CSI compression based on learned basis

Abstract

The disclosure relates to a 5G or 6G communication system for supporting a higher data transmission rate. Apparatuses and methods for channel state information (CSI) compression based on learned basis. A method performed by a user equipment (UE) includes receiving information about a CSI report. The CSI report is based on a pair of matrices (W1, Ŵ1), where W1 is a compression matrix and Ŵ1 is a reconstruction matrix. The method further includes determining the CSI report based on the compression matrix W1 and transmitting the CSI report. The pair of matrices is trained using data. The pair of matrices, the data, and the CSI report are associated with P ports and NSB subbands (SBs).

Description

CSI COMPRESSION BASED ON LEARNED BASIS

[0001] The present disclosure relates generally to wireless communication systems and, more specifically, the present disclosure is related to apparatuses and methods for channel state information (CSI) compression based on learned basis.

[0002] Wireless communication has been one of the most successful innovations in modern history. Recently, the number of subscribers to wireless communication services exceeded five billion and continues to grow quickly. The demand of wireless data traffic is rapidly increasing due to the growing popularity among consumers and businesses of smart phones and other mobile data devices, such as tablets, “note pad” computers, net books, eBook readers, and machine type of devices. In order to meet the high growth in mobile data traffic and support new applications and deployments, improvements in radio interface efficiency and coverage are of paramount importance. To meet the demand for wireless data traffic having increased since deployment of 4G communication systems, and to enable various vertical applications, 5G communication systems have been developed and are currently being deployed.

[0003] 5th generation (5G) mobile communication technologies define broad frequency bands such that high transmission rates and new services are possible, and can be implemented not only in “Sub 6GHz” bands such as 3.5GHz, but also in “Above 6GHz” bands referred to as mmWave including 28GHz and 39GHz. In addition, it has been considered to implement 6G mobile communication technologies (referred to as Beyond 5G systems) in terahertz bands (for example, 95GHz to 3THz bands) in order to accomplish transmission rates fifty times faster than 5G mobile communication technologies and ultra-low latencies one-tenth of 5G mobile communication technologies.

[0004] At the beginning of the development of 5G mobile communication technologies, in order to support services and to satisfy performance requirements in connection with enhanced Mobile BroadBand (eMBB), Ultra Reliable Low Latency Communications (URLLC), and massive Machine-Type Communications (mMTC), there has been ongoing standardization regarding beamforming and massive MIMO for mitigating radio-wave path loss and increasing radio-wave transmission distances in mmWave, supporting numerologies (for example, operating multiple subcarrier spacings) for efficiently utilizing mmWave resources and dynamic operation of slot formats, initial access technologies for supporting multi-beam transmission and broadbands, definition and operation of BWP (BandWidth Part), new channel coding methods such as a LDPC (Low Density Parity Check) code for large amount of data transmission and a polar code for highly reliable transmission of control information, L2 pre-processing, and network slicing for providing a dedicated network specialized to a specific service.

[0005] Currently, there are ongoing discussions regarding improvement and performance enhancement of initial 5G mobile communication technologies in view of services to be supported by 5G mobile communication technologies, and there has been physical layer standardization regarding technologies such as V2X (Vehicle-to-everything) for aiding driving determination by autonomous vehicles based on information regarding positions and states of vehicles transmitted by the vehicles and for enhancing user convenience, NR-U (New Radio Unlicensed) aimed at system operations conforming to various regulation-related requirements in unlicensed bands, NR UE Power Saving, Non-Terrestrial Network (NTN) which is UE-satellite direct communication for providing coverage in an area in which communication with terrestrial networks is unavailable, and positioning.

[0006] Moreover, there has been ongoing standardization in air interface architecture / protocol regarding technologies such as Industrial Internet of Things (IIoT) for supporting new services through interworking and convergence with other industries, IAB (Integrated Access and Backhaul) for providing a node for network service area expansion by supporting a wireless backhaul link and an access link in an integrated manner, mobility enhancement including conditional handover and DAPS (Dual Active Protocol Stack) handover, and two-step random access for simplifying random access procedures (2-step RACH for NR). There also has been ongoing standardization in system architecture / service regarding a 5G baseline architecture (for example, service based architecture or service based interface) for combining Network Functions Virtualization (NFV) and Software-Defined Networking (SDN) technologies, and Mobile Edge Computing (MEC) for receiving services based on UE positions.

[0007] As 5G mobile communication systems are commercialized, connected devices that have been exponentially increasing will be connected to communication networks, and it is accordingly expected that enhanced functions and performances of 5G mobile communication systems and integrated operations of connected devices will be necessary. To this end, new research is scheduled in connection with eXtended Reality (XR) for efficiently supporting AR (Augmented Reality), VR (Virtual Reality), MR (Mixed Reality) and the like, 5G performance improvement and complexity reduction by utilizing Artificial Intelligence (AI) and Machine Learning (ML), AI service support, metaverse service support, and drone communication.

[0008] Furthermore, such development of 5G mobile communication systems will serve as a basis for developing not only new waveforms for providing coverage in terahertz bands of 6G mobile communication technologies, multi-antenna transmission technologies such as Full Dimensional MIMO (FD-MIMO), array antennas and large-scale antennas, metamaterial-based lenses and antennas for improving coverage of terahertz band signals, high-dimensional space multiplexing technology using OAM (Orbital Angular Momentum), and RIS (Reconfigurable Intelligent Surface), but also full-duplex technology for increasing frequency efficiency of 6G mobile communication technologies and improving system networks, AI-based communication technology for implementing system optimization by utilizing satellites and AI (Artificial Intelligence) from the design stage and internalizing end-to-end AI support functions, and next-generation distributed computing technology for implementing services at levels of complexity exceeding the limit of UE operation capability by utilizing ultra-high-performance communication and computing resources.

[0009] The above information is presented as background information only to assist with an understanding of the disclosure. No determination has been made, and no assertion is made, as to whether any of the above might be applicable as prior art with regard to the disclosure.

[0010] The present disclosure relates to CSI compression based on learned basis.

[0011] The technical objects to be achieved by various embodiments of the disclosure are not limited to the technical objects mentioned above, and other technical objects not mentioned may be considered by those skilled in the art from various embodiments of the disclosure to be described below.

[0012] In one embodiment, a user equipment (UE) is provided. The UE includes a transceiver configured to receive information about a channel state information (CSI) report. The CSI report is based on a pair of matrices , where is a compression matrix and is a reconstruction matrix. The UE further includes a processor operably coupled to the transceiver. The processor is configured to determine the CSI report based on the compression matrix . The transceiver is further configured to transmit the CSI report. The pair of matrices is trained using data. The pair of matrices, the data, and the CSI report are associated with P ports and subbands (SBs).

[0013] In another embodiment, a base station (BS) is provided. The BS includes a processor and a transceiver operably coupled to the processor. The transceiver is configured to transmit, to a UE, information about a CSI report that is based on a pair of matrices , where is a compression matrix and is a reconstruction matrix, and receive the CSI report that is based on the compression matrix . The pair of matrices is trained using data. The pair of matrices, the data, and the CSI report are associated with P ports and SBs.

[0014] In yet another embodiment, a method performed by a UE is provided. The method includes receiving information about a CSI report. The CSI report is based on a pair of matrices , where is a compression matrix and is a reconstruction matrix. The method further includes determining the CSI report based on the compression matrix and transmitting the CSI report. The pair of matrices is trained using data. The pair of matrices, the data, and the CSI report are associated with P ports and SBs.

[0015] Other technical features may be readily apparent to one skilled in the art from the following figures, descriptions, and claims.

[0016] The present disclosure relates to CSI compression based on learned basis.

[0017] Before undertaking the DETAILED DESCRIPTION below, it may be advantageous to set forth definitions of certain words and phrases used throughout this patent document. The term “couple” and its derivatives refer to any direct or indirect communication between two or more elements, whether or not those elements are in physical contact with one another. The terms “transmit,” “receive,” and “communicate,” as well as derivatives thereof, encompass both direct and indirect communication. The terms “include” and “comprise,” as well as derivatives thereof, mean inclusion without limitation. The term “or” is inclusive, meaning and / or. The phrase “associated with,” as well as derivatives thereof, means to include, be included within, interconnect with, contain, be contained within, connect to or with, couple to or with, be communicable with, cooperate with, interleave, juxtapose, be proximate to, be bound to or with, have, have a property of, have a relationship to or with, or the like. The term “controller” means any device, system, or part thereof that controls at least one operation. Such a controller may be implemented in hardware or a combination of hardware and software and / or firmware. The functionality associated with any particular controller may be centralized or distributed, whether locally or remotely. The phrase “at least one of,” when used with a list of items, means that different combinations of one or more of the listed items may be used, and only one item in the list may be needed. For example, “at least one of: A, B, and C” includes any of the following combinations: A, B, C, A and B, A and C, B and C, and A and B and C.

[0018] Moreover, various functions described below can be implemented or supported by one or more computer programs, each of which is formed from computer readable program code and embodied in a computer readable medium. The terms “application” and “program” refer to one or more computer programs, software components, sets of instructions, procedures, functions, objects, classes, instances, related data, or a portion thereof adapted for implementation in a suitable computer readable program code. The phrase “computer readable program code” includes any type of computer code, including source code, object code, and executable code. The phrase “computer readable medium” includes any type of medium capable of being accessed by a computer, such as read only memory (ROM), random access memory (RAM), a hard disk drive, a compact disc (CD), a digital video disc (DVD), or any other type of memory. A “non-transitory” computer readable medium excludes wired, wireless, optical, or other communication links that transport transitory electrical or other signals. A non-transitory computer readable medium includes media where data can be permanently stored and media where data can be stored and later overwritten, such as a rewritable optical disc or an erasable memory device.

[0019] Definitions for other certain words and phrases are provided throughout this patent document. Those of ordinary skill in the art should understand that in many if not most instances, such definitions apply to prior as well as future uses of such defined words and phrases.

[0020] For a more complete understanding of the present disclosure and its advantages, reference is now made to the following description taken in conjunction with the accompanying drawings, in which like reference numerals represent like parts:

[0021] FIG. 1 illustrates an example wireless network according to embodiments of the present disclosure;

[0022] FIG. 2 illustrates an example gNodeB (gNB) according to embodiments of the present disclosure;

[0023] FIG. 3 illustrates an example UE according to embodiments of the present disclosure;

[0024] FIG. 4A illustrates an example of a wireless transmit path according to embodiments of the present disclosure;

[0025] FIG. 4B illustrates an example of a wireless receive path according to embodiments of the present disclosure;

[0026] FIG. 5 illustrates an example of a transmitter structure for beamforming according to embodiments of the present disclosure;

[0027] FIG. 6 illustrates example radio access network (RAN) configurations according to embodiments of the present disclosure;

[0028] FIG. 7 illustrates an example antenna port layout according to embodiments of the present disclosure;

[0029] FIG. 8 illustrates a timeline of example spatial-domain (SD) units and frequency-domain (FD) units according to embodiments of the present disclosure;

[0030] FIG. 9 illustrates an example codebook according to embodiments of the present disclosure;

[0031] FIG. 10 illustrates an example artificial intelligence (AI)-native CSI configuration according to embodiments of the present disclosure;

[0032] FIG. 11 illustrates an example complex-values matrix / vector according to embodiments of the present disclosure;

[0033] FIG. 12 illustrates an example neural network based auto encoder according to embodiments of the present disclosure;

[0034] FIG. 13 illustrates an example basis pair for CSI compression and decompression according to embodiments of the present disclosure;

[0035] FIG. 14 illustrates another example basis pair for CSI compression and decompression according to embodiments of the present disclosure;

[0036] FIG. 15 illustrates an example basis pair using separate SD / FD bases according to embodiments of the present disclosure;

[0037] FIG. 16 illustrates an example method for signaling according to embodiments of the present disclosure; and

[0038] FIG. 17 illustrates an example method performed by a UE in a wireless communication system according to embodiments of the present disclosure.

[0039] FIGS. 1-17 discussed below, and the various, non-limiting embodiments used to describe the principles of the present disclosure in this patent document are by way of illustration only and should not be construed in any way to limit the scope of the disclosure. Those skilled in the art will understand that the principles of the present disclosure may be implemented in any suitably arranged system or device.

[0040] To meet the demand for wireless data traffic having increased since deployment of 4G communication systems, and to enable various vertical applications, 5G / NR communication systems have been developed and are currently being deployed. The 5G / NR communication system is implemented in higher frequency (mmWave) bands, e.g., 28 GHz or 60GHz bands, so as to accomplish higher data rates or in lower frequency bands, such as 6 GHz, to enable robust coverage and mobility support. To decrease propagation loss of the radio waves and increase the transmission distance, the beamforming, massive MIMO, full dimensional MIMO (FD-MIMO), array antenna, an analog beam forming, large scale antenna techniques are discussed in 5G / NR communication systems.

