiving a message based on an output of the machine learning model of the wireless device, wherein the output is based on the first set of operations; receiving an indication to change a first operation in the first set of operations based on a change detected by the wireless device; and changing the first operation of the apparatus based on the indication of the change.

[0025] In another example, a non-transitory computer-readable medium having instructions stored thereon is provided. The instructions, when executed by at least one processor, cause the at least one processor to: send an indication of a first set of operations supported by one or more machine learning models of the apparatus; receive a message based on an output of a machine learning model of a wireless device, wherein the output is based on the first set of operations; receive an indication to change a first operation in the first set of operations based on a change detected by the wireless device; and change the first operation of the apparatus based on the indication of the change.

[0026] As another example, an apparatus for wireless communication is provided. The apparatus includes: means for sending an indication of a first set of operations supported by one or more machine learning models of the apparatus; means for receiving a message based on an output of a machine learning model of a wireless device, wherein the output is based on the first set of operations; means for receiving an indication to change a first operation in the first set of operations based on a change detected by the wireless device; and means for changing the first operation of the apparatus based on the indication of the change.

[0027] In another example, an apparatus for wireless communication is provided. The apparatus includes at least one memory and at least one processor coupled to the at least one memory. The at least one processor is configured to: receive a first set of operations supported by one or more machine learning models of a wireless device; receive a first parameter set associated with the first set of operations, the first parameter set indicating parameters supported by the one or more machine learning models of the wireless device; perform a first operation in the first set of operations using the machine learning model of the apparatus based on the received first set of operations; receive an indication to change the first operation in the first set of operations based on a change detected by the wireless device; and change the first operation of the apparatus based on the indication of the change.

[0028] As another example, a method for wireless communication is provided. The method includes: receiving a first set of operations supported by one or more machine learning models of a wireless device; receiving a first parameter set associated with the first set of operations, the first parameter set indicating parameters supported by the one or more machine learning models of the wireless device; performing a first operation in the first set of operations using the machine learning model of the device based on the received first set of operations; receiving an indication to change the first operation in the first set of operations based on a change detected by the wireless device; and changing the first operation of the device based on the indication of the change.

[0029] In another example, a non-transitory computer-readable medium having instructions stored thereon is provided. The instructions, when executed by at least one processor, cause the at least one processor to: receive a first set of operations supported by one or more machine learning models of a wireless device; receive a first parameter set associated with the first set of operations, the first parameter set indicating parameters supported by the one or more machine learning models of the wireless device; perform a first operation in the first set of operations using the machine learning model of the device based on the received first set of operations; receive an indication to change the first operation in the first set of operations based on a change detected by the wireless device; and change the first operation of the device based on the indication of the change.

[0030] As another example, an apparatus for wireless communication is provided. The apparatus includes means for receiving a first parameter set associated with a first set of operations, the first parameter set indicating parameters supported by one or more machine learning models of the wireless device; means for performing a first operation in the first set of operations using the machine learning model of the apparatus based on the received first set of operations; means for receiving an indication to change the first operation in the first set of operations based on a change detected by the wireless device; and means for changing the first operation of the apparatus based on the indication of the change.

[0031] Aspects generally include methods, apparatus, systems, computer program products, non-transitory computer-readable media, user equipment, base stations, wireless communication devices, and / or processing systems as fully described herein with reference to and as illustrated by the accompanying figures and description.

[0032] The features and technical advantages of the examples according to the present disclosure have been outlined quite broadly above so that the detailed description that follows may be better understood. Additional features and advantages will be described below. The concepts and specific examples disclosed may be readily utilized as a basis for modifying or designing other structures for achieving the same purposes of the present disclosure. Such equivalent constructions do not depart from the scope of the appended claims. The characteristics of the concepts disclosed herein, both in terms of their organization and method of operation, as well as the associated advantages, will be better understood by considering the following description in conjunction with the accompanying drawings. Each of the figures in the accompanying drawings is provided for the purpose of illustration and description and not as a definition of limitations to the claims.

[0033] While various aspects are described in this disclosure through illustration of certain examples, those skilled in the art will appreciate that such aspects can be implemented in many different arrangements and scenarios. The techniques described herein can be implemented using different platform types, devices, systems, shapes, sizes, and / or packaging arrangements. For example, some aspects can be implemented via integrated chip implementations or other non-module-based devices (e.g., end-user devices, vehicles, communications devices, computing devices, industrial equipment, retail / shopping devices, medical devices, and / or artificial intelligence devices). Various aspects can be implemented in chip-level components, modular components, non-modular components, non-chip-level components, device-level components, and / or system-level components. Devices incorporating the described aspects and features may include additional components and features for implementing and practicing the claimed and described aspects. For example, the transmission and reception of wireless signals may include one or more components for analog and digital purposes (e.g., hardware components including antennas, radio frequency (RF) chains, power amplifiers, modulators, buffers, processors, interleavers, adders, and / or summers). The various aspects described herein are intended to be practiced in a wide variety of devices, components, systems, distributed arrangements, and / or end-user devices of various sizes, shapes, and configurations.

[0034] Other objects and advantages associated with the aspects disclosed herein will be apparent to those skilled in the art based on the drawings and detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Examples of various specific implementations are described in detail below with reference to the following drawings:

[0036] Figure 1 is a block diagram illustrating an example of a wireless communication network according to some examples;

[0037] Figure 2 is a diagram illustrating a design of a base station and a user equipment (UE) device according to some examples, which enables transmission and processing of signals exchanged between the UE and the base station;

[0038] Figure 3is a diagram illustrating an example of a decomposed base station according to some examples;

[0039] Figure 4 is a block diagram illustrating components of user equipment according to some examples;

[0040] Figure 5 illustrates an example architecture of a neural network that may be used in accordance with some aspects of the present disclosure;

[0041] Figure 6A is a block diagram illustrating an ML engine according to aspects of the present disclosure;

[0042] Figure 6B is a diagram illustrating an example of a network including ML components according to aspects of the present disclosure;

[0043] Figure 6C is a diagram illustrating an example of a network including multiple ML components according to aspects of the present disclosure;

[0044] Figure 7 、 Figure 8 and Figure 9 is a sequence diagram illustrating an example technique for functionality-based, two-sided machine learning operations in accordance with aspects of the present disclosure;

[0045] Figure 10 、 Figure 11 、 Figure 12 、 Figure 13 、 Figure 14 and Figure 15 is a flowchart illustrating an example process for wireless communication according to aspects of the present disclosure; and

[0046] Figure 16 is a diagram illustrating an example of a system for implementing certain aspects of the present technology. DETAILED DESCRIPTION

[0047] Provide certain aspects and embodiments of the present disclosure below. Some of these aspects and embodiments can be applied independently, and some of them can be applied in combination, which will be apparent to those skilled in the art. In the following description, specific details are set forth for explanation purposes to provide a thorough understanding of each embodiment of the application. However, it will be apparent that each embodiment can be put into practice without these specific details. Each drawing and description are not intended to be restrictive.

[0048] The following description provides only example embodiments and is not intended to limit the scope, applicability or configuration of the present disclosure. On the contrary, the following description of the exemplary embodiments will provide an enabling description for implementing the exemplary embodiments to those skilled in the art. It should be understood that various changes may be made to the function and arrangement of elements without departing from the spirit and scope of the present application as set forth in the appended claims.

[0049] Wireless networks are deployed to provide various communication services, such as voice, video, packet data, messaging, and broadcast. Wireless networks may support two types of access links for communication between wireless devices. An access link may refer to any communication link between a client device (e.g., user equipment (UE), station (STA), or other client device) and a base station (e.g., a 3rd Generation Partnership Project (3GPP) gNodeB (gNB) for 5G / NR, a 3GPP eNodeB (eNB) for LTE, a Wi-Fi access point (AP), or other base station) or components of a decomposed base station (e.g., a central unit, distributed units, and / or radio units). In one example, the access link between a UE and a 3GPP gNB may be over the Uu interface. In some cases, the access link may support uplink signaling, downlink signaling, connection procedures, and the like.

[0050] Various systems and techniques are provided for wireless technologies (e.g., 3GPP 5G / New Radio (NR) standards) to provide improvements to wireless communications. A device (e.g., a user equipment terminal (UE)) may be configured to generate or determine control information related to a communication channel on which the device is communicating or is configured to communicate. For example, the UE may monitor the channel to determine information indicating the quality or state of the channel, which may be referred to as channel state information (CSI) or channel state feedback (CSF). The UE may send reports, messages, or other signaling including CSI or CSF to a network device, such as a base station (e.g., a gNB) or a portion of a base station (e.g., a central unit (CU), distributed unit (DU), radio unit (RU), near real-time (near-RT) radio access network (RAN) intelligent controller (RIC), or non-real-time (non-RT) RIC of a gNB).

[0051] In some cases, using a machine learning (ML)-based air interface, a first network device (e.g., a UE) and a second network device (e.g., a gNB) can implement functionality using trained ML models. For example, a UE intended to deliver CSI to a gNB can use a neural network (e.g., an encoder neural network model) to derive a compressed representation (also known as a latent representation) of the CSI for transmission to the gNB. The gNB can then use another neural network (e.g., a decoder neural network model) to reconstruct the target CSI from the compressed representation.

[0052] In some cases, both the UE and the network device may use multiple ML models to implement functionality that can be used for communication with other devices (e.g., UE to network device, network device to UE, etc.). If both the UE and the network entity are using ML models to perform corresponding operations, the UE and network entity should use compatible ML models. In some cases, either or both the UE and the network entity may include one or more ML models for performing certain operations. For example, for operations such as generating CSI information, the UE may include multiple ML models for generating and / or encoding CSI information for multiple frequency bands, antenna modes, etc. Each of these ML models may take different parameters as input, and the UE may use different ML models to generate CSI information based on which parameters are available / available. Similarly, a network device (e.g., a network entity) may also include different ML models for decoding CSI information, and the use of these different ML models may vary based on which parameters are used as input to generate / encode the CSI information. Therefore, techniques for coordinating ML model operations between two parties (e.g., between two networked devices, such as a UE and a network device) may be useful.

[0053] Described herein are systems, apparatuses, processes (also referred to as methods), and computer-readable media (collectively referred to herein as "systems and techniques") for providing functionality-based assistance information and / or indications between wireless devices. These systems, apparatuses, processes, and computer-readable media can be used to coordinate the use of ML models for compatibility while avoiding disclosing the identity of the ML model for one or more of the wireless devices. For example, the systems and techniques discussed herein can facilitate functionality-based, two-sided machine learning operations.

[0054] The systems and techniques can provide information associated with operations performed by machine learning techniques for a UE and / or network device. For example, a UE (or network device) can provide an indication of an operation that can be performed by an ML model to the network device (or UE). In some cases, parameters associated with the operation can also be provided. Based on the provided indication of the operation, the network device can detect a change in the operation performed by the ML model, or a change in a parameter used by the ML model, and the network can indicate to the UE, such as via an activation / deactivation message or assistance information, that adjustments to the ML model used by the UE to perform the corresponding operation can be changed. Similarly, the UE can detect a change in the operation performed by the ML model, or a change in a parameter used by the ML model, and the UE can indicate to the network device, such as via an activation / deactivation message, that adjustments to the ML model used by the network device to perform the corresponding operation can be changed.

[0055] Determining, encoding, decoding, and CSI (or CSF) will be used herein as examples of operations that can be performed by an ML model. However, the systems and techniques described herein can be used for other types of operations that can be performed by an ML model, such as those that can be used by a network.

[0056] Additional aspects of the disclosure are described in more detail below.

[0057] As used herein, the terms "user equipment" (UE) and "network entity" are not intended to be specific to or otherwise limited to any particular radio access technology (RAT), unless otherwise specified. Generally speaking, a UE can be any wireless communication device (e.g., a mobile phone, router, tablet, laptop, and / or tracking device), wearable device (e.g., smartwatch, smart glasses, wearable ring, and / or extended reality (XR) device (such as a virtual reality (VR) headset, augmented reality (AR) headset or glasses, or mixed reality (MR) headset)), vehicle (e.g., car, motorcycle, bicycle, etc.), and / or Internet of Things (IoT) device, etc.), used by a user to communicate over a wireless communication network. A UE can be mobile or can be stationary (e.g., at certain times) and can communicate with a radio access network (RAN). As used herein, the term "UE" may be interchangeably referred to as an "access terminal" or "AT," "client device," "wireless device," "subscriber equipment," "subscriber terminal," "subscriber station," "user terminal" or "UT," "mobile device," "mobile terminal," "mobile station," or variations thereof. Generally speaking, a UE may communicate with a core network via a RAN, and through the core network, the UE may connect to external networks such as the Internet and to other UEs. Of course, other mechanisms for connecting to the core network and / or the Internet are also possible for the UE, such as through a wired access network, a wireless local area network (WLAN) network (e.g., based on the IEEE 802.11 communication standard, etc.), and the like.

[0058] A network entity may be implemented in a converged or monolithic base station architecture, or alternatively, in a disaggregated base station architecture, and may include one or more of a central unit (CU), a distributed unit (DU), a radio unit (RU), a near-real-time (near-RT) RAN intelligent controller (RIC), or a non-real-time (non-RT) RIC. A base station (e.g., one having a converged / monolithic or disaggregated base station architecture) may operate according to one of several RATs for communicating with UEs (depending on the network in which it is deployed) and may be alternatively referred to as an access point (AP), a network node, a NodeB (NB), an evolved NodeB (eNB), a next-generation eNB (ng-eNB), a new radio (NR) NodeB (also referred to as a gNB or gNodeB), etc. A base station may primarily support radio access for UEs, including supporting data, voice, and / or signaling connections for the supported UEs. In some systems, a base station may provide edge node signaling functionality, while in other systems, a base station may provide additional control and / or network management functionality. The communication link through which a UE can transmit signals to a base station is called an uplink (UL) channel (e.g., a reverse traffic channel, a reverse control channel, an access channel, etc.). The communication link through which a base station can transmit signals to a UE is called a downlink (DL) or forward link channel (e.g., a paging channel, a control channel, a broadcast channel, or a forward traffic channel, etc.). As used herein, the term traffic channel (TCH) can refer to an uplink, a reverse or downlink, and / or a forward traffic channel.

[0059] The term "network entity" or "base station" (e.g., having a converged / monolithic base station architecture or a disaggregated base station architecture) can refer to a single physical transmit / receive point (TRP) or multiple physical TRPs that may or may not be co-located. For example, where the term "network entity" or "base station" refers to a single physical TRP, the physical TRP may be the base station antenna corresponding to the cell (or several cell sectors) of the base station. Where the term "network entity" or "base station" refers to multiple co-located physical TRPs, the physical TRPs may be the antenna array of the base station (e.g., as in a multiple-input multiple-output (MIMO) system or where the base station employs beamforming). Where the term "base station" refers to multiple non-co-located physical TRPs, the physical TRPs may be a distributed antenna system (DAS) (a network of spatially separated antennas connected to a common source via a transmission medium) or a remote radio head (RRH) (a remote base station connected to a serving base station). Alternatively, the non-co-located physical TRPs may be a serving base station that receives measurement reports from a UE and a neighbor base station whose reference radio frequency (RF) signal (or simply "reference signal") the UE is measuring. Because, as used herein, a TRP is the point through which a base station transmits and receives wireless signals, references to transmitting from or receiving at a base station should be understood to refer to the specific TRP of a base station.

[0060] In some implementations supporting UE positioning, a network entity or base station may not support wireless access by the UE (e.g., may not support data, voice, and / or signaling connections for the UE), but may instead transmit a reference signal to the UE to be measured by the UE and / or may receive and measure signals transmitted by the UE. Such a base station may be referred to as a positioning beacon (e.g., when transmitting a signal to the UE) and / or as a position measurement unit (e.g., when receiving and measuring a signal from the UE).

