Determining inclusion and omission of multiple channel state information prediction instances

Abstract

A WTRU may receive configuration information, for example from a network. The configuration information may be associated with inclusion and / or omission of one or more CSI predictions. The WTRU may determine a plurality of CSI measurements, for example based on CSI reference signals (CSI-RSs). The WTRU may determine a plurality of CSI predictions, for example based on the plurality of CSI measurements. The WTRU may determine a subset of CSI predictions of the plurality of CSI predictions, for example based on the configuration information. The WTRU may send, for example to the network, one or more of an indication of the subset of CSI predictions and / or an inclusion indicator associated with the subset CSI predictions.

Description

DETERMINING INCLUSION AND OMISSION OF MULTIPLE CHANNEL STATEINFORMATION PREDICTION INSTANCESCROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the benefit of United States Provisional Application No. 63 / 627,246 filed on January 31, 2024, the entire contents of which are incorporated herein by reference.BACKGROUND

[0002] Channel state information (CSI) prediction and CSI compression may utilize enablers for artificial intelligence (Al) / machine learning (ML)-based enhanced CSI measurement and reporting. CSI prediction may be performed by a wireless transmit / receive unit (WTRU), which may predict future CSI based on historical measured CSI.SUMMARY

[0003] A WTRU (e.g., capable of CSI prediction) may receive configuration information (e.g., one or more configurations) on inclusion and / or omission of CSI predictions. The WTRU may receive CSI-reference signal (RS), measure the current channel state, predict multiple future CSI instances, determine a subset of CSI prediction instances based on the configurations and a criteria (e.g., the consecutive differences of prediction), determine an inclusion indicator, and / or report the inclusion indicator and / or a subset of multiple CSI predictions.

[0004] A WTRU (e.g., capable of CSI prediction) may receive configuration information (e.g., one or more configurations) on CSI prediction and / or compression model selection. The WTRU may receive CSI-RS, measure the current channel state, predict multiple future CSI instances, determine the CSI compression model based on the configurations and criteria (e.g., based on the rate of change of predicted CSI), jointly compress all CSI predictions using the determined model, and / or report(e.g., jointly) compressed CSI predictions and / or model information.

[0005] A WTRU (e.g., capable of CSI prediction) may receive configuration information (e.g., one or more configurations) on determining a CSI prediction subset. The WTRU may receive CSI-RS, measure the current channel state, predict multiple future CSI instances, determine a subset of the CSI prediction instances to use as input to a configured model, jointly compress the determined subset of CSI predictions using the configured model, and / or report (e.g., jointly) compressed CSI predictions and / or identifiers of CSI prediction instances used in the compression.

[0006] A WTRU may receive configuration information, for example from a network. The configuration information may be associated with one or more CSI predictions. The WTRU may determine one or more CSI predictions, for example based on the configuration information. The WTRU may determine whether to include the one or more CSI predictions in a subset of CSI predictions, for example to be reported. The WTRU may transmit one or more of an indication of the subset of CSI predictions and / or an inclusion indicator associated with one or more CSI predictions, for example to the network.

[0007] The WTRU may determine whether to include the one or more CSI predictions in the subset of CSI predictions based on a value associated with a difference between the one or more determined CSI predictions and one or more previous CSIs. The WTRU may compress one or more values of the subset of CSI predictions, for example to obtain compressed CSI predictions. The WTRU may determine whether to include the one or more CSI predictions in the subset of CSI predictions by determining a difference associated with consecutive compressed CSI predictions. The WTRU may determine whether to include the one or more CSI predictions in the subset of CSI predictions by determining to include and / or omit one or more CSI predictions from the subset of CSI predictions, for example based on a comparison of consecutive CSI predictions.

[0008] The one or more CSI predictions in the subset of CSI predictions may include a minimum value of CSI predictions to be included in the subset of CSI predictions. The WTRU may determine to update one or more of the indication of the subset of CSI predictions and / or the inclusion indicator, for example based on the minimum value of CSI predictions to be included in the subset of CSI predictions. The WTRU may transmit one or more of a first included CSI prediction and / or an indication of a difference of consecutive included CSI predictions, for example to the network. The WTRU may transmit the indication of the subset of CSI predictions and the inclusion indicator (e.g., to the network), for example by transmitting the indication of the subset of CSI predictions after the inclusion indicator.

[0009] A WTRU may receive configuration information, for example from a network. The configuration information may be associated with one or more CSI predictions. The WTRU may determine one or more CSI predictions, for example based on the configuration information. The WTRU may compress one or more values of the one or more CSI predictions, for example to obtain one or more compressed CSI predictions. The WTRU may transmit the one or more compressed CSI predictions, for example to the network.

[0010] The WTRU may determine one or more parameters associated with the one or more CSI predictions. The WTRU may determine a CSI compression model, for example based onthe one or more parameters associated with the one or more CSI predictions. The WTRU may compress the one or more values of the one or more CSI predictions to obtain the one or more compressed CSI predictions by compressing the one or more values of the one or more CSI predictions, for example with the determined CSI compression model. The one or more parameters may include one or more of a rate of change between CSI predictions, an average squared generalized cosine similarity (SGCS) between CSI predictions, and / or a payload size.

[0011] The WTRU may transmit the determined one or more parameters associated with the one or more CSI predictions, for example to the network. The WTRU may determine a CSI compression model based on the one or more parameters after transmitting the determined one or more parameters associated with the one or more CSI predictions, for example to the network. The WTRU may determine a subset of the one or more CSI predictions for input to the determined model. The WTRU may transmit an indication of one or more indices associated with the subset of the one or more CSI predictions, for example to the network. The WTRU may transmit model information to the network.

[0012] A WTRU may receive configuration information, for example from a network. The configuration information may be associated with inclusion and / or omission of one or more CSI predictions. The WTRU may determine a plurality of CSI measurements, for example based on CSI reference signals (CSI-RSs). The WTRU may determine a plurality of CSI predictions, for example based on the plurality of CSI measurements. The WTRU may determine a subset of CSI predictions of the plurality of CSI predictions, for example based on the configuration information. The WTRU may send, for example to the network, one or more of an indication of the subset of CSI predictions and / or an inclusion indicator associated with the subset CSI predictions.

[0013] The configuration information may include an indication of one or more of a CSI prediction model, a number of historical CSI measurements, a threshold used to determine whether a CSI prediction should be included in the subset of CSI predictions, a maximum number of consecutive omissions of CSI predictions from the subset of CSI predictions, and / or a minimum number of inclusions of CSI predictions into the subset of CSI predictions. The CSI prediction of the plurality of CSI predictions may be determined to be part of the subset of CSI predictions determined based on a difference between the CSI prediction and one or more other CSI predictions. For example, the other CSI predictions may include one or more historical CSI predictions and / or one or more of the plurality of CSI predictions. The inclusion indicator may indicate an inclusion of a CSI prediction, for example when the difference is above a threshold.Additionally, or alternatively the inclusion indicator may indicate an omission of the CSI prediction, for example when the difference is below the threshold.

[0014] The WTRU may compress one or more values of the subset of CSI predictions, for example to obtain compressed CSI predictions. The indication of the subset of CSI predictions may include the compressed CSI predictions. The WTRU may compress each of the plurality of CSI predictions and / or determine a difference associated with consecutive compressed CSI predictions of the plurality of compressed CSI predictions, for example to determine the subset of CSI predictions.

[0015] The WTRU may receive, for example from the network, an uplink grant indicating resources for sending the indication of the subset of CSI predictions in response to the inclusion indicator associated with the subset of CSI predictions. The configuration information associated with inclusion or omission of one or more CSI predictions may include an indication of a number of CSI predictions. The WTRU may determine an artificial intelligence (Al) / machine learning (ML) model, for example based on the indication of the number of CSI predictions. The configuration information associated with inclusion or omission of one or more CSI predictions may include an indication of an (Al)Zmachine learning (ML) model. The AI / ML model may be used to determine the plurality of CSI predictions.BRIEF DESCRIPTION OF THE DRAWINGS

[0016] FIG. 1A is a system diagram illustrating an example communications system in which one or more disclosed embodiments may be implemented.

