WO2025117483A1

WO2025117483A1 - Performance monitoring for artificial intelligence based compression of channel state information

Info

Publication number: WO2025117483A1
Application number: PCT/US2024/057353
Authority: WO
Inventors: Huaning Niu; Weidong Yang; Wei Zeng; Haitong Sun; Hong He
Original assignee: Apple Inc
Current assignee: Apple Inc
Priority date: 2023-11-30
Filing date: 2024-11-25
Publication date: 2025-06-05
Anticipated expiration: 2026-05-30

Abstract

An apparatus of a user equipment (UE) comprising one or more processors coupled to a memory and configured to receive, from a next generation Node B (gNB), an indication to activate artificial intelligence (Al) based compression model performance monitoring at the UE; decode configuration information for the Al based compression model performance monitoring received from the gNB; decode channel state information (CSI) received from the gNB; compress the CSI, at the UE, using an Al based compression model to generate a compressed CSI; reconstruct the compressed CSI at the UE using an Al based reconstruction model to generate the reconstructed CSI for the Al based compression model monitoring; determine a similarity metric between the CSI and the reconstructed CSI; compare the similarity metric to a compression model threshold; and transmit a monitoring report to the gNB based on the comparison.

Description

PERFORMANCE MONITORING FOR ARTIFICIAL INTELLIGENCE BASED COMPRESSION OF CHANNEL STATE INFORMATION

FIELD

[0001] Embodiments of the invention relate to wireless communications, including apparatuses, systems, and methods for user equipment (UE) side performance monitoring for artificial intelligence (Al) based channel state information (CSI) compression model in a cellular communications network.

DESCRIPTION OF THE RELATED ART

[0002] Wireless communication systems are rapidly growing in usage. In recent years, wireless devices such as smart phones and tablet computers have become increasingly sophisticated. In addition to supporting telephone calls, many mobile devices now provide access to the internet, email, text messaging, and navigation using the global positioning system (GPS) and are capable of operating sophisticated applications that utilize these functionalities.

[0003] Long Term Evolution (LTE) has been the technology of choice for the majority of wireless network operators worldwide, providing mobile broadband data and high-speed Internet access to their subscriber base. LTE was first proposed in 2004 and was first standardized in 2008. Since then, as usage of wireless communication systems has expanded exponentially, demand has risen for wireless network operators to support a higher capacity for a higher density of mobile broadband users. In 2015, a study of a new radio access technology began and, in 2017, a first release of Fifth Generation New Radio (5G NR) was standardized.

[0004] 5G-NR, also simply referred to as NR, provides, as compared to LTE, a higher capacity for a higher density of mobile broadband users, while also supporting device-to-device, ultra-reliable, and massive machine type communications with lower latency and/or lower battery consumption. Further, NR may allow for more flexible UE scheduling as compared to current LTE. Consequently, efforts are being made in ongoing developments of 5G-NR to take advantage of higher throughputs possible at higher frequencies.

[0005] Wireless communication systems provide mobility by enabling user equipment (UEs) to move between cells via a process referred to as handover. Handover occurs when a mobile UE switches from one cell to another neighboring cell. Mechanisms have been established to help ensure a smooth transition between cells. NR supports different types of handover that were not supported in the previous 4G LTE specification. The basic handover in NR has been based on LTE handover mechanisms in which the network controls UE mobility based on UE measurement reporting. This measurement reporting typically involves Layer 3 (L3) measurements of neighbor cells and reporting from the UE to the eNB.

[0006] Additionally, in Wireless communication systems, Channel state information (CSI) feedback provides the network (e.g., a base station) with essential information about the downlink channel conditions. This allows the base station to optimize transmission strategies like beamforming and resource allocation. However, CSI feedback results in substantial overhead especially for massive MIMO systems. To reduce this overhead, Al-based compression techniques are emerging where the CSI is compressed at the user equipment (UE) using a neural network model and reconstructed at the base station. But the performance of such Al-based compression needs to be monitored to detect any model degradation. While base station-side monitoring is possible, a need exists for UE-side Al-based compression model monitoring to 1 ) provide localized detection and rapid reporting of any compression issues and 2) developing UE- side CSI reconstruction capabilities and associated monitoring operations to reliably track model performance over time.

SUMMARY

[0007] Embodiments relate to wireless communications, and more particularly to apparatuses, systems, and methods for an apparatus of a user equipment (UE), the apparatus comprising one or more processors, coupled to a memory, configured to: receive, from a next generation Node B (gNB), an indication to activate artificial intelligence (Al) based compression model performance monitoring at the UE; decode configuration information for the Al based compression model performance monitoring received from the gNB; decode channel state information (CSI) received from the gNB; compress the CSI, at the UE, using an Al based compression model to generate a compressed CSI; reconstruct the compressed CSI at the UE using an Al based reconstruction model to generate the reconstructed CSI for the Al based compression model monitoring; determine a similarity metric between the CSI and the reconstructed CSI; compare the similarity metric to a compression model threshold; and transmit a monitoring report to the gNB based on the comparison.

[0008] Other embodiments relate to an apparatus of a next generation Node B (gNB), the apparatus comprising one or more processors, coupled to a memory, configured to: encode, at the gNB, an indication to activate artificial intelligence (Al) based compression model performance monitoring; encode, at the gNB, channel state information (CSI); transmit, to a user equipment (UE); transmit, to the UE, the indication to activate the Al based compression model performance monitoring at the UE to enable the UE to perform the Al based compression model monitoring by: compressing the CSI using an Al based compression model to generate a compressed CSI; using a reconstructed CSI for the Al based compression model monitoring; determining a similarity metric between the CSI and the reconstructed CSI; and comparing the similarity metric to a compression model threshold; and decode, a monitoring report, received from the UE, based on the comparison.

[0009] The techniques described herein may be implemented in and/or used with a number of different types of devices, including but not limited to unmanned aerial vehicles (UAVs), unmanned aerial controllers (UACs), base stations, access points, cellular phones, tablet computers, wearable computing devices, portable media players, and any of various other computing devices.

[0010] This Summary is intended to provide a brief overview of some of the subject matter described in this document. Accordingly, it will be appreciated that the above-described features are merely examples and should not be construed to narrow the scope or spirit of the subject matter described herein in any way. Other features, aspects, and advantages of the subject matter described herein will become apparent from the following Detailed Description, Figures, and Claims.

BRIEF DESCRIPTION OF THE DRAWINGS

[0011] A better understanding of the present subject matter can be obtained when the following detailed description of various embodiments is considered in conjunction with the following drawings, in which:

[0012] FIG. 1 illustrates an example wireless communication system according to some embodiments.

[0013] FIG. 1 B illustrates an example of a base station and an access point in communication with a user equipment (UE) device, according to some embodiments.

[0014] FIG. 2 illustrates an example block diagram of a base station, according to some embodiments.

[0015] FIG. 3 illustrates an example block diagram of a server according to some embodiments.

[0016] FIG. 4 illustrates an example block diagram of a UE according to some embodiments.

[0017] FIG. 5 illustrates an example block diagram of cellular communication circuitry, according to some embodiments.

[0018] FIG. 6 illustrates an example of a baseband processor architecture for a UE, according to some embodiments.

[0019] FIG. 7 illustrates an example block diagram of an interface of baseband circuitry according to some embodiments.

[0020] FIG. 8A illustrates an example of a control plane protocol stack in accordance with some embodiments. [0021] FIG. 8B illustrates an example of an autoencoder-based two-sided framework for implicit CSI feedback enhancement in accordance with some embodiments.

[0022] FIG. 9 illustrates an example timing diagram signaling between a user equipment (UE) and next generation node B (gNB) for supporting UE performance monitoring for artificial intelligence (Al) based channel state information (CSI) compression model according to some embodiments.

[0023] FIG. 10A illustrates an example of using an evaluation window for UE performance monitoring for artificial intelligence (Al) based channel state information (CSI) compression model in accordance with some embodiments.

[0024] FIG. 10B illustrates an example of using a counter for UE performance monitoring for artificial intelligence (Al) based channel state information (CSI) compression model in accordance with some embodiments.

[0025] FIG. 10C illustrates an example of using a counter and timer for UE performance monitoring for artificial intelligence (Al) based channel state information (CSI) compression model in accordance with some embodiments.

[0026] FIGs. 11 A and 1 1 B illustrate examples of graphs depicting a cumulative distribution function comparing the squared generalized cosine similarity achieved by an Al-based CSI compression model versus a baseline compression in accordance with some embodiments.

[0027] FIGs. 12A and 12B illustrate examples of graphs comparing the performance difference between an Al-based CSI compression model and an optimized codebook baseline in accordance with some embodiments.

[0028] FIG. 13 illustrates an example timing diagram signaling between a user equipment (UE) and a next generation node B (gNB) for supporting UE performance monitoring for artificial intelligence (Al) based channel state information (CSI) compression model according to some embodiments.

[0029] FIG. 14 illustrates an example flow chart of a method of performing user equipment (UE) side performance monitoring for artificial intelligence (Al) based channel state information (CSI) compression model, at a user equipment (UE), according to some embodiments.

[0030] FIG. 15 illustrates an example flow chart of a method of performing user equipment (UE) side performance monitoring for artificial intelligence (Al) based channel state information (CSI) compression model, at a user equipment (UE), according to some embodiments.

[0031] FIG. 16 illustrates an example flow chart of a method of performing user equipment (UE) side performance monitoring for artificial intelligence (Al) based channel state information (CSI) compression model, at next generation node B (gNB), according to some embodiments.

[0032] While the features described herein may be susceptible to various modifications and alternative forms, specific embodiments thereof are shown by way of example in the drawings and are herein described in detail. It should be understood, however, that the drawings and detailed description thereto are not intended to be limiting to the particular form disclosed, but on the contrary, the intention is to cover all modifications, equivalents and alternatives falling within the spirit and scope of the subject matter as defined by the appended claims.

DETAILED DESCRIPTION

Terms

[0033] The following is a glossary of terms used in this disclosure:

[0034] Memory Medium - Any of various types of non-transitory memory devices or storage devices. The term “memory medium” is intended to include an installation medium, e.g., a CD-ROM, floppy disks, or tape device; a computer system memory or random-access memory such as DRAM, DDR RAM, SRAM, EDO RAM, Rambus RAM, etc.; a non-volatile memory such as a Flash, magnetic media, e.g., a hard drive, or optical storage; registers, or other similar types of memory elements, etc. The memory medium may include other types of non- transitory memory as well or combinations thereof. In addition, the memory medium may be located in a first computer system in which the programs are executed or may be located in a second different computer system which connects to the first computer system over a network, such as the Internet. In the latter instance, the second computer system may provide program instructions to the first computer for execution. The term “memory medium” may include two or more memory mediums which may reside in different locations, e.g., in different computer systems that are connected over a network. The memory medium may store program instructions (e.g., embodied as computer programs) that may be executed by one or more processors.

[0035] Carrier Medium - a memory medium as described above, as well as a physical transmission medium, such as a bus, network, and/or other physical transmission medium that conveys signals such as electrical, electromagnetic, or digital signals.

[0036] Programmable Hardware Element includes various hardware devices comprising multiple programmable function blocks connected via a programmable interconnect. Examples include FPGAs (Field Programmable Gate Arrays), PLDs (Programmable Logic Devices), FPOAs (Field Programmable Object Arrays), and CPLDs (Complex PLDs). The programmable function blocks may range from fine grained (combinatorial logic or look up tables) to coarse grained (arithmetic logic units or processor cores). A programmable hardware element may also be referred to as "reconfigurable logic”.

[0037] Computer System (or Computer) - any of various types of computing or processing systems, including a personal computer system (PC), mainframe computer system, workstation, network appliance, Internet appliance, personal digital assistant (PDA), television system, grid computing system, or other device or combinations of devices. In general, the term "computer system" can be broadly defined to encompass any device (or combination of devices) having at least one processor that executes instructions from a memory medium.

[0038] User Equipment (UE) (or “UE Device”) - any of various types of computer systems devices which are mobile or portable and which performs wireless communications. Examples of UE devices include mobile telephones or smart phones (e.g., iPhone™, Android™-based phones), portable gaming devices (e.g., Nintendo DS™, PlayStation Portable™, Gameboy Advance™, iPhone™), laptops, wearable devices (e.g., smart watch, smart glasses), PDAs, portable Internet devices, music players, data storage devices, other handheld devices, unmanned aerial vehicles (UAVs) (e.g., drones), UAV controllers (UACs), and so forth. In general, the term “UE” or “UE device” can be broadly defined to encompass any electronic, computing, and/or telecommunications device (or combination of devices) which is easily transported by a user and capable of wireless communication.

[0039] Base Station - The term "Base Station" has the full breadth of its ordinary meaning, and at least includes a wireless communication station installed at a fixed location and used to communicate as part of a wireless telephone system or radio system.

[0040] Processing Element (or Processor) - refers to various elements or combinations of elements that are capable of performing a function in a device, such as a user equipment or a cellular network device. Processing elements may include, for example: processors and associated memory, portions or circuits of individual processor cores, entire processor cores, processor arrays, circuits such as an ASIC (Application Specific Integrated Circuit), programmable hardware elements such as a field programmable gate array (FPGA), as well any of various combinations of the above.

[0041] Channel - a medium used to convey information from a sender (transmitter) to a receiver. It should be noted that since characteristics of the term “channel” may differ according to different wireless protocols, the term “channel” as used herein may be considered as being used in a manner that is consistent with the standard of the type of device with reference to which the term is used. In some standards, channel widths may be variable (e.g., depending on device capability, band conditions, etc.). For example, LTE may support scalable channel bandwidths from 1.4 MHz to 20MHz. 5G NR can support scalable channel bandwidths from 5 MHz to 100 MHz in Frequency Range 1 (FR1 ) and up to 400 MHz in FR2. In other radio access technologies, WLAN channels may be 22 MHz wide while Bluetooth channels may be 1 MHz wide. Other protocols and standards may include different definitions of channels. Furthermore, some standards may define and use multiple types of channels, e.g., different channels for uplink or downlink and/or different channels for different uses such as data, control information, etc.

