Method and apparatus for reducing CSI feedback overhead using compression

Abstract

The present disclosure relates to a method and apparatus for reporting channel state information (CSI) feedback in a wireless telecommunications network. In an example, a method implemented in a wireless transmit / receive unit (WTRU) may include receiving configuration information indicating a channel rank threshold; determining a channel rank associated with the channel measurement; selecting a CSI compression type based on the channel rank and the channel rank threshold; and transmitting CSI and information indicating the selected CSI compression type, the CSI being associated with the channel measurement and being compressed using the selected CSI compression type.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims priority to and the benefit of U.S. Provisional Application No. 63 / 392,734, filed in the U.S. Patent and Trademark Office on July 27, 2022, the entire contents of which are incorporated herein by reference as if fully set forth herein for all applicable purposes. Technical Field

[0003] The present disclosure generally relates to wireless and / or wired communication networks.For example, one or more embodiments disclosed herein relate to methods and apparatus for channel state information (CSI) reporting in wireless telecommunication networks. Summary of the invention

[0004] One or more embodiments disclosed herein relate to methods and apparatus for reducing CSI feedback overhead using eigenvector compression for wireless communications. For example, a wireless transmit / receive unit (WTRU) that enables full channel CSI compression and eigenvector-based CSI compression is able to select a CSI compression type. The WTRU measures a channel and determines a rank. If the determined rank is less than a configured channel rank threshold, the WTRU selects EV-based compression, which can reduce CSI feedback overhead.

[0005] In one embodiment, a method for wireless communication implemented by a WTRU includes: receiving configuration information indicating a channel rank threshold from a network entity; and determining a channel rank associated with a channel measurement. The method also includes: selecting a CSI compression type based on the determined channel rank and the channel rank threshold; and sending CSI and information indicating the selected CSI compression type to the network entity, wherein the CSI is associated with the channel measurement and is compressed using the selected CSI compression type. In some cases, the configuration information includes an indication to enable the CSI compression type to be selected from a set of CSI compression types, and the set of CSI compression types includes full channel based compression and feature vector (EV) based compression.

[0006] In one embodiment, a WTRU for wireless communication includes circuitry including a transmitter, a receiver, a processor, and a memory, the WTRU being configured to receive configuration information indicating a channel rank threshold from a network entity; determine a channel rank associated with a channel measurement; select a channel state information (CSI) compression type based on the determined channel rank and the channel rank threshold; and send CSI and information indicating the selected CSI compression type to the network entity, wherein the CSI is associated with the channel measurement and is compressed using the selected CSI compression type. In some cases, the configuration information includes an indication to enable the CSI compression type to be selected from a set of CSI compression types, and the set of CSI compression types includes full channel-based compression and feature vector (EV)-based compression. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] A more detailed understanding can be obtained from the following detailed description, which is given by way of example in conjunction with its accompanying drawings. As with the detailed description, the figures in such drawings are exemplary. Therefore, the drawings and detailed description should not be considered limiting, and other equally effective examples are possible and intended. In addition, like reference numerals in the accompanying drawings ("Figures") indicate like elements, and wherein:

[0008] Figure 1A is a system diagram illustrating an example communication system in which one or more disclosed embodiments may be implemented;

[0009] Figure 1B is an example of an embodiment in which Figure 1A A system diagram of an example wireless transmit / receive unit (WTRU) for use within an illustrated communication system;

[0010] Figure 1C is an example of an embodiment that can be used according to one or more embodiments Figure 1A A system diagram of an example Radio Access Network (RAN) and an example Core Network (CN) used within the illustrated communication system;

[0011] Figure 1D is an example of an embodiment that can be used according to one or more embodiments Figure 1A A system diagram of another example RAN and another example CN used within the illustrated communication system;

[0012] Figure 2 is a diagram showing an example of configuration for CSI report settings, resource settings, and links;

[0013] Figure 3 is a diagram illustrating codebook-based precoding using feedback information;

[0014] Figure 4 is a block diagram illustrating a zero padding method according to one or more embodiments;

[0015] Figure 5 is a diagram illustrating autoencoder inputs for zero padding for rank 1 transmission and rank 2 transmission according to one or more embodiments;

[0016] Figure 6 is a flow chart illustrating a method for selecting between feature vector compression and full channel compression according to one or more embodiments;

[0017] Figure 7 is a diagram illustrating an example network configuration of MU-MIMO;

[0018] Figure 8 is a flow chart illustrating a process for selecting between eigenvector compression and full channel compression for MU-MIMO according to one or more embodiments;

[0019] Fig. 9 is a block diagram illustrating burst-based compression according to one or more embodiments;

[0020] Fig.10 is a diagram illustrating a feature vector bursting technique according to one or more embodiments;

[0021] Fig.11 is a flow chart illustrating a process for selecting between burst feature vector based compression and burst full channel based compression according to one or more embodiments;

[0022] Fig.12 is a flow chart illustrating a process for selecting between burst eigenvector-based compression and burst full channel-based compression for MU-MIMO according to one or more embodiments;

[0023] Fig.13 is a block diagram illustrating a process for post-processing of CSI feedback in a potential domain according to one or more embodiments;

[0024] Fig.14 is a block diagram illustrating a process for adaptive quantization post-processing of CSI feedback according to one or more embodiments;

[0025] Fig.15 is a block diagram illustrating an example of a CSI feedback procedure using selection and indication of a CSI compression type according to one or more embodiments; and

[0026] Fig.16 is a flow chart illustrating an example of a CSI feedback procedure using a selected CSI compression type in accordance with one or more embodiments. DETAILED DESCRIPTION

[0027] introduction

[0028] In the following specific embodiments, many specific details are set forth to provide a thorough understanding of the embodiments and / or examples disclosed herein. However, it should be understood that such embodiments and examples can be practiced without some or all of the specific details set forth herein. In other cases, well-known methods, procedures, components, and circuits are not described in detail to avoid blurring the following description. In addition, embodiments and examples not specifically described herein can be practiced in place of embodiments and other examples explicitly, implicitly, and / or inherently described, disclosed, or otherwise provided (collectively referred to as "provided") herein, or practiced in combination with these embodiments and examples.

[0029] Although various embodiments are described and / or claimed herein in which devices, systems, equipment, etc. and / or any elements thereof perform operations, procedures, algorithms, functions, etc. and / or any portions thereof, it should be understood that any embodiment described and / or claimed herein assumes that any device, system, device, etc. and / or any element thereof is configured to perform any operation, procedure, algorithm, function, etc. and / or any portion thereof.

[0030] Example Communication System

[0031] The methods, devices, and systems provided herein are well suited for communications involving both wired and wireless networks. Wired networks are well known. Figures 1A to 1D An overview of various types of wireless devices and infrastructure is provided, wherein various elements of the network can utilize the methods, apparatuses, and systems provided herein, perform the methods, apparatuses, and systems provided herein, be arranged according to the methods, apparatuses, and systems provided herein, and / or be adapted and / or configured for the methods, apparatuses, and systems provided herein.

[0032] Figure 1A1 is a diagram illustrating an example communication system 100 in which one or more disclosed embodiments may be implemented. The communication system 100 may be a multiple access system that provides content such as voice, data, video, messaging, broadcast, etc. to multiple wireless users. The communication system 100 may enable multiple wireless users to access such content through sharing of system resources (including wireless bandwidth). For example, the communication system 100 may employ one or more channel access methods such as code division multiple access (CDMA), time division multiple access (TDMA), frequency division multiple access (FDMA), orthogonal FDMA (OFDMA), single carrier FDMA (SC-FDMA), zero tail unique word DFT spread OFDM (ZT UW DTS-s OFDM), unique word OFDM (UW-OFDM), resource block filtered OFDM, and filter bank multi-carrier (FBMC), etc.

[0033] like Figure 1A As shown, the communication system 100 may include wireless transmit / receive units (WTRUs) 102a, 102b, 102c, 102d, RAN 104 / 113, CN 106 / 115, public switched telephone network (PSTN) 108, Internet 110 and other networks 112, but it should be understood that the disclosed embodiments contemplate any number of WTRUs, base stations, networks, and / or network elements. Each of the WTRUs 102a, 102b, 102c, 102d may be any type of device configured to operate and / or communicate in a wireless environment. By way of example, the WTRUs 102a, 102b, 102c, 102d (any of which may be referred to as a “station” and / or “STA”) may be configured to send and / or receive wireless signals and may include user equipment (UE), a mobile station, a fixed or mobile subscriber unit, a subscription-based unit, a pager, a cellular phone, a personal digital assistant (PDA), a smart phone, a laptop, a netbook, a personal computer, a wireless sensor, a hotspot or Mi-Fi device, an Internet of Things (IoT) device, a watch or other wearable device, a head-mounted display (HMD), a vehicle, a drone, medical devices and applications (e.g., remote surgery), industrial devices and applications (e.g., robots and / or other wireless devices operating in an industrial and / or automated process chain environment), consumer electronic devices, and devices operating on a commercial and / or industrial wireless network, etc. Any of the WTRUs 102a, 102b, 102c, and 102d may be interchangeably referred to as a UE.

[0034] The communication system 100 may also include a base station 114a and / or a base station 114b. Each of the base stations 114a, 114b may be any type of device that is configured to wirelessly interface with at least one of the WTRUs 102a, 102b, 102c, 102d to facilitate access to one or more communication networks, such as the CN 106 / 115, the Internet 110, and / or other networks 112. By way of example, the base stations 114a, 114b may be a base transceiver station (BTS), a Node B, an eNode B, a Home Node B, a Home eNode B, a gNB, an NR Node B, a site controller, an access point (AP), a wireless router, and the like. Although the base stations 114a, 114b are each depicted as a single element, it should be appreciated that the base stations 114a, 114b may include any number of interconnected base stations and / or network elements.

[0035] Base station 114a may be part of RAN 104 / 113, which may also include other base stations and / or network elements (not shown), such as base station controllers (BSC), radio network controllers (RNC), relay nodes, etc. Base station 114a and / or base station 114b may be configured to send and / or receive wireless signals on one or more carrier frequencies (which may be referred to as cells (not shown)). These frequencies may be in a licensed spectrum, an unlicensed spectrum, or a combination of a licensed spectrum and an unlicensed spectrum. A cell may provide coverage of wireless services to a specific geographic area, which may be relatively fixed or may change over time. A cell may be further divided into cell sectors. For example, a cell associated with base station 114a may be divided into three sectors. Therefore, in one embodiment, base station 114a may include three transceivers, i.e., one transceiver for each sector of the cell. In an embodiment, base station 114a may employ multiple-input multiple-output (MIMO) technology and may utilize multiple transceivers for each sector of the cell. For example, beamforming may be used to send and / or receive signals in a desired spatial direction.

[0036] The base stations 114a, 114b may communicate with one or more of the WTRUs 102a, 102b, 102c, 102d over an air interface 116, which may be any suitable wireless communication link (e.g., radio frequency (RF), microwave, centimeter wave, micrometer wave, infrared (IR), ultraviolet (UV), visible light, etc.). The air interface 116 may be established using any suitable radio access technology (RAT).

[0037] More specifically, as noted above, the communication system 100 may be a multiple access system and may employ one or more channel access schemes, such as CDMA, TDMA, FDMA, OFDMA, and SC-FDMA. For example, the base station 114a in the RAN 104 / 113 and the WTRUs 102a, 102b, 102c may implement a radio technology such as Universal Mobile Telecommunications System (UMTS) Terrestrial Radio Access (UTRA), which may use Wideband CDMA (WCDMA) to establish the air interface 116. WCDMA may include communication protocols such as High Speed ​​Packet Access (HSPA) and / or Evolved HSPA (HSPA+). HSPA may include High Speed ​​Downlink Packet Access (HSDPA) and / or High Speed ​​Uplink Packet Access (HSUPA).

[0038] In an embodiment, the base station 114a and the WTRUs 102a, 102b, 102c may implement a radio technology such as Evolved UMTS Terrestrial Radio Access (E-UTRA) that may establish the air interface 116 using Long Term Evolution (LTE) and / or Advanced LTE (LTE-A) and / or Advanced LTE Pro (LTE-APro).

[0039] In an embodiment, the base station 114a and the WTRUs 102a, 102b, 102c may implement a radio technology such as NR radio access, which may establish the air interface 116 using New Radio (NR).

[0040] In an embodiment, the base station 114a and the WTRUs 102a, 102b, 102c may implement multiple radio access technologies. For example, the base station 114a and the WTRUs 102a, 102b, 102c may together implement LTE radio access and NR radio access, for example using the dual connectivity (DC) principle. Thus, the air interface utilized by the WTRUs 102a, 102b, 102c may be characterized by multiple types of radio access technologies and / or transmissions transmitted to / from multiple types of base stations (e.g., eNBs and gNBs).

[0041] In other embodiments, the base station 114a and the WTRUs 102a, 102b, 102c may implement radio technologies such as IEEE 802.11 (i.e., Wireless Fidelity (WiFi)), IEEE 802.16 (i.e., Worldwide Interoperability for Microwave Access (WiMAX)), CDMA2000, CDMA2000 1X, CDMA2000 EV-DO, Interim Standard 2000 (IS-2000), Interim Standard 95 (IS-95), Interim Standard 856 (IS-856), Global System for Mobile Communications (GSM), Enhanced Data rates for GSM Evolution (EDGE), and GSM EDGE (GERAN).

