Method and procedure for data collection for ai based csi prediction

Abstract

Apparatuses, systems, and methods for Al based CSI feedback with CSI prediction, including systems, methods, and mechanisms for a user equipment device (UE) to encode a data collection request, for training a CSI prediction Al-model, for transmission of the data collection request to a next generation node B (gNB). The UE may decode a data collection request response received from the gNB. The UE may perform UE measurements of CSI Reference Signals (CSI-RS) for training the CSI prediction Al-model based on the data collection request response from the gNB. The UE may encode a data collection stop request for transmission to the gNB. The UE may store the UE measurements of the CSI-RS in a memory coupled to the one or more processors.

Description

METHOD AND PROCEDURE FORDATA COLLECTION FOR Al BASED CSI PREDICTIONFIELD

[0001] The invention relates to wireless communications, and more particularly to apparatuses, systems, and methods for data collection for artificial intelligence (Al) based channel state information (CSI) prediction, including systems, methods, and mechanisms for a UE to initiate data collection to train an Al prediction model during 5G NR communications.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. Additionally, there exist numerous different wireless communication technologies and standards.

[0003] Long Term Evolution (LTE), also referred to as the Evolved Universal Terrestrial Radio Access Network (E-UTRAN, 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. Thus, in 2015 study of a new radio access technology began and, in 2017, a first release of the Third Generation Partnership Project (3GPP) Fifth Generation New Radio (5G NR) was standardized. 5th generation mobile networks or 5th generation wireless systems, referred to as 3GPP NR (otherwise known as 5G-NR or NR-5G for 5G New Radio, also simply referred to as NR). NR proposes a higher capacity for a higher density of mobile broadband users, also supporting device-to-device, ultra-reliable, and massive machine communications, as wellas lower latency and lower battery consumption, than LTE standards.

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

[0005] One aspect of wireless communication systems, e.g., systems for NR cellular wireless communications, is the transmission and measurement of reference signals, including channel- state information reference signals (CSI-RS).SUMMARY

[0006] Embodiments relate to wireless communications, and more particularly to apparatuses, systems, and methods for data collection for Al based CSI prediction, including systems, methods, and mechanisms for a UE to initiate and collect data to train an Al prediction model during 5G NR communications.

[0007] For example, in some embodiments, a UE may transmit, to a base station, a data collection request, for training a CSI prediction Artificial Intelligence (Al) model, for transmission of the data collection request to a base station (e.g., a next generation node B (gNB)). The UE may receive a data collection request response received from the gNB. The UE may perform UE measurements of CSI Reference Signals (CSI-RS) for training the CSI prediction Al-model based on the data collection request response from the gNB.

[0008] The UE may transmit, to the gNB, a data collection stop request. The UE may store the UE measurements of the CSI-RS in a memory coupled to one or more processors. The configuration for the data collection request may include at least a request to collect input data for the CSI prediction Al-model and output data from the CSI prediction AI- model to enable the gNB to train the CSI prediction Al-model. The configuration for the input data for the CSI prediction Al-model include a total number of samples used as input for the CSI prediction Al-model collected in a time domain, a measurement distances between one or more of the total number of samples collected in the time domain, and information used for categorizing the total number of samples. The configuration for theoutput data of the CSI prediction Al-model may include a total number of samples to predict and a prediction distance between one or more of the samples.

[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 base stations, access points, cellular phones, tablet computers, wearable computing devices, portable media players, vehicles, 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 abovedescribed 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] Figure 1 A illustrates an example wireless communication system according to some embodiments.

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

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

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

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

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

[0018] Figure 6A illustrates an example of a 5G network architecture that incorporatesboth 3GPP (e.g., cellular) and non-3GPP (e.g., non-cellular) access to the 5G CN, according to some embodiments.

[0019] Figure 6B illustrates an example of a 5G network architecture that incorporates both dual 3GPP (e.g., LTE and 5G NR) access and non-3GPP access to the 5G CN, according to some embodiments.

[0020] Figure 7 illustrates an example of a baseband processor architecture for a UE, according to some embodiments.

[0021] Figure 8 illustrates an example of a device in accordance with some embodiments.

[0022] Figure 9 illustrates an example baseband circuitry in accordance with some embodiments.

[0023] Figure 10 illustrates an example of a control plane protocol stack in accordance with some embodiments.

[0024] Figure 11 illustrates an example of CSI feedback according to 3GPP Release 17.

[0025] Figure 12 illustrates an example of CSI feedback according to 3GPP Release18.

[0026] Figure 13 illustrates an example of CSI feedback with CSI prediction, according to some embodiments.

[0027] Figure 14A illustrates an example of an Al model for CSI prediction, according to some embodiments.

[0028] Figure 14B illustrates an example of a one-dimensional LSTM Al model for CSI prediction using a time domain, according to some embodiments.

[0029] Figure 14C illustrates an example of a two-dimensional convolutional neural network (CNN) Al model for CSI prediction using a time domain and a frequency domain, according to some embodiments.

[0030] Figure 14D illustrates an example of a three-dimensional convolutional neural network (CNN) Al model for CSI prediction using a time domain and a frequency domain and a spatial (antenna) domain, according to some embodiments.

[0031] Figure 15 illustrates an example timing diagram signaling for data collection forAl based CSI prediction, according to some embodiments.

[0032] Figure 16 illustrates an example of UE data collection request for data collection for Al based CSI prediction, according to some embodiments.

[0033] Figure 17 illustrates an example of a base station sending a data collection command for data collection for Al based CSI prediction, according to some embodiments.

[0034] Figure 18 illustrates another example of an Al model for CSI prediction, according to some embodiments.

[0035] Figure 19 illustrates a block diagram of an example of a method for data collection for artificial intelligence (Al) based channel state information (CSI) prediction, according to some embodiments.

[0036] While the features described herein may be susceptible to various modifications and alternative forms, specific embodiments thereof are shown by way of example in the drawings and are herein described in detail. It should be understood, however, that the drawings and detailed description thereto are not intended to be limiting to the particular form disclosed, but on the contrary, the intention is to cover all modifications, equivalents and alternatives falling within the spirit and scope of the subject matter as defined by the appended claims.DETAILED DESCRIPTION

[0037] The following is a glossary of terms used in this disclosure:

[0038] 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 randomaccess memory such as DRAM, DDR RAM, SRAM, EDO RAM, Rambus RAM, etc.; a non-volatile memory such as a Flash, magnetic media, e.g., a hard drive, or optical storage; registers, or other similar types of memory elements, etc. The memory medium may include other types of non-transitory memory as well or combinations thereof. In addition, the memory medium may be located in a first computer system in which the programs are executed, or may be located in a second different computer system which connects to the first computer system over a network, such as the Internet. In the latter instance, the second computer system may provide program instructions to the first computer for execution. Theterm “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.

[0039] 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.

[0040] 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”.

[0041] 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.

[0042] User Equipment (UE) (or “UE Device”) - any of various types of computer systems devices which are mobile or portable and which performs wireless communications. Examples of UE devices include mobile telephones or smart phones (e.g., iPhone™, Android™-based phones), portable gaming devices (e.g., Nintendo DS™, PlayStation Portable™, Gameboy Advance™, iPhone™), laptops, wearable devices (e.g., smart watch, smart glasses), PDAs, portable Internet devices, music players, data storage devices, other handheld devices, unmanned aerial vehicles (UAVs) (e.g., drones), UAV controllers (UACs), and so forth. In general, the term “UE” or “UE device” can be broadly defined to encompass any electronic, computing, and / or telecommunications device (or combination of devices) which is easily transported by a user and capable of wirelesscommunication.

[0043] 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.

[0044] 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.

[0045] 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. In contrast, WLAN channels may be 22MHz wide while Bluetooth channels may be IMhz wide. Other protocols and standards may include different definitions of channels. Furthermore, some standards may define and use multiple types of channels, e.g., different channels for uplink or downlink and / or different channels for different uses such as data, control information, etc.

[0046] 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.

[0047] Wi-Fi - The term "Wi-Fi" (or WiFi) has the full breadth of its ordinary meaning, and at least includes a wireless communication network or RAT that is serviced by wireless LAN (WLAN) access points and which provides connectivity through these access points to the Internet. Most modem Wi-Fi networks (or WLAN networks) are based on IEEE 802.11 standards and are marketed under the name “Wi-Fi”. A Wi-Fi (WLAN) network is different from a cellular network.

[0048] 3GPP Access - refers to accesses (e.g., radio access technologies) that are specified by the Third Generation Partnership Project (3GPP) standards. These accesses include, but are not limited to, GSM / GPRS, LTE, LTE-A, and / or 5G NR. In general, 3GPP access refers to various types of cellular access technologies.

[0049] Non-3GPP Access - refers any accesses (e.g., radio access technologies) that are not specified by 3GPP standards. These accesses include, but are not limited to, WiMAX, CDMA2000, Wi-Fi, WLAN, and / or fixed networks. Non-3GPP accesses may be split into two categories, "trusted" and "untrusted": Trusted non-3GPP accesses can interact directly with an evolved packet core (EPC) and / or a 5G core (5GC) whereas untrusted non-3GPP accesses interwork with the EPC / 5GC via a network entity, such as an Evolved Packet Data Gateway and / or a 5G NR gateway. In general, non-3GPP access refers to various types on non-cellular access technologies.

[0050] 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 can 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.

[0051] Approximately - refers to a value that is almost correct or exact. For example,approximately may refer to a value that is within 1 to 10 percent of the exact (or desired) value. It should be noted, however, that the actual threshold value (or tolerance) may be application dependent. For example, in some embodiments, “approximately” may mean within 0.1% of some specified or desired value, while in various other embodiments, the threshold may be, for example, 2%, 3%, 5%, and so forth, as desired or as used by the particular application.

[0052] 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.

[0053] 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.

[0054] 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.Figures 1A and IB: Communication Systems

[0055] Figure 1A illustrates a simplified example wireless communication system, according to some embodiments. It is noted that the system of Figure 1 A is merely oneexample of a possible system, and that features of this disclosure may be implemented in any of various systems, as desired.