[0042] Band - The term "band" has the full breadth of its ordinary meaning, and at least includes a section of spectrum (e.g., radio frequency spectrum) in which channels are used or set aside for the same purpose.

[0043] Automatically - refers to an action or operation performed by a computer system (e.g., software executed by the computer system) or device (e.g., circuitry, programmable hardware elements, ASICs, etc.), without user input directly specifying or performing the action or operation. Thus, the term "automatically" is in contrast to an operation being manually performed or specified by the user, where the user provides input to directly perform the operation. An automatic procedure may be initiated by input provided by the user, but the subsequent actions that are performed "automatically” are not specified by the user, i.e., are not performed “manually”, where the user specifies each action to perform. For example, a user filling out an electronic form by selecting each field and providing input specifying information (e.g., by typing information, selecting check boxes, radio selections, etc.) is filling out the form manually, even though the computer system will update the form in response to the user actions. The form may be automatically filled out by the computer system where the computer system (e.g., software executing on the computer system) analyzes the fields of the form and fills in the form without any user input specifying the answers to the fields. As indicated above, the user may invoke the automatic filling of the form, but is not involved in the actual filling of the form (e.g., the user is not manually specifying answers to fields but rather they are being automatically completed). The present specification provides various examples of operations being automatically performed in response to actions the user has taken.

[0044] Approximately - refers to a value that is almost correct or exact. For example, approximately may refer to a value that is within 1 to 10 percent of the exact (or desired) value. It should be noted, however, that the actual threshold value (or tolerance) may be application dependent. For example, in some embodiments, “approximately” may mean within 0.1 % of some specified or desired value, while in various other embodiments, the threshold may be, for example, 2%, 3%, 5%, and so forth, as desired or as set by the particular application.

[0045] Concurrent - refers to parallel execution or performance, where tasks, processes, or programs are performed in an at least partially overlapping manner. For example, concurrency may be implemented using “strong” or strict parallelism, where tasks are performed (at least partially) in parallel on respective computational elements, or using “weak parallelism”, where the tasks are performed in an interleaved manner, e.g., by time multiplexing of execution threads.

[0046] Various components may be described as “configured to” perform a task or tasks. In such contexts, “configured to” is a broad recitation generally meaning “having structure that” performs the task or tasks during operation. As such, the component can be configured to perform the task even when the component is not currently performing that task (e.g., a set of electrical conductors may be configured to electrically connect a module to another module, even when the two modules are not connected). In some contexts, “configured to” may be a broad recitation of structure generally meaning “having circuitry that” performs the task or tasks during operation. As such, the component can be configured to perform the task even when the component is not currently on. In general, the circuitry that forms the structure corresponding to “configured to” may include hardware circuits.

[0047] Various components may be described as performing a task or tasks, for convenience in the description. Such descriptions should be interpreted as including the phrase “configured to.” Reciting a component that is configured to perform one or more tasks is expressly intended not to invoke 35 U.S.C. § 112(f) interpretation for that component.

[0048] The example embodiments may be further understood with reference to the following description and the related appended drawings, wherein like elements are provided with the same reference numerals. The example embodiments relate to UE side performance monitoring for artificial intelligence (Al) based channel state information (CSI) compression model.

[0049] The example embodiments are described with regard to communication between a next generation Node B (gNB) and a user equipment (UE). However, reference to a gNB or a UE is merely provided for illustrative purposes. The example embodiments may be utilized with any electronic component that may establish a connection to a network and is configured with the hardware, software, and/or firmware to support UE side performance monitoring for Al based CSI compression model. Therefore, the gNB or UE as described herein is used to represent any appropriate type of electronic component.

[0050] The example embodiments are also described with regard to a fifth generation (5G) New Radio (NR) network that may configure a UE to control the UE side performance monitoring. However, reference to a 5G NR network is merely provided for illustrative purposes. The example embodiments may be utilized with any appropriate type of network.

[0051] Throughout this description various information elements (lEs) are referred to by specific names. It should be understood that these names are only examples and the lEs carrying the information referred to throughout this description may be referred to by other names by various entities.

Figures 1 A and 1 B: Communication Systems

[0052] FIG. 1 A illustrates a simplified example wireless communication system, according to some embodiments. It is noted that the system of FIG. 1 A is merely one example of a possible system, and that features of this disclosure may be implemented in any of various systems, as desired.

[0053] As shown, the example wireless communication system includes a base station 102A which communicates over a transmission medium with one or more user devices 106A, 106B, etc., through 106N. Each of the user devices may be referred to herein as a “user equipment” (UE). Thus, the user devices 106 are referred to as UEs or UE devices.

[0054] The base station (BS) 102A may be a base transceiver station (BTS) or cell site (a “cellular base station”) and may include hardware that enables wireless communication with the UEs 106A through 106N.

[0055] The communication area (or coverage area) of the base station may be referred to as a “cell.” The base station 102A and the UEs 106 may be configured to communicate over the transmission medium using any of various radio access technologies (RATs), also referred to as wireless communication technologies, or telecommunication standards, such as GSM, UMTS (associated with, for example, WCDMA or TD-SCDMA air interfaces), LTE, LTE-Advanced (LTE-A), 5G new radio (5G NR), HSPA, 3GPP2 CDMA2000 (e.g., 1 xRTT, 1 xEV-DO, HRPD, eHRPD), etc. Note that if the base station 102A is implemented in the context of LTE, also referred to as the Evolved Universal Terrestrial Radio Access Network (E-UTRAN, it may alternately be referred to as an 'eNodeB' or ‘eNB’. Note that if the base station 102A is implemented in the context of 5G NR, it may alternately be referred to as ‘gNodeB’ or ‘gNB’.

[0056] As shown, the base station 102A may also be equipped to communicate with a network 100 (e.g., a core network of a cellular service provider, a telecommunication network such as a public switched telephone network (PSTN), and/or the Internet, among various possibilities). Thus, the base station 102A may facilitate communication between the user devices and/or between the user devices and the network 100. In particular, the cellular base station 102A may provide UEs 106 with various telecommunication capabilities, such as voice, SMS and/or data services.

[0057] Base station 102A and other similar base stations (such as base stations 102B...102N) operating according to the same or a different cellular communication standard may thus be provided as a network of cells, which may provide continuous or nearly continuous overlapping service to UEs 106A-N and similar devices over a geographic area via one or more cellular communication standards. [0058] Thus, while base station 102A may act as a “serving cell” for UEs 106A- N as illustrated in FIG. 1A, each UE 106 may also be capable of receiving signals from (and possibly within communication range of) one or more other cells (which might be provided by base stations 102B-N and/or any other base stations), which may be referred to as “neighboring cells”. Such cells may also be capable of facilitating communication between user devices and/or between user devices and the network 100. Such cells may include “macro” cells, “micro” cells, “pico” cells, and/or cells which provide any of various other granularities of service area size. For example, base stations 102A-B illustrated in FIG. 1 A might be macro cells, while base station 102N might be a micro cell. Other configurations are also possible.

[0059] In some embodiments, base station 102A may be a next generation base station, e.g., a 5G New Radio (5G NR) base station, or “gNB”. In some embodiments, a gNB may be connected to a legacy evolved packet core (EPC) network and/or to a NR core (NRC) network. In addition, a gNB cell may include one or more transition and reception points (TRPs). In addition, a UE capable of operating according to 5G NR may be connected to one or more TRPs within one or more gNBs.

[0060] Note that a UE 106 may be capable of communicating using multiple wireless communication standards. For example, the UE 106 may be configured to communicate using a wireless networking (e.g., Wi-Fi) and/or peer-to-peer wireless communication protocol (e.g., Bluetooth, Wi-Fi peer-to-peer, etc.) in addition to at least one cellular communication protocol (e.g., GSM, UMTS (associated with, for example, WCDMA or TD-SCDMA air interfaces), LTE, LTE-A, 5G NR, HSPA, 3GPP2 CDMA2000 (e.g., 1 xRTT, 1xEV-DO, HRPD, eHRPD), etc.). The UE 106 may also or alternatively be configured to communicate using one or more global navigational satellite systems (GNSS, e.g., GPS or GLONASS), one or more mobile television broadcasting standards (e.g., ATSC-M/H or DVB-H), and/or any other wireless communication protocol, if desired. Other combinations of wireless communication standards (including more than two wireless communication standards) are also possible. [0061] FIG. 1 B illustrates user equipment 106 (e.g., one of the devices 106A through 106N) in communication with a base station 102 and an access point 112, according to some embodiments. The UE 106 may be a device with both cellular communication capability and non-cellular communication capability (e.g., Bluetooth, Wi-Fi, and so forth) such as a mobile phone, a hand-held device, a computer or a tablet, or virtually any type of wireless device.

[0062] The UE 106 may include a processor that is configured to execute program instructions stored in memory. The UE 106 may perform any of the method embodiments described herein by executing such stored instructions. Alternatively, or in addition, the UE 106 may include a programmable hardware element such as an FPGA (field-programmable gate array) that is configured to perform any of the method embodiments described herein, or any portion of any of the method embodiments described herein.

[0063] The UE 106 may include one or more antennas for communicating using one or more wireless communication protocols or technologies. In some embodiments, the UE 106 may be configured to communicate using, for example, CDMA2000 (1 xRTT 1 1 xEV-DO / HRPD I eHRPD), LTE/LTE- Advanced, or 5G NR using a single shared radio and/or GSM, LTE, LTE-Advanced, or 5G NR using the single shared radio. The shared radio may couple to a single antenna, or may couple to multiple antennas (e.g., for MIMO) for performing wireless communications. In general, a radio may include any combination of a baseband processor, analog RF signal processing circuitry (e.g., including filters, mixers, oscillators, amplifiers, etc.), ordigital processing circuitry (e.g., for digital modulation as well as other digital processing). Similarly, the radio may implement one or more receive and transmit chains using the aforementioned hardware. For example, the UE 106 may share one or more parts of a receive and/or transmit chain between multiple wireless communication technologies, such as those discussed above.

[0064] In some embodiments, the UE 106 may include separate transmit and/or receive chains (e.g., including separate antennas and other radio components) for each wireless communication protocol with which it is configured to communicate. As a further possibility, the UE 106 may include one or more radios which are shared between multiple wireless communication protocols, and one or more radios which are used exclusively by a single wireless communication protocol. For example, the UE 106 might include a shared radio for communicating using either of LTE or 5G NR (or LTE or IxRTTor LTE or GSM), and separate radios for communicating using each of Wi-Fi and Bluetooth. Other configurations are also possible.

FIG. 2: Block Diagram of a Base Station

[0065] FIG. 2 illustrates an example block diagram of a base station 102, according to some embodiments. It is noted that the base station of FIG. 2 is merely one example of a possible base station. As shown, the base station 102 may include processor(s) 204 which may execute program instructions for the base station 102. The processor(s) 204 may also be coupled to memory management unit (MMU) 240, which may be configured to receive addresses from the processor(s) 204 and translate those addresses to locations in memory (e.g., memory 260 and read only memory (ROM) 250) or to other circuits or devices.

[0066] The base station 102 may include at least one network port 270. The network port 270 may be configured to couple to a telephone network and provide a plurality of devices, such as UE devices 106, access to the telephone network as described above in Figures 1 and 2.

[0067] The network port 270 (or an additional network port) may also or alternatively be configured to couple to a cellular network, e.g., a core network of a cellular service provider. The core network may provide mobility related services and/or other services to a plurality of devices, such as UE devices 106. In some cases, the network port 270 may couple to a telephone network via the core network, and/or the core network may provide a telephone network (e.g., among other UE devices serviced by the cellular service provider).

[0068] In some embodiments, base station 102 may be a next generation base station, e.g., a 5G New Radio (5G NR) base station, or “gNB”. In such embodiments, base station 102 may be connected to a legacy evolved packet core (EPC) network and/or to a NR core (NRC) network. In addition, base station 102 may be considered a 5G NR cell and may include one or more transition and reception points (TRPs). In addition, a UE capable of operating according to 5G NR may be connected to one or more TRPs within one or more gNBs.

[0069] The base station 102 may include at least one antenna 234, and possibly multiple antennas. The at least one antenna 234 may be configured to operate as a wireless transceiver and may be further configured to communicate with UE devices 106 via radio 230. The antenna 234 communicates with the radio 230 via communication chain 232. Communication chain 232 may be a receive chain, a transmit chain or both. The radio 230 may be configured to communicate via various wireless communication standards, including, but not limited to, 5G NR, LTE, LTE-A, GSM, UMTS, CDMA2000, Wi-Fi, etc.

[0070] The base station 102 may be configured to communicate wirelessly using multiple wireless communication standards. In some instances, the base station 102 may include multiple radios, which may enable the base station 102 to communicate according to multiple wireless communication technologies. For example, as one possibility, the base station 102 may include an LTE radio for performing communication according to LTE as well as a 5G NR radio for performing communication according to 5G NR. In such a case, the base station 102 may be capable of operating as both an LTE base station and a 5G NR base station. As another possibility, the base station 102 may include a multi-mode radio which is capable of performing communications according to any of multiple wireless communication technologies (e.g., 5G NR and Wi-Fi, LTE and Wi-Fi, LTE and UMTS, LTE and CDMA2000, UMTS and GSM, etc.).

[0071] As described further subsequently herein, the BS 102 may include hardware and software components for implementing or supporting implementation of features described herein. The processor 204 of the base station 102 may be configured to implement or support implementation of part or all of the methods described herein, e.g., by executing program instructions stored on a memory medium (e.g., a non-transitory computer-readable memory medium). Alternatively, the processor 204 may be configured as a programmable hardware element, such as an FPGA (Field Programmable Gate Array), or as an ASIC (Application Specific Integrated Circuit), or a combination thereof.

