Reconstructed CSI transmission from a network to a user equipment (UE)

Abstract

An apparatus of a UE comprising one or more processors coupled to a memory and configured to determine channel state information (CSI) at the UE; compress the CSI, at the UE, using an Al based compression model to generate a compressed CSI; encode, for transmission to the gNB, the compressed CSI; receive, a CSI-RS resource set, received from the from the gNB, configured for Al based compression model performance monitoring, wherein the CSI-RS resource set comprises one or more CSI-RS that are precoded using reconstructed CSI that are reconstructed from the compressed CSI using an Al based decoder model at the gNB; determine, at the UE, one or more performance monitoring metrics, for the Al based compression model performance monitoring, based on comparing the reconstructed CSI and the CSI; and encode, for transmission to a gNB, monitoring results to the gNB based on the one or more performance monitoring metrics.

Description

RECONSTRUCTED CSI TRANSMISSION FROM A NETWORK TO A USER EQUIPMENT (UE)FIELD

[0001] Embodiments of the invention relate to wireless communications, including apparatuses, systems, and methods for user equipment (UE) side performance monitoring for transmitting reconstructed channel state information (CSI) from a network to a user equipment (UE) 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 typecommunications 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, in two-sided Al models for CSI compression, the UE has the encoder input while the gNB has the decoder output. This presents a challenge for performance monitoring, as the UE needs the reconstructed CSI from the gNB to evaluate the model, while the gNB needs the original CSI from the UE. To enable effective monitoring, information exchange is required between the UE and gNB. The key options under discussion include intermediate key performance indicators (KPIs) like SGCS, eventual KPIs like BLER, legacy CSI reporting, and input / output data monitoring. Intermediate KPI monitoring has been proposed using UE-side evaluation based on reconstructed CSI from the gNB or target CSI measured via SRS. However, conveying the required information introduces overhead and latency. The core issue is transmitting the decoder output from the gNB to the UE to facilitate UE-side model monitoring with reasonable overhead and performance impact. Accordingly, a need exists to provide the UE with the necessaryreconstructed CSI information from the gNB while meeting constraints on overhead, complexity, and latency for practical systems.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: determine channel state information (CSI) at the UE; compress the CSI, at the UE, using an artificial intelligence (Al) based compression model to generate a compressed CSI; encode, for transmission to the gNB, the compressed CSI; receive, a channel state information reference signal (CSI-RS) resource set, received from the from the gNB, configured for Al based compression model performance monitoring, wherein the CSI-RS resource set comprises one or more CSI-RS that are precoded using reconstructed CSI that are reconstructed from the compressed CSI using an Al based decoder model at the gNB; determine, at the UE, one or more performance monitoring metrics, for the Al based compression model performance monitoring, based on comparing the reconstructed CSI and the CSI; and encode, for transmission to a gNB, monitoring results to the gNB based on the one or more performance monitoring metrics.

[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, for transmission to a user equipment (UE), channel state information (CSI) to enable the UE to compress the CSI, at the UE, using an artificial intelligence (Al) based compression model to generate a compressed CSI; decode the compressed CSI received from the UE; encode, for transmission to the UE, a channel state information reference signal (CSI-RS) resource set, received from the from the gNB, configured for Al based compression model performance monitoring, wherein the CSI-RS resource set comprises one or more CSI-RS that are precoded using reconstructed CSI that are reconstructed from the compressed CSI using an Al based decoder model at the gNB to enable the UE to determine,at the UE, one or more performance monitoring metrics, for the Al based compression model performance monitoring, based on comparing the reconstructed CSI and the CSI; and encode monitoring results, received from the UE, based on the one or more performance monitoring metrics.

[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 A 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 transmitting reconstructed channel state information (CSI) according to some embodiments.

[0023] FIG. 10A illustrates an example of transmitting reconstructed channel state information (CSI) in accordance with some embodiments.

[0024] FIGs. 10B and 10C illustrate examples of transmitting reconstructed channel state information (CSI) for intermediate KPI calculation in accordance with some embodiments.

[0025] Fig. 1 1 illustrates an example timing diagram signaling between a user equipment (UE) and a next generation node B (gNB) for transmitting reconstructed channel state information (CSI) to support UE performance monitoring for artificial intelligence (Al) based channel state information (CSI) compression model according to some embodiments.

[0026] FIG. 12 illustrates an example flow chart of a method of performing user equipment (UE) side performance monitoring for artificial intelligence (Al) basedchannel state information (CSI) compression model, at a user equipment (UE), according to some embodiments.

[0027] FIG. 13 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.

[0028] 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 DESCRIPTIONTerms

[0029] The following is a glossary of terms used in this disclosure:

[0030] 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 thefirst 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.

[0031] 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.

[0032] 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”.

[0033] 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.

[0034] 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, portableInternet 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.

[0035] 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.

[0036] 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.

[0037] 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 maydefine 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.

[0038] 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.

[0039] 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.

[0040] 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 thresholdvalue (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.

[0041] 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.

[0042] 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.

[0043] 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.

[0044] 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 relateto UE side performance monitoring for artificial intelligence (Al) based channel state information (CSI) compression model.

[0045] 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.

[0046] 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.

[0047] 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

[0048] 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.

[0049] 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 maybe referred to herein as a “user equipment” (UE). Thus, the user devices 106 are referred to as UEs or UE devices.

[0050] 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.

[0051] 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’.

[0052] 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.

[0053] 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 andsimilar devices over a geographic area via one or more cellular communication standards.

[0054] 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.

[0055] 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.

[0056] 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.

[0057] 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 11 , 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.

[0058] 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.

[0059] 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 xEV-DO I 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.

[0060] 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

[0061] 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.

[0062] 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.

[0063] 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 corenetwork, and / or the core network may provide a telephone network (e.g., among other UE devices serviced by the cellular service provider).

[0064] 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.

[0065] 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.

[0066] 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.).

[0067] 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.

[0068] 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.

[0069] 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.

[0070] 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 configurationinformation 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

[0071] 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.