[0056] 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. The user devices may be referred to herein as a “user equipment” (UE). Thus, the user devices 106 are referred to as UEs or UE devices.

[0057] 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 106 A through 106N.

[0058] 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., IxRTT, IxEV-DO, HRPD, eHRPD), etc. Note that if the base station 102 A is implemented in the context of LTE (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’.

[0059] As shown, the base station 102 A 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 102 A may provide UEs 106 with various telecommunication capabilities, such as voice, SMS and / or data services.

[0060] Base station 102A and other similar base stations (such as base stations 102B...102N) operating according to the same or a different cellular communication standard may thus be provided as a network of cells, which may provide continuous or nearly continuous overlapping service to UEs 106A-N and similar devices over a geographic area via one or more cellular communication standards.

[0061] Thus, while base station 102A may act as a “serving cell” for UEs 106A-N as illustrated in Figure 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 Figure 1A might be macro cells, while base station 102N might be a micro cell. Other configurations are also possible.

[0062] 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.

[0063] 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., IxRTT, IxEV-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.

[0064] Figure IB illustrates user equipment 106 (e.g., one of the devices 106A through 106N) in communication with a base station 102 and an access point 112, according to some embodiments. The UE 106 may be a device with both cellular communication capability and non-cellular communication capability (e.g., Bluetooth, Wi-Fi, and so forth) such as amobile phone, a hand- held device, a computer or a tablet, or virtually any type of wireless device.

[0065] 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.

[0066] 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 (IxRTT I IxEV- DO / HRPD I eHRPD), LTE / LTE- Advanced, or 5G NR using a single shared radio and / or GSM, LTE, LTE- Advanced, or 5G NR using the single shared radio. The shared radio may couple to a single antenna, or may couple to multiple antennas (e.g., for MIMO) for performing wireless communications. In general, a radio may include any combination of a baseband processor, analog RF signal processing circuitry (e.g., including filters, mixers, oscillators, amplifiers, etc.), or digital 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.

[0067] 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 (E-UTRAN) 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.Figure 2: Block Diagram of a Base Station

[0068] Figure 2 illustrates an example block diagram of a base station 102, according to some embodiments. It is noted that the base station of Figure 3 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.

[0069] 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.

[0070] The network port 270 (or an additional network port) may also or alternatively be configured to couple to a cellular network, e.g., a core network of a cellular service provider. The core network may provide mobility related services and / or other services to a plurality of devices, such as UE devices 106. In some cases, the network port 270 may couple to a telephone network via the core network, and / or the core network may provide a telephone network (e.g., among other UE devices serviced by the cellular service provider).

[0071] 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.

[0072] 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.

[0073] 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.).

[0074] 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.

[0075] 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.

[0076] 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 mayinclude circuitry (e.g., first circuitry, second circuitry, etc.) configured to perform the functions of radio 230.Figure 3 : Block Diagram of a Server

[0077] Figure 3 illustrates an example block diagram of a server 104, according to some embodiments. It is noted that the server of Figure 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.

[0078] 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.

[0079] 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.

[0080] As described further subsequently herein, the server 104 may include hardware and software components for implementing or supporting implementation of features described herein. The processor 344 of the server 104 may be configured to implement or support implementation of part or all of the methods described herein, e.g., by executing program instructions stored on a memory medium (e.g., a non-transitory computer-readable memory medium). Alternatively, the processor 344 may be configured as a programmable hardware element, such as an FPGA (Field Programmable Gate Array), or as an ASIC (Application Specific Integrated Circuit), or a combination thereof. Alternatively (or in addition) the processor 344 of the server 104, in conjunction with one or more of the other components 354, 364, and / or 374 may be configured to implement or support implementation of part or all of the features described herein.

[0081] In addition, as described herein, processor(s) 344 may be comprised of one ormore 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.Figure 4: Block Diagram of a UE

[0082] Figure 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 Figure 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.

[0083] 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 I / 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.

[0084] 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. Theshort 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.

[0085] 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 5 G NR). In addition, in some embodiments, cellular communication circuitry 430 may include a single transmit chain that may be switched between radios dedicated to specific RATs. For example, a first radio may be dedicated to a first RAT, e.g., LTE, and may be in communication with a dedicated receive chain and a transmit chain shared with an additional radio, e.g., a second radio that may be dedicated to a second RAT, e.g., 5G NR, and may be in communication with a dedicated receive chain and the shared transmit chain.

[0086] 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.

[0087] 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 eUICCs, one or more eSIMs, either removable or embedded, etc. In some embodiments, the UE 106 may includeat 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 (eUICCs), which are sometimes referred to as “eSIMs” or “eSIM cards”). In some embodiments (such as when the SIM(s) include an eUICC), one or more of the SlM(s) may implement embedded SIM (eSIM) functionality; in such an embodiment, a single one of the SIM(s) may execute multiple SIM applications. Each of the SIMs may include components such as a processor and / or a memory; instructions for performing SIM / eSIM functionality may be stored in the memory and executed by the processor. In some embodiments, the UE 106 may include a combination of removable smart cards and fixed / non-removable smart cards (such as one or more eUICC 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.

[0088] 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 eitherof 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 eUICC) that executes multiple SIM applications for different carriers and / or RATs.

[0089] As shown, the SOC 400 may include processor(s) 402, which may execute program instructions for the communication device 106 and display circuitry 404, which may perform graphics processing and provide display signals to the display 460. The processor(s) 402 may also be coupled to memory management unit (MMU) 440, which may be configured to receive addresses from the processor(s) 402 and translate those addresses to locations in memory (e.g., memory 406, read only memory (ROM) 450, NAND flash memory 410) and / or to other circuits or devices, such as the display circuitry 404, short to medium range wireless communication circuitry 429, cellular communication circuitry 430, connector I / 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.

[0090] As noted above, the communication device 106 may be configured to communicate using wireless and / or wired communication circuitry. The communication device 106 may be configured to perform methods for Al based CSI feedback with CSI prediction, including systems, methods, and mechanisms for a UE to indicate a predicted CSI report, network configuration of CSI feedback, UE PMI report format, and Al model life cycle management, e.g., in 5G NR systems and beyond, as further described herein.

[0091] 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.

[0092] 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.

[0093] Further, as described herein, cellular communication circuitry 430 and short to medium range wireless communication circuitry 429 may each include one or more processing elements. In other words, one or more processing elements may be included in cellular communication circuitry 430 and, similarly, one or more processing elements may be included in short to medium range wireless communication circuitry 429. Thus, cellular communication circuitry 430 may include one or more integrated circuits (ICs) that are configured to perform the functions of cellular communication circuitry 430. In addition, each integrated circuit may include circuitry (e.g., first circuitry, second circuitry, etc.) configured to perform the functions of cellular communication circuitry 430. Similarly, the short to medium range wireless communication circuitry 429 may include one or more ICs that are configured to perform the functions of short to medium range wireless communication circuitry 429. In addition, each integrated circuit may include circuitry (e.g., first circuitry, second circuitry, etc.) configured to perform the functions of short to medium range wireless communication circuitry 429.Figure 5: Block Diagram of Cellular Communication Circuitry

[0094] Figure 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 Figure 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 portablecomputing device), a tablet and / or a combination of devices, among other devices.

[0095] 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 Figure 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 Figure 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.

[0096] 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 530. RF front end 530 may include circuitry for transmitting and receiving radio signals. For example, RF front end 530 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.

[0097] 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.

[0098] In some embodiments, a switch 570 may couple transmit circuitry 534 to uplink (UL) front end 572. In addition, switch 570 may couple transmit circuitry 544 to UL front end 572. UL front end 572 may include circuitry for transmitting radio signals via antenna 336. Thus, when cellular communication circuitry 530 receives instructions to transmit according to the first RAT (e.g., as supported via modem 510), switch 570 may be switched to a first state that allows modem 510 to transmit signals according to the first RAT (e.g., via a transmit chain that includes transmit circuitry 534 and UL front end 572). Similarly, when cellular communication circuitry 530 receives instructions to transmit according tothe 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).

[0099] In some embodiments, the cellular communication circuitry 530 may be configured to perform methods for Al based CSI feedback with CSI prediction, including systems, methods, and mechanisms for a UE to indicate a predicted CSI report, network configuration of CSI feedback, UE PMI report format, and Al model life cycle management, e.g., in 5G NR systems and beyond, as further described herein.

[0100] 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, 550, 570, 572, 335 and 336 may be configured to implement part or all of the features described herein.

[0101] 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.

[0102] As described herein, the modem 520 may include hardware and software components for implementing the above features for Al based CSI feedback with CSI prediction, including systems, methods, and mechanisms for a UE to indicate a predicted CSI report, network configuration of CSI feedback, UE PMI report format, and Al model life cycle management, e.g., in 5G NR systems and beyond, as well as the various other techniques described herein. The processors 522 may be configured to implement part or all of the features described herein, e.g., by executing program instructions stored on a memory medium (e.g., a non-transitory computer- readable memory medium).Alternatively (or in addition), processor 522 may be configured as a programmable hardware element, such as an FPGA (Field Programmable Gate Array), or as an ASIC (Application Specific Integrated Circuit). Alternatively (or in addition) the processor 522, in conjunction with one or more of the other components 540, 542, 544, 550, 570, 572, 335 and 336 may be configured to implement part or all of the features described herein.