[0041] In addition, in 5G / NR communication systems, development for system network improvement is under way based on advanced small cells, cloud radio access networks (RANs), ultra-dense networks, device-to-device (D2D) communication, wireless backhaul, moving network, cooperative communication, coordinated multi-points (CoMP), reception-end interference cancelation and the like.

[0042] In the 5G system, Hybrid frequency shift keying (FSK) and QAM Modulation (FQAM) and sliding window superposition coding (SWSC) as an advanced coding modulation (ACM), and filter bank multi carrier(FBMC), non-orthogonal multiple access(NOMA), and sparse code multiple access (SCMA) as an advanced access technology have been developed.

[0043] The discussion of 5G systems and frequency bands associated therewith is for reference as certain embodiments of the present disclosure may be implemented in 5G systems. However, the present disclosure is not limited to 5G systems, or the frequency bands associated therewith, and embodiments of the present disclosure may be utilized in connection with any frequency band. For example, aspects of the present disclosure may also be applied to deployment of 5G communication systems, 6G, or even later releases which may use terahertz (THz) bands.

[0044] The following documents and standards descriptions are hereby incorporated by reference into the present disclosure as if fully set forth herein: [REF 1] 3GPP, TS 38.211, 5G; NR; Physical channels and modulation; [REF 2] 3GPP, TS 38.331, 5G; NR; Radio Resource Control (RRC); Protocol specification; [REF 3] 3GPP, TS 38.321, 5G; NR; Medium Access Control (MAC); Protocol specification; [REF 4] 3GPP, TS 38.214, 5G; NR; Physical layer procedures for data; [REF 5] https: / mathworld.wolfram.com / ToeplitzMatrix.html; [REF 6] M. Wax and T. Kailath, “Efficient inversion of a doubly block Toeplitz matrix”, in Proc. IEEE ICASSP, pp. 170-173, Apr. 14-16, 1983; [REF 7] https: / mathworld.wolfram.com / CirculantMatrix.html; [REF 8] A. Araujo, “Building Compact and Robust Deep Neural Networks with Toeplitz Matrices”, https: / arxiv.org / pdf / 2109.00959.pdf; [REF 9] 3GPP TS 38.212 v18.0.0, “E-UTRA, NR, Multiplexing and Channel coding;” [REF 10] 3GPP TS 38.213 v18.0.0, “E-UTRA, NR, Physical Layer Procedures for Control;” [REF 11] O-RAN.WG4.CONF.0-R003-v09.00, “O-RAN Working Group 4 (Fronthaul Working Group) Conformance Test Specification;” [REF 12] O-RAN.WG4.CUS.0-R003-v13.00, “O-RAN Working Group 4 (Open Fronthaul Interfaces WG) - Control, User and Synchronization Plane Specification; [REF 13] 3GPP TR 38.843, Study on Artificial Intelligence (AI) / Machine Learning (ML) for NR air interface; [REF 14] H. Ngo et al., “Cell-free massive MIMO versus small cells,” IEEE Transactions on Wireless Communications, vol. 16, no. 3, Mar. 2017; [REF 15] H. Ngo et al., “On the total energy efficiency of cell-free massive MIMO,” IEEE Transactions on Green Communications and Networking, vol. 2, no. 1, pp. 25 - 39, Mar. 2018; [REF 16] G. Interdonato et al., “Ubiquitous cell-free massive MIMO communications,” EURASIP J. Wireless Commun. Netw., vol. 2019, no. 197, Aug. 2019; [REF 17] J. Jeon et al., “MIMO evolution towards 6G: Modular massive MIMO in low-frequency bands,” IEEE Communications Magazine, vol. 59, pp. 52-58, Nov. 2021; [REF 18] E. Onggosanusi et al., “Modular and high-resolution channel state information and beam management for 5G new radio,” IEEE Communications Magazine, vol. 56, no. 3, pp. 48 - 55, Mar. 2018; [REF 19] 3GPP RAN1 contribution RP-213517, MIMO evolution for downlink and uplink, Samsung; and [REF 20] P. Madadi et al., “PolarDenseNet: A Deep Learning Model for CSI Feedback in MIMO Systems”, https: / arxiv.org / pdf / 2202.01246.pdf.

[0045] FIGS. 1-3 below describe various embodiments implemented in wireless communications systems and with the use of orthogonal frequency division multiplexing (OFDM) or orthogonal frequency division multiple access (OFDMA) communication techniques. The descriptions of FIGS. 1-3 are not meant to imply physical or architectural limitations to how different embodiments may be implemented. Different embodiments of the present disclosure may be implemented in any suitably arranged communications system.

[0046] FIG. 1 illustrates an example wireless network 100 according to embodiments of the present disclosure. The embodiment of the wireless network 100 shown in FIG. 1 is for illustration only. Other embodiments of the wireless network 100 could be used without departing from the scope of the present disclosure.

[0047] As shown in FIG. 1, the wireless network 100 includes a gNB 101 (e.g., base station, BS), a gNB 102, and a gNB 103. The gNB 101 communicates with the gNB 102 and the gNB 103. The gNB 101 also communicates with at least one network 130, such as the Internet, a proprietary Internet Protocol (IP) network, or other data network.

[0048] The gNB 102 provides wireless broadband access to the network 130 for a first plurality of user equipments (UEs) within a coverage area 120 of the gNB 102. The first plurality of UEs includes a UE 111, which may be located in a small business; a UE 112, which may be located in an enterprise; a UE 113, which may be a WiFi hotspot; a UE 114, which may be located in a first residence; a UE 115, which may be located in a second residence; and a UE 116, which may be a mobile device, such as a cell phone, a wireless laptop, a wireless PDA, or the like. The gNB 103 provides wireless broadband access to the network 130 for a second plurality of UEs within a coverage area 125 of the gNB 103. The second plurality of UEs includes the UE 115 and the UE 116. In some embodiments, one or more of the gNBs 101-103 may communicate with each other and with the UEs 111-116 using 5G / NR, long term evolution (LTE), long term evolution-advanced (LTE-A), WiMAX, WiFi, or other wireless communication techniques.

[0049] Depending on the network type, the term “base station” or “BS” can refer to any component (or collection of components) configured to provide wireless access to a network, such as transmit point (TP), transmit-receive point (TRP), an enhanced base station (eNodeB or eNB), a 5G / NR base station (gNB), a macrocell, a femtocell, a WiFi access point (AP), or other wirelessly enabled devices. Base stations may provide wireless access in accordance with one or more wireless communication protocols, e.g., 5G / NR 3rd generation partnership project (3GPP) NR, long term evolution (LTE), LTE advanced (LTE-A), high speed packet access (HSPA), Wi-Fi 802.11a / b / g / n / ac, etc. For the sake of convenience, the terms “BS” and “TRP” are used interchangeably in this patent document to refer to network infrastructure components that provide wireless access to remote terminals. Also, depending on the network type, the term “user equipment” or “UE” can refer to any component such as “mobile station,” “subscriber station,” “remote terminal,” “wireless terminal,” “receive point,” or “user device.” For the sake of convenience, the terms “user equipment” and “UE” are used in this patent document to refer to remote wireless equipment that wirelessly accesses a BS, whether the UE is a mobile device (such as a mobile telephone or smartphone) or is normally considered a stationary device (such as a desktop computer or vending machine).

[0050] The dotted lines show the approximate extents of the coverage areas 120 and 125, which are shown as approximately circular for the purposes of illustration and explanation only. It should be clearly understood that the coverage areas associated with gNBs, such as the coverage areas 120 and 125, may have other shapes, including irregular shapes, depending upon the configuration of the gNBs and variations in the radio environment associated with natural and man-made obstructions.

[0051] As described in more detail below, one or more of the UEs 111-116 include circuitry, programing, or a combination thereof for CSI compression based on learned basis. In certain embodiments, one or more of the BSs 101-103 include circuitry, programing, or a combination thereof to support CSI compression based on learned basis.

[0052] Although FIG. 1 illustrates one example of a wireless network, various changes may be made to FIG. 1. For example, the wireless network 100 could include any number of gNBs and any number of UEs in any suitable arrangement. Also, the gNB 101 could communicate directly with any number of UEs and provide those UEs with wireless broadband access to the network 130. Similarly, each gNB 102-103 could communicate directly with the network 130 and provide UEs with direct wireless broadband access to the network 130. Further, the gNBs 101, 102, and / or 103 could provide access to other or additional external networks, such as external telephone networks or other types of data networks.

[0053] FIG. 2 illustrates an example gNB 102 according to embodiments of the present disclosure. The embodiment of the gNB 102 illustrated in FIG. 2 is for illustration only, and the gNBs 101 and 103 of FIG. 1 could have the same or similar configuration. However, gNBs come in a wide variety of configurations, and FIG. 2 does not limit the scope of the present disclosure to any particular implementation of a gNB.

[0054] As shown in FIG. 2, the gNB 102 includes multiple antennas 205a-205n, multiple transceivers 210a-210n, a controller / processor 225, a memory 230, and a backhaul or network interface 235.

[0055] The transceivers 210a-210n receive, from the antennas 205a-205n, incoming radio frequency (RF) signals, such as signals transmitted by UEs in the wireless network 100. The transceivers 210a-210n down-convert the incoming RF signals to generate IF or baseband signals. The IF or baseband signals are processed by receive (RX) processing circuitry in the transceivers 210a-210n and / or controller / processor 225, which generates processed baseband signals by filtering, decoding, and / or digitizing the baseband or IF signals. The controller / processor 225 may further process the baseband signals.

[0056] Transmit (TX) processing circuitry in the transceivers 210a-210n and / or controller / processor 225 receives analog or digital data (such as voice data, web data, e-mail, or interactive video game data) from the controller / processor 225. The TX processing circuitry encodes, multiplexes, and / or digitizes the outgoing baseband data to generate processed baseband or IF signals. The transceivers 210a-210n up-converts the baseband or IF signals to RF signals that are transmitted via the antennas 205a-205n.

[0057] The controller / processor 225 can include one or more processors or other processing devices that control the overall operation of the gNB 102. For example, the controller / processor 225 could control the reception of uplink (UL) channel signals and the transmission of downlink (DL) channel signals by the transceivers 210a-210n in accordance with well-known principles. The controller / processor 225 could support additional functions as well, such as more advanced wireless communication functions. For instance, the controller / processor 225 could support beam forming or directional routing operations in which outgoing / incoming signals from / to multiple antennas 205a-205n are weighted differently to effectively steer the outgoing signals in a desired direction. As another example, the controller / processor 225 could support methods for CSI compression based on learned basis. Any of a wide variety of other functions could be supported in the gNB 102 by the controller / processor 225.

[0058] The controller / processor 225 is also capable of executing programs and other processes resident in the memory 230, such as processes to support CSI compression based on learned basis. The controller / processor 225 can move data into or out of the memory 230 as required by an executing process.

[0059] The controller / processor 225 is also coupled to the backhaul or network interface 235. The backhaul or network interface 235 allows the gNB 102 to communicate with other devices or systems over a backhaul connection or over a network. The interface 235 could support communications over any suitable wired or wireless connection(s). For example, when the gNB 102 is implemented as part of a cellular communication system (such as one supporting 5G / NR, LTE, or LTE-A), the interface 235 could allow the gNB 102 to communicate with other gNBs over a wired or wireless backhaul connection. When the gNB 102 is implemented as an access point, the interface 235 could allow the gNB 102 to communicate over a wired or wireless local area network or over a wired or wireless connection to a larger network (such as the Internet). The interface 235 includes any suitable structure supporting communications over a wired or wireless connection, such as an Ethernet or transceiver.

[0060] The memory 230 is coupled to the controller / processor 225. Part of the memory 230 could include a RAM, and another part of the memory 230 could include a Flash memory or other ROM.

[0061] Although FIG. 2 illustrates one example of gNB 102, various changes may be made to FIG. 2. For example, the gNB 102 could include any number of each component shown in FIG. 2. Also, various components in FIG. 2 could be combined, further subdivided, or omitted and additional components could be added according to particular needs.

[0062] FIG. 3 illustrates an example UE 116 according to embodiments of the present disclosure. The embodiment of the UE 116 illustrated in FIG. 3 is for illustration only, and the UEs 111-115 of FIG. 1 could have the same or similar configuration. However, UEs come in a wide variety of configurations, and FIG. 3 does not limit the scope of the present disclosure to any particular implementation of a UE.