Alternatively (or in addition) the processor 204 of the BS 102, in conjunction with one or more of the other components 230, 232, 234, 240, 250, 260, 270 may be configured to implement or support implementation of part or all of the features described herein.

[0072] In addition, as described herein, processor(s) 204 may be comprised of one or more processing elements. In other words, one or more processing elements may be included in processor(s) 204. Thus, processor(s) 204 may include one or more integrated circuits (ICs) that are configured to perform the functions of processor(s) 204. In addition, each integrated circuit may include circuitry (e.g., first circuitry, second circuitry, etc.) configured to perform the functions of processor(s) 204.

[0073] Further, as described herein, radio 230 may be comprised of one or more processing elements. In other words, one or more processing elements may be included in radio 230. Thus, radio 230 may include one or more integrated circuits (ICs) that are configured to perform the functions of radio 230. In addition, each integrated circuit may include circuitry (e.g., first circuitry, second circuitry, etc.) configured to perform the functions of radio 230.

[0074] In some embodiments, the base station or gNB 102, and/or processors 204 thereof, can be capable of and configured to receive, from a next generation Node B (gNB), an indication to activate artificial intelligence (Al) based compression model performance monitoring at the UE; decode configuration information for the Al based compression model performance monitoring received from the gNB; decode channel state information (CSI) received from the gNB; compress the CSI, at the UE, using an Al based compression model to generate a compressed CSI; reconstruct the compressed CSI at the UE using an Al based reconstruction model to generate the reconstructed CSI for the Al based compression model monitoring; determine a similarity metric between the CSI and the reconstructed CSI; compare the similarity metric to a compression model threshold; and transmit a monitoring report to the gNB based on the comparison. FIG. 3: Block Diagram of a Server

[0075] FIG. 3 illustrates an example block diagram of a server 104, according to some embodiments. It is noted that the server of FIG. 3 is merely one example of a possible server. As shown, the server 104 may include processor(s) 344 which may execute program instructions for the server 104. The processor(s) 344 may also be coupled to memory management unit (MMU) 374, which may be configured to receive addresses from the processor(s) 344 and translate those addresses to locations in memory (e.g., memory 364 and read only memory (ROM) 354) or to other circuits or devices.

[0076] The server 104 may be configured to provide a plurality of devices, such as base station 102, and UE devices 106 access to network functions, e.g., as further described herein.

[0077] In some embodiments, the server 104 may be part of a radio access network, such as a 5G New Radio (5G NR) radio access network. In some embodiments, the server 104 may be connected to a legacy evolved packet core (EPC) network and/or to a NR core (NRC) network.

[0078] As described herein, the server 104 may include hardware and software components for implementing or supporting implementation of features described herein. The processor 344 of the server 104 may be configured to implement or support implementation of part or all of the methods described herein, e.g., by executing program instructions stored on a memory medium (e.g., a non-transitory computer-readable memory medium). Alternatively, the processor 344 may be configured as a programmable hardware element, such as an FPGA (Field Programmable Gate Array), or as an ASIC (Application Specific Integrated Circuit), or a combination thereof. Alternatively (or in addition) the processor 344 of the server 104, in conjunction with one or more of the other components 354, 364, and/or 374 may be configured to implement or support implementation of part or all of the features described herein.

[0079] In addition, as described herein, processor(s) 344 may be comprised of one or more processing elements. In other words, one or more processing elements may be included in processor(s) 344. Thus, processor(s) 344 may include one or more integrated circuits (ICs) that are configured to perform the functions of processor(s) 344. In addition, each integrated circuit may include circuitry (e.g., first circuitry, second circuitry, etc.) configured to perform the functions of processor(s) 344.

FIG. 4: Block Diagram of a Base Station

[0080] FIG. 4 illustrates an example simplified block diagram of a communication device 106, according to some embodiments. It is noted that the block diagram of the communication device of FIG. 4 is only one example of a possible communication device. According to embodiments, communication device 106 may be a user equipment (UE) device, a mobile device or mobile station, a wireless device or wireless station, a desktop computer or computing device, a mobile computing device (e.g., a laptop, notebook, or portable computing device), a tablet, an unmanned aerial vehicle (UAV), a UAV controller (UAC) and/or a combination of devices, among other devices. As shown, the communication device 106 may include a set of components 400 configured to perform core functions. For example, this set of components may be implemented as a system on chip (SOO), which may include portions for various purposes. Alternatively, this set of components 400 may be implemented as separate components or groups of components for the various purposes. The set of components 400 may be coupled (e.g., communicatively; directly or indirectly) to various other circuits of the communication device 106.

[0081] For example, the communication device 106 may include various types of memory (e.g., including NAND flash 410), an input/output interface such as connector l/F 420 (e.g., for connecting to a computer system; dock; charging station; input devices, such as a microphone, camera, keyboard; output devices, such as speakers; etc.), the display 460, which may be integrated with or external to the communication device 106, and cellular communication circuitry 430 such as for 5G NR, LTE, GSM, etc., and short to medium range wireless communication circuitry 429 (e.g., Bluetooth™ and WLAN circuitry). In some embodiments, communication device 106 may include wired communication circuitry (not shown), such as a network interface card, e.g., for Ethernet.

[0082] The cellular communication circuitry 430 may couple (e.g., communicatively; directly or indirectly) to one or more antennas, such as antennas 435 and 436 as shown. The short to medium range wireless communication circuitry 429 may also couple (e.g., communicatively; directly or indirectly) to one or more antennas, such as antennas 437 and 438 as shown. Alternatively, the short to medium range wireless communication circuitry 429 may couple (e.g., communicatively; directly or indirectly) to the antennas 435 and 436 in addition to, or instead of, coupling (e.g., communicatively; directly or indirectly) to the antennas 437 and 438. The short to medium range wireless communication circuitry 429 and/or cellular communication circuitry 430 may include multiple receive chains and/or multiple transmit chains for receiving and/or transmitting multiple spatial streams, such as in a multiple-input multiple output (MIMO) configuration.

[0083] In some embodiments, as further described below, cellular communication circuitry 430 may include dedicated receive chains (including and/or coupled to, e.g., communicatively; directly or indirectly, dedicated processors and/or radios) for multiple RATs (e.g., a first receive chain for LTE and a second receive chain for 5G NR). In addition, in some embodiments, cellular communication circuitry 430 may include a single transmit chain that may be switched between radios dedicated to specific RATs. For example, a first radio may be dedicated to a first RAT, e.g., LTE, and may be in communication with a dedicated receive chain and a transmit chain shared with an additional radio, e.g., a second radio that may be dedicated to a second RAT, e.g., 5G NR, and may be in communication with a dedicated receive chain and the shared transmit chain.

[0084] The communication device 106 may also include and/or be configured for use with one or more user interface elements. The user interface elements may include any of various elements, such as display 460 (which may be a touchscreen display), a keyboard (which may be a discrete keyboard or may be implemented as part of a touchscreen display), a mouse, a microphone and/or speakers, one or more cameras, one or more buttons, and/or any of various other elements capable of providing information to a user and/or receiving or interpreting user input.

[0085] The communication device 106 may further include one or more smart cards 445 that include SIM (Subscriber Identity Module) functionality, such as one or more UICC(s) (Universal Integrated Circuit Card(s)) cards 445. Note that the term “SIM” or “SIM entity” is intended to include any of various types of SIM implementations or SIM functionality, such as the one or more UICC(s) cards 445, one or more eUlCCs, one or more eSIMs, either removable or embedded, etc. In some embodiments, the UE 106 may include at least two SIMs. Each SIM may execute one or more SIM applications and/or otherwise implement SIM functionality. Thus, each SIM may be a single smart card that may be embedded, e.g., may be soldered onto a circuit board in the UE 106, or each SIM 410 may be implemented as a removable smart card. Thus, the SIM(s) may be one or more removable smart cards (such as UICC cards, which are sometimes referred to as “SIM cards”), and/or the SIMs 410 may be one or more embedded cards (such as embedded UICCs (eUlCCs), which are sometimes referred to as “eSIMs” or “eSIM cards”). In some embodiments (such as when the SIM(s) include an eUlCC), one or more of the SIM(s) may implement embedded SIM (eSIM) functionality; in such an embodiment, a single one of the SIM(s) may execute multiple SIM applications. Each of the SIMs may include components such as a processor and/or a memory; instructions for performing SIM/eSIM functionality may be stored in the memory and executed by the processor. In some embodiments, the UE 106 may include a combination of removable smart cards and fixed/non-removable smart cards (such as one or more eUlCC cards that implement eSIM functionality), as desired. For example, the UE 106 may comprise two embedded SIMs, two removable SIMs, or a combination of one embedded SIMs and one removable SIMs. Various other SIM configurations are also contemplated.

[0086] As noted above, in some embodiments, the UE 106 may include two or more SIMs. The inclusion of two or more SIMs in the UE 106 may allow the UE 106 to support two different telephone numbers and may allow the UE 106 to communicate on corresponding two or more respective networks. For example, a first SIM may support a first RAT such as LTE, and a second SIM 410 can support a second RAT such as 5G NR. Other implementations and RATs are of course possible. In some embodiments, when the UE 106 comprises two SIMs, the UE 106 may support Dual SIM Dual Active (DSDA) functionality. The DSDA functionality may allow the UE 106 to be simultaneously connected to two networks (and use two different RATs) at the same time, or to simultaneously maintain two connections supported by two different SIMs using the same or different RATs on the same or different networks. The DSDA functionality may also allow the UE 106 to simultaneously receive voice calls or data traffic on either phone number. In certain embodiments the voice call may be a packet switched communication. In other words, the voice call may be received using voice over LTE (VoLTE) technology and/or voice over NR (VoNR) technology. In some embodiments, the UE 106 may support Dual SIM Dual Standby (DSDS) functionality. The DSDS functionality may allow either of the two SIMs in the UE 106 to be on standby waiting for a voice call and/or data connection. In DSDS, when a call/data is established on one SIM, the other SIM is no longer active. In some embodiments, DSDx functionality (either DSDA or DSDS functionality) may be implemented with a single SIM (e.g., a eUlCC) that executes multiple SIM applications for different carriers and/or RATs.

[0087] As shown, the SOC 400 may include processor(s) 402, which may execute program instructions for the communication device 106 and display circuitry 404, which may perform graphics processing and provide display signals to the display 460. The processor(s) 402 may also be coupled to memory management unit (MMU) 440, which may be configured to receive addresses from the processor(s) 402 and translate those addresses to locations in memory (e.g., memory 406, read only memory (ROM) 450, NAND flash memory 410) and/or to other circuits or devices, such as the display circuitry 404, short to medium range wireless communication circuitry 429, cellular communication circuitry 430, connector l/F 420, and/or display 460. The MMU 440 may be configured to perform memory protection and page table translation or set up. In some embodiments, the MMU 440 may be included as a portion of the processor(s) 402.

[0088] As described herein, the communication device 106 may include hardware and software components for implementing the above features for a communication device 106 to communicate a scheduling profile for power savings to a network. The processor 402 of the communication device 106 may be configured to implement part or all of the features described herein, e.g., by executing program instructions stored on a memory medium (e.g., a non-transitory computer-readable memory medium). Alternatively (or in addition), processor 402 may be configured as a programmable hardware element, such as an FPGA (Field Programmable Gate Array), or as an ASIC (Application Specific Integrated Circuit). Alternatively (or in addition) the processor 402 of the communication device 106, in conjunction with one or more of the other components 400, 404, 406, 410, 420, 429, 430, 440, 445, 450, 460 may be configured to implement part or all of the features described herein.

[0089] In addition, as described herein, processor 402 may include one or more processing elements. Thus, processor 402 may include one or more integrated circuits (ICs) that are configured to perform the functions of processor 402. In addition, each integrated circuit may include circuitry (e.g., first circuitry, second circuitry, etc.) configured to perform the functions of processor(s) 402.

[0090] Further, as described herein, cellular communication circuitry 430 and short to medium range wireless communication circuitry 429 may each include one or more processing elements. In other words, one or more processing elements may be included in cellular communication circuitry 430 and, similarly, one or more processing elements may be included in short to medium range wireless communication circuitry 429. Thus, cellular communication circuitry 430 may include one or more integrated circuits (ICs) that are configured to perform the functions of cellular communication circuitry 430. In addition, each integrated circuit may include circuitry (e.g., first circuitry, second circuitry, etc.) configured to perform the functions of cellular communication circuitry 430. Similarly, the short to medium range wireless communication circuitry 429 may include one or more ICs that are configured to perform the functions of short to medium range wireless communication circuitry 429. In addition, each integrated circuit may include circuitry (e.g., first circuitry, second circuitry, etc.) configured to perform the functions of short to medium range wireless communication circuitry 429.

[0091] In some embodiments, the gNB 102 and/or the processors 402 thereof can be configured to and/or capable of selecting, at the gNB, a dynamic measurement opportunity sharing scheme for L3 measurement opportunities relative to L1 measurement opportunities, as described herein.

FIG. 5: Block Diagram of Cellular Communication Circuitry

[0092] FIG. 5 illustrates an example simplified block diagram of cellular communication circuitry, according to some embodiments. It is noted that the block diagram of the cellular communication circuitry of FIG. 5 is only one example of a possible cellular communication circuit. According to embodiments, cellular communication circuitry 530, which may be cellular communication circuitry 430, may be included in a communication device, such as communication device 106 described above. As noted above, communication device 106 may be a user equipment (UE) device, a mobile device or mobile station, a wireless device or wireless station, a desktop computer or computing device, a mobile computing device (e.g., a laptop, notebook, or portable computing device), a tablet and/or a combination of devices, among other devices.