[0103] 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.Figures 6 A, 6B and 7: 5G Core Network Architecture - Interworking with Wi-Fi

[0104] In some embodiments, the 5G core network (CN) may be accessed via (or through) a cellular connection / interface (e.g., via a 3GPP communication architecture / protocol) and a non- cellular connection / interface (e.g., a non-3GPP access architecture / protocol such as Wi-Fi connection). Figure 6A illustrates an example of a 5G network architecture that incorporates both 3GPP (e.g., cellular) and non-3GPP (e.g., non- cellular) access to the 5G CN, according to some embodiments. As shown, a user equipment device (e.g., such as UE 106) may access the 5G CN through both a radio access network (RAN, e.g., such as gNB 604, which may be a base station 102) and an access point, such as AP 612. The AP 612 may include a connection to the Internet 600 as well as a connection to a non-3GPP inter-working function (N3IWF) 603 network entity. The N3IWF may include a connection to a core access and mobility management function (AMF) 605 of the 5G CN. The AMF 605 may include an instance of a 5G mobility management (5G MM) function associated with the UE 106. In addition, the RAN (e.g., gNB 604) may also have a connection to the AMF 605. Thus, the 5G CN may support unified authentication over both connections as well as allow simultaneous registration for UE 106 access via both gNB 604 and AP 612. As shown, the AMF 605 may include one or more functional entities associated with the 5G CN (e.g., network slice selection function (NSSF) 620, short message service function (SMSF) 622, application function (AF) 624, unified data management (UDM) 626, policy control function (PCF) 628, and / or authentication server function (AUSF) 630). Note that these functional entities may also besupported by a session management function (SMF) 606a and an SMF 606b of the 5G CN. The AMF 605 may be connected to (or in communication with) the SMF 606a. Further, the gNB 604 may in communication with (or connected to) a user plane function (UPF) 608a that may also be communication with the SMF 606a. Similarly, the N3IWF 603 may be communicating with a UPF 608b that may also be communicating with the SMF 606b. Both UPFs may be communicating with the data network (e.g., DN 610a and 610b) and / or the Internet 600 and Internet Protocol (IP) Multimedia Subsystem / IP Multimedia Core Network Subsystem (IMS) core network 610.

[0105] Figure 6B illustrates an example of a 5G network architecture that incorporates both dual 3GPP (e.g., LTE and 5G NR) access and non-3GPP access to the 5G CN, according to some embodiments. As shown, a user equipment device (e.g., such as UE 106) may access the 5G CN through both a radio access network (RAN, e.g., such as gNB 604 or eNB 602, which may be a base station 102) and an access point, such as AP 612. The AP 612 may include a connection to the Internet 600 as well as a connection to the N3IWF 603 network entity. The N3IWF may include a connection to the AMF 605 of the 5G CN. The AMF 605 may include an instance of the 5G MM function associated with the UE 106. In addition, the RAN (e.g., gNB 604) may also have a connection to the AMF 605. Thus, the 5G CN may support unified authentication over both connections as well as allow simultaneous registration for UE 106 access via both gNB 604 and AP 612. In addition, the 5G CN may support dual-registration of the UE on both a legacy network (e.g., LTE via eNB 602) and a 5G network (e.g., via gNB 604). As shown, the eNB 602 may have connections to a mobility management entity (MME) 642 and a serving gateway (SGW) 644. The MME 642 may have connections to both the SGW 644 and the AMF 605. In addition, the SGW 644 may have connections to both the SMF 606a and the UPF 608a. As shown, the AMF 605 may include one or more functional entities associated with the 5G CN (e.g., NSSF 620, SMSF 622, AF 624, UDM 626, PCF 628, and / or AUSF 630). Note that UDM 626 may also include a home subscriber server (HSS) function and the PCF may also include a policy and charging rules function (PCRF). Note further that these functional entities may also be supported by the SMF606a and the SMF 606b of the 5G CN. The AMF 605 may be connected to (or in communication with) the SMF 606a. Further, the gNB 604 may in communication with (or connected to) the UPF 608a that may also be communication with the SMF 606a. Similarly, the N3IWF 603 may be communicating with a UPF 608b that may also be communicating with the SMF 606b. Both UPFs may becommunicating with the data network (e.g., DN 610a and 610b) and / or the Internet 600 and IMS core network 610.

[0106] Note that in various embodiments, one or more of the above-described network entities may be configured to perform methods for Al based CSI feedback with CSI prediction, including systems, methods, and mechanisms for a UE to indicate a predicted CSI report, network configuration of CSI feedback, UE PMI report format, and Al model life cycle management, e.g., in 5G NR systems and beyond, e.g., as further described herein.

[0107] Figure 7 illustrates an example of a baseband processor architecture for a UE (e.g., such as UE 106), according to some embodiments. The baseband processor architecture 700 described in Figure 7 may be implemented on one or more radios (e.g., radios 429 and / or 430 described above) or modems (e.g., modems 510 and / or 520) as described above. As shown, the non-access stratum (NAS) 710 may include a 5G NAS 720 and a legacy NAS 750. The legacy NAS 750 may include a communication connection with a legacy access stratum (AS) 770. The 5G NAS 720 may include communication connections with both a 5G AS 740 and a non-3GPP AS 730 and Wi-Fi AS 732. The 5G NAS 720 may include functional entities associated with both access stratums. Thus, the 5G NAS 720 may include multiple 5G MM entities 726 and 728 and 5G session management (SM) entities 722 and 724. The legacy NAS 750 may include functional entities such as short message service (SMS) entity 752, evolved packet system (EPS) session management (ESM) entity 754, session management (SM) entity 756, EPS mobility management (EMM) entity 758, and mobility management (MM) / GPRS mobility management (GMM) entity 760. In addition, the legacy AS 770 may include functional entities such as LTE AS 772, UMTS AS 774, and / or GSM / GPRS AS 776.

[0108] Thus, the baseband processor architecture 700 allows for a common 5G-NAS for both 5G cellular and non-cellular (e.g., non-3GPP access). The baseband processor architecture 700 can be in communication with one or more UICC(s) 745. Note that as shown, the 5G MM may maintain individual connection management and registration management state machines for each connection. Additionally, a device (e.g., UE 106) may register to a single PLMN (e.g., 5G CN) using 5G cellular access as well as non-cellular access. Further, it may be possible for the device to be in a connected state in one access and an idle state in another access and vice versa. Finally, there may be common 5G-MMprocedures (e.g., registration, de-registration, identification, authentication, as so forth) for both accesses.

[0109] Note that in various embodiments, one or more of the above-described functional entities of the 5G NAS and / or 5G AS may be configured to perform methods for Al based CSI feedback with CSI prediction, including systems, methods, and mechanisms for a UE to indicate a predicted CSI report, network configuration of CSI feedback, UE PMI report format, and Al model life cycle management, e.g., in 5G NR systems and beyond, e.g., as further described herein.

[0110] FIG. 8 illustrates example components of a device 800 in accordance with some embodiments. In some embodiments, the device 800 may include application circuitry 802, baseband circuitry 804, Radio Frequency (RF) circuitry 806, front-end module (FEM) circuitry 808, one or more antennas 810, and power management circuitry (PMC) 812 coupled together at least as shown. The components of the illustrated device 800 may be included in a UE or a RAN node. In some embodiments, the device 800 may include less elements (e.g., a RAN node may not utilize application circuitry 802, and instead include a processor / controller to process IP data received from an EPC). In some embodiments, the device 800 may include additional elements such as, for example, memory / storage, display, camera, sensor, or input / output (I / O) interface. In other embodiments, the components described below may be included in more than one device (e.g., said circuitries may be separately included in more than one device for Cloud-RAN (C-RAN) implementations).

[0111] The application circuitry 802 may include one or more application processors. For example, the application circuitry 802 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 800. In some embodiments, processors of application circuitry 802 may process IP data packets received from an EPC.

[0112] The baseband circuitry 804 may include circuitry such as, but not limited to, one or more single-core or multi-core processors. The baseband circuitry 804 may include one or more baseband processors or control logic to process baseband signals received from areceive signal path of the RF circuitry 806 and to generate baseband signals for a transmit signal path of the RF circuitry 806. Baseband processing circuity 804 may interface with the application circuitry 802 for generation and processing of the baseband signals and for controlling operations of the RF circuitry 806. For example, in some embodiments, the baseband circuitry 804 may include a third generation (3G) baseband processor 804A, a fourth generation (4G) baseband processor 804B, a fifth generation (5G) baseband processor 804C, or other baseband processor(s) 804D for other existing generations, generations in development or to be developed in the future (e.g., second generation (2G), si8h generation (6G), etc.). The baseband circuitry 804 (e.g., one or more of baseband processors 804A-D) may handle various radio control functions that enable communication with one or more radio networks via the RF circuitry 806. In other embodiments, some or all of the functionality of baseband processors 804A-D may be included in modules stored in the memory 804G and executed via a Central Processing Unit (CPU) 804E. 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 804 may include Fast-Fourier Transform (FFT), precoding, or constellation mapping / demapping functionality. In some embodiments, encoding / decoding circuitry of the baseband circuitry 804 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.

[0113] In some embodiments, the baseband circuitry 804 may include one or more audio digital signal processor(s) (DSP) 804F. The audio DSP(s) 804F 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 804 and the application circuitry 802 may be implemented together such as, for example, on a system on a chip (SOC).

[0114] In some embodiments, the baseband circuitry 804 may provide for communication compatible with one or more radio technologies. For example, in some embodiments, the baseband circuitry 804 may support communication with an evolveduniversal 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 804 is configured to support radio communications of more than one wireless protocol may be referred to as multi-mode baseband circuitry.

[0115] RF circuitry 806 may enable communication with wireless networks using modulated electromagnetic radiation through a non-solid medium. In various embodiments, the RF circuitry 806 may include switches, filters, amplifiers, etc. to facilitate the communication with the wireless network. RF circuitry 806 may include a receive signal path which may include circuitry to down-convert RF signals received from the FEM circuitry 808 and provide baseband signals to the baseband circuitry 804. RF circuitry 806 may also include a transmit signal path which may include circuitry to up-convert baseband signals provided by the baseband circuitry 804 and provide RF output signals to the FEM circuitry 808 for transmission.

[0116] In some embodiments, the receive signal path of the RF circuitry 806 may include mixer circuitry 806a, amplifier circuitry 806b and filter circuitry 806c. In some embodiments, the transmit signal path of the RF circuitry 806 may include filter circuitry 806c and mixer circuitry 806a. RF circuitry 806 may also include synthesizer circuitry 806d for synthesizing a frequency for use by the mixer circuitry 806a of the receive signal path and the transmit signal path. In some embodiments, the mixer circuitry 806a of the receive signal path may be configured to down-convert RF signals received from the FEM circuitry 808 based on the synthesized frequency provided by synthesizer circuitry 806d. The amplifier circuitry 806b may be configured to amplify the down-converted signals and the filter circuitry 806c 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 804 for further processing. In some embodiments, the output baseband signals may be zero-frequency baseband signals, although this is not a requirement. In some embodiments, mixer circuitry 806a of the receive signal path may comprise passive mixers, although the scope of the embodiments is not limited in this respect.