[0017] FIG. 1 B is a system diagram illustrating an example wireless transmit / receive unit (WTRU) that may be used within the communications system illustrated in FIG. 1A according to an embodiment.

[0018] FIG. 1C is a system diagram illustrating an example radio access network (RAN) and an example core network (CN) that may be used within the communications system illustrated in FIG. 1A according to an embodiment.

[0019] FIG. 1 D is a system diagram illustrating a further example RAN and a further example CN that may be used within the communications system illustrated in FIG. 1A according to an embodiment.

[0020] FIG. 2 shows an example of codebook-based precoding with feedback information.

[0021] FIG. 3 shows an example AI / ML framework for CSI feedback with CSI compression.

[0022] FIG. 4 shows an example of multiple CSI prediction framework.

[0023] FIG. 5 shows an example of mode-a of operation for multiple CSI prediction.

[0024] FIG. 6 shows an example of mode-b of operation for multiple CSI prediction.

[0025] FIG. 7 shows an example of mode-c of operation for multiple CSI prediction.DETAILED DESCRIPTION

[0026] FIG. 1A is a diagram illustrating an example communications system 100 in which one or more disclosed embodiments may be implemented. The communications system 100 may be a multiple access system that provides content, such as voice, data, video, messaging, broadcast, etc., to multiple wireless users. The communications system 100 may enable multiple wireless users to access such content through the sharing of system resources, including wireless bandwidth. For example, the communications systems 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, filter bank multicarrier (FBMC), and the like.

[0027] As shown in FIG. 1A, the communications system 100 may include wireless transmit / receive units (WTRUs) 102a, 102b, 102c, 102d, a RAN 104 / 113, a CN 106 / 115, a public switched telephone network (PSTN) 108, the Internet 110, and other networks 112, though it will be appreciated 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 a “STA”, may be configured to transmit and / or receive wireless signals and may include a user equipment (UE), a mobile station, a fixed or mobile subscriber unit, a subscription-based unit, a pager, a cellular telephone, a personal digital assistant (PDA), a smartphone, a laptop, a netbook, a personal computer, a wireless sensor, a hotspot or Mi-Fi device, an Internet of Things (loT) device, a watch or other wearable, a head-mounted display (HMD), a vehicle, a drone, a medical device and applications (e.g., remote surgery), an industrial device and applications (e.g., a robot and / or other wireless devices operating in an industrial and / or an automated processing chain contexts), a consumer electronics device, a device operating on commercial and / or industrial wireless networks, and the like. Any of the WTRUs 102a, 102b, 102c and 102d may be interchangeably referred to as a WTRU.

[0028] The communications systems 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 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 the 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, a NR NodeB, a site controller, an access point (AP), a wireless router, and the like. While the base stations 114a, 114b are each depicted as a single element, it will be appreciated that the base stations 114a, 114b may include any number of interconnected base stations and / or network elements.

[0029] The base station 114a may be part of the RAN 104 / 113, which may also include other base stations and / or network elements (not shown), such as a base station controller (BSC), a radio network controller (RNC), relay nodes, etc. The base station 114a and / or the base station 114b may be configured to transmit and / or receive wireless signals on one or more carrier frequencies, which may be referred to as a cell (not shown). These frequencies may be in licensed spectrum, unlicensed spectrum, or a combination of licensed and unlicensed spectrum. A cell may provide coverage for a wireless service to a specific geographical area that may be relatively fixed or that may change over time. The cell may further be divided into cell sectors. For example, the cell associated with the base station 114a may be divided into three sectors. Thus, in one embodiment, the base station 114a may include three transceivers, i.e. , one for each sector of the cell. In an embodiment, the 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 transmit and / or receive signals in desired spatial directions.

[0030] 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).

[0031] More specifically, as noted above, the communications system 100 may be a multiple access system and may employ one or more channel access schemes, such as CDMA, TDMA, FDMA, OFDMA, SC-FDMA, and the like. 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), whichmay establish the air interface 115 / 116 / 117 using wideband CDMA (WCDMA). WCDMA may include communication protocols such as High-Speed Packet Access (HSPA) and / or Evolved HSPA (HSPA+). HSPA may include High-Speed Downlink (DL) Packet Access (HSDPA) and / or High-Speed UL Packet Access (HSUPA).

[0032] 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), which may establish the air interface 116 using Long Term Evolution (LTE) and / or LTE- Advanced (LTE-A) and / or LTE-Advanced Pro (LTE-A Pro).

[0033] 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).

[0034] 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 implement LTE radio access and NR radio access together, for instance using dual connectivity (DC) principles. Thus, the air interface utilized by WTRUs 102a, 102b, 102c may be characterized by multiple types of radio access technologies and / or transmissions sent to / from multiple types of base stations (e.g., a eNB and a gNB).

[0035] 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), GSM EDGE (GERAN), and the like.

[0036] The base station 114b in FIG. 1 A may be a wireless router, Home Node B, Home eNode B, or access point, for example, and may utilize any suitable RAT for facilitating wireless connectivity in a localized area, such as a place of business, a home, a vehicle, a campus, an industrial facility, an air corridor (e.g., for use by drones), a roadway, and the like. In one embodiment, the base station 114b and the WTRUs 102c, 102d may implement a radio technology 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 a radio technology 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 cellularbased RAT (e.g., WCDMA, CDMA2000, GSM, LTE, LTE-A, LTE-A Pro, NR etc.) to establish a picocell or femtocell. As shown in FIG. 1A, the base station 114b may have a direct connectionto the Internet 110. Thus, the base station 114b may not be required to access the Internet 110 via the CN 106 / 115.

[0037] 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 varying quality of service (QoS) requirements, such as differing throughput requirements, latency requirements, error tolerance requirements, reliability requirements, data throughput requirements, mobility requirements, and the like. The CN 106 / 115 may provide call control, billing services, mobile location-based services, pre-paid calling, Internet connectivity, video distribution, etc., and / or perform high-level security functions, such as user authentication. Although not shown in FIG. 1A, 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 be utilizing a NR radio technology, the CN 106 / 115 may also be in communication with another RAN (not shown) employing a GSM, UMTS, CDMA 2000, WiMAX, E-UTRA, or WiFi radio technology.

[0038] The CN 106 / 115 may also serve as a gateway for the WTRUs 102a, 102b, 102c, 102d to access the PSTN 108, the Internet 110, and / or the other networks 112. The PSTN 108 may include circuit-switched telephone networks that provide 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), 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 communications 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.

[0039] Some or all of the WTRUs 102a, 102b, 102c, 102d in the communications 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 over different wireless links). For example, the WTRU 102c shown in FIG. 1A 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.

[0040] FIG. 1 B is a system diagram illustrating an example WTRU 102. As shown in FIG. 1 B, the WTRU 102 may include 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, among others. It will be appreciated that the WTRU 102 may include any sub-combination of the foregoing elements while remaining consistent with an embodiment.

[0041] 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 in association with a DSP core, a controller, a microcontroller, Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) circuits, any other type of integrated circuit (IC), a state machine, and the like. The processor 118 may perform signal coding, 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. While FIG. 1 B depicts the processor 118 and the transceiver 120 as separate components, it will be appreciated that the processor 118 and the transceiver 120 may be integrated together in an electronic package or chip.

[0042] The transmit / receive element 122 may be configured to transmit signals to, or receive signals from, a base station (e.g., the base station 114a) over the air interface 116. For example, in one embodiment, the transmit / receive element 122 may be an antenna configured to transmit and / or receive RF signals. In an embodiment, the transmit / receive element 122 may be an emitter / detector configured to transmit and / or receive IR, UV, or visible light signals, for example. In yet another embodiment, the transmit / receive element 122 may be configured to transmit and / or receive both RF and light signals. It will be appreciated that the transmit / receive element 122 may be configured to transmit and / or receive any combination of wireless signals.

[0043] Although the transmit / receive element 122 is depicted in FIG. 1 B as a single element, 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.