[0042] Figure 1A The base station 114b in may be, for example, a wireless router, a Home NodeB, a Home eNodeB, or an access point, and may utilize any suitable RAT to facilitate wireless connectivity in local areas such as commercial venues, homes, vehicles, campuses, industrial facilities, sky corridors (e.g., for use by drones), and roads. In one embodiment, the base station 114b and the WTRUs 102c, 102d may implement radio technologies such as IEEE 802.11 to establish a wireless local area network (WLAN). In an embodiment, the base station 114b and the WTRUs 102c, 102d may implement radio technologies such as IEEE 802.15 to establish a wireless personal area network (WPAN). In yet another embodiment, the base station 114b and the WTRUs 102c, 102d may utilize a cellular-based RAT (e.g., WCDMA, CDMA2000, GSM, LTE, LTE-A, LTE-A Pro, NR, etc.) to establish a picocell or a femtocell. As Figure 1A As shown, base station 114b may have a direct connection to Internet 110. Therefore, base station 114b may not need to access Internet 110 via CN 106 / 115.

[0043] The RAN 104 / 113 may be in communication with the CN 106 / 115, which may be any type of network configured to provide voice, data, applications and / or Voice over Internet Protocol (VoIP) services to one or more of the WTRUs 102a, 102b, 102c, 102d. The data may have different quality of service (QoS) requirements, such as different throughput requirements, latency requirements, error tolerance requirements, reliability requirements, data throughput requirements, mobility requirements, etc. The CN 106 / 115 may provide call control, billing services, mobile location-based services, prepaid calling, Internet connectivity, video distribution, etc., and / or perform high-level security functions, such as user authentication. Although not described in detail in the text and video, the CN 106 / 115 may be configured to provide voice, data, applications and / or Voice over Internet Protocol (VoIP) services to one or more of the WTRUs 102a, 102b, 102c, 102d. The data may have different quality of service (QoS) requirements, such as different throughput requirements, latency requirements, error tolerance requirements, reliability requirements, data throughput requirements, mobility requirements, etc. The CN 106 / 115 may provide call control, billing services, mobile location-based services, prepaid calling, Internet connectivity, video distribution, etc., and / or perform high-level security functions, such as user authentication. Figure 1AAlthough not shown in the figure, it will be appreciated that the RAN 104 / 113 and / or the CN 106 / 115 may be in direct or indirect communication with other RANs that employ the same RAT as the RAN 104 / 113 or a different RAT. For example, in addition to being connected to the RAN 104 / 113, which may utilize NR radio technology, the CN 106 / 115 may also be in communication with another RAN (not shown) that employs GSM, UMTS, CDMA2000, WiMAX, E-UTRA, or WiFi radio technology.

[0044] The CN 106 / 115 may also act as a gateway for the WTRUs 102a, 102b, 102c, 102d to access the PSTN 108, the Internet 110, and / or other networks 112. The PSTN 108 may include a circuit-switched telephone network that provides plain old telephone service (POTS). The Internet 110 may include a global system of interconnected computer networks and devices that use common communication protocols, such as the Transmission Control Protocol (TCP), the User Datagram Protocol (UDP), and / or the Internet Protocol (IP) in the TCP / IP Internet protocol suite. The networks 112 may include wired and / or wireless communication networks owned and / or operated by other service providers. For example, the networks 112 may include another CN connected to one or more RANs, which may employ the same RAT as the RAN 104 / 113 or a different RAT.

[0045] Some or all of the WTRUs 102a, 102b, 102c, 102d in the communication system 100 may include multi-mode capabilities (e.g., the WTRUs 102a, 102b, 102c, 102d may include multiple transceivers for communicating with different wireless networks via different wireless links). Figure 1A The illustrated WTRU 102c may be configured to communicate with the base station 114a, which may employ a cellular-based radio technology, and with the base station 114b, which may employ an IEEE 802 radio technology.

[0046] Figure 1B is a system diagram illustrating an example WTRU 102. Figure 1B As shown, the WTRU 102 may include, among other things, a processor 118, a transceiver 120, a transmit / receive element 122, a speaker / microphone 124, a keypad 126, a display / touchpad 128, non-removable memory 130, removable memory 132, a power source 134, a global positioning system (GPS) chipset 136, and / or other peripherals 138. It will be appreciated that the WTRU 102 may include any sub-combination of the foregoing elements while remaining consistent with an embodiment.

[0047] The processor 118 may be a general purpose processor, a special purpose processor, a conventional processor, a digital signal processor (DSP), a plurality of microprocessors, one or more microprocessors associated with a DSP core, a controller, a microcontroller, an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) circuit, any other type of integrated circuit (IC) and state machine, etc. The processor 118 may perform signal decoding, data processing, power control, input / output processing, and / or any other functionality that enables the WTRU 102 to operate in a wireless environment. The processor 118 may be coupled to the transceiver 120, which may be coupled to the transmit / receive element 122. Although Figure 1B The processor 118 and the transceiver 120 are depicted as separate components, but it is understood that the processor 118 and the transceiver 120 may be integrated together in an electronic package or chip.

[0048] The send / receive element 122 may be configured to send a signal to a base station (e.g., base station 114a) or receive a signal from the base station via an air interface 116. For example, in one embodiment, the send / receive element 122 may be an antenna configured to send and / or receive an RF signal. In an embodiment, the send / receive element 122 may be a transmitter / detector configured to send and / or receive, for example, IR, UV or visible light signals. In another embodiment, the send / receive element 122 may be configured to send and / or receive both RF signals and optical signals. It should be understood that the send / receive element 122 may be configured to send and / or receive any combination of wireless signals.

[0049] Although the transmit / receive element 122 Figure 1B Although depicted as a single element in the figure, the WTRU 102 may include any number of transmit / receive elements 122. More specifically, the WTRU 102 may employ MIMO technology. Thus, in one embodiment, the WTRU 102 may include two or more transmit / receive elements 122 (e.g., multiple antennas) for transmitting and receiving wireless signals over the air interface 116.

[0050] The transceiver 120 may be configured to modulate signals to be transmitted by the transmit / receive element 122 and to demodulate signals received by the transmit / receive element 122. As noted above, the WTRU 102 may have multi-mode capabilities. For example, the transceiver 120 may include multiple transceivers for enabling the WTRU 102 to communicate via multiple RATs, such as NR and IEEE 802.11.

[0051] The processor 118 of the WTRU 102 may be coupled to and may receive user input data from a speaker / microphone 124, a keypad 126, and / or a display / touchpad 128 (e.g., a liquid crystal display (LCD) display unit or an organic light emitting diode (OLED) display unit). The processor 118 may also output user data to the speaker / microphone 124, the keypad 126, and / or the display / touchpad 128. In addition, the processor 118 may access information from and store data in any type of suitable memory, such as a non-removable memory 130 and / or a removable memory 132. The non-removable memory 130 may include a random access memory (RAM), a read-only memory (ROM), a hard disk, or any other type of memory storage device. The removable memory 132 may include a subscriber identity module (SIM) card, a memory stick, a secure digital (SD) memory card, and the like. In other embodiments, the processor 118 may access information from, and store data in, memory that is not physically located on the WTRU 102, such as on a server or a home computer (not shown).

[0052] The processor 118 may receive power from the power source 134, and may be configured to distribute and / or control power to the other components in the WTRU 102. The power source 134 may be any suitable device for powering the WTRU 102. For example, the power source 134 may include one or more dry cell batteries (e.g., nickel cadmium (NiCd), nickel zinc (NiZn), nickel metal hydride (NiMH), lithium ion (Li-ion), etc.), solar cells, fuel cells, etc.

[0053] The processor 118 may also be coupled to the GPS chipset 136, which may be configured to provide location information (e.g., longitude and latitude) regarding the current location of the WTRU 102. In addition to or in lieu of the information from the GPS chipset 136, the WTRU 102 may receive location information from a base station (e.g., base stations 114a, 114b) over the air interface 116 and / or determine its location based on the timing of signals received from two or more nearby base stations. It will be appreciated that the WTRU 102 may acquire location information by any suitable location-determination method while remaining consistent with an embodiment.

[0054] The processor 118 may be further coupled to other peripherals 138, which may include one or more software modules and / or hardware modules that provide additional features, functionality, and / or wired or wireless connectivity. For example, the peripherals 138 may include an accelerometer, an electronic compass, a satellite transceiver, a digital camera (for photos and / or video), a universal serial bus (USB) port, a vibration device, a television transceiver, a hands-free headset, module, a frequency modulation (FM) radio unit, a digital music player, a media player, a video game player module, an Internet browser, a virtual reality and / or augmented reality (VR / AR) device, and an activity tracker, etc. The peripheral device 138 may include one or more sensors, which may be one or more of the following: a gyroscope, an accelerometer, a Hall effect sensor, a magnetometer, an orientation sensor, a proximity sensor, a temperature sensor, a time sensor; a geolocation sensor; an altimeter, a light sensor, a touch sensor, a magnetometer, a barometer, a gesture sensor, a biometric sensor, and / or a humidity sensor.

[0055] The WTRU 102 may include a full-duplex radio for which transmission and reception of some or all signals (e.g., associated with specific subframes for both uplink (e.g., for transmission) and downlink (e.g., for reception)) may be concurrent and / or simultaneous. The full-duplex radio may include an interference management unit 139 for reducing and / or substantially eliminating self-interference via signal processing performed by hardware (e.g., a choke) or via a processor (e.g., a separate processor (not shown) or via the processor 118). In an embodiment, the WTRU 102 may include a half-duplex radio for which transmission and reception of some or all signals (e.g., associated with specific subframes for both uplink (e.g., for transmission) or downlink (e.g., for reception)) may be concurrent and / or simultaneous.

[0056] Figure 1C 1 is a system diagram illustrating the RAN 104 and the CN 106 in accordance with an embodiment. As noted above, the RAN 104 may employ an E-UTRA radio technology to communicate with the WTRUs 102a, 102b, 102c over the air interface 116. The RAN 104 may also be in communication with the CN 106.

[0057] The RAN 104 may include evolved Node-Bs 160a, 160b, 160c, though it will be appreciated that the RAN 104 may include any number of evolved Node-Bs while remaining consistent with an embodiment. The evolved Node-Bs 160a, 160b, 160c may each include one or more transceivers for communicating with the WTRUs 102a, 102b, 102c over the air interface 116. In one embodiment, the evolved Node-Bs 160a, 160b, 160c may implement MIMO technology. Thus, the evolved Node-B 160a, for example, may use multiple antennas to transmit wireless signals to and / or receive wireless signals from the WTRU 102a.

[0058] Each of the evolved Node Bs 160a, 160b, and 160c may be associated with a particular cell (not shown) and may be configured to handle radio resource management decisions, handover decisions, user scheduling in uplink (UL) and / or downlink (DL), etc. Figure 1C As shown, the eNode-Bs 160a, 160b, 160c may communicate with each other via an X2 interface.

[0059] Figure 1C The illustrated CN 106 may include a mobility management entity (MME) 162, a serving gateway (SGW) 164, and a packet data network (PDN) gateway (or PGW) 166. While each of the foregoing elements is depicted as being part of the CN 106, it should be understood that any of these elements may be owned and / or operated by an entity other than the CN operator.

[0060] The MME 162 may be connected to each of the evolved Node-Bs 162a, 162b, 162c in the RAN 104 via an S1 interface and may serve as a control node. For example, the MME 162 may be responsible for authenticating users of the WTRUs 102a, 102b, 102c, bearer activation / deactivation, and selecting a particular serving gateway during an initial attach of the WTRUs 102a, 102b, 102c. The MME 162 may provide a control plane function for switching between the RAN 104 and other RANs (not shown) that employ other radio technologies, such as GSM and / or WCDMA.

[0061] The SGW 164 may be connected to each of the evolved Node-Bs 160a, 160b, 160c in the RAN 104 via an S1 interface. The SGW 164 may generally route and forward user data packets to / from the WTRUs 102a, 102b, 102c. The SGW 164 may perform other functions, such as anchoring the user plane during an inter-evolved Node-B handover, triggering paging when DL data is available for the WTRUs 102a, 102b, 102c, and managing and storing the context of the WTRUs 102a, 102b, 102c.

[0062] The SGW 164 may be connected to the PGW 166, which may provide the WTRUs 102a, 102b, 102c with access to packet-switched networks, such as the Internet 110, to facilitate communications between the WTRUs 102a, 102b, 102c and IP-enabled devices.

[0063] The CN 106 may facilitate communications with other networks. For example, the CN 106 may provide the WTRUs 102a, 102b, 102c with access to circuit-switched networks, such as the PSTN 108, to facilitate communications between the WTRUs 102a, 102b, 102c and traditional land-line communications devices. For example, the CN 106 may include, or may be in communication with, an IP gateway (e.g., an IP Multimedia Subsystem (IMS) server) that acts as an interface between the CN 106 and the PSTN 108. In addition, the CN 106 may provide the WTRUs 102a, 102b, 102c with access to other networks 112, which may include other wired and / or wireless networks owned and / or operated by other service providers.

[0064] Although the WTRU Figures 1A to 1D Although described as wireless terminals, it is contemplated that in certain representative embodiments, such terminals may (eg, temporarily or permanently) use a wired communications interface with a communications network.

[0065] In a representative embodiment, the other network 112 may be a WLAN.

[0066] A WLAN in infrastructure basic service set (BSS) mode may have an access point (AP) for a BSS and one or more stations (STAs) associated with the AP. The AP may have access or an interface to a distribution system (DS) or another type of wired / wireless network that carries traffic to and / or out of the BSS. Traffic from outside the BSS to a STA may be reached by the AP and may be delivered to the STA. Traffic from a STA to a destination outside the BSS may be transmitted to the AP to be delivered to the corresponding destination. Traffic between STAs within the BSS may be transmitted by the AP, for example, where the source STA may transmit traffic to the AP, and the AP may deliver traffic to the destination STA. Traffic between STAs within the BSS may be considered and / or referred to as point-to-point traffic. Direct link establishment (DLS) may be used to transmit point-to-point traffic between a source STA and a destination STA (e.g., directly between them). In certain representative embodiments, the DLS may use 802.11e DLS or 802.11z tunnel DLS (TDLS). A WLAN using an independent BSS (IBSS) mode may not have an AP, and STAs within or using the IBSS (eg, all STAs in the STA) may communicate directly with each other. The IBSS communication mode may sometimes be referred to herein as an "ad hoc" communication mode.