[0117] In some embodiments, the mixer circuitry 806a of the transmit signal path may be configured to up-convert input baseband signals based on the synthesized frequencyprovided by the synthesizer circuitry 806d to generate RF output signals for the FEM circuitry 808. The baseband signals may be provided by the baseband circuitry 804 and may be filtered by filter circuitry 806c.

[0118] In some embodiments, the mixer circuitry 806a of the receive signal path and the mixer circuitry 806a of the transmit signal path may include two or more mixers and may be arranged for quadrature downconversion and upconversion, respectively. Tn some embodiments, the mixer circuitry 806a of the receive signal path and the mixer circuitry 806a 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 806a of the receive signal path and the mixer circuitry 806a may be arranged for direct downconversion and direct upconversion, respectively. In some embodiments, the mixer circuitry 806a of the receive signal path and the mixer circuitry 806a of the transmit signal path may be configured for super-heterodyne operation.

[0119] 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 806 may include analog-to-digital converter (ADC) and digital-to-analog converter (DAC) circuitry and the baseband circuitry 804 may include a digital baseband interface to communicate with the RF circuitry 806.

[0120] 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.

[0121] In some embodiments, the synthesizer circuitry 806d may be a fractional-N synthesizer or a fractional N / N+l 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 806d may be a delta-sigma synthesizer, a frequency multiplier, or a synthesizer comprising a phase-locked loop with a frequency divider.

[0122] The synthesizer circuitry 806d may be configured to synthesize an output frequency for use by the mixer circuitry 806a of the RF circuitry 806 based on a frequency input and a divider control input. In some embodiments, the synthesizer circuitry 806d may be a fractional N / N+l synthesizer.

[0123] In some embodiments, frequency input may be provided by a voltage controlled oscillator (VCO), although that is not a requirement. Divider control input may be provided by either the baseband circuitry 804 or the applications processor 802 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 802.

[0124] Synthesizer circuitry 806d of the RF circuitry 806 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+l (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.

[0125] In some embodiments, synthesizer circuitry 806d 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 806 may include an IQ / polar converter.

[0126] FEM circuitry 808 may include a receive signal path which may include circuitry configured to operate on RF signals received from one or more antennas 810, amplify the received signals and provide the amplified versions of the received signals to the RF circuitry 806 for further processing. FEM circuitry 808 may also include a transmit signal path which may include circuitry configured to amplify signals for transmission provided by the RF circuitry 806 for transmission by one or more of the one or more antennas 810. In various embodiments, the amplification through the transmit or receive signal paths may be done solely in the RF circuitry 806, solely in the FEM 808, or in both the RF circuitry 806 and the FEM 808.

[0127] In some embodiments, the FEM circuitry 808 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 806). The transmit signal path of the FEM circuitry 808 may include a power amplifier (PA) to amplify input RF signals (e.g., provided by RF circuitry 806), and one or more filters to generate RF signals for subsequent transmission (e.g., by one or more of the one or more antennas 810).

[0128] In some embodiments, the PMC 812 may manage power provided to the baseband circuitry 804. In particular, the PMC 812 may control power-source selection, voltage scaling, battery charging, or DC-to-DC conversion. The PMC 812 may often be included when the device 800 is capable of being powered by a battery, for example, when the device is included in a UE. The PMC 812 may increase the power conversion efficiency while providing desirable implementation size and heat dissipation characteristics.

[0129] While FIG. 8 shows the PMC 812 coupled only with the baseband circuitry 804. However, in other embodiments, the PMC 8 12 may be additionally or alternatively coupled with, and perform similar power management operations for, other components such as, but not limited to, application circuitry 802, RF circuitry 806, or FEM 808.

[0130] In some embodiments, the PMC 812 may control, or otherwise be part of, various power saving mechanisms of the device 800. For example, if the device 800 is in an RRC_Connected state, where it is still connected to the RAN node as it expects to receive traffic shortly, then it may enter a state known as Discontinuous Reception Mode (DRX) after a period of inactivity. During this state, the device 800 may power down for brief intervals of time and thus save power.

[0131] If there is no data traffic activity for an e8ended period of time, then the device 800 may transition off to an RRC_Idle state, where it disconnects from the network and does not perform operations such as channel quality feedback, handover, etc. The device 800 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 800 may not receive data in this state, in order to receive data, it can transition back to RRC_Connected state.

[0132] 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.

[0133] Processors of the application circuitry 802 and processors of the baseband circuitry 804 may be used to execute elements of one or more instances of a protocol stack. For example, processors of the baseband circuitry 804, alone or in combination, may be used to execute Layer 3, Layer 2, or Layer 1 functionality, while processors of the application circuitry 804 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 may comprise a radio resource control (RRC) layer, described in further detail below. As referred to herein, Layer 2 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 may comprise a physical (PHY) layer of a UE / RAN node, described in further detail below.

[0134] FIG. 9 illustrates example interfaces of baseband circuitry in accordance with some embodiments. As discussed above, the baseband circuitry 804 of FIG. 8 may comprise processors 804A-804E and a memory 804G utilized by said processors. Each of the processors 804A-804E may include a memory interface, 904A-904E, respectively, to send / receive data to / from the memory 804G.

[0135] The baseband circuitry 804 may further include one or more interfaces to communicatively couple to other circuitries / de vices, such as a memory interface 912 (e.g., an interface to send / receive data to / from memory e8emal to the baseband circuitry 804), an application circuitry interface 9f4 (e.g., an interface to send / receive data to / from the application circuitry 802 of FIG. 8), an RF circuitry interface 9f6 (e.g., an interface to send / receive data to / from RF circuitry 806 of FIG. 8), a wireless hardware connectivity interface 918 (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 920 (e.g., an interface to send / receive power or control signals to / from the PMC 812.

[0136] FIG. 10 is an illustration of a control plane protocol stack in accordance with some embodiments. In one embodiment, a control plane 1000 may be a communications protocolstack between one or more UEs such as, for example, UE 801 (or alternatively, the UE 802), and / or one or more RAN nodes 811 (or alternatively, the RAN node 812), and a mobility management entity (MME) 821.

[0137] The PHY layer 1001 may transmit or receive information used by the MAC layer1002 over one or more air interfaces. The PHY layer 1001 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 1005. The PHY layer 1001 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.

[0138] The MAC layer 1002 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, demultiplexing MAC SDUs to one or more logical channels from transport blocks (TB) delivered from the PHY via transport channels, multiplexing MAC SDUs onto TBs, scheduling information reporting, error correction through hybrid automatic repeat request (HARQ), and logical channel prioritization.

[0139] The RLC layer 1003 may operate in a plurality of modes of operation, including: Transparent Mode (TM), Unacknowledged Mode (UM), and Acknowledged Mode (AM). The RLC layer 1003 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 layer1003 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.

[0140] The PDCP layer 1004 may execute header compression and decompression of IP data, maintain PDCP Sequence Numbers (SNs), perform in-sequence delivery of upper layer PDUs at re-establishment of lower layers, eliminate duplicates of lower layer SDUs at reestablishment of lower layers for radio bearers mapped on RLC AM, cipher and deciphercontrol plane data, perform integrity protection and integrity verification of control plane data, control timer-based discard of data, and perform security operations (e.g., ciphering, deciphering, integrity protection, integrity verification, etc.).

[0141] The main services and functions of the RRC layer 1005 may include broadcast of system information (e.g., included in Master Information Blocks (MIBs) or System Information Blocks (SIBs) related to the non-access stratum (NAS)), broadcast of system information related to the access stratum (AS), paging, establishment, maintenance and release of an RRC connection between the UE and E-UTRAN (e.g., RRC connection paging, RRC connection establishment, RRC connection modification, and RRC connection release), establishment, configuration, maintenance and release of point to point Radio Bearers, security functions including key management, inter radio access technology (RAT) mobility, and measurement configuration for UE measurement reporting. Said MIBs and SIBs may comprise one or more information elements (IES), which may each comprise individual data fields or data structures.

[0142] In one example, a UE (e.g., UE 106A-N) and a RAN node (e.g., base station 102) 811may utilize a Uu interface (e.g., an LTE-Uu interface) to exchange control plane data via a protocol stack comprising the PHY layer 1001, the MAC layer 1002, the RLC layer 1003, the PDCP layer 1004, and the RRC layer 1005.

[0143] The non-access stratum (NAS) protocols 1006 form the highest stratum of the control plane between the UE (e.g., UE 106A-N) 801 and an MME 821. The NAS protocols 1006 support the mobility of the UE (e.g., UE 106A-N) 801and the session management procedures to establish and maintain IP connectivity between the UE (e.g., UE 106A-N) 80 land a P-GW.

[0144] The Sl Application Protocol (Sl-AP) layer 1015 may support the functions of the SI interface and comprise Elementary Procedures (EPs). An EP is a unit of interaction between a RAN node (e.g., base station 102)811and the CN. The Sl-AP layer 1015 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.

[0145] The Stream Control Transmission Protocol (SCTP) layer (alternatively referred to as the SCTP / IP layer) 1014 may ensure reliable delivery of signaling messages betweenthe RAN node (e.g., base station 102) 81 land a MME821 based, in part, on the IP protocol, supported by the IP layer 1013. The L2 layer 1012 and the LI layer 1011 may refer to communication links (e.g., wired or wireless) used by the RAN node (e.g., base station 102) and the MME to exchange information.

