Patent Publication Number: US-2023164817-A1

Title: Artificial Intelligence Capability Reporting for Wireless Communication

Description:
TECHNICAL FIELD 
     The present disclosure relates to wireless communications, and more specifically to artificial intelligence (AI) and wireless communication. 
     BACKGROUND 
     A wireless communications system may include one or multiple network communication devices, such as base stations, which may be otherwise known as an eNodeB (eNB), a next-generation NodeB (gNB), or other suitable terminology. Each network communication device, such as a base station, may support wireless communications for one or multiple user communication devices, which may be otherwise known as user equipment (UE), or other suitable terminology. The wireless communications system may support wireless communications with one or multiple user communication devices by utilizing resources of the wireless communication system, such as time resources (e.g., symbols, subslots, slots, subframes, frames, or the like) or frequency resources (e.g., subcarriers, carriers). Additionally, the wireless communications system may support wireless communications across various radio access technologies including third generation (3G) radio access technology, fourth generation (4G) radio access technology, fifth generation (5G) radio access technology, and other suitable radio access technologies beyond 5G. 
     To enable wireless connectivity of a UE to a wireless network, wireless capability information is exchanged between the UE and a base station of the wireless network. Examples of capability information include supported radio access technologies, supported encryption type(s), supported wireless band combinations, and so forth. This enables a wireless connection between a UE and a wireless network to be established that conforms to capabilities of the UE and a base station of the wireless network. 
     SUMMARY 
     The present disclosure relates to methods, apparatuses, and systems that support integration and implementations of AI capability reporting for wireless communication. For instance, the present disclosure provides a framework for reporting AI capability information exchange such as between a user equipment (UE) and a wireless network, e.g., a base station. For instance, the UE and the wireless network exchange AI capability information that describes AI features supported and/or not supported by the UE and/or the wireless network, and the UE and/or the wireless network implement one or more of the supported AI features in conjunction with establishing and/or participating in wireless connectivity between the UE and the wireless network. This enables the UE and/or the wireless network to utilize supported AI features for wireless communication and to avoid attempting to proceed with AI-enabled features and corresponding signaling that are not supported by UE and/or the base station. For instance, by avoiding attempting to proceed with unsupported AI-enabled features and corresponding signaling, this avoids wasting time, power, and/or computational resources corresponding to AI-enabled features that cannot be supported by the UE and/or the base station. 
     Accordingly, by exchanging capability information describing supported AI-enabled features, one or more of the supported AI-enabled features can be implemented such as to optimize wireless connectivity between the UE and the wireless network. For instance, in some implementations utilizing AI features enables signal transmission and processing overhead to be reduced, such as by implementing AI to reduce an amount of control and/or signaling data exchanged between a UE and a wireless network as part of establishing and/or managing wireless connectivity between the UE and the wireless network. 
     Some implementations of the methods and apparatuses described herein may further include wireless communication at a device (e.g., a UE), which includes generating a capability report configured to specify AI-enabled features of a first node (e.g., a UE) that pertain to at least one protocol layer of a wireless protocol stack; configuring the capability report to indicate that the first node includes AI capability and to specify at least one supported AI feature of the first node including selecting the at least one supported AI feature from available AI features for the capability report; communicating the configured capability report to a second node (e.g., a base station); and engaging in wireless connectivity between the first node and the second node including at least partially implementing the at least one supported AI-enabled feature for wireless connectivity. 
     In some implementations of the methods and apparatuses described herein, the available AI-enabled features for the capability report include one or more supported AI model types; one or more supported AI model training techniques; one or more supported AI learning frameworks; one or more AI training modes selected from at least an offline training mode, an online training mode, and a mixed training mode; and one or more transceiver application modes selected from at least a joint application mode and an individual block application mode. 
     Some implementations of the methods and apparatuses described herein may further include wireless communication at a device (e.g., a base station such as a gNB), which includes receiving at a first node (e.g., the base station) a capability report from a second node (e.g., a UE) that specifies one or more supported AI-enabled features of the second node that pertain to at least one protocol layer of a wireless protocol stack; processing the capability report and at least partially implementing at least one AI-enabled feature identified in the capability report; and managing wireless connectivity between the first node and the second node based on the at least partially implementing the at least one supported AI-enabled feature for wireless connectivity. 
     In some implementations of the method and apparatuses described herein, the available AI features implemented from the capability report include one or more supported AI model types; one or more supported AI model training techniques; one or more supported AI learning frameworks; one or more AI training modes selected from at least an offline training mode, an online training mode, and a mixed training mode; and one or more transceiver application modes selected from at least a joint application mode and an individual block application mode. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Various aspects of the present disclosure for AI capability reporting for wireless communication are described with reference to the following Figures. The same numbers may be used throughout to reference like features and components shown in the Figures. 
         FIG.  1    illustrates an example of a wireless communications system that supports AI capability reporting for wireless communication in accordance with aspects of the present disclosure. 
         FIG.  2    illustrates an example of an AI-enabled notification that supports AI capability reporting for wireless communication in accordance with aspects of the present disclosure. 
         FIG.  3    illustrates an example of capability reporting for AI-enabled physical (PHY) layer features for wireless communication in accordance with aspects of the present disclosure. 
         FIG.  4    illustrates an example of AI framework notification that supports AI capability reporting for wireless communication in accordance with aspects of the present disclosure. 
         FIG.  5    illustrates an example of learning notification that supports AI capability reporting for wireless communication in accordance with aspects of the present disclosure. 
         FIG.  6    illustrates an example of an integration mode notification that supports AI capability reporting for wireless communication in accordance with aspects of the present disclosure. 
         FIG.  7    illustrates an example of a training mode notification that supports AI capability reporting for wireless communication in accordance with aspects of the present disclosure. 
         FIG.  8    illustrates an example of an application mode notification that supports AI capability reporting for wireless communication in accordance with aspects of the present disclosure. 
         FIG.  9    illustrates an example of a PHY layer parameters notification that supports AI capability reporting for wireless communication in accordance with aspects of the present disclosure. 
         FIG.  10    illustrates an example of a channel state indicator-AI sub-feature notification that supports AI capability reporting for wireless communication in accordance with aspects of the present disclosure. 
         FIG.  11    illustrates an example of a beam management-AI sub-feature notification that supports AI capability reporting for wireless communication in accordance with aspects of the present disclosure. 
         FIG.  12    illustrates an example of a reference signal-AI sub-feature notification that supports AI capability reporting for wireless communication in accordance with aspects of the present disclosure. 
         FIG.  13    illustrates an example of a positioning-AI sub-feature notification that supports AI capability reporting for wireless communication in accordance with aspects of the present disclosure. 
         FIG.  14    illustrates an example system that supports AI capability reporting for wireless communication in accordance with aspects of the present disclosure. 
         FIG.  15    illustrates an example system that supports AI capability reporting for wireless communication in accordance with aspects of the present disclosure. 
         FIG.  16    illustrates an example block diagram of components of a device that supports AI capability reporting for wireless communication in accordance with aspects of the present disclosure. 
         FIG.  17    illustrates an example block diagram of components of a base station that supports AI capability reporting for wireless communication in accordance with aspects of the present disclosure. 
         FIGS.  18 - 22    illustrate flowcharts of methods that support AI capability reporting for wireless communication in accordance with aspects of the present disclosure. 
     
    
    
     DETAILED DESCRIPTION 
     Implementations of AI capability reporting for wireless communication are described, such as related to reporting AI capability information between a UE and a wireless network, e.g., a base station. For instance, by exchanging capability information describing supported AI-enabled features, one or more of the supported AI-enabled features can be implemented such as to optimize wireless connectivity between the UE and the wireless network. 
     Conventional wireless systems currently have no methods or protocols for enabling AI capability information to be propagated between UEs and other network nodes, such as network base stations. This will prevent AI-enabled features from being utilized for wireless communication between network nodes. For instance, the inability to propagate AI capability information among network nodes exhibited in conventional wireless systems may cause a particular network node or set of network nodes to avoid using AI-related wireless features and thus fail to utilize such features such as for optimizing wireless communication. 
     Accordingly, to overcome such deficiencies in conventional wireless systems, this disclosure introduces comprehensive techniques for exchanging AI capabilities between network nodes, such as between UEs and network base stations. For instance, different notifications are described that enable a network node to populate the notifications with supported AI capabilities and unsupported AI capabilities. In some implementations a base station communicates a request to a UE for reporting AI capabilities and/or the base station broadcasts an indication (e.g., a broadcast beacon) that the base station supports AI-enabled features functionality and/or specific AI-enabled feature functionality. Accordingly, a UE receives the request and/or detects the broadcast from the base station and generates a capability report that specifies whether the UE supports AI-enabled features and/or specifies specific AI-enabled features supported or not supported by the UE. Supported and non-supported AI features, for instance, can pertain to various layers of a wireless protocol stack such as a PHY layer, medium access control (MAC) protocol layer, radio link control (RLC) protocol layer, radio resource control (RRC) protocol layer, packet data convergence protocol (PDCP) layer, and/or combinations thereof. In at least one implementation where the base station identifies supported and/or not supported AI-enabled features of the base station, the UE generates the capability report to signal AI-enabled features that are also supported by the UE. 
     The UE transmits the capability report to the base station and the base station processes the capability report to identify supported AI-enabled features and/or non-supported AI-enabled features. In some implementations, where the capability report identifies AI-enabled features supported by the UE, the UE and/or the base station implement one or more of the supported AI-enabled features as part of wireless communication between the UE and the base station. A supported AI-enabled feature can be implemented separately by the UE, implemented separately by the base station, and/or cooperatively between the UE and the base station. For instance, supported AI-enabled features can pertain to signaling optimization between the UE and the base station, such as for signals exchanged as part of channel state information exchange, beam management, channel estimation (CE), UE position determination, and so forth. 
