Methods for accurate CSI prediction in wireless systems

Abstract

A WTRU receives configuration information including a set of reference signals (RSs) for channel state information (CSI) prediction, one or more CSI prediction parameters including one or more prediction window lengths (L) and resource sets, one or more prediction accuracy thresholds, and one or more sets of resources for CSI reporting. The WTRU receives and measures RSs associated with the configured set of RSs, determines predicted CSI values ​​for the resources of the one or more configured prediction windows, and determines preferred CSI prediction parameters as a function of the measurements of the received RSs and the configured prediction accuracy threshold. The WTRU reports the determined preferred CSI prediction parameters and the determined predicted CSI values ​​for one or more subsequent prediction windows to the base station using the configured one or more sets of resources for CSI reporting. Additional embodiments are disclosed.

Description

[Background technology]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This application claims the benefit of U.S. Provisional Patent Application No. 63 / 443,219, filed February 3, 2023, the contents of which are incorporated herein by reference.

[0002] Channel state information (CSI) is used in wireless systems to provide feedback regarding connectivity conditions. In some environments, the CSI may include at least one of a channel quality index (CQI), a rank indicator (RI), a precoding matrix index (PMI), a Layer 1 channel measurement, and / or any other measurement determined by a wireless transmit / receive unit (WTRU) based on a configured reference signal (RS). In a multiple-input multiple-output (MIMO) system, as the number of antenna ports increases, the overhead for transmitting and providing feedback on CSI also increases in both the uplink and downlink. This increase in overhead poses practical challenges in obtaining and maintaining accurate CSI, which relies on reference signals sent to the WTRU for measurement and subsequent reporting to the network access station. As the number of antennas is expected to continue to increase in next-generation systems, this overhead is also expected to increase. Artificial intelligence and machine learning (AIML) can be used to predict CSI using models based on previous measurements of reference signals. Accurate CSI reporting is critical to ensuring optimal system performance (including beamforming, scheduling, and link adaptation). In a system using CSI prediction, it is important to determine the prediction accuracy. Methods and devices are needed for monitoring and reporting CSI prediction accuracy and for reducing CSI feedback overhead when reporting predicted CSI. Summary of the Invention [Means for solving the problem]

[0003] According to some aspects, a CSI prediction technique for reducing CSI overhead in DL and UL is disclosed. In one aspect, a user equipment (UE), also referred to herein as a WTRU, is configured to perform prediction for CSI feedback and to determine and report preferred components for CSI prediction as a function of channel conditions and a configured prediction accuracy criterion.

[0004] A method for a WTRU to perform CSI prediction and determine and report the accuracy of the predicted CSI is also disclosed. Further aspects are disclosed for a WTRU to perform CSI prediction by determining a subset of CSI components to report (i.e., reduced CSI reporting) as a function of the predicted component change (or frequency of change) and the component prediction accuracy.

[0005] For WTRU-side CSI prediction, data collection is based on measurements of the CSI-RS, possibly over a period of time. The CSI prediction can be defined to operate on two time windows: a first window during which the WTRU accumulates measurements to use for inferring the predicted CSI, and a second window during which the predicted CSI is applicable.

[0006] Some benefits of CSI prediction may be reduced CSI-RS and CSI reporting overhead. The window duration affects the CSI-RS configuration (i.e., CSI-RS needs to be transmitted at least during the first window) and the CSI reporting configuration (i.e., the size of the window affects the timing and size of the CSI report). Furthermore, the appropriate window duration is affected by the prediction performance (e.g., of the AI / ML model), which can be affected by channel conditions. Therefore, a configurable window duration is desirable to enable efficient WTRU-side CSI prediction, regardless of whether AI / ML is used for WTRU-side CSI prediction.

[0007] The window durations can be controlled by the gNB. However, their configuration depends on WTRU feedback. Time-domain channel property (TDCP) feedback is currently specified in 3GPP Rel-18 and can be used by the gNB to determine the appropriate window size. Additional WTRU feedback, for example based on AI / ML prediction performance under specific channel conditions, is important for accurate CSI prediction.