[0146] The RAN node (e.g., base station 102) 81 land the MME 821may utilize an Sl- MME interface to exchange control plane data via a protocol stack comprising the LI layer 1011, the L2 layer 1012, the IP layer 1013, the SCTP layer 1014, and the Sl-AP layer 1015.Al based CSI Feedback with CSI Prediction

[0147] In 3GPP standards development, channel state information (CSI) feedback has been an important topic in almost every 3GPP standards release. Further, nearly all CSI codebook design has been focused on feedback based on a current CSI reference signal (CSI-RS) measurement, e.g., as illustrated by Figure 11. As shown, in traditional CSI feedback (e.g., through 3GPP Release 17), a base station (e.g., gNB) periodically / semi- persistently or aperiodically transmits a CSLRS to a UE and the UE periodically provides CSI feedback (CSLFB). The base station then applies the CSI feedback to generate percoding matrix index (PMI) until a next CSI feedback is available. Further, the base station may use an older CSI feedback assuming a channel does not substantially change over time (e.g., over a few milliseconds). Such a scheme works well for UE low mobility cases. One possible 3GPP Release 18 enhancement is illustrated by Figure 12. It should be noted that the 3GPP Release 18 design is targeting to enhance high speed UE with CSI prediction using traditional signal processing methods. As shown, a base station (e.g., gNB) may transmit a cluster of CSI-RSs and the UE may transmit CSI feedback to the base station based on the predicted CSI using at least one CSI-RS of the cluster of CSI-RSs. The base station then applies the CSI feedback to generate PMI (with time) until a next CSI feedback is available. In both instances, however, frequent CSI feedback is used. Thus, improvements are desired.

[0148] Embodiments described herein provide apparatus, systems, and methods for receiving CSI-RS and measuring the CSI-RS to use for training a channel state information prediction artificial intelligence (Al) based model. Massive multiple-input and multiple output (MIMO), in which the gNB uses a large array of antennas, can considerably improve the system performance. However, the benefit of massive MIMO is based on the knowledgeof downlink CSI. In frequency division duplexing (FDD) massive MIMO systems, the UE will feedback the downlink CSI-RS measurements to the BS through the uplink because of the lack of channel reciprocity. The feedback overhead can be substantial due to the high dimension of the CSI in massive MIMO systems. In addition, a large amount of power is used at the UE to provide the feedback. One way of reducing the amount of feedback and reducing power consumption at the UE is through the use of a CSI prediction Al based model. The output of the CSI prediction Al-model can be used by the UE rather than CSI- RS. This can reduce the number of CSI-RS needed to transmit from the gNB to the UE. However, it is still necessary to train the CSI prediction Al-model. This can be accomplished using CSI-RS transmitted from the gNB to the UE. The CSI-RS may be specifically designated for the CSI prediction Al-model. Alternatively, the CSI-RS communicated for other UE needs may also be used to train the CSI prediction Al-model.

[0149] For example, as illustrated by Figure 13, a base station, such as base station 102, may configure CSI-RS transmissions with a periodic pattern, e.g., at times TO, Tl, and T2. Further, at time T2, a UE, such as UE 106, having received and measured the CSI-RS transmissions at times TO, Tl, and T2, may predict the channel at times T2+tl, T2+t2, and so forth. The prediction may be based, at least in part, on CSI RS measurements, e.g., CSI- RS measurements at times TO, Tl, and T2. The prediction, in at least some instances, may be generated via an Al model, e.g., such as the Al model 1410 illustrated by Figure 14A. As shown in Figure 14A, the Al model 1410 may use past CSI-RS measurements (e.g., such as a times TO, Tl, and T2) as inputs and may output a predicted channel (e.g., at times T2+tl and T2+t2) at future times after a latest (or last) CSI-RS measurement but prior to a next CSI-RS measurement. The time tl and t2 can be a time less than the time period between TO, Tl, T2, T3 and T4, as shown in FIG. 13. In some instances, the UE may calculate CSI feedback for times T2, T2+tl, and T2+t2, where T2+t2 is less than T3 as well as a joint CSI feedback for T2, T2+tl, and T2+t2. Further, the UE may then provide CSI feedback (e.g., the UE may feedback a CSI codebook) at configured time T2’, where T2’ is at least a processing time after T2.

[0150] In other instances, the prediction may be generated via the Al model 1410, which may be a one-dimensional Long short-term memory (LSTM) Al model for CSI prediction using a time domain. As shown in Figure 14B, the Al model 1410 may be use for predicting time domain correlation only (such as, for example, the LSTM). The timeseries CSI measurements may be fed into the LSTM layer, which outputs a vector capturingtemporal dependencies. This vector is fed into a fully connected (FC) layer to generate the CSI prediction output.

[0151] In other instances, the prediction may be generated via the Al model 1410, which may be a two-dimensional convolutional neural network (“CNN”) Al model for CSI prediction using a time domain and a frequency domain. As shown in Figure 14C, the Al model 1410 may be used to predict time domain and a frequency domain correlation (such as, for example, a 2D CNN). That is, a 2D CNN can capture a batch of input data, where each sample can comprise a time series of CSI measurements across different frequency subcarriers. This input tensor is passed through a series of 2D convolutional layers (e.g., neural network) and enables the model to identify patterns in the CSI that extend across both time steps and subcarriers. The Al model 1410 may learn and provide CSI predictions across the future time steps and subcarriers based on the measurements of the CSI- RS input into the Al model as training.

[0152] In other instances, the prediction may be generated via the Al model 1410, which may be a three-dimensional convolutional neural network (CNN) Al model for CSI prediction using a time domain and a frequency domain and a spatial (antenna) domain. As shown in Figure 14D, the Al model 1410 may be used to predict time, frequency, and spatial domain correlation (such as 3D CNN). As the inputs pass through the 3D convolutional layers (e.g., neural network), the Al model 1410 may learn and predict the future CSI values across time, frequency, and antenna (spatial) domains. It should be noted that due to different designs, the model data collection categorization information can be different. In particular, 3D CNN can be sensitive to a network antenna panel design and virtualization. Assisted information for categorizing the input data may be needed in this case.

[0153] Figure 15 illustrates one possible example of signaling 1500 for performing data collection for channel state information (CSI) prediction such as, for example, data collection for UE side CSI prediction Al model training, according to some embodiments. The signaling shown in Figure 15 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.

[0154] The signaling may begin with a UE, such as UE 106, transmitting, to a base station, such as base station 102A ... 102N, a data collection request 1510. In one embodiment, the UE data collection request 1510 can be optional as the base station may directly trigger the data collection. For example, the bases station, without receiving the data collection request 1510 from the UE, may trigger the UE to perform the data collection request. When the data collection request 1510 is optional, the supported input / output configuration may be sent via UE capability or UE Assistance Information (“UAI”) information.

[0155] In one example, the UE may encode the data collection request using one or more processors, such as a baseband processor 804A...804D for transmission of the data collection request from the UE to the base station. The data collection request 1510 can be used to request the network (i.e., gNB) to send CSI-RS to the UE that can be used for CSI prediction Al model training. To enable supervised learning, gNB will transmit CSI-RS at the requested input location, as well as the requested output location. UE uses the measured output location as the training label for supervised learning.

[0156] The UE may indicate, in the data collection request 1510 for CSI prediction Al model training, whether assisted information for categorizing data is used. The assisted information for categorizing can include additional information that can be useful in categorizing the collected data into appropriate subsets. For example, the additional information can include information regarding the type of antenna used by the gNB, whether gNB antenna virtualization is used, the speed of the UE, and so forth. This assisted information can be used to categorize CSI-RS measurements to provide more accurate modeling using the CSI prediction Al model.

[0157] In some instances, the data collection request 1510 may further include any combination of, and / or all of (at least one of and / or one or more of) a request for the UE to collect input data for the CSI prediction Al model and the type of data to be output from the CSI prediction Al model to enable the UE to train the CSI prediction Al model and output data of the CSI prediction Al model. In one example, the data collection request 1510 can include information regarding the output of the CSI prediction Al model. For example, the data collection request can provide information that includes a total number of samples for the CSI prediction Al model to predict and a prediction distance between one or more of the samples. When the samples are periodic, the distance between eachsample will be substantially the same. The input data for the CSI prediction Al model may further include any, any combination of, and / or all of (at least one of and / or one or more of) a total number of samples used as input for the CSI prediction Al-model (i.e. CSI-RS measurements at the UE) that are collected in a time domain, measurement distances between one or more of the total number of samples collected in the time domain and / or information used for categorizing the total number of samples.

[0158] In response to the data collection request 1510, the UE may receive, from the base station (e.g., a gNB and / or a network), a trigger command 1512 to start data collection for training the CSI prediction Al model. The UE may receive, from the base station (e.g., a gNB and / or a network), CSI-RS transmissions 1514. The UE may perform UE measurements of the CSI-RS transmissions. The measurements of the CSI-RS transmissions can be used for training the CSI prediction Al model. In some examples, the UE measurements of the CSI-RS that are used for training the CSI prediction Al model may be buffered at the UE.

[0159] In some examples, the UE may transmit, to the base station, a request 1516 to stop data collection of the CSI-RS used for training the CSI prediction Al model. In response, the UE may receive, from the base station, a stop for data collection command 1518. In one embodiment, when the data collection request 1510 is optional, the UE stop request 1516 may not be used (e.g., the UE stop request can also be optional based upon whether the UE data collection request 1510 is used).

[0160] That is, the UE may measure the CSI-RS and collect the measurement data and then transmit to the base station a request to stop collection. In one aspect, when the CSI prediction Al model is a UE side Al prediction model, the UE may buffer the CSI-RS measurement data for training. In one example, the UE can transfer the data to an over- the-top (“OTT”) server (e.g., a server accessed over the internet rather than through a mobile network) for offline training of the CSI prediction Al model (e.g., data delivery of the CSI-RS measurements can be performed over a non-3GPP interface).

[0161] In one example, the UE can send the request to stop message 1516 using an uplink (UL) radio resource control (RRC) message. For example, the UL RRC message may be sent in a UE Assistance Information (“UAI”) message. Alternatively, the UE can send the request to stop message 1516 using an uplink media access control element (UL MAC CE) to stop data collection of the CSI-RS measurements. The base station can stopsending the CSI-RS transmissions used for training the CSI-prediction Al model and the UE can stop performing the CSI-RS measurements.