[0044] The transceiver 120 may be configured to modulate the signals that are to be transmitted by the transmit / receive element 122 and to demodulate the signals that are received by the transmit / receive element 122. As noted above, the WTRU 102 may have multi-mode capabilities. Thus, the transceiver 120 may include multiple transceivers for enabling the WTRU 102 to communicate via multiple RATs, such as NR and IEEE 802.11 , for example.

[0045] The processor 118 of the WTRU 102 may be coupled to, and may receive user input data from, the speaker / microphone 124, the keypad 126, and / or the display / touchpad 128 (e.g., a liquid crystal display (LCD) display unit or 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 the non-removable memory 130 and / or the removable memory 132. The non-removable memory 130 may include random-access memory (RAM), 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).

[0046] The processor 118 may receive power from the power source 134, and may be configured to distribute and / or control the 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, and the like.

[0047] 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 over the air interface 116 from a base station (e.g., base stations 114a, 114b) and / or determine its location based on the timing of the signals being received from two or more nearby base stations. It will be appreciated that the WTRU 102 may acquire location information by way of any suitable location-determination method while remaining consistent with an embodiment.

[0048] The processor 118 may further be coupled to other peripherals 138, which may include one or more software 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 e-compass, a satellite transceiver, a digital camera (for photographs and / or video), a universal serial bus (USB) port, a vibration device, a television transceiver, a hands free headset, a Bluetooth® module, a frequency modulated (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, an activity tracker, and the like. The peripherals 138may include one or more sensors, the sensors may be one or more of 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.

[0049] The WTRU 102 may include a full duplex radio for which transmission and reception of some or all of the signals (e.g., associated with particular subframes for both the UL (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 to reduce and or substantially eliminate self-interference via either hardware (e.g., a choke) or signal processing via a processor (e.g., a separate processor (not shown) or via processor 118). In an embodiment, the WRTU 102 may include a half-duplex radio for which transmission and reception of some or all of the signals (e.g., associated with particular subframes for either the UL (e.g., for transmission) or the downlink (e.g., for reception)).

[0050] FIG. 1C is a system diagram illustrating the RAN 104 and the ON 106 according to 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.

[0051] The RAN 104 may include eNode-Bs 160a, 160b, 160c, though it will be appreciated that the RAN 104 may include any number of eNode-Bs while remaining consistent with an embodiment. The eNode-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 eNode-Bs 160a, 160b, 160c may implement MIMO technology. Thus, the eNode-B 160a, for example, may use multiple antennas to transmit wireless signals to, and / or receive wireless signals from, the WTRU 102a.

[0052] Each of the eNode-Bs 160a, 160b, 160c may be associated with a particular cell (not shown) and may be configured to handle radio resource management decisions, handover decisions, scheduling of users in the UL and / or DL, and the like. As shown in FIG. 1 C, the eNode-Bs 160a, 160b, 160c may communicate with one another over an X2 interface.

[0053] The CN 106 shown in FIG. 1C 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 are depicted as part of the CN 106, it will be appreciated that any of these elements may be owned and / or operated by an entity other than the CN operator.

[0054] The MME 162 may be connected to each of the eNode-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, selecting a particular serving gateway during an initial attach of the WTRUs 102a, 102b, 102c, and the like. 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.

[0055] The SGW 164 may be connected to each of the eNode Bs 160a, 160b, 160c in the RAN 104 via the 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 user planes during inter-eNode B handovers, triggering paging when DL data is available for the WTRUs 102a, 102b, 102c, managing and storing contexts of the WTRUs 102a, 102b, 102c, and the like.

[0056] 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.

[0057] 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 communicate with, an IP gateway (e.g., an IP multimedia subsystem (IMS) server) that serves 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 the other networks 112, which may include other wired and / or wireless networks that are owned and / or operated by other service providers.

[0058] Although the WTRU is described in FIGS. 1A-1D as a wireless terminal, it is contemplated that in certain representative embodiments that such a terminal may use (e.g., temporarily or permanently) wired communication interfaces with the communication network.

[0059] In representative embodiments, the other network 112 may be a WLAN.

[0060] A WLAN in Infrastructure Basic Service Set (BSS) mode may have an Access Point (AP) for the BSS and one or more stations (STAs) associated with the AP. The AP may have an access or an interface to a Distribution System (DS) or another type of wired / wireless network that carries traffic in to and / or out of the BSS. Traffic to STAs that originates from outside the BSS may arrive through the AP and may be delivered to the STAs. Traffic originating from STAs to destinations outside the BSS may be sent to the AP to be delivered to respectivedestinations. Traffic between STAs within the BSS may be sent through the AP, for example, where the source STA may send traffic to the AP and the AP may deliver the traffic to the destination STA. The traffic between STAs within a BSS may be considered and / or referred to as peer-to-peer traffic. The peer-to-peer traffic may be sent between (e.g., directly between) the source and destination STAs with a direct link setup (DLS). In certain representative embodiments, the DLS may use an 802.11e DLS or an 802.11z tunneled DLS (TDLS). A WLAN using an Independent BSS (IBSS) mode may not have an AP, and the STAs (e.g., all of the STAs) within or using the IBSS may communicate directly with each other. The IBSS mode of communication may sometimes be referred to herein as an “ad-hoc” mode of communication.

[0061] When using the 802.11ac infrastructure mode of operation or a similar mode of operations, the AP may transmit a beacon on a fixed channel, such as a primary channel. The primary channel may be a fixed width (e.g., 20 MHz wide bandwidth) or a dynamically set width via signaling. The primary channel may be the operating channel of the BSS and may be used by the STAs to establish a connection with the AP. In certain representative embodiments, Carrier Sense Multiple Access with Collision Avoidance (CSMA / CA) may be implemented, for example in in 802.11 systems. For CSMA / CA, the STAs (e.g., every 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.

[0062] High Throughput (HT) STAs may use a 40 MHz wide channel for communication, for example, via a combination of the primary 20 MHz channel with an adjacent or nonadjacent 20 MHz channel to form a 40 MHz wide channel.

[0063] Very High Throughput (VHT) STAs may support 20MHz, 40 MHz, 80 MHz, and / or 160 MHz wide channels. The 40 MHz, and / or 80 MHz, channels may be formed by combining contiguous 20 MHz channels. A 160 MHz channel may be formed by combining 8 contiguous 20 MHz channels, or by combining two non-contiguous 80 MHz channels, which may be referred to as an 80+80 configuration. For the 80+80 configuration, the data, after channel encoding, may be passed through a segment parser that may divide the data into two streams. Inverse Fast Fourier Transform (IFFT) processing, and time domain processing, may be done on each stream separately. The streams may be mapped on to the two 80 MHz channels, and the data may be transmitted by a transmitting STA. At the receiver of the receiving STA, the above described operation for the 80+80 configuration may be reversed, and the combined data may be sent to the Medium Access Control (MAC).

[0064] Sub 1 GHz modes of operation are supported by 802.11 af and 802.11 ah. The channel operating bandwidths, and carriers, are reduced in 802.11af and 802.11 ah relative to those used in 802.11n, and 802.11ac. 802.11af supports 5 MHz, 10 MHz and 20 MHz bandwidths in the TV White Space (TVWS) spectrum, and 802.11ah supports 1 MHz, 2 MHz, 4 MHz, 8 MHz, and 16 MHz bandwidths using non-TVWS spectrum. According to a representative embodiment, 802.11ah may support Meter Type Control / Machine-Type Communications, such as MTC devices in a macro coverage area. MTC devices may have certain capabilities, for example, limited capabilities including support for (e.g., only support for) certain and / or limited bandwidths. The MTC devices may include a battery with a battery life above a threshold (e.g., to maintain a very long battery life).