[0008] Metrics for monitoring the WTRU-side CSI prediction model that are more indicative of end-to-end performance are disclosed. In one example, the gNB can also monitor / estimate the performance of the WTRU-side CSI prediction model through other means, including hybrid automatic repeat request (HARQ) feedback and other measurement reports from the WTRU. For example, if the gNB receives consecutive negative acknowledgments (NACKs) from scheduling based on the predicted CSI, the gNB can be configured to assume that the CSI prediction at the WTRU is not working well and fall back to legacy CSI reporting. The gNB can also periodically trigger CSI reporting for specific time occasions and compare between the predicted and measured CSI. In this case, monitoring of the CSI prediction model performance can be performed at the gNB side, which will be specification transparent because higher layers configure / reconfigure between the legacy CSI reporting mode and the CSI prediction mode, or use both simultaneously. As used herein, "legacy CSI reporting" refers to CSI reporting without using prediction or an AIML model.

[0009] According to one aspect, a method and device for improving CSI measurement and feedback is disclosed, in which CSI prediction, i.e., based on artificial intelligence and machine learning (AIML), can be utilized to reduce CSI overhead and reduce the number of transmission opportunities. According to another aspect, one or more methods are disclosed for evaluating and indicating the accuracy of AIML prediction to request and achieve reduced CSI reference signal (RS) transmission. Further aspects are disclosed relating to methods for reporting the accuracy of CSI prediction to monitor and supervise CSI prediction operations. Additional aspects may relate to methods and devices configured to leverage the accuracy of CSI prediction and varying each CSI component, or set of components, to obtain reduced CSI feedback without affecting the quality of the CSI feedback and utilization.

[0010] In one particular example, a method for a wireless transmit / receive unit (WTRU) includes receiving, from a base station, configuration information including a first set of reference signals (RSs) for channel state information (CSI) prediction, one or more CSI prediction parameters including lengths (L) and sets of resources of one or more prediction windows, one or more prediction accuracy thresholds, and one or more sets of resources for CSI reporting. The WTRU receives from the base station one or more RSs associated with the first set of RSs and determines predicted CSI values ​​for at least one resource of the one or more configured prediction windows. The WTRU determines preferred CSI prediction parameters as a function of the RS measurements and the configured prediction accuracy threshold(s), and reports the determined preferred CSI prediction parameters and the determined predicted CSI values ​​for one or more subsequent prediction windows to the base station using the configured set or sets of resources for CSI reporting.

[0011] According to some aspects, the predicted CSI value relates to any of a rank indicator (RI), a channel quality index (CQI), a precoding matrix indicator (PMI), a layer indicator (LI), a CSI-RS resource indicator (CRI), a signal interference-to-noise ratio (SINR), a reference signal received power (RSRP), a reference signal received quality (RSRQ), a received signal strength indicator (RSSI), a Doppler spread, an angle of arrival (AoA), an angle of departure (AoD), a delay spread, or a mean delay. In some embodiments, the preferred CSI prediction parameters include one or more of a length (L) of the prediction window and / or a number (K) of CSI-RS resources desired for the prediction window.

[0012] In one embodiment, the second set of RSs is included in the WTRU configuration information and can be used to verify or adjust the predicted CSI and / or preferred CSI prediction parameters by receiving one or more RSs associated with the second set of RSs from the base station and verifying or adjusting the determined preferred CSI prediction parameters. The WTRU reports CSI validation or adjustment information to the base station based on measurements of the received one or more RSs associated with the second set of RSs.

[0013] According to one aspect, the determined preferred CSI prediction parameters are validated when measurements of one or more RSs associated with the second set of RSs meet or exceed an accuracy criterion. In one embodiment, the WTRU requests a reduced number of RSs for one or more subsequent prediction windows when the accuracy criterion is exceeded by a predetermined amount, or requests an increased number of RSs for one or more subsequent prediction windows when the accuracy criterion is exceeded by less than the predetermined amount.

[0014] In some embodiments, the WTRU configuration information includes a third set of RSs, and the WTRU receives one or more RSs associated with the third set of RSs from the base station prior to receiving one or more RSs associated with the first set of RSs. The WTRU can train an artificial intelligence machine learning (AIML) model for CSI prediction using measurements of the RSs associated with the third set while the WTRU performs legacy CSI reporting. Additional features and aspects are further disclosed. [Brief explanation of the drawings]

[0015] A more detailed understanding may be had from the following description, given by way of example in conjunction with the accompanying drawings, in which like reference numerals indicate similar elements and in which:

[0016] [Figure 1A] FIG. 1 illustrates an exemplary communication system in which one or more disclosed embodiments may be implemented. [Figure 1B] 1B is a system diagram illustrating an exemplary wireless transmit / receive unit (WTRU) that may be used within the communications system shown in FIG. 1A, according to one embodiment. [Figure 1C] 1B illustrates an exemplary radio access network (RAN) and core network (CN) that may be used within the communication system of FIG. 1A, according to one embodiment. [Figure 1D] FIG. 1B is a system diagram illustrating a further example RAN and CN that may be used within the communication system shown in FIG. 1A, according to one embodiment. [Figure 2] FIG. 1 illustrates an example of a configuration for CSI reporting configuration, resource configuration, and linking. [Figure 3] FIG. 1 illustrates the basic concept of codebook-based precoding with feedback information. [Figure 4] FIG. 1 illustrates an exemplary recurrent neural network (RNN) architecture. [Figure 5] FIG. 1 illustrates an exemplary CSI prediction procedure, according to an example embodiment. [Figure 6] 1 is a timing diagram illustrating a WTRU process of reporting CSI with look-ahead window and reference signal (RS) adjustment, according to an example embodiment. [Figure 7] FIG. 10 is a timing diagram of a process for reporting and configuring CSI prediction parameters in an example embodiment. [Figure 8] 10 illustrates an example signal flow between a base station and a WTRU for CSI prediction, in accordance with some embodiments. [Figure 9] FIG. 10 illustrates an exemplary signal flow when the CSI prediction accuracy is greater than a configured threshold in one example. [Figure 10] FIG. 10 illustrates an exemplary signal flow when the CSI prediction accuracy is below a configured threshold in one example. [Figure 11] FIG. 10 illustrates an example representation of varying grades of CSI prediction accuracy over multiple windows. [Figure 12] 1 shows an example of K-adaptation as a function of the gradient of prediction accuracy along multiple windows. [Figure 13] FIG. 10 illustrates an example of graded CSI prediction accuracy over time across multiple windows. [Figure 14] FIG. 10 is a flow diagram illustrating an example method for determining the number of CSI-RS transmissions during the next look-ahead window based on per-component accuracy reporting. [Figure 15] FIG. 1 illustrates an exemplary signal flow for per-component accuracy reporting, according to an exemplary embodiment. [Figure 16] FIG. 1 illustrates an example of the variation of CSI components over multiple windows. [Figure 17] FIG. 1 is a flow diagram illustrating a method for CSI prediction and reporting, according to an example embodiment. [Figure 18] 1 is a flow diagram illustrating a method for a WTRU to perform CSI prediction, according to an example embodiment. [Figure 19] FIG. 1 is a flow diagram illustrating a method for CSI prediction accuracy reporting, according to one embodiment. [Figure 20] FIG. 1 is a flow diagram illustrating a method for reporting accuracy of CSI prediction and CSI components according to one embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0017] 1A is a system 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 the multiple wireless users to access such content through the 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 (ZT) unique-word (UW) discrete Fourier transform (DFT) spread OFDM (ZT UW DTS-s OFDM), unique word OFDM (UW-OFDM), resource block filtered OFDM, filter bank multicarrier (FBMC), etc.

[0018] 1A, communications system 100 may include wireless transmit / receive units (WTRUs) 102a, 102b, 102c, 102d, a radio access network (RAN) 104, a core network (CN) 106, a public switched telephone network (PSTN) 108, the Internet 110, and other networks 112, although it will be understood that the disclosed embodiments contemplate any number of WTRUs, base stations, networks, and / or network elements. Each of 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 transmit and / or receive wireless signals, and may include (or be) a 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 smartphone, 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, a head-mounted display (HMD), a vehicle, a drone, a medical device and application (e.g., remote surgery), an industrial device and application (e.g., robots and / or other wireless devices operating in an industrial and / or automated processing chain context), a consumer electronics device, a device operating on a commercial and / or industrial wireless network, etc. Any of the WTRUs 102a, 102b, 102c, and 102d may be referred to interchangeably as a UE.

[0019] The communications 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 configured to wirelessly interface with at least one of the WTRUs 102a, 102b, 102c, 102d to facilitate access to one or more communications networks, such as, for example, the CN 106, the Internet 110, and / or the network 112. By way of example, the base stations 114a, 114b may be any of a base transceiver station (BTS), a Node B (NB), an eNodeB (eNB), a Home Node B (HNB), a Home eNodeB (HeNB), a gNode B (gNB), a NR Node B (NR NB), a site controller, an access point (AP), a wireless router, etc. Although the base stations 114a, 114b are each shown as a single element, it will be understood that the base stations 114a, 114b may include any number of interconnected base stations and / or network elements.