     Accordingly, by enabling network nodes to share information pertaining to AI capabilities of the nodes, the implementations described in this disclosure enable AI feature capabilities to be propagated among the network nodes. For instance, using the described implementations, various network nodes can quickly and efficiently identify AI-enabled features that are supported and to implement instances of the supported AI-enabled features such as for optimizing various aspects of wireless communication among the nodes. 
     Aspects of the present disclosure are described in the context of a wireless communications system. Aspects of the present disclosure are further illustrated and described with reference to device diagrams and flowcharts that relate to AI capability reporting for wireless communication. 
       FIG.  1    illustrates an example of a wireless communications system  100  that supports AI capability reporting for wireless communication in accordance with aspects of the present disclosure. The wireless communications system  100  may include one or more base stations  102 , one or more UEs  104 , and a core network  106 . The wireless communications system  100  may support various radio access technologies. In some implementations, the wireless communications system  100  may be a 4G network, such as an LTE network or an LTE-Advanced (LTE-A) network. In some other implementations, the wireless communications system  100  may be a 5G network, such as an NR network. In other implementations, the wireless communications system  100  may be a combination of a 4G network and a 5G network. The wireless communications system  100  may support radio access technologies beyond 5G. Additionally, the wireless communications system  100  may support technologies, such as time division multiple access (TDMA), frequency division multiple access (FDMA), or code division multiple access (CDMA), etc. 
     The one or more base stations  102  may be dispersed throughout a geographic region to form the wireless communications system  100 . One or more of the base stations  102  described herein may be or include or may be referred to as a base transceiver station, an access point, a NodeB, an eNodeB (eNB), a next-generation NodeB (gNB), a Radio Head (RH), a relay node, an integrated access and backhaul (IAB) node, or other suitable terminology. A base station  102  and a UE  104  may communicate via a communication link  108 , which may be a wireless or wired connection. For example, a base station  102  and a UE  104  may perform wireless communication over a Uu interface. 
     A base station  102  may provide a geographic coverage area  110  for which the base station  102  may support services (e.g., voice, video, packet data, messaging, broadcast, etc.) for one or more UEs  104  within the geographic coverage area  110 . For example, a base station  102  and a UE  104  may support wireless communication of signals related to services (e.g., voice, video, packet data, messaging, broadcast, etc.) according to one or multiple radio access technologies. In some implementations, a base station  102  may be moveable, for example, a satellite associated with a non-terrestrial network. In some implementations, different geographic coverage areas  110  associated with the same or different radio access technologies may overlap, but the different geographic coverage areas  110  may be associated with different base stations  102 . Information and signals described herein may be represented using any of a variety of different technologies and techniques. For example, data, instructions, commands, information, signals, bits, symbols, and chips that may be referenced throughout the description may be represented by voltages, currents, electromagnetic waves, magnetic fields or particles, optical fields or particles, or any combination thereof. 
     The one or more UEs  104  may be dispersed throughout a geographic region of the wireless communications system  100 . A UE  104  may include or may be referred to as a mobile device, a wireless device, a remote device, a handheld device, a customer premise equipment (CPE), or a subscriber device, or some other suitable terminology. In some implementations, the UE  104  may be referred to as a unit, a station, a terminal, or a client, among other examples. Additionally, or alternatively, the UE  104  may be referred to as an Internet-of-Things (IoT) device, an Internet-of-Everything (IoE) device, or machine-type communication (MTC) device, among other examples. In some implementations, a UE  104  may be stationary in the wireless communications system  100 . In some other implementations, a UE  104  may be mobile in the wireless communications system  100 . 
     The one or more UEs  104  may be devices in different forms or having different capabilities. Some examples of UEs  104  are illustrated in  FIG.  1   . A UE  104  may be capable of communicating with various types of devices, such as the base stations  102 , other UEs  104 , or network equipment (e.g., the core network  106 , a relay device, an integrated access and backhaul (IAB) node, or another network equipment), as shown in  FIG.  1   . Additionally, or alternatively, a UE  104  may support communication with other base stations  102  or UEs  104 , which may act as relays in the wireless communications system  100 . 
     A UE  104  may also be able to support wireless communication directly with other UEs  104  over a communication link  112 . For example, a UE  104  may support wireless communication directly with another UE  104  over a device-to-device (D2D) communication link. In some implementations, such as vehicle-to-vehicle (V2V) deployments, vehicle-to-everything (V2X) deployments, or cellular-V2X deployments, the communication link  112  may be referred to as a sidelink. For example, a UE  104  may support wireless communication directly with another UE  104  over a PC5 interface. 
     A base station  102  may support communications with the core network  106 , or with another base station  102 , or both. For example, a base station  102  may interface with the core network  106  through one or more backhaul links  114  (e.g., via an S1, N2, N2, or another network interface). The base stations  102  may communicate with each other over the backhaul links  114  (e.g., via an X2, Xn, or another network interface). In some implementations, the base stations  102  may communicate with each other directly (e.g., between the base stations  102 ). In some other implementations, the base stations  102  may communicate with each other indirectly (e.g., via the core network  106 ). In some implementations, one or more base stations  102  may include subcomponents, such as an access network entity, which may be an example of an access node controller (ANC). An ANC may communicate with the one or more UEs  104  through one or more other access network transmission entities, which may be referred to as a radio heads, smart radio heads, or transmission-reception points (TRPs). 
     The core network  106  may support user authentication, access authorization, tracking, connectivity, and other access, routing, or mobility functions. The core network  106  may be an evolved packet core (EPC), or a 5G core (5GC), which may include a control plane entity that manages access and mobility (e.g., a mobility management entity (MME), an access and mobility management functions (AMF)) and a user plane entity that routes packets or interconnects to external networks (e.g., a serving gateway (S-GW), a Packet Data Network (PDN) gateway (P-GW), or a user plane function (UPF)). In some implementations, the control plane entity may manage non-access stratum (NAS) functions, such as mobility, authentication, and bearer management for the one or more UEs  104  served by the one or more base stations  102  associated with the core network  106 . 
     According to implementations for AI capability reporting for wireless communication, a UE  104  can exchange AI capability information with a particular base station  102 . For instance, in the wireless communications system  100 , a UE  104  and a base station  102  establish wireless connectivity via a communication link  108 , e.g., an RRC_CONNECTED state. Further, the base station  102  communicates an AI capability enquiry  116  to the UE  104  over the communication link  108 . The AI capability enquiry  116 , for instance, requests AI capability information from the UE  104 . Based on the AI capability enquiry  116  the UE  104  generates an AI capability response  118  and communicates the AI capability response  118  to the base station  102  over the communication link  108 . The AI capability response  118 , for example, indicates whether the UE  104  supports AI functionality and if so, details concerning supported AI functionality. Detailed examples of AI functionality that can be indicated with the AI capability response  118  are presented below. Accordingly, the base station  102  and/or the UE  104  can leverage AI functionality identified in the AI capability response  118  such as to optimize wireless communication between the UE  104  and the base station  102 . 
     As part of setting up wireless connectivity between a UE and a wireless network in conventional wireless systems, UE capability information can be exchanged between the UE and the wireless network. See, for example, 3GPP TS 38.331 (V16.6.0) clause 5.6, which provides an example framework and signaling flow for conventional ways for enabling capability exchange between a UE and a wireless network. For instance, when a UE connects to a base station (RRC_CONNECTED), the base station and the UE undergo access stratum (AS) security activation to enable secure data transfer between the UE and the base station. The base station then transmits a UE capability enquiry (e.g., UECapabilityEnquiry) to the UE and the UE returns a capability response (e.g., UECapabilityInformation) indicating various capabilities of the UE pertaining to wireless communication. The capability response can include various types of UE capabilities pertaining to wireless communication such as supported radio access technologies, supported encryption type(s), supported wireless band combinations, and so forth. Accordingly, utilizing the UE capabilities, the wireless network can configure wireless communication with the UE to comply with capabilities of the UE. Such conventional techniques for exchanging capability information, however, are limited and do not provide ways for identifying AI-related capabilities of a UE or a base station. This prevents a UE and a connected wireless network from leveraging available AI functionality for optimizing wireless connectivity. 
     This disclosure provides a framework for exchange of AI capability information between a UE  104  and a wireless network (e.g., a base station  102 ) to enable implementation of AI functionality to improve wireless communications. Reporting of supported AI features can occur in various ways, such as using the described notifications. For instance, a UE  104  can report its AI capability for individual frequency ranges. A UE  104 , for example, can indicate that AI capability in frequency range 1 (FR1) is not supported, while AI capability in frequency range 2 (FR2) is supported. Further, in some examples, reporting of AI capability of a UE  104  for certain AI features and/or frequency range is optional, while for other AI features and/or frequency range, reporting of AI capability of a UE  104  can be mandatory. In at least one implementation, whether reporting of AI-enabled features is optional or mandatory can be enforced on a per-cell and/or per-network basis. 
     In some implementations, a wireless network can request that a UE  104  provide one or more AI capabilities during an initial network access procedure, e.g., before an RRC connection to the wireless network is established. This can allow for a base station  102  of the wireless network and/or a UE  104  to apply AI-enabled procedures at the initial access stage itself, e.g., as part of establishing an RRC_CONNECTED state. 
     According to one or more implementations, a wireless network can indicate to a UE  104  that it supports AI-enabled features and based on the received indication, the UE  104  can apply the supported AI-enabled features such as AI algorithms, AI procedures, AI signaling, and so forth. A wireless network, for example, indicates its AI capability during the initial access procedures via for example master information block (OB), system information block 1 (SIB1), or other suitable SIB. For instance, network support for AI capability and/or specific AI-enabled features can be broadcast by a base station  102  and received by a UE  104 . 
     In some implementations, implementation of various AI features may be controlled based on permissions. For instance, a UE  104  may initiate a request to a base station  102  of a wireless network requesting whether the UE  104  is allowed to perform certain AI-enabled features. In one implementation, the base station  102  may respond with a positive or negative response for specific AI-enabled features to allow or disallow specific AI features. In at least one implementation, if a base station  102  doesn&#39;t respond to the request from a UE  104  for permitted AI features, the UE  104  determines that AI-enabled features are not supported unless the base station  102  communicates some other form of AI feature capability to the UE  104 , e.g., other than a response to the request from the UE  104 . 