[0065] WLAN systems, which may support multiple channels, and channel bandwidths, such as 802.11n, 802.11ac, 802.11af, and 802.11 ah, include a channel which may be designated as the primary channel. The primary channel may have a bandwidth equal to the largest 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, from among all STAs in operating in a BSS, which supports the smallest bandwidth operating mode. In the example of 802.11ah, the primary channel may be 1 MHz wide for STAs (e.g., MTC type devices) that support (e.g., only support) a 1 MHz mode, even if the AP, and other STAs in the BSS support 2 MHz, 4 MHz, 8 MHz, 16 MHz, and / or other channel bandwidth operating modes. Carrier sensing and / or Network Allocation Vector (NAV) settings may depend on the status of the primary channel. If the primary channel is busy, for example, due to a STA (which supports only a 1 MHz operating mode), transmitting to the AP, the entire available frequency bands may be considered busy even though a majority of the frequency bands remains idle and may be available.

[0066] In the United States, the available frequency bands, which may be used by 802.11 ah, are from 902 MHz to 928 MHz. In Korea, the available frequency bands are from 917.5 MHz to 923.5 MHz. In Japan, the available frequency bands are from 916.5 MHz to 927.5 MHz. The total bandwidth available for 802.11ah is 6 MHz to 26 MHz depending on the country code.

[0067] FIG. 1 D is a system diagram illustrating the RAN 113 and the CN 115 according to an embodiment. As noted above, the RAN 113 may employ an 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.

[0068] 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 withthe WTRUs 102a, 102b, 102c over the air interface 116. In one embodiment, the gNBs 180a, 180b, 180c may implement MIMO technology. For example, gNBs 180a, 108b may utilize beamforming to transmit signals to and / or receive signals from the gNBs 180a, 180b, 180c. Thus, the gNB 180a, for example, may use multiple antennas to transmit 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 unlicensed spectrum while the remaining component carriers may be on licensed spectrum. In an embodiment, the gNBs 180a, 180b, 180c may implement Coordinated Multi-Point (CoMP) technology. For example, WTRU 102a may receive coordinated transmissions from gNB 180a and gNB 180b (and / or gNB 180c).

[0069] The WTRUs 102a, 102b, 102c may communicate with gNBs 180a, 180b, 180c using transmissions associated with a scalable numerology. For example, the 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 gNBs 180a, 180b, 180c using subframe or transmission time intervals (TTIs) of various or scalable lengths (e.g., containing varying number of OFDM symbols and / or lasting varying lengths of absolute time).

[0070] 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 the standalone configuration, WTRUs 102a, 102b, 102c may communicate with gNBs 180a, 180b, 180c without also accessing other RANs (e.g., such as eNode-Bs 160a, 160b, 160c). In the standalone configuration, WTRUs 102a, 102b, 102c may utilize one or more of gN Bs 180a, 180b, 180c as a mobility anchor point. In the standalone configuration, WTRUs 102a, 102b, 102c may communicate with gNBs 180a, 180b, 180c using signals in an unlicensed band. In a non-standalone configuration WTRUs 102a, 102b, 102c may communicate with / connect to gNBs 180a, 180b, 180c while also communicating with / connecting to another RAN such as eNode-Bs 160a, 160b, 160c. For example, WTRUs 102a, 102b, 102c may implement DC principles to communicate with one or more gNBs 180a, 180b, 180c and one or more eNode-Bs 160a, 160b, 160c substantially simultaneously. In the non-standalone configuration, eNode-Bs 160a, 160b, 160c may serve as a mobility anchor for WTRUs 102a, 102b, 102c and gNBs 180a, 180b, 180c may provide additional coverage and / or throughput for servicing WTRUs 102a, 102b, 102c.

[0071] Each of the gNBs 180a, 180b, 180c may be associated with a particular cell (not shown) and may be configured to handle radio resource management decisions, handover decisions, scheduling of users in the UL and / or DL, support of network slicing, dual connectivity, interworking between NR and E-UTRA, routing of user plane data towards User Plane Function (UPF) 184a, 184b, routing of control plane information towards Access and Mobility Management Function (AMF) 182a, 182b and the like. As shown in FIG. 1 D, the gNBs 180a, 180b, 180c may communicate with one another over an Xn interface.

[0072] The CN 115 shown in FIG. 1D may include at least one AMF 182a, 182b, at least one UPF 184a, 184b, at least one Session Management Function (SMF) 183a, 183b, and possibly a Data Network (DN) 185a, 185b. While each of the foregoing elements are depicted as part of the CN 115, it will be appreciated that any of these elements may be owned and / or operated by an entity other than the CN operator.

[0073] The AMF 182a, 182b may be connected to one or more of the gNBs 180a, 180b, 180c in the RAN 113 via an N2 interface and may serve as a control node. For example, the AMF 182a, 182b may be responsible for authenticating users of the WTRUs 102a, 102b, 102c, support for network slicing (e.g., handling of different PDU sessions with different requirements), selecting a particular SMF 183a, 183b, management of the registration area, termination of NAS signaling, mobility management, and the like. Network slicing may be used by the AMF 182a, 182b in order to customize CN support for WTRUs 102a, 102b, 102c based on the types of services being utilized WTRUs 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 massive mobile broadband (eMBB) access, services for machine type communication (MTC) access, and / or the like. The AMF 162 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-A Pro, and / or non- 3GPP access technologies such as WiFi.

[0074] The SMF 183a, 183b may be connected to an AMF 182a, 182b in the CN 115 via an N11 interface. The SMF 183a, 183b may also be connected to a UPF 184a, 184b in the CN 115 via an N4 interface. The SMF 183a, 183b may select and control the UPF 184a, 184b and configure the routing of traffic through the UPF 184a, 184b. The SMF 183a, 183b may perform other functions, such as managing and allocating WTRU IP address, managing PDU sessions, controlling policy enforcement and QoS, providing downlink data notifications, and the like. A PDU session type may be IP-based, non-IP based, Ethernet-based, and the like.

[0075] The UPF 184a, 184b may be connected to one or more of the gNBs 180a, 180b, 180c in the RAN 113 via an 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, providing mobility anchoring, and the like.

[0076] 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 serves 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 the other networks 112, which may include other wired and / or wireless networks that are owned and / or operated by other service providers. In one embodiment, the WTRUs 102a, 102b, 102c may be connected to a local Data Network (DN) 185a, 185b through the UPF 184a, 184b via the N3 interface to the UPF 184a, 184b and an N6 interface between the UPF 184a, 184b and the DN 185a, 185b.

[0077] In view of Figures 1A-1 D, and the corresponding description of Figures 1A-1 D, one or more, or all, of the functions described herein with regard to one or more of: WTRU 102a-d, Base Station 114a-b, eNode-B 160a-c, MME 162, SGW 164, PGW 166, gNB 180a-c, AMF 182a-ab, UPF 184a-b, SMF 183a-b, DN 185a-b, and / or any other device(s) described herein, may be performed by one or more emulation devices (not shown). The emulation devices may be one or more devices configured to emulate one or more, or all, of the functions described herein. For example, the emulation devices may be used to test other devices and / or to simulate network and / or WTRU functions.

[0078] The emulation devices may be designed to implement one or more tests of other devices in a lab environment and / or in an operator network environment. For example, the one or more emulation devices may perform the one or more, 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. The one or more emulation devices may perform the one or more, or all, functions while being temporarily implemented / deployed as part of a wired and / or wireless communication network. The emulation device may be directly coupled to another device for purposes of testing and / or may performing testing using over-the-air wireless communications.

[0079] The one or more emulation devices may perform the one or more, including all, functions while not being implemented / deployed as part of a wired and / or wireless communicationnetwork. For example, the emulation devices may be utilized in a testing scenario in a testing laboratory and / or a non-deployed (e.g., testing) wired and / or wireless communication network in order to implement testing of one or more components. The one or more emulation devices may be test equipment. Direct RF coupling and / or wireless communications via RF circuitry (e.g., which may include one or more antennas) may be used by the emulation devices to transmit and / or receive data.