[0020] The base station 114a may be part of the RAN 104, 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 base station 114b may be configured to transmit and / or receive wireless signals on one or more carrier frequencies, sometimes referred to as a cell (not shown). These frequencies may be licensed spectrum, unlicensed spectrum, or a combination of licensed and unlicensed spectrum. A cell may provide coverage for wireless services in a particular geographic area, which may be relatively fixed or may change over time. A cell may be further 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 one embodiment, the base station 114a may employ multiple-input multiple-output (MIMO) technology and utilize multiple transceivers for each or any sector of the cell. For example, beamforming may be used to transmit and / or receive signals in desired spatial directions.

[0021] 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).

[0022] More particularly, as mentioned 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, etc. For example, the base station 114a in the RAN 104 and the WTRUs 102a, 102b, 102c may implement a radio technology such as Universal Mobile Telecommunications System (UMTS) Terrestrial Radio Access (UTRA), which may establish the air interface 116 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 Packet Access (HSDPA) and / or High Speed ​​Uplink Packet Access (HSUPA).

[0023] In one 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).

[0024] In one embodiment, the base station 114a and the WTRUs 102a, 102b, 102c may implement a radio technology, such as New Radio (NR) radio access, which may establish the air interface 116 using NR.

[0025] In one 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 jointly implement LTE radio access and NR radio access, e.g., using dual connectivity (DC) principles. Thus, the air interface utilized by the WTRUs 102a, 102b, 102c may be characterized by multiple types of radio access technologies and / or transmissions from / to multiple types of base stations (e.g., eNBs and gNBs).

[0026] In one embodiment, the base station 114a and the WTRUs 102a, 102b, 102c may implement a wireless technology such as IEEE 802.11 (i.e., Wireless Fidelity (Wi-Fi)), 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), or the like.

[0027] 1A may be, for example, a wireless router, a Home NodeB, a Home eNodeB, or an access point and may utilize any suitable RAT for facilitating wireless connectivity in a localized area, such as a business, a home, a vehicle, a campus, an industrial facility, an air corridor (e.g., for use by drones), a road, etc. 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 one 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 one 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 either a small cell, a picocell, or a femtocell. 1A, the base station 114b may have a direct connection to the Internet 110. Thus, the base station 114b may not be required to access the Internet 110 through the CN 106.

[0028] The RAN 104 may be in communication with the CN 106, which may be any type of network configured to provide voice, data, application, and / or voice over internet protocol (VoIP) services to one or more of the WTRUs 102a, 102b, 102c, 102d. The data may have various quality of service (QoS) requirements, such as different throughput, latency, error resilience, reliability, data throughput, mobility, etc. The CN 106 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 shown in FIG. 1A , it will be understood that the RAN 104 and / or CN 106 may be in direct or indirect communication with other RANs employing the same RAT as the RAN 104 or a different RAT. For example, in addition to being connected to the RAN 104, which may utilize NR radio technology, the CN 106 may also be in communication with another RAN (not shown) that employs any of GSM, UMTS, CDMA2000, WiMAX, E-UTRA, or Wi-Fi radio technologies.

[0029] The CN 106 may also serve 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 providing 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 TCP, User Datagram Protocol (UDP), and / or IP in the Transmission Control Protocol / Internet Protocol (TCP / IP) Internet protocol suite. The network 112 may include wired and / or wireless communication networks owned and / or operated by other service providers. For example, the network 112 may include another CN connected to one or more RANs, which may employ the same RAT as the RAN 104 or a different RAT.

[0030] 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 a base station 114a that may employ cellular-based wireless technology and may be configured to communicate with a base station 114b that may employ IEEE 802.11 wireless technology.

[0031] 1B is a system diagram illustrating an example WTRU 102. As shown in FIG. 1B, 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 elements / peripherals 138. It will be understood that the WTRU 102 may include any sub-combination of the above elements while remaining consistent with an embodiment.

[0032] The processor 118 may be a general-purpose processor, a special-purpose processor, a conventional processor, a digital signal processor (DSP), multiple 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), a state machine, etc. 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. 1B depicts the processor 118 and the transceiver 120 as separate components, it will be understood that the processor 118 and the transceiver 120 may be incorporated together, for example, in an electronic package or chip.