     According to one or more implementations whether a particular AI-enabled feature is supported or not supported can be implied based on other AI-enabled features that are supported or not supported. For instance, where a particular AI-enabled feature is dependent on and/or a sub-feature of another AI-enabled feature that is indicated as not supported, it can be inferred that the dependent AI feature and/or AI sub-feature is not supported. Further, where a particular AI-enabled feature is a sub-feature of another AI feature that is indicated as supported, it can be inferred that the AI sub-feature is supported. Reporting of specific AI-enabled features can be conditioned on a particular node supporting AI capability. For instance, where a UE  104  indicates that it does not support AI functionality, no further reporting regarding specific AI functionality is requested, e.g., by a base station  102 . In some instances, if a UE  104  is not required and/or requested by network to report AI functionality, then further reporting regarding specific AI functionality can be requested by network. 
     In some implementations various combinations of AI-enabled features can be preconfigured according to a particular AI protocol such that if a particular AI-enabled feature of a particular feature combination is indicated as supported, other AI-enabled features included the feature combination can be inferred as being supported. Additionally or alternatively, if a particular AI-enabled feature of a particular feature combination is indicated as not supported, other AI-enabled features included the feature combination can be inferred as not being supported. 
     According to one or more implementations, some AI features may be associated with different processing thresholds such as maximum processor latency, maximum processing overhead, minimum processing bandwidth, minimum processor units (e.g., minimum processor cores), minimum processor resources dedicated to AI-enabled features, etc. Accordingly, a UE  104  can report its processing capability to a base station  102  and if the UE  104  does not meet a specified processing threshold for a particular AI feature, the base station  102  can determine that the particular AI feature is not supported by the UE  104 . In at least one implementation a UE  104  can report its processing capability that is allocated specifically to AI-enabled functionality and/or to specific AI-enabled features. Further, some AI features may be associated with different memory thresholds such minimum memory bandwidth, minimum data storage capacity, minimum memory dedicated to AI-enabled features, etc. Accordingly, a UE  104  can report its memory capability to a base station  102  and if the UE  104  does not meet a specified memory threshold for a particular AI-enabled feature, the base station  102  can determine that the particular AI-enabled feature is not supported by the UE  104 . In at least one implementation a UE  104  can report its memory capability that is allocated specifically to AI-enabled functionality and/or to specific AI-enabled features. 
     The following discussion presents some example notification types that can be implemented to report AI capability of a UE  104  to a wireless network to enable AI functionality to be implemented, such as to optimize wireless communication between a UE  104  and a wireless network. The described notifications, for example, represent information that can be included in the AI capability response  118  described with reference to the wireless communications system  100 . Further, the notifications can be implemented as separate notifications and/or can be combined to generate integrated notifications that describe a variety of different supported and/or not supported AI-enabled features. The described notifications can be implemented in various ways such as information elements, new capability notifications, novel extensions of existing capability notifications, and so forth. Further, the notifications and/or AI features described in the notifications can be combined into instances of capability reports and/or sets of capability reports for reporting AI capabilities. The described notifications are presented for purpose of example only and it is to be appreciated that a variety of different types and forms of notifications can be utilized in accordance with the described and claimed implementations. 
     In some implementations, a particular node (e.g., a base station  102 ) can request reporting from another node (e.g., a UE  104 ) on the various AI-enabled features described in the example notifications below, and/or other instances of AI features not specifically mentioned. Further, a UE  104  can request reporting from a base station  102  on its supported AI features. Alternatively or additionally, a UE  104  can proactively communicate supported and/or not supported AI features to a base station  102 , and/or a base station  102  can proactively communicate supported and/or not supported AI features to a UE  104 . Further, a particular node can request information concerning specific combinations of the various AI features described in the example notifications below, and/or other instances of AI features not specifically mentioned. 
       FIG.  2    illustrates an example of an AI-enabled notification  200  that supports AI capability reporting for wireless communication in accordance with aspects of the present disclosure. The AI-enabled notification  200  may implement or be implemented by aspects of the wireless communications system  100 . For example, the AI-enabled notification  200  may include an AI supported field  202  that is configurable to indicate whether a particular UE supports AI functionality. For instance, if the AI supported field  202  is configured to indicate “supported,” this indicates that a UE has the capability to support AI functionality. If the AI supported field  202  is configured to indicate “notSupported,” this indicates that a UE does not support AI functionality. In an example implementation when the AI supported field is indicated as “notSupported,” a base station and/or other network node may refrain from enquiring with a UE concerning further AI related capability. However, if the AI supported field is indicated as “supported,” a base station and/or other network node may further enquire with a UE concerning details about supported AI capability, such as for AI capabilities concerning supported AI algorithms, supported AI procedures, supported AI signaling, and so forth. 
       FIG.  3    illustrates an example of a PHY support notification  300  that supports AI capability reporting for wireless communication in accordance with aspects of the present disclosure. The PHY support notification  300  may implement or be implemented by aspects of the wireless communications system  100 . The PHY support notification  300  includes a phy-parameters field  302  and a phy-AI-capability field  304 . The phy-parameters field  302  is configured to receive identifiers for different PHY layer features of a UE, such as wireless PHY layer features supported by the UE. The phy-AI-capability field  304  is configurable to indicate whether a UE supports AI functionality at the PHY layer (“supported”) or does not support AI functionality at the PHY layer (“notSupported”). In some instances, if the UE reports “supported” for phy-AI-capability, then the network may assume that UE is capable of supporting all the AI-enabled PHY layer procedures and signaling. In some other instances, if the UE reports “supported” for phy-AI-capability, then the network may further enquire about specific AI-enabled PHY layer features, if they are supported or not by the UE. On the other hand, if the UE reports “notSupported” for phy-AI-capability, then the network may assume that UE is incapable of supporting all the AI-enabled PHY layer procedures and signaling, and hence the network would not further enquire about specific AI-enabled PHY layer features, and hence the UE would not be expected to receive configuration signaling, or feedback parameters that are related to the aforementioned AI-enabled feature(s). 
       FIG.  4    illustrates an example of an AI framework notification  400  that supports AI capability reporting for wireless communication in accordance with aspects of the present disclosure. The AI framework notification  400  may implement or be implemented by aspects of the wireless communications system  100 . The AI framework notification  400  includes a supported framework field  402  that is configurable to indicate different AI frameworks that are supported or not supported. An AI framework can refer to various model structures and model types of AI algorithms such as neural network architectures, machine learning model (MLM) architectures, combinations of different frameworks, and so forth. While the examples presented in the AI framework notification  400  are indicated in the context of neural network types, it is to be appreciated that a wide variety of different AI frameworks and AI model types can be identified via the AI framework notification  400 . 
     In some implementations, as part of reporting AI framework capability, a maximum supported depth (e.g., number of layers) and a maximum supported width (e.g., number of nodes) that a UE can support for an AI framework is reported. For example, the maximum depth and width of a particular AI framework is reported as a pair of values selected from a codebook of pair values. In another example, each of the maximum supported depth and width supported for a particular AI framework are reported separately. 
       FIG.  5    illustrates an example of a learning notification  500  that supports AI capability reporting for wireless communication in accordance with aspects of the present disclosure. The learning notification  500  may implement or be implemented by aspects of the wireless communications system  100 . The learning notification  500  includes a learning capability field  502  that is configurable to indicate different AI learning (e.g., model training) frameworks that are supported or not supported. For instance, unsupervised learning utilizes unlabeled data sets, while supervised learning utilizes labelled data sets and the desired output is already known at the time of training. Federated learning utilizes training across multiple model nodes that contain local data, but avoids exchange of training data between different nodes. Further, in federated learning parameters obtained from training local models can be used to create a global training mode. Another category of learning is reinforcement learning that is done in absence of a training data set and is based on an agent and a reward until a model being trained converges to a specified training target. These specific examples of learning methods are presented for purpose of example only, and it is to be appreciated that a variety of different learning methods can be implemented in accordance with the described and claimed implementations. 
       FIG.  6    illustrates an example of an integration mode notification  600  that supports AI capability reporting for wireless communication in accordance with aspects of the present disclosure. The integration mode notification  600  may implement or be implemented by aspects of the wireless communications system  100 . The integration mode notification  600  includes an integration mode field  602  that is configurable to indicate different AI integration modes that are supported or not supported. An integration mode refers to how a node such as a UE is capable of integrating AI features. For instance, in a non-assisted mode a node is able to utilize an AI algorithm without assisted signaling from another node such as a base station. In an assisted mode, a node is able to utilize an AI algorithm with assistance from another node such as a base station. In a distributed node, both a transmitter node and a receiver node (e.g., a UE and a base station) can apply AI algorithms independently with or without assisted signaling. Further, in a joint mode, two or more nodes may apply a same AI algorithm for joint application of an AI procedure at both nodes, e.g., at a transmitter node and a receiver node. In some implementations implementing a joint mode involves model transfer between participating nodes. These examples of integration modes are presented for purpose of example, and it is to be appreciated that a variety of different integration modes can be implemented in accordance with the described and claimed implementations. The indication for support of one or more of these modes can also imply which specific AI-enabled feature may or not be supported by UE and/or network. 
       FIG.  7    illustrates an example of a training mode notification  700  that supports AI capability reporting for wireless communication in accordance with aspects of the present disclosure. The training mode notification  700  may implement or be implemented by aspects of the wireless communications system  100 . The training mode notification  700  includes a training mode field  702  that is configurable to indicate different AI training modes that are supported or not supported. For instance, in an offline mode an AI model implemented by a node is trained prior to node activation, e.g., before the node is deployed and engages in network connectivity. In an online mode, a node causes an AI model to be trained after being deployed, e.g., after node activation. For a mixed mode, an AI model implemented by a node can be partially training in the offline mode, and partially trained in the online mode. These specific examples of training modes are presented for purpose of example only, and it is to be appreciated that a variety of training integration modes can be implemented in accordance with the described and claimed implementations. In some implementations a UE reports its processor and/or memory capabilities pertaining to AI-related training, such as a maximum threshold data size for a training data set and/or a number of model input nodes that processing resources of the UE can support. Thus, if a particular training mode is predicted to exceed the identified processor and/or memory capabilities of a UE, the training mode can be determined to be not supported by the UE. 