[0072] 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.

[0073] 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.

[0074] 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-transitorycomputer-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.

[0075] 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

[0076] 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 (SOC), 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.

[0077] 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.

[0078] 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.

[0079] 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 beswitched 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.

[0080] 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.

[0081] 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 andexecuted 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 elllCC 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.

[0082] 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 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.

[0083] As shown, the SOC 400 may include processor(s) 402, which may execute program instructions for the communication device 106 and displaycircuitry 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.

[0084] 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.

[0085] 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.

[0086] Further, as described herein, cellular communication circuitry 430 and short to medium range wireless communication circuitry 429 may each include oneor 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.

[0087] 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

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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 receivesinstructions 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).

[0093] 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.

[0094] 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.

[0095] 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 anASIC (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.

[0096] 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.

[0097] 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

[0098] 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.

[0099] 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., saidcircuitries may be separately included in more than one device for Cloud-RAN (C- RAN) implementations).

[0100] 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.

[0101] 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.

[0102] 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 be 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).

[0103] 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.

[0104] 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 RFsignals 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.

[0105] 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.

[0106] 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.

[0107] 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 moremixers 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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 alsoinclude 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.

[0116] 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).

[0117] 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.

[0118] 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.

[0119] 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 isstill 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.

[0120] If there is no data traffic activity for an extended period of time, then the device 600 may transition off to an RRCJdle 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.

[0121] 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.

[0122] 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 transmissionbetween a UE and a gNB, or decode a message received between a UE and a gNB.

[0123] For example, the RF circuitry 606 and FEM circuitry 608 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; application circuitry 602 can be used to compress the CSI, at the UE, using an Al based compression model to generate a compressed CSI; and 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 application circuitry 602 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 the RF circuitry 606 and FEM circuitry 608 can 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

[0124] 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.

[0125] 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.

[0126] The baseband circuitry 604 may further include one or more interfaces to communicatively couple to other circuitries / devices, such as a memory interface712 (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

[0127] 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 .

[0128] 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.

[0129] 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 channelsfrom 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.

[0130] 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.

[0131] 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.).

[0132] 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 RadioBearers, 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.

[0133] The UE 106 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.

[0134] The non-access stratum (NAS) protocols 806 form the highest stratum of the control plane between the UE 106 and the MME 621. The NAS protocols 806 support the mobility of the UE 106 and the session management procedures to establish and maintain IP connectivity between the UE 106 and the P-GW.

[0135] 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 core network. 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.

[0136] 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.

[0137] 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.

[0138] FIG. 8B illustrates an example of an autoencoder-based two-sided framework for implicit CSI feedback enhancement in accordance with some embodiments.

[0139] 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.

[0140] 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 VAto 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

[0141] When using an Al-based compression of channel state information(CSI), there is a need to monitor the ongoing performance of the Al model to detectany 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.

[0142] To overcome these challenges, embodiments provide herein enable UE- side monitoring and reporting of Al-based CSI compression model performance. This is achieved by the base station (e.g., gNB) to encode and transmit the reconstructed CSI to the UE to enable UE side model performance monitoring. In one embodiment, a first solution utilizes implicit CSI transmission by precoded CSI reference signals (CSI-RS), allowing the UE to derive reconstructed CSI and calculate key performance indicators (KPIs) by comparing to original CSI. This leverages existing CSI-RS but the changed channel over time must be accounted for.

[0143] In an additional embodiment, a second solution utilizes explicit transmission of reconstructed CSI through control signaling or data channel communication. This allows accurate monitoring but introduces overhead. An optimal balance between overhead, performance impact, and monitoring accuracy is desired. Therefore, some embodiments provide implicit or explicit CSI transmission solutions that enable UE side monitoring for Al based CSI compression and reconstruction. The solutions aim to balance overhead minimization, accuracy, and performance factors.

[0144] 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 monitoringprocedures 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.

[0145] FIG. 9 provides an example illustration 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 FIG. 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.

[0146] In one embodiment, FIG. 9 illustrates an example timing diagram 900 signaling between a user equipment (UE) and a base station (e.g., a next generation node B (gNB)) for transmitting reconstructed channel state information (CSI). The signaling may begin with a UE, such as UE 106, receiving 910, from a gNB, a CSI-RS configuration information configured for Al based compression model performance monitoring. For example, the base station 102 (e.g., network “NW” or gNB) may further send to the UE 106 configuration information 912 to enable, at the UE, the Al based compression model performance monitoring.

[0147] The signaling may include the UE receiving 912, from the gNB, CSI-RS transmission. The CSI-RS can be a CSI-RS resource that includes one or more CSI-RS that are precoded using reconstructed CSI that are reconstructed from the compressed CSI using an Al based decoder model at the gNB. The signaling mayinclude the UE transmitting 914 performance monitoring results to the gNB based on the one or more performance monitoring metrics.

[0148] 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.

[0149] The RRC configuration information for the Al based compression model performance monitoring can include 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. The UE may monitor the performance of the Al based compression model. In some embodiments, the UE may monitor the performance of the Al based compression model by determining the similarity metric over an evaluation window, as illustrated in FIGs. 10A-10C.

[0150] Thus, in one example, reconstructed CSI may be transmitted from the gNB to the UE to enable performance monitoring. In one example, implicit CSI transmission can be used utilizing precoded CSI reference signals (CSI-RS). Using implicit CSI transmission, the gNB can configure a new CSI-RS resource set specifically for performance monitoring. The CSI-RS ports in this new CSI-RS resource set are precoded using the reconstructed CSI from the Al decoder model. The new usage of a CSI-RS resource set for Al based CSI compression performance monitoring can be indicated as part of the RRC configuration. The UE can measure the precoded CSI-RS and derive the reconstructed CSI to calculate performance metrics like hypothetical block error rate (BLER) or squared generalized cosine similarity (SGCS) by comparison with the original CSI. Thisleverages existing CSI-RS mechanisms for implicit conveyance while minimizing overhead. In one example, a new information element (IE) can be used, such as an NZP-CCSI-RS-ResourceSet IE for “AI-CSI performance monitoring ENUMBERATED {true]”.