[0061] RF signals consist of electromagnetic waves of a given frequency that transmit information through the space between a transmitter and a receiver. As used herein, a transmitter may transmit a single "RF signal" or multiple "RF signals" to a receiver. However, due to the propagation characteristics of RF signals through multipath channels, a receiver may receive multiple "RF signals" corresponding to each transmitted RF signal. The same transmitted RF signal on different paths between the transmitter and receiver may be referred to as a "multipath" RF signal. As used herein, an RF signal may also be referred to as a "wireless signal" or simply as a "signal" when the context clearly indicates that the term "signal" refers to either a wireless signal or an RF signal.

[0062] Various aspects of the systems and techniques described herein are discussed below with respect to the accompanying drawings. According to various aspects, Figure 1An example of a wireless communication system 100 is illustrated. Wireless communication system 100 (which may also be referred to as a wireless wide area network (WWAN)) may include various base stations 102 and various UEs 104. In some aspects, base stations 102 may also be referred to as "network entities" or "network nodes." One or more of base stations 102 may be implemented in a converged or monolithic base station architecture. Additionally or alternatively, one or more of base stations 102 may be implemented in a disaggregated base station architecture and may include one or more of a central unit (CU), a distributed unit (DU), a radio unit (RU), a near-real-time (near-RT) RAN intelligent controller (RIC), or a non-real-time (non-RT) RIC. Base stations 102 may include macrocell base stations (high-power cellular base stations) and / or small cell base stations (low-power cellular base stations). In one aspect, macrocell base stations may include eNBs and / or ng-eNBs (where wireless communication system 100 corresponds to a Long Term Evolution (LTE) network), or gNBs (where wireless communication system 100 corresponds to an NR network), or a combination of both, and small cell base stations may include femtocells, picocells, microcells, and the like.

[0063] The base stations 102 may collectively form a RAN and interface with a core network 170 (e.g., an evolved packet core (EPC) or a 5G core (5GC)) via backhaul links 122. The base stations 102 may interface with one or more location servers 172 (which may be part of or external to the core network 170) via the core network 170. Among other functions, the base stations 102 may perform functions related to one or more of the following: delivering user data, radio channel encryption and decryption, integrity protection, header compression, mobility control functions (e.g., handover, dual connectivity), inter-cell interference coordination, connection establishment and release, load balancing, distribution of non-access stratum (NAS) messages, NAS node selection, synchronization, RAN sharing, multimedia broadcast multicast service (MBMS), subscriber and equipment tracking, RAN information management (RIM), paging, positioning, and delivery of warning messages. The base stations 102 may communicate with each other directly or indirectly (e.g., via the EPC or 5GC) via backhaul links 134 (which may be wired and / or wireless).

[0064] Base stations 102 can communicate wirelessly with UEs 104. Each of base stations 102 can provide communication coverage for a corresponding geographic coverage area 110. In one aspect, base stations 102 in each coverage area 110 can support one or more cells. A "cell" is a logical communication entity used for communicating with a base station (e.g., on a certain frequency resource, referred to as a carrier frequency, component carrier, carrier, frequency band, etc.) and can be associated with an identifier (e.g., a physical cell identifier (PCI), a virtual cell identifier (VCI), or a cell global identifier (CGI)) to distinguish between cells operating on the same or different carrier frequencies. In some cases, different cells may be configured according to different protocol types (e.g., machine type communication (MTC), narrowband IoT (NB-IoT), enhanced mobile broadband (eMBB), or other protocol types) that can provide access to different types of UEs. Because a cell is supported by a specific base station, the term "cell" can refer to either or both the logical communication entity and the base station supporting the logical communication entity, depending on the context. Furthermore, because a Transmission Point (TRP) is generally the physical transmission point of a cell, the terms "cell" and "TRP" can be used interchangeably. In some cases, the term "cell" may also refer to a geographic coverage area (eg, a sector) of a base station, as long as a carrier frequency can be detected within some portion of the geographic coverage area 110 and is used for communications within that portion.

[0065] While the geographic coverage areas 110 of adjacent macrocell base stations 102 may partially overlap (e.g., in a handover area), some areas of the geographic coverage areas 110 may substantially overlap with the larger geographic coverage area 110. For example, a small cell base station 102' may have a coverage area 110' that substantially overlaps with the coverage areas 110 of one or more macrocell base stations 102. A network that includes both small cell base stations and macrocell base stations may be referred to as a heterogeneous network. A heterogeneous network may also include a Home eNB (HeNB), which may provide service to a restricted group known as a Closed Subscriber Group (CSG).

[0066] The communication link 120 between the base station 102 and the UE 104 may include uplink (also referred to as a reverse link) transmissions from the UE 104 to the base station 102 and / or downlink (also referred to as a forward link) transmissions from the base station 102 to the UE 104. The communication link 120 may utilize MIMO antenna technology, including spatial multiplexing, beamforming, and / or transmit diversity. The communication link 120 may be over one or more carrier frequencies. The allocation of carriers may be asymmetric for the downlink and uplink (e.g., more or fewer carriers may be allocated to the downlink than to the uplink).

[0067] The wireless communication system 100 may further include a WLAN AP 150 in communication with a WLAN station (STA) 152 via a communication link 154 in an unlicensed spectrum (e.g., 5 gigahertz (GHz)). When communicating in the unlicensed spectrum, the WLAN STA 152 and / or the WLAN AP 150 may perform a clear channel assessment (CCA) or a listen-before-talk (LBT) procedure before communicating to determine whether the channel is available. In some examples, the wireless communication system 100 may include devices (e.g., UEs, etc.) that utilize an ultra-wideband (UWB) spectrum to communicate with one or more UEs 104, base stations 102, AP 150, etc. The UWB spectrum may range from 3.1 GHz to 10.5 GHz.

[0068] The small cell base station 102' can operate in licensed and / or unlicensed spectrum. When operating in the unlicensed spectrum, the small cell base station 102' can employ LTE or NR technology and use the same 5 GHz unlicensed spectrum used by the WLAN AP 150. Small cell base stations 102' employing LTE and / or 5G in the unlicensed spectrum can improve coverage and / or increase capacity of the access network. NR in the unlicensed spectrum may be referred to as NR-U. LTE in the unlicensed spectrum may be referred to as LTE-U, Licensed Assisted Access (LAA), or MulteFire.

[0069] The wireless communication system 100 may also include a millimeter wave (mmW) base station 180 that can operate at mmW and / or near-mmW frequencies to communicate with UE 182. The mmW base station 180 may be implemented in a converged or monolithic base station architecture, or alternatively, in a disaggregated base station architecture (e.g., including one or more of a CU, DU, RU, near-RT RIC, or non-RTRIC). Extremely high frequency (EHF) is a portion of the RF spectrum in the electromagnetic spectrum. EHF has a range of 30 GHz to 300 GHz, with wavelengths between 1 mm and 10 mm. Radio waves in this frequency band may be referred to as millimeter waves. Near-mmW frequencies extend down to frequencies of 3 GHz, with wavelengths of 100 mm. Super high frequency (SHF) frequency bands extend between 3 GHz and 30 GHz and are also referred to as centimeter waves. Communications using mmW and / or near-mmW radio frequency bands suffer from high path loss and relatively short range. The mmW base station 180 and the UE 182 can utilize beamforming (transmit and / or receive) on the mmW communication link 184 to compensate for the extremely high path loss and short distance. Furthermore, it should be understood that in alternative configurations, one or more base stations 102 can also use mmW or near-mmW and beamforming for transmission. Therefore, it should be understood that the foregoing illustrations are merely examples and should not be construed as limiting the various aspects disclosed herein.

[0070] In some aspects related to 5G, the spectrum in which wireless network nodes or entities (e.g., base stations 102 / 180, UEs 104 / 182) operate is divided into multiple frequency ranges: FR1 (from 450 megahertz (MHz) to 6000 MHz), FR2 (from 24250 MHz to 52600 MHz), FR3 (above 52600 MHz), and FR4 (between FR1 and FR2). In multi-carrier systems such as 5G, one of the carrier frequencies is referred to as the "primary carrier," "anchor carrier," or "primary serving cell," or "PCell," and the remaining carrier frequencies are referred to as "secondary carriers," "secondary serving cells," or "SCells." In carrier aggregation, the anchor carrier is the carrier operating on the primary frequency (e.g., FR1) utilized by the UE 104 / 182 and the cell in which the UE 104 / 182 performs an initial radio resource control (RRC) connection establishment procedure or initiates an RRC connection re-establishment procedure. The primary carrier carries all common control channels as well as UE-specific control channels and can be a carrier in a licensed frequency (however, this is not always the case). A secondary carrier is a carrier operating on a second frequency (e.g., FR2) that can be configured and used to provide additional radio resources once an RRC connection is established between the UE 104 and the anchor carrier. In some cases, the secondary carrier can be a carrier in an unlicensed frequency. The secondary carrier may contain only necessary signaling information and signals. For example, since the primary uplink and downlink carriers are typically UE-specific, UE-specific signaling information and signals may not be present in the secondary carrier. This means that different UEs 104 / 182 in a cell can have different downlink primary carriers. The same is true for the uplink primary carrier. The network can change the primary carrier for any UE 104 / 182 at any time. This can be done, for example, to balance load across different carriers. Because a "serving cell" (whether a PCell or SCell) corresponds to the carrier frequency and / or component carrier through which some base station is communicating, the terms "cell," "serving cell," "component carrier," "carrier frequency," etc. may be used interchangeably.

[0071] For example, still referring to Figure 1One of the frequencies utilized by macrocell base station 102 may be the anchor carrier (or "PCell"), and the other frequencies utilized by macrocell base station 102 and / or mmW base station 180 may be secondary carriers ("SCells"). In carrier aggregation, base station 102 and / or UE 104 may use up to Y MHz (e.g., 5 MHz, 10 MHz, 15 MHz, 20 MHz, 100 MHz) of spectrum bandwidth per carrier, with up to a total of Yx MHz (x component carriers) available for transmission in each direction. Component carriers may or may not be spectrally adjacent to one another. The allocation of carriers may be asymmetric with respect to downlink and uplink (e.g., more or fewer carriers may be allocated to the downlink compared to the uplink). Simultaneous transmission and / or reception of multiple carriers enables UE 104 / 182 to significantly increase its data transmission and / or data reception rates. For example, two 20 MHz aggregated carriers in a multi-carrier system would theoretically double the data rate (i.e., 40 MHz) compared to the data rate achieved with a single 20 MHz carrier.

[0072] To operate on multiple carrier frequencies, base station 102 and / or UE 104 may be equipped with multiple receivers and / or transmitters. For example, UE 104 may have two receivers, "Receiver 1" and "Receiver 2," where "Receiver 1" is a multi-band receiver that can be tuned to either frequency band (i.e., carrier frequency) "X" or frequency band "Y," and "Receiver 2" is a single-band receiver that can be tuned to only frequency band "Z." In this example, if UE 104 is being served in frequency band "X," frequency band "X" will be referred to as the PCell, or active carrier frequency, and "Receiver 1" will need to tune from frequency band "X" to frequency band "Y" (SCell) to measure frequency band "Y" (and vice versa). In contrast, regardless of whether UE 104 is being served in frequency band "X" or frequency band "Y," due to the separate "Receiver 2," UE 104 can measure frequency band "Z" without interrupting service on frequency band "X" or frequency band "Y."

[0073] The wireless communication system 100 may further include a UE 164 that may communicate with the macrocell base station 102 over a communication link 120 and / or with the mmW base station 180 over a mmW communication link 184. For example, the macrocell base station 102 may support a PCell and one or more SCells for the UE 164, and the mmW base station 180 may support one or more SCells for the UE 164.

[0074] The wireless communication system 100 may also include one or more UEs, such as UE 190, that are indirectly connected to one or more communication networks via one or more device-to-device (D2D) peer-to-peer (P2P) links (referred to as "side links"). Figure 1 In the example, UE 190 has a D2D P2P link 192 with one of UEs 104 connected to one of base stations 102 (e.g., UE 190 can indirectly obtain cellular connectivity via the D2D P2P link), and has a D2D P2P link 194 with WLAN STA 152 connected to WLAN AP 150 (UE 190 can indirectly obtain WLAN-based Internet connectivity via the D2D P2P link). In one example, D2D P2P links 192 and 194 can use any well-known D2D RAT (such as LTE Direct (LTE-D), Wi-Fi Direct (Wi-Fi-D), Bluetooth ® etc.) to support.

[0075] Figure 2 A block diagram of a design of a base station 102 and a UE 104 is shown that enables sending and processing of signals exchanged between the UE and the base station according to some aspects of the present disclosure. Design 200 includes components of the base station 102 and the UE 104, which may be Figure 1 One of base stations 102 and one of UE 104. Base station 102 may be equipped with T antennas 234a through 234t, and UE 104 may be equipped with R antennas 252a through 252r, where in general T≧1 and R≧1.

[0076] At base station 102, transmit processor 220 may receive data for one or more UEs from data source 212, select one or more modulation and coding schemes (MCS) for each UE based at least in part on a channel quality indicator (CQI) received from the UE, process (e.g., encode and modulate) the data for each UE based at least in part on the MCS selected for the UE, and provide data symbols for all UEs. Transmit processor 220 may also process system information (e.g., for semi-static resource partitioning information (SRPI)) and control information (e.g., CQI requests, grants, upper layer signaling, channel state information, channel state feedback, etc.), and provide overhead symbols and control symbols. Transmit processor 220 may also generate reference symbols for reference signals (e.g., cell-specific reference signals (CRS)) and synchronization signals (e.g., primary synchronization signals (PSS) and secondary synchronization signals (SSS)). The transmit (TX) multiple-input, multiple-output (MIMO) processor 230 may perform spatial processing (e.g., precoding) on ​​data symbols, control symbols, overhead symbols, and / or reference symbols, as applicable, and may provide T output symbol streams to T modulators (MODs) 232a through 232t. Modulators 232a through 232t are illustrated as combined modulator-demodulators (MOD-DEMODs). In some cases, the modulator and demodulator may be separate components. Each modulator 232a through 232t may process a corresponding output symbol stream (e.g., for an orthogonal frequency division multiplexing (OFDM) scheme, etc.) to obtain an output sample stream. Each modulator 232a through 232t may further process (e.g., convert to analog, amplify, filter, and frequency upconvert) the output sample stream to obtain a downlink signal. The T downlink signals may be transmitted from modulators 232a through 232t via T antennas 234a through 234t, respectively. According to certain aspects described in greater detail below, position encoding may be utilized to generate a synchronization signal to convey additional information.

[0077] At UE 104, antennas 252a through 252r may receive downlink signals from base station 102 and / or other base stations and may provide received signals to demodulators (DEMODs) 254a through 254r, respectively. Demodulators 254a through 254r are illustrated as a combined modulator-demodulator (MOD-DEMOD). In some cases, the modulator and demodulator may be separate components. Each of demodulators 254a through 254r may condition (e.g., filter, amplify, downconvert, and digitize) the received signal to obtain input samples. Each of demodulators 254a through 254r may further process the input samples (e.g., for OFDM, etc.) to obtain received symbols. A MIMO detector 256 may obtain received symbols from all R demodulators 254a through 254r, perform MIMO detection on the received symbols where applicable, and provide detected symbols. The receive processor 258 may process (e.g., demodulate and decode) the detected symbols, provide decoded data for the UE 104 to a data sink 260, and provide decoded control information and system information to a controller / processor 280. The channel processor may determine, among other things, a reference signal received power (RSRP), a received signal strength indicator (RSSI), a reference signal received quality (RSRQ), and / or a channel quality indicator (CQI).