[0162] Thus, the CSI prediction Al model may execute / run on the UE side so that the UE performs the CSI-RS measurements and collects the data. In one example, the UE has two options for handling the collected data: 1) buffer the CSI-RS measurement data locally on the UE for model training, and / or 2) transfer the collected data to an OTT (over-the-top) server over a non-3GPP interface like WiFi. This allows the data to be stored on a server over the internet for offline training rather than relying on the mobile operator's network. Once the UE has collected sufficient training data, the UE may signal to the base station to stop the data collection procedure. The UE can send an UL RRC message (e.g., a UAI message) to indicate the UE wants to stop the data collection. Alternatively, the UE can send an UL MAC Control Element (CE) to indicate stopping data collection. Upon receiving the stop collection message, the base station may stop the data collection procedure by deactivating the CSI-RS transmissions that were configured for data collection.

[0163] In one example, if the base station (e.g., network “NW”) trains the CSI prediction Al-model, the UE may further send the CSI-RS measurements 1520 to the base station. The CSI-RS measurements can be encoded for transmission from the UE to the base station or network using a user plane solution, such as a physical uplink shared channel (PUSCH) or a control plane solution, such as a physical uplink control channel (PUCCH) transmission.

[0164] Figure 16 illustrates an example of a UE data collection request for data collection for Al based CSI prediction, according to some embodiments. A UE, such as UE 106, may transmit to a base station, such as base station 102a. . . 102n, a data collection request 1510 for training a UE side CSI Al prediction model. The data collection request 1510 may further include any combination of, and / or all of (at least one of and / or one or more of) a request to collect input data for the CSI Al prediction model and output data from the CSI Al prediction model to enable the CSI prediction Al-model to be trained. The data collection request 1510 can include information regarding the expected output of the CSI Al prediction model, such as a total number of samples for the model to predict and a prediction distance between one or more of the samples. When the samples are periodic, the prediction distance between each sample will be substantially the same. Also, the UE data collection request 1510 may include an indication whether assisted information forcategorizing data is needed, as previously discussed. For example, the CSI prediction Al model designs may need additional information from the network to categorize / label the data during training. For example, a model using spatial domain information may need to know antenna virtualization parameters. Spatial domain information can be used to categorize the CSI-RS measurement data.

[0165] The input data for the CST prediction AT model may further include any combination of, and / or all of (at least one of and / or one or more of) a total number of samples used as input for the CSI prediction Al model collected in a time domain, the measurement distances between one or more of the total number of samples collected in the time domain and / or information used for categorizing the total number of samples. When the samples are periodic, the distance between the samples will be substantially the same. More specifically, as illustrated in Figure 16, the UE data collection request content may include 1) a number of samples used to provide training for the CSI Al prediction model. The number of samples can be the number of CSI-RS measurements (samples) that the UE's Al model will use to train the model to output a selected number of CSI predictions. For example, the Al model may use n CSI measurements, where n is a positive integer. Accordingly, the UE data collection request 1510 can identify that n CSI-RS transmissions may be used for the UE to obtain the measurements of the CSI-RS to train the CSI Al prediction model.

[0166] The UE data collection request content may also include a measurement distance (e.g., nth distance) between CSI-RS measurements samples. The CSI-RS may be sent periodically, such as every 5 milliseconds (ms). For example, the CSI measurements samples may be taken a periodic pattern, e.g. , at times TO, T1 , and T2. In this example, TO, Tl, and T2 are each received by the UE approximately 5 ms apart. The UE can be moving at a certain speed, such as 30 kilometers per hour (kph). This will result in each CSI-RS measurement occurring a set distance apart. In this example, each CSI-RS measurement will occur approximately 42 millimeters apart. If the speed of the UE were to change significantly, the distance between the measurements will change in proportion with the change in speed of the UE.

[0167] The data collection request may further include the expected CSI prediction Al model output data. The output data of the CSI prediction Al model may further include any combination of, and / or all of (at least one of and / or one or more of) a total number ofsamples to predict and a prediction distance between one or more of the samples. When the samples are periodic, the distance between the samples will be substantially the same. Thus, the UE, such as UE 106, having received and measured the CSI-RS transmissions at times TO, Tl, and T2, may predict a number of samples tO, tl, and t2 and a prediction distance 1604 between the samples. Unlike the CSI-RS, which are received periodically at the UE and measured as the UE is moving, the output of the CSI prediction Al-model is not dependent on speed. Each sample may be configured to have approximately the same distance apart as the CSI-RS measurements used as an input to train the CSI prediction AI- model. Alternatively, the CSI prediction Al-model may output the samples at a faster (i.e. more) or slower (i.e. less) rate than the CSI-RS measurements used as input. For example, the CSI-RS transmissions may occur once every 5 ms. The channel predictions output by the CSI prediction Al-model may occur at the same rate, such as once every 5 ms, or at a different rate, such as once every 0.5 ms, 1 ms, 2 ms, 3 ms, 4ms, 6 ms, 7 ms, 8ms, 9ms, 10ms, and so forth. This list of input and output rates is not intended to be limiting. The actual CSI-RS transmission times and CSI prediction Al-model channel prediction sample output rate can be set based on system requirements, including but not limited to, the speed of the UE and the desired accuracy of the CSI prediction Al-model channel sample output. This can result in the predicted channel state information form the CSI prediction Al-model having a shorter or greater prediction-distance 1604 than the measurement-distance 1602 illustrated in the example of Figure 16.

[0168] For example, output related information may include 1) number of samples to predict, which may refer to how many future CSI values the Al model will predict at a time, based on the input samples, and 2) a prediction distance. The prediction distance may refer to a time distance in the future for the predicted CSI values, which enables a physical distance to be calculated based on a speed at which the UE is moving relative to the base station.

[0169] In some examples, the UE may send the data collection request message in an uplink Radio Resource Control (RRC) message. Specifically, the UE Assistance Information (UAI) message can be used. That is, the RRC message may be a UAI message. The UAI message would contain the input and output information for the CSI prediction Al-model. Alternatively, the UE can send the data collection request in an uplink Medium Access Control (MAC) Control Element.

[0170] In one example, the UE may determine whether to trigger the data collection request 1510 based on certain conditions. For example, the UE may trigger the request if the environment is changing compared to what the model has been trained on before, such as when the UE moves from indoors to outdoors. This would result in a significant change in the CSI-RS measurement values. The UE may also trigger the request if its speed has changed, since the model may have been trained for specific speed ranges. For example, when the speed of the UE changes from 30 kph to 60 kph, then the UE may trigger the data collection request 1510. In addition, the UE may trigger the data collection request 1510 when the UE travels to a cell with a different type of antenna that may result in different CSI-RS measurements. Thus, the UE may determine when to trigger the data collection request based on one or more of: the UE has not been trained using the CSI prediction AI- model; the UE has moved from outdoors to indoors; the UE has moved from indoors to outdoors; the UE has moved to an environment with a number of obstructions that have increased or decreased by a predetermined threshold level; the UE has changed speed by a predetermined amount; and / or the UE has moved into a cell with a different type of antenna used by the gNB of the cell.

[0171] Figure 17 illustrates an example of a base station triggering a CSI-RS transmission for data collection for Al based CSI prediction, according to some embodiments. In one example, based on the UE's data collection request and indicated input / output preferences for the CSI prediction Al-model, the base station may send a triggering command to start data collection.

[0172] A base station such as, for example, one of base stations 102A...102N can configure CSI Reference Signal (CSI-RS) resources to be transmitted to the UE specifically for the data collection purpose. The data collection RS periodicity configures the repetition rate of the CSI-RS for data collection. Within each time period, the measurement CSI-RS communicated from the base station to the UE and the prediction CSI are configured to align with the channel measurement and prediction needs of the UE's Al model. This provides customized CSI-RS resources tailored for efficiently collecting training data for the CSI prediction Al-model.

[0173] In one aspect, if periodic CSI-RS resources are currently configured for normal CSI measurement by the UE, and the periodicity aligns with a UE's desired input sample spacing for the CSI prediction Al-model, these existing CSI-RS resources can be reusedfor efficiency.

[0174] For example, the data collection RS can reuse periodic CSI-RS resources (“p- CSI-RS”) for CSI if the p-CSI-RS for CSI is already configured for regular / normal CSI measurements and reporting, and the sample distance is aligned with the input / output distance. Thus, if the p-CSI-RS resources are already configured, and their periodicity happens to align with the measurement spacing needed for the CSI prediction Al-model data collection, then the existing p-CSI-RS can be reused for dual purpose. In this way, the UE can use the same p-CSI-RS used for normal CSI measurements to report to network, as well as collect the measurements as training data for its CSI prediction Al-model. This avoids the base station having to configure, and the UE having to measure, entirely separate CSI-RS resources just for data collection, thereby improving efficiency and reducing power use at the UE. For the p-CSI-RS to be reusable in this way, the periodicity (or measurement spacing) of the p-CSI-RS can align with the data collection CSI-RS periodicity measurement spacing used as an input to the CSI prediction Al-model, as indicated in the UE's data collection request 1510.

[0175] Alternatively, the base station may configure separate CSI-RS resource sets specifically for the data collection needs of the CSI prediction Al model. This configuration can include the periodicity or transmission timing, frequency / time and locations of ports for the CSI-RS for the UE to measure and provide as an input to the CSI prediction AI- model. That is, the data collection CSI-RS can be a separately configured CSI-RS resource set based on transmission periodicity, ports, time and / or frequency location of the CSI-RS.

[0176] For example, if there are no existing periodic CSI-RS resources (p-CSI-RS) already configured, or their periodicity does not match the measurement spacing needed for the CSI prediction Al-model data collection, then the base station may configure separate CSI-RS resource sets dedicated specifically for the CSI prediction Al-model data collection purpose. These dedicated CSI resource sets for data collection may be configured with a periodicity / transmission timing that matches the measurement sample spacing requested by the UE's data collection request 1510. The base station may also specify the ports, time, and frequency locations for these dedicated data collection CSI-RS within the system bandwidth. This allows the network to fully customize the CSI-RS configuration to meet the specific spacing and quality needs for the data collection indicated in the UE's data collection request 1510. Hence, if the reuse of existing p-CSI-RS is notpossible, the base station may configure separate CSI-RS resource sets just for CSI prediction Al-model training data collection, with full control over the transmission periodicity and resource allocation to meet the UE's requested spacing and quality needs.