[0067] When using the 802.11ac infrastructure operating mode or a similar operating mode, the AP may send beacons on a fixed channel, such as a primary channel. The primary channel may be a fixed width (e.g., a 20 MHz wide bandwidth) or a width dynamically set via signaling. The primary channel may be an operating channel of the BSS and may be used by the STA to establish a connection with the AP. In certain representative embodiments, carrier sense multiple access / collision avoidance (CSMA / CA) may be implemented, for example, in an 802.11 system. For CSMA / CA, a STA (e.g., each STA) (including the AP) may sense the primary channel. If the primary channel is sensed / detected and / or determined to be busy by a particular STA, the particular STA may back off. One STA (e.g., only one station) may transmit at any given time in a given BSS.

[0068] High throughput (HT) STAs may communicate using a 40 MHz wide channel, for example, via a combination of a primary 20 MHz channel and adjacent or non-adjacent 20 MHz channels to form a 40 MHz wide channel.

[0069] Very high throughput (VHT) STA can support 20MHz, 40MHz, 80MHz and / or 160MHz wide channels. 40MHz channels and / or 80MHz channels can be formed by combining continuous 20MHz channels. 160MHz channels can be formed by combining 8 continuous 20MHz channels, or by combining two non-continuous 80MHz channels (this can be called 80+80 configuration). For 80+80 configuration, after channel coding, the data can pass through a segment parser that can divide the data into two streams. Each stream can be processed by inverse fast Fourier transform (IFFT) and time domain processing separately. These streams can be mapped to two 80MHz channels, and data can be sent by the transmitter STA. At the receiver of the receiver STA, the above-mentioned operation for the 80+80 configuration can be reversed, and the combined data can be transmitted to the medium access control (MAC).

[0070] 802.11af and 802.11ah support operating modes below 1GHz. Channel operating bandwidths and carriers are reduced in 802.11af and 802.11ah relative to those used in 802.11n and 802.11ac. 802.11af supports 5MHz, 10MHz, and 20MHz bandwidths in the TV White Space (TVWS) spectrum, and 802.11ah supports 1MHz, 2MHz, 4MHz, 8MHz, and 16MHz bandwidths using non-TVWS spectrum. According to a representative embodiment, 802.11ah may support instrument type control / machine type communications, such as MTC devices in macro coverage areas. MTC devices may have certain capabilities, such as limited capabilities, including support for (e.g., only support for) certain bandwidths and / or limited bandwidths. MTC devices may include batteries with battery life above a threshold (e.g., to maintain very long battery life).

[0071] WLAN systems that can support multiple channels and channel bandwidths (such as 802.11n, 802.11ac, 802.11af, and 802.11ah) include channels that can be designated as primary channels. The primary channel may have a bandwidth equal to the maximum common operating bandwidth supported by all STAs in the BSS. The bandwidth of the primary channel may be set and / or limited by a STA (which supports the minimum bandwidth operating mode) from all STAs operating in the BSS. In the example of 802.11ah, for STAs (e.g., MTC-type devices) that support (e.g., only support) a 1MHz mode, the primary channel may be 1MHz wide, even if the AP and other STAs in the BSS support 2MHz, 4MHz, 8MHz, 16MHz, and / or other channel bandwidth operating modes. Carrier sensing and / or network allocation vector (NAV) settings may depend on the state of the primary channel. If the primary channel is busy, for example, because a STA (supporting only a 1MHz operating mode) is sending to the AP, the entire available band may be considered busy even if most of the band remains idle and may be available.

[0072] In the United States, the available frequency band for 802.11ah is 902MHz to 928MHz. In South Korea, the available frequency band is 917.5MHz to 923.5MHz. In Japan, the available frequency band is 916.5MHz to 927.5MHz. The total bandwidth available for 802.11ah is 6MHz to 26MHz, depending on the country code.

[0073] Figure 1D 1 is a system diagram illustrating the RAN 113 and the CN 115 in accordance with an embodiment. As noted above, the RAN 113 may employ NR radio technology to communicate with the WTRUs 102a, 102b, 102c over the air interface 116. The RAN 113 may also be in communication with the CN 115.

[0074] The RAN 113 may include gNBs 180a, 180b, 180c, though it will be appreciated that the RAN 113 may include any number of gNBs while remaining consistent with an embodiment. The gNBs 180a, 180b, 180c may each include one or more transceivers for communicating with the WTRUs 102a, 102b, 102c over the air interface 116. In one embodiment, the gNBs 180a, 180b, 180c may implement MIMO technology. For example, the gNBs 180a, 180b may utilize beamforming to send signals to and / or receive signals from the gNBs 180a, 180b, 180c. Thus, the gNB 180a may, for example, use multiple antennas to send wireless signals to and / or receive wireless signals from the WTRU 102a. In an embodiment, the gNBs 180a, 180b, 180c may implement carrier aggregation technology. For example, the gNB 180a may transmit multiple component carriers to the WTRU 102a (not shown). A subset of these component carriers may be on an unlicensed spectrum, while the remaining component carriers may be on a licensed spectrum. In an embodiment, the gNBs 180a, 180b, 180c may implement coordinated multi-point (CoMP) technology. For example, the WTRU 102a may receive coordinated transmissions from the gNB 180a and the gNB 180b (and / or the gNB 180c).

[0075] The WTRUs 102a, 102b, 102c may communicate with the gNBs 180a, 180b, 180c using transmissions associated with parameter sets that may be scalable. For example, OFDM symbol spacing and / or OFDM subcarrier spacing may vary for different transmissions, different cells, and / or different portions of the wireless transmission spectrum. The WTRUs 102a, 102b, 102c may communicate with the gNBs 180a, 180b, 180c using subframes or transmit time intervals (TTIs) of varying or scalable lengths (e.g., containing different numbers of OFDM symbols and / or varying absolute time lengths over time).

[0076] The gNBs 180a, 180b, 180c may be configured to communicate with the WTRUs 102a, 102b, 102c in a standalone configuration and / or a non-standalone configuration. In a standalone configuration, the WTRUs 102a, 102b, 102c may communicate with the gNBs 180a, 180b, 180c while not accessing other RANs (e.g., such as the eNodeBs 160a, 160b, 160c). In a standalone configuration, the WTRUs 102a, 102b, 102c may use one or more of the gNBs 180a, 180b, 180c as mobility anchors. In a standalone configuration, the WTRUs 102a, 102b, 102c may communicate with the gNBs 180a, 180b, 180c using signals in an unlicensed band. In a non-standalone configuration, the WTRUs 102a, 102b, 102c may communicate / connect with the gNB 180a, 180b, 180c while also communicating / connecting with another RAN, such as the eNode-B 160a, 160b, 160c. For example, the WTRUs 102a, 102b, 102c may implement the DC principle to communicate with one or more gNBs 180a, 180b, 180c and one or more eNode-Bs 160a, 160b, 160c substantially simultaneously. In a non-standalone configuration, the eNode-B 160a, 160b, 160c may act as a mobility anchor for the WTRUs 102a, 102b, 102c, and the gNB 180a, 180b, 180c may provide additional coverage and / or throughput for serving the WTRUs 102a, 102b, 102c.

[0077] Each of the gNBs 180a, 180b, 180c may be associated with a specific cell (not shown) and may be configured to handle radio resource management decisions, handover decisions, scheduling of users in uplink (UL) and / or downlink (DL), support of network slicing, dual connectivity, interworking between NR and E-UTRA, routing of user plane data towards a user plane function (UPF) 184a, 184b, routing of control plane information towards an access and mobility management function (AMF) 182a, 182b, etc. As shown in FIG. Figure 1D As shown, gNBs 180a, 180b, and 180c may communicate with each other via an Xn interface.

[0078] Figure 1DThe illustrated CN 115 may include at least one AMF 182a, 182b, at least one UPF 184a, 184b, at least one session management function (SMF) 183a, 183b, and possible data networks (DNs) 185a, 185b. Although each of the foregoing elements is depicted as part of the CN 115, it should be understood that any of these elements may be owned and / or operated by an entity other than the CN operator.

[0079] The AMF 182a, 182b may be connected to one or more of the gNBs 180a, 180b, 180c in the RAN 113 via the N2 interface and may act as a control node. For example, the AMF 182a, 182b may be responsible for authenticating users of the WTRU 102a, 102b, 102c, support of network slicing (e.g., handling of different PDU sessions with different requirements), selecting a specific SMF 183a, 183b, management of registration areas, termination of NAS signaling and mobility management, etc. The AMF 182a, 182b may use network slicing to customize CN support for the WTRU 102a, 102b, 102c based on the type of services utilized by the WTRU 102a, 102b, 102c. For example, different network slices may be established for different use cases, such as services relying on ultra-reliable low latency (URLLC) access, services relying on enhanced mobile broadband (eMBB) access, and / or services for machine type communication (MTC) access, etc. The AMF a82a, 182b may provide a control plane function for switching between the RAN 113 and other RANs (not shown) that employ other radio technologies, such as LTE, LTE-A, LTE-APro, and / or non-3GPP access technologies, such as WiFi.

[0080] The SMF 183a, 183b may be connected to the AMF 182a, 182b in the CN 115 via the N11 interface. The SMF 183a, 183b may also be connected to the UPF 184a, 184b in the CN 115 via the N4 interface. The SMF 183a, 183b may select and control the UPF 184a, 184b, and configure the service routing performed by the UPF 184a, 184b. The SMF 183a, 183b may perform other functions, such as managing and allocating UE IP addresses, managing PDU sessions, controlling policy implementation and QoS, and providing downlink data notifications. The PDU session type may be IP-based, non-IP-based, Ethernet-based, etc.

[0081] The UPF 184a, 184b may be connected to one or more of the gNBs 180a, 180b, 180c in the RAN 113 via the N3 interface, which may provide the WTRUs 102a, 102b, 102c with access to packet-switched networks, such as the Internet 110, to facilitate communications between the WTRUs 102a, 102b, 102c and IP-enabled devices. The UPF 184, 184b may perform other functions, such as routing and forwarding packets, enforcing user plane policies, supporting multi-homed PDU sessions, handling user plane QoS, buffering downlink packets, and providing mobility anchoring, etc.

[0082] The CN 115 may facilitate communications with other networks. For example, the CN 115 may include or may communicate with an IP gateway (e.g., an IP Multimedia Subsystem (IMS) server) that acts as an interface between the CN 115 and the PSTN 108. In addition, the CN 115 may provide the WTRUs 102a, 102b, 102c with access to other networks 112, which may include other wired and / or wireless networks owned and / or operated by other service providers. In one embodiment, the WTRUs 102a, 102b, 102c may be connected to local data networks (DNs) 185a, 185b via the UPF 184a, 184b via the N3 interface to the UPF 184a, 184b and the N6 interface between the UPF 184a, 184b and the DN 185a, 185b.

[0083] Given that Figures 1A to 1D as well as Figures 1A to 1D Corresponding to the description of the present invention, one or more or all of the functions described herein with respect to one or more of the following may be performed by one or more simulation devices (not shown): WTRU102a-102d, base station 114a-114b, evolved Node B 160a-160c, MME 162, SGW 164, PGW 166, gNB 180a-180c, AMF 182a-182b, UPF 184a-184b, SMF 183a-183b, DN 185a-185b and / or any other device described herein. The simulation device may be one or more devices configured to mimic one or more or all of the functions described herein. For example, the simulation device may be used to test other devices and / or simulate network and / or WTRU functions.

[0084] The simulation device may be designed to implement one or more tests of other devices in a laboratory environment and / or in an operator network environment. For example, one or more simulation devices may perform one or more functions or all functions while being fully or partially implemented and / or deployed as part of a wired and / or wireless communication network in order to test other devices within the communication network. One or more simulation devices may perform one or more functions or all functions while being temporarily implemented / deployed as part of a wired and / or wireless communication network. The simulation device may be directly coupled to another device for testing purposes and / or may use over-the-air wireless communications to perform testing.

[0085] One or more simulation devices can perform one or more (including all) functions without being implemented / deployed as part of a wired and / or wireless communication network. For example, the simulation device can be used in a test scenario in a test laboratory and / or a non-deployed (e.g., testing) wired and / or wireless communication network to implement testing of one or more components. One or more simulation devices can be test equipment. Direct RF coupling and / or wireless communication via RF circuits (e.g., which may include one or more antennas) can be used by the simulation device to send and / or receive data.

[0086] Channel State Information (CSI) reporting

[0087] The channel state information (CSI) may include at least one of the following: a channel quality index (CQI), a rank indicator (RI), a precoding matrix index (PMI), an L1 channel measurement (e.g., a reference signal received power (RSRP), such as L1-RSRP or SINR), a CSI-reference signal (CSI-RS) resource indicator (CRI), a SS / PBCH block resource indicator (SSBRI), a layer indicator (LI), and / or any other measurement quantity measured by the WTRU from a configured reference signal (e.g., a CSI-RS or a SS / PBCH block or any other reference signal).

[0088] CSI Reporting Framework

[0089] The WTRU may be configured to report CSI via the uplink control channel on the physical uplink control channel (PUCCH) or based on a request granted by the gNB on the uplink (UL) PUSCH. Depending on the configuration, the CSI-RS may cover the entire bandwidth of the bandwidth part (BWP) or only a small portion of it. Within the CSI-RS bandwidth, the CSI-RS may be configured in every physical resource block (PRB) or every other PRB. In the time domain, the CSI-RS resources may be configured as periodic, semi-persistent, or aperiodic. Semi-persistent CSI-RS is similar to periodic CSI-RS, except that the resource may be activated (deactivated) by a MAC CE; and the WTRU reports the relevant measurements only when the resource is activated. For aperiodic CSI-RS, the WTRU is triggered to report the CSI-RS measured on the PUSCH through a request in the downlink control information (DCI). Periodic reports are carried over the PUCCH, while semi-persistent reports may be carried on the PUCCH or the physical uplink shared channel (PUSCH).