[0151] In an additional example, since the changing channel over time must be accounted for, explicit CSI transmission can be used, where the gNB directly transmits the reconstructed CSI to the UE via downlink control signaling or using a data channel. This allows accurate monitoring but introduces higher overhead. In both cases, the transmitted CSI is associated with the original CSI measurement and report from the UE based on timing relationships. The solutions aim to balance overhead, performance impact, and monitoring accuracy.

[0152] 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.FIG. 10A-B: Transmitting reconstructed channel state information (CSI) for UE performance monitoring

[0153] FIG. 10A illustrates an example of transmitting reconstructed channel state information (CSI) for UE performance monitoring for artificial intelligence (Al) based channel state information (CSI) compression model in accordance with some embodiments.

[0154] In one example, as depicted in FIG. 10A, for CSI-RS transmission, CSI- RS resources for performance monitoring can be configured in a same subframe as existing CSI-RS resources meant for inference, or in a different subframe.

[0155] The inference CSI-RS can be periodic (P), semi-persistent (Sp), or aperiodic triggered (e.g., P / Sp) CSI-RS resource set for inferencing. It should be noted, as used in FIG. 10A, the variable (P) can refer to periodic CSI-RS configuration where the CSI-RS is transmitted at regular, pre-configured intervals and the variable (Sp) can refer to semi-persistent CSI-RS configuration where the CSI-RS can be transmitted in regular bursts but the periodicity can be reconfigureddynamically via signaling. The variable (P / Sp) can refer to the two options for configuring regular / recurring CSI-RS transmission as either strictly periodic, or semi-persistent which allows some flexibility in changing the transmission periodicity.

[0156] For example, FIG. 10A depicts a P / Sp configuration 1010 of CSI-RS resource sets for inference and CSI-RS resource sets for performance monitoring. The inference CSI-RS and monitoring CSI-RS can be configured in the same subframe or different subframes. The monitoring CSI-RS may have a larger periodicity than the inference CSI-RS.

[0157] For example, a gNB may configure 32 CSI-RS ports for downlink MIMO measurement by the UE. The inference CSI-RS resource set is configured as periodic, semi-persistent, or aperiodic triggered. This inference resource set has multiple CSI-RS ports transmitted - for example 32 ports corresponding to the gNB's number of ports used to measure the downlink MIMO measurement. In one embodiment, the inference CSI-RS is configured as a non-zero power CSI-RS (NZP-CSI-RS) resource set. NZP-CSI-RS refers to reference signals for CSI measurement that have non-zero power. The inference NZP-CSI-RS resource set can be periodic, semi-persistent, or aperiodic triggered. This inference set has multiple CSI-RS ports. For example, the inference set can have the same number of ports as the gNB's number of ports used for measurement of a downlink MIMO transmission.

[0158] In one embodiment, the monitoring CSI-RS can be configured as a separate resource set (e.g., P / Sp CSI-RS resource set for monitoring). This monitoring CSI-RS resource set can be periodic or semi-persistent but with a larger periodicity compared to the inference resource set. In one example, the monitoring CSI-RS is also configured as an NZP-CSI-RS resource set, but with a larger periodicity compared to the inference set. The gNB configures the number of layers, and correspondingly CSI-RS ports that are on the same layer, to be measured for monitoring purposes. In one embodiment, the network may always configure only layer 1. As an alternative, the network can configure a maximumrank. The maximum rank can determine the number of CSI-RS ports as each port corresponds to a layer. In the event the UE reported rank is lower than the maximum rank (i.e., precoder rank is less than the maximum ran), a number of the monitoring CSI-RS ports may not be transmitted. The UE can adapt based on the UE’s own reported rank.

[0159] In one example, those of the periodic or semi-persistent CSI-RS resources configured for performance monitoring can be configured by the gNB. These monitoring resources can have a larger periodicity compared to the inference CSI-RS. The key aspect is that the monitoring CSI-RS ports are precoded by the gNB using the reconstructed CSI obtained from the UE's feedback. This enables the UE to derive the reconstructed CSI by measuring the precoded monitoring CSI-RS. For example, the CSI-RS in the CSI-RS resource set, configured for the Al based compression model performance monitoring, can be a non-zero-power (NZP) CSI-RS and can be scheduled periodically or semi- persistently, where the NZP-CSI-RS in the CSI-RS resource set are configured in a same subframe as a CSI-RS resources used for inferencing at the UE.

[0160] A precoding delay can be configured through an offset value to account for the processing time at the gNB between the UE's CSI report and transmitting the monitoring CSI-RS precoded with the reconstructed CSI, where the UE’s CSI report can be used as a reference to calculate the processing time. Alternatively, the processing time can use DL CSI-RS transmission as a reference and the processing time, which may include the UE receiving a CSI-RS, calculating the CSI report, and gNB processing time for reconstructed CSI and applying the reconstructed CSI on precoded CSI-RS transmission.

[0161] For example, as illustrated in example 1012 of FIG. 10B, a gNB may continuously transmit the periodic / semi-persistent CSI-RS resource set for inference at time intervals TO, T1 , T2 etc. The UE measures the inference CSI-RS at these intervals to generate CSI reports. The precoding delay is between T1 and T2 and T3 and T4 where the gNB sends the P / Sp CSI-RS resources configured for performance monitoring.

[0162] In addition to periodic / semi-persistent transmission, the monitoring CSI- RS can also be aperiodic triggered (e.g., ap triggering), as illustrated in Fig. 10B, by downlink control information (DCI) signaling either along with inference CSI-RS or separately. When triggered separately, the number of monitoring CSI-RS ports can be adapted based on the rank indicator in the UE's previous CSI report.