[0033] The transmit / receive element 122 may be configured to transmit signals to or receive signals from a base station (e.g., 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 one 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 one embodiment, the transmit / receive element 122 may be configured to transmit and / or receive both RF and light signals. It will be understood that the transmit / receive element 122 may be configured to transmit and / or receive any combination of wireless signals.

[0034] 1B as a single element, the WTRU 102 may include any number of transmit / receive elements 122. For example, 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.

[0035] The transceiver 120 may be configured to modulate signals to be transmitted by the transmit / receive element 122 and demodulate signals received by the transmit / receive element 122. As mentioned above, the WTRU 102 may have multi-mode capabilities. Thus, the transceiver 120 may include multiple transceivers to enable the WTRU 102 to communicate via multiple RATs, such as, for example, NR and IEEE 802.11.

[0036] 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. Furthermore, the processor 118 may access information from and store data in any type of suitable memory, such as non-removable memory 130 and / or 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, etc. 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 home computer (not shown).

[0037] The processor 118 may receive power from the power source 134 and may be configured to distribute and / or control the power to 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 batteries (e.g., nickel-cadmium (NiCd), nickel-zinc (NiZn), nickel-metal hydride (NiMH), lithium-ion (Li-ion), etc.), solar cells, fuel cells, etc.

[0038] The processor 118 may also be coupled to a 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 instead of, information from the GPS chipset 136, the WTRU 102 may receive location information from base stations (e.g., base stations 114a, 114b) over the air interface 116 and / or determine its location based on the timing of when signals are received from two or more nearby base stations. It will be understood that the WTRU 102 may acquire location information via any suitable location determination method while remaining consistent with an embodiment.

[0039] The processor 118 may further be coupled to other peripherals 138, which may include one or more software and / or hardware modules / units 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 videos), 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, etc. The peripherals 138 may include one or more sensors, which 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.

[0040] The WTRU 102 may include a full-duplex radio where transmission and reception of some or all of the signals (e.g., associated with a particular subframe for both the uplink (e.g., for transmission) and the downlink (e.g., for reception) may be parallel and / or simultaneous. The full-duplex radio may include an interference management unit for reducing and or substantially eliminating self-interference via either hardware (e.g., a choke) or signal processing via a processor (e.g., a separate processor (not shown) or via the processor 118). In one embodiment, the WTRU 102 may include a half-duplex radio that is for transmission and reception of some or all of the signals (e.g., associated with a particular subframe for either the uplink (e.g., for transmission) or the downlink (e.g., for reception)).

[0041] 1C is a system diagram illustrating the RAN 104 and the CN 106, according to one embodiment. As mentioned above, the RAN 104 may employ E-UTRA radio technology to communicate with the WTRUs 102a, 102b, and 102c over the air interface 116. The RAN 104 may also be in communication with the CN 106.

[0042] The RAN 104 may include eNodeBs 160a, 160b, and 160c, although it will be understood that the RAN 104 may include any number of eNodeBs while remaining consistent with an embodiment. The eNodeBs 160a, 160b, and 160c may each include one or more transceivers for communicating with the WTRUs 102a, 102b, and 102c over the air interface 116. In an embodiment, the eNodeBs 160a, 160b, and 160c may implement MIMO technology. Thus, the eNodeB 160a, for example, may use multiple antennas to transmit wireless signals to, and receive wireless signals from, the WTRU 102a.

[0043] Each of the eNodeBs 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, scheduling of users in the uplink (UL) and / or downlink (DL), etc. As shown in FIG. 1C, the eNodeBs 160a, 160b, 160c may communicate with one another over an X2 interface.

[0044] 1C may include a mobility management entity (MME) 162, a serving gateway (SGW) 164, and a packet data network (PDN) gateway (PGW) 166. Although each of the above elements is shown as part of the CN 106, it will be understood that any one of these elements may be owned and / or operated by an entity other than the CN operator.

[0045] The MME 162 may be connected to each of the eNodeBs 160a, 160b, and 160c in the RAN 104 via an S1 interface and may act 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 initial attach of the WTRUs 102a, 102b, 102c, etc. 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.

[0046] The SGW 164 may be connected to each of the eNodeBs 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 inter-eNodeB handover, triggering paging when DL data is available for the WTRUs 102a, 102b, 102c, managing and storing the context of the WTRUs 102a, 102b, 102c, etc.