[0090] The reported CSI may be used by the scheduler when allocating the best resource blocks, possibly based on the time-frequency selectivity of the channel; when determining the precoding matrix; when determining the beam; when determining the transmit mode; and when selecting the appropriate modulation and coding scheme (MCS). The reliability, accuracy, and timeliness of WTRU CSI reporting may be critical to meeting Ultra-Reliable Low Latency Communication (URLLC) service requirements.

[0091] The WTRU may be configured with a CSI measurement setup, which may include one or more CSI reporting setups, resource setups, and / or links between one or more CSI reporting setups and one or more resource setups. Figure 2 Examples of configuration for CSI reporting settings, resource settings, and links are shown.

[0092] In the CSI measurement setting, one or more of the following configuration parameters may be provided: N≥1 CSI report settings, M≥1 resource settings, and / or a CSI measurement setting linking N CSI report settings with M resource settings.

[0093] The CSI reporting settings include at least one of the following:

[0094] Temporal behavior: for example, aperiodic, periodic, or semi-persistent;

[0095] Frequency granularity, at least for Precoding Matrix Indicator (PMI) and Channel Quality Indicator (CQI);

[0096] CSI report type (e.g., PMI, CQI, rank indicator (RI), CSI resource indicator (CRI), etc.); and / or

[0097] PMI type (Type I or II) and codebook configuration (if PMI is reported).

[0098] Resource settings include at least one of the following:

[0099] Time domain behavior: aperiodic, periodic, or semi-persistent;

[0100] RS type (e.g., for channel measurement or interference measurement); and / or

[0101] S ≥ 1 resource sets, each of which can contain K s resources.

[0102] The CSI measurement setting includes at least one of: 1) a CSI report setting, 2) a resource setting, and / or 3) a reference transmission scheme setting for CQI.

[0103] For CSI reporting for a component carrier, one or more of the following frequency granularities may be supported: wideband CSI, partial-band CSI, and / or sub-band CSI.

[0104] Codebook-based pre-decoding

[0105] Figure 3 The basic concept of codebook-based precoding using feedback information is shown. The feedback information may include a PMI, which may be referred to as Figure 3 The codeword index in the codebook shown.

[0106] like Figure 3 As shown, the codebook includes a set of precoding vectors / matrices for each rank and number of antenna ports, and each precoding vector / matrix has its own index so that the receiver can inform the transmitter of the preferred precoding vector / matrix index. Compared with non-codebook-based precoding, codebook-based precoding may have degraded performance due to its limited number of precoding vectors / matrices. However, the main advantage of codebook-based precoding is lower control signaling / feedback overhead.

[0107] Table 1 shows an example of a codebook for 2Tx.

[0108] Table 1.2 Tx downlink codebook

[0109]

[0110] CSI Processing Standards

[0111] A CSI processing unit (CPU) may be referred to as a minimum CSI processing unit, and a WTRU may support one or more CPUs (e.g., N CPUs). A WTRU with N CPUs may estimate N CSI feedback calculations in parallel, where N may be the WTRU capability. If a WTRU is requested to estimate more than N CSI feedbacks simultaneously, the WTRU may only perform the N highest priority CSI feedbacks and may not estimate the remaining CSI feedbacks.

[0112] The start and end of the CPU may be determined based on the CSI reporting type (eg, aperiodic, periodic, or semi-persistent) as follows.

[0113] For example, for aperiodic CSI reporting, the CPU is occupied starting from the first Orthogonal Frequency Division Multiplexing (OFDM) symbol after a physical downlink control channel (PDCCH) trigger until the last OFDM symbol of the PUSCH carrying the CSI report.

[0114] As another example, for periodic and semi-persistent CSI reporting, the CPU is occupied starting from the first OFDM symbol of one or more associated measurement resources (no earlier than the CSI reference resource) until the last OFDM symbol of the CSI report.

[0115] The amount of CPU occupied may vary based on the CSI measurement type (e.g., beam-based or non-beam-based), for example as shown below:

[0116] Non-beam related reports

[0117] ●K s CPUs, when there are K in the CSI-RS resource set used for channel measurement s CSI-RS resources

[0118] Beam-related reports (e.g., "cri-RSRP", "ssb-Index-RSRP" or

[0119] "none")

[0120] ○1 CPU, ignoring the CSI-RS resource set used for channel measurement

[0121] The number of RS resources

[0122] ○ “none” is used for P3 beam management operations or aperiodic tracking reference signals

[0123] (TRS)Send

[0124] ● For aperiodic CSI reporting with a single CSI-RS resource, 1 CPU is occupied

[0125] ●For Report K s The CSI of CSI-RS resources, K s CPUs are occupied because the WTRU needs to perform CSI measurements for each CSI-RS resource

[0126] When the number of unused CPUs (N u ) is less than the number of CPUs required for CSI reporting (N r ), the following WTRU behaviors may be implemented. For example, in the case where there is no data / HARQ (Hybrid Automatic Repeat Request) in the uplink control information (UCI) on PUSCH, the WTRU may discard Nr–Nu CSI reporting instances based on priority. For another example, the WTRU may report dummy information in Nr–Nu CSI reporting instances based on priority to avoid rate matching processing for PUSCH.

[0127] Artificial Intelligence (AI)

[0128] Artificial intelligence can be broadly defined as behaviors exhibited by machines. Such behaviors can, for example, mimic the cognitive functions of sensing, inferring, adapting, and acting.

[0129] Machine Learning (ML)

[0130] General principles of ML

[0131] Machine learning may refer to algorithms that learn to solve problems based on experience ("data") without explicit programming ("configuration rule sets"). Machine learning may be considered a subset of artificial intelligence (AI). Different machine learning paradigms may be envisioned based on the nature of the data or feedback available to the learning algorithm. For example, supervised learning methods may involve learning a function that maps inputs to outputs based on labeled training examples, where each training example may be a pair consisting of an input and a corresponding output. On the other hand, for example, unsupervised learning methods may involve detecting patterns in data without pre-existing labels. As another example, reinforcement learning methods may involve performing a series of actions in an environment to maximize a cumulative reward.

[0132] In some solutions, it is possible to apply a machine learning algorithm using a combination or interpolation of any of the above mentioned methods. For example, a semi-supervised learning method can use a combination of a small amount of labeled data and a large amount of unlabeled data during training. In this regard, semi-supervised learning falls between unsupervised learning (no labeled training data) and supervised learning (only labeled training data).

[0133] Deep Learning (DL)

[0134] Deep learning refers to a class of machine learning algorithms that employ artificial neural networks, specifically deep neural networks (DNNs), that are generally inspired by biological systems. DNNs are a special class of machine learning models inspired by the human brain, in which the input is linearly transformed and passed through a nonlinear activation function multiple times. DNNs typically include multiple layers, each of which includes a linear transformation function and a given nonlinear activation function. DNNs can be trained via a back-propagation algorithm using training data. Recently, DNNs have demonstrated state-of-the-art performance in various fields such as speech, vision, natural language, etc. and for various machine learning settings, including supervised, unsupervised, and semi-supervised.

[0135] AI / ML-based methods / processes may refer to achieving behaviors and / or compliance with requirements by learning based on data without explicitly configuring a sequence of action steps. Such methods may enable learning of complex behaviors that may be difficult to specify and / or achieve using traditional methods.

[0136] CSI Overhead

[0137] For downlink scheduling and link adaptation purposes for both single-user (SU)-MIMO and multi-user (MU)-MIMO, accurate knowledge of the channel is required. This is achieved by using the DL CSI Reference Signal (CSI-RS) to enable channel estimation at the WTRU and by feeding back the estimated CSI (e.g., implicit CSI: CQI, PMI, RI, LI) in the WTRU CSI report. However, since NR supports a large number of antenna ports, there is a large amount of overhead associated with CSI feedback reporting. The overhead is particularly high for CSI Type II codebooks (e.g., Rel-15 CSI Type II, Rel-16 / 17 Type II / eType IICSI codebooks).

[0138] This overhead is expected to increase further as the system bandwidth and number of antennas will increase in B5G massive MIMO systems.

[0139] In some examples, AI / ML-based compression reduces CSI feedback overhead. However, the typical approach is to compress the full channel matrix. The overhead can be further reduced by compressing the eigenvectors instead.

[0140] For AI / ML based feature vector compression, new or improved methods may be needed to support scenarios where the channel rank is greater than 1. Additionally, for feature vector based compression, new or improved methods may be needed to support MU-MIMO.

[0141] In some current implementations, there is a large overhead associated with CSI feedback reporting, and this overhead is expected to increase further in current and future wireless communication networks (e.g., 5G advanced networks or 6G networks) as system bandwidth and number of antennas will increase. CSI compression (e.g., AI / ML-based CSI compression) can reduce CSI feedback overhead. However, current implementations tend to compress the full channel matrix. In some examples described herein, the overhead can be further reduced by compressing one or more eigenvectors (EVs).

[0142] Therefore, new or improved methods and procedures are needed to address the following problems: 1) reducing CSI feedback overhead by compressing channel eigenvectors; 2) using eigenvector compression to support different channel ranks; 3) using eigenvector compression to support MU-MIMO configuration; 4) determining and selecting the CSI compression type (channel matrix compression or eigenvector compression); 5) post-processing the compressed CSI; 6) indicating the determined CSI compression type to the network; and / or 7) reporting compressed CSI feedback to the network.

[0143] Representative procedures for configuring a WTRU

[0144] AI / ML-based CSI compression capabilities

[0145] In an example, the WTRU may use one or more autoencoders (AEs) to support AI / ML based CSI compression. Different types of AI / ML models (e.g., AEs) may be used for CSI compression. For example, a dedicated (or separate) model may be constructed and trained using a training data set including a full channel response matrix H. The model may be used to compress the full channel matrix H. Another dedicated (or separate) model may be trained using an eigenvector (EV) of the channel response; the model may be used to compress the channel eigenvector. A generalized model may be constructed and trained to compress the full channel matrix H or the channel eigenvector (EV).

[0146] The WTRU may report its AI / ML based CSI compression capabilities to the network (e.g., gNB) and / or may report the configured CSI compression model. Parameters describing the WTRU CSI compression model may include one or more of the following.

[0147] CSI compression types may include: 1) Full Channel (H) - the WTRU compresses the full channel matrix estimate; and / or 2) Eigenvector (EV) - the WTRU compresses the eigenvectors (e.g., one or more eigenvectors, including all eigenvectors) of the channel estimate.

[0148] AI / ML CSI compression model types may include: 1) separate (or dedicated) models, whereby full-channel matrix compression and eigenvector compression employ separate ML models (e.g., dedicated full-channel or dedicated EV); and / or 2) generalized models, whereby full-channel matrix compression and eigenvector compression may use the same ML model.

[0149] The maximum channel rank R supported at the AE model input m .

[0150] Type of stacking or formatting at the input of the autoencoder:

[0151] Zero padding (ZP): The WTRU may be configured to support the full channel H as input (in this case R m =N r ) of a (fixed-size) generalized model; alternatively, the WTRU may be configured with a single model that supports the maximum number of eigenvectors (corresponding to the full rank of the channel). In these examples, if the actual channel rank is less than the maximum channel rank R supported at the model input m , then the data at the ML (e.g., AE) model input may be zero-padded.

[0152] Burst: The WTRU may be configured with a dedicated model for EV compression, with a fixed input size; when the model compresses one feature vector at a time, the WTRU may format the data at the ML (e.g., AE) model input to compress each feature vector separately. For example, when the actual channel rank is 1, the WTRU will compress a single feature vector (associated with the largest eigenvalue); when the actual channel rank is 2, the WTRU will compress 2 feature vectors (e.g., the ML encoder model may be run twice).

[0153] AI / ML model ID, which may include: input size, stacking or formatting information of model input (ZP or burst), output size, other model parameters (e.g., number of layers), and / or training dataset information.

[0154] AI / ML-based CSI compression configuration

[0155] A WTRU capable of AI / ML based CSI compression may be configured to report compressed CSI. The configuration may include:

[0156] AI / ML CSI compression model types: single model (full channel matrix H and / or channel feature vector EV) and / or generalized model

[0157] ●Single User (SU) or Multi User (MU) configuration

[0158] Channel rank threshold

[0159] ● CSI feedback type. The WTRU may be configured to report:

[0160] ○ The compressed channel matrix H, or

[0161] ○ Compressed feature vector

[0162] ●The type of post-processing to be applied at the output of the ML encoder.

[0163] Representative Procedure for Eigenvector (EV)-Based CSI Compression Using Zero Padding

[0164] The WTRU may be configured to generate ML-based CSI feedback reports. In one solution, the AI / ML model may be an autoencoder that is used to compress the channel matrix or principal eigenvector for a single-layer transmission case, or to compress the dominant eigenvector for a multi-layer transmission case. In contrast to the full matrix compression approach, eigenvector-based compression is expected to yield more efficient compression capabilities, especially when the number of layers is less than the channel rank. On the other hand, since the AE model typically handles a fixed input size, handling the case of multiple layers / ranks for the eigenvector approach requires the use of multiple models (one for each rank); this approach significantly increases the computational and memory burden at the WTRU. To overcome this problem, a single autoencoder model may be trained to handle the case of multiple layers / ranks by including a zero padding step before using the model.