[0163] For example, in one example, a single DCI triggers both the aperiodic inference CSI-RS and monitoring CSI-RS together and one DCI signal triggers the aperiodic transmission of both resource sets. Alternatively, in one example, a first DCI triggers the aperiodic CSI-RS for inference and second DCI triggers the aperiodic CSI-RS for monitoring. Thus, separate DCI signals can trigger the inference and monitoring CSI-RS aperiodically.FIG. 10C: Transmitting reconstructed channel state information (CSI) using Eventual KPI for UE performance monitoring

[0164] Fig. 10C illustrates an example 1050 of transmitting reconstructed channel state information (CSI) using intermediate KPI for UE for artificial intelligence (Al) based channel state information (CSI) compression model in accordance with some embodiments. It should be noted that as used herein the “eventual KPI” can be a hypothetical BLER and intermediate KPI’s can be the SGSC. In some embodiments, there can be a mixed use of hypothetical BLER and SGSC.

[0165] In one example, the performance monitoring metrics, for the Al based compression model performance monitoring, can be a hypothetical block error rate (BLER) for performance evaluation. The UE can calculate a hypothetical block error rate (BLER) based on the received CSI-RS resources for performance monitoring. The hypothetical BLER can be compared to a reference hypothetical BLER calculated using ideal CSI.

[0166] On CSI-RS for inference, a target CSI can be obtained by calculating the target CSI (ideal eigenvector of the measured channel matrix H and rank). A number of layers used for performance monitoring determines how the idealhypothetical BLER can be calculated based on the target CSL The hypothetical BLER can be calculated based on H * Videal(e.g., (detailed mapping is implementation based using physical (PHY) layer abstraction).

[0167] On CSI-RS for monitoring, the hypothetical BLER can be determined based on measured CSI-RS, which may be H * Vai)

[0168] A correlation link between the monitoring CSI-RS and inference CSI-RS can be configured, either through explicit configuration signaling or implicit reference based on the latest CSI-RS transmission timing. By comparing the hypothetical BLER to the ideal hypothetical BLER, the Al model performance degradation can be determined. If the monitoring BLER is less than the ideal BLER by a threshold, the Al model performance is considered degraded. The threshold can be adjusted to account for channel change between measurement and monitoring CSI-RS due to UE mobility or application delay.

[0169] It should be noted that since the Al precoding includes a delay, and in the event the UE has significant mobility, the channel can change resulting and monitoring CSI-RS, the threshold can be configured in relation to UE mobility to compensate for this channel change.Intermediate KPI

[0170] In one embodiment, one or more intermediate key performance indicators (e.g., SGSC) can be determined. For example, the UE can determine the SGSC as the performance monitoring metric by comparing the CSI-RS measurement with a hypothetical received vector (e.g., H * Videai, wherein H is a measured channel based on CSI-RS for inferencing, and Videaiis a perfect precoding matrix. In one example, the UE can determine / calculate the SGCS based on the received CSI-RS resources for performance monitoring and compare it to CSI-RS measurement using an ideal eigenvector.

[0171] The ideal eigenvector and rank are obtained by calculating the target CSI from separate CSI-RS resources used for channel measurement. Ahypothetical received vector ITVJdeal (H is the measured channel based on CSI- RS for inferencing) is calculated using the target CS I, assuming layer 1 is used for performance monitoring. For the monitoring CSI-RS, the precoded received vector H*V_ai is measured. The cosine similarity between the hypothetical and monitoring CSI-RS measurement and the hypothetical received vector is calculated per subband. GCS_subband = (V_ai’ * H’ * H * VJdeal) I VJdeal’ * H' * H * VJdeal). The squared cosine similarity averaging over all subbands is then calculated as: Ave(GCS_subband2) to obtain the final SGCS. This SGCS is compared to a threshold to determine Al model performance degradation. The threshold can be adapted based on UE mobility to account for channel change between measurement and monitoring due to application delay. If the SGCS falls below the threshold it indicates degraded performance.

[0172] To compensate for a processing delay between the original CSI measurement and subsequent monitoring CSI-RS transmission, the Al based compression model can be matched with an original channel. In one example, the CSI-RS resources can be configured for monitoring at the same slot as the same CSI-RS set for inference. An Al based precoder (e.g., the precoding matrix Vaithat is reconstructed at the network side using the Al decoder), can be based on measured CSI-RS channel at tO. In one example, the precoding can be based on CSI-RS at tO and denoted as Vai tn

[0173] Thus, Al reconstructed precoder Vai t0is derived from channel measurement H at time tO. The UE reports CSI at t1 , and monitoring CSI-RS is received at t2. Due to this delay between tO and t2, monitoring metrics face inaccuracy from channel aging. To address this, two techniques can be used.

[0174] The Al reconstructed precoder Vai t0is derived from channel measurement H at time tO. The UE reports CSI at t1. The CSI-RS received by the UE for performance monitoring purposes is transmitted at time t2. The channel measured by the UE at time t2 is represented as Ht2based on CSI-RS for inferencing Ht2.

[0175] In one example, the UE determines / calculates the SGCS as Videalt0„ * Ht2> *t2* VaitQwhere the UE can buffer Vait0in the previous CSI-RS measurement HtQ. The SGCS can be compared to a threshold for indication.

[0176] In another example, the UE can determine / calculate the Vai tQexplicitly by inverting Ht2(e.g., a zero forcing solution, higher complexity), where Vait0is equal to (Ht2, * Ht2)~1Ht2> * Ht2* Vai tQ. The UE can also calculate the SGCS ofFIG. 1 1 : Explicit CSI Reconstruction

[0177] Fig. 1 1 illustrates an example timing diagram signaling between a next generation node B (gNB) and a user equipment (UE) for transmitting reconstructed channel state information (CSI) to support UE performance monitoring for artificial intelligence (Al) based channel state information (CSI) compression model according to some embodiments.

[0178] FIG. 1 1 provides an example illustration 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 FIG. 1 1 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.