[0047] The SGW 164 may be connected to a PGW 166, which may provide the WTRUs 102a, 102b, 102c with access to packet-switched networks, such as the Internet 110, to facilitate communication between the WTRUs 102a, 102b, 102c and IP-enabled devices.

[0048] The CN 106 may facilitate communication 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 communication between the WTRUs 102a, 102b, 102c and legacy landline communication devices. For example, the CN 106 may include or communicate 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. Additionally, 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.

[0049] Although the WTRU is described in Figures 1A-1D as a wireless terminal, it is contemplated that in certain representative embodiments, such a terminal may use a wired communication interface with the communication network (e.g., temporarily or permanently).

[0050] In a representative embodiment, the other network 112 may be a WLAN.

[0051] 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 access to or interface with a distribution system (DS) or another type of wired / wireless network that carries traffic during and / or from the BSS. Traffic to the STA originating from outside the BSS may arrive through the AP and be delivered to the STA. Traffic originating from the STA to a destination outside the BSS may be sent to the AP for delivery to the respective destination. Traffic between STAs within the BSS may be sent through the AP, e.g., where a source STA may send traffic to the AP, and the AP may deliver the traffic to the destination STA. Traffic between STAs within the BSS may be considered and / or referred to as peer-to-peer traffic. Peer-to-peer traffic may be sent between (e.g., directly between) a source STA and a destination STA via a direct link setup (DLS). In some representative embodiments, the DLS may use 802.11e DLS or 802.11z tunneled DLS (TDLS). A WLAN using an Independent BSS (IBSS) mode may not have an AP, and the STAs within or using the IBSS (e.g., all of the STAs) can communicate directly with each other. The IBSS communication mode is sometimes referred to herein as an "ad hoc" communication mode.

[0052] When using the 802.11ac infrastructure mode of operation or a similar mode of operation, an AP can transmit beacons on a fixed channel, such as a primary channel. The primary channel can be a fixed width (e.g., a 20 MHz wide bandwidth) or a width dynamically set via signaling. The primary channel can be the operating channel of the BSS and can be used by STAs to establish a connection with the AP. In some representative embodiments, carrier sense multiple access with collision avoidance (CSMA / CA) can be implemented, for example, in an 802.11 system. In CSMA / CA, STAs (e.g., every STA), including the AP, can sense the primary channel. If the primary channel is sensed / detected and / or determined to be busy by a particular STA, the particular STA can back off. One STA (e.g., only one station) can transmit at any given time in a given BSS.

[0053] High-throughput (HT) STAs may use, for example, a 40 MHz wide channel for communication via a combination of a primary 20 MHz channel with adjacent or non-adjacent 20 MHz channels to form a 40 MHz wide channel.

[0054] A very high throughput (VHT) STA can support 20 MHz, 40 MHz, 80 MHz, and / or 160 MHz wide channels. A 40 MHz channel and / or an 80 MHz channel can be formed by combining contiguous 20 MHz channels. A 160 MHz channel can be formed by combining eight contiguous 20 MHz channels or by combining two non-contiguous 80 MHz channels, sometimes referred to as an 80+80 configuration. For the 80+80 configuration, after channel encoding, the data can be passed through a segment parser that can split the data into two streams. Inverse fast Fourier transform (IFFT) processing and time-domain processing can be performed separately for each stream. The streams can be mapped onto two 80 MHz channels, and the data can be transmitted by the transmitting STA. At the receiver of the receiving STA, the operations described above for the 80+80 configuration can be reversed, and the combined data can be sent to a medium access control (MAC) layer, entity, etc.

[0055] Sub-1 GHz operating modes are supported by 802.11af and 802.11ah. 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 5 MHz, 10 MHz, and 20 MHz bandwidths in the TV white space (TVWS) spectrum, while 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 can support meter-type control / machine-type communication (MTC), such as MTC devices in macro coverage areas. MTC devices can have limited capabilities, including, for example, support for some and / or limited bandwidths (e.g., only support for that). MTC devices can include batteries with above-threshold battery life (e.g., to maintain very long battery life).

[0056] WLAN systems that can support multiple channels and channel bandwidths, such as 802.11n, 802.11ac, 802.11af, and 802.11ah, include a channel that can be designated as a primary channel. The primary channel can have a bandwidth equal to the largest common operating bandwidth supported by all STAs in the BSS. The bandwidth of the primary channel can be set and / or limited by a STA, from among all STAs operating in the BSS, that supports the smallest bandwidth operating mode. In an 802.11ah example, the primary channel may be 1 MHz wide for a STA (e.g., an MTC-type device) that supports (e.g., only supports) the 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. For example, if a STA (that only supports 1 MHz operating mode) transmits to an AP such that the primary channel is busy, the entire available frequency band may be considered busy, even though most of the frequency band may remain idle and available for use.