[0078] On the uplink, at the UE 104, a transmit processor 264 may receive and process data from a data source 262 and control information (e.g., for reports including RSRP, RSSI, RSRQ, CQI, channel state information, and / or channel state feedback) from the controller / processor 280. The transmit processor 264 may also generate reference symbols for one or more reference signals (e.g., based at least in part on a beta value or set of beta values ​​associated with the one or more reference signals). The symbols from the transmit processor 264 may be pre-decoded by the TX MIMO processor 266, if applicable, further processed by the modulators 254a through 254r (e.g., for DFT-s-OFDM and / or CP-OFDM, etc.), and transmitted to the base station 102. At base station 102, uplink signals from UE 104 and other UEs may be received by antennas 234a through 234t, processed by demodulators 232a through 232t, detected by MIMO detector 236 where applicable, and further processed by receive processor 238 to obtain decoded data and control information transmitted by UE 104. Receive processor 238 may provide the decoded data to a data sink 239 and the decoded control information to controller (processor) 240. Base station 102 may include a communication unit 244 and communicate with network controller 231 via communication unit 244. Network controller 231 may include a communication unit 294, a controller / processor 290, and a memory 292.

[0079] In some aspects, one or more components of the UE 104 may be included in a housing. The controller 240 of the base station 102, the controller / processor 280 of the UE 104, and / or Figure 2 Any other component of may perform one or more techniques associated with implicit UCI β value determination for NR.

[0080] Memories 242 and 282 may store data and program codes for base station 102 and UE 104, respectively. A scheduler 246 may schedule UEs for data transmission on the downlink, uplink, and / or sidelink.

[0081] In some aspects, the deployment of a communication system, such as a 5G New Radio (NR) system, can be arranged in a variety of ways with various components or parts. In a 5G NR system or network, a network node, a network entity, a mobility element of a network, a radio access network (RAN) node, a core network node, a network element, or network equipment (such as a base station (BS)), or one or more units (or one or more components) performing base station functions can be implemented in a converged or disaggregated architecture. For example, a BS (such as a Node B (NB), an evolved NB (eNB), an NR BS, a 5G NB, an access point (AP), a transmit / receive point (TRP), or a cell) can be implemented as a converged base station (also known as a standalone BS or a monolithic BS) or a disaggregated base station.

[0082] A converged base station can be configured to utilize a radio protocol stack that is physically or logically integrated within a single RAN node. A disaggregated base station can be configured to utilize a protocol stack that is physically or logically distributed across two or more units, such as one or more central or centralized units (CUs), one or more distributed units (DUs), or one or more radio units (RUs). In some aspects, a CU can be implemented within a RAN node, and one or more DUs can be co-located with the CU, or alternatively, can be geographically or virtually distributed across one or more other RAN nodes. A DU can be implemented to communicate with one or more RUs. Each of the CU, DU, and RU can also be implemented as a virtual unit, namely a virtual central unit (VCU), a virtual distributed unit (VDU), or a virtual radio unit (VRU).

[0083] Base station-type operation or network design may take into account the aggregated nature of base station functionality. For example, disaggregated base stations may be utilized in integrated access backhaul (IAB) networks, open radio access networks (O-RAN, such as those promoted by the O-RAN Alliance), or virtualized radio access networks (vRAN, also known as cloud radio access networks (C-RAN)). Disaggregation can include distributing functionality across two or more units at various physical locations, as well as virtually distributing the functionality of at least one unit, which enables flexibility in network design. Each unit of a disaggregated base station or disaggregated RAN architecture can be configured for wired or wireless communication with at least one other unit.

[0084] Figure 3A diagram illustrating an example disaggregated base station 300 architecture is shown. The disaggregated base station 300 architecture may include one or more central units (CUs) 310, which may communicate directly with a core network 320 via a backhaul link, or indirectly through one or more disaggregated base station units, such as a near real-time (near-RT) RAN intelligent controller (RIC) 325 via an E2 link, or a non-real-time (non-RT) RIC 315 associated with a service management and orchestration (SMO) framework 305, or both. The CUs 310 may communicate with one or more distributed units (DUs) 330 via corresponding midhaul links, such as the F1 interface. The DUs 330 may communicate with one or more radio units (RUs) 340 via corresponding fronthaul links. The RUs 340 may communicate with corresponding UEs 104 via one or more radio frequency (RF) access links. In some implementations, a UE 104 may be served simultaneously by multiple RUs 340.

[0085] Each of these units (e.g., CU 310, DU 330, RU 340, as well as near-RT RIC 325, non-RT RIC 315, and SMO framework 305) may include or be coupled to one or more interfaces configured to receive or transmit signals, data, or information (collectively, signals) via a wired transmission medium or a wireless transmission medium. Each of these units, or an associated processor or controller providing instructions to the communication interface of these units, may be configured to communicate with one or more of the other units via the transmission medium. For example, these units may include a wired interface configured to receive signals or transmit signals to one or more of the other units via the wired transmission medium. Additionally, these units may include a wireless interface, which may include a receiver, transmitter, or transceiver (such as a radio frequency (RF) transceiver) configured to receive signals or transmit signals to one or more of the other units via the wireless transmission medium, or both.

[0086] In some aspects, the CU 310 may host one or more higher-layer control functions. Such control functions may include radio resource control (RRC), packet data convergence protocol (PDCP), or service data adaptation protocol (SDAP), among others. Each control function may be implemented using an interface configured to communicate signals with other control functions hosted by the CU 310. The CU 310 may be configured to handle user plane functionality (i.e., central unit-user plane (CU-UP)), control plane functionality (i.e., central unit-control plane (CU-CP)), or a combination thereof. In some implementations, the CU 310 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 units may communicate bidirectionally with the CU-CP units via an interface, such as an E1 interface. As needed, the CU 310 may be implemented to communicate with the DU 330 for network control and signaling.

[0087] The DU 330 may correspond to a logical unit that includes one or more base station functions for controlling the operation of one or more RUs 340. In some aspects, the DU 330 may host one or more of a radio link control (RLC) layer, a medium access control (MAC) layer, and one or more higher physical (PHY) layers (such as modules for forward error correction (FEC) encoding and decoding, scrambling, modulation and demodulation, etc.), depending at least in part on a functional split (such as that defined by the Third Generation Partnership Project (3GPP)). In some aspects, the DU 330 may also host one or more lower PHY layers. Each layer (or module) may be implemented using an interface configured to communicate signals with other layers (and modules) hosted by the DU 330 or with control functions hosted by the CU 310.

[0088] Lower layer functionality may be implemented by one or more RUs 340. In some deployments, a RU 340 controlled by a DU 330 may correspond to a logical node that hosts RF processing functionality or low PHY layer functionality (such as performing Fast Fourier Transforms (FFTs), Inverse FFTs (iFFTs), digital beamforming, Physical Random Access Channel (PRACH) extraction and filtering, etc.), or both, based at least in part on a functional split (such as a lower layer functional split). In such an architecture, the RU 340 may be implemented to handle over-the-air (OTA) communications with one or more UEs 104. In some implementations, both real-time and non-real-time aspects of control and user plane communications with the RU 340 may be controlled by the corresponding DU 330. In some scenarios, this configuration may enable the implementation of the DU 330 and CU 310 in a cloud-based RAN architecture, such as a vRAN architecture.

[0089] The SMO framework 305 can be configured to support RAN deployment and provisioning of both non-virtualized and virtualized network elements. For non-virtualized network elements, the SMO framework 305 can be configured to support the deployment of dedicated physical resources for RAN coverage requirements, which can be managed via an operations and maintenance interface (such as the O1 interface). For virtualized network elements, the SMO framework 305 can be configured to interact with a cloud computing platform (such as Open Cloud (O-Cloud) 390) to perform network element lifecycle management (such as instantiating virtualized network elements) via cloud computing platform interfaces (such as the O2 interface). Such virtualized network elements may include, but are not limited to, the CU 310, DU 330, RU 340, and near-RT RIC 325. In some implementations, the SMO framework 305 can communicate with 4G RAN hardware (such as Open eNB (O-eNB) 311) via the O1 interface. Additionally, in some implementations, the SMO framework 305 can communicate directly with one or more RUs 340 via the O1 interface. The SMO framework 305 may also include a non-RT RIC 315 configured to support the functionality of the SMO framework 305 .

[0090] The non-RT RIC 315 can be configured to include logic that enables non-real-time control and optimization of RAN elements and resources, artificial intelligence / machine learning (AI / ML) workflows including model training and updating, or policy-based guidance of applications / features in the near-RT RIC 325. The non-RT RIC 315 can be coupled to or in communication with the near-RT RIC 325 (e.g., via an A1 interface). The near-RT RIC 325 can be configured to include logic that enables near-real-time control and optimization of RAN elements and resources through data collection and actions via an interface (e.g., via an E2 interface) that connects one or more CUs 310, one or more DUs 330, or both, and the O-eNB with the near-RT RIC 325.

[0091] In some implementations, to generate AI / ML models to be deployed in the near-RT RIC 325, the non-RT RIC 315 may receive parameters or external enrichment information from an external server. Such information may be utilized by the near-RT RIC 325 and may be received from non-network data sources or from network functions at the SMO framework 305 or the non-RT RIC 315. In some examples, the non-RT RIC 315 or the near-RT RIC 325 may be configured to tune RAN behavior or performance. For example, the non-RT RIC 315 may monitor long-term trends and patterns in performance and employ AI / ML models to execute corrective actions through the SMO framework 305 (such as via reconfiguration of O1) or through the creation of RAN management policies (such as A1 policies).

[0092] Figure 4 An example of a computing system 470 of a wireless device 407 is illustrated. Wireless device 407 may include a client device such as a UE (e.g., UE 104, UE 152, UE 190) or other type of device usable by an end user (e.g., a station (STA) configured to communicate using a Wi-Fi interface). For example, wireless device 407 may include a mobile phone, a router, a tablet, a laptop, a tracking device, a wearable device (e.g., a smartwatch, glasses, an extended reality (XR) device (such as a virtual reality (VR), augmented reality (AR), or mixed reality (MR) device), etc.), an Internet of Things (IoT) device, an access point, and / or another device configured to communicate via a wireless communication network. Computing system 470 includes software and hardware components that may be electrically or communicatively coupled via a bus 489 (or may otherwise communicate, as appropriate). For example, computing system 470 includes one or more processors 484. The one or more processors 484 may include one or more central processing units (CPUs), digital signal processors (DSPs), general-purpose microprocessors, application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), application processors (APs), graphics processing units (GPUs), visual processing units (VPUs), neural processing units (NPUs), neural signal processors (NSPs), microcontrollers, dedicated hardware, any combination thereof, and / or other processing devices or systems. The one or more processors 484 may use a bus 489 to communicate between cores and / or with one or more memory devices 486.

[0093] The computing system 470 may also include one or more memory devices 486, one or more digital signal processors (DSPs) 482, one or more subscriber identity modules (SIMs) 474, one or more modems 476, one or more wireless transceivers 478, one or more antennas 487, one or more input devices 472 (e.g., a camera, a mouse, a keyboard, a touch-sensitive screen, a touchpad, a keypad, a microphone, and / or the like), and one or more output devices 480 (e.g., a display, a speaker, a printer, and / or the like).

[0094] In some aspects, computing system 470 may include one or more RF interfaces configured to transmit and / or receive radio frequency (RF) signals. In some examples, the RF interface may include components such as a modem 476, a wireless transceiver 478, and / or an antenna 487. The one or more wireless transceivers 478 may transmit and receive wireless signals (e.g., signals 488) via antenna 487 from one or more other devices, such as other wireless devices, network devices (e.g., base stations such as eNBs and / or gNBs, Wi-Fi access points (APs) such as routers or range extenders, etc.), and / or cloud networks. In some examples, computing system 470 may include multiple antennas or antenna arrays to facilitate simultaneous transmit and receive functionality. Antenna 487 may be an omnidirectional antenna, enabling RF signals to be received from all directions and transmitted in all directions. Wireless signals 488 may be transmitted via a wireless network. The wireless network may be any wireless network, such as a cellular or telecommunications network (e.g., 3G, 4G, 5G, etc.), a wireless local area network (e.g., a WiFi network), a Bluetooth™ network, and / or other networks.

[0095] In some examples, wireless signals 488 can be transmitted directly to other wireless devices using sidelink communications (e.g., using a PC5 interface, using a DSRC interface, etc.). Wireless transceiver 478 can be configured to transmit RF signals via antenna 487 for performing sidelink communications according to one or more transmit power parameters that can be associated with one or more regulatory modes. Wireless transceiver 478 can also be configured to receive sidelink communication signals having different signal parameters from other wireless devices.

[0096] In some examples, one or more wireless transceivers 478 may include an RF front end that includes one or more components such as an amplifier, a mixer for downconverting a signal (also known as a signal multiplier), a frequency synthesizer (also known as an oscillator) that provides the signal to the mixer, a baseband filter, an analog-to-digital converter (ADC), one or more power amplifiers, and other components. The RF front end generally handles the selection of wireless signals 488 and the conversion of the wireless signals to baseband frequencies or intermediate frequencies, and may convert the RF signals to the digital domain.

[0097] In some cases, computing system 470 may include a coding-decoding device (or CODEC) configured to encode and / or decode data transmitted and / or received using one or more wireless transceivers 478. In some cases, computing system 470 may include an encryption-decryption device or component configured to encrypt and / or decrypt data transmitted and / or received by one or more wireless transceivers 478 (e.g., according to the AES and / or DES standards).

[0098] One or more SIM cards 474 can each securely store an International Mobile Subscriber Identity (IMSI) number and associated keys assigned to the user of wireless device 407. The IMSI and keys can be used to identify and authenticate the subscriber when accessing a network provided by a network service provider or operator associated with one or more SIM cards 474. One or more modems 476 can modulate one or more signals to encode information for transmission using one or more wireless transceivers 478. One or more modems 476 can also demodulate signals received by one or more wireless transceivers 478 to decode the transmitted information. In some examples, one or more modems 476 can include a Wi-Fi modem, a 4G (or LTE) modem, a 5G (or NR) modem, and / or other types of modems. One or more modems 476 and one or more wireless transceivers 478 can be used to communicate data from one or more SIM cards 474.

[0099] The computing system 470 may also include (and / or be in communication with) one or more non-transitory machine-readable storage media or storage devices (e.g., one or more memory devices 486), which may include, but are not limited to, local and / or network-accessible storage, disk drives, drive arrays, optical storage devices, solid-state storage devices (such as RAM and / or ROM), which may be programmable and / or flash-updatable, etc. Such storage devices may be configured to implement any suitable data storage, including, but not limited to, various file systems and / or database structures, etc.

[0100] In various embodiments, the functionality may be stored as one or more computer program products (e.g., instructions or code) in the memory device 486 and executed by the one or more processors 484 and / or the one or more DSPs 482. The computing system 470 may also include software elements (e.g., located within the one or more memory devices 486) including, for example, an operating system, device drivers, executable libraries, and / or other code, such as one or more application programs, which may include computer programs that implement the functionality provided by the various embodiments and / or may be designed to implement methods and / or configure systems, as described herein.

[0101] Figure 5 An example architecture of a neural network 500 that can be used according to some aspects of the present disclosure is illustrated. The example architecture of the neural network 500 can be defined by an example neural network description 502 in a neural controller 501. The neural network 500 is an example of a machine learning model that can be deployed and implemented at a base station 102, a central unit (CU) 310, a distributed unit (DU) 330, a radio unit (RU) 340, and / or a UE 104. The neural network 500 can be a feed-forward neural network or any other known or yet to be developed neural network or machine learning model.

[0102] Neural network description 502 may include a complete specification of neural network 500, including Figure 5 For example, neural network description 502 may include: a description or specification of the architecture of neural network 500 (e.g., layers, layer interconnections, number of nodes in each layer, etc.); input and output descriptions indicating how inputs and outputs are formed or processed; indications of activation functions in the neural network, operations or filters in the neural network, etc.; neural network parameters such as weights, biases, etc.; etc.