       FIG.  8    illustrates an example of an application mode notification  800  that supports AI capability reporting for wireless communication in accordance with aspects of the present disclosure. The application mode notification  800  may implement or be implemented by aspects of the wireless communications system  100 . An application mode, for instance, represents a mode in which an AI model (e.g., algorithm) is applied to radio frequency signal transmitted and/or received by a transceiver, such as to optimize signal quality of transmitted and/or received signal. The application mode notification  800  includes an application mode field  802  that is configurable to indicate different AI application modes that are supported or not supported. One example application mode is an individual mode, where AI is applied to a particular signal block/feature individually in a transceiver chain. For instance, an AI algorithm individually processes one signal block/feature, while the AI algorithm is either not applied to another signal block/feature and/or is individually applied independent of another signal block/feature. In some examples, AI-enabled CE is applied at the UE, but CSI feedback is not processed by AI. In another example, two signal blocks/features are separately/individually processed by AI, for instance, one AI-enabled algorithm is applied for CE and another AI-enabled algorithm is applied for CSI feedback. This can effectively result in applying two separate AI models to signal blocks/features. Another example application mode is a joint mode that represents AI application where block AI application occurs across multiple blocks across a transceiver chain, e.g., in a joint manner. In some examples, a single AI model is applied to optimize both CE and CSI feedback at the UE. 
       FIG.  9    illustrates an example of a PHY parameters notification  900  that supports AI capability reporting for wireless communication in accordance with aspects of the present disclosure. The PHY parameters notification  900  may implement or be implemented by aspects of the wireless communications system  100 . In at least one implementation the phy parameter notification represents an extension of and/or variation on the PHY support notification  300  and identifies specific PHY-level AI functionalities that are supported. The PHY parameters notification  900  includes a PHY parameters field  902  that is configurable to identify different specific PHY-AI parameters that are supported or not supported. In this example the PHY-AI parameters include channel state information (CSI)-AI capabilities. For instance, as part of CSI-AI capabilities a node reports its capability to indicate whether it supports AI-enabled CSI feedback compression. A new type of codebook, for example, can be introduced for AI-enabled CSI codebook compression. In some implementations support of AI-enabled CSI reporting can be introduced and indicated via the CSI-ReportConfig information element. In an additional or alternative implementation, the AI-enabled CSI codebook compression capability can be indicated in a capability information element called CodebookParameters. 
     Another example PHY-AI parameter is a beam management (BM)-AI parameter that specifies AI-enabled capabilities pertaining to beam management. For instance, as part of BM-AI capabilities a node can report is capability to support AI-enabled beam failure detection and beam recovery. In one implementation, the BeamFailureRecoveryConfig information element can be enhanced to report a node&#39;s capability for AI-enabled beam failure and recovery. In some implementations a node can report its capability to support AI-enabled beam indication and prediction. For instance, the TCI-State information element can be enhanced to indicate its capability in terms of Transmission Configuration Indicator (TCI) state prediction, which can be implemented as part of beam prediction. In another example implementation, beam measurements and reporting configuration specific for AI-enabled methods can be indicated and/or reported by a node. 
     Another example PHY-AI parameter is a channel estimation (CE)-AI parameter that indicates a nodes capability to support AI-enabled CE and/or channel prediction. In some implementations, CE-AI capability can be reported via a node&#39;s capability information element and/or can be separately indicated by reporting the node&#39;s capability to support a new DeModulation Reference Signal (DMRS) configuration and/or pattern associated with AI-enabled CE and/or channel prediction. 
     Another example PHY-AI parameter is a positioning (POS)-AI parameter that indicates a nodes capability to support AI-enabled node position determination. For instance, POS-AI capability can be reported in the LocationMeasurementInfo information element. In some implementations an AI-POS capability is indicated for positioning techniques. Alternatively or additionally, positioning technique and/or scenario specific POS capability is reported. 
     These specific examples of PHY-AI parameters are presented for purpose of example only, and it is to be appreciated that a variety of different PHY-AI parameters can be implemented in accordance with the described and claimed implementations. 
       FIG.  10    illustrates an example of a CSI-AI sub-feature notification  1000  that supports AI capability reporting for wireless communication in accordance with aspects of the present disclosure. The CSI-AI sub-feature notification  1000  may implement or be implemented by aspects of the wireless communications system  100 . In at least one implementation the CSI-AI sub-feature notification  1000  can be implemented in conjunction with the PHY parameters notification  900 , such as where the PHY parameters notification  900  indicates that CSI-AI capabilities are supported by a node. The CSI-AI sub-feature notification  1000  includes a CSI-AI sub-feature field  1002  that is configurable to indicate different CSI-AI sub-features that are supported or not supported. For instance, AI-PMIprediction represents AI-assisted precoding matrix indicator prediction; AI-RIprediction represents AI-assisted rank indicator prediction; AI-CQIprediction represents AI-assisted channel quality indicator; AI-Spatial-domainCompression represents AI assisted compression of spatial domain signals; AI-Frequency-domainCompression represents AI-assisted compression of frequency domain signals; and AI-CSI-RScompression represents AI-assisted compression of CSI-reference signals (RS). These specific examples of CSI-AI sub-features are presented for purpose of example only, and it is to be appreciated that a variety of different CSI-AI sub-features can be implemented in accordance with the described and claimed implementations. 
     CSI-AI sub-features can be communicated in various ways, such as part of a Phy-Parameters information element, as part of a multiple-input and multiple-output (MIMO)-related information element, as part of an information element that is associated with AI capabilities (e.g., the PHY parameters notification  900 ), as part of an information element associated with CSI-AI-capability (e.g., the CSI-AI sub-feature notification  1000 ), and so forth. 
       FIG.  11    illustrates an example of a BM-AI sub-feature notification  1100  that supports AI capability reporting for wireless communication in accordance with aspects of the present disclosure. The BM-AI sub-feature notification  1100  may implement or be implemented by aspects of the wireless communications system  100 . In at least one implementation the BM-AI sub-feature notification can be implemented in conjunction with the PHY parameters notification  900 , such as where the PHY parameters notification  900  indicates that BM-AI capabilities are supported by a node. The BM-AI sub-feature notification  1100  includes a BM-AI sub-feature field  1102  that is configurable to indicate different BM-AI sub-features that are supported or not supported. For instance, AI-BeamPrediction represents AI-assisted wireless beam prediction; AI-BlockagePrediction represents AI-assisted blockage prediction (e.g. for predicting when signal blockage may occur); AI-SSB compression represents AI-assisted synchronization signal block (SSB) compression; AI-CSI-RScompression represents AI-assisted compression of CSI-reference signals (RS); AI-ReportingCompression represents AI-assisted wireless beam reporting; and AI-BeamFailurePrediction represents AI-assisted prediction of wireless beam failure. These specific examples of BM-AI sub-features are presented for purpose of example only, and it is to be appreciated that a variety of different BM-AI sub-features can be implemented in accordance with the described and claimed implementations. 
     BM-AI sub-features can be communicated in various ways, such as part of a Phy-Parameters information element, as part of a MIMO-related information element, as part of an information element that is associated with AI capabilities (e.g., the PHY parameters notification  900 ), as part of an information element associated with BM-AI-capability (e.g., the BM-AI sub-feature notification  1100 ), and so forth. 
       FIG.  12    illustrates an example of a RS-AI sub-feature notification  1200  that supports AI capability reporting for wireless communication in accordance with aspects of the present disclosure. The RS-AI sub-feature notification  1200  may implement or be implemented by aspects of the wireless communications system  100 . In at least one implementation the RS-AI sub-feature notification can be implemented in conjunction with the CSI-AI sub-feature notification  1000 , such as where the CSI-AI sub-feature notification  1000  indicates that AI-assisted compression of CSI-RS are supported by a node. The RS-AI sub-feature notification  1200  includes a RS-AI sub-feature field  1202  that is configurable to indicate different RS-AI sub-features that are supported or not supported. For instance, DMRSforPDSCH-comp represents AI-assisted compression of DeModulation Reference Signal (DMRS) for Physical Downlink Shared Channel (PDSCH); AI-DMRSforPDCCH-comp represents AI-assisted compression of DMRS for Physical Downlink Control Channel (PDCCH); AI-SSBcomp represents AI-assisted compression of synchronization signal blocks (SSB); AI-CSI-RScomp represents AI-assisted compression of CSI-RS; and AI-PRScomp represents AI-assisted compression of positioning reference signals. These specific examples of RS-AI sub-features are presented for purpose of example only, and it is to be appreciated that a variety of different RS-AI sub-features can be implemented in accordance with the described and claimed implementations. 
     RS-AI sub-features can be communicated in various ways, such as part of a Phy-Parameters information element, as part of a MIMO-related information element, as part of an information element that is associated with AI capabilities (e.g., the CSI-AI sub-feature notification  1000  and/or the BM-AI sub-feature notification  1100 ), as part of an information element associated with RS-AI-capability (e.g., the RS-AI sub-feature notification  1200 ), and so forth. 
       FIG.  13    illustrates an example of a POS-AI sub-feature notification  1300  that supports AI capability reporting for wireless communication in accordance with aspects of the present disclosure. The POS-AI sub-feature notification  1300  may implement or be implemented by aspects of the wireless communications system  100 . In at least one implementation the POS-AI sub-feature notification can be implemented in conjunction with the PHY parameters notification  900 , such as where the PHY parameters notification  900  indicates that a node supports AI-assisted positioning services. The POS-AI sub-feature notification  1300  includes a POS-AI sub-feature field  1302  that is configurable to indicate different POS-AI sub-features that are supported or not supported. For instance, AI-LoSpos-prediction represents AI-assisted position prediction in Line of Sight (LoS) scenarios, AI-NLoSpos-prediction represents AI-assisted position prediction in non-Line of Sight (NLoS) scenarios, and AI-PRScompression represents AI-assisted compression of Positioning Reference Signals (PRS). These specific examples of POS-AI sub-features are presented for purpose of example only, and it is to be appreciated that a variety of different POS-AI sub-features can be implemented in accordance with the described and claimed implementations. 