[0063] As shown in FIG. 3, the UE 116 includes antenna(s) 305, a transceiver(s) 310, and a microphone 320. The UE 116 also includes a speaker 330, a processor 340, an input / output (I / O) interface (IF) 345, an input 350, a display 355, and a memory 360. The memory 360 includes an operating system (OS) 361 and one or more applications 362.

[0064] The transceiver(s) 310 receives from the antenna(s) 305, an incoming RF signal transmitted by a gNB of the wireless network 100. The transceiver(s) 310 down-converts the incoming RF signal to generate an intermediate frequency (IF) or baseband signal. The IF or baseband signal is processed by RX processing circuitry in the transceiver(s) 310 and / or processor 340, which generates a processed baseband signal by filtering, decoding, and / or digitizing the baseband or IF signal. The RX processing circuitry sends the processed baseband signal to the speaker 330 (such as for voice data) or is processed by the processor 340 (such as for web browsing data).

[0065] TX processing circuitry in the transceiver(s) 310 and / or processor 340 receives analog or digital voice data from the microphone 320 or other outgoing baseband data (such as web data, e-mail, or interactive video game data) from the processor 340. The TX processing circuitry encodes, multiplexes, and / or digitizes the outgoing baseband data to generate a processed baseband or IF signal. The transceiver(s) 310 up-converts the baseband or IF signal to an RF signal that is transmitted via the antenna(s) 305.

[0066] The processor 340 can include one or more processors or other processing devices and execute the OS 361 stored in the memory 360 in order to control the overall operation of the UE 116. For example, the processor 340 could control the reception of DL channel signals and the transmission of uplink (UL) channel signals by the transceiver(s) 310 in accordance with well-known principles. In some embodiments, the processor 340 includes at least one microprocessor or microcontroller.

[0067] The processor 340 is also capable of executing other processes and programs resident in the memory 360. For example, the processor 340 may execute processes for CSI compression based on learned basis as described in embodiments of the present disclosure. The processor 340 can move data into or out of the memory 360 as required by an executing process. In some embodiments, the processor 340 is configured to execute the applications 362 based on the OS 361 or in response to signals received from gNBs or an operator. The processor 340 is also coupled to the I / O interface 345, which provides the UE 116 with the ability to connect to other devices, such as laptop computers and handheld computers. The I / O interface 345 is the communication path between these accessories and the processor 340.

[0068] The processor 340 is also coupled to the input 350, which includes, for example, a touchscreen, keypad, etc., and the display 355. The operator of the UE 116 can use the input 350 to enter data into the UE 116. The display 355 may be a liquid crystal display, light emitting diode display, or other display capable of rendering text and / or at least limited graphics, such as from web sites.

[0069] The memory 360 is coupled to the processor 340. Part of the memory 360 could include a random-access memory (RAM), and another part of the memory 360 could include a Flash memory or other read-only memory (ROM).

[0070] Although FIG. 3 illustrates one example of UE 116, various changes may be made to FIG. 3. For example, various components in FIG. 3 could be combined, further subdivided, or omitted and additional components could be added according to particular needs. As a particular example, the processor 340 could be divided into multiple processors, such as one or more central processing units (CPUs) and one or more graphics processing units (GPUs). In another example, the transceiver(s) 310 may include any number of transceivers and signal processing chains and may be connected to any number of antennas. Also, while FIG. 3 illustrates the UE 116 configured as a mobile telephone or smartphone, UEs could be configured to operate as other types of mobile or stationary devices.

[0071] FIG. 4A and FIG. 4B illustrate an example of wireless transmit and receive paths 400 and 450, respectively, according to embodiments of the present disclosure. For example, a transmit path 400 may be described as being implemented in a gNB (such as gNB 102), while a receive path 450 may be described as being implemented in a UE (such as UE 116). However, it will be understood that the receive path 450 can be implemented in a gNB and that the transmit path 400 can be implemented in a UE. In some embodiments, the transmit path 400 and / or receive path 450 is configured for CSI compression based on learned basis as described in embodiments of the present disclosure.

[0072] As illustrated in FIG. 4A, the transmit path 400 includes a channel coding and modulation block 405, a serial-to-parallel (S-to-P) block 410, a size N Inverse Fast Fourier Transform (IFFT) block 415, a parallel-to-serial (P-to-S) block 420, an add cyclic prefix block 425, and an up-converter (UC) 430. The receive path 450 includes a down-converter (DC) 455, a remove cyclic prefix block 460, a S-to-P block 465, a size N Fast Fourier Transform (FFT) block 470, a parallel-to-serial (P-to-S) block 475, and a channel decoding and demodulation block 480.

[0073] In the transmit path 400, the channel coding and modulation block 405 receives a set of information bits, applies coding (such as a low-density parity check (LDPC) coding), and modulates the input bits (such as with Quadrature Phase Shift Keying (QPSK) or Quadrature Amplitude Modulation (QAM)) to generate a sequence of frequency-domain modulation symbols. The serial-to-parallel block 410 converts (such as de-multiplexes) the serial modulated symbols to parallel data in order to generate N parallel symbol streams, where N is the IFFT / FFT size used in the gNB and the UE. The size N IFFT block 415 performs an IFFT operation on the N parallel symbol streams to generate time-domain output signals. The parallel-to-serial block 420 converts (such as multiplexes) the parallel time-domain output symbols from the size N IFFT block 415 in order to generate a serial time-domain signal. The add cyclic prefix block 425 inserts a cyclic prefix to the time-domain signal. The up-converter 430 modulates (such as up-converts) the output of the add cyclic prefix block 425 to a RF frequency for transmission via a wireless channel. The signal may also be filtered at a baseband before conversion to the RF frequency.

[0074] As illustrated in FIG. 4B, the down-converter 455 down-converts the received signal to a baseband frequency, and the remove cyclic prefix block 460 removes the cyclic prefix to generate a serial time-domain baseband signal. The serial-to-parallel block 465 converts the time-domain baseband signal to parallel time-domain signals. The size N FFT block 470 performs an FFT algorithm to generate N parallel frequency-domain signals. The (P-to-S) block 475 converts the parallel frequency-domain signals to a sequence of modulated data symbols. The channel decoding and demodulation block 480 demodulates and decodes the modulated symbols to recover the original input data stream.

[0075] Each of the gNBs 101-103 may implement a transmit path 400 that is analogous to transmitting in the downlink to UEs 111-116 and may implement a receive path 450 that is analogous to receiving in the uplink from UEs 111-116. Similarly, each of UEs 111-116 may implement a transmit path 400 for transmitting in the uplink to gNBs 101-103 and may implement a receive path 450 for receiving in the downlink from gNBs 101-103.

[0076] Each of the components in FIGS. 4A and 4B can be implemented using only hardware or using a combination of hardware and software / firmware. As a particular example, at least some of the components in FIGS. 4A and 4B may be implemented in software, while other components may be implemented by configurable hardware or a mixture of software and configurable hardware. For instance, the FFT block 470 and the IFFT block 415 may be implemented as configurable software algorithms, where the value of size N may be modified according to the implementation.

[0077] Furthermore, although described as using FFT and IFFT, this is by way of illustration only and should not be construed to limit the scope of the present disclosure. Other types of transforms, such as Discrete Fourier Transform (DFT) and Inverse Discrete Fourier Transform (IDFT) functions, can be used. It will be appreciated that the value of the variable N may be any integer number (such as 1, 2, 3, 4, or the like) for DFT and IDFT functions, while the value of the variable N may be any integer number that is a power of two (such as 1, 2, 4, 8, 16, or the like) for FFT and IFFT functions.

[0078] Although FIGS. 4A and 4B illustrate examples of wireless transmit and receive paths 400 and 450, respectively, various changes may be made to FIGS. 4A and 4B. For example, various components in FIGS. 4A and 4B can be combined, further subdivided, or omitted, and additional components can be added according to particular needs. Also, FIGS. 4A and 4B are meant to illustrate examples of the types of transmit and receive paths that can be used in a wireless network. Any other suitable architectures can be used to support wireless communications in a wireless network.

[0079] FIG. 5 illustrates an example of a transmitter structure 500 for beamforming according to embodiments of the present disclosure. In certain embodiments, one or more of gNB 102 or UE 116 includes the transmitter structure 500. For example, one or more of antenna 205 and its associated systems or antenna 305 and its associated systems can be included in transmitter structure 500. This example is for illustration only and other embodiments can be used without departing from the scope of the present disclosure.

[0080] Accordingly, embodiments of the present disclosure recognize that Rel-14 LTE and Rel-15 NR support up to 32 channel state indication / information CSI reference signal (CSI-RS) antenna ports which enable an eNB or a gNB to be equipped with a large number of antenna elements (such as 64 or 128). A plurality of antenna elements can then be mapped onto one CSI-RS port. For mmWave bands, although a number of antenna elements can be larger for a given form factor, a number of CSI-RS ports, that can correspond to the number of digitally precoded ports, can be limited due to hardware constraints (such as the feasibility to install a large number of analog-to-digital converters (ADCs) / digital-to-analog converters (DACs) at mmWave frequencies) as illustrated in FIG. 5. Then, one CSI-RS port can be mapped onto a large number of antenna elements that can be controlled by a bank of analog phase shifters 501. One CSI-RS port can then correspond to one sub-array which produces a narrow analog beam through analog beamforming 505. This analog beam can be configured to sweep across a wider range of angles 520 by varying the phase shifter bank across symbols or slots / subframes. The number of sub-arrays (equal to the number of RF chains) is the same as the number of CSI-RS ports NCSI-PORT. A digital beamforming unit 510 performs a linear combination across NCSI-PORT analog beams to further increase a precoding gain. While analog beams are wideband (hence not frequency-selective), digital precoding can be varied across frequency sub-bands or resource blocks. Receiver operation can be conceived analogously.

[0081] Since the transmitter structure 500 of FIG. 5 utilizes multiple analog beams for transmission and reception (wherein one or a small number of analog beams are selected out of a large number, for instance, after a training duration that is occasionally or periodically performed), the term “multi-beam operation” is used to refer to the overall system aspect. This includes, for the purpose of illustration, indicating the assigned DL or UL TX beam (also termed “beam indication”), measuring at least one reference signal for calculating and performing beam reporting (also termed “beam measurement” and “beam reporting”, respectively), and receiving a DL or UL transmission via a selection of a corresponding RX beam. The system of FIG. 5 is also applicable to higher frequency bands such as >52.6GHz (also termed frequency range 4 or FR4). In this case, the system can employ only analog beams. Due to the O2 absorption loss around 60 GHz frequency (~10 dB additional loss per 100 m distance), a larger number and narrower analog beams (hence a larger number of radiators in the array) are essential to compensate for the additional path loss.

[0082] In next generation cellular standards (e.g. 6G), in addition to FR1 and FR2, new carrier frequency bands can be evaluated, e.g., FR4 (>52.6GHz), terahertz (>100GHz) and upper mid-band (10-15GHz). The number of CSI-RS ports that can be supported for these new bands is likely to be different from FR1 and FR2. In particular, for 10-15GHz band, the max number of CSI-RS antenna ports is likely to be more than FR1, due to smaller antenna form factors, and feasibility of fully digital beamforming (as in FR1) at these frequencies. For instance, the number of CSI-RS antenna ports can grow up to 128. Besides, the NW deployment / topology at these frequencies is also expected to be denser / distributed, for example, antenna ports distributed at multiple (non-co-located, hence geographically separated) TRPs within a cellular region can be the main scenario of interest, due to which the number of CSI-RS antenna ports for MIMO can be even larger (e.g. up to 256).

[0083] Likewise, for a cellular system operating in low carrier frequency in general, a sub-1GHz frequency range (e.g. less than 1 GHz) as an example, supporting large number of CSI-RS antenna ports (e.g. 32) or many antenna elements at a single location or remote radio head (RRH) or TRP is challenging due to a larger antenna form factor size needed evaluating carrier frequency wavelength than a system operating at a higher frequency such as 2 GHz or 4 GHz. At such low frequencies, the maximum number of CSI-RS antenna ports that can be co-located at a site (or RRH or TRP) can be limited, for example to 8. This limits the spectral efficiency of such systems. In particular, the multiple user multiple-input-multiple-output (MU-MIMO) spatial multiplexing gains offered due to large number of CSI-RS antenna ports (such as 32) can’t be achieved due to the antenna form factor limitation. One plausible way to operate a system with large number of CSI-RS antenna ports at low carrier frequency is to distribute the physical antenna ports to different panels / RRHs / TRPs, which can be non-collocated. The multiple sites or panels / RRHs / TRPs can still be connected to a single (common) base unit forming a single antenna system, hence the signal transmitted / received via multiple distributed RRHs / TRPs can still be processed at a centralized location.