[0103] Neural network 500 may reflect the neural architecture defined in neural network description 502 . Neural network 500 may comprise any suitable neural or deep learning type of network. In some cases, neural network 500 may comprise a feedforward neural network. In other cases, neural network 500 may comprise a recurrent neural network, which may have loops that allow information to be carried across nodes when reading inputs. Neural network 500 may comprise any other suitable neural network or machine learning model. One example includes a convolutional neural network (CNN), which includes an input layer and an output layer with multiple hidden layers between the input and output layers. The hidden layers of a CNN include a series of hidden layers, such as convolutional layers, nonlinear layers, pooling layers (for downsampling), and fully connected layers, as described below. In other examples, neural network 500 may represent any other neural network or deep learning network, such as an autoencoder, a deep belief network (DBN), a recurrent neural network (RNN), a generative adversarial network (GAN), and the like.

[0104] exist Figure 5 In a non-limiting example, neural network 500 includes an input layer 503 that can receive one or more sets of input data. The input data can be any type of data (e.g., image data, video data, network parameter data, user data, etc.). Neural network 500 can include hidden layers 504A through 504N (hereinafter collectively referred to as "504"). Hidden layers 504 can include n hidden layers, where n is an integer greater than or equal to one. The n hidden layers can include as many layers as are necessary for a desired processing result and / or presentation intent. In one illustrative example, any of hidden layers 504 can include data representing one or more of the data provided at input layer 503. Neural network 500 also includes an output layer 506 that provides output resulting from the processing performed by hidden layer 504. Output layer 506 can provide output data based on the input data.

[0105] exist Figure 5In the example shown, neural network 500 is a multi-layer neural network of interconnected nodes. Each node can represent a piece of information. The information associated with these nodes is shared between different layers, and each layer retains the information as it processes it. Information can be exchanged between nodes via node-to-node interconnections between layers. Nodes in input layer 503 can activate a set of nodes in first hidden layer 504A. For example, as shown, each input node in input layer 503 is connected to each node in first hidden layer 504A. Nodes in hidden layer 504A can transform the information from each input node by applying an activation function to that information. The information derived from this transformation can then be passed to nodes in the next hidden layer (e.g., 504B) and activated, and these nodes can perform their own designated functions. Example functions include convolution, upsampling, data transformation, pooling, and / or any other suitable function. The output of a hidden layer (e.g., 504B) can then activate nodes in the next hidden layer (e.g., 504N), and so on. The output of the last hidden layer can activate one or more nodes in output layer 506, at which point the output is provided. In some cases, although nodes in neural network 500 (eg, nodes 508A, 508B, 508C) are shown as having multiple output lines, the node may have a single output and all lines shown as output from the node may represent the same output value.

[0106] In some cases, each node or the interconnections between nodes can have a weight, which is a set of parameters derived from training neural network 500. For example, an interconnection between nodes can represent a piece of information learned about the interconnected nodes. The interconnections can have numerical weights that can be tuned (e.g., based on a training data set), thereby allowing neural network 500 to adapt to inputs and learn as more data is processed.

[0107] The neural network 500 can be pre-trained to process features from the data in the input layer 503 using different hidden layers 504 in order to provide output via the output layer 506. For example, in some cases, the neural network 500 can use a training process known as backpropagation to adjust the weights of the nodes. Backpropagation can include a forward pass, a loss function, a backward pass, and weight updates. The forward pass, loss function, backward pass, and parameter updates can be performed for one training iteration. This process can be repeated for each training data set for a certain number of iterations until the weights of the layers are accurately tuned (e.g., meeting a configurable threshold determined based on experiments and / or empirical studies).

[0108] More and more ML (eg, AI) algorithms (eg, models) are being incorporated into various technologies including wireless telecommunications standards. Figure 6Ais a block diagram illustrating an ML engine 600 according to aspects of the present disclosure. As an example, one or more devices in a wireless system may include the ML engine 600. In some cases, the ML engine 600 may be similar to the neural network 500. In this example, the ML engine 600 includes three parts: an input 602 to the ML engine 600, the ML engine, and an output 604 from the ML engine 600. The input 602 to the ML engine 600 may be data that the ML engine 600 may use to make predictions or otherwise operate on it. As an example, the ML engine 600 configured to select an RF beam may use data regarding current RF conditions, location information, network load, etc. as input 602. As another example, data related to packets transmitted to a UE, along with historical packet data, may be used as input 602 to the ML engine 600 configured to predict a discontinuous reception (DRX) schedule for the UE. In some cases, the output 604 may be a prediction or other information generated by the ML engine 600, and the output 604 may be used to configure the wireless device, adjust settings, parameters, operating modes, etc. Continuing with the previous example, the ML engine 600 configured to select an RF beam may output 604 an RF beam or set of RF beams that may be used. Similarly, the ML engine 600 configured to predict a DRX schedule for a UE may output the DRX schedule for the UE.

[0109] In some cases, an ML engine (such as ML engine 600) can be used to generate and / or process various types of control information and / or system information. In another example, ML engine 600 can be an encoder for compressing information determined by a UE (e.g., channel state information (CSI) or channel state feedback (CSF)) to generate a representation (e.g., a latent representation) of the information. In some cases, the ML model can also be used by a network entity to implement operations. In another example, ML engine 600 can be a decoder used by a network entity (e.g., a base station) to decode a representation (e.g., a latent representation) of information (e.g., CSI) generated by a UE.

[0110] Figure 6B 6 is a diagram illustrating an example of a network 650 including a UE 651 and a base station 653 (e.g., a gNB or a portion of a gNB, such as a CU, DU, RU, etc. of a gNB with a disaggregated architecture). Figure 6BAs shown, a downlink channel estimate 652 (e.g., CSI or CSF) is provided to an encoder 654 of a UE 651. The CSI encoder 654 encodes the CSI and the UE 651 transmits the encoded CSI (e.g., a potential representation of the CSI, such as an eigenvector representing the CSI) to a receive antenna 662 of a base station 653 via a data or control channel 656 over a wireless or air interface 660 using antennas 658. In some cases, the UE 651 may transmit a potential message representing the CSI.

[0111] The coded CSI is provided to a CSI decoder 667 of the base station 653 via a data or control channel 664, which can decode the coded CSI to generate a reconstructed downlink channel estimate 668 (or reconstructed CSI). In some cases, the base station 653 can then determine a precoding matrix, a modulation and coding scheme (MCS), and / or a rank associated with one or more antennas of the base station. Based on the precoding matrix, MCS, and / or rank, the base station 653 can determine the configuration of control resources (e.g., via a physical downlink control channel (PDCCH)) or data resources (e.g., via a physical downlink shared channel (PDSCH)).

[0112] Figure 6C FIG6 is a diagram illustrating an example of a network 670 including a UE 671 and a base station 673 (e.g., a gNB with a disaggregated architecture or a portion of a gNB, such as a CU, DU, or RU of the gNB), according to various aspects of the present disclosure. As described above, an ML engine can be used to generate and / or process various types of operations. In some cases, multiple ML models of the UE 671 can generate and / or process multiple control information and / or system information based on multiple parameters. Different parameter sets can be input to different ML models for processing to obtain different outputs for different operations.

[0113] As an example, a UE may include operations 1 675A, ..., and 675O (collectively, operations 675). Each operation in operations 675 may be performed by one or more ML models. For example, operation 1 675A may be performed by ML models 1 680A, ..., and 680M (collectively, ML models 680), while operation 0 may be performed by ML models B 682B, ..., and N 682N (collectively, ML models 682). Each ML model (e.g., ML models 680 and 682) may accept one or more different parameters as input, such as parameter A 684A, parameter B 684B, ..., parameter Y 684Y, and parameter 1 686A, parameter 2 686B, ..., parameter X 686X.

[0114] As a more specific example, operation 1 675A may be using ML model 680 to generate a CSI report for transmission. ML model 680 may perform a portion of generating the CSI report. For example, an ML model in ML model 680 may perform space-frequency CSI compression, another ML model in ML model 680 may perform space-frequency-time CSI compression, another ML model in ML model 680 may use a UE-side model to perform time-domain CSI prediction, and so on. ML models (such as ML model 680 and ML model 682) may take various parameters as input. For example, ML model 1 680A may take parameter 1 686A and parameter X 686X as input, while ML model M 680M may take parameter 1 686A and parameter 2 686B as input. Examples of parameters may include, but are not limited to, frequency band / frequency information, subcarrier spacing, number of TX and RX antenna ports, different scenarios and / or configurations, antenna patterns, environmental information and / or settings, and the like. The output of ML model 680 may be combined for CSI reporting by operation 1 675A and sent to BS 673 via antenna 685 using a data or control channel.

[0115] In some cases, BS 673 may receive information sent by UE 671 via receive antenna 687. In some cases, one or more operations 690 may be performed to decode, process, transform, etc. the information sent by UE 671. In some cases, these operations 690 may be performed by one or more ML models (such as ML model C 692A, ..., ML model P 692P) to generate output data 694.

[0116] As discussed above, ML models can be used by either or both the UE and a network entity (e.g., BS 673) to implement functionality. If both the UE and the network entity are using ML models to perform corresponding operations (e.g., encoding a type of CSI information and decoding encoded CSI information), the UE and network entity should use compatible ML models. In some cases, either or both the UE and the network entity may include one or more ML models for performing certain operations. For example, the UE may include a specific ML model for performing an operation such as spatial-frequency CSI compression, while the network entity includes multiple ML models for performing a corresponding operation such as decompressing spatial-frequency CSI, and may select between the multiple ML models for performing the corresponding operation (e.g., decompression). Similarly, the UE may switch from a first ML model used to perform an operation to a second ML model used to perform the operation based on a changed condition (e.g., based on a change in an input parameter). In some cases, based on the UE's change from the first ML model to the second ML model, the network entity may also switch the ML model used to perform the corresponding operation (or cease using the ML model) to ensure compatibility. Similarly, the network entity may switch between the third ML model and the fourth ML model based on the changed parameters, for example, and based on such changes to the ML model by the network entity, the UE may also switch the ML model (or stop using the ML model). Functionality-based two-sided machine learning operations may help facilitate the maintenance of compatible ML models by providing a framework for providing functionality-based assistance information and / or indications to corresponding wireless devices (e.g., UEs or network entities) to ensure ML model compatibility.

[0117] In some cases, functionality-based assistance information and / or indications between wireless devices can be used to coordinate ML model usage for compatibility while avoiding disclosure of which ML models are being used. For example, a UE can keep confidential the specific details of how the UE is using a particular ML model or which ML model is being used, but still disclose what operations the ML model is performing and what parameters are being input to the ML model (indicating the scenario, configuration, and other functional parameters that the UE has the relevant model to use) to allow network entities to select a compatible corresponding ML model (or no ML model at all).

[0118] Figure 7is a sequence diagram illustrating an example technique 700 for functional, two-sided machine learning operations according to aspects of the present disclosure. In technique 700, a UE 702 may be communicatively coupled to a network entity 704, and the UE 702 may send 706 to the network entity 704 an indication of one or more UE operations (e.g., a set) performed by the UE 702 using an ML model (e.g., a second set of operations). For example, the UE 702 may indicate that the ML model may be used to generate UE operations, such as certain CSI measurements reported by the UE 702. In some cases, the UE 702 may send 706 a set of UE parameters associated with the ML model being used (e.g., a second set of parameters). For example, one or more ML models may be used to generate portions of the CSI measurement, and the UE parameters used by those one or more ML models may be indicated to the network entity 704. In some cases, one or more ML models corresponding to the indicated UE parameters may not be indicated. UE parameters that may be used to generate another portion of the CSI measurement using non-ML techniques may not be indicated to the network entity. In some cases, a UE parameter set may be those parameters that are input to an ML model being used by the UE. In some cases, a parameter set (UE or network entity) may be a list of parameters that are input to an ML model (e.g., of the UE or network entity). In other cases, the UE parameter sets may be grouped so that the grouping indicates the UE parameters that may be input per ML model. In some cases, there may be an indication of the associated ML model for the grouped UE parameters. In some cases, the parameters indicate the scenarios, configurations, and other operating parameters for which the UE has an associated model for a given operation or set of operations. In some cases, the UE operation set may be sent 706 via control signaling, such as RRC signaling, as part of a capability exchange, such as capability information or higher-level signaling. The UE parameter set may be sent 808 in conjunction with or in place of the UE operation set.

[0119] In some cases, the network entity 704 may optionally send 708 to the UE 702 an indication of one or more network entity operations (e.g., a set) to be performed by the network entity 704 using the ML model (e.g., a first set of operations). In some cases, the network entity 704 may send 708 a set of network entity parameters associated with the ML model being used (e.g., the first set of parameters). The set of network entity parameters may be sent 808 in conjunction with or in lieu of the set of network entity operations. In some cases, the network entity 704 may indicate the set of network entity operations and network entity parameters associated with the set of network entity operations in a manner substantially similar to that of the UE 702, as discussed above. In some cases, the network entity 704 may send 708 the indication of the set of network entity operations before, concurrently with, or after the UE 702 sends 706 the indication of the set of network entity operations.

[0120] Based on the indicated set of UE operations received from UE 702, the network entity may determine a set of compatible network entity operations to be performed by the ML model of the network entity 704 and / or a set of network entity parameters to be used by the ML model of the network entity. Based on the determined set of compatible network entity operations and / or set of network entity parameters, the network entity 704 may select one or more UE operations and / or UE parameters from the set of UE operations and / or set of UE parameters received from UE 702 (e.g., in transmission 706). For example, the network entity 704 may support more, all, or less than all of the UE operations and / or UE parameters in the set of UE operations received from UE 702, and the network entity may select the UE operations and / or UE parameters from the set of UE operations and / or set of UE parameters received from UE 702. The network entity 704 may then configure 710 the UE 702 to use the one or more UE operations and / or use the one or more UE parameters. For example, the network entity 704 may indicate a UE operation set, a UE parameter set, and / or a UE operation set and an associated UE parameter set that the UE 702 may use to communicate with the network entity. In some cases, the network entity 704 may configure 710 the UE 702 using a UE configuration update procedure or any other procedure for network-directed configuration updates for wireless devices.

[0121] In some cases, network entity 704 or UE 702 may detect a change in operation and / or parameters applicable to (e.g., adapted for) operation. For example, an environmental change (such as interference from other wireless devices or weather changes) may cause network entity 704 to determine that the frequency band, frequency, and / or antenna pattern, etc., should be adjusted to reduce interference. In some cases, these adjustments may affect network entity operation and / or network entity parameters associated with network entity operation. For example, network entity operation for decoding a portion of a CSI report may include a first ML model that is parameterized to operate within a specific frequency range. Therefore, a change in the frequency of a portion of the CSI report may result in the first ML model not being used to decode that portion of the CSI report. Instead, a second ML model may be used (or a fallback to non-ML-based operation).

[0122] To ensure compatibility with corresponding UE ML models, the network entity 704 may detect potential changes 712 in network entity operations and / or network entity parameters to determine whether corresponding changes in UE operations and / or UE parameters may be required. This determination may be based on a UE operation set and / or UE parameter set received from the UE 702 (e.g., in a transmission 706). In some cases, the network entity 704 may transmit an indication to activate and / or deactivate 714 the use of one or more UE operations and / or UE parameters. In some cases, the indication to activate and / or deactivate 714 may be transmitted via control signaling (e.g., using a PDCCH / PDSCH message, an RRC message, a MAC CE, etc.). The UE 702 may then activate / deactivate the UE operations and / or use of certain ML models accordingly.