[0179] In one embodiment, FIG. 1 1 illustrates an example timing diagram 1 100 signaling between a user equipment (UE) and a base station 102 (e.g., a next generation node B (gNB)) for transmitting reconstructed channel state information (CSI). The signaling may begin with a UE, such as UE 106, receiving 1 110, from a gNB, a reconstructed CSI-RS for enabling the UE to perform US side Al basedcompression model performance monitoring. That is, the gNB sends configuration to the UE setting up the parameters for performance monitoring based on reconstructed CSI provided by the gNB. The reconstructed CSI needs to be quantized in order for efficient transmission. In one example, a traditional codebook can be used to quantize the reconstructed CSI to transmit. In another example, a scaler quantization of the reconstructed CSI can be performed good for PDSCH transmission due to larger payload size.

[0180] The signaling may include the base station 102 (e.g., network “NW” or gNB) sending 1 112 CSI-RS information to the UE 106. The gNB transmits CSI- RS resources meant for the UE to measure and estimate downlink CSI that will be compressed and fed back. At the UE side, an Al encoder model compresses the downlink CSI into a bitstream to be fed back to the gNB.

[0181] The signaling can include transmitting 1 1 14 the CSI report (e.g., the monitoring results, based on the one or more performance monitoring metrics.

[0182] The signaling may include the UE receiving 1 1 16, from the gNB, a decoder output / reconstructed CSI from the Al decoder of the gNB to the UE for performance monitoring analysis. At the UE side, the UE can determine / calculate the SGCS for performance monitoring for artificial intelligence (Al) based channel state information (CSI) compression model. The UE calculates the SGCS metric between the original measured CSI and the reconstructed CSI that is explicitly provided later by the gNB for comparison in performance monitoring.

[0183] The signaling can include the UE sending 11 18 the monitoring report to the gNB upon detecting an error event. For example, if a comparison of original CIS and reconstructed CSI yields degradation higher than a threshold, the UE reports back a monitoring error event to the gNB.FIG. 12: Flow Chart for a Method of transmitting reconstructed channel state information (CSI) for performance monitoring for artificial intelligence (Al) based channel state information (CSI) compression model, at a UE.

[0184] FIG. 12 illustrates a flow chart of an example of a method for performing user equipment (UE) side performance monitoring for artificial intelligence (Al) based channel state information (CSI) compression model, at a UE, according to some embodiments. The method shown in FIG. 12 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.

[0185] In accordance with an embodiment, a method 1200 for performing monitoring of an artificial intelligence (Al) based channel state information (CSI) compression model, determine channel state information (CSI) at the UE, as shown in block 1202.

[0186] The method 1200 further comprises compressing the CSI, at the UE, using an artificial intelligence (Al) based compression model to generate a compressed CSI, as shown in block 1204. The method 1200 further comprises encoding, for transmission to the gNB, the compressed CSI, as shown in block 1206. The method 1200 further comprises receiving, a channel state information reference signal (CSI-RS) resource set, received from the from the gNB, configured for Al based compression model performance monitoring, where the CSI-RS resource set comprises one or more CSI-RS that are precoded using reconstructed CSI that are reconstructed from the compressed CSI using an Al based decoder model at the gNB, as shown in block 1208.

[0187] The method 1200 further comprises determining, at the UE, one or more performance monitoring metrics, for the Al based compression model performance monitoring, based on comparing the reconstructed CSI and the CSI, as shown in block 1210.

[0188] The method 1200 further comprises encode, for transmission to the gNB, monitoring results based on the one or more performance monitoring metrics, as shown in block 1212.

[0189] In some embodiments, the CSI-RS in the CSI-RS resource set, configured for the Al based compression model performance monitoring, can be a non-zero-power (NZP) CSI-RS and are scheduled periodically or semi-persistently, wherein the NZP-CSI-RS in the CSI-RS resource set are configured in a same subframe as a CSI-RS resources used for inferencing at the UE.

[0190] In some embodiments, the method 1200 can further comprise decoding channel state information (CSI) received from the gNB, compress the CSI, at the UE, using an artificial intelligence (Al) based compression model to generate a compressed CSI, and encode, for transmission to the gNB, the compressed CSI.

[0191] In some embodiments, the method 1200 can further comprise determining one or more performance monitoring metrics, for the Al based compression model performance monitoring, based on comparing the reconstructed CSI and the CSI. In one example, a precoding delay is configured, by the gNB, for the one or more CSI-RS in the CSI-RS resource set using an offset value.

[0192] In some embodiments, a number of the CSI-RS in the CSI-RS resource set is equal to a number of ports used to measure for a downlink multiple-input- multiple-output (MIMO) transmission.

[0193] In some embodiments, the method 1200 can further comprise decoding configuration information, received from the gNB, matching a number of ports to a number of layers indicated in an initial CSI report for the one or more CSI-RS in the CSI-RS resource set.

[0194] In some embodiments, the one or more CSI-RS in the CSI-RS resource set are triggered aperiodically based on downlink control information (DCI) received from the gNB. A number of the one or more CSI-RS is equal to a number of CSI-RS ports, and the CSI-RS transmission is triggered based on a rank indicator (Rl) of previous feedback. Additionally, the various performance monitoring metrics can include at least one of a hypothetical block error rate (BLER) or a squared generalized cosine similarity (SGCS).

[0195] In some embodiments, the method 1200 can further comprise determining a hypothetical BLER based on the one or more CSI-RS in the CSI-RS resource set, comprising H*V_ideal, wherein H is a measured channel based on CSI-RS for inferencing, and V ideal is a perfect precoding matrix.

[0196] In some embodiments, the method 1200 can further comprise determining a target CSI based on an eigenvector and rank based on the one or more CSI-RS in the CSI-RS resource set for channel measurement; determine a first reference hypothetical BLER based on the target CSI; and determine a second reference hypothetical BLER based a most recent CSI-RS transmitted from the gNB.

[0197] In some embodiments, the method 1200 can further comprise comparing the first reference hypothetical BLER to the second reference hypothetical BLER; and determine a performance degradation level of the Al based compression model based on comparing the first and second hypothetical BLER.