[0093] The cellular communication circuitry 530 may couple (e.g., communicatively; directly or indirectly) to one or more antennas, such as antennas 435a-b and 436 as shown (in FIG. 4). In some embodiments, cellular communication circuitry 530 may include dedicated receive chains (including and/or coupled to, e.g., communicatively; directly or indirectly, dedicated processors and/or radios) for multiple RATs (e.g., a first receive chain for LTE and a second receive chain for 5G NR). For example, as shown in FIG. 5, cellular communication circuitry 530 may include a modem 510 and a modem 520. Modem 510 may be configured for communications according to a first RAT, e.g., such as LTE or LTE-A, and modem 520 may be configured for communications according to a second RAT, e.g., such as 5G NR.

[0094] As shown, modem 510 may include one or more processors 512 and a memory 516 in communication with processors 512. Modem 510 may be in communication with a radio frequency (RF) front end 535. RF front end 535 may include circuitry for transmitting and receiving radio signals. For example, RF front end 535 may include receive circuitry (RX) 532 and transmit circuitry (TX) 534. In some embodiments, receive circuitry 532 may be in communication with downlink (DL) front end 550, which may include circuitry for receiving radio signals via antenna 335a.

[0095] Similarly, modem 520 may include one or more processors 522 and a memory 526 in communication with processors 522. Modem 520 may be in communication with an RF front end 540. RF front end 540 may include circuitry for transmitting and receiving radio signals. For example, RF front end 540 may include receive circuitry 542 and transmit circuitry 544. In some embodiments, receive circuitry 542 may be in communication with DL front end 560, which may include circuitry for receiving radio signals via antenna 335b.

[0096] In some embodiments, a switch 570 may couple transmit circuitry 534 to uplink (UL) front end 572. In addition, switch 570 may couple transmit circuitry 544 to UL front end 572. UL front end 572 may include circuitry for transmitting radio signals via antenna 336. Thus, when cellular communication circuitry 530 receives instructions to transmit according to the first RAT (e.g., as supported via modem 510), switch 570 may be switched to a first state that allows modem 510 to transmit signals according to the first RAT (e.g., via a transmit chain that includes transmit circuitry 534 and UL front end 572). Similarly, when cellular communication circuitry 530 receives instructions to transmit according to the second RAT (e.g., as supported via modem 520), switch 570 may be switched to a second state that allows modem 520 to transmit signals according to the second RAT (e.g., via a transmit chain that includes transmit circuitry 544 and UL front end 572).

[0097] As described herein, the modem 510 may include hardware and software components for implementing the above features or for time division multiplexing UL data for NSA NR operations, as well as the various other techniques described herein. The processors 512 may be configured to implement part or all of the features described herein, e.g., by executing program instructions stored on a memory medium (e.g., a non-transitory computer-readable memory medium). Alternatively (or in addition), processor 512 may be configured as a programmable hardware element, such as an FPGA (Field Programmable Gate Array), or as an ASIC (Application Specific Integrated Circuit). Alternatively (or in addition) the processor 512, in conjunction with one or more of the other components 530, 532, 534, 535, 550, 570, 572, 335a, 335b, and 336 may be configured to implement part or all of the features described herein.

[0098] In addition, as described herein, processors 512 may include one or more processing elements. Thus, processors 512 may include one or more integrated circuits (ICs) that are configured to perform the functions of processors 512. In addition, each integrated circuit may include circuitry (e.g., first circuitry, second circuitry, etc.) configured to perform the functions of processors 512.

[0099] The processors 522 may be configured to implement part or all of the features described herein, e.g., by executing program instructions stored on a memory medium (e.g., a non-transitory computer-readable memory medium). Alternatively (or in addition), processor 522 may be configured as a programmable hardware element, such as an FPGA (Field Programmable Gate Array), or as an ASIC (Application Specific Integrated Circuit). Alternatively (or in addition) the processor 522, in conjunction with one or more of the other components 540, 542, 544, 550, 570, 572, 335a, 335b, and 336 may be configured to implement part or all of the features described herein.

[00100] In addition, as described herein, processors 522 may include one or more processing elements. Thus, processors 522 may include one or more integrated circuits (ICs) that are configured to perform the functions of processors 522. In addition, each integrated circuit may include circuitry (e.g., first circuitry, second circuitry, etc.) configured to perform the functions of processors 522.

[00101] In some embodiments, the processors 512, 522 can be configured for selecting a dynamic measurement opportunity sharing scheme for L3 measurement opportunities relative to L1 measurement opportunities, as further described herein.

FIG. 6: Block Diagram of a Baseband Processor Architecture for a UE

[00102] FIG. 6 illustrates example components of a device 600 in accordance with some embodiments. It is noted that the device of FIG. 6 is merely one example of a possible system, and that features of this disclosure may be implemented in any of various UEs, as desired.

[00103] In some embodiments, the device 600 may include application circuitry 602, baseband circuitry 604, Radio Frequency (RF) circuitry 606, front-end module (FEM) circuitry 608, one or more antennas 610, and power management circuitry (PMC) 612 coupled together at least as shown. The components of the illustrated device 600 may be included in a UE 106 or a RAN node 102A. In some embodiments, the device 600 may include less elements (e.g., a RAN node may not utilize application circuitry 602, and instead include a processor/controller to process IP data received from an EPC). In some embodiments, the device 600 may include additional elements such as, for example, memory/storage, display, camera, sensor, or input/output (I/O) interface. In other embodiments, the components described below may be included in more than one device (e.g., said circuitries may be separately included in more than one device for Cloud-RAN (C- RAN) implementations).

[00104] The application circuitry 602 may include one or more application processors. For example, the application circuitry 602 may include circuitry such as, but not limited to, one or more single-core or multi-core processors. The processor(s) may include any combination of general-purpose processors and dedicated processors (e.g., graphics processors, application processors, etc.). The processors may be coupled with or may include memory/storage and may be configured to execute instructions stored in the memory/storage to enable various applications or operating systems to run on the device 600. In some embodiments, processors of application circuitry 602 may process IP data packets received from an EPC.

[00105] The baseband circuitry 604 may include circuitry such as, but not limited to, one or more single-core or multi-core processors. The baseband circuitry 604 may include one or more baseband processors or control logic to process baseband signals received from a receive signal path of the RF circuitry 606 and to generate baseband signals for a transmit signal path of the RF circuitry 606. Baseband processing circuity 604 may interface with the application circuitry 602 for generation and processing of the baseband signals and for controlling operations of the RF circuitry 606. For example, in some embodiments, the baseband circuitry 604 may include a third generation (3G) baseband processor 604A, a fourth generation (4G) baseband processor 604B, a fifth generation (5G) baseband processor 604C, or other baseband processor(s) 604D for other existing generations, generations in development or to be developed in the future (e.g., second generation (2G), sixth generation (6G), etc.). The baseband circuitry 604 (e.g., one or more of baseband processors 604A-D) may handle various radio control functions that enable communication with one or more radio networks via the RF circuitry 606. In other embodiments, some or all of the functionality of baseband processors 604A-D may be included in modules stored in the memory 604G and executed via a Central Processing Unit (CPU) 604E. The radio control functions may include, but are not limited to, signal modulation/demodulation, encoding/decoding, radio frequency shifting, etc. In some embodiments, modulation/demodulation circuitry of the baseband circuitry 604 may include Fast- Fourier Transform (FFT), precoding, or constellation mapping/demapping functionality. In some embodiments, encoding/decoding circuitry of the baseband circuitry 604 may include convolution, tail-biting convolution, turbo, Viterbi, or Low Density Parity Check (LDPC) encoder/decoder functionality. Embodiments of modulation/demodulation and encoder/decoder functionality are not limited to these examples and may include other suitable functionality in other embodiments.

[00106] In some embodiments, the baseband circuitry 604 may include one or more audio digital signal processor(s) (DSP) 604F. The audio DSP(s) 604F may include elements for compression/decompression and echo cancellation and may include other suitable processing elements in other embodiments. Components of the baseband circuitry may be suitably combined in a single chip, a single chipset, or disposed on a same circuit board in some embodiments. In some embodiments, some or all of the constituent components of the baseband circuitry 604 and the application circuitry 602 may be implemented together such as, for example, on a system on a chip (SOC).

[00107] In some embodiments, the baseband circuitry 604 may provide for communication compatible with one or more radio technologies. For example, in some embodiments, the baseband circuitry 604 may support communication with an evolved universal terrestrial radio access network (EUTRAN) or other wireless metropolitan area networks (WMAN), a wireless local area network (WLAN), a wireless personal area network (WPAN). Embodiments in which the baseband circuitry 604 is configured to support radio communications of more than one wireless protocol may be referred to as multi-mode baseband circuitry.

[00108] RF circuitry 606 may enable communication with wireless networks using modulated electromagnetic radiation through a non-solid medium. In various embodiments, the RF circuitry 606 may include switches, filters, amplifiers, etc. to facilitate the communication with the wireless network. RF circuitry 606 may include a receive signal path which may include circuitry to down-convert RF signals received from the FEM circuitry 608 and provide baseband signals to the baseband circuitry 604. RF circuitry 606 may also include a transmit signal path which may include circuitry to up-convert baseband signals provided by the baseband circuitry 604 and provide RF output signals to the FEM circuitry 608 for transmission.

[00109] In some embodiments, the receive signal path of the RF circuitry 606 may include mixer circuitry 606a, amplifier circuitry 606b and filter circuitry 606c. In some embodiments, the transmit signal path of the RF circuitry 606 may include filter circuitry 606c and mixer circuitry 606a. RF circuitry 606 may also include synthesizer circuitry 606d for synthesizing a frequency for use by the mixer circuitry 606a of the receive signal path and the transmit signal path. In some embodiments, the mixer circuitry 606a of the receive signal path may be configured to down-convert RF signals received from the FEM circuitry 608 based on the synthesized frequency provided by synthesizer circuitry 606d. The amplifier circuitry 606b may be configured to amplify the down-converted signals and the filter circuitry 606c may be a low-pass filter (LPF) or band-pass filter (BPF) configured to remove unwanted signals from the down-converted signals to generate output baseband signals. Output baseband signals may be provided to the baseband circuitry 604 for further processing. In some embodiments, the output baseband signals may be zero-frequency baseband signals, although this is not a necessity. In some embodiments, mixer circuitry 606a of the receive signal path may comprise passive mixers, although the scope of the embodiments is not limited in this respect.

[00110] In some embodiments, the mixer circuitry 606a of the transmit signal path may be configured to up-convert input baseband signals based on the synthesized frequency provided by the synthesizer circuitry 606d to generate RF output signals for the FEM circuitry 608. The baseband signals may be provided by the baseband circuitry 604 and may be filtered by filter circuitry 606c.

[00111] In some embodiments, the mixer circuitry 606a of the receive signal path and the mixer circuitry 606a of the transmit signal path may include two or more mixers and may be arranged for quadrature downconversion and upconversion, respectively. In some embodiments, the mixer circuitry 606a of the receive signal path and the mixer circuitry 606a of the transmit signal path may include two or more mixers and may be arranged for image rejection (e.g., Hartley image rejection). In some embodiments, the mixer circuitry 606a of the receive signal path and the mixer circuitry 606a may be arranged for direct downconversion and direct upconversion, respectively. In some embodiments, the mixer circuitry 606a of the receive signal path and the mixer circuitry 606a of the transmit signal path may be configured for super-heterodyne operation.

[00112] In some embodiments, the output baseband signals and the input baseband signals may be analog baseband signals, although the scope of the embodiments is not limited in this respect. In some alternate embodiments, the output baseband signals and the input baseband signals may be digital baseband signals. In these alternate embodiments, the RF circuitry 606 may include analog- to-digital converter (ADC) and digital-to-analog converter (DAC) circuitry and the baseband circuitry 604 may include a digital baseband interface to communicate with the RF circuitry 606.

[00113] In some dual-mode embodiments, a separate radio IC circuitry may be provided for processing signals for each spectrum, although the scope of the embodiments is not limited in this respect.

[00114] In some embodiments, the synthesizer circuitry 606d may be a fractional-N synthesizer or a fractional N/N+1 synthesizer, although the scope of the embodiments is not limited in this respect as other types of frequency synthesizers may be suitable. For example, synthesizer circuitry 606d may be a delta-sigma synthesizer, a frequency multiplier, or a synthesizer comprising a phase-locked loop with a frequency divider.

[00115] The synthesizer circuitry 606d may be configured to synthesize an output frequency for use by the mixer circuitry 606a of the RF circuitry 606 based on a frequency input and a divider control input. In some embodiments, the synthesizer circuitry 606d may be a fractional N/N+1 synthesizer.

[00116] In some embodiments, frequency input may be provided by a voltage controlled oscillator (VCO), although that is not a necessity. Divider control input may be provided by either the baseband circuitry 604 or the applications processor 602 depending on the desired output frequency. In some embodiments, a divider control input (e.g., N) may be determined from a look-up table based on a channel indicated by the applications processor 602.

[00117] Synthesizer circuitry 606d of the RF circuitry 606 may include a divider, a delay-locked loop (DLL), a multiplexer and a phase accumulator. In some embodiments, the divider may be a dual modulus divider (DMD) and the phase accumulator may be a digital phase accumulator (DPA). In some embodiments, the DMD may be configured to divide the input signal by either N or N+1 (e.g., based on a carry out) to provide a fractional division ratio. In some example embodiments, the DLL may include a set of cascaded, tunable, delay elements, a phase detector, a charge pump and a D-type flip-flop. In these embodiments, the delay elements may be configured to break a VCO period up into Nd equal packets of phase, where Nd is the number of delay elements in the delay line. In this way, the DLL provides negative feedback to help ensure that the total delay through the delay line is one VCO cycle.

[00118] In some embodiments, synthesizer circuitry 606d may be configured to generate a carrier frequency as the output frequency, while in other embodiments, the output frequency may be a multiple of the carrier frequency (e.g., twice the carrier frequency, four times the carrier frequency) and used in conjunction with quadrature generator and divider circuitry to generate multiple signals at the carrier frequency with multiple different phases with respect to each other. In some embodiments, the output frequency may be a LO frequency (fLO). In some embodiments, the RF circuitry 606 may include an IQ/polar converter.