[0123] As another example, UE 702 may determine that UE operation and / or UE parameters may need to be changed. For example, in some cases, UE 702 may detect certain environmental changes, such as moving from an indoor environment to an outdoor environment, or vice versa. Based on these environmental changes, UE 702 may detect potential changes 716 in UE operation and / or UE parameters and determine whether corresponding changes in UE operation and / or UE parameters may be required. If UE 702 determines that changes to UE operation and / or UE parameters should be made, in some cases, UE 702 may autonomously activate / deactivate UE operation 718 and / or use of certain ML models accordingly. When UE 702 is configured to autonomously activate / deactivate UE operation 718 and / or use of certain ML models, UE 702 may notify 720 network entity 704 of such UE operation and / or UE parameter changes. In some cases, UE 702 may then implement the UE operation and / or UE parameter changes, or fall back to non-ML operation. In some cases, notification 720 of UE operation and / or UE parameter changes may be conveyed via control signaling, such as using PUCCH / PUSCH messages, RRC messages, MAC CEs, and the like.

[0124] In some cases, such as when the UE 702 is not configured to autonomously activate / deactivate UE operation 718 and / or use of certain ML models, the UE 702 may notify 722 the network entity 704 of changes in UE operation and / or UE parameters. In some cases, the notification 722 of the change in UE operation and / or UE parameters may be conveyed via control signaling (such as using PUCCH / PUSCH messages, RRC messages, MAC CEs, etc.). Based on the notification 722, the network entity 704 may then configure 724 the UE 702 to use another UE operation and / or one or more UE parameters (or fall back to non-ML operation). In some cases, configuring 724 the UE 702 may be performed in a manner substantially similar to configuring 710 the UE 702.

[0125] Figure 8is a sequence diagram illustrating another example technique 800 for functional, two-sided machine learning operations in accordance with aspects of the present disclosure. Generally, in technique 800, the network entity 804 may play a reduced role in determining which operations and / or features should be used. In technique 800, a UE 802 may be communicatively coupled to the network entity 804. Optionally, in some cases, the UE 802 may send 806 to the network entity 804 an indication of a set of UE operations (e.g., a second set of operations) and / or a set of UE parameters (e.g., a second set of parameters) to be performed by the UE 802 using the ML model. In some cases, the UE operations performed and / or the UE parameters used may be sent 706 via control signaling, such as RRC signaling, as part of a capability exchange, such as capability information or higher-level signaling.

[0126] In some cases, the network entity 804 may send 808 to the UE 802 an indication of a set of network entity operations (e.g., a first set of operations) performed by the network entity 804 using an ML model. In some cases, the network entity 804 may send 808 a set of network entity parameters associated with the ML model being used (e.g., the first set of parameters). The set of network entity parameters may be sent 808 in conjunction with or in lieu of the set of network entity operations. In some cases, the set of network entity parameters may be grouped such that the grouping indicates network entity parameters that may be input per ML model. In some cases, there may be an indication of an associated ML model for the grouped network entity parameters. In some cases, the set of network entity operations may be sent 808 by the network entity 804 via unicast or multicast control signaling, or broadcast by the network entity.

[0127] In some examples, UE 802 may determine a compatible set of UE operations performed by an ML model of UE 802 and / or a set of UE parameters used by the ML model of the UE. Based on the determined compatible set of UE operations and / or UE parameter sets, UE 802 may perform one or more UE operations and / or UE parameters.

[0128] In some cases, the network entity 804 may detect a potential change 810 in network entity operation and / or network entity parameters. For example, environmental changes (such as interference from other wireless devices or weather changes) may cause the network entity 804 to determine that the frequency band, frequency, and / or antenna pattern, etc., may be adjusted to reduce interference. In some cases, such as if the network entity 804 or UE 802 has previously activated / deactivated UE operation and / or use of certain parameters, the network entity 804 may determine that reactivating / deactivating the previously activated / deactivated UE operation and / or use of certain parameters may be useful to adapt to the potential change in network entity operation and / or network entity parameters. In such cases, the network entity may transmit an indication to activate / deactivate 812 the UE operation and / or use of certain parameters. The UE 802 may then activate / deactivate the UE operation and / or use of certain ML models accordingly.

[0129] In some cases, the network entity 804 may determine whether a corresponding change in UE operation and / or UE parameters may be required. This determination may be based on a UE operation set and / or UE parameter set received from the UE 802 (e.g., in transmission 806). In some cases, the network entity 804 may transmit an indication to activate and / or deactivate 812 the use of one or more UE operations and / or UE parameters. In some cases, the indication to activate and / or deactivate 812 may be transmitted via control signaling (such as using PDCCH / PDSCH messages and MAC CEs). The UE 802 may then activate / deactivate the UE operation and / or use of certain ML models accordingly.

[0130] In some cases, UE 802 may determine that changes to UE operation and / or UE parameters are necessary. For example, in some cases, UE 802 may detect certain environmental changes, such as moving from an indoor environment to an outdoor environment or vice versa. Based on these environmental changes, UE 802 may detect potential changes 814 to UE operation and / or UE parameters and determine whether corresponding changes to UE operation and / or UE parameters may be necessary. Whether corresponding changes to UE operation and / or UE parameters may be necessary may be determined based on an indication sent 808 by network entity 804 of a set of network entity operations performed and / or a set of network entity parameters used by network entity 804. If UE 802 determines that changes to UE operation and / or UE parameters should be made, UE 802 may autonomously activate / deactivate UE operation and / or use of certain ML models accordingly. When UE 802 is configured to autonomously activate / deactivate UE operation and / or use of certain ML models, UE 802 may notify 816 the network entity 804 of such changes to UE operation and / or UE parameters. In some cases, the UE 802 may then implement the UE operation and / or UE parameter change, or fall back to non-ML operation. In some cases, the notification 816 of the UE operation and / or UE parameter change may be transmitted via control signaling (such as using PUCCH / PUSCH messages, MAC CE, and RRC messages).

[0131] Figure 9 is a sequence diagram illustrating another example technique 900 for two-sided machine learning operations based on functionality according to aspects of the present disclosure. Figure 8 The technique 800 is similar in that the network entity 904 may play a reduced role in determining which operations and / or features the UE 902 should use. In the technique 900 , the UE 902 is communicatively coupled to the network entity 904 .

[0132] In some cases, the network entity 904 may send 906 a set of operation identifiers (e.g., a first set of operation identifiers) and an indication of the applicability associated with the operation identifiers. The operation identifiers may be associated with processes (e.g., applications or technologies) that can be used to perform the operations, and the applicability may be an indication of the operations associated with the processes. In some cases, the applicability may be a list of scenarios (e.g., configurations) in which the ML model (or set of ML models) may be used. In some cases, the network entity 904 may also send 906 to the UE 902 an indication of a set of network entity operations (e.g., the first set of operations) performed by the network entity 904 using the ML model and / or a set of network entity parameters (e.g., the first set of parameters) associated with the ML model being used. The set of network entity parameters may be sent 908 along with or in place of the set of network entity operations. In some cases, the set of network entity parameters may be grouped such that the grouping indicates network entity parameters that may be input per ML model. In some cases, there may be an indication of the associated ML model for the grouped network entity parameters. In some cases, the network entity operation set may be sent 906 via control signaling and may be sent by the network entity 904 as a unicast, multicast, or broadcast.

[0133] In some cases, the network entity 904 may detect a potential change 908 in network entity operation and / or network entity parameters. For example, environmental changes (such as interference from other wireless devices or weather changes) may cause the network entity 904 to determine that the frequency band, frequency, and / or antenna pattern, etc., may be adjusted to reduce interference. In some cases, the network entity 904 may detect a potential change 908 in network entity operation and / or network entity parameters and determine that a change in the network entity operation and / or network entity parameters may be useful. Based on determining that the network entity operation and / or network entity parameters may be changed, the network entity 904 may transmit assistance information 910 to the UE 902. In some cases, the assistance information 910 may indicate to the UE that the network entity operation and / or network entity parameters may have changed. In some cases, the UE 902 may determine, based on the assistance information and the transmitted 906 set of operation identifiers and the indication of applicability associated with the operation identifiers, whether the corresponding UE operator and / or UE parameters should also be changed to ensure compatibility between the ML model of the UE 902 and the ML model of the network entity 904. The UE 902 may then activate / deactivate UE operation, use of certain ML models, and / or fall back to non-ML techniques accordingly. In some cases, the assistance information may be sent in a UE Assistance Information message, which may be an RRC message that may be used to indicate various internal states / conditions of the network to the UE.

[0134] In some cases, UE 902 may determine that UE operation and / or UE parameters may need to be changed. For example, in some cases, UE 902 may detect certain environmental changes, such as moving from an indoor environment to an outdoor environment, or vice versa. Based on these environmental changes, UE 902 may determine that potential changes 912 to UE operation and / or UE parameters may be useful. UE 902 may determine, based on the potential changes, the set of operation identifiers sent 906, and the indication of applicability associated with the operation identifiers, whether corresponding network entity operators and / or network entity parameters should also be changed to ensure compatibility between the ML model of UE 902 and the ML model of network entity 904. In some cases, UE 902 may notify 914 network entity 904 of such UE operation and / or UE parameter changes. In some cases, UE 902 may then implement the UE operation and / or UE parameter changes, or fall back to non-ML operation. In some cases, notification 914 of UE operation and / or UE parameter changes may be conveyed via control signaling, such as using PUCCH / PUSCH messages, MAC CE, RRC messages, and the like.

[0135] Figure 10 is a flow chart illustrating a process 1000 for performing wireless communication. Process 1000 may be performed by a wireless device (e.g., Figure 1 、 Figure 2 、 Figure 7 、 Figure 8 and Figure 9 The process 1000 may be performed by a component or system of a wireless device (e.g., a chipset, one or more processors such as one or more microcontrollers, CPUs, DSPs, NPUs, NSPs, GPUs, ASICs, FPGAs, VPUs, etc.). The wireless device may be a mobile device (e.g., a mobile phone), a network-connected wearable device such as a watch, an extended reality (XR) device such as a virtual reality (VR) device or an augmented reality (AR) device, a vehicle or a component or system of a vehicle, or other type of computing device. The operations of process 1000 may be implemented as a processor (e.g., Figure 4 Processor 484, Figure 16 In addition, the software components can be executed and run on the processor 1610 or other processors of the present invention. Figure 2 Antenna 252, Figure 4 antenna 487) and / or one or more transceivers (e.g., Figure 4 The wireless transceiver 478 of the wireless device is used to enable the wireless device to send and receive signals in process 1000.

[0136] At block 1002, a computing device (or a component thereof) may receive a first set of operations supported by one or more machine learning models of a network entity. The computing device (or a component thereof) may determine a second set of operations supported by the one or more machine learning models of the device. The computing device (or a component thereof) may determine a second set of parameters associated with the second set of operations. In some cases, the second set of parameters is supported by the one or more machine learning models of the device. The computing device (or a component thereof) may send the second set of operations and the second set of parameters to the network entity.

[0137] At block 1004, the computing device (or a component thereof) may receive a first parameter set associated with the first set of operations. In some cases, the first parameter set is supported by the one or more machine learning models of the network entity. In some cases, the first set of operations and the first parameter set are based on the second set of operations and the second parameter set. In some cases, the first set of operations and the first parameter set are received from the network entity via unicast, multicast, or broadcast.

[0138] At block 1006, the computing device (or a component thereof) may select a machine learning model for performing a first operation in the first set of operations based on the first parameter set. The computing device (or a component thereof) may receive an activation message. In some cases, the activation message is configured to activate a second operation in the first set of operations. The computing device (or a component thereof) may select a machine learning model to perform the second operation. The computing device (or a component thereof) may receive a deactivation message. In some cases, the deactivation message specifies the first operation in the first set of operations. The computing device (or a component thereof) may, based on the deactivation message, cease using the selected machine learning model to perform the first operation. In some cases, the first operation comprises an encoding operation. In some cases, the first operation comprises encoding channel state information (CSI) feedback information.

[0139] At block 1008 , the computing device (or a component thereof) may detect a change in at least one of: the first operation; or a parameter associated with the first operation.

[0140] At block 1010, the computing device (or a component thereof) may send an instruction to change the first operation based on the detected change. In some cases, the instruction to change the first operation based on the detected change includes one of: an instruction to deactivate the first operation; or an instruction to activate a second operation. The computing device (or a component thereof) may send a message to the network entity based on the output of the selected machine learning model. The computing device (or a component thereof) may be configured to fall back to a non-machine learning model-based approach to perform the first operation.

[0141] Figure 11is a flow chart illustrating a process 1100 for performing wireless communication. Process 1100 may be performed by a wireless device (e.g., Figure 1 、 Figure 2 、 Figure 7 、 Figure 8 and Figure 9 The process 1100 may be performed by a component or system of a wireless device (e.g., a chipset, one or more processors such as one or more microcontrollers, CPUs, DSPs, NPUs, NSPs, GPUs, ASICs, FPGAs, VPUs, etc.). The wireless device may be a mobile device (e.g., a mobile phone), a network-connected wearable device such as a watch, an extended reality (XR) device such as a virtual reality (VR) device or an augmented reality (AR) device, a vehicle or a component or system of a vehicle, or other type of computing device. The operations of process 1100 may be implemented as a processor (e.g., Figure 4 Processor 484, Figure 16 In addition, the software components can be executed and run on the processor 1610 or other processors of the present invention. Figure 2 Antenna 252, Figure 4 antenna 487) and / or one or more transceivers (e.g., Figure 4 The wireless transceiver 478 of the wireless device is used to enable the wireless device to send and receive signals in process 1100.

[0142] At block 1102, a computing device (or a component thereof) may receive an indication of a first set of operations supported by one or more machine learning models of a network entity. In some cases, the indication of the first set of operations includes a set of identifiers. In some cases, the set of identifiers includes a respective identifier for each of the one or more machine learning models of the network entity. The computing device (or a component thereof) may determine a first set of parameters associated with the one or more machine learning models identified by the set of identifiers. In some cases, the first set of parameters indicates parameters supported by the one or more machine learning models of the network entity. In some cases, the set of identifiers indicates operational applicability of operations in the first set of operations and parameters associated with the operations in the first set of operations. In some cases, the set of identifiers indicates a configuration for using the operations in the first set of operations. In some cases, the indication of the first set of operations is received from the network entity via unicast, multicast, or broadcast.

[0143] At block 1104, the computing device (or a component thereof) may select a machine learning model for performing a first operation in the first set of operations. The computing device (or a component thereof) may send a message to the network entity based on an output of the selected machine learning model. In some cases, the first operation includes encoding. In some cases, the first operation includes encoding channel state information (CSI) feedback information.

[0144] At block 1106, the computing device (or a component thereof) may detect a change in at least one of: the first operation; or a parameter associated with the first operation. The computing device (or a component thereof) may receive an assistance message. The computing device (or a component thereof) may select a second operation from the first set of operations based on the assistance message.

[0145] At block 1108, the computing device (or a component thereof) may send an instruction to change the first operation based on the detected change. In some cases, the instruction to change the first operation based on the detected change includes one of: an instruction to deactivate the first operation; or an instruction to activate a second operation. The computing device (or a component thereof) may fall back to a non-machine learning model-based approach to perform the first operation.

[0146] Figure 12 is a flow chart illustrating a process 1000 for performing wireless communication. The process 1200 may be performed by a wireless device (e.g., Figure 1 、 Figure 2 、 Figure 7 、 Figure 8 and Figure 9 The process 1200 may be performed by a component or system of a wireless device (e.g., a chipset, one or more processors such as one or more microcontrollers, CPUs, DSPs, NPUs, NSPs, GPUs, ASICs, FPGAs, VPUs, etc.). The wireless device may be a mobile device (e.g., a mobile phone), a network-connected wearable device such as a watch, an extended reality (XR) device such as a virtual reality (VR) device or an augmented reality (AR) device, a vehicle or a component or system of a vehicle, or other type of computing device. The operations of process 1200 may be implemented as a processor (e.g., Figure 4 Processor 484, Figure 16 In addition, the software components can be executed and run on the processor 1610 or other processors of the present invention. Figure 2 Antenna 252, Figure 4 antenna 487) and / or one or more transceivers (e.g., Figure 4The wireless transceiver 478 of the wireless device is used to enable the wireless device to send and receive signals in process 1200.