[0198] In some embodiments, the method 1200 can further comprise determining the SGCS based on the one or more CSI-RS in the CSI-RS resource set. The UE Can determine a target CSI based on an eigenvector and a rank based on the one or more CSI-RS in the CSI-RS resource set for channel measurement; determine a hypothetical received vector based on the target CSI; determine the SGCS based on a delta between the hypothetical received vector and a measurement of the one or more CSI-RS resources.

[0199] In some embodiments, the method 1200 can further comprise determining a performance degradation level of the Al based compression model based on the SGCS. The UE can determine a performance threshold based on mobility of the UE in order to compensate for a channel change between a measurement of the CSI-RS for a channel measurement and reception of the one or more CSI-RS resources due to the mobility of the UE; and compare the SGCS to a performance threshold.

[0200] In an additional embodiment, the reconstructed CSI is received from the gNB via explicit downlink control channel or data channel signaling. In oneexample, the reconstructed CSI can be received in a medium access control (MAC) control element, reconstructed CSI is received over a physical downlink shared channel (PDSCH). In another example, the reconstructed CSI is quantized by the gNB prior to reception using scalar or vector quantization.

[0201] In an additional embodiment, the one or more performance monitoring metrics comprise at least one of: squared generalized cosine similarity, Kullback- Leibler divergence, or mean squared error.

[0202] In some embodiments, a baseband processor is configured to perform operations of the method 1200.

[0203] In some embodiments, an apparatus is configured to cause a user equipment (UE) to perform operations of the method 1200.

[0204] In some embodiments, a computer program product is disclosed, comprising computer instructions which, when executed by one or more processors, perform any of the operations described with respect to method 1200.FIG. 13: Flow Chart for a Method of transmitting reconstructed channel state information (CSI) for performance monitoring for artificial intelligence (Al) based channel state information (CSI) compression model, at a gNB

[0205] FIG. 13 illustrates a flow chart of an example of a method for transmitting reconstructed channel state information (CSI) for performance monitoring for 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. 13 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.

[0206] In accordance with an embodiment, a method 1300 for transmitting reconstructed channel state information (CSI), encode, for transmission to a userequipment (UE), channel state information (CSI) to enable the UE to compress the CSI, at the UE, using an artificial intelligence (Al) based compression model to generate a compressed CSI, as shown in block 1304.

[0207] The method 1300 further comprises decoding, at the gNB, the compressed CSI received from the UE, as in block 1306. The method 1300 further comprises encoding, for transmission to the UE, a channel state information reference signal (CSI-RS) resource set configured for Al based compression model performance monitoring, where the CSI-RS resource set comprises one or more CSI-RS that are precoded using reconstructed CSI that are reconstructed from the compressed CSI using an Al based decoder model at the gNB to enable the UE to determine, at the UE, one or more performance monitoring metrics, for the Al based compression model performance monitoring, based on comparing the reconstructed CSI and the CSI, as in block 1308.

[0208] The method 1300 further comprises decoding, at the gNB, monitoring results, received from the UE, based on the one or more performance monitoring metrics, as in block 1310. In some embodiments, the CSI-RS in the CSI-RS resource set, configured for the Al based compression model performance monitoring, can be a non-zero-power (NZP) CSI-RS and are scheduled periodically or semi-persistently, wherein the NZP-CSI-RS in the CSI-RS resource set are configured in a same subframe as a CSI-RS resources used for inferencing at the UE.

[0209] In some embodiments, the method 1300 can further comprise encoding, for transmission to the UE, channel state information (CSI) to enable the UE to compress the CSI, at the UE, using an artificial intelligence (Al) based compression model to generate a compressed CSI, and decode the compressed CSI received from the UE.

[0210] In some embodiments, the method 1300 can further comprise determining one or more performance monitoring metrics, for the Al based compression model performance monitoring, based on comparing the reconstructed CSI and the CSI. In one example, a precoding delay is configured,by the gNB, for the one or more CSI-RS in the CSI-RS resource set using an offset value.

[0211] In some embodiments, a number of the CSI-RS in the CSI-RS resource set is equal to a number of ports used to measure for a downlink multiple-input- multiple-output (MIMO) transmission.

[0212] In some embodiments, the method 1300 can further comprise encode configuration information, for transmission to the UE, matching a number of ports to a number of layers indicated in an initial CSI report for the one or more CSI-RS in the CSI-RS resource set.

[0213] In some embodiments, the one or more CSI-RS in the CSI-RS resource set are triggered aperiodically based on downlink control information (DCI) received from the gNB. A number of the one or more CSI-RS is equal to a number of CSI-RS ports, and the CSI-RS transmission is triggered based on a rank indicator (Rl) of previous feedback. Additionally, the various performance monitoring metrics can include at least one of a hypothetical block error rate (BLER) or a squared generalized cosine similarity (SGCS).

[0214] In an additional embodiment, encode the reconstructed CSI for transmission to the UE via explicit downlink control channel or data channel signaling. In one example, the reconstructed CSI can be transmitted in a medium access control (MAC) control element. In an additional embodiment, the reconstructed CSI can be transmitted by the gNB over a physical downlink shared channel (PDSCH). In another example, the reconstructed CSI is quantized by the gNB prior to reception using scalar or vector quantization.

[0215] 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 1300.

[0216] In some embodiments, a baseband processor is configured to perform operations of the method 1300.

[0217] In some embodiments, a computer program product is disclosed, comprising computer instructions which, when executed by one or more processors, perform any of the operations described with respect to method 1300.

[0218] 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.

[0219] 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.

[0220] 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.

[0221] 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 Ytransmitted in the uplink by the UE as a message / signal Y received by the base station.

[0222] 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

CLAIMSWhat is claimed is:1 . An apparatus of a user equipment (UE) comprising: one or more processors, coupled to a memory, configured to: determine channel state information (CSI) at the UE; compress the CSI, at the UE, using an artificial intelligence (Al) based compression model to generate a compressed CSI; encode, for transmission to a next generation Node B (gNB), the compressed CSI; receive, a channel state information reference signal (CSI-RS) resource set, received from the from the gNB, configured for Al based compression model performance monitoring, wherein the CSI-RS resource set comprises one or more CSI-RS that are precoded using reconstructed CSI that are reconstructed from the compressed CSI using an Al based decoder model at the gNB; determine, at the UE, one or more performance monitoring metrics, for the Al based compression model performance monitoring, based on comparing the reconstructed CSI and the CSI; and encode, for transmission to a gNB, monitoring results to the gNB based on the one or more performance monitoring metrics.