[0165] Figure 4 A high level block diagram of the proposed zero padding method is depicted in FIG. As shown in the figure, the CSI-RS is received by the WTRU, and the WTRU performs channel estimation on the CSI-RS. A singular value decomposition (SVD) is performed on the channel estimate. After the SVD, zero padding is performed before the autoencoder process. Then, the compressed CSI feedback information is sent to the network.

[0166] The WTRU may derive a zero-filled feature vector stack based on the maximum number of layers supported by the AE model.

[0167] The WTRU may be configured to derive the nth principal eigenvector of the estimated channel matrix at the nth sub-band, where n=1,…,Nsub. The AE model may support up to a predetermined number of layers R m The eigenvector compression of R m ≤N r The AE model can use the zero-filling method described below to handle values ​​less than or equal to R m any number of layers.

[0168] If the WTRU is configured for R m The WTRU first estimates the channel matrix Then derive R from the matrix H[n] mdominant eigenvector in represents the i-th dominant eigenvector associated with the matrix H[n], where The WTRU may then construct the matrix in The jth dominant eigenvector associated with the nth subband channel matrix is ​​retained in its nth column. The WTRU may then use the trained autoencoder model to compress the input to obtain and transmit back a codeword of size M. If the WTRU is configured for (R m -1) Layer reports CSI feedback, the WTRU may then construct The number of zeros filled is the dimension N t × Nsub, to compensate for R m layer information, and Similarly, if the WTRU is configured to report CSI feedback for a single layer, it is expected that the WTRU will input the model Build as The number of zeros filled is the dimension N t ×(R m -1)N sub For example, given N t =8, N r =8 and R m =4, the WTRU constructs the following inputs for all possible ranks:

[0169] ●For single-layer transmission,

[0170] ●For double-layer sending,

[0171] ●For Layer 3 transmission,

[0172] ●For Layer 4 transmission,

[0173] The same AE model can be trained to output codewords whose size depends on the sparsity of the input. m The model can be trained to output the maximum codeword size by providing feedback from layers (bottleneck layer size), while for the single-layer case, only The first M1 in is non-zero and needs to be transmitted back to the gNB. Similarly, for rank 2 transmission and rank 3 transmission, the autoencoder produces outputs of size M2 and M3 respectively, where Figure 5 The input to the same AE model in a single layer transmission and a dual layer transmission is shown.

[0174] The WTRU may determine whether to use full channel compression or special channel compression based on the estimated rank, the amount of computational resources, and / or the UCI allocation. Eigenvector compression

[0175] In an embodiment, the WTRU may be configured to select between compressing and reporting the full channel matrix or compressing and reporting the associated eigenvectors based on the estimated rank. Although the eigenvector compression method can potentially achieve higher compression rates than full matrix compression, it requires performing singular value decomposition (SVD) on each channel matrix across all subbands, which is computationally demanding relative to the full matrix method.

[0176] In an embodiment, the WTRU may determine which method to use (i.e., whether to compress and report the full channel matrix or the channel eigenvectors) based on any one or more of the following: (1) available computing resources (number of CPUs N r ); (2) the number of allocated bits for CSI reporting (i.e., UCI) on PUCCH or PUSCH; or (3) the preconfigured number of layers (i.e., rank) for CSI reporting. For example, if R m is chosen to be equal to N r , then both the feature vector compression method using zero padding and the full channel compression method can use the same AE model because the input samples associated with these two methods have the same dimension N t ×N r N sub . The WTRU may select between full channel compression or eigenvector compression based on the estimated rank. For example, if the estimated rank R is less than or equal to a pre-configured threshold, eigenvector compression may be selected because eigenvector compression potentially achieves higher compression performance compared to the full channel matrix case. The WTRU may be configured to select between compressing the full channel matrix or compressing the eigenvectors based on a pre-configured threshold or estimated rank indicator. The decision whether to compress the full channel matrix or the eigenvectors alternatively or additionally may be based on the available computational resources. For example, if the amount of computational resources is limited or below a certain threshold, it may be beneficial to use the full channel matrix approach because no SVD step is required, regardless of the estimated rank (or possibly in combination with consideration of the estimated rank).

[0177] Alternatively, instead of using one generalized model that can handle both channel samples and zero-filled eigenvector samples, the WTRU may choose between two AE models: one optimized for full channel based compression and the other optimized for eigenvector compression, such as Figure 6 Depicted.

[0178] Figure 6is a flow chart illustrating an exemplary embodiment of a process for compressing CSI. At step 601, the channel matrix H is measured. At step 603, the WTRU determines whether it is configured to compress the full channel matrix or the eigenvectors. For example, the WTRU may be pre-configured by the gNB to: (1) compress the full channel matrix; (2) compress the eigenvectors; or (3) select between compressing the full channel matrix or the eigenvectors based on certain conditions (such as estimated rank).

[0179] If the WTRU is configured to compress the full channel matrix, the flow will proceed from step 603 to step 615. As noted above, the WTRU may be configured with two AI / ML models for compression, namely, a generalized model (Model #1) that may be well suited for compressing both the channel eigenvectors and the full channel matrix, and a separate model (Model #2) that is best suited for compressing the full channel matrix. Therefore, in step 615, the WTRU will determine which model to use for compressing the full channel matrix. In particular, if the WTRU is provided with a separate model for full channel matrix compression, the WTRU will use that model (Model #2) in step 617. Otherwise, the WTRU will use the generalized model (Model #1) in step 613.

[0180] On the other hand, if in step 603, the WTRU is configured to compress the eigenvectors (or at least consider compressing the eigenvectors based on conditions such as the estimated channel rank), the flow proceeds from step 603 to step 605 instead. In step 605, the WTRU checks whether it is configured with a rank threshold to be considered when deciding whether to compress the eigenvectors or the full channel matrix. If so, the flow proceeds from step 605 to step 607 to make that determination. If the estimated rank indicator is above the pre-configured threshold, the flow proceeds from step 607 to step 615 to perform model selection as previously discussed. On the other hand, if the estimated rank is not above the threshold, the flow proceeds from step 607 to step 609, where the WTRU continues to use the SVD process to obtain the eigenvectors for all subbands. Next, at step 611, the WTRU builds a stack of eigenvectors. Then, at step 613, the WTRU compresses the eigenvectors using model #1.

[0181] The WTRU may measure the signal quality of the MU-MIMO system based on reference signal measurements (e.g., RSSI, RSRQ, RSRP, SINR) or based on MU-MIMO configuration to recommend whether to use the channel matrix compression method or the eigenvector compression method

[0182] Alternatively or additionally, the WTRU may be configured to use a channel matrix compression method or an eigenvector compression method based on reference signal measurements. SVD-based precoding is not optimal in the case of MU and may be more suitable for compressing the full channel matrices from different WTRUs in the case of MU-MIMO. Therefore, in the case of MU-MIMO, the WTRU may be configured to compress the full channel matrix and feed it back to the gNB so that the gNB can design the precoder based on the full channel matrix information from all co-scheduled WTRUs. However, in the case of MU-MIMO (such as Figure 7 ), there is still a possibility that SVD precoding may produce acceptable performance (e.g., for the case of inter-cell interference when co-scheduled WTRUs are far away from each other). The WTRU may use some reference signal measurements to suggest whether compressing the eigenvectors or channel matrices is beneficial. For example, the WTRU may use a signal to interference plus noise ratio (SINR) measurement and compare it to a pre-configured threshold.

[0183] Figure 8 is a flow chart of an example embodiment in which a WTRU determines whether to use full channel compression or eigenvector compression based on whether the WTRU is configured for MU-MIMO.

[0184] As shown, at 801, a channel matrix H is obtained via measurement. Then, at 803, the WTRU determines whether it is configured for MU-MIMO.

[0185] If the WTRU is configured for MU-MIMO, it will compress the full channel matrix; and if the WTRU is not configured for MU-MIMO, it will compress the eigenvectors. Therefore, if the WTRU is not configured for MU-MIMO, the flow proceeds from step 803 to step 805, where the WTRU continues to use the SVD process to obtain the eigenvectors for all sub-bands. Then, at step 807, the WTRU builds a stack of eigenvectors and at 809, inputs the eigenvectors to the autoencoder model #1.

[0186] On the other hand, at 803, if the WTRU determines that it is configured for MU-MIMO, then the flow instead proceeds to step 811 where the WTRU checks if it is configured with a separate model (Model #2) for MU-MIMO. If configured with the separate model, then the flow proceeds from step 811 to 813 to use Model #2. If not configured with the separate model, then the flow proceeds from step 811 to step 809 to use generalized Model #1.

[0187] If a generalized model is used, flow proceeds from 1213 to step 1209 where the channel bursts are input to a generalized autoencoder (which is trained using all eigenvector bursts and H-bursts).

[0188] On the other hand, if a separate model is used, flow instead proceeds from step 1213 to step 1215, where the channel bursts are input to a separate autoencoder (which is trained using only H bursts).

[0189] The WTRU may transmit a channel matrix containing the compressed eigenvectors and filled with zeros or the dimensions of the compressed channel matrix CSI Report

[0190] The WTRU may be configured to report either the compressed channel matrix or the compressed eigenvectors as part of the CSI report. The WTRU may also be configured to report a recommendation between the two approaches in case of multi-user or single-user or single-layer or multi-layer or any combination thereof (the gNB may choose to follow or not follow the recommendation for future CSI feedback reports). In case of eigenvector compression using zero padding, the WTRU may be configured to explicitly indicate the number of padded zeros for meaningful reconstruction at the gNB side. In another option, the number of padded zeros may be implicitly known from the rank indicator and the maximum number of layers supported by the AE model.

[0191] Representative Procedure for Eigenvector (EV)-Based CSI Compression Using Bursts

[0192] The WTRU may be configured with AI / ML based bursts where the autoencoder may compress the channel matrix burst or eigenvector burst for a single layer transmission case or compress the dominant eigenvector for a multi-layer transmission case. In this context, an EV / H burst is defined as EV / H to a fixed size block (i.e., N t ×N sub When processing multiple layers / ranks for both channel burst methods and feature vector burst methods, fixing the size of the input burst to the AE allows the AE model to process fixed input sizes, such as Fig. 9 The schematic block diagram is shown in the figure.

[0193] The WTRU may perform measurements on the acquired channel and then use these measurements to select between using channel matrix bursts or eigenvector bursts (i.e., based on the estimated rank from these measurements) as input to the autoencoder. Alternatively or additionally, the WTRU may determine the input type based on any one or more of the following: (1) available computational resources; (2) the number of allocated bits for CSI reporting on PUCCH or PUSCH; and (3) the pre-configured number of layers for CSI reporting. For example, the WTRU may be configured to check the rank of the channel and based on a particular threshold, may select the channel matrix or eigenvector. Additionally, the WTRU may be configured to select between using channel matrix bursts or eigenvector bursts in a multi-user scenario.

[0194] In one embodiment, the WTRU may be configured with a channel matrix burst as follows. The WTRU may be configured with a channel matrix burst where N sub channels are input to the burst block. All N sub The nth sub-band r The row vectors are mapped into a single block to construct a vector of size (N t ×N sub For all N channel matrices in all sub-bands r Repeat the same operation for each row vector, thus generating a vector of size (N t ×N sub ) of the total N r For example, for some N sub channels, It is expressed as:

[0195]

[0196] in It is nth sub The nth channel r row vectors, totaling N r A burst can be converted into:

[0197]

[0198] All N r bursts are fed into the autoencoder to generate N r The WTRU may be configured to input each channel matrix burst into a parallel autoencoder.

[0199] In one embodiment, the WTRU may be configured with EV bursts as follows. When the WTRU is configured with eigenvector bursts, for all N sub SVD decomposition is performed on N channels. sub eigenvectors (i.e., the main eigenvector for the single-layer transmission case, or the dominant eigenvector for the multi-layer transmission case) are fed into a burst block, where all N sub The nth sub-band t The column vectors are mapped into a single block to construct a vector of size (N t ×N sub ) block. For all R m Repeat the same operation for row vectors, where R m represents the maximum rank defined by the WTRU (e.g., R m =N r , Thus, the total R m A sudden incident.

[0200] For example, N sub The EV of the channel in a sub-band can be expressed as:

[0201]

[0202] in It is nth sub The rth sub-band m feature vectors, and a total of N r An EV burst can be converted into:

[0203]

[0204] All R m bursts are fed into an autoencoder to generate R m The WTRU may be configured to input each feature vector burst into a parallel autoencoder.

[0205] In one embodiment, the WTRU may be configured with an eigenvector burst based on a specific rank of R as follows.

[0206] For example, the WTRU is configured to use a specific R (i.e., sub After performing SVD decomposition on each channel, the feature vector burst is performed. The output N sub feature vectors are fed into the burst block, where for all N sub The first R column vectors of the sub-band feature vectors are mapped to R blocks, each of which has a size of (N t ×Ns x b ).

[0207] In the example, for R=1, the first eigenvectors of all subbands are mapped to a single eigenvector of size (N t ×N sub ) of EV1, such as Fig.10 Here, a burst of feature vectors is input to the autoencoder, which outputs a single compressed burst out1 of size M.

[0208] For R = 2, the first eigenvectors in all subbands are mapped to a t ×N sub ), then the second eigenvectors in all subbands are mapped to a single EV1 of size (N t ×N sub ) of another EV2, such as Fig.10 Here, two feature vector bursts are input to the autoencoder, which outputs two compressed bursts of size M, out1 and out2.

[0209] For R=3, the first eigenvectors in all subbands are mapped to a t ×N sub ), the second eigenvectors in all subbands are mapped to a single EV1 of size (N t ×N sub ) and the third eigenvectors in all subbands are mapped to another block EV2 of size (N t ×N sub ) of another EV3, such as Fig.10 Here, all feature vector bursts are input to the autoencoder, which outputs three compressed bursts of size M, out1, out2, and out3.