     POS-AI sub-features can be communicated in various ways, such as part of a Phy-Parameters information element, as part of a positioning-related information element, as part of an information element that is associated with AI capabilities (e.g., the PHY parameters notification  900 ), as part of an information element associated with POS-AI-capability (e.g., the POS-AI sub-feature notification  1300 ), and so forth. 
       FIG.  14    illustrates an example system  1400  for utilizing AI capability reporting for wireless communication in accordance with aspects of the present disclosure. In the system  1400  a base station  102  and a UE  104  intercommunicate to establish a wireless connection. As part of the establishing wireless connectivity, the base station  102  generates an AI capability enquiry  1402  requesting AI capability information and communicates the AI capability enquiry  1402  to the UE  104 . Based on the AI capability enquiry  1402  the UE  104  generates an AI capability response  1404  indicating AI capability of the UE  104  and communicates the AI capability response  1404  to the base station  102 . The AI capability response  1404  can be formatted in various ways and include various types of information pertaining to AI capability of the UE  104 . Examples of different formats and AI-related information types that can be included in the AI capability response  1404  are detailed above. 
     In the example of  FIG.  14    the AI capability response  1404  indicates that the UE  104  supports AI functionality and specific AI functionality including AI-enabled CE. The base station  102  receives the AI capability response  1404  and determines that the UE  104  supports CE-AI capability. Accordingly, based on determining that the UE  104  supports AI capability and specifically CE-AI capability, the base station  102  generates AI-based CE signals  1406  (e.g., DMRS, pilot signals) and transmits the CE signals  1406  for receipt by the UE  104 . In some implementations, for example, the base station  102  is configured to transmit a first configuration of CE signals to UEs that that don&#39;t support CE-AI functionality, and a second configuration of CE signals to UEs that support CE-AI functionality. The second configuration of CE signals, for instance, includes fewer signals than the first configuration of CE signals. The CE signals  1406 , for example, are based on the second configuration of CE signals. 
     The UE  104  receives the CE signals  1406  and implements an AI channel estimator module  1408  to process the CE signals  1406  and generate CE information  1410 . For instance, as part of generating the CE information  1410  the AI channel estimator module  1408  determines channel coefficients and channel noise estimates for a wireless channel over which the AI CE signals  1406  are received. The UE  104  can utilize the CE information  1410  to optimize wireless communication between the UE  104  and the base station  102 . Accordingly, the system  1400  illustrates that AI capabilities can be utilized to optimize various aspects of wireless communication. For instance, in this particular example, utilizing CE-AI techniques enables fewer CE-related signals to be exchanged between the base station  102  and the UE  104  as compared with non-AI techniques, thus reducing signal transmission burden on the base station  102  and signal processing burden on the UE  104 . 
       FIG.  15    illustrates an example system  1500  for utilizing AI capability reporting for wireless communication in accordance with aspects of the present disclosure. Based on the AI capability reporting discussed within the context of this document, the AI-based CSI decompression or reconstruction is applied at the network based on the CSI related capability information reported by the UE. As illustrated in  FIG.  10   , a UE can report that AI-based CSI feedback compression is supported. Consequently, the network can assume CSI compression at UE and then correspondingly apply AI-enabled CSI decompression at the network side, as illustrated in  FIG.  15   . The system  1500  includes an AI model  1502  which in this particular example represents a neural network with an input layer  1504 , a hidden layer  1506  (or multiple hidden layers  1506 ) and an output layer  1508 . Further, the hidden layer(s)  1506  include neurons  1510 . The AI model  1502  can be utilized to process and predict various types of data pertaining to wireless communication. For instance, in this particular example the AI model  1502  takes compressed CSI feedback  1512  as input (x values), applies weighting values w to the compressed CSI feedback  1512 , processes the weighted compressed CSI feedback  1512  at the hidden layer  1506 , and generates decompressed CSI feedback  1514  (y values) at the output layer  1508  by predicting the decompressed CSI feedback  1514  from the compressed CSI feedback  1512 . In at least one implementation, prior to generating the decompressed CSI feedback  1514  from the compressed CSI feedback, the AI model  1502  is trained utilizing a training data set to enable the AI model to accurately predict decompressed CSI feedback from an input set of compressed CSI feedback. 
     In addition to utilizing the AI model  1502  for decompression of the compressed CSI feedback  1512 , the AI model  1502  and/or other AI model may be leveraged by a UE to generate the compressed CSI feedback  1512  from input CSI feedback. For instance, based on determining that a UE and a base station support AI-enabled CSI compression and decompression, the UE applies AI-enable CSI compression to compress CSI and generate the compressed CSI feedback  1512 . The UE then transmits the compressed CSI feedback  1512  to the base station, and the base station applies AI-enabled decompression of the compressed CSI feedback  1512  to generate the decompressed CSI feedback  1514 , as described above. 
     The AI model  1502  can be leveraged in various was for efficient generation of compressed CSI feedback and prediction of decompressed CSI feedback. For instance, the compressed CSI feedback  1512  can be generated by a UE  104  and transmitted to a base station  102  and the base station  102  can leverage the AI model  1502  to generate the decompressed CSI feedback  1514 . The base station  102  can utilize the decompressed CSI feedback  1514  to optimize wireless communication between the UE  104  and the base station  102 . 
       FIG.  16    illustrates an example of a block diagram  1600  of a device  1602  that supports AI capability reporting for wireless communication in accordance with aspects of the present disclosure. The device  1602  may be an example of a UE  104  as described herein. The device  1602  may support wireless communication with one or more base stations  102 , UEs  104 , or any combination thereof. The device  1602  may include components for bi-directional communications including components for transmitting and receiving communications, such as a communication manager  1604 , a processor  1606 , a memory  1608 , a receiver  1610 , a transmitter  1612 , and an I/O controller  1614 . These components may be in electronic communication or otherwise coupled (e.g., operatively, communicatively, functionally, electronically, electrically) via one or more interfaces (e.g., buses). 
     The communication manager  1604 , the receiver  1610 , the transmitter  1612 , or various combinations thereof or various components thereof may be examples of means for performing various aspects of the present disclosure as described herein. For example, the communication manager  1604 , the receiver  1610 , the transmitter  1612 , or various combinations or components thereof may support a method for performing one or more of the functions described herein. 
     In some implementations, the communication manager  1604 , the receiver  1610 , the transmitter  1612 , or various combinations or components thereof may be implemented in hardware (e.g., in communications management circuitry). The hardware may include a processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic device, a discrete gate or transistor logic, discrete hardware components, or any combination thereof configured as or otherwise supporting a means for performing the functions described in the present disclosure. In some implementations, the processor  1606  and the memory  1608  coupled with the processor  1606  may be configured to perform one or more of the functions described herein (e.g., by executing, by the processor  1606 , instructions stored in the memory  1608 ). 
     Additionally or alternatively, in some implementations, the communication manager  1604 , the receiver  1610 , the transmitter  1612 , or various combinations or components thereof may be implemented in code (e.g., as communications management software or firmware) executed by the processor  1606 . If implemented in code executed by the processor  1606 , the functions of the communication manager  1604 , the receiver  1610 , the transmitter  1612 , or various combinations or components thereof may be performed by a general-purpose processor, a DSP, a central processing unit (CPU), an ASIC, an FPGA, or any combination of these or other programmable logic devices (e.g., configured as or otherwise supporting a means for performing the functions described in the present disclosure). 
     In some implementations, the communication manager  1604  may be configured to perform various operations (e.g., receiving, monitoring, transmitting) using or otherwise in cooperation with the receiver  1610 , the transmitter  1612 , or both. For example, the communication manager  1604  may receive information from the receiver  1610 , send information to the transmitter  1612 , or be integrated in combination with the receiver  1610 , the transmitter  1612 , or both to receive information, transmit information, or perform various other operations as described herein. Although the communication manager  1604  is illustrated as a separate component, in some implementations, one or more functions described with reference to the communication manager  1604  may be supported by or performed by the processor  1606 , the memory  1608 , or any combination thereof. For example, the memory  1608  may store code, which may include instructions executable by the processor  1606  to cause the device  1602  to perform various aspects of the present disclosure as described herein, or the processor  1606  and the memory  1608  may be otherwise configured to perform or support such operations. 
     In some implementations, the communication manager  1604  represents and/or implements a dedicated AI module that is configured to apply at least in part the various AI features discussed herein. For instance, to support various AI-features (e.g., algorithms, procedures, signaling, etc.) the communication manager  1604  can be trained or is already trained to accept a set of input parameters and based on the inputs and its trained/learned algorithms provide output for one or more wireless-related procedures, algorithms, signals, and so forth. For instance, AI features implemented by the communication manager  1604  are able to provide output (e.g., via inference) with more highly optimized performance in comparison to a node (e.g., a UE) that does not support AI features. Such performance enabled by supported AI features can be improved in terms of accuracy, latency, overhead, complexity, or combinations thereof. Further, supported basic AI features can be applied at the transmitter  1612 , at the receiver  1610 , and/or a combination thereof. In some implementations a supported AI feature, unless otherwise indicated, is applicable to both the transmitter chain as well a receiver chain of the device  1602 . 