[0084] As described herein, for low (FR1), high (FR2 and beyond), or mid (6-15GHz) band, the NW topology / architecture is likely to be more and more distributed in future due to reasons explained herein (e.g. use cases, HW requirements, antenna form factors, mobility etc.). In this disclosure, such a distributed system is referred to as a DMIMO or multiple TRP (mTRP) system (multiple antenna port groups, which can be non-co-located). The transmission in such a system can be coherent joint transmission (CJT), i.e., a layer can be transmitted across / using multiple TRPs, or non-coherent joint transmission (NCJT). Due to distributed nature of operation, the groups of antenna ports (or TRPs) need to be calibrated / synchronized by compensating for the non-idealities such as time / frequency / phase offsets non-ideal backhaul across TRPs, due to HW impairments, different delay profiles, and Doppler profile (in high-speed scenarios) associated with different TRPs.

[0085] In one example, a TRP or RRH can be functionally equivalent to (hence can be replaced with) or is interchangeable with one of more of the following: an antenna, or an antenna group (multiple antennae), an antenna port, an antenna port group (multiple ports), a CSI-RS resource, multiple CSI-RS resources, a CSI-RS resource set, multiple CSI-RS resource sets, an antenna panel, multiple antenna panels, a Tx-Rx entity, a (analog) beam, a (analog) beam group, a cell, a cell group.

[0086] The present disclosure relates generally to wireless communication systems and, more specifically, to Deep-learning-based precoding in next generation of communication (e.g. 6G) systems.

[0087] There are two types of frequency range (FR) defined in 3GPP 5G NR specifications. The sub-6 GHz range is called frequency range 1 (FR1) and millimeter wave range is called frequency range 2 (FR2). An example of the frequency range for FR1 and FR2 is shown in Table 1. Whenever the FR2 is referred, both FR2-1 and FR2-2 frequency sub-ranges shall be provided, unless otherwise stated.

[0088] Table 1: Definition of frequency ranges

[0089]

[0090] In next generation cellular standards (e.g. 6G), in addition to FR1 and FR2, new carrier frequency bands can be taken into account, e.g. terahertz (>100GHz) and FR3 or upper mid-band (7-24GHz). The number of antenna ports that can be supported for these new bands is likely to be different from FR1 and FR2. In particular, for 7-15GHz band, the max number of antenna ports is likely to be more than FR1, due to smaller antenna form factors, and feasibility of fully digital beamforming (as in FR1) at these frequencies. For instance, the number of CSI-RS antenna ports can grow up to 128. Besides, the NW deployment / topology at these frequencies is also expected to be denser / distributed, for example, antenna ports distributed at multiple (potentially non-co-located, hence geographically separated) TRPs or O-RUs within a cellular region can be the main scenario of interest, due to which the number of CSI-RS antenna ports for MIMO can be even larger (e.g. up to 256).

[0091] A (spatial or digital) precoding / beamforming can be used across these large number of antenna ports in order to achieve MIMO gains. Depending on the carrier frequency, and the feasibility of RF / HW-related components, the (spatial) precoding / beamforming can be fully digital or hybrid analog-digital. In fully digital beamforming, there can be one-to-one mapping between an antenna port and an antenna element, or a ‘static / fixed’ virtualization of multiple antenna elements to one antenna port can be used. Each antenna port can be digitally controlled. Hence, a spatial multiplexing across antenna ports is provided.

[0092] FIG. 6 illustrates a diagram of example RAN configurations 600 according to embodiments of the present disclosure. For example, RAN configurations 600 can be implemented by the BS 102 of FIG. 1. This example is for illustration only and other embodiments can be used without departing from the scope of the present disclosure.

[0093] Likewise, for O-RAN, a TRP can be functionally equivalent to (hence can be replaced with) or is interchangeable with one of more of the following:

[0094] ● One RU or O-RU: a logical node that includes a subset of the eNB / gNB functions (e.g. as listed in clause 4.2 split option 7-2x)

[0095] ● More than one RUs or O-RUs

[0096] ● One or more than one RUs or O-RUs

[0097] Two examples are shown in FIG. 6.

[0098] The following are defined in [REF11 and REF12].

[0099]

[0100] The 5th generation (5G) standard supports several features, but only a handful of them is implemented in real products. The main reason is owing to complexity, feasibility, and market need of those features. 6G should therefore be (a) aimed for realistic antenna structures, deployment scenarios, and feasibility of features, (b) simpler than 5G (to ease implementations), whenever feasible (c) learning-based (for adaptability and future-proofness). Just like 5G, multiple-input multiple-output (MIMO) is expected to encompass key enabling technologies / features to meet data-rate requirements in the 6th generation (6G) as well. In particular, a codebook-based channel state information (CSI) acquisition at the network (NW) is likely to remain crucial for frequency division duplexing (FDD) as well as time division duplexing (TDD) bands in real 6G NW deployments.

[0101] The codebook-based CSI in 5G is based on a fixed-basis. In spatial domain (SD), the fixed-basis is designed expecting a structured (e.g. planar dual-polarized) antenna port layout, taking into account one or multiple of such layouts located at transmit-receive points (TRPs). The fixed-basis is optimized depending on deployment of these TRPs (co-located vs distributed) and transmission hypotheses, i.e., TRP selection vs non-coherent joint transmission (NCJT) vs coherent JT (CJT). For further CSI compression, the fixed-basis is extended in frequency domain (FD) and Doppler domain (DD). As many as 20 codebooks (at least one codebook per 5G release) have been specified thus far, cf. Table 1. This approach of designing codebooks is becoming untenable. Especially in 6G, the fixed-basis is quite limited in its utility due to (i) more diverse and less-structured and non-planar antenna types, e.g. reconfigurable intelligent surface (RIS), three-dimensional (3D) cylindrical / semi-spherical antenna may be used, while two-dimensional (2D) planar array is still relevant, and (ii) NW deployment / topology is expected to be more distributed due to large antenna form factor (in low band), channel-sparsity, or rank-deficiency (in higher bands), implying larger number of antenna ports (e.g. up to 256) than 5G. Taking into account the herein, a unified future-proof design while still highly performing for key scenarios is deemed necessary. The design should be upgradable (based on parameterized components), scalable (as number of antenna ports grows or geometry or distribution evolves), and learning-based (if / when feasible). A new CSI paradigm, namely artificial intelligence (AI)-native CSI provided in this disclosure can be instrumental in this regard.

[0102] Up to a 5G network (NW) can be described in terms of transmit-receive points (TRPs). For a first frequency range (FR1), i.e., <6GHz, a TRP can comprise one or more antenna ports, and is fully-digital (i.e. each antenna port is driven by a dedicated baseband processing chain); and for a second frequency range 24.25 - 52.6 GHz (FR2), i.e., for mmWave frequencies, a TRP comprises one of more antenna panels (sub-arrays), each comprising one or two antenna ports that are controlled by analog phase shifters that result in an analog beam (pointing in certain spatial direction). An antenna port in FR1 can also be beamformed (aka virtualization); however, such a beamforming (BF) is generally static (non-adaptive, hence not requiring measurement and reporting). In FR2, due to large propagation loss at mmWave frequencies, each antenna panel requires dynamic / frequent update of the analog BF, which is often based on (analog) beam measurement and reporting.

[0103] A communication between the 5G NW and a user is broadly based on: (A1) NW resources, and (A2) signaling components, where the former corresponds to spatial-domain, frequency-domain, and time-domain (SD, FD, TD) resources allocated to the user for the communication, and the latter corresponds to components that are signaled over the NW resources. The SD resources can be based on a single TRP (sTRP) or multiple TRPs (mTRP), where mTRP can be (B1) co-located at a site / location or (B2) non-co-located / distributed at multiple sites / locations, where the latter corresponds to a distributed SD resource, hence the corresponding communication hypothesis can be (C1) non-coherent joint transmission (NCJT) where a data stream (layer) is transmitted from one of the mTRPs, or (C2) coherent JT (CJT), where a data stream (layer) can be transmitted from multiple of the mTRPs. The FD resources can comprise a set of physical resource blocks (PRBs), and the TD resources can comprise one or multiple time slots (i.e., 1 slot = Nsymconsecutive symbols).

[0104] The signaling components include signaling associated with (D1) measurement, (D2) channel state information (CSI) report, and (D3) DL reception or UL transmission.

[0105] For (D1), the user measures channel measurement RSs (CMRs) to estimate the channel condition between the sTRP / mTRP and the user. In case of sTRP, the user can measure a set comprising one or multiple DL measurement resources. For mTRP, the measurement resources can be (E1) one resource set comprising one group per TRP, or (E2) one resource set per TRP. The user can also measure the interference based on interference measurement RSs (IMRs). A CMR can correspond to an analog beam, and can be repeated in multiple symbols for determining user’s analog beam.

[0106] For (D2), the user, based on the measurement, determines the CSI and reports it to the NW, where the CSI can be (F1) (analog) beam-related CSI, or (F2) (digital) non-beam-related CSI. For (F1), the user determines one or multiple pairs (indicator, metric), where the indicator indicates a CMR and the metric indicates a (beam) quality (e.g. reference signal received power (RSRP), signal-to-interference-plus-noise ratio (SINR)).

[0107] 5G NR codebooks (CBs) compress the CSI in the spatial / angle (introduced in Rel-15), frequency / delay (introduced in Rel-16), and time / Doppler (introduced in Rel-18) domains. The 5G NR CBs employ DFT basis vectors-based compression exploiting the sparsity of the channel (fewer significant coefficients) in certain domain (angle / delay / Doppler), DFT basis vectors-based representation of precoding vectors is computationally advantageous, e.g., O(n2) complexity for basis matrix inversion. However, basis vectors-based representation may incur a non-trivial approximation error due to incomplete basis representation, fixed basis sampling, fixed (RRC-configured) number of basis vectors, etc. An example in which the channel strength in the spatial-frequency domain and angel-delay domain for a single layer precoding vectors (32 ports and 13 subbands) is taken into account. Rel-16 eType II codebook exploits the sparsity of the strong angle-delay coefficients for feedback overhead reduction, i.e., e.g., reports coefficients, say, corresponding to L=4 angle (beam) per polarization and M=3 delay components (basis vectors). The components, which are still significant but not reported by the eType II-based CSI feedback, contribute to the performance (accuracy) gap from the ideal feedback.

[0108] Taking into account the issues mentioned herein with 5G NR (DFT-based fixed) CBs, it may be advantageous to configure a UE to support alternate methods of compressing DL CSI. For instance, deep-learning or AI / ML-based CSI feedback has a potential of providing better accuracy-overhead trade-off via non-linear compression. The following are the potential benefits of AI / ML-based CSI feedback.

[0109] ● Better performance, i.e., CSI feedback accuracy-overhead trade-off

[0110] ● Antenna panels / arrangements agnostic as opposed to the limitation of NR CBs to ULA

[0111] ● Better flexibility to support variable CSI feedback payload size

[0112] ● Capability to scale with a larger CSI dimension (large number of ports, SD / FD / TD granularities, etc.)

[0113] The 5G CSI is based on a fixed-basis codebook. Embodiments of the present disclosure recognize that the fixed-basis approach is unscalable and non-future-proof, since it requires specific designs tailored for CSI compression / resolution type, deployment scenario, transmission hypothesis, and operating carrier frequency, as is evident from close to two dozen codebooks specified in 5G. AI-native is expected to be an integral part of a 6G system, hence can be instrumental in designing a scenario-driven learning-based basis, whenever feasible, as a replacement for the fixed-basis. Examples of such AI-native CSI are provided in this disclosure.

[0114] The present disclosure describes a framework for learning-based (aka AI-native) CSI. Details on the support of CNN-based methods for generating / reporting CSI are disclosed, including information elements to be exchanged between a transmitter and a receiver. The following aspects are provided in the disclosure:

[0115] ● SD, FD basis matrices for compression and decompression

[0116] ● Joint and separate bases across SD and FD

[0117] ● Signaling details

[0118] In the following, for brevity, both FDD and TDD are regarded as the duplex method for both DL and UL signaling.

[0119] Although exemplary descriptions and embodiments to follow expect orthogonal frequency division multiplexing (OFDM) or orthogonal frequency division multiple access (OFDMA), this disclosure can be extended to other OFDM-based transmission waveforms or multiple access schemes such as filtered OFDM (F-OFDM).