[0080] Systems and methods disclosed provide solutions for the efficient reporting of multiple channel state information (CSI) prediction instances, for example within a single report. CSI may be reported, for example by a WTRU. FIG. 2 shows an example of codebook-based precoding 200 with feedback information. A transmitter 202 may include a precoding matrix 204. Input signals (e.g., Xi, Xwt) may be input into the precoding matrix 204, which for example may include a weight, W = Pi. Output signals (e.g., Zi, Z ) from the precoding matrix 204 may be input into a MIMO channel 206. The MIMO channel 206 may output signals (e.g., y1 , yN1) based on the input signals and / or may transmit the output signals (e.g., yi , YNY) to a receiver 208. The receiver may include a machine learning (ML) algorithm and / or may perform a minimum mean square error (MMSE) process, for example on the output signals (e.g., y1 , yN1) from the MIMO channel 206. The receiver 208 may output signals (e.g., X;, Xwt) based on the output signals (e.g., y1 , yN1) from the MIMO channel 206. Additionally, or alternatively, the receiver 208 may output feedback, for example to the transmitter 202. The feedback information may include a precoding matrix index (PMI). The feedback information (e.g., PMI) may include a feedback codeword index.

[0081] A codebook may include a set of precoding vectors / matrices, for example for a (e.g., each) rank and number of antenna ports. A (e.g., each) precoding vector / matrix may include an index, for example such that a receiver may inform a preferred precoding vector / matrix index to a transmitter. Codebook-based precoding may have performance degradation due to a finite number of precoding vector / matrix, for example as compared with non-codebook-based precoding. An advantage of a codebook-based precoding may include lower control signaling / feedback overhead.

[0082] There may be artificial intelligence (Al) / machine learning (ML) based CSI feedback. An autoencoder (AE) may be used, for example for CSI compression for AI / ML based CSI feedback. Systems and methods may include two sides, which for example may include an encoder and decoder. FIG. 3 shows an example AI / ML framework 300 for CSI feedback with CSI compression. Estimated CSI may be compressed at a WTRU 302 side. The WTRU side may include the encoder 304. The estimated CSI feedback 306 may be sent to a network (NW)308 side (e.g., a gNB). The network 308 side may include the decoder 310. The estimated CSI feedback 306 may (e.g., then) be decompressed, for example at the network 308 (e.g., gNB). An advantage of AI / ML based CSI compression may include improved performance, for example compared to legacy CSI feedback using a similar payload size. A disadvantage of AI / ML based CSI feedback may include a compression error that, for example may occasionally lead to a (e.g., significant) mismatch between a precoder computed at the WTRU 302 and a decompressed precoder at NW 308, X and X.

[0083] FIG. 4 shows an example of multiple CSI prediction framework 400. AI / ML based feedback may be used for CSI prediction. A WTRU may predict one or more instances of future CSI (e.g., channel matrices H(t + 1), ..., H(t + P)), for example by using the current and / or historical (e.g., previous) CSI measurements H(t — N), ..., H(t). CSI prediction may reduce CSI reporting overhead and / or reduce the number of downlink CSI-RS. The WTRU may perform CSI predictions.

[0084] Systems and methods may report multiple CSI prediction instances. A CSI prediction model may predict more than one future CSI depending on the trained model. A WTRU may report all predicted future CSI in a (e.g., single) report. Predicted CSI instances may show correlation, for example when a CSI prediction model predicts more than one future CSI. For example, predicted CSI instances may show correlation among consecutive predicted CSI instances. Systems and methods to leverage the correlation between consecutive CSI prediction instances may be defined to reduce the feedback overhead and / or increase reporting efficiency.

[0085] Systems and methods may determine inclusion and / or omission of predicted CSI instances. For example there may be multiple predicted CSI instances. A WTRU may be capable of (e.g., configured for) CSI prediction. The WTRU may receive configuration information (e.g., configurations) on inclusion and / or omission of CSI predictions. The WTRU may receive one or more CSI-reference signal (RS). The WTRU may measure a (e.g., current) channel state, predict a (e.g., multiple) future CSI instance, determine a subset of one or more CSI prediction instances, determine an inclusion indicator, and / or report the inclusion indicator and / or a subset of multiple CSI predictions. The WTRU may determine the subset of CSI prediction instances based on the configuration and / or a criteria. The criteria may include consecutive differences of prediction, for example between predicted CSI and previous (e.g., predicted) CSI. Criteria may additionally, or alternatively, include one or more parameters and / or thresholds as herein.

[0086] The WTRU (e.g., capable of CSI prediction) may be configured with one or more (e.g., of a plurality of) CSI predictions. The one or more CSI predictions may be for one or more of a (e.g., predetermined) window length, a (e.g., predetermined) number of slots, one or more CSI reporting instances, a number of historical CSI predictions to be used as input to the model, one or more model ID, one or more thresholds to determine the inclusion and / or omission of CSI predictions, a maximum number of consecutive omissions, a minimum number of inclusions, and / or the mode of operation of CSI prediction feedback.

[0087] The WTRU may receive CSI-RS, determine CSI based on received CSI-RS, and / or predict multiple CSI (e.g., P), for example for the configured window. There may be multiple modes of WTRU operation. For example in a mode of operation (e.g., mode-a), the WTRU may determine omission and / or inclusion of reporting for CSI predictions. The WTRU may determine a subset of CSI predictions, for example by computing the difference of predicted CSI with the previous predicted CSIs. The WTRU may omit a predicted CSI instance in the CSI report and / or may indicate the omission (e.g., 0, in the inclusion indicator), for example if the difference is below a threshold. The WTRU may include the predicted CSI instance in the CSI report and / or indicate the inclusion (e.g., 1 , in the inclusion indicator), for example if the difference is above a threshold. For example, if the WTRU is configured to feedback 8 future CSI predictions, the inclusion indicator may be in the form 11011101. 1s may indicate inclusion and / or 0s may indicate omission. The WTRU may include (e.g., not omit) the corresponding CSI prediction, for example if the number of consecutive omissions would become larger (e.g., exceeds) than the configured threshold.

[0088] In another example mode of operation (e.g., mode-b), the WTRU may compress one or more values for the included predicted CSI instances (e.g., the determined subset of CSI predictions). The WTRU may compress each predicted CSI instance in the subset using an autoencoder, for example if the WTRU is configured to compress the CSI predictions.

[0089] In another example mode of operation (e.g., mode-c), the WTRU may compress a (e.g., each) predicted CSI instance and / or (e.g., then) compute the difference of the consecutive compressed CSI instances. The WTRU may decide on inclusion for (e.g., each) CSI instance. The WTRU may omit one or more CSI prediction groups. For example, an inclusion indicator of 01110011 may mean the first CSI prediction instance is omitted, for example due to a similarity to the last CSI prediction of the previous group. The WTRU may (e.g., further) update the inclusion indicator and / or the subset of CSI predictions, for example based on the configured minimum number of inclusions. The WTRU may feedback (e.g., send to a network) the inclusion flag and / or the determined subset of predicted CSI instances. The WTRU may report (e.g., tothe network) the first included CSI prediction and / or the differences of consecutive included CSI predictions.

[0090] The WTRU may report predicted multiple CSI in phases, for example in two phases. The WTRU may report the inclusion indicator and / or (e.g., then) receive an uplink grant to report the CSI predictions in a first phase. The WTRU may report the included multiple CSI predictions in a second phase. The included multiple CSI predictions may include the determined subset of CSI predictions. Alternatively, the WTRU may report the inclusion indicator and / or (e.g., then) receive an uplink grant to report the CSI predictions in a first phase and report the included multiple CSI predictions in one phase. For example, the uplink grant may indicate resources to report the CSI predictions.

[0091] CSI as herein may refer to any measurement and / or computation performed by the WTRU regarding a channel between WTRU and the NW. CSI may additionally, or alternatively, refer to one or more of channel matrix, representation of a channel matrix, and / or processed channel matrix. CSI prediction as herein may refer to the predicting future CSI, for example based on the historical measured CSI. Additionally, or alternatively, CSI prediction as herein may refer to using an analytical or AI / ML-based model. Multiple CSI prediction as herein may refer to a prediction model that is capable of prediction more than one future CSI predictions, for example using historical measured CSI. CSI prediction group as herein may refer to the multiple CSI predictions, for example obtained at the output of the CSI prediction model. CSI compression as herein may refer to the compression of CSI, for example using a compression model such as autoencoder based model. Joint CSI compression as herein may refer to the compression of more than one CSI using a single model, for example simultaneously. Inclusion indicator as herein may refer to the indicator information reported from the WTRU, for example to the NW. The inclusion indicator may provide information on (e.g., which) CSI instances included in the report. The inclusion indicator may include one or more of the indices, slot numbers, time stamps, and / or etc. of the CSI predictions included in the report.