[00119] FEM circuitry 608 may include a receive signal path which may include circuitry configured to operate on RF signals received from one or more antennas 610, amplify the received signals and provide the amplified versions of the received signals to the RF circuitry 606 for further processing. FEM circuitry 608 may also include a transmit signal path which may include circuitry configured to amplify signals for transmission provided by the RF circuitry 606 for transmission by one or more of the one or more antennas 610. In various embodiments, the amplification through the transmit or receive signal paths may be done solely in the RF circuitry 606, solely in the FEM 608, or in both the RF circuitry 606 and the FEM 608.

[00120] In some embodiments, the FEM circuitry 608 may include a TX/RX switch to switch between transmit mode and receive mode operation. The FEM circuitry may include a receive signal path and a transmit signal path. The receive signal path of the FEM circuitry may include an LNA to amplify received RF signals and provide the amplified received RF signals as an output (e.g., to the RF circuitry 606). The transmit signal path of the FEM circuitry 608 may include a power amplifier (PA) to amplify input RF signals (e.g., provided by RF circuitry 606), and one or more filters to generate RF signals for subsequent transmission (e.g., by one or more of the one or more antennas 610).

[00121] In some embodiments, the PMC 612 may manage power provided to the baseband circuitry 604. In particular, the PMC 612 may control power-source selection, voltage scaling, battery charging, or DC-to-DC conversion. The PMC 612 may often be included when the device 600 is capable of being powered by a battery, for example, when the device is included in a UE. The PMC 612 may increase the power conversion efficiency while providing desirable implementation size and heat dissipation characteristics.

[00122] While FIG. 6 shows the PMC 612 coupled only with the baseband circuitry 604, in other embodiments the PMC 612 may be additionally or alternatively coupled with, and perform similar power management operations for, other components such as, but not limited to, application circuitry 602, RF circuitry 606, or FEM 608.

[00123] In some embodiments, the PMC 612 may control, or otherwise be part of, various power saving mechanisms of the device 600. For example, if the device 600 is in a radio resource control_Connected (RRC_Connected) state, where it is still connected to the RAN node as it expects to receive traffic shortly, then it may enter a state known as Discontinuous Reception Mode (DRX) after a period of inactivity. During this state, the device 600 may power down for brief intervals of time and thus save power.

[00124] If there is no data traffic activity for an extended period of time, then the device 600 may transition off to an RRC Idle state, where it disconnects from the network and does not perform operations such as channel quality feedback, handover, etc. The device 600 goes into a very low power state and it performs paging where again it periodically wakes up to listen to the network and then powers down again. The device 600 may not receive data in this state, in order to receive data, it will transition back to RRC_Connected state.

[00125] An additional power saving mode may allow a device to be unavailable to the network for periods longer than a paging interval (ranging from seconds to a few hours). During this time, the device is totally unreachable to the network and may power down completely. Any data sent during this time incurs a large delay and it is assumed the delay is acceptable.

[00126] Processors of the application circuitry 602 and processors of the baseband circuitry 604 may be used to execute elements of one or more instances of a protocol stack. For example, processors of the baseband circuitry 604, alone or in combination, may be used execute Layer 3, Layer 2, or Layer 1 functionality, while processors of the application circuitry 604 may utilize data (e.g., packet data) received from these layers and further execute Layer 4 functionality (e.g., transmission communication protocol (TCP) and user datagram protocol (UDP) layers). As referred to herein, Layer 3 (L3) may comprise a radio resource control (RRC) layer, described in further detail below. As referred to herein, Layer 2 (L2) may comprise a medium access control (MAC) layer, a radio link control (RLC) layer, and a packet data convergence protocol (PDCP) layer, described in further detail below. As referred to herein, Layer 1 (L1 ) may comprise a physical (PHY) layer of a UE/RAN node, described in further detail below. Accordingly, the baseband circuitry 604 can be used to encode a message for transmission between a UE and a gNB, or decode a message received between a UE and a gNB.

[00127] For example, the baseband circuitry 604 can be used to receive, from a gNB, an indication to activate artificial intelligence (Al) based compression model performance monitoring at the UE; decode, at the UE, configuration information for the Al based compression model performance monitoring received from the gNB. In another embodiment, the baseband circuitry 604 can be used to decode channel state information (CSI) received from the gNB; compress the CSI, at the UE, using an Al based compression model to generate a compressed CSI; reconstruct the compressed CSI at the UE using an Al based reconstruction model to generate the reconstructed CSI for the Al based compression model monitoring. In another embodiment, the baseband circuitry 604 can be used to determine a similarity metric between the CSI and the reconstructed CSI and/or compare the similarity metric to a compression model threshold; and transmit a monitoring report to the gNB based on the comparison. These examples are not intended to be limiting. The baseband circuitry can be used as previously described.

FIG. 7: Block Diagram of an Interface of Baseband Circuitry

[00128] FIG. 7 illustrates example interfaces of baseband circuitry in accordance with some embodiments. It is noted that the baseband circuitry of FIG. 7 is merely one example of a possible circuitry, and that features of this disclosure may be implemented in any of various systems, as desired.

[00129] As discussed above, the baseband circuitry 604 of FIG. 6 may comprise processors 604A-604E and a memory 604G utilized by said processors. Each of the processors 604A-604E may include a memory interface, 704A-704E, respectively, to send/receive data to/from the memory 604G.

[00130] The baseband circuitry 604 may further include one or more interfaces to communicatively couple to other circuitries/devices, such as a memory interface 712 (e.g., an interface to send/receive data to/from memory external to the baseband circuitry 604), an application circuitry interface 714 (e.g., an interface to send/receive data to/from the application circuitry 602 of FIG. 6), an RF circuitry interface 716 (e.g., an interface to send/receive data to/from RF circuitry 606 of FIG. 6), a wireless hardware connectivity interface 718 (e.g., an interface to send/receive data to/from Near Field Communication (NFC) components, Bluetooth® components (e.g., Bluetooth® Low Energy), Wi-Fi® components, and other communication components), and a power management interface 720 (e.g., an interface to send/receive power or control signals to/from the PMC 612.

FIG. 8A: Control Plane Protocol Stack

[00131] FIG. 8A is an illustration of a control plane protocol stack in accordance with some embodiments. In this embodiment, a control plane 800 is shown as a communications protocol stack between the UE 106a (or alternatively, the UE 106b), the RAN node 102A (or alternatively, the RAN node 102B), and the mobility management entity (MME) 621 .

[00132] The PHY layer 801 may transmit or receive information used by the MAC layer 802 over one or more air interfaces. The PHY layer 801 may further perform link adaptation or adaptive modulation and coding (AMC), power control, cell search (e.g., for initial synchronization and handover purposes), and other measurements used by higher layers, such as the RRC layer 805. The PHY layer 801 may still further perform error detection on the transport channels, forward error correction (FEC) coding/decoding of the transport channels, modulation/demodulation of physical channels, interleaving, rate matching, mapping onto physical channels, and Multiple Input Multiple Output (MIMO) antenna processing.

[00133] The MAC layer 802 may perform mapping between logical channels and transport channels, multiplexing of MAC service data units (SDUs) from one or more logical channels onto transport blocks (TB) to be delivered to PHY via transport channels, de-multiplexing MAC SDUs to one or more logical channels from transport blocks (TB) delivered from the PHY via transport channels, multiplexing MAC SDUs onto TBs, scheduling information reporting, error correction through hybrid automatic repeat request (HARQ), and logical channel prioritization.

[00134] The RLC layer 803 may operate in a plurality of modes of operation, including: Transparent Mode (TM), Unacknowledged Mode (UM), and Acknowledged Mode (AM). The RLC layer 803 may execute transfer of upper layer protocol data units (PDUs), error correction through automatic repeat request (ARQ) for AM data transfers, and concatenation, segmentation and reassembly of RLC SDUs for UM and AM data transfers. The RLC layer 803 may also execute re-segmentation of RLC data PDUs for AM data transfers, reorder RLC data PDUs for UM and AM data transfers, detect duplicate data for UM and AM data transfers, discard RLC SDUs for UM and AM data transfers, detect protocol errors for AM data transfers, and perform RLC re-establishment.

[00135] The PDCP layer 804 may execute header compression and decompression of IP data, maintain PDCP Sequence Numbers (SNs), perform insequence delivery of upper layer PDUs at re-establishment of lower layers, eliminate duplicates of lower layer SDUs at re-establishment of lower layers for radio bearers mapped on RLC AM, cipher and decipher control plane data, perform integrity protection and integrity verification of control plane data, control timerbased discard of data, and perform security operations (e.g., ciphering, deciphering, integrity protection, integrity verification, etc.).

[00136] The main services and functions of the RRC layer 805 may include broadcast of system information (e.g., included in Master Information Blocks (MIBs) or System Information Blocks (SIBs) related to the non-access stratum (NAS)), broadcast of system information related to the access stratum (AS), paging, establishment, maintenance and release of an RRC connection between the UE and E-UTRAN (e.g., RRC connection paging, RRC connection establishment, RRC connection modification, and RRC connection release), establishment, configuration, maintenance and release of point to point Radio Bearers, security functions including key management, inter radio access technology (RAT) mobility, and measurement configuration for UE measurement reporting. Said MIBs and SIBs may comprise one or more information elements (lEs), which may each comprise individual data fields or data structures.

[00137] The UE 601 and the RAN node 102A may utilize a Uu interface (e.g., an LTE-Uu interface) to exchange control plane data via a protocol stack comprising the PHY layer 801 , the MAC layer 802, the RLC layer 803, the PDCP layer 804, and the RRC layer 805.

[00138] The non-access stratum (NAS) protocols 806 form the highest stratum of the control plane between the UE 601 and the MME 621. The NAS protocols 806 support the mobility of the UE 601 and the session management procedures to establish and maintain IP connectivity between the UE 601 and the P-GW 623.

[00139] The S1 Application Protocol (S1 -AP) layer 815 may support the functions of the S1 interface and comprise Elementary Procedures (EPs). An EP is a unit of interaction between the RAN node 102A and the CN 1020. The S1 -AP layer services may comprise two groups: UE-associated services and non UE- associated services. These services perform functions including, but not limited to: E-UTRAN Radio Access Bearer (E-RAB) management, UE capability indication, mobility, NAS signaling transport, RAN Information Management (RIM), and configuration transfer.

[00140] The Stream Control Transmission Protocol (SCTP) layer (alternatively referred to as the SCTP/IP layer) 814 may ensure reliable delivery of signaling messages between the RAN node 102A and the MME 621 based, in part, on the IP protocol, supported by the IP layer 813. The L2 layer 812 and the L1 layer 81 1 may refer to communication links (e.g., wired or wireless) used by the RAN node and the MME to exchange information.

[00141] The RAN node 102A and the MME 621 may utilize an S1 -MME interface to exchange control plane data via a protocol stack comprising the L1 layer 81 1 , the L2 layer 812, the IP layer 813, the SCTP layer 814, and the S1 -AP layer 815.

FIG. 8B: autoencoder-based two-sided framework for implicit CSI feedback.

[00142] FIG. 8B illustrates an example of an autoencoder-based two-sided framework for implicit CSI feedback enhancement in accordance with some embodiments.

[00143] The two-sided framework for implicit CSI feedback is based on autoencoder-based image compression, a neural network (NN)-based encoder is adopted at the UE to compress and quantize generated precoding matrix and the generated bitstream in this framework can be considered as a precoding matrix indicator (PMI) in an existing codebook-based feedback strategy. After obtaining the feedback bitstream, the NN-based decoder reconstructs the original precoding matrix.

[00144] For example, at the UE side, the downlink channel state information (CSI) matrix H captures the characteristics of the wireless channel from the gNB. The UE can perform a singular value decomposition (SVD) on this CSI matrix to extract the precoding matrix V which contains weights for optimally transmitting through the channel described by H. The UE has an Al encoder which takes this precoding matrix V as input and generates a compressed bitstream feedback to transmit back to the gNB efficiently utilizing neural network processing. At the gNB, an Al decoder module reconstructs the precoding matrix V from the compressed feedback from the UE. The reconstructed estimate of the precoding matrix is labeled / to denote it may contain inaccuracies. By tuning the Al encoder and decoder as a combined autoencoder system across a wireless link, the precoding reconstruction can be significantly enhanced versus standardized codebooks. This CSI feedback framework with Al powered compression and enhancement effectively provides downlink channel knowledge for massive Ml MO base stations to optimize transmission.

FIG. 9: UE performance monitoring for Al based CSI compression model.

[00145] Using an Al-based compression of channel state information (CSI), there is a need to monitor the ongoing performance of the Al model to detect any degradation. While network-side monitoring is possible, additional user equipment (UE) side monitoring can provide localized and timely detection of compression issues. However, existing solutions lack adequate mechanisms for CSI reconstruction and dynamic monitoring at the UE side based on intermediate metrics. The lack of reconstruction capability and defined monitoring procedures at the UE prevents robust supervision of the Al compression model's quality over time.

[00146] To overcome these challenges, embodiments provided herein enable UE-side monitoring and reporting of Al-based CSI compression model performance. This is achieved by providing CSI reconstruction capabilities at the UE using an Al-based reconstruction model, or Al-based proxy model that generate the SGCS directly. The original CSI, prior to compression, can then be compared to the reconstructed CSI at the UE side using intermediate metrics like squared generalized cosine similarity (SGCS). Defined monitoring procedures allow evaluation of compression model quality over periodic windows based on thresholding the intermediate metric. Network configuration of parameters such as an evaluation monitoring window, thresholds, etc. allows flexible supervision. By determining CSI reconstruction and intermediate metric monitoring procedures performed at the UE, issues with the Al compression model can be localized and reported to the network rapidly. The solutions improve reliability of Al-based CSI compression deployments.