[0147] At block 1202 , a computing device (or a component thereof) may determine a first set of operations supported by one or more machine learning models of an apparatus.

[0148] At block 1204, the computing device (or a component thereof) may determine a first parameter set associated with the first set of operations. In some cases, the first parameter set is supported by the one or more machine learning models of the apparatus. The computing device (or a component thereof) may receive configuration information for a first operation in the first set of operations. The computing device (or a component thereof) may perform the first operation based on the configuration information. In some cases, the received configuration information is based on the first set of operations and the first parameter set. The computing device (or a component thereof) may receive an activation message. In some cases, the activation message is configured to activate a second operation in the first set of operations. The computing device (or a component thereof) may select a machine learning model to perform the second operation. The computing device (or a component thereof) may receive a deactivation message. In some cases, the deactivation message specifies the first operation in the first set of operations. Based on the deactivation message, the computing device (or a component thereof) may stop performing the first operation.

[0149] At block 1206 , the computing device (or a component thereof) may send the first set of operations and the first set of parameters to a network entity.

[0150] At block 1208, the computing device (or a component thereof) may detect a change in at least one of: a first operation in the first set of operations or a parameter associated with the first operation. In some cases, the first operation includes an encoding operation. In some cases, the first operation includes encoding channel state information (CSI) feedback information.

[0151] At block 1210, the computing device (or a component thereof) may send an indication of a change from the first operation based on the detected change. In some cases, the indication of the change includes an indication to activate a second operation based on the detected change. In some cases, the indication of the change includes an indication to deactivate the first operation based on the detected change. In some cases, the indication of the change includes an indication of the detected change to the network entity. The computing device (or a component thereof) may receive at least one of a deactivation message or an activation message. In some cases, at least one of the deactivation message or the activation message is based on the indication of the detected change. The computing device (or a component thereof) may fall back to a non-machine learning model-based approach to perform the first operation.

[0152] Figure 13is a flow chart illustrating a process 1300 for performing wireless communication. Process 1300 may be performed by a network entity (e.g., Figure 7 、 Figure 8 and Figure 9 The network entity may be a base station (e.g., a base station), or a component or system of the network entity (e.g., a chipset, one or more processors such as one or more microcontrollers, CPUs, DSPs, NPUs, NSPs, GPUs, ASICs, FPGAs, VPUs, etc.). Figure 1 and Figure 2 The operations of process 1300 may be implemented as a process on one or more processors (e.g., Figure 4 Processor 484, Figure 16 processor 1610, Figure 16 In addition, the software components can be executed and run on the processor 1610 or other processors of the present invention. Figure 2 antenna 234) and / or one or more transceivers (e.g., wireless transceivers such as Figure 2 The transmitting processor 220 and receiving processor 238 of the first network entity are used to enable the first network entity to send and receive signals in the process 1300.

[0153] At block 1302, a computing device (or a component thereof) may transmit a first set of operations supported by one or more machine learning models of an apparatus. The computing device (or a component thereof) may receive, from the wireless device, a second set of operations supported by one or more machine learning models of the wireless device and a second set of parameters associated with the second set of operations. The computing device (or a component thereof) may determine the first set of operations based on the second set of operations.

[0154] At block 1304, the computing device (or a component thereof) may send a first parameter set associated with the first set of operations. In some cases, the first parameter set indicates parameters supported by the one or more machine learning models of the apparatus. The computing device (or a component thereof) may send the first set of operations and the first parameter set as a unicast message, a multicast message, or a broadcast message.

[0155] At block 1306, the computing device (or a component thereof) may receive a message based on an output of a machine learning model of the wireless device. In some cases, the output is based on the first set of operations and the first set of parameters. The computing device (or a component thereof) may send an activation message. In some cases, the activation message is configured to activate a second operation in the first set of operations. The computing device (or a component thereof) may send a deactivation message. In some cases, the deactivation message specifies a first operation in the first set of operations.

[0156] At block 1308, the computing device (or a component thereof) may receive an indication to change a first operation in the first set of operations based on a change detected by the wireless device. In some cases, the indication of the change includes an indication to activate a second operation. In some cases, to change the first operation, the computing device (or a component thereof) may activate the second operation. In some cases, the indication of the change includes an indication to deactivate the first operation. In some cases, to change the first operation, the computing device (or a component thereof) may deactivate the first operation. In some cases, the first operation includes a decoding operation. In some cases, the first operation includes encoding channel state information (CSI) feedback information.

[0157] At block 1310 , the computing device (or a component thereof) may change the first operation based on the indication of a change.

[0158] Figure 14 is a flow chart illustrating a process 1400 for performing wireless communication. Process 1400 may be performed by a network entity (e.g., Figure 7 、 Figure 8 and Figure 9 The network entity may be a base station (e.g., a base station), or a component or system of the network entity (e.g., a chipset, one or more processors such as one or more microcontrollers, CPUs, DSPs, NPUs, NSPs, GPUs, ASICs, FPGAs, VPUs, etc.). Figure 1 and Figure 2 The operations of process 1400 may be implemented as a process on one or more processors (e.g., Figure 4 Processor 484, Figure 10 processor 1010, Figure 16 In addition, the software components can be executed and run on the processor 1610 or other processors of the present invention. Figure 2 antenna 234) and / or one or more transceivers (e.g., wireless transceivers such as Figure 2 The transmitting processor 220 and receiving processor 238 of the first network entity are used to enable the first network entity to send and receive signals in the process 1400.

[0159] At block 1402, a computing device (or a component thereof) may send an indication of a first set of operations supported by one or more machine learning models of an apparatus. In some cases, the indication of the first set of operations includes a set of identifiers for the one or more machine learning models of a network entity. In some cases, the set of identifiers indicates operational applicability of operations in the first set of operations and parameters associated with the operations in the first set of operations.

[0160] At block 1404, the computing device (or a component thereof) may receive a message based on an output of a machine learning model of the wireless device. In some cases, the output is based on the first set of operations. The computing device (or a component thereof) may detect a change in at least one of: a first operation in the first set of operations; or a parameter associated with the first operation. The computing device (or a component thereof) may send an assistance message to the wireless device.

[0161] At block 1406, the computing device (or a component thereof) may receive an indication to change a first operation in the first set of operations based on a change detected by the wireless device (also referred to as a "change indication"). In some cases, the change indication includes an indication to the wireless device that a wireless device operation has been activated based on the change detected by the wireless device. In some cases, the change indication includes an indication to the wireless device that a wireless device operation has been deactivated based on the change detected by the wireless device. In some cases, the first operation includes a decoding operation. In some cases, the first operation includes decoding channel state information (CSI) feedback information.

[0162] At block 1408, the computing device (or a component thereof) may change the first operation of the apparatus based on the indication of the change. In some cases, to change the first operation, the computing device (or a component thereof) may perform at least one of: deactivating the first operation; activating the first operation; or falling back to a non-machine learning model-based approach to perform the first operation.

[0163] Figure 15 is a flow chart illustrating a process 1500 for performing wireless communication. Process 1500 may be performed by a network entity (e.g., Figure 7 、 Figure 8 and Figure 9 The network entity may be a base station (e.g., a base station), or a component or system of the network entity (e.g., a chipset, one or more processors such as one or more microcontrollers, CPUs, DSPs, NPUs, NSPs, GPUs, ASICs, FPGAs, VPUs, etc.). Figure 1 and Figure 2 The operations of process 1500 may be implemented as a process on one or more processors (e.g., Figure 4 Processor 484, Figure 10 processor 1010, Figure 16 In addition, the software components can be executed and run on the processor 1610 or other processors of the present invention. Figure 2antenna 234) and / or one or more transceivers (e.g., wireless transceivers such as Figure 2 The transmitting processor 220 and receiving processor 238 of the first network entity are used to enable the first network entity to send and receive signals in the process 1500.

[0164] At block 1502, a computing device (or a component thereof) may receive a first set of operations supported by one or more machine learning models of a wireless device. The computing device (or a component thereof) may send configuration information for a first operation in the first set of operations to the wireless device.

[0165] At block 1504, the computing device (or a component thereof) may receive a first parameter set associated with the first set of operations. In some cases, the first parameter set indicates parameters supported by the one or more machine learning models of the wireless device.

[0166] At block 1506, the computing device (or a component thereof) may perform a first operation in the first set of operations using the device's machine learning model based on the received first set of operations. The computing device (or a component thereof) may detect a change in at least one of the first operation or a parameter associated with the first operation. The computing device (or a component thereof) may send an activation message. In some cases, the activation message activates a second operation in the first set of operations. The computing device (or a component thereof) may detect a change in at least one of the first operation or a parameter associated with the first operation. The computing device (or a component thereof) may send an indication to deactivate the first operation based on the detected change. In some cases, the first operation includes a decoding operation. In some cases, the first operation includes decoding channel state information (CSI) feedback information.

[0167] At block 1508, the computing device (or a component thereof) may receive an indication to change a first operation in the first set of operations based on the change detected by the wireless device (also referred to as a change indication). In some cases, the change indication includes an indication to the wireless device to activate or deactivate a wireless device operation based on the change detected by the wireless device.

[0168] At block 1510, the computing device (or a component thereof) may change the first operation of the apparatus based on the indication of the change. The computing device (or a component thereof) may determine to deactivate the first operation based on the received indication of the change. In some cases, based on the determination to deactivate the first operation, the computing device (or a component thereof) may send a deactivation message to the wireless device. The computing device (or a component thereof) may determine to activate a second operation based on the received indication of the change. In some cases, based on the determination to activate the second operation, the computing device (or a component thereof) may send an activation message to the wireless device.

[0169] Figure 16 is a diagram illustrating an example of a system for implementing certain aspects of the present technology. Specifically, Figure 16 An example of a computing system 1600 is illustrated, which can be any computing device, for example, constituting an internal computing system, a remote computing system, a camera, or any component thereof, wherein the components of the system communicate with each other using a connection 1605. The connection 1605 can be a physical connection using a bus, or a direct connection to the processor 1610, such as in a chipset architecture. The connection 1605 can also be a virtual connection, a networked connection, or a logical connection.

[0170] In some embodiments, computing system 1600 is a distributed system, in which the functionality described in this disclosure can be distributed across a data center, multiple data centers, a peer-to-peer network, etc. In some embodiments, one or more of the described system components represent a number of such components that each perform some or all of the functionality for which the component is described. In some embodiments, a component can be a physical device or a virtual device.

[0171] Example system 1600 includes at least one processing unit (CPU or processor) 1610 and connections 1605 that communicatively couple various system components, including system memory 1615, such as read-only memory (ROM) 1620 and random access memory (RAM) 1625, to processor 1610. Computing system 1600 may include a cache 1612 of high-speed memory directly connected to, in close proximity to, or integrated as part of processor 1610.

[0172] Processor 1610 may include any general-purpose processor and hardware or software services (such as services 1632, 1634, and 1636 configured to control processor 1610, stored in storage device 1630), as well as specialized processors where software instructions are incorporated into the actual processor design. Processor 1610 may essentially be a completely independent computing system containing multiple cores or processors, buses, memory controllers, caches, etc. Multi-core processors may be symmetric or asymmetric.

[0173] To enable user interaction, computing system 1600 includes input devices 1645, which can represent any number of input mechanisms, such as a microphone for voice, a touch-sensitive screen for gesture or graphical input, a keyboard, a mouse, motion input, voice, etc. Computing system 1600 can also include output devices 1635, which can be one or more of a plurality of output mechanisms. In some cases, a multimodal system can enable a user to provide multiple types of input / output to communicate with computing system 1600.

[0174] The computing system 1600 may include a communication interface 1640, which generally governs and manages user input and system output. The communication interface may perform or facilitate receiving and / or sending wired or wireless communications using wired and / or wireless transceivers, including using audio jacks / plugs, microphone jacks / plugs, Universal Serial Bus (USB) ports / plugs, Apple ™ Lightning ™ Ports / plugs, Ethernet ports / plugs, fiber optic ports / plugs, dedicated wired ports / plugs, 3G, 4G, 5G and / or other cellular data network wireless signal transmission, Bluetooth ™ Wireless signal transmission, Bluetooth ™ Low energy (BLE) wireless signal transmission, IBEACON ™ The communication interface 1640 may also include one or more global navigation satellite system (GNSS) receivers or transceivers for determining the location of the computing system 1600 based on one or more signals received from one or more satellites associated with the one or more GNSS systems. GNSS systems include, but are not limited to, the United States' Global Positioning System (GPS), Russia's Global Navigation Satellite System (GLONASS), China's BeiDou Navigation Satellite System (BDS), and Europe's Galileo GNSS. There is no restriction on operating on any particular hardware arrangement, and thus the base features herein may be readily substituted for improved hardware or firmware arrangements as they are developed.

[0175] The storage device 1630 may be a non-volatile and / or non-transitory and / or computer-readable memory device and may be a hard disk or other type of computer-readable medium that can store data accessible by a computer, such as a magnetic cassette, a flash memory card, a solid-state memory device, a digital versatile disk, a cassette, a floppy disk, a floppy disk, a hard disk, a magnetic tape, a magnetic stripe / magnetic stripe, any other magnetic storage medium, flash memory, a memristor memory, any other solid-state memory, a compact disc read-only memory (CD-ROM) disc, a rewritable compact disc (CD) disc, a digital video disc (DVD) disc, a Blu-ray disc (BDD) disc, a holographic disc, another optical medium, a secure digital (SD) card, a micro secure digital (microSD) card, a Memory Stick ® card, a smart card chip, an EMV chip, a subscriber identity module (SIM) card, a mini / micro / nano / pico SIM card, another integrated circuit (IC) chip / card, a random access memory (RAM), a static RAM (SRAM), a dynamic RAM (DRAM), a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a flash EPROM (FLASHEPROM), a cache memory (e.g., a level 1 (L1) cache, a level 2 (L2) cache, a level 3 (L3) cache, a level 4 (L4) cache, a level 5 (L5) cache, other (L#) cache), a resistive random access memory (RRAM / ReRAM), a phase change memory (PCM), a spin transfer torque RAM (STT-RAM), another memory chip or cartridge, and / or a combination thereof.

[0176] Storage devices 1630 may include software services, servers, services, and the like. When the code defining such software is executed by processor 1610, the code enables the system to perform functions. In some embodiments, hardware services that perform specific functions may include software components for performing functions stored on a computer-readable medium connected to the necessary hardware components (such as processor 1610, connection 1605, output device 1635, etc.). The term "computer-readable medium" includes, but is not limited to, portable or non-portable storage devices, optical storage devices, and various other media capable of storing, containing, or carrying instructions and / or data. Computer-readable media may include non-transitory media that can store data and does not include carrier waves and / or transient electronic signals propagated wirelessly or via wired connections. Examples of non-transitory media may include, but are not limited to, magnetic disks or tapes, optical storage media (such as compact discs (CDs) or digital versatile discs (DVDs)), flash memory, memory, or storage devices. Computer-readable media can store thereon code and / or machine-executable instructions that can represent a procedure, function, subroutine, program, routine, subroutine, module, software package, category, or any combination of instructions, data structures, or program statements. A code segment can be coupled to another code segment or a hardware circuit by passing and / or receiving information, data, independent variables, parameters, or memory contents. Information, independent variables, parameters, data, etc. can be passed, forwarded, or sent via any suitable means, including memory sharing, message passing, token passing, network sending, etc.