[0120] This disclosure covers several components which can be used in conjunction or in combination with one another, or can operate as standalone schemes.

[0121] All the following components and embodiments are applicable for UL transmission with CP-OFDM (cyclic prefix OFDM) waveform as well as DFT-SOFDM (DFT-spread OFDM) and SC-FDMA (single-carrier FDMA) waveforms. Furthermore, the following components and embodiments are applicable for UL transmission when the scheduling unit in time is either one subframe (which can include one or multiple slots) or one slot.

[0122] In the present disclosure, the frequency resolution (reporting granularity) and span (reporting bandwidth) of CSI reporting can be defined in terms of frequency “subbands” and “CSI reporting band” (CRB), respectively.

[0123] A subband for CSI reporting is defined as a set of contiguous PRBs which represents the smallest frequency unit for CSI reporting. The number of PRBs in a subband can be fixed for a given value of DL system bandwidth, configured either semi-statically via higher-layer / RRC signaling, or dynamically via L1 DL control signaling or MAC control element (MAC CE). The number of PRBs in a subband can be included in CSI reporting setting.

[0124] “CSI reporting band” is defined as a set / collection of subbands, either contiguous or non-contiguous, wherein CSI reporting is performed. For example, CSI reporting band can include the subbands within the DL system bandwidth. This can also be termed “full-band”. Alternatively, CSI reporting band can include only a collection of subbands within the DL system bandwidth. This can also be termed “partial band”.

[0125] The term “CSI reporting band” is used only as an example for representing a function. Other terms such as “CSI reporting subband set” or “CSI reporting bandwidth” or bandwidth part (BWP) can also be used.

[0126] In terms of UE configuration, a UE (e.g., the UE 116) can be configured with at least one CSI reporting band. This configuration can be semi-static (via higher-layer signaling or RRC) or dynamic (via MAC CE or L1 DL control signaling). When configured with multiple (N) CSI reporting bands (e.g. via RRC signaling), a UE can report CSI associated with n ≤ N CSI reporting bands. For instance, >6GHz, large system bandwidth may require multiple CSI reporting bands. The value of n can either be configured semi-statically (via higher-layer signaling or RRC) or dynamically (via MAC CE or L1 DL control signaling). Alternatively, the UE can report a recommended value of n via an UL channel.

[0127] Therefore, CSI parameter frequency granularity can be defined per CSI reporting band as follows. A CSI parameter is configured with “single” reporting for the CSI reporting band with Mn subbands when one CSI parameter for the Mn subbands within the CSI reporting band. A CSI parameter is configured with “subband” for the CSI reporting band with Mn subbands when one CSI parameter is reported for each of the Mn subbands within the CSI reporting band.

[0128] FIG. 7 illustrates an example antenna port layout 700 according to embodiments of the present disclosure. For example, antenna port layout 700 can be implemented in the wireless network 100 of FIG. 1. This example is for illustration only and can be used without departing from the scope of the present disclosure.

[0129] In the following, N1and N2are the number of antenna ports with the same polarization in the first and second dimensions, respectively. For 2D antenna port layouts, N1> 1, N2> 1, and for 1D antenna port layouts either have N1> 1 and N2= 1 or N2> 1 and N1= 1. In the rest of the disclosure, 1D antenna port layouts with N1> 1 and N2= 1 is taken into account. The disclosure, however, is applicable to the other 1D port layouts with N2> 1 and N1= 1. Also, in the rest of the disclosure, N1≥N2. The disclosure, however, is applicable to the case when N1<N2, and the embodiments for N1>N2apply to the case N1<N2by swapping / switching (N1,N2) with (N2,N1). For a single-polarized (or co-polarized) antenna port layout, the total number of antenna ports is . And, for a dual-polarized antenna port layout, the total number of antenna ports is . An illustration is shown in FIG. 7 where “X” represents two antenna polarizations (dual-pol, s=2) and “ / ” represents one antenna polarization (co-pol, s=1). In this disclosure, the term “polarization” refers to a group of antenna ports with the same polarization. For example, antenna ports comprise a first antenna polarization, and antenna ports comprise a second antenna polarization, where is a number of CSI-RS antenna ports and X is a starting antenna port number (e.g. X=3000, then antenna ports are 3000, 3001, 3002, …). Unless stated otherwise, dual-polarized antenna layouts are expected in this disclosure. The embodiments (and examples) in this disclosure however are general and are applicable to single-polarized antenna layouts as well.

[0130] Let s denotes the number of antenna polarizations (or groups of antenna ports with the same polarization). Then, for co-polarized antenna ports, s=1, and for dual- or cross (X)-polarized antenna ports s=2. So, the total number of antenna ports .

[0131] Let Ngbe a number of antenna / port groups (PGs). When there are multiple antenna / port groups (Ng>1), each group comprises and ports in two dimensions. This is illustrated in FIG. 8. Note that the antenna port layouts may be the same in different antenna / port groups, or they can be different across antenna / port groups. For group g, the number of antenna ports is (for co-polarized or dual-polarized respectively), i.e., where sg=1 or 2.

[0132] In one example, an antenna / port group corresponds to an antenna panel. In one example, an antenna / port group corresponds to a TRP. In one example, an antenna / port group corresponds to an RRH. In one example, an antenna / port group corresponds to CSI-RS antenna ports of a NZP CSI-RS resource. In one example, an antenna / port group corresponds to a subset of CSI-RS antenna ports of a NZP CSI-RS resource (comprising multiple antenna / port groups). In one example, an antenna / port group corresponds to CSI-RS antenna ports of multiple NZP CSI-RS resources (e.g. comprising a CSI-RS resource set).

[0133] In one example, an antenna / port group corresponds to a reconfigurable intelligent surface (RIS) in which the antenna / port group can be (re-)configured more dynamically (e.g. via MAC CE or / and downlink control information (DCI)). For example, the number of antenna ports associated with the antenna / port group can be changed dynamically.

[0134] In one example, the antenna architecture of the MIMO system is structured. For example, the antenna structure at each PG or O-RU (or RU) is dual-polarized (single or multi-panel as shown in FIG. 7. The antenna structure at each PG or O-RU (or RU) can be the same. Or the antenna structure at an PG or O-RU (or RU) can be different from another PG or O-RU (or RU). Likewise, the number of ports at each PG (OR O-RU OR RU) can be the same. Or the number of ports at one PG (OR O-RU OR RU) can be different from another PG (OR O-RU OR RU).

[0135] In another example, the antenna architecture of the MIMO system is unstructured. For example, the antenna structure at one PG (OR O-RU OR RU) can be different from another PG (OR O-RU OR RU).

[0136] A structured antenna architecture is provided in the rest of the disclosure. For simplicity, each PG (OR O-RU OR RU) is equivalent to a panel (cf. FIG. 7), although, an PG (OR O-RU OR RU) can have multiple panels in practice. The disclosure however is not restrictive to a single panel expectation at each PG (OR O-RU OR RU), and can easily be extended (covers) the case when an PG (OR O-RU OR RU) has multiple antenna panels.

[0137] In one embodiment, an PG (OR O-RU OR RU) constitutes (or corresponds to or is equivalent to) at least one of the following:

[0138] ○ In one example, an PG OR O-RU (OR RU) corresponds to a TRP.

[0139] ○ In one example, an PG or O-RU (or RU) corresponds to a CSI-RS resource. A UE is configured with K=Ng>1 non-zero-power (NZP) CSI-RS resources, and a CSI reporting is configured to be across multiple CSI-RS resources. This is similar to Class B, K > 1 configuration in Rel. 14 LTE. The K NZP CSI-RS resources can belong to a CSI-RS resource set or multiple CSI-RS resource sets (e.g. K resource sets each comprising one CSI-RS resource). The details are as explained in this disclosure herein.

[0140] ○ In one example, an PG or O-RU (or RU) corresponds to a CSI-RS resource group, where a group comprises one or multiple NZP CSI-RS resources. A UE is configured with K≥Ng>1 non-zero-power (NZP) CSI-RS resources, and a CSI reporting is configured to be across multiple CSI-RS resources from resource groups. This is similar to Class B, K > 1 configuration in Rel. 14 LTE. The K NZP CSI-RS resources can belong to a CSI-RS resource set or multiple CSI-RS resource sets (e.g. K resource sets each comprising one CSI-RS resource). The details are as explained in this disclosure herein. In particular, the K CSI-RS resources can be partitioned into Ngresource groups. The information about the resource grouping can be provided together with the CSI-RS resource setting / configuration, or with the CSI reporting setting / configuration, or with the CSI-RS resource configuration.

[0141] ○ In one example, an PG or O-RU (or RU) corresponds to a subset (or a group) of CSI-RS ports. A UE is configured with at least one NZP CSI-RS resource comprising (or associated with) CSI-RS ports that can be grouped (or partitioned) multiple subsets / groups / parts of antenna ports, each corresponding to (or constituting) an PG or O-RU (or RU). The information about the subsets of ports or grouping of ports can be provided together with the CSI-RS resource setting / configuration, or with the CSI reporting setting / configuration, or with the CSI-RS resource configuration.

[0142] ○ In one example, an PG or O-RU (or RU) corresponds to one or more examples described herein depending on a configuration. For example, this configuration can be explicit via a parameter (e.g. an RRC parameter). Or it can be implicit.

[0143] ○ In one example, when implicit, it could be based on the value of K. For example, when K>1 CSI-RS resources, an PG or O-RU (or RU) corresponds to one or more examples described herein, and when K=1 CSI-RS resource, an PG or O-RU (or RU) corresponds to one or more examples described herein.

[0144] ○ In another example, the configuration could be based on the configured codebook. For example, an PG or O-RU (or RU) corresponds to a CSI-RS resource (according to one or more examples described herein) or resource group (according to one or more examples described herein) when the codebook corresponds to a decoupled codebook (modular or separate codebook for each PG or O-RU (or RU)), and an PG or O-RU (or RU) corresponds to a subset (or a group) of CSI-RS ports (according to one or more examples described herein) when codebook corresponds to a coupled (joint or coherent) codebook (one joint codebook across PGs).

[0145] In one example, when PG or O-RU (or RU) maps (or corresponds to) a CSI-RS resource or resource group (according to one or more examples described herein), and a UE can select a subset of PGs (resources or resource groups) and report the CSI for the selected PGs (resources or resource groups), the selected PGs can be reported via an indicator. For example, the indicator can be a CQI report interval (CRI) or a PMI (component) or a new indicator.

[0146] In one example, when PG or O-RU (or RU) maps (or corresponds to) a CSI-RS port group (according to one or more examples described herein), and a UE can select a subset of PGs (port groups) and report the CSI for the selected PGs (port groups), the selected PGs can be reported via an indicator. For example, the indicator can be a CRI or a PMI (component) or a new indicator.

[0147] In one example, when multiple (K>1) CSI-RS resources are configured for NgPGs (according to one or more examples described herein), a decoupled (modular) codebook is used / configured, and when a single (K=1) CSI-RS resource for NgPGs (according to one or more examples described herein), a joint codebook is used / configured.

[0148] In one embodiment, a UE is configured (e.g. via a higher layer CSI configuration information) with a CSI report, where the CSI report is based on a channel measurement (and interference measurement) and a codebook. When the CSI report is configured to be aperiodic, it is reported when triggered via a DCI field (e.g. a CSI request field) in a DCI.

[0149] FIG. 8 illustrates a timeline 800 of example SD units and FD units according to embodiments of the present disclosure. For example, timeline 800 can be followed by any of the UEs 111-116 of FIG. 1, such as the UE 116. This example is for illustration only and other embodiments can be used without departing from the scope of the present disclosure.

[0150] The channel measurement can be based on K≥1 channel measurement resources (CMRs) that are transmitted from a plurality of spatial-domain (SD) units (e.g. a SD unit = a CSI-RS antenna port), and are measured via a plurality of frequency-domain (FD) units (e.g. a FD unit = one or more PRBs / SBs) and via either a time-domain (TD) unit or a plurality of TD units (e.g. a TD unit = one or more time slots). In one example, a CMR can be a NZP-CSI-RS resource.

[0151] The CSI report can be associated with the plurality of FD units and the plurality of TD units associated with the channel measurement. Alternatively, the CSI report can be associated with a second set of FD units (different from the plurality of FD units associated with the channel measurement) or / and a second set of TD units (different from the plurality of TD units associated with the channel measurement). In this later case, the UE, based on the channel measurement, can perform prediction (interpolation or extrapolation) in the second set of FD units or / and the second set of TD units associated with the CSI report.