[0057] In the United States, the available frequency bands that can be used by 802.11ah are 902 MHz to 928 MHz. In South Korea, the available frequency bands are 917.5 MHz to 923.5 MHz. In Japan, the available frequency bands are 916.5 MHz to 927.5 MHz. The total available bandwidth for 802.11ah is 6 MHz to 26 MHz, depending on the country code.

[0058] 1D is a system diagram illustrating the RAN 104 and the CN 106, according to one embodiment. As mentioned above, the RAN 104 may employ NR 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.

[0059] The RAN 104 may include gNBs 180a, 180b, and 180c, although it will be understood that the RAN 104 may include any number of gNBs while remaining consistent with an embodiment. The gNBs 180a, 180b, and 180c may each include one or more transceivers for communicating with the WTRUs 102a, 102b, and 102c over the air interface 116. In an embodiment, the gNBs 180a, 180b, and 180c may implement MIMO technology. For example, the gNBs 180a and 180b may utilize beamforming to transmit signals to and / or receive signals from the WTRUs 102a, 102b, and 102c. Thus, the gNB 180a may, for example, use multiple antennas to transmit wireless signals to and / or receive wireless signals from the WTRU 102a. In one 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 one embodiment, the gNBs 180a, 180b, 180c may implement coordinated multi-point (CoMP) technology. For example, the WTRU 102a may receive coordinated transmissions from the gNBs 180a and 180b (and / or 180c).

[0060] The WTRUs 102a, 102b, 102c may communicate with the gNBs 180a, 180b, 180c using transmissions associated with scalable numerologies. For example, the OFDM symbol spacing and / or OFDM subcarrier spacing may be different 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 transmission time intervals (TTIs) of varying or scalable lengths (e.g., including varying numbers of OFDM symbols and / or varying lengths of absolute time duration).

[0061] 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 can communicate with the gNBs 180a, 180b, 180c without accessing any other RAN (e.g., eNodeBs 160a, 160b, 160c). In a standalone configuration, the WTRUs 102a, 102b, 102c can utilize one or more of the gNBs 180a, 180b, 180c as mobility anchor points. In a standalone configuration, the WTRUs 102a, 102b, 102c can communicate with the gNBs 180a, 180b, 180c using signals in unlicensed bands. In a non-standalone configuration, the WTRUs 102a, 102b, 102c may communicate / connect with a gNB 180a, 180b, 180c while also communicating / connecting with another RAN, such as an eNodeB 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 eNodeBs 160a, 160b, 160c substantially simultaneously. In a non-standalone configuration, the eNodeBs 160a, 160b, 160c may act as mobility anchors for the WTRUs 102a, 102b, 102c, and the gNBs 180a, 180b, 180c may provide additional coverage and / or throughput for serving the WTRUs 102a, 102b, 102c.

[0062] 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 for network slicing, dual connectivity, interworking between NR and E-UTRA, routing of user plane data to user plane functions (UPFs) 184a, 184b, routing of control plane information to access and mobility management functions (AMFs) 182a, 182b, etc. As shown in FIG. 1D, the gNBs 180a, 180b, 180c may communicate with one another via an Xn interface.

[0063] 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 at least one Data Network (DN) 185a, 185b. While each of the above elements is shown as part of the CN 106, it will be understood that any of these elements may be owned and / or operated by an entity other than the CN operator.

[0064] The AMF 182a, 182b may be connected to one or more of the gNBs 180a, 180b, 180c in the RAN 104 via an N2 interface and may act as a control node. For example, the AMF 182a, 182b may be responsible for authenticating users of the WTRUs 102a, 102b, 102c, supporting network slicing (e.g., handling different protocol data unit (PDU) sessions with different requirements), selecting a particular SMF 183a, 183b, managing registration areas, terminating NAS signaling, mobility management, etc. Network slicing may be used by the AMF 182a, 182b to customize CN support for the WTRUs 102a, 102b, 102c, for example, based on the type of service being utilized by the 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 MTC access, etc. The AMFs 182a, 182b may provide a control plane function for switching between the RAN 104 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 Wi-Fi.