[00147] For example, in one example, a two-sided framework may utilize an autoencoder architecture with neural network models at both the UE and base station. In one example, the UE encoder compresses the CSI which is reconstructed by the base station decoder. The original CSI, prior to compression, can then be compared to the reconstructed CSI at the UE side using intermediate metrics like squared generalized cosine similarity (SGCS). Defined monitoring procedures allow evaluation of compression model quality over periodic windows based on thresholding the intermediate metric. Network configuration of parameters such as an evaluation monitoring window, thresholds, etc. allows flexible supervision. By determining CSI reconstruction and intermediate metric monitoring procedures performed at the UE, issues with the Al compression model can be localized and reported to the network rapidly. The solutions improve reliability of Al-based CSI compression deployments.

[00148] FIG. 9 provides an example illustration of timing diagram 900 of a UE 106 communicating with a gNB. In various embodiments, some of the signaling shown may be performed concurrently, in a different order than shown, or may be omitted. Additional signaling may also be performed as desired. As shown, this signaling may flow as follows as one example embodiment. The signaling shown in Figure 9 may be used in conjunction with any of the systems, methods, and/or devices. In various embodiments, some of the signaling shown may be performed concurrently, in a different order than shown, or may be omitted. Additional signaling may also be performed as desired. As shown, this signaling may flow as follows as one example embodiment.

[00149] The signaling may begin with a UE, such as UE 106, receiving, from a gNB, an indication 910 to activate Al based compression. For example, the base station may trigger the UE to perform the Al based compression model at the UE. In one example, the base station 102 (e.g., network “NW”) may further send to the UE 106 configuration information 912 to enable, at the UE, the Al based compression model performance monitoring.

[00150] The configuration information may include, for example, radio resource control (RRC) configuration information indicating whether the Al based compression model performance monitoring is activated and one or more parameters configured for the Al based compression model performance monitoring, wherein the one or more parameters include a compression model threshold, an evaluation window, or a layer-1 (L1 ) indication duration, which can be applicable to Figs 10A-C. The parameters can also include a maxCounter value maxTimer value. Typically, a range potential values can be included in a specification, such as 3GPP TS 38.331 , where the gNB may configure one of the values or range of values. The configuration information may be used to assist in training an Al performance monitoring model offline using a training dataset.

[00151] The RRC configuration information for the Al based compression model performance monitoring includes defining a time duration of the evaluation window and a time duration of the time between each L1 indication, defined criteria indicating a success or failure based on the compression model threshold or a number of success or failures in relation to the compression model threshold during the evaluation window. The UE may monitor the performance of the Al based compression model. In some embodiment, the UE may monitor the performance of the Al based compression model by determining the similarity metric over an evaluation window, as illustrated in Figures 10A-10C.

[00152] In one example, as illustrated in Figs 10A-10C, configuration information for the Al based compression model performance monitoring may include radio resource control (RRC) configuration information indicating both the Al based compression model performance monitoring is activated and one or more parameters for the Al based compression model performance monitoring, wherein the one or more parameters include a compression model threshold, an evaluation window, or a layer-1 (L1 ) indication duration [00153] The signaling may also include the UE, such as UE 106, transmitting a monitoring report 914 (e.g., UE side monitoring report) to the gNB. The monitoring report can be sent when a failure event happens. In one example, the monitoring report can be sent via UL MAC CE when the failure event happens. In another example, the monitoring report can be sent via an UL control channel.

FIG. 10A: Using an Evaluation window for UE performance monitoring.

[00154] FIG. 10A illustrates an example of using an evaluation window for UE performance monitoring for artificial intelligence (Al) based channel state information (CSI) compression model in accordance with some embodiments.

[00155] The performance monitoring procedure, as illustrated herein, such as, for example, as depicted in FIG. 9, can utilize an evaluation window that includes multiple L1 indications to make pass/fail decisions. In some embodiments, a gNB (e.g., gNB 102) can configure parameters such as, for example, an evaluation window duration, a time interval between L1 indications, and criteria for determining pass/fail decisions within a window, as illustrated in Figure 10A.

[00156] For example, the L1 indication interval can be periodically configured by the gNB or set to a minimum duration determined by the UE. Within an evaluation window, the similarity metric can be compared to a threshold on each L1 indication, and the number of indications that fail the threshold checked. Alternatively, the similarity metrics can be averaged over the window and compared to a threshold. As illustrated in FIG. 10A, the UE performs the monitoring procedure over time by calculating the similarity metric such as, for example, a squared generalized cosine similarity (SGCS) at each L1 indication triggered by receiving configured reference signals. Failures (as indicated by “X” in FIG. 10A) can be counted within each evaluation window and reported to the gNB if above a configured failure count threshold (e.g., SGCS is greater than or less than a threshold). In this manner, the gNB can customize the window-based evaluation approach as needed through configuration parameters.

[00157] It should be noted that the performance monitoring requires configured reference signal transmission from the gNB. The gNB can configure periodic or semi-persistent CSI-RS for similarity metric calculation at the UE based on the received reference signals. Aperiodic reference signals and reporting can also be supported for each L1 indication. While SGCS is shown as an example similarity metric, alternatives like hypothetical block error rate (BLER) can be used, where the hypothetical BLER is calculated and compared to a threshold. When the similarity metric indicates an Al model failure within the evaluation window, the UE can generate an uplink medium access control (MAC) control element (CE) report to the gNB indicating the failure event.

[00158] For example, as illustrated in FIG. 10A, the UE may determine a number of times (a check mark illustrates a pass/success, and an “x” indicates a fail/failure) the similarity metric indicates a pass or failure in relation to the compression model threshold for one or more evaluations performed during the evaluation window. That is, the UE computes a similarity metric like SGCS at each L1 indication triggered by receiving a reference signal from the gNB. The UE tracks failures where the similarity metric falls below a configured threshold (e.g., threshold, threshold 1 , and threshold 2) for each indication. In other words, the UE determines the similarity metric for each evaluation interval within the evaluation window.

[00159] Within each evaluation window, if the number of failures exceeds a configured failure count threshold (such as, for example X-failure), the UE generates a monitoring report to the gNB indicating the Al model degradation. The periodic L1 indications for evaluation are spaced at least a minimum duration apart. The gNB can configure the evaluation window duration, L1 indication periodicity, similarity thresholds per indication, and failure count threshold for the window.

FIG. 10B: Using a Counter for UE performance monitoring.

[00160] FIG. 10B illustrates an example of using a counter for UE performance monitoring for artificial intelligence (Al) based channel state information (CSI) compression model in accordance with some embodiments.

[00161] The performance monitoring procedure, as illustrated herein, such as, for example, as depicted in FIG. 9, can utilize a counter that includes multiple L1 indications to make pass/fail decisions. In one embodiment, a counter-based approach can alternatively be used for performance monitoring as illustrated in Figure 10B.

[00162] For example, a counter can be initialized and incremented each time the similarity metric indicates failure (i.e., falls below a configured threshold) for an L1 indication. The counter can be reset when the metric indicates the Al based compression model has passed for an L1 indication. If the counter exceeds a preconfigured threshold, the UE declares an Al model failure event. This approach requires periodic transmission of reference signals like CSI-RS from the gNB to enable computing the similarity metric at each L1 indication. The counter-based method allows aggregation of multiple failures or passes over time to make a failure decision, providing robustness against temporary variations.

[00163] In one example, a counter-based approach for monitoring performance tracks failures and passes of the similarity metric threshold over periodic L1 indications spaced by the indication duration. Each L1 indication triggers computation of the similarity metric which is compared to a configured threshold to determine pass or fail. If the metric is below the threshold, a failure is registered by incrementing the counter. For example, at each L1 indication, the SGCS is calculated and compared to a configured threshold, with SGCS values above the threshold indicating a pass (e.g., SGCS > threshold) and SGCS values (e.g., SGCS < threshold) below indicating a failure. Each failure causes the counter to increment (e.g., counter + 1 ), while a pass resets the counter (e.g., counter = 0). For example, at the second L1 indication in FIG. 10B, the SGCS is less than threshold 1 , so the counter increments to 1. This continues over multiple L1 indications, with the counter aggregating the number of failures. Finally, after a series of failures at the end, the counter exceeds the configured failure count threshold (e.g., Counter > threshold), triggering the UE to report the Al model failure event to the gNB. In this manner, the counter mechanism aggregates failures over time to make a pass/fail decision. FIG. 10C: Using a Counter and Timer for UE performance monitoring.

[00164] FIG. 10C illustrates an example of using a counter and timer for UE performance monitoring for artificial intelligence (Al) based channel state information (CSI) compression model in accordance with some embodiments.

[00165] In one embodiment, as illustrated in FIG. 10C, a combined counter and timer can be utilized for performance monitoring. In one example, a counter tracks aggregated failures across periodic L1 indications, incrementing when the similarity metric is below a configured threshold for an indication. A timer is started or restarted whenever an L1 indication failure occurs. If no failures occur for the duration of the timer, the timer expires and the counter is reset (e.g., timer expires, and counter is reset to 0). This prevents transient failures from accumulating indefinitely, particularly for aperiodic CSI-RS based monitoring, where gNB did not trigger ap-CSI-RS transmission for certain time. The UE reports an Al model failure event to the gNB when the counter exceeds a configured threshold, indicating consistent degradation over the time window captured by the timer duration. Together, the counter and timer provide reliable performance monitoring and failure detection across multiple L1 evaluation instances over time.

[00166] For example, as illustrated in FIG. 10C, the periodic L1 indications with fixed duration trigger computation of the similarity metric (e.g., SGCS), which is compared to a threshold. When SGCS falls below the threshold (e.g., SGCS < threshold), a failure is registered by incrementing the counter and restarting the timer. As long as failures continue to occur, the counter aggregates the failures and the timer restarts. Once an L1 indication occurs with SGCS above the threshold (e.g., SGCS > threshold), the timer expires since no recent failures have occurred. This resets the counter. The process continues with new failures incrementing the counter and restarting the timer. Finally, after aggregating enough consecutive failures to exceed the failure count threshold, the UE reports the Al model failure event to the gNB. In this manner, the counter aggregates failures while the timer prevents transient failures from accumulating indefinitely, together enabling reliable Al model performance monitoring. FIGs. 1 1A and 1 1 B: Cumulative Distribution Function Comparison Graph based on absolute squared generalized cosine (SGSC)

[00167] FIGs. 11 A and 1 1 B illustrate examples of graphs depicting a cumulative distribution function comparing the squared generalized cosine similarity achieved by an Al-based CSI compression model versus a baseline compression in accordance with some embodiments.

[00168] In one embodiment, as illustrated in FIG. 1 1 A, the squared generalized cosine similarity (SGCS) is used as the similarity metric to evaluate the Al compression model performance. SGCS measures how closely the reconstructed CSI matches the original target CSI, with values from 0 to 1 indicating worse to better similarity.

[00169] FIGs. 1 1 A and 11 B illustrate example cumulative distribution function (CDF) plots of the SGCS values for an Al-based compression model and a baseline e-type 2 compression scheme over a range of channel conditions. The CDFs demonstrate the variability and distribution of SGCS values, with the Al-based model generally achieving higher SGCS values than the e-type 2 baseline. However, both schemes exhibit variations in SGCS across different channels. With higher overhead, the percentage of low SGCS values decreases for the Al model. The SGCS metric provides an effective way to statistically evaluate and compare CSI reconstruction accuracy.

[00170] That is, FIG. 11 A depicts a graph illustrating cumulative distribution function (CDF) plots for comparing the squared generalized cosine similarity (SGCS) achieved by an Al-based CSI compression model versus an e-type 2 baseline scheme. Graph 1 1 10A depicts a CDF plot illustrating the distribution of SGCS values for the Al-based compression model. In FIG. 1 1 B, Graph 1 1 10B depicts a CDF plot illustrating the distribution for the e-type 2 compression scheme.

[00171] The x-axis shows the range of possible SGCS values from 0 to 1 , with 1 representing maximum similarity between original and reconstructed CSI. The y- axis shows the CDF which represents the cumulative probability that the SGCS value is less than or equal to the corresponding x-axis value.

[00172] The CDF curve for the Al-based model in graph 1 1 10A is shifted to the right compared to the e-type 2 baseline curve. This demonstrates that the Al model yields higher SGCS values overall. For example, the Al model has a 0.8 cumulative probability (80% chance) of producing an SGCS value greater than 0.8. However, in graph 1 1 1 B, using an e-type 2 compression scheme, there is only a 0.8 probability of getting SGCS higher than 0.65. This indicates that the Al model results in higher SGCS values overall compared to e-type 2. For example, the Al CDF reaches 0.8 cumulative probability at around 0.8 SGCS. But the e-type 2 CDF reaches 0.8 probability at only around 0.65 SGCS.

[00173] Thus, the Al model is more likely to produce high SGCS values close to 1 , while e-type 2 produces lower SGCS values concentrated around 0.6-0.7.

[00174] In summary, the CDF plot comparison visually conveys that the Al-based CSI compression model achieves significantly improved SGCS reconstruction accuracy compared to the legacy e-type 2 compression scheme across a wide range of channel conditions. This demonstrates the Al model's capabilities for robust CSI feedback performance.

FIGs. 12A and 12B: Cumulative Distribution Function Comparison Graph based on relative squared generalized cosine (SGSC)

[00175] FIGs. 12A and 12B illustrate examples of graphs comparing the performance difference between an Al-based CSI compression model and an optimized codebook baseline in accordance with some embodiments. That is, FIGs. 12A and 12B illustrate cumulative distribution function (CDF) plots comparing the performance difference between an Al-based CSI compression mode in graph 1210A in FIG. 12A and an optimized e-type 2 codebook baseline in graph 1210B in FIG. 12B. The plots show the distribution of the delta key performance indicators (KPI) metric, calculated as the difference in squared generalized cosine similarity (SGCS) between the Al and codebook models. It should be noted that in codebook-based CSI feedback, codebook-based feedback, the UE searches the nearest codeword in a predefined codebook, which is shared by the UE and the base station (e.g., gNB). Then, the UE feedbacks the index of the selected codeword to the BS. The BS obtains the corresponding codeword by looking up the codebook.