[0177] Specific details are provided in the above description to provide a thorough understanding of each embodiment and each example provided herein, but those skilled in the art will recognize that the present application is not limited thereto. Thus, although the exemplary embodiments of the present application have been described in detail herein, it is to be understood that the inventive concept can be embodied and adopted in various other ways, and the appended claims are intended to be interpreted as including such variations, unless limited by the prior art. The various features and aspects of the above-mentioned applications may be used individually or in combination. In addition, without departing from the broader scope of this specification, the embodiments may be used in any number of environments and applications beyond the environments and applications described herein. Therefore, the description and the accompanying drawings should be considered as illustrative rather than restrictive. For illustrative purposes, each method is described in a particular order. It should be understood that in an alternative embodiment, each method may be performed in a different order than described.

[0178] For clarity of explanation, in some cases, the present technology can be presented as including a separate functional block, which includes a device, device component, step or routine in a method embodied in software or a combination of hardware and software. Additional components other than those components shown in the drawings and / or described herein can be used. For example, circuits, systems, networks, processes and other components can be shown as components in block diagram form to avoid these embodiments becoming difficult to understand in unnecessary details. In other cases, known circuits, processes, algorithms, structures and techniques can be shown in the absence of necessary details to avoid making each embodiment difficult to understand.

[0179] In addition, it will be understood by those skilled in the art that the various illustrative logic blocks, modules, circuits, and algorithmic steps described in conjunction with the various aspects disclosed herein can be implemented as electronic hardware, computer software, or a combination of the two. In order to clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been generally described above in terms of their functionality. Whether such functionality is implemented as hardware or software depends on the specific application and the design constraints imposed on the entire system. Those skilled in the art may implement the described functionality in different ways for each specific application, but such specific implementation decisions should not be interpreted as resulting in a departure from the scope of this disclosure.

[0180] Individual embodiments may be described above as processes or methods depicted as flowcharts, flow diagrams, data flow diagrams, structure diagrams, or block diagrams. Although a flowchart may describe operations as a sequential process, many of the operations may be performed in parallel or concurrently. Furthermore, the order of the operations may be rearranged. A process is terminated when its operations are completed, but a process may have additional steps not included in the accompanying figures. A process may correspond to a method, function, procedure, subroutine, subprogram, etc. When a process corresponds to a function, termination of the process may correspond to the function returning to the calling function or main function.

[0181] The processes and methods according to the examples described above can be implemented using stored computer-executable instructions or computer-executable instructions otherwise available from a computer-readable medium. Such instructions may include, for example, instructions and data that cause or otherwise configure a general-purpose computer, a special-purpose computer, or a processing device to perform a certain function or group of functions. Portions of the computer resources used may be accessible via a network. The computer-executable instructions may be, for example, binary, intermediate format instructions such as assembly language, firmware, source code. Examples of computer-readable media that can be used to store instructions, information used, and / or information created during the methods according to the described examples include magnetic or optical disks, flash memory, USB devices with non-volatile memory, networked storage devices, and the like.

[0182] In some embodiments, computer-readable storage devices, media, and memories may include wired or wireless signals containing bit streams, etc. However, when referred to, non-transitory computer-readable storage media specifically excludes media such as power consumption, carrier signals, electromagnetic waves, and signals themselves.

[0183] Those skilled in the art will understand that information and signals can be represented using any of a variety of different techniques and methods. For example, data, instructions, commands, information, signals, bits, symbols, and chips that may be mentioned throughout the above description may, in some cases, be represented by voltage, current, electromagnetic waves, magnetic fields or magnetic particles, light fields or light particles, or any combination thereof, depending in part on the specific application, in part on the desired design, in part on the corresponding technology, etc.

[0184] The various illustrative logical blocks, modules, and circuits described in conjunction with the various aspects disclosed herein may be implemented or executed using hardware, software, firmware, middleware, microcode, hardware description languages, or any combination thereof, and may be implemented in any of a variety of form factors. When implemented in software, firmware, middleware, or microcode, program code or code segments (e.g., a computer program product) for performing the necessary tasks may be stored in a computer-readable or machine-readable medium. A processor may perform the necessary tasks. Examples of form factors include laptop computers, smartphones, mobile phones, tablet devices, or other small form factor personal computers, personal digital assistants, rack-mounted devices, stand-alone devices, and the like. The functionality described herein may also be embodied in peripheral devices or add-in cards. By way of further example, such functionality may also be implemented on circuit boards in different chips or processes executed on a single device.

[0185] Instructions, media for conveying such instructions, computing resources for executing them, and other structures for supporting such computing resources are example means for providing the functionality described in this disclosure.

[0186] The techniques described herein may also be implemented in electronic hardware, computer software, firmware, or any combination thereof. Such techniques may be implemented in any of a variety of devices, such as general-purpose computers, wireless communication devices, or integrated circuit devices with multiple uses, including applications in wireless communication devices and other devices. Any features described as modules or components may be implemented together in an integrated logic device or separately as discrete but interoperable logic devices. If implemented in software, the techniques may be implemented at least in part by a computer-readable data storage medium containing program code, which, when executed, includes instructions for performing one or more of the methods, algorithms, and / or operations described above. The computer-readable data storage medium may form part of a computer program product, which may include packaging materials. The computer-readable medium may include memory or data storage media, such as random access memory (RAM) (such as synchronous dynamic random access memory (SDRAM)), read-only memory (ROM), non-volatile random access memory (NVRAM), electrically erasable programmable read-only memory (EEPROM), flash memory, magnetic or optical data storage media, and the like. Additionally or alternatively, the techniques may be implemented at least in part by a computer-readable communication medium that carries or communicates program code in the form of instructions or data structures and that can be accessed, read, and / or executed by a computer, such as a propagated signal or wave.

[0187] The program code may be executed by a processor, which may include one or more processors, such as one or more digital signal processors (DSPs), general-purpose microprocessors, application-specific integrated circuits (ASICs), field-programmable logic arrays (FPGAs), or other equivalent integrated or discrete logic circuitry. Such a processor may be configured to perform any of the techniques described in this disclosure. A general-purpose processor may be a microprocessor; however, in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing devices, such as a combination of a DSP and a microprocessor, multiple microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration. Thus, as used herein, the term "processor" may refer to any of the foregoing structures, any combination of the foregoing structures, or any other structure or device suitable for implementing the techniques described herein.

[0188] It should be understood by those skilled in the art that the less than ("<") symbol and greater than (">") symbol or term used herein may be replaced by less than or equal to (" ”) symbol and the greater than or equal to (“ ) symbol instead.

[0189] Where a component is described as being “configured to” perform certain operations, such configuration may be achieved, for example, by designing electronic circuits or other hardware to perform the operations, by programming programmable electronic circuits (e.g., a microprocessor or other suitable electronic circuits) to perform the operations, or any combination thereof.

[0190] The phrases “coupled to” or “communicatively coupled to” refer to any component being physically connected directly or indirectly to another component, and / or any component being in communication, directly or indirectly, with another component (e.g., connected to the other component via a wired or wireless connection and / or other suitable communication interface).

[0191] Claim language or other language reciting "at least one of" a set and / or "one or more of" a set indicates that one member of the set or multiple members of the set (in any combination) satisfies the claim. For example, claim language reciting "at least one of A and B" or "at least one of A or B" means A, B, or A and B. In another example, claim language reciting "at least one of A, B, and C" or "at least one of A, B, or C" means A, B, C, or A and B, or A and C, or B and C, or any repetition of information or data (e.g., A and A, B and B, C and C, A and A and B, etc.), or any other ordering, repetition, or combination of A, B, and C. The language "at least one of" a set and / or "one or more of" a set does not limit the set to the items listed in the set. For example, claim language stating "at least one of A and B" or "at least one of A or B" can mean A, B, or A and B, and may additionally include items not listed in the set of A and B. The phrases "at least one" and "one or more" are used interchangeably herein.

[0192] Claim language or other language that recites "at least one processor configured to," "at least one processor configured to," "one or more processors configured to," "one or more processors configured to," etc., indicates that one processor or multiple processors (in any combination) can perform the associated operations. For example, claim language that recites "at least one processor configured to: X, Y, and Z" means that a single processor can be used to perform operations X, Y, and Z; or that multiple processors are each tasked with a particular subset of operations X, Y, and Z, such that the multiple processors together perform X, Y, and Z; or that a group of multiple processors work together to perform operations X, Y, and Z. In another example, claim language that recites "at least one processor configured to: X, Y, and Z" can mean that any single processor can perform only at least a subset of operations X, Y, and Z.

[0193] When referring to one or more elements that perform functions (e.g., steps of a method), one element may perform all of the functions, or more than one element may collectively perform the functions. When more than one element collectively performs the functions, each function need not be performed by each of those elements (e.g., different functions may be performed by different elements), and / or each function need not be performed as a whole by only one element (e.g., different elements may perform different sub-functions of the function). Similarly, when referring to one or more elements that are configured to cause another element (e.g., a device) to perform a function, one element may be configured to cause the other element to perform all of the functions, or more than one element may collectively be configured to cause the other element to perform the functions.

[0194] When referring to an entity (e.g., any entity or device described herein) that performs functions or is configured to perform functions (e.g., steps of a method), the entity may be configured to cause one or more elements (individually or collectively) to perform those functions. The one or more components of the entity may include at least one memory, at least one processor, at least one communication interface, another component configured to perform one or more (or all) of the functions, and / or any combination thereof. When referring to an entity that performs functions, the entity may be configured to cause one component to perform all functions, or to cause more than one component to collectively perform those functions. When the entity is configured to cause more than one component to collectively perform those functions, each function need not be performed by each of those components (e.g., different functions may be performed by different components), and / or each function need not be performed as a whole by only one component (e.g., different components may perform different sub-functions of a function).

[0195] Illustrative aspects of the present disclosure include:

[0196] Aspect 1. A device for wireless communication, the device comprising: at least one memory; and at least one processor, the at least one processor being coupled to the at least one memory, the at least one processor being configured to: receive a first set of operations supported by one or more machine learning models of a network entity; receive a first set of parameters associated with the first set of operations, wherein the first set of parameters is supported by the one or more machine learning models of the network entity; select a machine learning model for performing a first operation in the first set of operations based on the first set of parameters; detect a change in at least one of: the first operation; or a parameter associated with the first operation; and send an indication to change the first operation based on the detected change.

[0197] Aspect 2. An apparatus according to Aspect 1, wherein the at least one processor is further configured to: determine a second set of operations supported by one or more machine learning models of the apparatus; determine a second set of parameters associated with the second set of operations, wherein the second set of parameters is supported by the one or more machine learning models of the apparatus; and send the second set of operations and the second set of parameters to the network entity.

[0198] Aspect 3. The apparatus according to aspect 2, wherein the first set of operations and the first set of parameters are based on the second set of operations and the second set of parameters.

[0199] Aspect 4. The apparatus according to any one of aspects 1 to 3, wherein the first set of operations and the first set of parameters are received from the network entity in unicast, multicast or broadcast.

[0200] Aspect 5. An apparatus according to any one of Aspects 1 to 4, wherein the at least one processor is further configured to: receive an activation message, wherein the activation message is configured to activate a second operation in the first set of operations; and select a machine learning model to perform the second operation.

[0201] Aspect 6. An apparatus according to any one of Aspects 1 to 5, wherein the at least one processor is further configured to: receive a deactivation message, wherein the deactivation message specifies the first operation in the first set of operations; and based on the deactivation message, stop using the selected machine learning model to perform the first operation.

[0202] Aspect 7. An apparatus according to any one of aspects 1 to 6, wherein the instruction to change the first operation based on the detected change comprises one of: an instruction to deactivate the first operation; or an instruction to activate a second operation.

[0203] Aspect 8. An apparatus according to any one of Aspects 1 to 7, wherein the at least one processor is further configured to: send a message to the network entity based on the output of the selected machine learning model.

[0204] Aspect 9. The apparatus according to any one of aspects 1 to 8, wherein the first operation comprises an encoding operation.

[0205] Aspect 10. The apparatus of aspect 9, wherein the first operation comprises encoding channel state information (CSI) feedback information.

[0206] Aspect 11. An apparatus according to any one of Aspects 1 to 10, wherein the at least one processor is further configured to: fall back to a non-machine learning model-based approach to perform the first operation.

[0207] Aspect 12. A device for wireless communication, the device comprising: at least one memory; and at least one processor, the at least one processor coupled to the at least one memory, the at least one processor configured to: receive an indication of a first set of operations supported by one or more machine learning models of a network entity; select a machine learning model for performing a first operation in the first set of operations; detect a change in at least one of: the first operation; or a parameter associated with the first operation; and send an indication to change the first operation based on the detected change.

[0208] Aspect 13. An apparatus according to Aspect 12, wherein the indication of the first set of operations includes a set of identifiers, the set of identifiers including a corresponding identifier for each of the one or more machine learning models of the network entity.

[0209] Aspect 14. An apparatus according to Aspect 13, wherein the at least one processor is further configured to: determine a first parameter set associated with the one or more machine learning models identified by the identifier set, the first parameter set indicating parameters supported by the one or more machine learning models of the network entity.

[0210] Aspect 15. An apparatus according to any one of aspects 13 to 14, wherein the set of identifiers indicates operational applicability of operations in the first set of operations and parameters associated with the operations in the first set of operations.

[0211] Aspect 16. An apparatus according to any one of aspects 13 to 15, wherein the set of identifiers indicates a configuration for using operations in the first set of operations.

[0212] Aspect 17. The apparatus according to any one of aspects 12 to 16, wherein the at least one processor is configured to: receive the indication of the first set of operations from the network entity in unicast, multicast or broadcast.

[0213] Aspect 18. The apparatus according to any one of aspects 12 to 17, wherein the at least one processor is further configured to: receive an assistance message; and select a second operation from the first set of operations based on the assistance message.

[0214] Aspect 19. An apparatus according to any one of aspects 12 to 18, wherein the instruction to change the first operation based on the detected change comprises one of: an instruction to deactivate the first operation; or an instruction to activate a second operation.

[0215] Aspect 20. An apparatus according to any one of Aspects 12 to 19, wherein the at least one processor is further configured to: send a message to the network entity based on the output of the selected machine learning model.

[0216] Aspect 21. An apparatus according to any one of aspects 12 to 20, wherein the first operation comprises an encoding operation.

[0217] Aspect 22. The apparatus according to any one of aspects 12 to 21, wherein the first operation comprises encoding channel state information (CSI) feedback information.

[0218] Aspect 23. An apparatus according to any one of Aspects 12 to 22, wherein the at least one processor is further configured to: fall back to a non-machine learning model-based approach to perform the first operation.

[0219] Aspect 24. A device for wireless communication, the device comprising: at least one memory; and at least one processor, the at least one processor coupled to the at least one memory, the at least one processor configured to: determine a first set of operations supported by one or more machine learning models of the device; determine a first set of parameters associated with the first set of operations, wherein the first set of parameters is supported by the one or more machine learning models of the device; send the first set of operations and the first set of parameters to a network entity; detect a change in at least one of the following: a first operation in the first set of operations; or a parameter associated with the first operation; and send an indication of a change from the first operation based on the detected change.

[0220] Aspect 25. The apparatus according to aspect 24, wherein the at least one processor is further configured to: receive configuration information for a first operation in the first set of operations; and perform the first operation based on the configuration information.

[0221] Aspect 26. The apparatus of aspect 25, wherein the received configuration information is based on the first set of operations and the first set of parameters.

[0222] Aspect 27. An apparatus according to any one of Aspects 24 to 26, wherein the at least one processor is further configured to: receive an activation message, wherein the activation message is configured to activate a second operation in the first set of operations; and select a machine learning model to perform the second operation.

[0223] Aspect 28. An apparatus according to any one of Aspects 24 to 27, wherein the at least one processor is further configured to: receive a deactivation message, wherein the deactivation message specifies a first operation in the first set of operations; and stop performing the first operation based on the deactivation message.