[0092] Systems and methods herein may address overhead in reporting of multiple CSI predictions. Systems and methods herein may reduce the uplink control channel overhead, for example by selective inclusion of multiple CSI predictions in a CSI prediction report. Systems and methods herein may include a CSI prediction module that, for example takes the historical measured CSI as input and / or may output multiple CSI prediction instances (e.g., as in FIG. 4). The number input CSIs and / or the number of predicted future CSIs may be configured by the NW. The WTRU may indicate the available configurations of the CSI prediction module, forexample within a capability signaling message. Each configuration of the CSI prediction module may correspond to a different model trained for the given number of input and output.

[0093] Systems and methods may determine inclusion and / or omission of multiple predicted CSI instances. A WTRU may receive one or more configurations for inclusion and / or omission on CSI predictions. A WTRU (e.g., capable of multiple CSI predictions) may be configured with one or more of a number of multiple CSI predictions, a number of historical CSI measurements, a model ID for the CSI prediction model, one or more thresholds to determine inclusion of CSI predictions, a maximum number of consecutive omissions, a minimum number of inclusions, and / or a mode of operation for multiple CSI prediction feedback. The WTRU may use the configuration to determine the inclusion and / or omission of predicted CSI for the CSI report.

[0094] The WTRU may be configured with a number of multiple CSI predictions, P. The WTRU may activate a (e.g., different) CSI prediction model based on the configured number of multiple CSI predictions, P. The configured number of multiple CSI predictions P instances may correspond to pre-configured prediction instances (e.g., slots). The WTRU may (e.g., further) receive configurations on the prediction instances (e.g., slot identifiers, timing, and / or etc.) of the P CSI predictions. The WTRU may receive configurations on (e.g., only) the prediction instances (e.g., slot identifiers, timing, etc.) and / or may (e.g., then) derive the configured number of multiple CSI predictions P based on the configured prediction instances.

[0095] The WTRU may be configured with the number of historical CSI measurements, N. The WTRU may receive configuration on the number of the inputs for the CSI prediction model, for example from the NW. The WTRU may use a number of input CSI measurements N and / or the number of CSI predictions P to determine the CSI prediction model. The WTRU may be configured with a model ID for the CSI prediction model. The WTRU may receive a model ID of the CSI prediction model, for example from the NW. The model ID may implicitly determine (e.g., may be used to implicitly determine) the number of inputs N and / or the number of outputs P of the prediction model. The WTRU may be configured with one or more thresholds to determine inclusion of CSI predictions. The WTRU may receive a threshold value to be used for the process, for example to determine whether to include and / or omit a CSI prediction instance from the multiple CSI prediction report.

[0096] The WTRU may be configured with a maximum number of consecutive omissions, Nomit. The WTRU may receive configuration on the maximum number of omissions, for example to limit the impact of prediction error and / or to prevent prediction error propagation. The maximum distance between (e.g., two) included CSI predictions may be Nomit, for example if a WTRU is configured with the maximum number of omissions for CSI prediction.

[0097] The WTRU may be configured with a minimum number of inclusions, Ninc. The WTRU may receive a configuration on the minimum number of inclusions, for example such that a minimum number of CSI predictions is reported by the WTRU. The WTRU may be configured with the mode of operation for multiple CSI prediction feedback. For example, there may be three modes of operation for multiple CSI prediction feedback. In one of the modes of operation, the WTRU may use uncompressed CSI predictions for inclusion determination and / or reporting. In another mode of operation, the WTRU may use uncompressed CSI predictions for inclusion determination and / or compressed CSI predictions for reporting. In the other mode of operation, the WTRU may use compressed CSI predictions for inclusion determination and / or compressed CSI predictions for reporting.

[0098] A WTRU may determine inclusion and / or omission of multiple predicted CSI instances. A WTRU, for example a WTRU configured with multiple CSI predictions, may receive CSI-RS and / or compute CSI based on the received CSI-RS. The WTRU may compute the channel matrix as the output of the CSI computations. The WTRU may compute a processed channel matrix such as a two-dimensional - discrete Fourier transform (2D-DFT) of the channel matrix and / or compute eigenvectors of the channel matrix as the output of the CSI computations.

[0099] The WTRU may activate a CSI prediction model to compute multiple CSI predictions, for example based on one or more of the indicated number of historical CSI measurements N, the number of CSI predictions P, and / or the model ID. The WTRU may (e.g., then) use N historical CSI measurements as input to the CSI prediction model and / or obtain P CSI predictions (e.g., a group of P CSI predictions). The (e.g., group of) P CSI predictions may correspond to the slots and / or timing, for example as configured by the NW. S = {Ht+1, ..., Ht+P} may denote the set of multiple CSI predictions, for example where N historical CSI measurements..., Ht-Nare used as input to the predictor.

[0100] The WTRU may (e.g., begin) to determine the inclusion and / or omission of the (e.g., multiple) CSI predictions, for example based on the configured mode of operation for multiple CSI prediction feedback. Example modes of operation (e.g., three different modes of operation) are shown in FIGS. 5, 6, and 7.

[0101] FIG. 5 shows an example of mode-a of operation 500 for multiple CSI prediction. The WTRU may predict CSI at 502. At 504 the WTRU configured with mode-a for example, may determine the inclusion of the CSI predictions based on the consecutive differences and / or similarities of the CSI predictions, Dj. The WTRU may compute the sum of the absolute differences between consecutive predicted CSI instances (e.g., channel matrices, Hj). TheWTRU may compute the SGCS between consecutive predicted CSI instances (e.g., channel matrices). For example, the WTRU may compute the difference between consecutive differences of the CSI predictions as Dj = fD(Hj, H^, ) where fDdenotes the differencing function.

[0102] If for example the WTRU computes that a consecutive difference Dnis above (e.g., or below in case of a similarity metric like SGCS) the configured threshold value T, the WTRU may (e.g., then) determine to include the CSI prediction Hnin the multiple CSI prediction report. The WTRU may compute a subset of the multiple CSI predictions S' =D, > T}. Additionally, or alternatively, the WTRU may determine an inclusion indicator set I, for example to feedback information about the included and / or omitted CSI predictions to the NW. The inclusion indicator set entries Ij may include a set of binary values, for example computed as:!=fl if Hj e S'; 1< j <pto otherwise

[0103] For example if P = 5 and I = {1,1, 0,1,0}, the 1st, 2nd, and 4thCSI predictions out of 5 may be included in the CSI prediction report. The WTRU may feedback the subset of multiple CSI predictions S' and / or the inclusion indicator I.

[0104] FIG. 6 shows an example of mode-b of operation 600 for multiple CSI prediction. The WTRU may predict CSI at 602. At 604 the WTRU configured with mode-b for example, may determine the inclusion of the CSI predictions. The WTRU configured with mode-b for example may (e.g., in addition to mode-a) compress (e.g., further compress) the subset of multiple CSI predictions S'D; > T}. At 606 the WTRU may compress the CSI predictions using an encoder-based compression model, for example an autoencoder-based compression model. For example, the WTRU may compute the compressed subset of CSI predictions as S' = {fe(Hj) | Kj G S' } where femay denote the compression function. The WTRU may compute the inclusion indicator similar to mode-a. The WTRU may feedback the subset of compressed multiple CSI predictions S(, and / or the corresponding inclusion indicator I.