[00176] In one example, the delta KPI is calculated as being equal to the difference between an Al model SGCS and a codebook SGCS (e.g., delta KPI = Al based solution - eType 2 based solution, as illustrated in equation 1 : Pldiff ⁼ PIgenie ' ~ KPI_actuai, 0),

[00177] where KPI_diff KPI difference, which is the performance metric used in Figure 13 for monitoring the Al model, KPI_genie refers to the KPI achieved by an ideal model (e.g., relative SGCS) achieved by the baseline codebook model, and the KPI_actual refers to the actual KPI achieved in operation where the SGCS is achieved by the Al CSI compression model being monitored.

[00178] Thus, the KPI_diff is calculating the difference between the SGCS achieved by the baseline codebook model and the Al model. If KPl_diff is negative, it means the Al model achieved better SGCS than the codebook (desirable). If KPldiff is positive, it means the Al model performed worse than the codebook in terms of SGCS (undesirable). By monitoring this difference metric over time, the performance of the Al model can be evaluated relative to the standardized codebook baseline.

[00179] For example, the x-axis shows the range of delta KPI values, with negative values indicating the Al model achieved better SGCS in graph 1210A in FIG. 12A compared to the codebook graph 1210B. The e-type 2 codebook parameters in graph 1210B in FIG. 12B are optimized for maximum performance.

[00180] The CDF plot of graphs 1210A and 1210B demonstrates that most of the delta KPI values are negative, meaning the Al model results in higher SGCS and outperforms the optimized codebook for a majority of channel conditions. However, there is some variability in the positive region, indicating for certain channels the legacy codebook can achieve better SGCS. For performance monitoring purposes, positive deltas are undesirable as they imply the Al model is underperforming significantly compared to the standardized codebook scheme. The distribution gives insight into the likelihood of these events occurring.

[00181] Thus, the CDF plot comparison visually conveys how the Al-based CSI compression model outperforms the optimized codebook baseline in terms of SGCS similarity metric across a variety of channels. The delta KPI metric and distribution provides useful insights for performance monitoring.

FIG. 13: UE performance monitoring for Al based CSI compression model.

[00182] FIG. 13 provides an example illustration of a timing diagram 1300 with a UE 106 communicating with a gNB. In various embodiments, some of the signaling shown may be performed concurrently, in a different order than shown, or may be omitted. Additional signaling may also be performed as desired. As shown, this signaling may flow as follows as one example embodiment. The signaling shown in FIG. 13 may be used in conjunction with any of the systems, methods, and/or devices. In various embodiments, some of the signaling shown may be performed concurrently, in a different order than shown, or may be omitted. Additional signaling may also be performed as desired. As shown, this signaling may flow as follows as one example embodiment.

[00183] The signaling may begin with a UE, such as UE 106, receiving, from a gNB, an indication 1310 to activate Al based compression. For example, the base station may trigger the UE to perform the Al based compression model at the UE. In one example, the base station 102 (e.g., network “NW”) may further send to the UE 106 configuration information 1320 to enable, at the UE, the Al based compression model performance monitoring.

[00184] The configuration information may include, for example, radio resource control (RRC) configuration information indicating whether the Al based compression model performance monitoring is activated and one or more parameters configured for the Al based compression model performance monitoring, wherein the one or more parameters include a compression model threshold, an evaluation window, and a layer-1 (L1 ) indication duration. The configuration information may be used to assist in training an Al performance monitoring model offline using a training dataset.

[00185] The similarity metric may be a difference between a squared generalized cosine similarity (SGCS) of the reconstructed CSI and an SGCS of a defined CSI determined using a non-AI based compression model, wherein the difference is a delta SGSC. The defined CSI can be determined using a codebook-based feedback model. The UE can receive, from the gNB, radio resource control (RRC) configuration information indicating whether the Al based compression model performance monitoring is activated and an indication the non-AI based compression model is used for determining the delta SGSC. The RRC configuration information for the Al based compression model performance monitoring includes defining a time duration of the evaluation window and a time duration for between each L1 indication, defined criteria indicating a success or failure based on the compression model threshold or a number of success or failures in relation to the compression model threshold during the evaluation window.

[00186] The UE can receive from the reconstructed CSI, from the gNB, for the Al based compression model monitoring to the perform the Al based compression model monitoring at the UE. In one example, the UE side may monitor performance of the Al based compression model and determine the delta SGCS for one or more evaluation intervals within an evaluation window and determine a number of times the delta SGCS exceeds the compression model threshold within the evaluation window.

[00187] Additionally, as part of the UE monitoring performance, in one embodiment, an evaluation window can be used where the KPI difference is evaluated over periodic L1 indications within a window. Each L1 indication calculates the delta KPI between the Al and codebook models. If the delta KPI exceeds a positive threshold, indicating the Al model underperformed the codebook, that instance is counted as a failure. Within the evaluation window, if the number of failures exceeds a configured limit, a failure event is triggered. The delta KPI is typically defined so that a negative value indicates the Al outperformed the codebook. But the threshold is positive, so only cases where the codebook exceeds the Al are counted. In this manner, the evaluation window aggregates model performance over time.

[00188] In another embodiment, a counter-based method can alternatively be utilized. A counter can be used and incremented each time the delta KPI indicates the codebook outperformed the Al model for an L1 indication. The counter is reset when the Al model KPI exceeds the codebook KPI. When the counter exceeds a configured threshold, the UE reports an Al model failure event. This approach accumulates failures and passes over time to make reliable performance decisions.

[00189] In a further embodiment, a combined counter and timer can be used where the counter tracks aggregated failures and the timer is reset upon failures, expiring if no failures occur for a duration. The counter can be reset when the timer expires. When the counter exceeds a threshold prior to timer expiry, the UE reports failure. Together, the counter aggregates failures while the timer prevents transient failures from accumulating indefinitely. The integrated approach enables reliable performance monitoring.

[00190] The signaling may also include the UE, such as UE 106, transmitting a monitoring report 1330 (e.g., UE side monitoring report) to the gNB.

FIG. 14: Flow Chart for a Method of performing monitoring of an artificial intelligence (Al) based channel state information (CSI) compression model at a UE

[00191] FIG. 14 illustrates a flow chart of an example of a method 1400 for performing monitoring of an artificial intelligence (Al) based channel state information (CSI) compression model, at a UE, according to some embodiments. The method shown in FIG. 14 may be used in conjunction with any of the systems, methods, or devices illustrated in the Figures, among other devices. In various embodiments, some of the method elements shown may be performed concurrently, in a different order than shown, or may be omitted. Additional method elements may also be performed as desired.

[00192] In accordance with an embodiment, a method 1400 for performing monitoring of an artificial intelligence (Al) based channel state information (CSI) compression model, receive, from a next generation Node B (gNB), an indication to activate artificial intelligence (Al) based compression model performance monitoring at the UE, as shown in block 1402.

[00193] The method 1400 further comprises decoding configuration information for the Al based compression model performance monitoring received from the gNB, as shown in block 1404. The method 1400 further comprises measuring a channel state information (CSI) resource set (CSI-RS), received from the gNB, and calculating the CSI at the UE, as shown in block 1406. The method 1400 further comprises compressing the CSI, at the UE, using an Al based compression model to generate a compressed CSI, as shown in block 1408. The method 1400 further comprises reconstructing the compressed CSI at the UE using an Al based reconstruction model to generate the reconstructed CSI for the Al based compression model monitoring, as shown in block 1410.

[00194] The method 1400 further comprises determining a similarity metric between the CSI and the reconstructed CSI, as shown in block 1412. The method 1400 further comprises comparing the similarity metric to a compression model threshold, as shown in block 1414. The method 1400 further comprises transmitting a monitoring report to the gNB based on the comparison, as in block 1416. In one example, 1410 and 1412 can be implemented together using one Al model that output SGCS directly.

[00195] In some embodiments, the similarity metric is a squared generalized cosine similarity (SGCS) between the CSI and the reconstructed CSI. In some embodiments, the method 1400 can further comprise receiving, from the gNB, radio resource control (RRC) configuration information indicating whether the Al based compression model performance monitoring is activated and one or more parameters configured for the Al based compression model performance monitoring, wherein the one or more parameters include a compression model threshold, an evaluation window, and a layer-1 (L1 ) indication duration.

[00196] In some embodiments, the method 1400 can further comprise configured to train an Al performance monitoring model offline using a training dataset.

[00197] In some embodiments, the method 1400 can further comprise evaluating performance of the Al based compression model by determining the similarity metric over an evaluation window. In some embodiments, the RRC configuration information for the Al based compression model performance monitoring includes defining a time duration of the evaluation window and a time duration for between each L1 indication, defined criteria indicating a success or failure based on the compression model threshold or a number of success or failures in relation to the compression model threshold during the evaluation window.

[00198] In some embodiments, the method 1400 can further comprise determining a number of times the similarity metric indicates a pass or failure in relation to the compression model threshold for one or more evaluations performed during the evaluation window.

[00199] In some embodiments, the method 1400 can further comprise determining the similarity metric for at one or more evaluation intervals within an evaluation window, determining each of the evaluation intervals for which the similarity metric indicates a pass or failure in comparison to the compression model threshold during the evaluation window, and transmitting the monitoring report upon determining a number failures of the similarity metric exceeds a configured failure count above the compression model threshold for the evaluation window.

[00200] In some embodiments, the method 1400 can further comprise performing the Al based compression model monitoring using a counter and time.

[00201] In some embodiments, an apparatus is configured to cause a user equipment (UE) to perform operations of the method 1400. In some embodiments, an apparatus is disclosed that is configured to cause a user equipment (UE) to perform any of the operations of the method 1400.

[00202] In some embodiments, a computer program product, comprising computer instructions which, when executed by one or more processors, perform any of the operations described in the method 1400.

FIG. 15: Flow Chart for a Method of performing monitoring of an artificial intelligence (Al) based channel state information (CSI) compression model at a UE.

[00203] FIG. 15 illustrates a flow chart of an example of a method 1500 for performing monitoring of an artificial intelligence (Al) based channel state information (CSI) compression model, at a UE, according to some embodiments. The method shown in FIG. 15 may be used in conjunction with any of the systems, methods, or devices illustrated in the Figures, among other devices. In various embodiments, some of the method elements shown may be performed concurrently, in a different order than shown, or may be omitted. Additional method elements may also be performed as desired.

[00204] In accordance with an embodiment, a method 1500 for performing monitoring of an artificial intelligence (Al) based channel state information (CSI) compression model, may receive, from a next generation Node B (gNB), an indication to activate artificial intelligence (Al) based compression model performance monitoring at the UE, as shown in block 1502.

[00205] The method 1500 further comprises decoding configuration information for the Al based compression model performance monitoring received from the gNB, as shown in block 1504. The method 1500 further comprises measuring channel state information (CSI) resource set (CSI-RS) received from the gNB and determine CSI at the UE, as shown in block 1506. The method 1500 further comprises compressing the CSI, at the UE, using an Al based compression model to generate a compressed CSI, as shown in block 1508.

[00206] The method 1500 further comprises decoding a reconstructed CSI, received from the gNB, for the Al based compression model monitoring performed at the UE, as shown in block 1510.

[00207] The method 1500 further comprises determining a similarity metric between the CSI and the reconstructed CSI, as shown in block 1512. The method 1400 further comprises comparing the similarity metric to a compression model threshold, as shown in block 1514. The method 1500 further comprises transmitting a monitoring report to the gNB based on the comparison, as in block 1516.

[00208] In some embodiments, the similarity metric is a similarity metric is a difference between a squared generalized cosine similarity (SGCS) of the reconstructed CSI and an SGCS of a defined CSI determined using a non-AI based compression model, wherein the difference is a delta SGSC. In some embodiments, the defined CSI is determined using a codebook-based feedback model.

[00209] In some embodiments, the method 1500 can further comprise receiving, from the gNB, radio resource control (RRC) configuration information indicating whether the Al based compression model performance monitoring is activated and an indication the non-AI based compression model is used for determining the delta SGSC.

[00210] In some embodiments, the method 1500 can further comprise receiving the reconstructed CSI, from the gNB, for the Al based compression model monitoring to the perform the Al based compression model monitoring at the UE.

[00211] In some embodiments, the method 1500 can further comprise determining the delta SGCS for one or more evaluation intervals within an evaluation window; determining a number of times the delta SGCS exceeds the compression model threshold within the evaluation window; and transmitting the monitoring report upon determining a number of times the delta SGCS exceeds the compression model threshold is above a configured failure count.

[00212] In some embodiments, the method 1500 can further comprise incrementing a counter when the delta SGCS indicates the Al based compression model fails in comparison to the non-AI based compression model; resetting the counter based on expiration of a timer; and transmitting the monitoring report when the counter exceeds the compression model threshold.

[00213] In some embodiments, the method 1500 can further comprise restarting a timer when the delta SGCS indicates the Al based compression model fails in comparison to the non-AI based compression model.

[00214] In some embodiments, an apparatus is configured to cause a user equipment (UE) to perform operations of the method 1500.

[00215] In some embodiments, a computer program product, comprising computer instructions which, when executed by one or more processors, perform any of the operations described in the method 1500.

FIG. 16: Flow Chart for a Method of assisting a UE to perform monitoring of an artificial intelligence (Al) based channel state information (CS I) compression model at a gNB.

[00216] FIG. 16 illustrates a flow chart of an example of a method 1600 for assisting a UE to perform monitoring of an artificial intelligence (Al) based channel state information (CSI) compression model, at a base station (e.g., a gNB), according to some embodiments. The method shown in FIG. 16 may be used in conjunction with any of the systems, methods, or devices illustrated in the Figures, among other devices. In various embodiments, some of the method elements shown may be performed concurrently, in a different order than shown, or may be omitted. Additional method elements may also be performed as desired.