2. The apparatus of claim 1 , wherein the one or more CSI-RS in the CSI-RS resource set, configured for the Al based compression model performance monitoring, are non-zero-power (NZP) CSI-RS and are scheduled periodically or semi-persistently, wherein theNZP-CSI-RS in the CSI-RS resource set are configured as a CSI- RS resources used for inferencing at the UE.

3. The apparatus of claim 2, wherein a precoding delay is configured, by the gNB, for the one or more CSI-RS in the CSI-RS resource set using an offset value.

4. The apparatus of claim 1 , wherein a number of the CSI-RS in the CSI-RS resource set is equal to a number of ports used to measure for a downlink multiple-input-multiple-output (MIMO) transmission.

5. The apparatus of claim 1 , wherein the one or more processors are further configured to decode configuration information, from the gNB, matching a number of ports to a number of layers indicated in an initial CSI report for the one or more CSI-RS in the CSI-RS resource set.

6. The apparatus of claim 1 , wherein the one or more CSI-RS in the CSI-RS resource set are triggered aperiodically based on downlink control information (DCI) received from the gNB.

7. The apparatus of claim 6, wherein a number of the one or more CSI-RS is equal to a number of CSI-RS ports, and the transmission of the compressed CSI-RS is triggered based on a rank indicator (Rl) of previous feedback.

8. The apparatus of claim 1 , wherein the one or more performance monitoring metrics comprise at least one of a hypothetical block error rate (BLER) or a squared generalized cosine similarity (SGCS).

9. The apparatus of claim 8, wherein the one or more processors are further configured to determine the hypothetical BLER based on the one or more CSI-RS in the CSI-RS resource set, comprising HVideal, wherein H is a measured channel based on CSI-RS for inferencing, and Videcdis a perfect precoding matrix.

10. The apparatus of claim 8, wherein the one or more processors are further configured to: determine a target CSI based on an eigenvector and rank based on the one or more CSI-RS in the CSI-RS resource set for channel measurement; determine a first reference hypothetical BLER based on the target CSI; and determine a second reference hypothetical BLER based a most recent CSI-RS transmitted from the gNB.1 1 . The apparatus of claim 10, wherein the one or more processors are further configured to: compare the first reference hypothetical BLER to the second reference hypothetical BLER; and determine a performance degradation level of the Al based compression model based on comparing the first and second hypothetical BLER.

12. The apparatus of claim 8, wherein the one or more processors are further configured to determine the SGCS based on the one or more CSI-RS in the CSI-RS resource set.

13. The apparatus of claim 8, wherein the one or more processors are further configured to:determine a target CSI based on an eigenvector and a rank based on the one or more CSI-RS in the CSI-RS resource set for channel measurement; determine a hypothetical received vector based on the target CSI; and determine the SGCS based on a delta between the hypothetical received vector and a measurement of the one or more CSI-RS resources.

14. The apparatus of claim 11 , wherein the one or more processors are further configured to determine the performance degradation level of the Al based compression model based on the SGCS.

15. The apparatus of claim 14, wherein the one or more processors are further configured to: determine a performance threshold based on mobility of the UE to compensate for a channel change between a measurement of the CSI-RS for a channel measurement and reception of the one or more CSI-RS resources due to the mobility of the UE; and compare the SGCS to a performance threshold.

16. The apparatus of claim 1 , wherein the reconstructed CSI is received from the gNB via explicit downlink control channel or data channel signaling.

17. The apparatus of claim 1 , wherein the reconstructed CSI is received in a medium access control (MAC) control element.

18. The apparatus of claim 1 , wherein the reconstructed CSI is received over a physical downlink shared channel (PDSCH).

19. The apparatus of claim 1 , wherein the reconstructed CSI is quantized by the gNB prior to reception using scalar or vector quantization.

20. The apparatus of claim 1 , wherein the one or more performance monitoring metrics comprise at least one of: squared generalized cosine similarity, Kullback-Leibler divergence, or mean squared error.21 . A method of performing artificial intelligence (Al) based compression model performance monitoring by a user equipment (UE), the method comprising: determining channel state information (CSI) at the UE; compressing the CSI, at the UE, using an artificial intelligence (Al) based compression model to generate a compressed CSI; encoding, for transmission to a next generation Node B (gNB), the compressed CSI; receiving, a channel state information reference signal (CSI- RS) resource set, received from the from the gNB, configured for Al based compression model performance monitoring, wherein the CSI- RS resource set comprises one or more CSI-RS that are precoded using reconstructed CSI that are reconstructed from the compressed CSI using an Al based decoder model at the gNB; determining, at the UE, one or more performance monitoring metrics, for the Al based compression model performance monitoring, based on comparing the reconstructed CSI and the CSI; and transmitting, from the UE, monitoring results to the gNB based on the one or more performance monitoring metrics.

22. The method of claim 21 , wherein the one or more CSI-RS in the CSI-RS resource set, configured for the Al based compression model performance monitoring, are non-zero-power (NZP) CSI-RS and are scheduled periodically or semi-persistently, wherein the NZP-CSI-RS in the CSI-RS resource set are configured in a same subframe as a CSI-RS resources used for inferencing at the UE.

23. The method of claim 21 , wherein a precoding delay is configured, by the gNB, for the one or more CSI-RS in the CSI-RS resource set using an offset value.

24. The method of claim 21 , wherein a number of the CSI-RS in the CSI-RS resource set is equal to a number of ports used to measure for a downlink multiple-input-multiple-output (MIMO) transmission.

25. The method of claim 21 , further comprising decoding configuration information, from the gNB, matching a number of ports to a number of layers indicated in an initial CSI report for the one or more CSI- RS in the CSI-RS resource set.