     For example, the communication manager  1604  may support wireless communication at a first device (e.g., the device  1602 ) in accordance with examples as disclosed herein. The communication manager  1604  and/or other device components may be configured as or otherwise support a means for wireless communication at a device, including generating a capability report indicating artificial intelligence enabled features of a first node associated with at least one protocol layer of a wireless protocol stack, where the capability report is generated to indicate that the first node includes artificial intelligence capability and to specify at least one supported artificial intelligence enabled feature of the first node including selecting the at least one supported artificial intelligence enabled feature from available artificial intelligence features for the capability report including: one or more supported AI model types; one or more supported AI model training techniques; one or more supported artificial intelligence integration modes; one or more supported AI learning frameworks; one or more AI training modes selected from at least an offline training mode, an online training mode, and a mixed training mode; and one or more transceiver application modes selected from at least a joint application mode and an individual block application mode; communicating the generated capability report to a second node; and engaging in wireless connectivity between the first node and the second node based at least in part on the at least one supported AI feature. 
     Additionally, wireless communication at the device includes any one or combination of: generating the capability report in response to detection of a broadcast signal from the second node that indicates that the second node supports AI capability; generating the capability report to indicate one or more of processing resources available for AI capability or memory resources available for AI capability; generating the capability report in response to a request from the second node for AI capability of the first node; generating the capability report independent of a request from the second node for AI capability of the first node; receiving a communication from the second node indicating at least one of: that the second node supports AI capability or that the second node supports one or more of the available AI-enabled features for the capability report, and generating the capability report in response to the communication from the second node; where: the one or more supported AI model types are selected from at least a convolutional neural network, a recurrent neural network, a modular neural network, or a combination thereof; the one or more supported AI learning frameworks are selected from at least unsupervised learning, supervised learning, federated learning, reinforced learning, or a combination thereof; and the one or more supported AI integration modes are selected from at least non-assisted mode, assisted mode, distributed mode, joint mode, or a combination thereof; where the available AI features for the capability report correspond to one or more PHY layer procedures in a wireless network, the one or more PHY layer procedures including: channel state information framework, beam management framework, CE AI application framework, reference signal AI application framework, positioning enhancements framework, channel coding or a combination thereof; where one or more of: an available AI feature for the capability report corresponding to the channel state information framework further includes capability sub-features for one or more of PMI prediction, RI prediction, CQI prediction, spatial-domain compression, frequency-domain compression, or CSI-RS compression; an available AI feature for the capability report corresponding to the beam management framework further includes capability sub-features for one or more of beam prediction, blockage prediction, beam failure prediction, SSB compression, CSI-RS compression, or reporting compression; an available AI feature for the capability report corresponding to the reference signal AI application framework further includes capability sub-features for one or more of DMRS-for-PDSCH overhead reduction, DMRS-for-PDCCH overhead reduction, SSB compression, CSI-RS overhead reduction, or PRS overhead reduction; and an available AI feature for the capability report corresponding to the positioning enhancements framework further includes capability sub-features for one or more of line-of-sight positioning prediction, non-line-of-sight positioning prediction, or PRS compression; communicating the generated capability report via one or more of an AI information element or a radio resource control information element; and communicating the generated capability report in response to receiving one or more of a master information block or a system information block; generating the capability report to indicate that the first node supports AI-enabled channel state information compression, and applying an AI model to channel state information to generate compressed channel state information, and transmitting the compressed channel state information for reception by the second node as part of the wireless connectivity between the first node and the second node; generating the capability report to indicate that the first node supports AI-enabled CE; receiving one or more CE signals configured for AI-enabled CE; performing AI-enabled CE utilizing the CE signals configured for AI-enabled CE. 
     Additionally, a device for wireless communication includes a communication manager configured to: generate a capability report to indicate artificial intelligence enabled features of a first node associated with at least one protocol layer of a wireless protocol stack, where the capability report is generated to indicate that the first node includes artificial intelligence capability and to specify at least one supported artificial intelligence enabled feature of the first node including to select the at least one supported artificial intelligence enabled feature from available artificial intelligence features for the capability report that include: one or more supported AI model types; one or more supported AI model training techniques; one or more supported artificial intelligence integration modes; one or more supported AI learning frameworks; one or more AI training modes selected from at least an offline training mode, an online training mode, and a mixed training mode; and one or more transceiver application modes selected from at least a joint application mode and an individual block application mode; and a transceiver configured to transmit the generated capability report for receipt by a second node, where the communication manager is further configured to engage in wireless connectivity between the first node and the second node based at least in part on the at least one supported AI feature. 
     Additionally, the device for wireless communication includes any one or combination of: where the communication manager is configured to generate the capability report in response to detection of a broadcast signal from the second node that indicates that the second node supports AI capability; where the communication manager is further configured to generate the capability report to indicate one or more of processing resources available for AI capability or memory resources available for AI capability; where the communication manager is further configured to: generate the capability report in response to a request from the second node for AI capability of the first node; and generate the capability report independent of a request from the second node for AI capability of the first node; where the communication manager is further configured to receive a communication from the second node indicating at least one of: that the second node supports AI capability or that the second node supports one or more of the available AI-enabled features for the capability report, and to generate the capability report in response to the communication from the second node; where: the one or more supported AI model types are selected from at least a convolutional neural network, a recurrent neural network, a modular neural network, or a combination thereof; the one or more supported AI learning frameworks are selected from at least unsupervised learning, supervised learning, federated learning, reinforced learning, or a combination thereof; and the one or more supported AI integration modes are selected from at least non-assisted mode, assisted mode, distributed mode, joint mode, or a combination thereof; where the available AI features for the capability report correspond to one or more PHY layer procedures in a wireless network, the one or more PHY layer procedures including: channel state information framework, beam management framework, CE AI application framework, reference signal AI application framework, positioning enhancements framework, channel coding or a combination thereof; where one or more of: an available AI feature for the capability report corresponding to the channel state information framework further includes capability sub-features for one or more of PMI prediction, RI prediction, CQI prediction, spatial-domain compression, frequency-domain compression, or CSI-RS compression; an available AI feature for the capability report corresponding to the beam management framework further includes capability sub-features for one or more of beam prediction, blockage prediction, beam failure prediction, SSB compression, CSI-RS compression, or reporting compression; an available AI feature for the capability report corresponding to the reference signal AI application framework further includes capability sub-features for one or more of DMRS-for-PDSCH overhead reduction, DMRS-for-PDCCH overhead reduction, SSB compression, CSI-RS overhead reduction, or PRS overhead reduction; and an available AI feature for the capability report corresponding to the positioning enhancements framework further includes capability sub-features for one or more of line-of-sight positioning prediction, non-line-of-sight positioning prediction, or PRS compression; where the communication manager is further configured to: communicate the generated capability report via one or more of an AI information element or a radio resource control information element; and communicate the generated capability report in response to receiving one or more of a master information block or a system information block; where the communication manager is further configured to generate the capability report to indicate that the first node supports AI-enabled channel state information compression, and to apply an AI model to channel state information to generate compressed channel state information, and where the transceiver is further configured to transmit the compressed channel state information for reception by the second node as part of the wireless connectivity between the first node and the second node; where: the communication manager is further configured to generate the capability report to indicate that the first node supports AI-enabled CE; the transceiver is further configured to receive one or more CE signals configured for AI-enabled CE; and the communication manager is further configured to perform AI-enabled CE utilizing the CE signals configured for AI-enabled CE. 
     The processor  1606  may include an intelligent hardware device (e.g., a general-purpose processor, a DSP, a CPU, a microcontroller, an ASIC, an FPGA, a programmable logic device, a discrete gate or transistor logic component, a discrete hardware component, or any combination thereof). In some implementations, the processor  1606  may be configured to operate a memory array using a memory controller. In some other implementations, a memory controller may be integrated into the processor  1606 . The processor  1606  may be configured to execute computer-readable instructions stored in a memory (e.g., the memory  1608 ) to cause the device  1602  to perform various functions of the present disclosure. 
     The memory  1608  may include random access memory (RAM) and read-only memory (ROM). The memory  1608  may store computer-readable, computer-executable code including instructions that, when executed by the processor  1606  cause the device  1602  to perform various functions described herein. The code may be stored in a non-transitory computer-readable medium such as system memory or another type of memory. In some implementations, the code may not be directly executable by the processor  1606  but may cause a computer (e.g., when compiled and executed) to perform functions described herein. In some implementations, the memory  1608  may include, among other things, a basic I/O system (BIOS) which may control basic hardware or software operation such as the interaction with peripheral components or devices. 
     The I/O controller  1614  may manage input and output signals for the device  1602 . The I/O controller  1614  may also manage peripherals not integrated into the device  1602 . In some implementations, the I/O controller  1614  may represent a physical connection or port to an external peripheral. In some implementations, the I/O controller  1614  may utilize an operating system such as iOS®, ANDROID®, MS-DOS®, MS-WINDOWS®, OS/2®, UNIX®, LINUX®, or another known operating system. In some implementations, the I/O controller  1614  may be implemented as part of a processor, such as the processor  1606 . In some implementations, a user may interact with the device  1602  via the I/O controller  1614  or via hardware components controlled by the I/O controller  1614 . 
     In some implementations, the device  1602  may include a single antenna  1616 . However, in some other implementations, the device  1602  may have more than one antenna  1616 , which may be capable of concurrently transmitting or receiving multiple wireless transmissions. The receiver  1610  and the transmitter  1612  may communicate bi-directionally, via the one or more antennas  1616 , wired, or wireless links as described herein. For example, the receiver  1610  and the transmitter  1612  may represent a wireless transceiver and may communicate bi-directionally with another wireless transceiver. The transceiver may also include a modem to modulate the packets, to provide the modulated packets to one or more antennas  1616  for transmission, and to demodulate packets received from the one or more antennas  1616 . 
       FIG.  17    illustrates an example of a block diagram  1700  of a device  1702  that supports AI capability reporting for wireless communication in accordance with aspects of the present disclosure. The device  1702  may be an example of a base station  102 , such as a gNB as described herein. The device  1702  may support wireless communication with one or more base stations  102 , UEs  104 , or any combination thereof. The device  1702  may include components for bi-directional communications including components for transmitting and receiving communications, such as a communications manager  1704 , a processor  1706 , a memory  1708 , a receiver  1710 , a transmitter  1712 , and an I/O controller  1714 . These components may be in electronic communication or otherwise coupled (e.g., operatively, communicatively, functionally, electronically, electrically) via one or more interfaces (e.g., buses). 