[0210] All R m bursts are fed into an autoencoder to generate R m A compressed burst of size M is transmitted as part of the CSI feedback.

[0211] The WTRU may be configured to input each of the R feature vector bursts into a parallel autoencoder.

[0212] In another embodiment, the WTRU may choose between using eigenvector bursting or channel bursting based on some measurement of the channel. For example, the WTRU determines the rank of the channel matrix and based on that rank, chooses between using eigenvector compression or full channel compression. For example, when the WTRU measures a low rank channel, it may choose to use eigenvector bursting (e.g., in the case where R=1, the WTRU uses the primary eigenvector). However, when the WTRU measures a high rank, the WTRU may choose to use the full channel matrix. For example, when the channel matrix is ​​full rank (R=N r ), the WTRU may choose to compress the channel matrix since the output of the autoencoder will be the same in both cases. In addition, the WTRU may determine whether the channel is low rank or high rank based on a certain threshold.

[0213] The WTRU may be configured to compress channel matrix bursts or eigenvector bursts. In addition, the WTRU may be configured to consider compressing eigenvector bursts or always compressing channel matrix bursts. In this case, assuming that the WTRU is configured to consider the use of eigenvectors, the WTRU may be configured to consider whether to use a threshold.

[0214] The detailed process flow chart is as follows Fig.11 As shown in the figure, at 1101, the channel matrix H is obtained through measurement. Then, at 1103, given N sub For a set of channels, the WTRU decides whether to burst the channel matrix or the eigenvectors in all sub-bands. For example, the WTRU may be pre-configured by the gNB to: (1) compress the full channel matrix; (2) compress the eigenvectors; or (3) choose between compressing the full channel matrix or the eigenvectors based on some condition (such as estimated rank).

[0215] If the WTRU determines that it is configured to report eigenvectors, the flow proceeds to step 1105. At step 1105, the WTRU determines whether it is further configured with an estimated rank threshold to be considered before deciding to report an eigenvector. If the WTRU determines that it is not configured with such a threshold, the flow proceeds directly to step 1109, where the WTRU continues to use the SVD process to obtain eigenvectors for all sub-bands. Then, at step 1111, the WTRU creates an eigenvector burst and, at 1113, inputs the eigenvector to the autoencoder.

[0216] On the other hand, at 1105, if the WTRU determines that it is configured with an estimated rank threshold, the process proceeds from 1105 to 1107, where the WTRU compares the estimated rank of the channel to the threshold. The threshold may be predefined or configured by the gNB. If the rank is greater than the threshold, then at step 1115, the process continues to leave the EV branch of the process and enter the full channel branch of the process, which is further described below. On the other hand, at step 1107, if it is determined that rank < threshold, the process instead proceeds from step 1107 to steps 1109, 1111, and 1113, as described above, i.e., eigenvectors for all subbands are obtained (step 1109), eigenvector bursts are then created (step 1111), and they are fed into the autoencoder (step 1113).

[0217] Returning to step 1103, if the WTRU initially determines that it is configured to burst the full channel matrix instead of EV (H), then the flow proceeds from step 1103 to step 115, where the WTRU generates N r bursts (these bursts include the channel matrix information). Next, at step 1117, the WTRU checks which model to use based on the specific configuration. If the WTRU chooses the generalized model, the channel bursts are input to the generalized autoencoder (which is trained using all eigenvector bursts and H-bursts) as shown at 1113. On the other hand, if the WTRU chooses to use the individual model, the flow instead proceeds from step 1117 to step 1119, where the channel bursts are input to the individual autoencoder (which is trained using only H-bursts).

[0218] In one embodiment, AI / ML based CSI feedback may be used to enable MU-MIMO transmissions. The selection of CSI feedback for a WTRU depends on the feedback from all simultaneously scheduled devices. Therefore, in order to infer the appropriate type of CSI feedback in a MU-MIMO scenario, the network needs to have a full understanding of the channels experienced by all scheduled devices. For example, if the scheduled devices experience high correlation, the network may configure the WTRU to report H bursts. However, if the scheduled devices experience independent channels, the WTRU may be configured to transmit eigenvector bursts. It should be noted that the cost of transmitting a channel matrix instead of an eigenvector is higher signaling overhead.

[0219] Fig.12 is a flow chart illustrating channel condition reporting for MU-MIMO transmission according to an exemplary embodiment. The WTRU may be configured to use H-bursts or Eigenvector bursts.

[0220] As shown, at 1201, a channel matrix H is obtained via measurement. Then, at 1203, the WTRU determines whether it is configured to compress the channel matrix H or eigenvectors EV for MU-MIMO to send to the network.

[0221] If the WTRU is configured to compress and send feature vectors, the flow proceeds from step 1203 to step 1205 where the WTRU continues to use the SVD process to obtain feature vectors for all sub-bands. Then, at step 1207, the WTRU creates a feature vector burst and at 1209, the feature vector is input to the auto-encoder.

[0222] On the other hand, if the WTRU determines to compress and transmit the channel matrix at step 1203, the WTRU generates N r A burst (including channel matrix information) is then checked at step 1213 which model to use based on the specific configuration.

[0223] If a generalized model is used, flow proceeds from 1213 to step 1209 where the channel bursts are input to a generalized autoencoder (which is trained using all eigenvector bursts and H-bursts).

[0224] On the other hand, if a separate model is used, flow instead proceeds from step 1213 to step 1215, where the channel bursts are input to a separate autoencoder (which is trained using only H bursts).

[0225] Representative Procedure for Post-Processing Compressed CSI

[0226] In certain embodiments, the WTRU may post-process the compressed eigenvector / H stack / burst at the output of the AE. The WTRU may perform measurements on the compressed H / eigenvector output in the latent domain to further reduce the dimensionality of the feedback.

[0227] For example, the WTRU may be configured with one or more post-processing types that are applied in potential domains, such as Fig.13 Each post-processing type may be defined using an index. For each post-processing type, a WTRU configuration may be associated with a set of parameters, some of which may be used for one or more post-processing types. Fig.13 As shown, if there is post-processing at the WTRU, corresponding de-processing will need to be performed on the network side.

[0228] Post-processing (ie, H / eigenvector zero padding or bursting) may be performed per input type, and parameters may be configured per post-processing type. Additionally, the WTRU may be configured with updates to one or more parameters based on the post-processing type.

[0229] The WTRU may be configured with a set of parameters for use with one or more post-processing types, wherein the set of parameters may include at least one of the following:

[0230] Compressed channel / eigenvector (e.g., a single output of an autoencoder) correlation threshold. This parameter may be associated with the type of post-processing using latent domain post-processing. The WTRU may use the compressed channel / eigenvector correlation of the same output to determine the correlation.

[0231] Compressed channel / eigenvector burst correlation threshold (eg, correlation between multiple output compression outputs). The WTRU may compare the compressed channel / eigenvector burst correlation value to a certain threshold.

[0232] Compressed channel / eigenvector burst correlation threshold in the time domain (e.g., correlation between multiple output compression outputs). The WTRU may compare the compressed channel / eigenvector zero-fill or burst correlation value with a specific threshold in the time domain (compressed eigenvector / H outputs in different time slots).

[0233] Quantizer information for adaptive quantization. This parameter may be associated with the type of post-processing that uses the sparsity information of the input zero-padded feature vectors. Depending on the sparsity level of the input zero-padded feature vectors, the WTRU may switch between different quantizers. For example, a low-resolution quantizer may be used with a stack of high-sparse input feature vectors, while a high-resolution quantizer may be used with a stack of low-sparse input feature vectors.

[0234] Adaptive Quantization :

[0235] The WTRU may be configured with adaptive quantization at the output based on the eigenvector / H input sparsity, such as Fig.14 In one solution, the WTRU may perform measurements on the input to the autoencoder, for example, checking the sparsity of the input in the case of using zero-padded feature vectors / H, to determine which quantizer to use. For example, the WTRU may support multiple quantizers (e.g., Fig.14 , Q#1, ..., Q#K in , where each quantizer is applicable to a specific set of measurements. For example, when the input to the autoencoder is highly sparse (e.g., a large number of zeros), then a low-resolution quantizer may be used, but when the input to the autoencoder is less sparse (e.g., a high-rank zero-filled feature vector stack), then the WTRU may select another quantizer with a higher resolution. The WTRU may define each quantizer using an index that may be reported to the network to apply an equivalent dequantization procedure.

[0236] Single output combination :

[0237] In an embodiment, the WTRU may be configured to perform post-processing on the single feature vector / H compressed output. Here, the WTRU may perform measurements on the output of the autoencoder in the latent domain to reduce the dimensionality of the feedback. For example, the WTRU checks the correlation between the coefficients of the compressed information, and based on a certain threshold of the correlation, the post-processor may average the adjacent coefficients (e.g., before quantization). For example, for a given output L of size M:

[0238] L=[l1 l2 … l M ],

[0239] The WTRU may check N neighboring coefficients (ie, l m ,l m+1 ,…,l m+N ), then if the correlation level exceeds a certain threshold, the WTRU may combine (e.g., average) the M neighbors. Therefore, the size of the output of the post-processor will be

[0240] Multiple output combinations :

[0241] The WTRU may be configured to perform post-processing on the multiple feature vectors / H compressed outputs. Here, the WTRU may perform post-processing on the R of the autoencoder in the latent domain. m The output is measured to reduce the dimensionality of the feedback.

[0242] If the burst method is used, then N sub Each of the channels can be obtained at the output of the autoencoder R m (For example, R m =R or R m =N r ) feature vector compressed bursts or channel response H compressed bursts, each burst size is M. The WTRU may perform measurements on the outputs to identify similarities between multiple outputs. For example, for a given output of the encoder The size of the matrix L can be defined as R m ×M:

[0243]

[0244] Where L includes all output In this case, the WTRU may check N1 horizontally neighboring coefficients (ie, ), and the correlation between the N2 vertical adjacent coefficients (i.e., If the correlation level exceeds certain thresholds in the horizontal and vertical directions, the WTRU may select the M horizontal neighbors and / or R m vertical neighbors to be combined (e.g., averaged). Therefore, the size of the output of the postprocessor will be Therefore, the following compression matrix is ​​obtained

[0245]

[0246] Additionally, the WTRU may receive indications to use post-processing within set time periods and / or across groups of frequency sub-bands.

[0247] If the WTRU supports time domain post-processing in the latent domain, the WTRU may perform measurements on the output of the autoencoder over a specific time period to reduce the dimensionality of the output spanning that time period.

[0248] In one example, the WTRU may perform a calculation for the compressed N T The WTRU may perform measurements on the outputs (e.g., correlations between different outputs), and based on a certain threshold, the WTRU may, for example, average the outputs into a single output.

[0249] Representative procedure for reporting compressed CSI

[0250] The WTRU may be configured to report compressed CSI based on the estimated rank and / or the calculated number of resources and / or uplink feedback allocation, using a CSI compression type (or model or model type) including full channel compression (H) or eigenvector compression (EV) or a combination of the two (e.g., multi-resolution EV / H).

[0251] In one embodiment, the WTRU may report compressed CSI using a combined report (e.g., a multi-resolution EV / H report) that includes a combination of full channel-based compression and EV-based compression. For example, the WTRU may report compressed CSI using full channel compression (H) for one or more wideband CSI reports, and may report compressed CSI using eigenvector (EV) compression for one or more sub-band CSI reports. For another example, the WTRU may report compressed CSI using full channel compression (H) for some sub-bands (e.g., when the rank exceeds a configured threshold), and may report compressed CSI using EV compression for other sub-bands of the allocated bandwidth. In some cases where EV compression is used, methods may include, but are not limited to, using zero padding and / or bursting.

[0252] The feedback and reporting procedures are applicable to any AI / ML model solutions (including AE methods) and post-processing solutions (including dimensionality reduction methods) for different compressed CSI model types.

[0253] The AI / ML model may be configured by the network, predefined, or implemented based on the WTRU. In some solutions, the WTRU may be configured to report AI / ML model details, such as neural network architecture and hyperparameters. In other solutions, the AI / ML model details may be derived implicitly based on certain WTRU behavior.

[0254] The configuration of the CSI compression model for compressed CSI reporting may be based on an indication from the gNB (e.g., explicitly through RRC, MAC-CE, or PUCCH / DCI). When certain performance measurements on the downlink are observed, the WTRU may autonomously indicate a new or modified compression model type to the gNB, either explicitly through uplink signaling or implicitly through some selection of UL resources.

[0255] The following two implementations may be considered for the WTRU to determine or update the CSI compression model type used for compressed CSI reporting: namely, (i) semi-static operation, where the WTRU determines and reports the model type to the gNB based on certain channel measurements (e.g., using EV for low-rank channels), and the gNB then configures the AI / ML model for EV; and (ii) dynamic operation, where the WTRU determines the model and applies it to the compressed CSI feedback, and the gNB then determines the model used through blind detection or specific header indications from the report.

[0256] Compressed CSI may be explicitly reported via PUCCH or PUSCH based on the configured time domain behavior (aperiodic or periodic / semi-persistent) and other options. In some embodiments, for example, for EVs with low rank, compressed CSI may also be implicitly reported via a certain selection of UL resources (RACH, PUCCH, PUSCH, SRS, SpatialRelationInfo, etc.).

[0257] The selection between H, EV, or EV / H may affect the reporting of other CSI quantities and / or any other measurement quantities measured by the WTRU from CSI-RS or SSB.

[0258] In some embodiments, rank may be implicitly extracted from reports of compressed CSI, including implicitly extracting rank from full channel compression (H), implicitly extracting rank from EV bursts, or implicitly extracting rank from post-processed feedback information used for decompression, all of which may reduce the need to report RI.

[0259] The CSI processing criteria may affect the reporting of compressed CSI.In some solutions, the WTRU may decide between H, EV or EV / H based on the number of CPUs and CSI calculation requests.