[0177] In one example, the CSI-RS resource sets just for CSI prediction Al-model training data collection may be transmitted periodically, semi-persistently, or even aperiodically if needed. If a CSI prediction Al-model uses additional spatial domain or antenna information for categorizing the data, the base station can also configure and transmit these assisted categorization data parameters. In one aspect, the assisted information may include a model type of a UE side CSI prediction Al-model, an antenna type of the base station, and / or a use of antenna virtualization by the base station. The base station may configure and transmit this assisted information along with the CSI-RS to help the UE properly categorize the training data.

[0178] For improved data collection accuracy, the CSI-RS resources can be enhanced to enable higher precision CSI measurements over multiple OFDM symbols. For example, for enhanced CSI-RS for higher accuracy, the CSI-RS can be enhanced to allow for higher accuracy channel measurements to be collected as training data for the CSI prediction AI- model. One way to achieve higher accuracy is to enable CSI measurements over multiple OFDM symbols rather than just a single symbol. Spreading the CSI-RS measurement over multiple symbols allows more samples to be collected, reducing noise and allowing channel characteristics to be determined more precisely. This enables a higher accuracy of training of the CSI prediction Al-model, thereby providing a higher accuracy output from the CSI prediction Al-model. That is, rather than transmitting the CSI-RS on a single symbol per slot, the CSI-RS can be transmitted on multiple symbols in a slot or across consecutive slots. Spreading the CSI-RS over multiple symbols will allow the UE to obtain multiple samples of the CSI-RS within a coherent time interval, effectively averaging the measurements and reducing noise / interference. This in turn provides a higher accuracy estimate of the channel conditions.

[0179] In some instances, an Al model may be used for CSI prediction, e.g., as illustrated by Figure 18. For example, a CSI prediction Al model 1810 may be UE implementation based and may be trained and managed locally by the UE. Thus, as shown, the UE may use past CSI-RS measurements as input to the Al model 1810 and the Al model 1810 may output a predicted channel. Further, the predicted channel may then beused as input to a CSI encoder 1812 to provide uplink feedback to a base station, e.g., to a CSI decoder 1820 of the base station. Note that if the UE is capable of training to fine tune the Al model using the received CSI- RS, the UE may request more frequent CSI-RS transmission from the base station to facilitate faster fine-tune.

[0180] Figure 19 illustrates a block diagram of an example of a method 1900 for data collection for artificial intelligence (Al) based channel state information (CSI) prediction, according to some embodiments. The method shown in Figure 19 may be used in conjunction with any of the systems, methods, or devices shown 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. As shown, this method may operate as follows.

[0181] At 1910, a user equipment device (UE), such as UE 106, may transmit, to a base station, such as base station 102, a data collection request for training a CSI prediction AI- model. The data collection request may further include any combination of, and / or all of (at least one of and / or one or more of) a request to collect input data for the CSI prediction Al-model and output data from the CSI prediction Al-model to enable the CSI prediction Al-model to be trained. The data collection request can also identify the expected output data of the CSI prediction Al-model, which can include a total number of samples to predict and a prediction distance between each of the samples. The input data for the CSI prediction Al-model may further include any combination of, and / or all of (at least one of and / or one or more of) a total number of samples used as input for the CSI prediction AI- model collected in a time domain, measurement distances between each of the total number of samples collected in the time domain and / or information used for categorizing the total number of samples. The output data of the CSI prediction Al-model may further include any combination of, and / or all of (at least one of and / or one or more of) a total number of samples to predict and a prediction distance between each of the samples.

[0182] At 1920, the UE may receive, from the base station, a data collection request response. In some instances, the data collection request response received may be a command to perform the UE measurements of the CSI-RS for training the CSI prediction Al-model.

[0183] At 1930, the UE may receive, from the base station, CSI Reference Signals (CSI-RS). At 1940, the UE may perform UE measurements of the CSI-RS for training theCSI prediction Al-model based on the data collection request response from the base station.

[0184] At 1950, the UE 106, may transmit, to the base station, a data collection stop request. At 1960, the UE 106, may store the UE measurements of the CSI-RS in a memory.

[0185] In some instances, the UE may decode a data collection stop request response received from the base station. The UE may also encode the UE measurements for transmission to the base station to enable the base station to train the CSI prediction AI- model. In some instances, the UE may buffer the UE measurements used for training the CSI prediction Al-model.

[0186] Further, in some instances, the UE may include a transceiver configured to receive the CSI-RS from the base station for training the CSI prediction Al-model, transmit the data collection request to the UE, and / or transmit the UE measurements performed by the UE to the base station.

[0187] In some instances, the UE may transmit the data collection request from the UE to the base station via one or more of an uplink radio resource control (RRC) message, and / or an uplink Medium Access Control (MAC) control element (CE).

[0188] Further, in some instances, the UE may determine when to trigger the data collection request based on any, any combination of, and / or all of (at least one of and / or one or more of) the UE has not been trained using the CSI prediction Al- model, the UE has moved from outdoors to indoors, the UE has moved from indoors to outdoors, the UE has moved to an environment with a number of obstructions that have increased or decreased by a predetermined threshold level; the UE has changed speed by a predetermined amount; and or the UE has moved into a cell with a different type of antenna relative to a previous cell.

[0189] In some instances, the UE may encode a request to the base station for assisted information to categorize the UE measurements based on the assisted information. The assisted information may include any, any combination of, and / or all of (at least one of and / or one or more of) a model type of a UE side CSI prediction Al-model, an antenna type of the base station, and / or a use of antenna virtualization by the base station.

[0190] In some instances, the UE may use a UE side CSI prediction Al-model that may include any of, any combination of, and / or all of (at least one of and / or one or more of) along short-term memory (LSTM) deep recurrent neural network model, a two-dimensional convolutional neural network model with an input tensor and an output tensor including dimensions of time and frequency, and / or a three-dimensional convolutional neural network model with an input tensor and an output tensor including dimensions of time and frequency and antenna type. The CSI prediction Al-model may include any, any combination of, and / or all of (at least one of and / or one or more of) a UE side prediction Al-model, and / or a network side prediction Al-model.

[0191] In some instances, the UE may encode the UE measurements of the CSI-RS used for training the CSI prediction Al-model, to enable the UE measurements to be communicated to an external server for offline training of the CSI prediction Al-model. In some instances, the UE may receive from the base station time domain repetition CSI-RS port transmissions to enable increased CSI-RS measurement accuracy. In some instances, the UE may receive CSI-RS measurements from multiple OFDM symbols decoded from the base station to enable a higher accuracy CSI-RS measurement.

[0192] It is well understood that the use of personally identifiable information should follow privacy policies and practices that are generally recognized as meeting or exceeding industry or governmental requirements for maintaining the privacy of users. In particular, personally identifiable information data should be managed and handled so as to minimize risks of unintentional or unauthorized access or use, and the nature of authorized use should be clearly indicated to users.

[0193] 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.

[0194] 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.

[0195] In some embodiments, a device (e.g., a UE 106) may be configured to include a processor (or a set of processors) including one or more baseband processors and one or more application 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.

[0196] Any of the methods described herein for operating a user equipment (UE) may be the basis of a corresponding method for operating a base station, by interpreting each message / signal X received by the UE in the downlink as message / signal X transmitted by the base station, and each message / signal Y transmitted in the uplink by the UE as a message / signal Y received by the base station.

[0197] 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) configured to perform data collection for channel state information (CSI) prediction with an artificial intelligence (Al) model, the apparatus comprising: one or more processors configured to: encode a data collection request, for training a CSI prediction AI- model, for transmission of the data collection request to a next generation node B (gNB); decode a data collection request response received from the gNB; and perform UE measurements of CSI Reference Signals (CSI-RS) for training the CSI prediction Al-model based on the data collection request response from the gNB; and a memory coupled to the one or more processors and configured to store the UE measurements of the CSI-RS.

2. The apparatus of claim 1 , wherein the one or more processors are further configured to: encode a data collection stop request for transmission to the gNB ; or decode a data collection stop request response received from the gNB.

3. The apparatus of claim 1, wherein the one or more processors are further configured to encode the UE measurements for transmission to the gNB to enable the gNB to train the CSI prediction Al-model.

4. The apparatus of claim 1, wherein the one or more processors are further configured to buffer the UE measurements for training the CSI prediction AI- model.

5. The apparatus of claim 1, further comprising a transceiver configured to:receive the CSI-RS from the gNB for training the CSI prediction Al- model; transmit the data collection request to the UE; and transmit the UE measurements performed by the UE to the gNB.

6. The apparatus of claim 1, wherein the data collection request includes at least a request to collect input data for the CSI prediction Al-model and output data from the CSI prediction Al-model to enable the gNB to train the CSI prediction Al- model.

7. The apparatus of any of claims 1 to 6, wherein the input data for the CSI prediction Al-model comprises one or more of: a total number of samples used as input for the CSI prediction Al-model collected in a time domain; measurement distances between one or more of the total number of samples collected in the time domain; and information used for categorizing the total number of samples.

8. The apparatus of any of claims 1 to 7, wherein output data of the CSI prediction Al-model includes a total number of samples to predict and a prediction distance between one or more of the samples.

9. The apparatus of claim 1, wherein the one or more processors are further configured to encode the data collection request for transmission from the UE to the gNB via one or more of: an uplink radio resource control (RRC) message; or an uplink Medium Access Control (MAC) control element (CE).

10. The apparatus of claim 9, wherein the one or more processors are further configured to determine when to trigger the data collection request based on one or more of: the UE has not been trained using the CSI prediction Al-model; the UE has moved from outdoors to indoors;the UE has moved from indoors to outdoors; the UE has moved to an environment with a number of obstructions that have increased or decreased by a predetermined threshold level; or the UE has changed speed by a predetermined amount.

11. The apparatus of claim 1, wherein the one or more processors are further configured to encode a request for transmission to the gNB for assisted information to categorize the UE measurements based on the assisted information.

12. The apparatus of claim 11, wherein the assisted information comprises one or more of: a model type of a UE side CSI prediction Al-model; an antenna type of the gNB ; or a use of antenna virtualization by the gNB.