     The communications manager  1704 , the receiver  1710 , the transmitter  1712 , or various combinations thereof or various components thereof may be examples of means for performing various aspects of the present disclosure as described herein. For example, the communications manager  1704 , the receiver  1710 , the transmitter  1712 , or various combinations or components thereof may support a method for performing one or more of the functions described herein. 
     In some implementations, the communications manager  1704 , the receiver  1710 , the transmitter  1712 , or various combinations or components thereof may be implemented in hardware (e.g., in communications management circuitry). The hardware may include a processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic device, a discrete gate or transistor logic, discrete hardware components, or any combination thereof configured as or otherwise supporting a means for performing the functions described in the present disclosure. In some implementations, the processor  1706  and the memory  1708  coupled with the processor  1706  may be configured to perform one or more of the functions described herein (e.g., by executing, by the processor  1706 , instructions stored in the memory  1708 ). 
     Additionally or alternatively, in some implementations, the communications manager  1704 , the receiver  1710 , the transmitter  1712 , or various combinations or components thereof may be implemented in code (e.g., as communications management software or firmware) executed by the processor  1706 . If implemented in code executed by the processor  1706 , the functions of the communications manager  1704 , the receiver  1710 , the transmitter  1712 , or various combinations or components thereof may be performed by a general-purpose processor, a DSP, a central processing unit (CPU), an ASIC, an FPGA, or any combination of these or other programmable logic devices (e.g., configured as or otherwise supporting a means for performing the functions described in the present disclosure). 
     In some implementations, the communications manager  1704  may be configured to perform various operations (e.g., receiving, monitoring, transmitting) using or otherwise in cooperation with the receiver  1710 , the transmitter  1712 , or both. For example, the communications manager  1704  may receive information from the receiver  1710 , send information to the transmitter  1712 , or be integrated in combination with the receiver  1710 , the transmitter  1712 , or both to receive information, transmit information, or perform various other operations as described herein. Although the communications manager  1704  is illustrated as a separate component, in some implementations, one or more functions described with reference to the communications manager  1704  may be supported by or performed by the processor  1706 , the memory  1708 , or any combination thereof. For example, the memory  1708  may store code, which may include instructions executable by the processor  1706  to cause the device  1702  to perform various aspects of the present disclosure as described herein, or the processor  1706  and the memory  1708  may be otherwise configured to perform or support such operations. 
     In some implementations, the communication manager  1704  represents and/or implements a dedicated AI module that is configured to apply at least in part the various AI features discussed herein. For instance, to support various AI-features (e.g., algorithms, procedures, signaling, etc.) the communication manager  1704  can be trained or is already trained to accept a set of input parameters and based on the inputs and its trained/learned algorithms provide output for one or more wireless-related procedures, algorithms, signals, and so forth. For instance, AI features implemented by the communication manager  1704  are able to provide output (e.g., via inference) with more highly optimized performance in comparison to a node (e.g., a base station) that does not support AI features. Such performance enabled by supported AI features can be improved in terms of accuracy, latency, overhead, complexity, or combinations thereof. Further, supported basic AI features can be applied at the transmitter  1712 , at the receiver  1710 , and/or a combination thereof. In some implementations a supported AI feature, unless otherwise indicated, is applicable to both the transmitter chain as well a receiver chain of the device  1702 . 
     For example, the communications manager  1704  may support wireless communication at a first device (e.g., the base station as device  1702 ) in accordance with examples as disclosed herein. The communications manager  1704  and/or other device components may be configured as or otherwise support a means for wireless communication at a base station, including receiving a capability report from a second node that indicates one or more supported AI-enabled features of the second node associated with at least one protocol layer of a wireless protocol stack; processing the capability report and to identify at least one AI-enabled feature in the capability report including at least one of: one or more supported AI model types; one or more supported AI model training techniques; one or more supported AI learning frameworks; one or more AI training modes selected from at least an offline training mode, an online training mode, and a mixed training mode; and one or more transceiver application modes selected from at least a joint application mode and an individual block application mode; and managing wireless connectivity between the first node and the second node based at least in part on the at least one supported AI-enabled feature. 
     Additionally, wireless communication at the base station includes any one or combination of: where the first node includes a base station of a wireless network, and the second node includes a UE; generating a request for AI capability, and transmitting the request for receipt by the second node; generating a further capability report specifying one or more supported AI-enabled features of the first node, and communicating the further capability report for receipt by the second node; generating a further capability report specifying one or more supported AI-enabled features of the first node, and broadcasting the further capability report via a wireless network; processing the capability report to determine that the second node supports AI-enabled CE, and managing wireless connectivity between the first node and the second node includes generating one or more CE signals configured for AI-enabled CE; transmitting the CE signals configured for AI-enabled CE for receipt by the second node; receiving compressed channel state information feedback from the second node; inputting the compressed channel state information feedback to an AI model and receiving decompressed channel state information feedback as output from the AI model. 
     Additionally, a base station for wireless communication includes a transceiver at a first node configured to receive a capability report from a second node that indicates one or more supported AI-enabled features of the second node associated with at least one protocol layer of a wireless protocol stack; and a communication manager at the first node configured to: process the capability report and to identify at least one AI-enabled feature in the capability report including at least one of: one or more supported AI model types; one or more supported AI model training techniques; one or more supported AI learning frameworks; one or more AI training modes selected from at least an offline training mode, an online training mode, and a mixed training mode; and one or more transceiver application modes selected from at least a joint application mode and an individual block application mode; and manage wireless connectivity between the first node and the second node based at least in part on the at least one supported AI-enabled feature. 
     Additionally, the base station for wireless communication includes any one or combination of: where the first node includes a base station of a wireless network, and the second node includes a UE; where the communication manager is further configured to generate a request for AI capability, and the transceiver is further implemented to transmit the request for receipt by the second node; where the communication manager is further configured to generate a further capability report specifying one or more supported AI-enabled features of the first node, and the transceiver is further configured to communicate the further capability report for receipt by the second node; where the communication manager is further configured to generate a further capability report specifying one or more supported AI-enabled features of the first node, and the transceiver is further configured to broadcast the further capability report via a wireless network; where: the communication manager is further configured to process the capability report to determine that the second node supports AI-enabled CE, and where to manage wireless connectivity between the first node and the second node includes to generate one or more CE signals configured for AI-enabled CE; and the transceiver is further configured to transmit the CE signals configured for AI-enabled CE for receipt by the second node; where: the transceiver is further configured to receive compressed channel state information feedback from the second node; and the communication manager is further configured to input the compressed channel state information feedback to an AI model and to receive decompressed channel state information feedback as output from the AI model. 
     The processor  1706  may include an intelligent hardware device (e.g., a general-purpose processor, a DSP, a CPU, a microcontroller, an ASIC, an FPGA, a programmable logic device, a discrete gate or transistor logic component, a discrete hardware component, or any combination thereof). In some implementations, the processor  1706  may be configured to operate a memory array using a memory controller. In some other implementations, a memory controller may be integrated into the processor  1706 . The processor  1706  may be configured to execute computer-readable instructions stored in a memory (e.g., the memory  1708 ) to cause the device  1702  to perform various functions of the present disclosure. 
     The memory  1708  may include random access memory (RAM) and read-only memory (ROM). The memory  1708  may store computer-readable, computer-executable code including instructions that, when executed by the processor  1706  cause the device  1702  to perform various functions described herein. The code may be stored in a non-transitory computer-readable medium such as system memory or another type of memory. In some implementations, the code may not be directly executable by the processor  1706  but may cause a computer (e.g., when compiled and executed) to perform functions described herein. In some implementations, the memory  1708  may include, among other things, a basic I/O system (BIOS) which may control basic hardware or software operation such as the interaction with peripheral components or devices. 
     The I/O controller  1714  may manage input and output signals for the device  1702 . The I/O controller  1714  may also manage peripherals not integrated into the device  1702 . In some implementations, the I/O controller  1714  may represent a physical connection or port to an external peripheral. In some implementations, the I/O controller  1714  may utilize an operating system such as iOS®, ANDROID®, MS-DOS®, MS-WINDOWS®, OS/2®, UNIX®, LINUX®, or another known operating system. In some implementations, the I/O controller  1714  may be implemented as part of a processor, such as the processor  1706 . In some implementations, a user may interact with the device  1702  via the I/O controller  1714  or via hardware components controlled by the I/O controller  1714 . 
     In some implementations, the device  1702  may include a single antenna  1716 . However, in some other implementations, the device  1702  may have more than one antenna  1716 , which may be capable of concurrently transmitting or receiving multiple wireless transmissions. The receiver  1710  and the transmitter  1712  may communicate bi-directionally, via the one or more antennas  1716 , wired, or wireless links as described herein. For example, the receiver  1710  and the transmitter  1712  may represent a wireless transceiver and may communicate bi-directionally with another wireless transceiver. The transceiver may also include a modem to modulate the packets, to provide the modulated packets to one or more antennas  1716  for transmission, and to demodulate packets received from the one or more antennas  1716 . 
       FIG.  18    illustrates a flowchart of a method  1800  that supports AI capability reporting for wireless communication in accordance with aspects of the present disclosure. The operations of the method  1800  may be implemented by a device or its components as described herein. For example, the operations of the method  1800  may be performed by a device, such as a UE  104  as described with reference to  FIGS.  1  through  17   . In some implementations, the device may execute a set of instructions to control the function elements of the device to perform the described functions. Additionally, or alternatively, the device may perform aspects of the described functions using special-purpose hardware. 
     At  1802 , the method may include generating a capability report indicating AI-enabled features of a first node associated with at least one protocol layer of a wireless protocol stack. The operations of  1802  may be performed in accordance with examples as described herein. In some implementations, aspects of the operations of  1802  may be performed by a device as described with reference to  FIG.  1   . 