[0105] FIG. 7 shows an example of mode-c of operation 700 for multiple CSI prediction. The WTRU may predict CSI at 702. The WTRU configured with mode-b for example, may determine the inclusion of the CSI predictions. At 704 the WTRU may compress one or more CSI predictions. For example, a WTRU configured with mode-c for example may compress (e.g., first compress) the (e.g., whole) set of CSI predictions S = {Ht+1, ..., Ht+P} separately and / or obtain the compressed set of CSI predictions Se= {fe(Ht+1), ..., fe(Ht+P)} where fedenotes thecompression function. The WTRU may use compression techniques such as autoencoderbased models.

[0106] At 706 the WTRU may (e.g., then) determine the inclusion of the CSI predictions, for example based on the consecutive differences and / or similarities of the compressed CSI predictions, D,. For example, the WTRU may compute the sum of the absolute differences between consecutive compressed predicted CSI instances. The WTRU may compute the squared generalized cosine similarity (SGCS) between consecutive compressed predicted CSI instances. The WTRU may compute the difference between consecutive differences of the compressed CSI predictions as Dj = fD(fe(Hj),) where fDdenotes the differencing function. If the WTRU computes that a consecutive difference Dnis above (e.g., or below in case of a similarity metric like SGCS) the configured threshold value T for example, the WTRU may (e.g., then) determine to include the compressed CSI prediction fe(Hn) 'nthe multiple CSI prediction report. The WTRU may compute a subset of the multiple CSI predictions S(, = {fe(Hj) e Se| Dj > T}. The WTRU may feedback the subset of multiple CSI predictions S^,. The WTRU may additionally, or alternatively, determine an inclusion indicator set I to feedback information about the included and / or omitted CSI predictions, for example to the NW. For example, the inclusion indicator set entries I may be a set of binary values computed as: i . = fiJ o otherwise

[0107] The WTRU may feedback, for example to the network, the subset of compressed multiple CSI predictionsand / or the corresponding inclusion indicator I. The WTRU may compute the subset of CSI predictions and inclusion indicator (e.g., accordingly), for example if the WTRU is configured with the maximum number of consecutive omissions, Nomit. For example, for P = 8 and Nomit= 2, if the WTRU has computed the inclusion indicator as I = {1, 1,0, 0,0, 1,1,0} (where 3rd, 4thand 5thCSI predictions are omitted consecutively), (e.g., then) the WTRU may update the inclusion indicator as I = {1,1, 0,0, 1,1, 1,0} to include the 5thCSI prediction, instead of omitting the 5thCSI prediction. The WTRU may update the subset of reported CSI predictions, for example to comply with the updated inclusion indicator.

[0108] The WTRU may compute the subset of CSI predictions and / or inclusion indicator (e.g., accordingly), for example if the WTRU is configured with the minimum number of total inclusions, Ninc. The WTRU may include (e.g., add more) CSI predictions in the group to the subset until the minimum number of total inclusions is reached, for example if the WTRU computes the number of inclusions to be lower than the configured Ninc. The WTRU mayadditionally, or alternatively, include one or more CSI predictions of the group in the subset, for example based on the value of the consecutive differences D The WTRU may include additional Hj, for example based on the sorted consecutive differences D

[0109] The WTRU may determine (e.g., compute) inclusion and / or omission among the groups of CSI predictions. The WTRU may determine an inclusion indicator of I = {0,1, 0,1,1}, which may indicate that the first CSI prediction in the current group of CSI predictions may not be included in the CSI prediction report, for example as the first CSI prediction in the current group may be similar to the last CSI prediction in the previous CSI prediction group (e.g., the difference between the two CSI predictions in two consecutive groups may be below the threshold). The inclusion indicator may include the indices of the included CSI predictions, for example instead of or in addition to binary values that may denote the inclusion and / or omission. The inclusion indicator may contain indices of a lookup table. The inclusion indicator may contain the indices of slots and / or the timing of the CSI predictions.

[0110] The inclusion and / or omission of CSI predictions may be based on (e.g., include) a function of the prediction accuracy. The WTRU may decrease the number of omissions and / or increase the number inclusions, for example if the CSI prediction accuracy (e.g., measured in terms of SGCS) is below a configured threshold. For example, if the number of CSI predictions P = 8 and the CSI prediction accuracy is above a threshold, (e.g., then) the WTRU may be configured to omit a maximum of 4 CSI predictions. If the number of CSI predictions P = 8 and / or the CSI prediction accuracy is below a threshold, (e.g., then) the WTRU may be configured to omit a maximum of 2 CSI predictions. Systems and methods as herein may be applied to WTRU reports which, for example may be based on WTRU measurements and / or computations. Systems and methods as herein may be applied to WTRU side CSI predictions. Predictions may refer to one or more of time, frequency, and / or space domain, for example for CSI predictions.

[0111] Systems and methods may report an inclusion indicator and / or a (e.g., determined) subset of multiple predicted CSI instances. A WTRU (e.g., capable of performing multiple CSI prediction instances) may be configured to report the CSI measurements, for example in a CSI feedback report. A CSI feedback report may include an inclusion indicator and / or the (e.g., determined) subset of predicted CSI. The feedback report may include measured differences between consecutive CSI predictions, for example if configured by the NW.

[0112] The inclusion indicator may include an indication which informs the NW of the instances of the predicted CSI (e.g., within the configured prediction window), that for example may be included in the CSI feedback report. The inclusion indicator may include one or more of abitmap, an index in a table, a list of one or more slot indices, and / or a size of a (e.g., determined) subset of CSI prediction instances.

[0113] The bitmap may include a length P, where P may be the configured number of CSI predictions for example. A bit value of 1 in the bitmap may indicate that the corresponding value of the predicted CSI is included in the CSI report For example, when the jthbit in the bitmap is 1 , where 1 < j < P, the bitmap may indicate that the CSI report includes the jthvalue of the predicted CSI with respect to the start of the CSI prediction window. The index in a table may include an index in a table of inclusion patterns, for example for the configured number of CSI predictions, P. The table of inclusion patterns may be predefined. Additionally, or alternatively, the WTRU may receive an indication and / or may determine to choose a table, for example when the NW configures the number of CSI predictions. The list of one or more slot indices may include a list of up to P slot indices, for example for which the predicted CSI is to be reported.

[0114] The WTRU may report the predicted CSI, for example corresponding to the included predicted instances. The predicted CSI may include one or more of uncompressed predicted CSI and / or compressed predicted CSI. Predicted CSI may be expressed as the raw (e.g., full) channel matrix and / or as the eigenvectors of the channel matrix, for example for uncompressed predicted CSI. Compressed predicted CSI may include one or more of a raw channel matrix and / or eigenvector.

[0115] The WTRU may report (e.g., to the network) the measured difference between consecutive predicted CSI values (e.g., for all consecutive differences in the prediction window P and / or or for the instances corresponding to omitted CSI), for example if configured. For example, if P = 5 and instances 1 , 2 and 4 are included in the WTRU CSI report, (e.g., then) the WTRU may report the measured differences corresponding to the 3rdand the 5thCSI instances (e.g., to enable the NW-side reconstruction of the predicted CSI for the 3rdand 5thprediction instance).

[0116] The WTRU may report (e.g., both) the inclusion indicator and / or the predicted CSI (e.g., the determined subset of CSI predictions) within the configured PUSCH CSI report, for example if the WTRU is configured for CSI reporting on the PUSCH (e.g., for semi-persistent and / or for aperiodic CSI reporting). The WTRU may report the inclusion indicator using the PUCCH, for example for periodic CSI reporting. The WTRU may (e.g., then) monitor for an UL grant and / or report the determined subset of predicted CSI values, for example based on the resources allocated in the UL grant. For example when the WTRU determined a set of M predictions (where 1 < M < P), the WTRU may only include a subset of Mxpredictions (where 1 < Mx< M), for example when the configured resource size for CSI reporting is smaller than M CSI reports.