26. The method of claim 21 , wherein the one or more CSI-RS in the CSI-RS resource set are triggered aperiodically based on downlink control information (DCI) received from the gNB.

27. The method of claim 26, wherein a number of the one or more CSI- RS is equal to a number of CSI-RS ports, and the CSI-RS transmission is triggered based on a rank indicator (Rl) of previous feedback.

28. The method of claim 21 , wherein the one or more performance monitoring metrics comprise at least one of a hypothetical blockerror rate (BLER) or a squared generalized cosine similarity (SGCS).

29. The method of claim 28, further including determining the hypothetical BLER based on the one or more CSI-RS in the CSI- RS resource set, comprising HVideawherein H is a measured channel based on CSI-RS for inferencing, and Videaiis a perfect precoding matrix.

30. The method of claim 21 , further comprising: determining a target CSI based on an eigenvector and rank based on the one or more CSI-RS in the CSI-RS resource set for channel measurement; determining a first reference hypothetical BLER based on the target CSI; and determining a second reference hypothetical BLER based on a most recent CSI-RS transmitted from the gNB.31 . The method of claim 30, further comprising: comparing the first reference hypothetical BLER to the second reference hypothetical BLER; and determining a performance degradation level of the Al based compression model based on comparing the first and second hypothetical BLER.

32. The method of claim 28, further comprising determining the SGCS based on the one or more CSI-RS in the CSI-RS resource set.

33. The method of claim 28, further including:determining a target CSI based on an eigenvector and a rank based on the one or more CSI-RS in the CSI-RS resource set for channel measurement; determining a hypothetical received vector based on the target CSI; and determining the SGCS based on a delta between the hypothetical received vector and a measurement of the one or more CSI-RS resources.

34. The method of claim 33, further comprising determining a performance degradation level of the Al based compression model based on the SGCS.

35. The method of claim 32, further comprising: determining a performance threshold based on mobility of the UE in order to compensate for a channel change between a measurement of the CSI-RS for a channel measurement and reception of the one or more CSI-RS resources due to the mobility of the UE; and comparing the SGCS to a performance threshold.

36. The method of claim 21 , wherein the reconstructed CSI is received from the gNB via explicit downlink control channel or data channel signaling.

37. The method of claim 21 , wherein the reconstructed CSI is received in a medium access control (MAC) control element.

38. The method of claim 21 , wherein the reconstructed CSI is received over a physical downlink shared channel (PDSCH).

39. The method of claim 21 , wherein the reconstructed CSI is quantized by the gNB prior to reception using scalar or vector quantization.

40. The method of claim 21 , wherein the one or more performance monitoring metrics comprise at least one of: squared generalized cosine similarity, Kullback-Leibler divergence, or mean squared error.

41. An apparatus configured to cause a user equipment (UE) to perform any of the methods of claims 21 to 40.

42. A baseband processor configured to perform one or more of the method claims 21 to 40.

43. An apparatus of a next generation Node B (gNB) comprising: one or more processors, coupled to a memory, configured to: encode, for transmission to a user equipment (UE), channel state information (CSI) to enable the UE to compress the CSI, at the UE, using an artificial intelligence (Al) based compression model to generate a compressed CSI; decode, at the gNB, the compressed CSI received from the UE, encode, for transmission to the UE, a channel state information reference signal (CSI-RS) resource set configured for Al based compression model performance monitoring, wherein the CSI-RS resource set comprises one or more CSI- RS that are precoded using reconstructed CSI that are reconstructed from the compressed CSI using an Al based decoder model at the gNB to enable the UE to determine, at the UE, one or more performance monitoring metrics, for theAl based compression model performance monitoring, based on comparing the reconstructed CSI and the CSI; and decode, at the gNB, monitoring results, received from the UE, based on the one or more performance monitoring metrics.

44. The apparatus of claim 43, wherein the one or more CSI-RS in the CSI-RS resource set, configured for the Al based compression model performance monitoring, are non-zero-power (NZP) CSI-RS and are scheduled periodically or semi-persistently, wherein the NZP-CSI-RS in the CSI-RS resource set are configured in a same subframe as a CSI-RS resources used for inferencing at the UE.

45. The apparatus of claim 44, wherein a precoding delay is configured, by the gNB, for the one or more CSI-RS in the CSI-RS resource set using an offset value.

46. The apparatus of claim 43, wherein a number of the CSI-RS in the CSI-RS resource set is equal to a number of ports used to measure for a downlink multiple-input-multiple-output (MIMO) transmission.

47. The apparatus of claim 43, wherein the one or more processors are further configured to encode, for transmission to the UE, configuration information matching a number of ports to a number of layers indicated in an initial CSI report for the one or more CSI- RS in the CSI-RS resource set.

48. The apparatus of claim 43, wherein the one or more CSI-RS in the CSI-RS resource set are triggered aperiodically based on downlink control information (DCI) received from the gNB.

49. The apparatus of claim 48, wherein a number of the one or more CSI-RS is equal to a number of CSI-RS ports, and the CSI-RS transmission is triggered based on a rank indicator (Rl) of previous feedback.

50. The apparatus of claim 43, wherein the one or more performance monitoring metrics comprise at least one of a hypothetical block error rate (BLER) or a squared generalized cosine similarity (SGCS).

51. The apparatus of claim 43, wherein the reconstructed CSI is transmitted from the gNB via explicit downlink control channel or data channel signaling.

52. The apparatus of claim 43, wherein the reconstructed CSI is transmitted in a medium access control (MAC) control element.

53. The apparatus of claim 43, wherein the reconstructed CSI is transmitted from the gNB over a physical downlink shared channel (PDSCH).

54. The apparatus of claim 43, wherein the reconstructed CSI is quantized by the gNB prior to reception using scalar or vector quantization.

55. The apparatus of claim 43, wherein the one or more performance monitoring metrics comprise at least one of: squared generalized cosine similarity, Kullback-Leibler divergence, or mean squared error.

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

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

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

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