[0152] An illustration of the SD units (in 1st and 2nd antenna dimensions), FD units, and, and TD units is shown in FIG. 8.

[0153] ○ The first dimension is associated with the 1st antenna port dimension and comprises N1units,

[0154] ○ The second dimension is associated with the 2nd antenna port dimension and comprises N2units,

[0155] ○ The third dimension is associated with the frequency dimension and comprises N3units, and

[0156] ○ The fourth dimension is associated with the time / Doppler dimension and comprises N4units.

[0157] Alternatively, the SD units, FD units, and TD units are as follows.

[0158] ○ The first dimension is associated with the antenna port dimension and comprises units,

[0159] ○ The second dimension is associated with the frequency dimension and comprises N3units, and

[0160] ○ The third dimension is associated with the time / Doppler dimension and comprises N4units.

[0161] The plurality of SD units can be associated with antenna ports (e.g. co-located at one site or distributed across multiple sites) comprising one or multiple antenna / port groups (i.e., Ng≥1), and dimensionalizes the spatial-domain profile of the channel measurement.

[0162] When K=1, there is one CMR comprising P_CSIRS CSI-RS antenna ports.

[0163] When Ng=1, there is one PG or O-RU (or RU) comprising ports, and the CSI report is based on the channel measurement from the one PG or O-RU (or RU).

[0164] When Ng>1, there are multiple PGs, and the CSI report is based on the channel measurement from / across the multiple PGs.

[0165] When K>1, there are multiple CMRs, and the CSI report is based on the channel measurement across the multiple CMRs. In one example, a CMR corresponds to an PG or O-RU (or RU) (one-to-one mapping). In one example, multiple CMRs can correspond to an PG or O-RU (or RU) (many-to-one mapping).

[0166] In one example, when the antenna ports are co-located at one site, Ng=1. In one example, when the antenna ports are distributed (non-co-located) across multiple sites, Ng>1.

[0167] In one example, when antenna ports are co-located at one site and within a single antenna panel, Ng=1. In one example, when the antenna ports are distributed across multiple antenna panels (can be co-located or non-co-located), Ng>1.

[0168] The value of Ngcan be configured, e.g. via higher layer RRC parameter. Or it can be indicated via a MAC CE. Or it can be provided via a DCI field.

[0169] Likewise, the value of K can be configured, e.g. via higher layer RRC parameter. Or it can be indicated via a MAC CE. Or it can be provided via a DCI field.

[0170] In one example, K=Ng=X. The value of X can be configured, e.g. via higher layer RRC parameter. Or it can be indicated via a MAC CE. Or it can be provided via a DCI field.

[0171] In one example, the value of K is determined based on the value of Ng. In one example, the value of Ngis determined based on the value of K.

[0172] The plurality of FD units can be associated with a frequency domain allocation of resources (e.g. one or multiple CSI reporting bands, each comprising multiple PRBs) and dimensionalizes the frequency (or delay)-domain profile of the channel measurement.

[0173] The plurality of TD units can be associated with a time domain allocation of resources (e.g. one or multiple CSI reporting windows, each comprising multiple time slots) and dimensionalizes the time (or Doppler)-domain profile of the channel measurement.

[0174] FIG. 9 illustrates an example codebook 900 according to embodiments of the present disclosure. For example, codebook 900 can be utilized by any of the UEs 111-116 of FIG. 1, such as the UE 112. This example is for illustration only and other embodiments can be used without departing from the scope of the present disclosure.

[0175] As discussed herein, a fixed codebook (expecting a uniform array and phase wave-front) is no longer sufficient in 6G due to (1) ‘new’ antenna types / architectures / geometries, (2) distributed (e.g. CJT), open (e.g. O-RAN), and “less”-structured (e.g. dynamic port adaptation for energy saving) NW topology, (3) dynamic duplexing (e.g. subband full duplex (SBFD), single frequency full duplex (SFFD)) operations, and advanced technologies such as RIS and near-field effects, and (4) new frequency bands with sparser (low-rank) channels (e.g. FR3) requiring mTRP-like MIMO operations. These necessitate a scenario-driven learning-based codebook-design. AI-native could be instrumental in this regard. For a UE not capable of AI-native, the fixed-basis codebook can be used as a last resort as illustrated in FIG. 9.

[0176] FIG. 10 illustrates an example AI-native CSI configuration 1000 according to embodiments of the present disclosure. For example, the UE 113 and the network 130 and / or the BS 103 of FIG. 1 can implement AI-native CSI configuration 1000. This example is for illustration only and other embodiments can be used without departing from the scope of the present disclosure.

[0177] In the provided AI-native CSI, the precoding is based on two stages: (i) first-stage for basis (W1) and (ii) second-stage for coefficients (W2). The first-stage includes a deep-learning-based basis, if the user is AI-native capable, and a unified fixed-basis, otherwise. The fixed-basis can also be used for fallback, initialization. An illustration of the AI-native CSI is shown in FIG. 10. The user measures CSI-RS and uses the measurement to determine uncompressed CSI. The CSI is then compressed in (SD, FD) or (SD, FD, DD), if DD compression is ON, utilizing the deep-learning-basis (auto-encoder). The compressed coefficients are then fed back as part of the AI-native CSI report. The deep-learning auto-decoder is then used to de-compress or reconstruct the CSI, which then is applied to subsequent downlink (DL) transmissions. The details of fixed- and deep-learning bases are provided next.

[0178] As antenna geometries get less-structured or more-distributed, (SD, FD, TD) properties can no longer be quantified with only fixed-basis, they rather need to be learnt depending on scenarios and deployments. Here, (SD, FD, TD) properties include antenna geometry, compression dimensions, SD / FD / TD units, prediction, and second order channel statistics. One can adopt a learning-based basis that replaces the fixed-basis. In one example, the learning-based basis can have some structure such as a convolutional (CNN)-based deep-learning basis. In one example, the learning-based basis is unstructured such as a fully connected deep (linear) layer. Mathematically, a one-dimensional (1D) operation is equivalent to: , where is a learning-basis matrix and is a data matrix, e.g. channel eigenvector matrix with columns being eigenvectors for N_SB SBs. For 2D (e.g. SD and FD), we can have two separate 1D bases, one for each dimension. Two separate 1D convolutions is equivalent to: , where and are basis matrices for SD and FD, respectively. The matrix or matrices can be constructed based on a Kernel (basis) B.

[0179] FIG. 11 illustrates an example complex-values matrix / vector 1100 according to embodiments of the present disclosure. For example, complex-values matrix / vector 1100 can be utilized by any of the UEs 111-116 of FIG. 1, such as the UE 116. This example is for illustration only and other embodiments can be used without departing from the scope of the present disclosure.

[0180] In one example, the input is a complex-valued matrix (or vector). In one example, the input X is a real-valued matrix (or vector), which is formed by concatenation of real and imaginary parts of complex data values. At least one of the following examples shown in FIG. 11 is used for the concatenation.

[0181] FIG. 12 illustrates an example neural network based auto encoder 1200 according to embodiments of the present disclosure. For example, the UE 116 of FIG. 3 can be configured to use the neural network based auto encoder 1200. This example is for illustration only and other embodiments can be used without departing from the scope of the present disclosure.

[0182] In one embodiment, as shown in FIG. 12, a UE (e.g., the UE 116) is configured to use a neural network (NN)-based auto-encoder (AE) model to determine a CSI, where the CSI is based on compression in at least one of SD, FD, and DD. The AE takes an input (data), e.g. eigenvectors of DL channel measurements (via CSI-RS) or DL channel estimate itself, performs operations (linear or / and non-linear) and outputs a bit sequence which is transmitted by the UE as part of the CSI report. The bit sequence is used by the NW to reconstruct the CSI.

[0183] In one embodiment, the model for CSI compression is one-sided, i.e., AE only. That is, there is no associated NN-based auto-decoder (AD) needed at the gNB (e.g., the BS 102) to reconstruct the CSI. In one example, the one-sided model is downloadable, hence can be referred to as a downloadable codebook.

[0184] In one embodiment, a UE is configured to use a neural network (NN)-based two-sided model comprising an auto-encoder (AE) part and an auto-decoder (AD) part. The AE part of the model is used to determine a CSI, where the CSI is based on compression in at least one of SD, FD, and DD. The AE takes an input (data), e.g., eigenvectors of DL channel measurements (via CSI-RS) or DL channel estimate itself, performs operations (linear or / and non-linear) and outputs a bit sequence which is transmitted by the UE as part of the CSI report. The bit sequence is used by the NW as input to the AD part of the model. The output of the AD part corresponds to a reconstructed CSI.

[0185] The rest of the disclosure focusses on one-sided models.

[0186] FIG. 13 illustrates an example basis pair for CSI compression and decompression 1300 according to embodiments of the present disclosure. For example, the UE 116 of FIG. 3 can be configured to use the basis pair for CSI compression and decompression 1300. This example is for illustration only and other embodiments can be used without departing from the scope of the present disclosure.

[0187] In one embodiment, a UE is configured with a CSI report based on a learning-based or neural network (NN)-based model, where the model includes (or can be described based on) a pair of basis matrices as illustrated in FIG. 13. In one example, is an size CSI compression basis matrix, where is the size (or dimension) of the target or raw or uncompressed CSI data x, and is the size (or dimension) of the reported or compressed CSI data y. Mathematically, . The compression CSI y is quantized for reporting. In one example, the quantization is based on the W2 amplitude or / and phase quantization scheme of 5G NR Rel-16 enhanced Type II codebook. In one example, corresponds to (or can be based on) at least one fully connected (FC) or dense layer. In one example, is an size CSI decompression or reconstruction matrix. In one example, corresponds to (or can be based on) at least one fully connected (FC) or dense layer. Mathematically, . In one example, when (unquantized) y can be reported (e.g., genie-aided), and then is referred to as a generalized inverse of . When has full column rank, the generalized inverse is also referred to as a pseudo-inverse of and is given by .

[0188] The information about either only or both basis matrices can be provided to the UE based on at least one of the following examples.

[0189] ○ In one example, the UE is configured with this information together with a higher layer (RRC) message or configuration via a dedicated channel (e.g. an RRC PDSCH). In one example, the higher layer (RRC) message or configuration can correspond to a CSI report setting or configuration or a codebook configuration.

[0190] ○ In one example, the UE is configured with this information together with the system related information, e.g. via a broadcast channel. In one example, the system information can correspond to SIB1.

[0191] ○ In one example, the UE is configured with this information together with a MAC CE message or activation command via a dedicated channel (e.g. a MAC CE PDSCH). In one example, the MAC CE can correspond to a MAC CE for a CSI report or codebook.

[0192] The information about either only or both basis matrices can be provided to UEs via a UE-common or UE-group-common DL channel that is used to provide information to multiple UEs connected to a cell. In one example, this information is via a cell-specific (but UE-common) message. The information provided therefore is applicable to all users in the cell.

[0193] The information about either only or both basis matrices can be provided to UEs via a UE-common or UE-group-common DL channel that is used to provide information to multiple UEs connected to a cellular site, where the site can include one or multiple co-located cells.

[0194] The information about either only or both basis matrices can be provided to UEs via a UE-common or UE-group-common DL channel that is used to provide information to multiple UEs connected to one or multiple cells / sites, where the sites / cells can include one or multiple co-located or non-co-located cells.

[0195] The information about either only or both basis matrices can be provided to UEs via a UE-specific or UE-dedicated DL channel that is used to provide information to the corresponding UE(s).

[0196] When number of layers of the CSI can be more than one, e.g., when the rank or RI-restriction allows reporting high rank CSI, at least one of the following examples is used.

[0197] ○ In one example, the information about either only or both basis matrices is provided in a per layer manner, i.e., for each layer of the number of layers.

[0198] ○ In one example, the information about either only or both basis matrices is provided in a per rank manner, i.e., for each rank or the value of number of layers. The information is common (the same) for all layers of a given rank or number of layers, but can vary across different values of rank or number of layers.

[0199] ○ In one example, the information about either only or both basis matrices is rank-common and layer-common, i.e., one information is provided that is common (the same) for all rank values, and all layers of a rank value.

[0200] ○ In one example, the information about either only or both basis matrices is common (the same) for a subset of rank values (e.g. rank pair).

[0201] At least one of the following examples is used / configured regarding the value of (ND,NR).

[0202] ○ In one example, only one value can be used / configured for each of NDand NR. The one value is fixed in the specification. Or the one value is reported by the UE via the UE capability information.