13. The apparatus of claim 1, wherein the one or more processors are configured to use a UE side CSI prediction Al-model comprising one or more of: a long short-term memory (LSTM) deep recurrent neural network model; a two-dimensional convolutional neural network model with an input tensor and an output tensor including dimensions of time and frequency; or a three-dimensional convolutional neural network model with an input tensor and an output tensor including dimensions of time and frequency and antenna type.

14. The apparatus of claim 1, wherein the CSI prediction Al-model is a UE side prediction model.

15. The apparatus of claim 1, wherein the one or more processors are configured to encode the UE measurements of the CSI-RS used for training the CSI prediction Al-model, to enable the UE measurements to be communicated to an external server for offline training of the CSI prediction Al-model.

16. The apparatus of claim 1, wherein the one or more processors are configured to receive, from the gNB, time domain repetition CSI-RS port transmissions to enable increased CSI-RS measurement accuracy.

17. The apparatus of claim 1, wherein the one or more processors are configured to measure CSI-RS from multiple OFDM symbols received from the gNB to enable a greater number of CSI-RS measurements to provide to the CSI prediction AI- model to enable higher accuracy training of the CSI prediction Al-model.

18. The apparatus of claim 1, wherein the data collection request response received from the gNB is a command for the UE to perform the UE measurements of the CSI-RS to collect input samples for training the CSI prediction Al-model.

19. An apparatus of a user equipment (UE) configured to perform data collection for channel state information (CSI) prediction with an Artificial Intelligence (Al) model, the apparatus comprising: one or more processors configured to: encode a data collection request, for training a CSI prediction AI- model, for transmission of the data collection request to a next generation node B (gNB); decode a data collection request response received from the gNB, wherein the data collection request response received from the gNB includes a command to perform UE measurements of CSI Reference Signals (CSI- RS) for training the CSI prediction Al-model; perform the UE measurements of CSI-RS for training the CSI prediction Al-model based on the data collection request response from the gNB; buffer the UE measurements for training the CSI prediction AI- model; encode a data collection stop request for transmission to the gNB; and decode a data collection stop request response received from the gNB; anda memory coupled to the one or more processors and configured to store the UE measurements of the CSI-RS.

20. The apparatus of claim 19, wherein the one or more processors are further configured to encode the UE measurements for transmission to the gNB to enable a network side training of the CSI prediction Al-model.

21. The apparatus of claim 19, further comprising a transceiver configured to receive the CSI-RS from the gNB for training the CSI prediction Al-model.

22. The apparatus of claim 19, further comprising a transceiver configured to: transmit the data collection request to the UE; and transmit the UE measurements performed by the UE to the gNB.

23. The apparatus of claim 19, wherein the data collection request includes at least a request to collect input data for the CSI prediction Al-model and output data of the CSI prediction Al-model to enable the gNB to train the CSI prediction AI- model, wherein: the input data for the CSI prediction Al-model comprises one or more of: a total number of samples used as input for the CSI prediction AI- model collected in a time domain; measurement distances between one or more of the total number of samples collected in the time domain; and information used for categorizing the total number of samples; and the output data of the CSI prediction Al-model includes a total number of samples to predict and a prediction distance between one or more of the samples.

24. The apparatus of claim 19, wherein the one or more processors are further configured to: encode a request to the gNB for assisted information to categorize the UE measurements based on the assisted information, wherein the assisted information comprises one or more of: a model type of the CSI prediction Al-model;an antenna type of the gNB; or a use of antenna virtualization by the gNB.

25. The apparatus of claim 19, wherein the one or more processors are further configured to transmit the data collection request from the UE to the gNB via one or more of: an uplink radio resource control (RRC) message; or an uplink Medium Access Control (MAC) control element (CE).

26. The apparatus of claim 25, wherein the one or more processors are further configured to determine when to transmit the data collection request based on one or more of: the UE has not been trained using the CSI prediction Al-model; the UE has moved from outdoors to indoors; the UE has moved from indoors to outdoors; the UE has moved to an environment with a number of obstructions that have increased or decreased by a predetermined threshold level; or the UE has changed speed by a predetermined amount.

27. The apparatus of claim 19, wherein the one or more processors are configured to use a CSI prediction Al-model comprising one or more of: a long short-term memory (LSTM) deep recurrent neural network model; a two-dimensional convolutional neural network model with an input tensor and an output tensor including dimensions of time and frequency; or a three-dimensional convolutional neural network model with an input tensor and an output tensor including dimensions of time and frequency and antenna type.

28. The apparatus of claim 19, wherein the CSI prediction Al-model is one of: a UE side prediction Al-model; or a network side prediction Al-model.

29. The apparatus of claim 19, wherein the one or more processors are configured to encode the UE measurements of the CSI-RS used for training the CSI prediction Al-model, to enable the UE measurements to be communicated to an external server for offline training of the CSI prediction Al- model.

30. The apparatus of claim 19, wherein the one or more processors are configured to receive CSI-RS measurements from multiple OFDM symbols decoded from the gNB to enable a higher number of CSI-RS measurements for training the CSI prediction Al-model to enable a higher accuracy output from the CSI prediction Al-model.

31. An apparatus of a Next Generation NodeB (gNB) operable to assist with data collection for channel state information (CSI) prediction, the apparatus comprising: one or more processors configured to: decode, at the gNB, a data collection request received from a user equipment (UE), for training a UE side CSI prediction Al-model; encode a data collection request response for transmission of the data collection request response to the UE; encode, at the gNB, CSI Reference Signals (CSI-RS) for transmission from the gNB to the UE to enable the UE to perform UE measurements of the CSI-RS for training the UE side CSI prediction Al-model; and decode, at the gNB, a data collection stop request to stop transmission of the CSI-RS from the gNB; and a memory configured to store data contained in the data collection request.

32. The apparatus of claim 31, further comprising a transceiver configured to: send the CSI-RS to the UE for training the UE side CSI prediction AI- model; and transmit the data collection request response to the UE.

33. The apparatus of claim 31, wherein the data collection request includes at least a request to collect input data for the UE side CSI prediction Al-model and outputdata from the UE side CSI prediction Al-model to enable the gNB to train the UE side CSI prediction Al-model.

34. The apparatus of any of claims 31 to 33, wherein the input data for the UE side CSI prediction Al-model comprises one or more of: a total number of samples used as input for the UE side CSI prediction AI- model collected in a time domain; a measurement distance between one or more of the total number of samples collected in the time domain; and information used for categorizing the total number of samples.

35. The apparatus of any of claims 31 to 33, wherein output data of the UE side CSI prediction Al-model includes a total number of samples to predict and a prediction distance between one or more of the samples.

36. The apparatus of claim 31, wherein the one or more processors are further configured to decode, at the gNB, the data collection request received from the UE via one or more of: an uplink radio resource control (RRC) message; or an uplink Medium Access Control (MAC) control element (CE).

37. The apparatus of claim 31, wherein the one or more processors are further configured to decode, at the gNB, a request for assisted information by the UE to enable the UE to categorize the UE measurements based on the assisted information.

38. The apparatus of claim 37, wherein the assisted information comprises one or more of: a model type of the UE side CSI prediction Al-model; an antenna type of the gNB ; or a use of antenna virtualization by the gNB.

39. The apparatus of claim 31, wherein the one or more processors are configured to use a UE side CSI prediction Al-model comprising one or more of: a long short-term memory (LSTM) deep recurrent neural network model; a two-dimensional convolutional neural network model with an input tensor and an output tensor including dimensions of time and frequency; or a three-dimensional convolutional neural network model with an input tensor and an output tensor including dimensions of time and frequency and antenna type.

40. The apparatus of claim 31, wherein the data collection request response sent to the UE is a command to perform the UE measurements of the CSI-RS for training the UE side CSI prediction Al-model.

41. A method to perform data collection for channel state information (CSI) prediction using an artificial intelligence (Al) model, the method comprising: transmitting, to a base station, a data collection request for training a UE side CSI prediction Al-model; receiving, from the base station, a data collection request response; receiving, from the base station, CSI Reference Signals (CSI-RS); perform UE measurements of the CSI-RS for training the CSI prediction Al-model based on the data collection request response from the base station; and transmitting, to the base station, a data collection stop request.

42. An apparatus of a user equipment (UE) configured to perform data collection for channel state information (CSI) prediction with an artificial intelligence (Al) model, the apparatus comprising: one or more processors configured to: decode a data collection request, for training a CSI prediction AI- model, received from a next generation node B (gNB); and perform UE measurements of CSI Reference Signals (CSI-RS) for training the CSI prediction Al-model based on the data collection request received from the gNB ; and a memory coupled to the one or more processors and configured to store theUE measurements of the CSI-RS.

43. The apparatus of claim 42, wherein the one or more processors are further configured to decode a data collection stop request response received from the gNB.

44. The apparatus of claim 42, wherein the data collection request includes at least a request to collect input data for the CSI prediction Al-model and output data of the CSI prediction Al-model to enable the gNB to train the CSI prediction AI- model, wherein: the input data for the CSI prediction Al-model comprises one or more of: a total number of samples used as input for the CSI prediction AI- model collected in a time domain; measurement distances between one or more of the total number of samples collected in the time domain; and information used for categorizing the total number of samples; and the output data of the CSI prediction Al-model includes a total number of samples to predict and a prediction distance between one or more of the samples.

45. The apparatus of claim 44, wherein the one or more processors are further configured to: decode assisted information, received from the gNB, for the UE to categorize the UE measurements based on the assisted information, wherein the assisted information comprises one or more of: a model type of the CSI prediction Al-model; an antenna type of the gNB; or a use of antenna virtualization by the gNB.

46. The apparatus of claim 42, wherein the one or more processors are further configured to receive, from the gNB, the data collection request to perform the data collection request based on one or more of: the UE has not been trained using the CSI prediction Al-model; the UE has moved from outdoors to indoors;the UE has moved from indoors to outdoors; the UE has moved to an environment with a number of obstructions that have increased or decreased by a predetermined threshold level; or the UE has changed speed by a predetermined amount.

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