[00217] In accordance with an embodiment, a method 1600 for assisting a UE to perform monitoring of an artificial intelligence (Al) based channel state information (CSI) compression model, receive, from a next generation Node B (gNB), an indication to activate artificial intelligence (Al) based compression model performance monitoring at the UE, as shown in block 1602.

[00218] The method 1600 further comprises encoding, at the gNB, an indication to activate artificial intelligence (Al) based compression model performance monitoring, as shown in block 1602. The method 1600 further comprises encoding, at the gNB, channel state information (CSI), as shown in block 1604. The method 1600 further comprises transmitting, to the UE, the indication to activate the Al based compression model performance monitoring at the UE to enable the UE to perform the Al based compression model monitoring by: compressing the CSI using an Al based compression model to generate a compressed CSI; using a reconstructed CSI for the Al based compression model monitoring; determining a similarity metric between the CSI and the reconstructed CSI; and comparing the similarity metric to a compression model threshold, as shown in block 1606. The method 1600 further comprises decoding, a monitoring report, received from the UE, based on the comparison, as shown in block 1608.

[00219] In some embodiments, an apparatus is configured to cause a next generation Node B (gNB) to perform operations of the method 1600. An apparatus of a next generation Node B (gNB) can comprise one or more processors, coupled to a memory, configured to perform operations of the method 1600.

[00220] In some embodiments, a computer program product, comprising computer instructions which, when executed by one or more processors, perform any of the operations described in the method 1600.

[00221] Embodiments of the present disclosure may be realized in any of various forms. For example, some embodiments may be realized as a computer- implemented method, a computer readable memory medium, or a computer system. Other embodiments may be realized using one or more custom-designed hardware devices such as ASICs. Still other embodiments may be realized using one or more programmable hardware elements such as FPGAs.

[00222] In some embodiments, a non-transitory computer-readable memory medium may be configured so that it stores program instructions and/or data, where the program instructions, if executed by a computer system, cause the computer system to perform a method, e.g., any of the method embodiments described herein, or, any combination of the method embodiments described herein, or, any subset of any of the method embodiments described herein, or, any combination of such subsets.

[00223] In some embodiments, a device (e.g., a UE 106) may be configured to include a processor (or a set of processors) and a memory medium, where the memory medium stores program instructions, where the processor is configured to read and execute the program instructions from the memory medium, where the program instructions are executable to implement any of the various method embodiments described herein (or, any combination of the method embodiments described herein, or, any subset of any of the method embodiments described herein, or, any combination of such subsets). The device may be realized in any of various forms.

[00224] Any of the methods described herein for operating a user equipment (UE) may be the basis of a corresponding method for operating a base station, by interpreting each message/signal X received by the UE in the downlink as message/signal X transmitted by the base station, and each message/signal Y transmitted in the uplink by the UE as a message/signal Y received by the base station.

[00225] Although the embodiments above have been described in considerable detail, numerous variations and modifications will become apparent to those skilled in the art once the above disclosure is fully appreciated. It is intended that the following claims be interpreted to embrace all such variations and modifications.

Claims

CLAIMS What is claimed is:

1 . An apparatus of a user equipment (UE) comprising: one or more processors, coupled to a memory, configured to: receive, from a next generation Node B (gNB), an indication to activate artificial intelligence (Al) based compression model performance monitoring at the UE; decode configuration information for the Al based compression model performance monitoring received from the gNB; measure channel state information (CSI) resource set (CSI-RS), received from the gNB, and determine CSI at the UE; compress the CSI, at the UE, using an Al based compression model to generate a compressed CSI; reconstruct the compressed CSI at the UE using an Al based reconstruction model to generate the reconstructed CSI for the Al based compression model monitoring; determine a similarity metric between the CSI and the reconstructed CSI; compare the similarity metric to a compression model threshold; and transmit a monitoring report to the gNB based on the comparison.

2. The apparatus of claim 1 , wherein the similarity metric is a squared generalized cosine similarity (SGCS) between the CSI and the reconstructed CSI.

3. The apparatus of claim 1 , wherein the one or more processors are further configured to receive, from the gNB, radio resource control (RRC) configuration information indicating whether the Al based compression model performance monitoring is activated and one or more parameters configured for the Al based compression model performance monitoring, wherein the one or more parameters include a compression model threshold, an evaluation window, and a layer-1 (L1 ) indication duration.

4. The apparatus of claim 1 , wherein the one or more processors are further configured to train an Al performance monitoring model offline using a training dataset.

5. The apparatus of claim 1 , wherein the one or more processors are further configured to evaluate performance of the Al based compression model by determining the similarity metric over an evaluation window.

6. The apparatus of claim 3, the wherein RRC configuration information for the Al based compression model performance monitoring includes defining a time duration of the evaluation window and a time duration for between each layer-1 (L1 ) indication, defined criteria indicating a success or failure based on the compression model threshold or a number of success or failures in relation to the compression model threshold during the evaluation window.

7. The apparatus of claim 6, wherein the one or more processors are further configured to determine a number of times the similarity metric indicates a pass or failure in relation to the compression model threshold for one or more evaluations performed during the evaluation window.

8. The apparatus of claim 6, wherein the one or more processors are further configured to: determine the similarity metric for at one or more evaluation intervals within an evaluation window; determine each of the evaluation intervals for which the similarity metric indicates a pass or failure in comparison to the compression model threshold during the evaluation window; and transmit the monitoring report upon determining a number of failures of the similarity metric exceeds a configured failure count above the compression model threshold for the evaluation window.

9. The apparatus of claim 1 , wherein the one or more processors are further configured to perform the Al based compression model monitoring using a counter and time.

10. The apparatus of claim 1 , wherein the configuration information for the Al based compression model performance monitoring includes radio resource control (RRC) configuration information indicating both the Al based compression model performance monitoring is activated and one or more parameters for the Al based compression model performance monitoring, wherein the one or more parameters include a compression model threshold, an evaluation window, or a layer-1 (L1 ) indication duration.

1 1 . An apparatus of a user equipment (UE) comprising: one or more processors, coupled to a memory, configured to: receive, from a next generation Node B (gNB), an indication to activate artificial intelligence (Al) based compression model performance monitoring at the UE; decode configuration information for the Al based compression model performance monitoring received from the gNB; decode channel state information (CSI) received from the gNB; compress the CSI, at the UE, using an Al based compression model to generate a compressed CSI; decode reconstructed CSI, received the gNB, for the Al based compression model monitoring performed at the UE; determine a similarity metric between the CSI and the reconstructed CSI; compare the similarity metric to a compression model threshold; and transmit a monitoring report to the gNB based on the comparison.

12. The apparatus of claim 1 1 , wherein the similarity metric is a difference between a squared generalized cosine similarity (SGCS) of the reconstructed CSI and an SGCS of a defined CSI determined using a non-AI based compression model, wherein the difference is a delta SGSC.

13. The apparatus of claim 12, wherein the defined CSI is determined using a codebook based feedback model.

14. The apparatus of claim 12, wherein the one or more processors are further configured to receive, from the gNB, radio resource control (RRC) configuration information indicating whether the Al based compression model performance monitoring is activated and an indication the non-AI based compression model is used for determining the delta SGSC.

15. The apparatus of claim 12, wherein the one or more processors are further configured to: determine the delta SGCS for one or more evaluation intervals within an evaluation window; determine a number of times the delta SGCS exceeds the compression model threshold within the evaluation window; and transmit the monitoring report upon determining a number of times the delta SGCS exceeds the compression model threshold is above a configured failure count.

16. The apparatus of claim 12, wherein the one or more processors are further configured to: increment a counter when the delta SGCS indicates the Al based compression model fails in comparison to the non-AI based compression model; reset the counter based on expiration of a timer; and transmit the monitoring report when the counter exceeds the compression model threshold.

17. The apparatus of claim 12, wherein the one or more processors are further configured to restart a timer when the delta SGCS indicates the Al based compression model fails in comparison to the non-AI based compression model.

18. A method of performing artificial intelligence (Al) based compression model performance monitoring by a user equipment (UE), the method comprising: receiving, from a next generation Node B (gNB), an indication to activate artificial intelligence (Al) based compression model performance monitoring at the UE; decoding configuration information for the Al based compression model performance monitoring received from the gNB; measuring channel state information (CSI) received from the gNB; compressing the CSI, at the UE, using an Al based compression model to generate a compressed CSI; reconstructing the compressed CSI at the UE using an Al based reconstruction model to generate the reconstructed CSI for the Al based compression model monitoring; determining a similarity metric between the CSI and the reconstructed CSI; comparing the similarity metric to a compression model threshold; and transmitting a monitoring report to the gNB based on the comparison.

19. The method of claim 18, wherein the similarity metric is a squared generalized cosine similarity (SGCS) between the CSI and the reconstructed CSI.

20. The method of claim 18, further comprising receiving, from the gNB, radio resource control (RRC) configuration information indicating whether the Al based compression model performance monitoring is activated and one or more parameters configured for the Al based compression model performance monitoring, wherein the one or more parameters include a compression model threshold, an evaluation window, and a layer-1 (L1 ) indication duration.

21. The method of claim 18, further comprising training an Al performance monitoring model offline using a training dataset.

22. The method of claim 20, further comprising evaluating performance of the Al based compression model by determining the similarity metric over an evaluation window.

23. The method of claim 22, wherein the RRC configuration information for the Al based compression model performance monitoring includes defining a time duration of the evaluation window and a time duration for between each layer-1 (L1 ) indication, defined criteria indicating a success or failure based on the compression model threshold or a number of success or failures in relation to the compression model threshold during the evaluation window.

24. The method of claim 22, further comprising determining a number of times the similarity metric indicates a pass or failure in relation to the compression model threshold for one or more evaluations performed during the evaluation window.

25. The method of claim 18, further comprising: determining the similarity metric for at one or more evaluation intervals within an evaluation window; determining each of the evaluation intervals for which the similarity metric indicates a pass or failure in comparison to the compression model threshold during the evaluation window; and transmitting the monitoring report upon determining a number of failures of the similarity metric exceeds a configured failure count above the compression model threshold for the evaluation window.

26. The method of claim 18, further comprising performing the Al based compression model monitoring using a counter and time.

27. An apparatus configured to cause a user equipment (UE) to perform any of the methods of claims 18 to 26.

28. An apparatus of a next generation Node B (gNB) comprising: one or more processors, coupled to a memory, configured to: encode, at the gNB, an indication to activate artificial intelligence (Al) based compression model performance monitoring; encode, at the gNB, channel state information (CSI); transmit, to a user equipment (UE), the indication to activate the Al based compression model performance monitoring at the UE to enable the UE to perform the Al based compression model monitoring by: compressing the CSI using an Al based compression model to generate a compressed CSI; using a reconstructed CSI for the Al based compression model monitoring; determining a similarity metric between the CSI and the reconstructed CSI; and comparing the similarity metric to a compression model threshold; and decoding, a monitoring report, received from the UE, based on the comparison.

29. The apparatus of claim 28, wherein the similarity metric is a squared generalized cosine similarity (SGCS) between the CSI and the reconstructed CSI.

30. The apparatus of claim 28, wherein the one or more processors are further configured to transmit, to the UE, radio resource control (RRC) configuration information indicating whether the Al based compression model performance monitoring is activated and one or more parameters configured for the Al based compression model performance monitoring, wherein the one or more parameters include a compression model threshold, an evaluation window, and a layer-1 (L1 ) indication duration.

31. The apparatus of claim 30, wherein the RRC configuration information for the Al based compression model performance monitoring includes defining a time duration of the evaluation window and a time duration for between each L1 indication, defined criteria indicating a success or failure based on the compression model threshold or a number of success or failures in relation to the compression model threshold during the evaluation window.

32. The apparatus of claim 28, wherein the monitoring report includes a number of times the similarity metric indicates a pass or failure in relation to the compression model threshold for one or more evaluations performed during an evaluation window.

33. The apparatus of claim 28, wherein the monitoring report indicates: the similarity metric determined for at one or more evaluation intervals within an evaluation window; each of the evaluation intervals for which the similarity metric indicates a pass or failure in comparison to the compression model threshold during the evaluation window.

34. The apparatus of claim 33, wherein the one or more processors are further configured to receive, from the UE, the monitoring report upon determining a number of failures of the similarity metric exceeds a configured failure count above the compression model threshold for the evaluation window.

35. The apparatus of claim 28, wherein the similarity metric is a difference between a squared generalized cosine similarity (SGCS) of the reconstructed CSI and an SGCS of a defined CSI determined using a non-AI based compression model, wherein the difference is a delta SGSC.

36. The apparatus of claim 35, wherein the defined CSI is determined using a codebook-based feedback model.

37. The apparatus of claim 35, wherein the one or more processors are further configured to transmit, to the UE, radio resource control (RRC) configuration information indicating whether the Al based compression model performance monitoring is activated and an indication the non-AI based compression model is used for determining the delta SGSC.

38. The apparatus of claim 35, wherein the one or more processors are further configured to transmit, the reconstructed CSI, to the UE, to enable the UE to the perform the Al based compression model monitoring at the UE.

39. The apparatus of claim 35, wherein the monitoring report includes: the delta SGCS determined for one or more evaluation intervals within an evaluation window; and a number of times the delta SGCS exceeds the compression model threshold within the evaluation window.

40. The apparatus of claim 35, wherein the one or more processors are further configured to receive, from the UE, the monitoring report upon determining a number of times the delta SGCS exceeds the compression model threshold is above a configured failure count.

41 . The apparatus of claim 35, wherein the one or more processors are further configured to receive, from the UE, the monitoring report upon a counter, which is incremented when the delta SGCS indicates the Al based compression model fails in comparison to the non-AI based compression model and based on expiration of a timer, exceeds the compression model threshold.

42. A user equipment (UE) configured to perform any of the operations described herein.

43. A next generation node B (gNB) configured to perform any of the operations described herein.

44. A computer program product, comprising computer instructions which, when executed by one or more processors, perform any of the operations described herein.

45. A baseband processor configured to perform one or more of the method claims 18 to 26.