[0203] ○ In one example, only one value can be used / configured for NDbut one or multiple values can be used / configured for NR, i.e., it takes a value from a set comprising multiple candidate or supported values. The one value of NDeither fixed in the specification or reported by the UE via the UE capability information. The one from the set of values for NRis either configured to the UE via higher layer RRC or MAC CE or DCI based signaling, or reported by the UE (e.g. as part of the CSI report via UCI part 1 of a two-part UCI).

[0204] ○ In one example, only one value can be used / configured for NRbut one or multiple values can be used / configured for ND, i.e., it takes a value from a set comprising multiple candidate or supported values. The one value of N_R either fixed in the specification or reported by the UE via the UE capability information. The one from the set of values for NDis either configured to the UE via higher layer RRC or MAC CE or DCI based signaling, or reported by the UE (e.g. as part of the CSI report via UCI part 1 of a two-part UCI).

[0205] ○ In one example, (ND,NR) takes a value from a set comprising multiple candidate or supported value pairs. The one pair from the set of value pairs for (ND,NR) is either configured to the UE via higher layer RRC or MAC CE or DCI based signaling, or reported by the UE (e.g. as part of the CSI report via UCI part 1 of a two-part UCI).

[0206] For a (ND,NR), at least one of the following examples is used / configured regarding of size ND×NR.

[0207] ○ In one example, only one candidate can be used / configured for . The one candidate can be fixed in the specification or can be reported by the UE via the UE capability information.

[0208] ○ In one example, only one value can be used / configured for but one or multiple values can be used / configured for , i.e., it takes a value from a set comprising multiple candidate or supported values. The one value of either fixed in the specification or reported by the UE via the UE capability information. The one from the set of values for is either configured to the UE via higher layer RRC or MAC CE or DCI based signaling, or reported by the UE (e.g. as part of the CSI report via UCI part 1 of a two-part UCI).

[0209] ○ In one example, only one value can be used / configured for but one or multiple values can be used / configured for , i.e., it takes a value from a set comprising multiple candidate or supported values. The one value of either fixed in the specification or reported by the UE via the UE capability information. The one from the set of values for is either configured to the UE via higher layer RRC or MAC CE or DCI based signaling, or reported by the UE (e.g. as part of the CSI report via UCI part 1 of a two-part UCI).

[0210] ○ In one example, ( , ) takes a value from a set comprising multiple candidate or supported value pairs. The one pair from the set of value pairs for ( , ) is either configured to the UE via higher layer RRC or MAC CE or DCI based signaling, or reported by the UE (e.g. as part of the CSI report via UCI part 1 of a two-part UCI).

[0211] FIG. 14 illustrates another example basis pair for CSI compression and decompression 1400 according to embodiments of the present disclosure. For example, the UE 116 of FIG. 3 can be configured to use the basis pair for CSI compression and decompression 1400. This example is for illustration only and other embodiments can be used without departing from the scope of the present disclosure.

[0212] In one embodiment, as shown in FIG. 14, the data x is a vector which is obtained by vectorization of size data matrix, where NPand NSBare a number of antenna ports (e.g. CSI-RS ports) and a number of SBs that are configured for the CSI report. The vectorization can be denoted as x=vec(X). Note that . In one example, columns of X are eigenvectors associated with NSBfrequency subbands (SBs). The CSI compression and decompression procedures can be described as follows.

[0213] ●CSI compression: , where is a compression basis matrix, NPand NSBare as described above, and is a number of (unquantized) coefficients, comprising a compressed vector y.

[0214] ●CSI report:the vector y is quantized and the quantized vector is denoted as .

[0215] ○ In one example, the quantization is based on 5G NR Rel-16 enhanced Type II W2 quantization codebook, wherein each element of y is quantized using a pair ( of bits for amplitude quantization and c=4 bits for phase quantization. The bits represent a reference (common) amplitude for all K elements of y, and represents a different amplitude (w. r. t. to the reference) for the i-th of the K elements of y, and i=1,2,…,K.

[0216] ○ In one example, the real and imaginary parts / components of all K elements of y are concatenated together in one vector of real numbers comprising 2K real values. In one example, the quantization 2K real values is based on 5G NR Rel-16 enhanced Type II W2 amplitude quantization codebook, as described above.

[0217] ●CSI decompression or reconstruction: , where is a decompression basis matrix, e.g. a generalized inverse. Note that when has full column rank, . The reconstructed vector is de-vectorized to obtain as a reconstruction of input X.

[0218] In one example, a NN-based training is used to learn the pair such that and minimize a mean-squared-error (MSE) between X and .

[0219] The 5G NR Rel-16 enhanced Type II W2 quantization is described as follows. The amplitude coefficient indicators and are

[0220]

[0221] for

[0222] Table 2: Mapping:

[0223]

[0224] Table 3: Mapping:

[0225]

[0226] The phase is given by , the phase coefficient indicator is for layer l=1,…,υ.

[0227] In one embodiment, a number of non-zero (NZ) coefficients in the quantized vector , denoted as , is upper bounded by a number , i.e., . In one example, , where β≤1. The CSI report additionally includes an indication or indicator indicating indices of NZ coefficients. In one example, this indication can be via a bitmap of length-K. The bitmap whose nonzero bits identify which coefficients in and are reported, is indicated by

[0228]

[0229] for l=1,…,υ, such that is the number of nonzero coefficients for layer l=1,…,υ and is the total number of nonzero coefficients. In one example, , when υ≥2.

[0230] In one example, this indication can be via a combinatorial indicator , where

[0231] .

[0232] In one example, the coefficient selection is a part of (or included) the model training. In one example, the model can be trained for K coefficients, but for reporting, a subset selection, as described above, can be performed.

[0233] In one example, for per layer reporting, layers need to be extracted from the CSI. In one example, this extraction is performed in a per SB manner.

[0234] ● In one example, this extraction is performed in the uncompressed CSI domain, i.e., a size SVD is performed per SB.

[0235] ● In one example, this extraction is performed in the compressed CSI domain, i.e., size K SVD is performed per SB.

[0236] FIG. 15 illustrates an example basis pair using separate SD / FD bases 1500 according to embodiments of the present disclosure. For example, the UE 116 of FIG. 3 can be configured to use the basis pair using separate SD / FD bases 1500. This example is for illustration only and other embodiments can be used without departing from the scope of the present disclosure.

[0237] In one embodiment, a UE is configured with a CSI report based on a learning-based or neural network (NN)-based model, where the model includes (or can be described based on) two pairs of basis matrices and for SD and FD, respectively, as illustrated in FIG. 15. In one example, and respectively are and size CSI compression basis matrices in SD and FD, where NPand NSBare the sizes (or dimensions) of the target or raw or uncompressed CSI data X in SD (row) and FD (column), respectively, as described earlier. Likewise, L and M are the sizes (or dimensions) of the reported or compressed CSI data matrix Y, in SD (row) and FD (column), respectively. Mathematically, . The compression CSI Y is quantized for reporting. In one example, the quantization is based on the W2 amplitude or / and phase quantization scheme of 5G NR Rel-16 enhanced Type II codebook. In one example, and correspond to (or can be based on) at least one fully connected (FC) or dense layer separately in SD and FD, respectively. In one example, and are an L×NPand M×NSBsize CSI decompression or reconstruction matrix in SD and FD. In one example, and correspond to (or can be based on) at least one fully connected (FC) or dense layer, in SD and FD, respectively. Mathematically, . In one example, when (unquantized) Y can be reported (e.g., genie-aided), and then and are referred to as a generalized inverse of and , resepctively. When and has full column rank, the generalized inverse is also referred to as a pseudo-inverse of and and is given by , where xD∈{SD,FD}.

[0238] The information about either only and or both pairs can be provided to the UE based on at least one of the following examples.

[0239] ● In one example, the UE is configured with this information together with a higher layer (RRC) message or configuration via a dedicated channel (e.g. an RRC PDSCH). In one example, the higher layer (RRC) message or configuration can correspond to a CSI report setting or configuration or a codebook configuration.

[0240] ● In one example, the UE is configured with this information together with the system related information, e.g. via a broadcast channel. In one example, the system information can correspond to SIB1.

[0241] ● In one example, the UE is configured with this information together with a MAC CE message or activation command via a dedicated channel (e.g. a MAC CE PDSCH). In one example, the MAC CE can correspond to a MAC CE for a CSI report or codebook.

[0242] The information about either only and or both basis matrices and can be provided to UEs via a UE-common or UE-group-common DL channel that is used to provide information to multiple UEs connected to a cell. In one example, this information is via a cell-specific (but UE-common) message. The information provided therefore is applicable to all users in the cell.

[0243] The information about either only and or both basis matrices and can be provided to UEs via a UE-common or UE-group-common DL channel that is used to provide information to multiple UEs connected to a cellular site, where the site can include one or multiple co-located cells.

[0244] The information about either only and or both basis matrices and can be provided to UEs via a UE-common or UE-group-common DL channel that is used to provide information to multiple UEs connected to one or multiple cells / sites, where the sites / cells can include one or multiple co-located or non-co-located cells.

[0245] The information about either only and or both basis matrices and can be provided to UEs via a UE-specific or UE-dedicated DL channel that is used to provide information to the corresponding UE(s).

[0246] When a number of layers of the CSI can be more than one, e.g., when the rank or RI-restriction allows reporting high rank CSI, at least one of the following examples is used.

[0247] ● In one example, the information about either only and or both basis matrices and is provided in a per layer manner, i.e., for each layer of the number of layers.

[0248] ● In one example, the information about either only and or both basis matrices and is provided in a per rank manner, i.e., for each rank or the value of number of layers. The information is common (the same) for all layers of a given rank or number of layers, but can vary across different values of rank or number of layers.

[0249] ● In one example, the information about either only and or both basis matrices and is rank-common and layer-common, i.e., one information is provided that is common (the same) for all rank values, and all layers of a rank value.

[0250] ● In one example, the information about either only and or both basis matrices and is common (the same) for a subset of rank values (e.g. rank pair).

[0251] At least one of the following examples is used / configured regarding the value of .

[0252] ● In one example, only one value can be used / configured for each of NPand L. The one value is fixed in the specification. Or the one value is reported by the UE via the UE capability information.

[0253] ● In one example, only one value can be used / configured for NPbut one or multiple values can be used / configured for L, i.e., it takes a value from a set comprising multiple candidate or supported values. The one value of NPeither fixed in the specification or reported by the UE via the UE capability information. The one from the set of values for L is either configured to the UE via higher layer RRC or MAC CE or DCI based signaling, or reported by the UE (e.g. as part of the CSI report via UCI part 1 of a two-part UCI).

[0254] ● In one example, only one value can be used / configured for L but one or multiple values can be used / configured for NP, i.e., it takes a value from a set comprising multiple candidate or supported values. The one value of L either fixed in the specification or reported by the UE via the UE capability information. The one from the set of values for NPis either configured to the UE via higher layer RRC or MAC CE or DCI based signaling, or reported by the UE (e.g. as part of the CSI report via UCI part 1 of a two-part UCI).

[0255] ● In one example, (NP,L) takes a value from a set comprising multiple candidate or supported value pairs. The one pair from the set of value pairs for (NP,L) is either configured to the UE via higher layer RRC or MAC CE or DCI based signaling, or reported by the UE (e.g. as part of the CSI report via UCI part 1 of a two-part UCI).

[0256] At least one of the following examples is used / configured regarding the value of (NSB,M).

[0257] ● In one example, only one value can be used / configured for each of N_SB and M. The one value is fixed in the specification. Or the one value is reported by the UE via the UE capability information.

[0258] ● In one example, only one value can be used / configured for N_SB but one or multiple values can be used / configured for M, i.e., it takes a value from a set comprising multiple candidate or supported values. The one value of N_SB either fixed in the specification or reported by the UE via the UE capability information. The one from the set of values for M is either configured to the UE via higher layer RRC or MAC CE or DCI based signaling, or reported by the UE (e.g. as part of the CSI report via UCI part 1 of a two-part UCI).

[0259] ● In one example, only one value can be used / configured for M but one or multiple values can be used / configured for NSB, i.e., it takes a value from a set comprising multiple candidate or supported values. The one value of M either fixed in the specification or reported by the UE via the UE capability information. The one from the set of values for NSBis either configured to the UE via higher layer RRC or MAC CE or DCI based signaling, or reported by the UE (e.g. as part of the CSI report via UCI part 1 of a two-part UCI).

[0260] ● In one example, (NSB,M) takes a value from a set comprising multiple candidate or supported value pairs. The one pair from the set of value pairs for (NSB,M) is either configured to the UE via higher layer RRC or MAC CE or DCI based signaling, or reported by the UE (e.g. as part of the CSI report via UCI part 1 of a two-part UCI).