[0224] Aspect 29. An apparatus according to any one of aspects 24 to 28, wherein the indication of a change comprises an indication to activate a second operation based on the detected change.

[0225] Aspect 30. An apparatus according to any one of aspects 24 to 29, wherein the indication of a change comprises an indication to disable the first operation based on the detected change.

[0226] Aspect 31. An apparatus according to any one of aspects 24 to 30, wherein the indication of a change comprises an indication of the detected change to the network entity.

[0227] Aspect 32. An apparatus according to aspect 31, wherein the at least one processor is further configured to: receive at least one of a deactivation message or an activation message, wherein the at least one of the deactivation message or the activation message is based on the indication of the detected change.

[0228] Aspect 33. An apparatus according to any one of aspects 24 to 32, wherein the first operation comprises an encoding operation.

[0229] Aspect 34. The apparatus of aspect 33, wherein the first operation comprises encoding channel state information (CSI) feedback information.

[0230] Aspect 35. An apparatus according to any one of Aspects 24 to 34, wherein the at least one processor is further configured to: fall back to a non-machine learning model-based approach to perform the first operation.

[0231] Aspect 36. An apparatus for wireless communication, the apparatus comprising: at least one memory; and at least one processor, the at least one processor coupled to the at least one memory, the at least one processor configured to: send a first set of operations supported by one or more machine learning models of the apparatus; send a first set of parameters associated with the first set of operations, the first set of parameters indicating parameters supported by the one or more machine learning models of the apparatus; receive a message based on an output of a machine learning model of a wireless device, wherein the output is based on the first set of operations and the first set of parameters; receive an indication to change a first operation in the first set of operations based on a change detected by the wireless device; and change the first operation based on the indication of the change.

[0232] Aspect 37. The apparatus of aspect 36, wherein the at least one processor is configured to send the first set of operations and the first set of parameters as a unicast message, a multicast message, or a broadcast message.

[0233] Aspect 38. An apparatus according to any one of Aspects 36 to 37, wherein the at least one processor is further configured to: receive from the wireless device a second set of operations supported by one or more machine learning models of the wireless device and a second set of parameters associated with the second set of operations; and determine the first set of operations based on the second set of operations.

[0234] Aspect 39. The apparatus according to aspect 38, wherein the at least one processor is further configured to: send an activation message, wherein the activation message is configured to activate a second operation in the first set of operations.

[0235] Aspect 40. The apparatus according to any one of aspects 38 to 39, wherein the at least one processor is further configured to: send a deactivation message, wherein the deactivation message specifies a first operation in the first set of operations.

[0236] Aspect 41. An apparatus according to any one of Aspects 38 to 40, wherein the indication of the change comprises an indication of activating a second operation; and wherein, in order to change the first operation, the at least one processor is configured to activate the second operation.

[0237] Aspect 42. An apparatus according to any one of Aspects 38 to 41, wherein the indication of the change comprises an indication to deactivate the first operation; and wherein, in order to change the first operation, the at least one processor is configured to deactivate the first operation.

[0238] Aspect 43. An apparatus according to any one of aspects 38 to 42, wherein the first operation comprises a decoding operation.

[0239] Aspect 44. The apparatus of aspect 43, wherein the first operation comprises encoding channel state information (CSI) feedback information.

[0240] Aspect 45. An apparatus for wireless communication, the apparatus comprising: at least one memory; and at least one processor, the at least one processor coupled to the at least one memory, the at least one processor configured to: send an indication of a first set of operations supported by one or more machine learning models of the apparatus; receive a message based on an output of a machine learning model of a wireless device, wherein the output is based on the first set of operations; receive an indication to change a first operation in the first set of operations based on a change detected by the wireless device; and change the first operation of the apparatus based on the indication of the change.

[0241] Aspect 46. An apparatus according to Aspect 45, wherein the indication of the first set of operations includes a set of identifiers of the one or more machine learning models for the network entity.

[0242] Aspect 47. The apparatus of aspect 46, wherein the set of identifiers indicates operational applicability of operations in the first set of operations and parameters associated with the operations in the first set of operations.

[0243] Aspect 48. An apparatus according to any one of aspects 46 to 47, wherein the set of identifiers indicates a configuration for using operations in the first set of operations.

[0244] Aspect 49. An apparatus according to any one of Aspects 45 to 49, wherein the at least one processor is further configured to: detect a change in at least one of: a first operation in the first set of operations; or a parameter associated with the first operation; and send an auxiliary message to the wireless device.

[0245] Aspect 50. An apparatus according to any one of aspects 45 to 49, wherein the indication of a change comprises an indication that the wireless device activated wireless device operation based on the change detected by the wireless device.

[0246] Aspect 51. An apparatus according to any one of aspects 45 to 50, wherein the indication of a change comprises an indication that the wireless device has disabled wireless device operation based on the change detected by the wireless device.

[0247] Aspect 52. An apparatus according to any one of Aspects 45 to 51, wherein, in order to change the first operation, the at least one processor is configured to perform at least one of the following: deactivate the first operation; activate the first operation; or fall back to a non-machine learning model-based manner to perform the first operation.

[0248] Aspect 53. An apparatus according to any one of aspects 45 to 52, wherein the first operation comprises a decoding operation.

[0249] Aspect 54. The apparatus of aspect 53, wherein the first operation comprises decoding channel state information (CSI) feedback information.

[0250] Aspect 55. An apparatus for wireless communication, the apparatus comprising: at least one memory; and at least one processor, the at least one processor coupled to the at least one memory, the at least one processor configured to: receive a first set of operations supported by one or more machine learning models of a wireless device; receive a first set of parameters associated with the first set of operations, the first set of parameters indicating parameters supported by the one or more machine learning models of the wireless device; perform a first operation in the first set of operations using the machine learning model of the apparatus based on the received first set of operations; receive an indication to change the first operation in the first set of operations based on a change detected by the wireless device; and change the first operation of the apparatus based on the indication of the change.

[0251] Aspect 56. The apparatus of aspect 55, wherein the at least one processor is further configured to send configuration information for the first operation in the first set of operations to the wireless device.

[0252] Aspect 57. An apparatus according to any one of Aspects 55 to 56, wherein the at least one processor is further configured to: detect a change in at least one of: the first operation; or a parameter associated with the first operation; and send an activation message, wherein the activation message activates a second operation in the first set of operations.

[0253] Aspect 58. An apparatus according to any one of Aspects 55 to 57, wherein the at least one processor is further configured to: detect a change in at least one of: the first operation; or a parameter associated with the first operation; and send an indication to deactivate the first operation based on the detected change.

[0254] Aspect 59. An apparatus according to any one of aspects 55 to 58, wherein the indication of a change comprises an indication that the wireless device has activated or deactivated wireless device operation based on the change detected by the wireless device.

[0255] Aspect 60. An apparatus according to any one of Aspects 55 to 59, wherein the at least one processor is further configured to: determine to deactivate the first operation based on the received indication of the change; and send a deactivation message to the wireless device based on the determination to deactivate the first operation.

[0256] Aspect 61. An apparatus according to any one of Aspects 55 to 60, wherein the at least one processor is further configured to: determine to activate a second operation based on the received indication of the change; and send an activation message to the wireless device based on the determination to activate the second operation.

[0257] Aspect 62. An apparatus according to any one of aspects 55 to 61, wherein the first operation comprises a decoding operation.

[0258] Aspect 63. The apparatus of any one of aspects 55 to 62, wherein the first operation comprises decoding channel state information (CSI) feedback information.

[0259] Aspect 64. A method for wireless communication, the method comprising performing the operations of any one of aspects 1 to 11.

[0260] Aspect 65. A non-transitory computer-readable storage medium comprising instructions stored thereon, which, when executed by at least one processor, cause the at least one processor to perform the operations of any one of aspects 1 to 11.

[0261] Aspect 66. An apparatus for wireless communication, the apparatus comprising one or more means for performing the operations of any one of aspects 1 to 11.

[0262] Aspect 67. A method for wireless communication, the method comprising performing the operations of any one of aspects 12 to 23.

[0263] Aspect 68. A non-transitory computer-readable storage medium comprising instructions stored thereon, which, when executed by at least one processor, cause the at least one processor to perform the operations of any one of aspects 12 to 23.

[0264] Aspect 69. An apparatus for wireless communication, the apparatus comprising one or more means for performing the operations of any one of aspects 12 to 23.

[0265] Aspect 70. A method for wireless communication, the method comprising performing the operations of any one of aspects 24 to 35.

[0266] Aspect 71. A non-transitory computer-readable storage medium comprising instructions stored thereon, which, when executed by at least one processor, cause the at least one processor to perform operations according to any one of aspects 24 to 35.

[0267] Aspect 72. An apparatus for wireless communication, the apparatus comprising one or more means for performing the operations of any one of aspects 24 to 35.

[0268] Aspect 73. A method for wireless communication, the method comprising performing the operations of any one of aspects 36 to 44.

[0269] Aspect 74. A non-transitory computer-readable storage medium comprising instructions stored thereon, which, when executed by at least one processor, cause the at least one processor to perform operations according to any one of aspects 36 to 44.

[0270] Aspect 75. An apparatus for wireless communications, the apparatus comprising one or more means for performing the operations of any one of aspects 36 to 44.

[0271] Aspect 78. A method for wireless communication, the method comprising performing the operations of any one of aspects 45 to 54.

[0272] Aspect 79. A non-transitory computer-readable storage medium comprising instructions stored thereon, which, when executed by at least one processor, cause the at least one processor to perform operations according to any one of aspects 45 to 54.

[0273] Aspect 80. An apparatus for wireless communications, the apparatus comprising one or more means for performing the operations of any one of aspects 45 to 54.

[0274] Aspect 81. A method for wireless communication, the method comprising performing the operations of any one of aspects 55 to 63.

[0275] Aspect 82. A non-transitory computer-readable storage medium comprising instructions stored thereon, which, when executed by at least one processor, cause the at least one processor to perform operations according to any one of aspects 55 to 63.

[0276] Aspect 83. An apparatus for wireless communications, the apparatus comprising one or more means for performing the operations of any one of aspects 55 to 63.

Claims

1. A device for wireless communication, the device comprising: at least one memory; and at least one processor coupled to the at least one memory, the at least one processor configured to: receiving a first set of operations supported by one or more machine learning models of a network entity; receiving a first set of parameters associated with the first set of operations, wherein the first set of parameters is supported by the one or more machine learning models of the network entity; selecting, based on the first set of parameters, a machine learning model for performing a first operation in the first set of operations; Detects changes in at least one of the following: the first operation; or parameters associated with the first operation; and An indication to change the first operation is sent based on the detected change.

2. The apparatus of claim 1 , wherein the at least one processor is further configured to: determining a second set of operations supported by one or more machine learning models of the apparatus; determining a second set of parameters associated with the second set of operations, wherein the second set of parameters is supported by the one or more machine learning models of the apparatus; and The second operation set and the second parameter set are sent to the network entity. 3 . The apparatus of claim 2 , wherein the first set of operations and the first set of parameters are based on the second set of operations and the second set of parameters. 4 . The apparatus of claim 1 , wherein the first set of operations and the first set of parameters are received from the network entity in a unicast, multicast, or broadcast.

5. The apparatus of claim 1 , wherein the at least one processor is further configured to: receiving an activation message, wherein the activation message is configured to activate a second operation in the first set of operations; and A machine learning model is selected to perform the second operation.

6. The apparatus of claim 1 , wherein the at least one processor is further configured to: receiving a deactivation message, wherein the deactivation message specifies the first operation in the first set of operations; and Based on the deactivation message, stop using the selected machine learning model to perform the first operation.

7. The apparatus of claim 1 , wherein the at least one processor is further configured to send a message to the network entity based on an output of the selected machine learning model.

8. An apparatus for wireless communication, the apparatus comprising: at least one memory; and at least one processor coupled to the at least one memory, the at least one processor configured to: receiving an indication of a first set of operations supported by one or more machine learning models of the network entity; selecting a machine learning model for performing a first operation in the first set of operations; Detects changes in at least one of the following: the first operation; or parameters associated with the first operation; and An indication to change the first operation is sent based on the detected change.

9. An apparatus according to claim 8, wherein the indication of the first set of operations includes a set of identifiers, the set of identifiers including a corresponding identifier for each of the one or more machine learning models of the network entity.

10. An apparatus according to claim 9, wherein the at least one processor is further configured to: determine a first parameter set associated with the one or more machine learning models identified by the identifier set, the first parameter set indicating parameters supported by the one or more machine learning models of the network entity. 11 . The apparatus of claim 9 , wherein the set of identifiers indicates operational applicability of operations in the first set of operations and parameters associated with the operations in the first set of operations.

12. The apparatus of claim 9, wherein the set of identifiers indicates a configuration for using operations in the first set of operations.

13. The apparatus of claim 8, wherein the at least one processor is configured to receive the indication of the first set of operations from the network entity in a unicast, multicast, or broadcast.

14. The apparatus of claim 8, wherein the at least one processor is further configured to: receiving an assistance message; and A second operation is selected from the first set of operations based on the assistance message.

15. The apparatus of claim 8, wherein the at least one processor is further configured to send a message to the network entity based on an output of the selected machine learning model.

16. An apparatus for wireless communication, the apparatus comprising: at least one memory; and at least one processor coupled to the at least one memory, the at least one processor configured to: determining a first set of operations supported by one or more machine learning models of the device; determining a first set of parameters associated with the first set of operations, wherein the first set of parameters is supported by the one or more machine learning models of the apparatus; Sending the first operation set and the first parameter set to a network entity; Detects changes in at least one of the following: a first operation in the first operation set; or parameters associated with the first operation; as well as An indication of a change from the first operation is sent based on the detected change.

17. The apparatus of claim 16, wherein the at least one processor is further configured to: receiving configuration information for a first operation in the first set of operations; and The first operation is performed based on the configuration information.

18. The apparatus of claim 17, wherein the received configuration information is based on the first set of operations and the first set of parameters.

19. The apparatus of claim 16, wherein the at least one processor is further configured to: receiving an activation message, wherein the activation message is configured to activate a second operation in the first set of operations; and A machine learning model is selected to perform the second operation.

20. The apparatus of claim 16, wherein the at least one processor is further configured to: receiving a deactivation message, wherein the deactivation message specifies a first operation in the first set of operations; and Based on the deactivation message, the first operation is stopped.

21. The apparatus of claim 16, wherein the indication of a change comprises an indication of the detected change to the network entity.

22. An apparatus for wireless communication, the apparatus comprising: at least one memory; and at least one processor coupled to the at least one memory, the at least one processor configured to: sending a first set of operations supported by one or more machine learning models of the device; sending a first parameter set associated with the first set of operations, the first parameter set indicating parameters supported by the one or more machine learning models of the device; receiving a message based on an output of a machine learning model of a wireless device, wherein the output is based on the first set of operations and the first set of parameters; receiving an indication to change a first operation in the first set of operations based on a change detected by the wireless device; as well as The first operation is changed based on the indication of a change.

23. The apparatus of claim 22, wherein the at least one processor is configured to send the first set of operations and the first set of parameters as a unicast message, a multicast message, or a broadcast message.

24. The apparatus of claim 22, wherein the at least one processor is further configured to: receiving, from the wireless device, a second set of operations supported by one or more machine learning models of the wireless device and a second set of parameters associated with the second set of operations; and The first set of operations is determined based on the second set of operations.

25. The apparatus of claim 22, wherein the at least one processor is further configured to send an activation message, wherein the activation message is configured to activate a second operation in the first set of operations.

26. The apparatus of claim 22, wherein the at least one processor is further configured to send a deactivation message, wherein the deactivation message specifies a first operation in the first set of operations.

Images