[0260] The WTRU may be configured to report compressed CSI based on the estimated rank and / or the amount of computational resources and / or uplink feedback allocation, whether full channel compression (H) or eigenvector compression (EV) or a combination of both (multi-resolution EV / H).

[0261] The configuration of compressed CSI reporting may be based on previous indications from the WTRU either explicitly through uplink signaling or implicitly through a specific selection of UL resources.

[0262] Compressed CSI may be explicitly reported via PUCCH or PUSCH based on the configured time domain behavior (aperiodic or periodic / semi-persistent). The AI / ML model may be configured by the network, predefined, or based on WTRU specific implementation. In some embodiments, the WTRU may be configured to report AI / ML model specific details, such as neural network architecture and hyperparameters. In other embodiments, the AI / ML model specific details may be derived implicitly, for example, via the ZP method or bursts.

[0263] Fig.15 is a block diagram illustrating an example of a CSI feedback procedure between a WTRU and a network (e.g., a gNB) using selection and indication of a CSI compression type in accordance with one or more of the above-described embodiments. In this example, the WTRU is capable of full channel CSI compression and eigenvector-based CSI compression, and is capable of selecting one or more CSI compression types. For example, the WTRU performs a channel measurement and determines a rank associated with the measured channel. If the determined rank is less than a configured channel rank threshold, the WTRU selects EV-based compression, which may reduce CSI feedback overhead. Otherwise, the WTRU selects full channel compression. The WTRU may report compressed CSI and / or the selected CSI compression type to the network (e.g., a gNB).

[0264] Fig.16 An example of a CSI feedback procedure using a selected CSI compression type is illustrated. In this example, a WTRU that is enabled for eigenvector (EV) CSI compression is able to select a CSI compression type (full channel based or EV based). The WTRU is configured with a channel rank threshold for CSI compression type selection. For example, the WTRU may receive configuration information indicating a channel rank threshold from a network entity. The WTRU may determine a channel rank associated with a channel measurement. For example, the WTRU may measure a downlink channel and determine a rank associated with the measured channel.

[0265] In an example, the WTRU may select the CSI compression type based on the determined channel rank and the channel rank threshold. In another example, the WTRU may select the CSI compression type based on any of: estimated rank, number of computational resources, and / or uplink feedback allocation.

[0266] In some aspects, the selected CSI compression type includes full channel based compression, eigenvector (EV) based compression, or 3) a combination of full channel based compression and eigenvector (EV) based compression.

[0267] In one example, when the determined channel rank is less than a channel rank threshold, the WTRU may select EV-based compression. In another example, when the determined channel rank is equal to or greater than the channel rank threshold, the WTRU may select full channel-based compression.

[0268] The WTRU may send CSI associated with the channel measurement and compressed using the selected CSI compression type and information indicating the selected CSI compression type to a network entity.

[0269] In some embodiments, the configuration information includes an indication to enable selection of the CSI compression type from a set of CSI compression types, wherein the set of CSI compression types includes full channel-based compression and EV-based compression.

[0270] In some embodiments, the full channel based compression includes compressing the full channel matrix. In an example, the full channel matrix is ​​an estimated channel matrix.

[0271] In some embodiments, the EV-based compression includes compressing one or more channel eigenvectors. In an example, each of the one or more channel eigenvectors is a respective eigenvector of a channel estimate.

[0272] In some embodiments, the WTRU is configured to perform CSI compression using a selected CSI compression type: full channel-based compression, EV-based compression, or a combination of full channel-based compression and EV-based compression.

[0273] In some embodiments, when performing CSI compression, the WTRU may compress the CSI via an artificial intelligence / machine learning (AI / ML) model.

[0274] Representative Procedure for Compressed CSI Reporting for MU-MIMO

[0275] Network side information used as input to AI / ML models for CSI compression

[0276] In an embodiment, the WTRU may be configured with one or more operating modes for compressed CSI reporting, where the compressed CSI may be the output of an AI / ML model (e.g., an autoencoder), and the WTRU may report the output of the AI / ML model as a CSI report in the determined uplink.

[0277] The operating mode may be determined or identified based on whether the input to the AI / ML model for CSI compression includes auxiliary information from the gNB. For example, in a first operating mode, the input to the AI / ML model for CSI compression may be based on measurements of reference signals (e.g., channel matrix: H; eigenvector: EV); and in a second operating mode, the input to the AI / ML model for CSI compression may be based on measurements of reference signals plus auxiliary information provided by the gNB, wherein the auxiliary information may be at least one of the following: 1) Channel information of another WTRU that may be scheduled in the same time / frequency resources as the WTRU (e.g., channel matrix, eigenvector, beam direction, position, PMI). For example, the WTRU may be provided with MIMO transmission scheme information (e.g., MU-MIMO transmission scheme) that may be used at the gNB. The MIMO transmission scheme information may determine the AI / ML model for CSI compression. 2) Channel information of the interfering WTRU. 3) WTRU location information of the interfering WTRU. 4) Scheduling mode (e.g., SU-MIMO, MU-MIMO). 5) MU-MIMO transmission schemes (e.g., ZF-BF, non-linear pre-decoder); and / or 6) AI / ML models for CSI compression.

[0278] The operating mode can be determined or identified based on the CSI type input to the AI / ML model for CSI compression, where the CSI type can be at least one of: channel information (e.g., channel matrix, eigenvector, PMI), beam information (e.g., beam direction, beam index), channel quality information (e.g., CQI, RSRP, reference signal received quality (RSRQ), RI, etc.) and / or channel information format (e.g., zero-padding-based eigenvector, burst-based eigenvector, number of subbands, subband size).

[0279] The WTRU may indicate or report its ability to support one or more operating modes for CSI compression using an AI / ML model.

[0280] In the following, the term "auxiliary information" may be used interchangeably with "additional information," "channel information of interfering WTRU," "co-channel information," "co-channel information for MU-MIMO," "interfering channel information," and "interfering beam information."

[0281] One or more operating modes for CSI compression may be used, and the WTRU may determine the operating mode based on one or more of the following.

[0282] Availability of additional information provided (or indicated) by the gNB. For example, if the additional information is available as input for CSI compression, then the first mode of operation may be used (e.g., the input to the AI / ML model includes the additional information). Otherwise, the second mode of operation may be used (e.g., the input to the AI / ML model is based only on measurements at the WTRU side).

[0283] A validity timer or validity time window for the additional information may be used. For example, the additional information provided by the gNB may be valid for a time period starting from the receipt of the additional information. For example, if the WTRU receives additional information from the gNB at time slot #n, the additional information may be valid until time slot #n+K. Starting from time slot #n+K+1, the WTRU may consider the additional information invalid (i.e., unavailable or not applicable). The value K may be determined based on one or more of: configuration from the gNB, WTRU mobility (e.g., WTRU speed), subcarrier spacing, and / or accuracy of the AI / ML model. The additional information may be provided by the gNB as part of the CSI report configuration via higher layer signaling (e.g., RRC, MAC-CE). Alternatively, the additional information may be provided by the gNB as part of the aperiodic CSI report trigger information.

[0284] CSI reporting resources. For example, one or more CSI reporting resources may be configured and the CSI reporting resources may be determined based on at least one of the CSI reporting timing, an indication in a trigger signal, and one or more pre-configured conditions. The WTRU may determine an operating mode based on the determined CSI reporting resources.

[0285] Network side information for processing input to AI / ML models for CSI compression

[0286] In an embodiment, the WTRU may determine, estimate and / or process inputs to an AI / ML model for CSI compression using auxiliary information provided by the gNB. For example, when auxiliary information is not available / applicable, the WTRU may determine a subset (or rank) of eigenvectors as inputs to the AI / ML model for CSI compression based on the order of maximum eigenvalues ​​(e.g., determining the eigenvector with the largest eigenvalue when a single eigenvector is reported, and determining two eigenvectors with the first largest eigenvalue and the second largest eigenvalue when two eigenvectors are reported). On the other hand, when auxiliary information is available / applicable, the WTRU may determine a subset of eigenvectors as inputs to the AI / ML model for CSI compression, taking into account auxiliary information (e.g., co-channel interference from another WTRU) that may maximize a metric (e.g., system throughput, total capacity, etc.), where the metric may be determined by the WTRU, configured by the gNB, or predetermined.

[0287] The number of eigenvectors in a subset may be determined based on a rank determined by the WTRU, which may be implicitly or explicitly reported with the subset of eigenvectors.

[0288] When the assistance information provided by the gNB is used for CSI reporting and / or CSI compression, the availability / applicability of the assistance information may be determined based on the reception time of the assistance information or the time duration of the received assistance information.

[0289] in conclusion

[0290] Although features and elements are provided above in specific combinations, it will be understood by those of ordinary skill in the art that each feature or element may be used alone or in any combination with other features and elements. The present disclosure is not limited to the specific embodiments described in this patent application, which are intended to be illustrative of various aspects. Many modifications and variations can be made without departing from the essence and scope of the present invention, as they will be apparent to those skilled in the art. Unless explicitly provided as such, any element, action or description used in the present application specification should not be understood as being essential or necessary to the present invention. According to the foregoing description, in addition to those listed herein, functionally equivalent methods and devices within the scope of the present disclosure will be apparent to those skilled in the art. Such modifications and variations are intended to fall within the scope of the appended claims. The present disclosure is limited only to the terms of the appended claims and the full range of equivalent forms of such entitled claims. It should be understood that the present disclosure is not limited to a specific method or system.

[0291] For simplicity, the foregoing embodiments are discussed with reference to the terminology and structure of infrared capable devices (i.e., infrared transmitters and receivers). However, the embodiments discussed are not limited to these systems, but can be applied to other systems using other forms of electromagnetic waves or non-electromagnetic waves (such as sound waves).

[0292] It should also be understood that the terms used herein are for the purpose of describing specific embodiments only and are not intended to be limiting. As used herein, the term "video" or the term "image" may mean any of a snapshot, a single image, and / or a plurality of images displayed on a temporal basis. For example, when referred to herein, the term "user equipment" and its abbreviation "UE", the term "remote" and / or the term "head-mounted display" or its abbreviation "HMD" may mean or include (i) a wireless transmit and / or receive unit (WTRU); (ii) any of a plurality of embodiments of the WTRU; (iii) a wireless and / or wired (e.g., tetherable) device configured with (in particular) some or all of the structure and functionality of the WTRU; (iii) a wireless and / or wired device configured with less than all of the structure and functionality of the WTRU; or (iv), etc. This document is relative to Figures 1A to 1D Details of an example WTRU are provided that may represent any WTRU described herein. As another example, various disclosed embodiments herein above and below are described as utilizing a head mounted display. One skilled in the art will recognize that devices other than head mounted displays may be utilized and that some or all of the embodiments of the present disclosure and various disclosed embodiments may be modified accordingly without undue experimentation. Examples of such other devices may include drones or other devices configured to stream information to provide an adapted reality experience.

[0293] In addition, the methods provided herein may be implemented in a computer program, software, or firmware incorporated into a computer-readable medium for execution by a computer or processor. Examples of computer-readable media include electronic signals (sent via a wired or wireless connection) and computer-readable storage media. Examples of computer-readable storage media include, but are not limited to, read-only memory (ROM), random access memory (RAM), registers, cache memory, semiconductor memory devices, magnetic media (such as built-in hard disks and removable disks), magneto-optical media, and optical media (such as CD-ROM disks and digital versatile disks (DVDs)). A processor associated with software may be used to implement a radio frequency transceiver for a WTRU, UE, terminal, base station, RNC, MME, EPC, AMF, or any host computer.

[0294] Variations of the methods, devices, and systems provided above are possible without departing from the scope of the present invention. In view of the various embodiments that can be applied, it should be understood that the illustrated embodiments are merely examples and should not be considered to limit the scope of the following claims. For example, the embodiments provided herein include handheld devices that may include or be used with any suitable voltage source (such as a battery, etc.) that provides any suitable voltage.

[0295] In addition, in the embodiments provided above, processing platforms, computing systems, controllers and other devices including processors are indicated. These devices may include at least one central processing unit ("CPU") and memory. According to the practice of those skilled in the art of computer programming, references to the symbolic representations of actions and operations or instructions may be performed by various CPUs and memories. Such actions and operations or instructions may be referred to as being "executed", "computer executed" or "CPU executed".

[0296] Those of ordinary skill in the art will appreciate that the actions and symbolic representations of operations or instructions include manipulation of electrical signals by the CPU. The electrical system represents data bits, which may result in the ultimate transformation of electrical signals or reduction of electrical signals and the retention of data bits at memory locations in the memory system, thereby reconfiguring or otherwise changing the operation of the CPU and performing other processing of signals. The memory location that holds the data bits is a physical location that has specific electrical, magnetic, optical, or organic properties that correspond to or represent the data bits. It should be understood that the embodiments are not limited to the above-mentioned platforms or CPUs, and other platforms and CPUs may also support the provided methods.

[0297] The data bits may also be maintained on a computer-readable medium, including magnetic disks, optical disks, and any other volatile (e.g., random access memory (RAM)) or non-volatile (e.g., read-only memory (ROM)) mass storage system readable by the CPU. The computer-readable medium may include cooperating or interconnected computer-readable media that exist solely on a processing system or distributed across multiple interconnected processing systems that may be local or remote relative to the processing system. It should be understood that the embodiments are not limited to the above-mentioned memories, and other platforms and memories may also support the provided methods.

[0298] In an exemplary embodiment, any of the operations, processes, etc. described herein may be implemented as computer-readable instructions stored on a computer-readable medium. The computer-readable instructions may be executed by a processor of a mobile unit, a network element, and / or any other computing device.