[0261] At least one of the following examples is used / configured regarding the value of (L,M).

[0262] ● In one example, (L,M) is configured via higher layer parameter paraComb, akin to 5G NR Rel-16 enhanced Type II codebook. In particular, the parameter paraComb can indicate a value for (L,M) or (L, ) or (L,M,β) or (L, ,β) as described in 5G NR Rel-16 enhanced Type II codebook.

[0263] At least one of the following examples is used / configured regarding the value of NSB.

[0264] ● In one example, NSBis a number of SBs configured for CQI reporting.

[0265] ● In one example, NSBis a number of SBs or FD units configured for PMI reporting. In one example, SB sizes (number of PRBs) for CQI and PMI are the same. In one example, they can be different.

[0266] ● In one example, is number of SBs for PMI, and R is an integer, e.g. from {1,2}. When . When , and hence NSBin embodiments of this disclosure can be replaced with N3when the model training is for PMI or precoding matrices.

[0267] For a , at least one of the following examples is used / configured regarding .

[0268] ● In one example, only one candidate can be used / configured for . The one candidate can be fixed in the specification or can be reported by the UE via the UE capability information.

[0269] ● In one example, only one value can be used / configured for but one or multiple values can be used / configured for , i.e., it takes a value from a set comprising multiple candidate or supported values. The one value of either fixed in the specification or reported by the UE via the UE capability information. The one from the set of values for is either configured to the UE via higher layer RRC or MAC CE or DCI based signaling, or reported by the UE (e.g. as part of the CSI report via UCI part 1 of a two-part UCI).

[0270] ● In one example, only one value can be used / configured for but one or multiple values can be used / configured for , i.e., it takes a value from a set comprising multiple candidate or supported values. The one value of either fixed in the specification or reported by the UE via the UE capability information. The one from the set of values for is either configured to the UE via higher layer RRC or MAC CE or DCI based signaling, or reported by the UE (e.g. as part of the CSI report via UCI part 1 of a two-part UCI).

[0271] ● In one example, takes a value from a set comprising multiple candidate or supported value pairs. The one pair from the set of value pairs for is either configured to the UE via higher layer RRC or MAC CE or DCI based signaling, or reported by the UE (e.g. as part of the CSI report via UCI part 1 of a two-part UCI).

[0272] For a (NSB,M), at least one of the following examples is used / configured regarding .

[0273] ● In one example, only one candidate can be used / configured for . The one candidate can be fixed in the specification or can be reported by the UE via the UE capability information.

[0274] ● In one example, only one value can be used / configured for but one or multiple values can be used / configured for , i.e., it takes a value from a set comprising multiple candidate or supported values. The one value of either fixed in the specification or reported by the UE via the UE capability information. The one from the set of values for is either configured to the UE via higher layer RRC or MAC CE or DCI based signaling, or reported by the UE (e.g. as part of the CSI report via UCI part 1 of a two-part UCI).

[0275] ● In one example, only one value can be used / configured for but one or multiple values can be used / configured for , i.e., it takes a value from a set comprising multiple candidate or supported values. The one value of either fixed in the specification or reported by the UE via the UE capability information. The one from the set of values for is either configured to the UE via higher layer RRC or MAC CE or DCI based signaling, or reported by the UE (e.g. as part of the CSI report via UCI part 1 of a two-part UCI).

[0276] ● In one example, takes a value from a set comprising multiple candidate or supported value pairs. The one pair from the set of value pairs for is either configured to the UE via higher layer RRC or MAC CE or DCI based signaling, or reported by the UE (e.g. as part of the CSI report via UCI part 1 of a two-part UCI).

[0277] In one embodiment IV.1, as shown in FIG. 15, the data X in embodiment IV is a data matrix, where NPand NSBare a number of antenna ports (e.g. CSI-RS ports) and a number of SBs that are configured for the CSI report. In one example, columns of X are eigenvectors associated with NSBfrequency subbands (SBs). The CSI compression and decompression procedures can be described as follows.

[0278] ●(Separate) CSI compression: , where basis matrix, FD basis matrix, and (L,M): parameter combination, and LM=K is a number of (unquantized) coefficients, comprising a compressed matrix Y.

[0279] ●CSI report:the matrix Y is quantized and the quantized matrix is denoted as .

[0280] ○ In one example, the quantization is based on 5G NR Rel-16 enhanced Type II W2 quantization codebook, as described above.

[0281] ○ In one example, the real and imaginary parts / components of all LM elements of Y are concatenated together in one matrix of real numbers comprising 2LM real values. In one example, the quantization 2LM real values is based on 5G NR Rel-16 enhanced Type II W2 amplitude quantization codebook, as described above.

[0282] ●CSI decompression or reconstruction: where is a decompression basis matrix in SD and is a NSB×M is a decompression basis matrix in FD. Note that for xD∈{SD,FD}, when has full column rank, .

[0283] In one example, a NN-based training is used to learn where xD∈{SD,FD} such that Y and minimize MSE between X and .

[0284] In one embodiment, a number of non-zero (NZ) coefficients in the quantized vector , denoted as KNZ, is upper bounded by a number K0, i.e., . In one example, , where β≤1. The CSI report additionally includes an indication or indicator indicating indices of NZ coefficients. In one example, this indication can be via a bitmap of length-LM. The bitmap whose nonzero bits identify which coefficients in and are reported, is indicated by , as described above.

[0285] In one example, the coefficient selection is a part of (or included) the model training. In one example, the model can be trained for K coefficients, but for reporting, a subset selection, as described above, can be performed.

[0286] In one example, for per layer reporting, layers need to be extracted from the CSI. In one example, this extraction is performed in a per SB manner.

[0287] ● In one example, this extraction is performed in the uncompressed CSI domain, i.e., a size SVD is performed per SB.

[0288] ● In one example, this extraction is performed in the compressed CSI domain, i.e., size K SVD is performed per SB.

[0289] FIG. 16 illustrates an example method for signaling 1600 according to embodiments of the present disclosure. This example is for illustration only and other embodiments can be used without departing from the scope of the present disclosure.

[0290] In one embodiment, the procedure / method and signaling associated with the proposed scheme is as illustrated in FIG. 16. The NW transfers the downloadable codebook comprising or where xD∈{SD,FD} to the UE (1610). The NW further configures NZP CSI-RS(s) for measurement (1620). The UE in response measures the NZP CSI-RS(s) and use the measurement to determine the data x or X (1630), which in turn is used together with the download codebook to obtain unquantized y or Y (1640). The resultant is then quantized for reporting (1650). The NW receives the quantized coefficients (1660), and uses the downloadable codebook for reconstruct the CSI (1670), which can then be used for link adaptation and precoder and CQI calculation (1680).

[0291] FIG. 17 illustrates an example method 1700 performed by a UE in a wireless communication system according to embodiments of the present disclosure. The method 1700 of FIG. 17 can be performed by any of the UEs 111-116 of FIG. 1, such as the UE 116 of FIG. 3, and a corresponding method can be performed by any of the BSs 101-103 of FIG. 1, such as BS 102 of FIG. 2. The method 1700 is for illustration only and other embodiments can be used without departing from the scope of the present disclosure.

[0292] The method 1700 begins with the UE receiving information about a CSI report (1710). For example, the CSI report is based on a pair of matrices , where is a compression matrix and is a reconstruction matrix. In various embodiments, the pair of matrices is trained using data. The pair of matrices, the data, and the CSI report are associated with P ports and NSBSBs. In various embodiments, the UE receives the compression matrix via higher layer RRC or SIB1. In various embodiments, the UE receives the pair of matrices via higher layer RRC or SIB1. In various embodiments, .

[0293] The UE then determines the CSI report based on the compression matrix (1720). In various embodiments, the CSI report is based on a model including the pair of matrices .

[0294] The UE then transmits the CSI report (1730). In various embodiments, a vector x is compressed as a vector y such that , the vector y is quantized to a quantized vector , and the CSI report includes at least one indicator indicating the quantized vector . For example, the vector x is reconstructed as a vector such that .

[0295] Any of the above variation embodiments can be utilized independently or in combination with at least one other variation embodiment. The above flowcharts illustrate example methods that can be implemented in accordance with the principles of the present disclosure and various changes could be made to the methods illustrated in the flowcharts herein. For example, while shown as a series of steps, various steps in each figure could overlap, occur in parallel, occur in a different order, or occur multiple times. In another example, steps may be omitted or replaced by other steps.

[0296] Although the figures illustrate different examples of user equipment, various changes may be made to the figures. For example, the user equipment can include any number of each component in any suitable arrangement. In general, the figures do not limit the scope of the present disclosure to any particular configuration(s). Moreover, while figures illustrate operational environments in which various user equipment features disclosed in this patent document can be used, these features can be used in any other suitable system.

[0297] Although the present disclosure has been described with exemplary embodiments, various changes and modifications may be suggested to one skilled in the art. It is intended that the present disclosure encompass such changes and modifications as fall within the scope of the appended claims. None of the descriptions in this application should be read as implying that any particular element, step, or function is an essential element that must be included in the claims scope. The scope of patented subject matter is defined by the claims.

Claims

1.A user equipment (UE) in a communication system, the UE comprising:a transceiver; andoperably coupled to the transceiver, the processor configured to:receive information about a channel state information (CSI) report, wherein the CSI report is based on a pair of matrices, whereis a compression matrix andis a reconstruction matrix,determine the CSI report based on the compression matrix, andtransmit the CSI report,wherein the pair of matrices is trained using data, andwherein the pair of matrices, the data, and the CSI report are associated with P ports and NSBsubbands (SBs).2.The UE of claim 1, wherein the CSI report is based on a model including the pair of matrices .3.The UE of claim 1, wherein the processor is further configured to receive the compression matrix via higher layer radio resource control (RRC) or a system information block 1 (SIB1).4.The UE of claim 1, wherein the processor is further configured to receive the pair of matrices via higher layer radio resource control (RRC) or a system information block 1 (SIB1).5.The UE of claim 1, wherein:a vector x is compressed as a vector y such that,the vector y is quantized to a quantized vector,the CSI report includes at least one indicator indicating the quantized vector, andthe vector x is reconstructed as a vectorsuch that.6.The UE of claim 1, wherein .7.A base station (BS) in a communication system, the BS comprising:a transceiver; andoperably coupled to the transceiver, the processor configured to:transmit information about a channel state information (CSI) report, wherein the CSI report is based on a pair of matrices, whereis a compression matrix andis a reconstruction matrix; andreceive the CSI report that is based on the compression matrix,wherein the pair of matrices is trained using data, andwherein the pair of matrices, the data, and the CSI report are associated with P ports and NSBsubbands (SBs).8.The BS of claim 7, wherein the CSI report is based on a model including the pair of matrices .9.The BS of claim 7, wherein the processor is further configured to transmit the compression matrix via higher layer radio resource control (RRC) or a system information block 1 (SIB1).10.The BS of claim 7, wherein the processor is further configured to transmit the pair of matrices via higher layer radio resource control (RRC) or a system information block 1 (SIB1).11.The BS of claim 7, wherein:a vector x is compressed as a vector y such that,the vector y is quantized to a quantized vector,the CSI report includes at least one indicator indicating the quantized vector, andthe vector x is reconstructed as a vectorsuch that.12.The BS of claim 7, wherein .13.A method performed by a user equipment (UE) in a communication system, the method comprising:receiving information about a channel state information (CSI) report, wherein the CSI report is based on a pair of matrices, whereis a compression matrix andis a reconstruction matrix;determining the CSI report based on the compression matrix; andtransmitting the CSI report,wherein the pair of matrices is trained using data, andwherein the pair of matrices, the data, and the CSI report are associated with P ports and NSBsubbands (SBs).14.The method of claim 13, wherein the CSI report is based on a model including the pair of matrices ,wherein the method further comprises receiving at least one of the compression matrixor the pair of matricesvia higher layer radio resource control (RRC) or a system information block 1 (SIB1), andwherein:a vector x is compressed as a vector y such that,the vector y is quantized to a quantized vector,the CSI report includes at least one indicator indicating the quantized vector, andthe vector x is reconstructed as a vectorsuch that.15.A method performed by a base station (BS) in a communication system, the method comprising:transmitting information about a channel state information (CSI) report, wherein the CSI report is based on a pair of matrices, whereis a compression matrix andis a reconstruction matrix; andreceiving the CSI report that is based on the compression matrix,wherein the pair of matrices is trained using data, andwherein the pair of matrices, the data, and the CSI report are associated with P ports and NSBsubbands (SBs).

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