[0117] A WTRU (e.g., capable of CSI prediction) may receive configuration information (e.g., one or more configurations) on inclusion and / or omission of CSI predictions. The WTRU may receive CSI-RS, measure the current channel state, predict multiple future CSI instances, determine a subset of CSI prediction instances based on the configurations and a criteria (e.g., the consecutive differences of prediction), determine an inclusion indicator, and / or report the inclusion indicator and / or a subset of multiple CSI predictions. The WTRU (e.g., capable of CSI prediction) may be configured with one or more of (e.g., multiple) CSI predictions for a window length, CSI predictions for a number of slots, CSI predictions for a number of CSI reporting instances, the number of historical CSI predictions to be used as input to the model, a model ID, one or more thresholds to determine the inclusion and omission of CSI predictions, a maximum number of consecutive omissions, a minimum number of inclusions, and / or the mode of operation of CSI prediction feedback. The WTRU may receive CSI-RS, determine CSI based on received CSI-RS, and / or predict multiple CSI (e.g., P) for the configured window.

[0118] The WTRU may determine omission and / or inclusion of reporting for CSI predictions, for example in one of the modes of operation (e.g., mode-a). The WTRU may determine a subset of CSI predictions, for example by computing the difference of predicted CSI with the previous predicted CSIs. The WTRU may omit the predicted CSI instance in the CSI report and / or indicate the omission (e.g., 0, in the inclusion indicator), for example if the difference is below a threshold. The WTRU may include the predicted CSI instance in the CSI report and / or indicate the inclusion (e.g., 1 , in the inclusion indicator), for example if the difference is above a threshold. The inclusion indicator may be 11011101 , where 1s indicate inclusion and 0s indicate omission, for example if the WTRU is configured to feedback 8 future CSI predictions. The WTRU may include (e.g., does not omit) the corresponding CSI prediction, for example if the number of consecutive omissions would become larger than (e.g., exceeds) a configured threshold.

[0119] The WTRU may compress one or more values for the included predicted CSI instances, (e.g., the determined subset of CSI predictions), for example in an example mode of operation (e.g., mode-b). The WTRU may compress each predicted CSI instance in the subset (e.g., using an autoencoder), for example if the WTRU is configured to compress the CSI predictions.

[0120] The WTRU may compress a (e.g., each) predicted CSI instance and / or (e.g., then) compute the difference of the consecutive compressed CSI instances to decide on inclusion, for example in another example mode of operation (e.g., mode-c). The WTRU may apply omission among CSI prediction groups. For example an inclusion indicator of 01110011, may mean thefirst CSI prediction instance is omitted. The WTRU may omit the first CSI prediction instance due to a similarity to the last CSI prediction of the previous group.

[0121] The WTRU may (e.g., further) update the inclusion indicator and / or the subset of CSI predictions, for example based on the configured minimum number of inclusions. The WTRU may feedback the inclusion flag and / or the (e.g., determined) subset of predicted CSI instances. The WTRU may report the first included CSI prediction and / or the differences of consecutive included CSI predictions. The WTRU may report predicted multiple CSI in two phases. For example in a first phase, the WTRU may report the inclusion indicator and / or (e.g., then) receive an uplink grant to report the CSI predictions. In a second phase for example, the WTRU may report the included multiple CSI predictions (e.g., the determined subset of CSI predictions).

Claims

CLAIMS:

1. A method implemented by a wireless transmit / receive unit (WTRU), the method comprising: receiving configuration information from a network, the configuration information associated with inclusion or omission of one or more channel state information (CSI) predictions; determining a plurality of CSI measurements based on CSI reference signals (CSI-RSs); determining a plurality of CSI predictions based on the plurality of CSI measurements; determining a subset of CSI predictions of the plurality of CSI predictions based on the configuration information; and sending, to the network, an indication of the subset of CSI predictions and an inclusion indicator associated with the subset CSI predictions.

2. The method of claim 1 , wherein the configuration information comprises an indication of one or more of a CSI prediction model, a number of historical CSI measurements, a threshold used to determine whether a CSI prediction should be included in the subset of CSI predictions, a maximum number of consecutive omissions of CSI predictions from the subset of CSI predictions, or a minimum number of inclusions of CSI predictions into the subset of CSI predictions.

3. The method of claim 1 , wherein a CSI prediction of the plurality of CSI predictions is determined to be part of the subset of CSI predictions based on a difference between the CSI prediction and one or more other CSI predictions.

4. The method of claim 3, wherein the inclusion indicator indicates an inclusion of a CSI prediction when the difference is above a threshold or indicates an omission of the CSI prediction when the difference is below the threshold.

5. The method of claim 3, further comprising compressing one or more values of the subset of CSI predictions to obtain compressed CSI predictions, wherein the indication of the subset of CSI predictions comprises the compressed CSI predictions.

6. The method of claim 1 , further comprising compressing each of the plurality of CSI predictions, and determining a difference associated with consecutive compressed CSI predictions of the plurality of compressed CSI predictions to determine the subset of CSI predictions.

7. The method of claim 1 , further comprising receiving, from the network, an uplink grant indicating resources for sending the indication of the subset of CSI predictions in response to the inclusion indicator associated with the subset of CSI predictions.

8. The method of claim 1 , wherein the configuration information associated with inclusion or omission of one or more CSI predictions comprises an indication of a number of CSI predictions.

9. The method of claim 8, further comprising determining an artificial intelligence (Al) / machine learning (ML) model based on the indication of the number of CSI predictions, wherein the AI / ML model is used to determine the plurality of CSI predictions.

10. The method of claim 1 , wherein the configuration information associated with inclusion or omission of one or more CSI predictions comprises an indication of an (Al) / machine learning (ML) model, wherein the AI / ML model is used to determine the plurality of CSI predictions.

11. A wireless transmit / receive unit (WTRU) comprising: a processor, the processor configured to: receive configuration information from a network, the configuration information associated with inclusion or omission of one or more channel state information (CSI) predictions; determine a plurality of CSI measurements based on CSI reference signals (CSI-RSs); determine a plurality of CSI predictions based on the plurality of CSI measurements; determine a subset of CSI predictions of the plurality of CSI predictions based on the configuration information; and send, to the network, an indication of the subset of CSI predictions and an inclusion indicator associated with the subset CSI predictions.

12. The WTRU of claim 11 , wherein the configuration information comprises an indication of one or more of a CSI prediction model, a number of historical CSI measurements, a threshold used to determine whether a CSI prediction should be included in the subset of CSI predictions, a maximum number of consecutive omissions of CSI predictions from the subset of CSI predictions, or a minimum number of inclusions of CSI predictions into the subset of CSI predictions.

13. The WTRU of claim 11 , wherein a CSI prediction of the plurality of CSI predictions is determined to be part of the subset of CSI predictions based on a difference between the CSI prediction and one or more other CSI predictions.

14. The WTRU of claim 13, wherein the inclusion indicator indicates an inclusion of a CSI prediction when the difference is above a threshold or indicates an omission of the CSI prediction when the difference is below the threshold.

15. The WTRU of claim 13, wherein the processor is further configured to compress one or more values of the subset of CSI predictions to obtain compressed CSI predictions, wherein the indication of the subset of CSI predictions comprises the compressed CSI predictions.

16. The WTRU of claim 11 , wherein the processor is further configured to compress each of the plurality of CSI predictions, and determine a difference associated with consecutive compressed CSI predictions of the plurality of compressed CSI predictions to determine the subset of CSI predictions.

17. The WTRU of claim 11 , wherein the processor is further configured to receive, from the network, an uplink grant indicating resources for sending the indication of the subset of CSI predictions in response to the inclusion indicator associated with the subset of CSI predictions.

18. The WTRU of claim 11 , wherein the configuration information associated with inclusion or omission of one or more CSI predictions comprises an indication of a number of CSI predictions.

19. The WTRU of claim 18, wherein the processor is further configured to determine an artificial intelligence (Al) / machine learning (ML) model based on the indication of the number of CSI predictions, wherein the AI / ML model is used to determine the plurality of CSI predictions.

20. The WTRU of claim 11 , wherein the configuration information associated with inclusion or omission of one or more CSI predictions comprises an indication of an (Al) / machine learning (ML) model, wherein the AI / ML model is used to determine the plurality of CSI predictions.

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