[0299] There is little difference between hardware implementation and software implementation of various aspects of the system. The use of hardware or software is usually (but not always, because in some contexts, the choice between hardware and software may become important) a design choice that represents a trade-off between cost and efficiency. There may be various media (e.g., hardware, software, and / or firmware) that can implement the processes and / or systems and / or other technologies described herein, and the preferred media may vary with the context of the deployment process and / or system and / or other technologies. For example, if the implementer determines that speed and accuracy are most important, the implementer may choose a media that is primarily hardware and / or firmware. If flexibility is most important, the implementer may choose a specific implementation that is primarily software. Alternatively, the implementer may choose some combination of hardware, software, and / or firmware.

[0300] The above specific embodiments have been listed various embodiments of devices and / or processes by using block diagrams, flow charts and / or examples. Where such block diagrams, flow charts and / or examples include one or more functions and / or operations, it will be understood by those skilled in the art that each function and / or operation within such block diagrams, flow charts or examples may be implemented individually and / or collectively by a wide range of hardware, software, firmware or almost any combination thereof. In embodiments, several parts of the subject matter described herein may be implemented via application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), digital signal processors (DSPs) and / or other integrated formats. However, those skilled in the art will recognize that some aspects of the embodiments disclosed herein may be equivalently implemented in integrated circuits as a whole or in part as one or more computer programs running on one or more computers (e.g., one or more programs running on one or more computer systems), one or more programs running on one or more processors (e.g., one or more programs running on one or more microprocessors), firmware or almost any combination thereof, and according to the present disclosure, designing circuits and / or writing software and / or firmware codes will be completely within the technical scope of those skilled in the art. Furthermore, those skilled in the art will appreciate that the mechanisms of the subject matter described herein can be distributed as a program product in a variety of forms, and that the illustrative embodiments of the subject matter described herein apply regardless of the specific type of signal-bearing medium used to actually perform the distribution. Examples of signal-bearing media include, but are not limited to, the following: recordable type media (such as floppy disks, hard drives, CDs, DVDs, digital tapes, computer memory, etc.); and transmission type media (such as digital and / or analog communication media (e.g., fiber optic cables, waveguides, wired communication links, wireless communication links, etc.)).

[0301] Those skilled in the art will recognize that it is common in the art to describe devices and / or processes in the manner set forth herein, and thereafter use engineering practices to integrate such described devices and / or processes into a data processing system. That is, at least a portion of the devices and / or processes described herein can be integrated into a data processing system via a reasonable amount of experimentation. Those skilled in the art will recognize that a typical data processing system generally may include one or more of the following: a system unit housing; a video display device; a memory, such as a volatile memory and a non-volatile memory; a processor, such as a microprocessor and a digital signal processor; a computing entity, such as an operating system, a driver, a graphical user interface, and an application; one or more interactive devices, such as a touch pad or a screen; and / or a control system, including a feedback loop and a control motor (e.g., a feedback for sensing position and / or velocity, a control motor for moving and / or adjusting components and / or quantities). A typical data processing system may be implemented using any suitable commercially available components, such as those typically found in data computing / communication and / or network computing / communication systems.

[0302] The subject matter described herein sometimes illustrates different components included in or connected to different other components. It should be understood that such depicted architectures are merely examples, and in fact many other architectures that achieve the same function can be realized. In a conceptual sense, any arrangement of components that achieve the same function is effectively "associated" so that the desired function can be achieved. Therefore, any two components that are combined to achieve a specific functionality in this article can be considered to be "associated" with each other so that the desired functionality is achieved, regardless of the architecture or intermediate components. Similarly, any two components so associated can also be considered to be "operably connected" or "operably coupled" to each other to achieve the desired functionality, and any two components that can be so associated can also be considered to be "operably couplable" to each other to achieve the desired functionality. Specific examples of operable coupling include, but are not limited to, components that can physically cooperate and / or physically interact and / or components that can wirelessly interact and / or wirelessly interact and / or components that can logically interact and / or components that can logically interact.

[0303] With respect to substantially any plural and / or singular terms used herein, those skilled in the art may translate from the plural to the singular and / or from the singular to the plural as appropriate, as appropriate to the context and / or application. For clarity, various singular / plural arrangements may be explicitly listed herein.

[0304] It will be understood by those skilled in the art that, in general, the terms used herein, and particularly in the appended claims (e.g., the bodies of the appended claims), are generally intended to be "open" terms (e.g., the term "including" should be interpreted as "including but not limited to," the term "having" should be interpreted as "having at least," the term "comprising" should be interpreted as "including but not limited to," etc.). It will also be understood by those skilled in the art that if a specific number of an introduced claim recitation is intended, such intent will be explicitly recited in the claim, and in the absence of such recitation, no such intent is present. For example, where only one item is intended, the term "single" or similar language may be used. To aid understanding, the following appended claims and / or the description herein may include the use of the introductory phrases "at least one" and "one or more" to introduce claim recitations. However, the use of such phrases should not be construed as implying that any particular claim that includes such introduced claim recitation subject matter is limited to embodiments that include only one such recitation subject matter by the indefinite article "a" or "an" to introduce the claim recitation subject matter, even when the same claim includes the introductory phrases "one or more" or "at least one" and an indefinite article such as "a" or "an" (e.g., "a" and / or "an" should be interpreted as meaning "at least one" or "one or more"). The same applies to the use of definite articles to introduce claim recitation subjects. Furthermore, even if a specific number of introduced claim recitation subjects is explicitly recited, one skilled in the art will recognize that such recitation should be interpreted to mean at least the recited number (e.g., the bare recitation of "two recitation subjects" means at least two recitation subjects or two or more recitation subjects in the absence of other modifiers). Additionally, in those instances where a convention similar to “at least one of A, B, and C, etc.” is used, generally speaking, such construction is meant to be understood by one skilled in the art (e.g., “a system having at least one of A, B, and C” would include, but is not limited to, systems having A alone, B alone, C alone, both A and B, both A and C, both B and C, and / or both A, B, and C, etc.). In those instances where a convention similar to “at least one of A, B, or C, etc.” is used, generally speaking, such construction is meant to be understood by one skilled in the art (e.g., “a system having at least one of A, B, or C” would include, but is not limited to, systems having A alone, B alone, C alone, both A and B, both A and C, both B and C, and / or both A, B, and C, etc.). It should also be understood by those skilled in the art that, in fact, any separate words and / or phrases presenting two or more alternative terms, whether in the specification, claims or drawings, should be understood to contemplate the possibility of including one of the terms, any one of the terms, or both of the terms.For example, the phrase "A or B" will be understood to include the possibilities of "A" or "B" or "A and B." Additionally, as used herein, the term "any of" followed by a listing of multiple items and / or multiple categories of items is intended to include "any of," "any combination," "any multiples," and / or "any combination of multiples" of the items and / or categories of items, either alone or in combination with other items and / or other categories of items. Additionally, as used herein, the term "set" is intended to include any number of items, including zero. Additionally, as used herein, the term "quantity" is intended to include any quantity, including zero. And, as used herein, the term "plurality" is intended to be synonymous with "multiple."

[0305] In addition, where features or aspects of the disclosure are described in terms of Markush groups, those skilled in the art will recognize that the disclosure is also described in terms of any individual member or subgroup of members of the Markush group.

[0306] As will be understood by those skilled in the art, for any and all purposes (such as for providing a written description), all ranges disclosed herein also encompass any and all possible sub-ranges and combinations of their sub-ranges. Any listed range can be easily identified as fully describing and enabling the same range to be divided into at least equal halves, one-third, one-quarter, one-fifth, one-tenth, etc. As a non-limiting example, each range discussed herein can be easily divided into a lower third, a middle third, and an upper third, etc. As will be understood by those skilled in the art, all languages ​​such as "up to", "at least", "greater than", and "less than", etc. include the cited numbers and refer to the ranges that can be subsequently divided into sub-ranges as described above. Finally, as will be understood by those skilled in the art, the range includes each individual number. Therefore, for example, a group with 1 to 3 units refers to a group with 1, 2, or 3 units. Similarly, a group with 1 to 5 units refers to a group with 1, 2, 3, 4, or 5 units, etc.

[0307] Furthermore, the claims should not be read as limited to the order or elements provided unless otherwise stated. Furthermore, the use of the term "means for..." in any claim is intended to invoke 35 U.S.C. § 112, 6 or parts-plus-function claim format, and any claim without the term "means for..." is not intended to be so.

[0308] Suitable processors include, by way of example, a general purpose processor, a special purpose processor, a conventional processor, a digital signal processor (DSP), a plurality of microprocessors, one or more microprocessors in association with a DSP core, a controller, a microcontroller, an application specific integrated circuit (ASIC), an application specific standard product (ASSP), a field programmable gate array (FPGA) circuit, any other type of integrated circuit (IC), and / or state machine.

[0309] The WTRU may be used in conjunction with a module, which may be implemented in hardware and / or software including: software defined radio (SDR) and other components such as a camera, a video camera module, a videophone, a speakerphone, a vibration device, a speaker, a microphone, a television transceiver, a hands-free headset, a keyboard, module, a frequency modulation (FM) radio unit, a near field communication (NFC) module, a liquid crystal display (LCD) display unit, an organic light emitting diode (OLED) display unit, a digital music player, a media player, a video game player module, an internet browser, and / or any wireless local area network (WLAN) or ultra-wideband (UWB) module.

[0310] Although various embodiments have been described in terms of a communication system, it is contemplated that the system may be implemented in software on a microprocessor / general purpose computer (not shown). In certain embodiments, one or more of the functions of the various components may be implemented in software controlling a general purpose computer.

[0311] Furthermore, while the invention has been illustrated and described herein with reference to specific embodiments, the invention is not intended to be limited to the details shown. Rather, various modifications may be made to the details within the scope and range of equivalents of the claims without departing from the invention.

Claims

1. A method implemented in a wireless transmit / receive unit (WTRU) for wireless communication, the method comprising: receiving configuration information indicating a channel rank threshold from a network entity; determining a channel rank associated with the channel measurement; selecting a channel state information (CSI) compression type based on 1) the determined channel rank and 2) the channel rank threshold; as well as CSI and information indicating the selected CSI compression type are sent to the network entity, wherein the CSI is associated with the channel measurement and is compressed using the selected CSI compression type.

2. The method of claim 1, wherein the configuration information comprises an indication enabling selection of the CSI compression type from a set of CSI compression types, wherein the set of CSI compression types comprises full channel based compression and feature vector (EV) based compression.

3. The method of claim 2, wherein the full-channel based compression comprises compressing a full-channel matrix. The method of claim 3 , wherein the full channel matrix is ​​an estimated channel matrix.

5. The method of claim 2, wherein the EV-based compression comprises compressing one or more channel feature vectors.

6. The method of claim 5, wherein each of the one or more channel eigenvectors is a corresponding eigenvector of a channel estimate.

7. The method according to claim 1, further comprising: CSI compression is performed using the selected CSI compression type.

8. The method of claim 7, wherein performing the CSI compression comprises compressing the CSI via an artificial intelligence / machine learning (AI / ML) model.

9. The method according to any one of the preceding claims, wherein the selected CSI compression type comprises: 1) full channel based compression, 2) feature vector (EV) based compression or 3) a combination of the full channel based compression and the feature vector (EV) based compression.

10. The method of any preceding claim, wherein selecting the CSI compression type comprises selecting an eigenvector (EV) based compression based on a determined channel rank being less than the channel rank threshold.

11. The method of any preceding claim, wherein selecting the CSI compression type comprises selecting full channel based compression based on the determined channel rank being equal to or greater than the channel rank threshold.

12. The method according to any of the preceding claims, wherein the CSI compression type is selected based on any of: estimation rank, number of computational resources and / or uplink feedback allocation.

13. A wireless transmit / receive unit (WTRU) for wireless communications, the WTRU comprising circuitry, the circuitry comprising a transmitter, a receiver, a processor, and a memory, the WTRU being configured to: receiving configuration information indicating a channel rank threshold from a network entity; determining a channel rank associated with the channel measurement; selecting a channel state information (CSI) compression type based on 1) the determined channel rank and 2) the channel rank threshold; as well as CSI and information indicating the selected CSI compression type are sent to the network entity, wherein the CSI is associated with the channel measurement and is compressed using the selected CSI compression type.

14. The WTRU of claim 13, wherein the configuration information includes an indication to enable selection of the CSI compression type from a set of CSI compression types, wherein the set of CSI compression types includes full channel based compression and feature vector (EV) based compression.

15. The WTRU of claim 14, wherein the full channel based compression comprises compressing a full channel matrix.

16. The WTRU of claim 15, wherein the full channel matrix is ​​an estimated channel matrix.

17. The WTRU of claim 15, wherein the EV-based compression comprises compressing one or more channel eigenvectors.

18. The WTRU of claim 17, wherein each of the one or more channel eigenvectors is a corresponding eigenvector of a channel estimate.

19. The WTRU of any one of claims 13 to 18, wherein the WTRU is further configured to perform CSI compression using the selected CSI compression type.

20. The WTRU of any one of claims 13 to 19, wherein the WTRU is further configured to compress the CSI via an artificial intelligence / machine learning (AI / ML) model when performing the CSI compression.

21. The WTRU of any one of claims 13 to 20, wherein the selected CSI compression type comprises: 1) full channel based compression, 2) feature vector (EV) based compression or 3) a combination of the full channel based compression and the feature vector (EV) based compression.

22. The WTRU of any one of claims 13 to 21, wherein the WTRU is further configured to select feature vector (EV) based compression based on the determined channel rank being less than the channel rank threshold.

23. The WTRU of any one of claims 13 to 22, wherein the WTRU is further configured to select full channel based compression based on the determined channel rank being equal to or greater than the channel rank threshold.

24. The WTRU of any one of claims 13 to 22, wherein the CSI compression type is selected based on any one of: an estimation rank, a number of computational resources, and / or an uplink feedback allocation.

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