     At  1804 , the method may include indicating in the capability report that the first node includes AI capability and specifying at least one supported AI-enabled feature of the first node including selecting the at least one supported AI-enabled feature from available AI features for the capability report. The operations of  1804  may be performed in accordance with examples as described herein. In some implementations, aspects of the operations of  1804  may be performed by a device as described with reference to  FIG.  1   . Examples of different AI features that are identifiable in the capability report are detailed above. 
     At  1806 , the method may include communicating the capability report to a second node. The operations of  1806  may be performed in accordance with examples as described herein. In some implementations, aspects of the operations of  1806  may be performed by a device as described with reference to  FIG.  1   . 
     At  1808 , the method may include engaging in wireless connectivity between the first node and the second node based at least in part on the at least one supported AI-enabled feature. The operations of  1808  may be performed in accordance with examples as described herein. In some implementations, aspects of the operations of  1808  may be performed by a device as described with reference to  FIG.  1   . 
       FIG.  19    illustrates a flowchart of a method  1900  that supports AI capability reporting for wireless communication in accordance with aspects of the present disclosure. The operations of the method  1900  may be implemented by a device or its components as described herein. For example, the operations of the method  1900  may be performed by a base station  102 , such as a gNB as described with reference to  FIGS.  1  through  17   . In some implementations, the device may execute a set of instructions to control the function elements of the device to perform the described functions. Additionally, or alternatively, the device may perform aspects of the described functions using special-purpose hardware. 
     At  1902 , the method may include receiving a capability report from a second node that indicates one or more supported AI-enabled features of the second node associated with at least one protocol layer of a wireless protocol stackAI-enabled. The operations of  1902  may be performed in accordance with examples as described herein. In some implementations, aspects of the operations of  1902  may be performed by a device as described with reference to  FIG.  1   . 
     At  1904 , the method may include processing the capability report and to identify at least one AI-enabled feature in the capability report. The operations of  1904  may be performed in accordance with examples as described herein. In some implementations, aspects of the operations of  1904  may be performed by a device as described with reference to  FIG.  1   . Examples of different AI features that are identifiable in the capability report are detailed above. 
     At  1906 , the method may include managing wireless connectivity between the first node and the second node based at least in part on the at least one supported AI-enabled feature. The operations of  1906  may be performed in accordance with examples as described herein. In some implementations, aspects of the operations of  1906  may be performed by a device as described with reference to  FIG.  1   . 
       FIG.  20    illustrates a flowchart of a method  2000  that supports AI capability reporting for wireless communication in accordance with aspects of the present disclosure. The operations of the method  2000  may be implemented by a device or its components as described herein. For example, the operations of the method  2000  may be performed by a device, such as a UE  104  as described with reference to  FIGS.  1  through  17   . In some implementations, the device may execute a set of instructions to control the function elements of the device to perform the described functions. Additionally, or alternatively, the device may perform aspects of the described functions using special-purpose hardware. 
     At  2002 , the method may include configuring a capability report to indicate that a first node supports AI-enabled CE. The operations of  2002  may be performed in accordance with examples as described herein. In some implementations, aspects of the operations of  2002  may be performed by a device as described with reference to  FIG.  1   . 
     At  2004 , the method may include communicating the capability report for receipt by a second node. The operations of  2004  may be performed in accordance with examples as described herein. In some implementations, aspects of the operations of  2004  may be performed by a device as described with reference to  FIG.  1   . 
     At  2006 , the method may include receiving from the second node one or more CE signals configured for AI-enabled CE. The operations of  2006  may be performed in accordance with examples as described herein. In some implementations, aspects of the operations of  2006  may be performed by a device as described with reference to  FIG.  1   . 
     At  2008 , the method may include performing AI-enabled CE utilizing the CE signals configured for AI-enabled CE. The operations of  2008  may be performed in accordance with examples as described herein. In some implementations, aspects of the operations of  2008  may be performed by a device as described with reference to  FIG.  1   . 
       FIG.  21    illustrates a flowchart of a method  2100  that supports AI capability reporting for wireless communication in accordance with aspects of the present disclosure. The operations of the method  2100  may be implemented by a device or its components as described herein. For example, the operations of the method  2100  may be performed by a base station  102 , such as a gNB as described with reference to  FIGS.  1  through  17   . In some implementations, the device may execute a set of instructions to control the function elements of the device to perform the described functions. Additionally, or alternatively, the device may perform aspects of the described functions using special-purpose hardware. 
     At  2102 , the method may include processing at a first node a capability report to determine that a second node supports AI-enabled CE. The operations of  2102  may be performed in accordance with examples as described herein. In some implementations, aspects of the operations of  2102  may be performed by a device as described with reference to  FIG.  1   . 
     At  2104 , the method may include generating one or more CE signals configured for AI-enabled CE. The operations of  2104  may be performed in accordance with examples as described herein. In some implementations, aspects of the operations of  2104  may be performed by a device as described with reference to  FIG.  1   . 
     At  2106 , the method may include transmitting the CE signals configured for AI-enabled CE for receipt by the second node. The operations of  2106  may be performed in accordance with examples as described herein. In some implementations, aspects of the operations of  2106  may be performed by a device as described with reference to  FIG.  1   . 
       FIG.  22    illustrates a flowchart of a method  2200  that supports AI capability reporting for wireless communication in accordance with aspects of the present disclosure. The operations of the method  2200  may be implemented by a device or its components as described herein. For example, the operations of the method  2200  may be performed by a base station  102 , such as a gNB as described with reference to  FIGS.  1  through  17   . In some implementations, the device may execute a set of instructions to control the function elements of the device to perform the described functions. Additionally, or alternatively, the device may perform aspects of the described functions using special-purpose hardware. 
     At  2202 , the method may include receiving at a first node compressed channel state information feedback from a second node. The operations of  2202  may be performed in accordance with examples as described herein. In some implementations, aspects of the operations of  2202  may be performed by a device as described with reference to  FIG.  1   . 
     At  2204 , the method may include inputting the compressed channel state information feedback to an AI model and to receive decompressed channel state information feedback as output from the AI model. The operations of  2204  may be performed in accordance with examples as described herein. In some implementations, aspects of the operations of  2204  may be performed by a device as described with reference to  FIG.  1   . 
     It should be noted that the methods described herein describe possible implementations, and that the operations and the steps may be rearranged or otherwise modified and that other implementations are possible. Further, aspects from two or more of the methods may be combined. The order in which the methods are described is not intended to be construed as a limitation, and any number or combination of the described method operations can be performed in any order to perform a method, or an alternate method. 
     The various illustrative blocks and components described in connection with the disclosure herein may be implemented or performed with a general-purpose processor, a DSP, an ASIC, a CPU, an FPGA or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general-purpose processor may be a microprocessor, but in the alternative, the processor may be any processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing devices (e.g., 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. 
     The functions described herein may be implemented in hardware, software executed by a processor, firmware, or any combination thereof. If implemented in software executed by a processor, the functions may be stored on or transmitted over as one or more instructions or code on a computer-readable medium. Other examples and implementations are within the scope of the disclosure and appended claims. For example, due to the nature of software, functions described herein may be implemented using software executed by a processor, hardware, firmware, hardwiring, or combinations of any of these. Features implementing functions may also be physically located at various positions, including being distributed such that portions of functions are implemented at different physical locations. 
     Computer-readable media includes both non-transitory computer storage media and communication media including any medium that facilitates transfer of a computer program from one place to another. A non-transitory storage medium may be any available medium that may be accessed by a general-purpose or special-purpose computer. By way of example, and not limitation, non-transitory computer-readable media may include RAM, ROM, electrically erasable programmable ROM (EEPROM), flash memory, compact disk (CD) ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other non-transitory medium that may be used to carry or store desired program code means in the form of instructions or data structures and that may be accessed by a general-purpose or special-purpose computer, or a general-purpose or special-purpose processor. 
     Any connection may be properly termed a computer-readable medium. For example, if the software is transmitted from a website, server, or other remote source using a coaxial cable, fiber optic cable, twisted pair, digital subscriber line (DSL), or wireless technologies such as infrared, radio, and microwave, then the coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technologies such as infrared, radio, and microwave are included in the definition of computer-readable medium. Disk and disc, as used herein, include CD, laser disc, optical disc, digital versatile disc (DVD), floppy disk and Blu-ray disc where disks usually reproduce data magnetically, while discs reproduce data optically with lasers. Combinations of the above are also included within the scope of computer-readable media. 
     As used herein, including in the claims, “or” as used in a list of items (e.g., a list of items prefaced by a phrase such as “at least one of” or “one or more of”) indicates an inclusive list such that, for example, a list of at least one of A, B, or C means A or B or C or AB or AC or BC or ABC (i.e., A and B and C). Also, as used herein, the phrase “based on” shall not be construed as a reference to a closed set of conditions. For example, an example step that is described as “based on condition A” may be based on both a condition A and a condition B without departing from the scope of the present disclosure. In other words, as used herein, the phrase “based on” shall be construed in the same manner as the phrase “based at least in part on. Further, as used herein, including in the claims, a “set” may include one or more elements. 
     The description set forth herein, in connection with the appended drawings, describes example configurations and does not represent all the examples that may be implemented or that are within the scope of the claims. The term “example” used herein means “serving as an example, instance, or illustration,” and not “preferred” or “advantageous over other examples.” The detailed description includes specific details for the purpose of providing an understanding of the described techniques. These techniques, however, may be practiced without these specific details. In some instances, known structures and devices are shown in block diagram form to avoid obscuring the concepts of the described example. 
     The description herein is provided to enable a person having ordinary skill in the art to make or use the disclosure. Various modifications to the disclosure will be apparent to a person having ordinary skill in the art, and the generic principles defined herein may be applied to other variations without departing from the scope of the disclosure. Thus, the disclosure is not limited to the examples and designs described herein but is to be accorded the broadest scope consistent with the principles and novel features disclosed herein.