[0065] The SMFs 183a and 183b may be connected to the AMFs 182a and 182b in the CN 106 via an N11 interface. The SMFs 183a and 183b may also be connected to the UPFs 184a and 184b in the CN 106 via an N4 interface. The SMFs 183a and 183b may select and control the UPFs 184a and 184b and configure the routing of traffic through the UPFs 184a and 184b. The SMFs 183a and 183b may perform other functions, such as managing and allocating UE IP addresses, managing PDU sessions, controlling policy enforcement and QoS, providing downlink data notification, etc. The PDU session type may be IP-based, non-IP-based, Ethernet-based, etc.

[0066] The UPFs 184a, 184b may be connected to one or more of the gNBs 180a, 180b, 180c in the RAN 104 via an N3 interface, which may provide the WTRUs 102a, 102b, 102c with access to packet-switched networks such as the Internet 110, for example, to facilitate communications between the WTRUs 102a, 102b, 102c and IP-enabled devices. The UPFs 184a, 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, etc.

[0067] The CN 106 may facilitate communication with other networks. For example, the CN 106 may include or communicate 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. Additionally, 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. In one embodiment, the WTRUs 102a, 102b, 102c may be connected to local data networks (DNs) 185a, 185b through UPFs 184a, 184b via an N3 interface to the UPFs 184a, 184b and an N6 interface between the UPFs 184a, 184b and the DNs 185a, 185b.

[0068] 1A-1D and the corresponding description thereof, one or more, or all, of the functions described herein with respect to any of the WTRUs 102a-d, base stations 114a-b, eNodeBs 160a-c, MME 162, SGW 164, PGW 166, gNBs 180a-c, AMFs 182a-b, UPFs 184a-b, SMFs 183a-b, DNs 185a-b, and / or any other element / device(s) described herein may be performed by one or more emulation elements / 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 functionality.

[0069] The emulation device may be designed to implement one or more tests of other devices in a laboratory environment and / or in a carrier network environment. For example, one or more emulation devices may perform one or more, or all, functions while fully or partially implemented and / or deployed as part of a wired and / or wireless communication network to test other devices in the communication network. One or more emulation devices may perform one or more, or all, functions while temporarily implemented / deployed as part of a wired and / or wireless communication network. The emulation device may be directly coupled to another device for testing purposes and / or may perform testing using over-the-air wireless communication.

[0070] The one or more emulation devices may perform one or more functions, inclusive, while not being implemented / deployed as part of a wired and / or wireless communication network. For example, the emulation devices may be utilized in test labs and / or test scenarios in non-deployed (e.g., test) wired and / or wireless communication networks to implement testing of one or more components. The one or more emulation devices may be test equipment. Direct RF coupling and / or wireless communication via RF circuitry (which may, for example, include one or more antennas) may be used by the emulation devices to transmit and / or receive data.

[0071] As mentioned above, the channel state information (CSI) may include at least one of 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), e.g., L1-RSRP, or a signal-to-interference-to-noise ratio (SINR), a CSI-RS resource indicator (CRI), a synchronization signal / physical broadcast channel (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 (RS) (e.g., a CSI-RS or SS / PBCH block or any other reference signal).

[0072] CSI reporting framework: The WTRU can be configured to report CSI through the physical uplink control channel (PUCCH) or per gNB request on a UL physical uplink shared channel (PUSCH) grant. Depending on the configuration, the CSI-RS can cover the entire bandwidth of the bandwidth portion (BWP) or only a portion of it. Within the CSI-RS bandwidth, the CSI-RS can be configured in each physical resource block (PRB) or every other PRB. In the time domain, the CSI-RS resources can be configured either periodic, semi-persistent, or aperiodic.

[0073] Semi-persistent CSI-RS is similar to periodic CSI-RS, except that resources can be (de)activated by a medium access control (MAC) control element (CE), and the WTRU reports the associated measurements only when the resources are activated. In aperiodic CSI-RS, the WTRU is triggered to report the measured CSI-RS on the PUSCH by a request in the downlink control information (DCI). Periodic reports are carried on the PUCCH, while semi-persistent reports can be carried on either the PUCCH or the PUSCH.

[0074] The reported CSI can be used by the scheduler when allocating optimal resource blocks, determining precoding matrices, beams, transmission modes, and selecting a preferred modulation and coding scheme (MCS), possibly based on the time-frequency selectivity of the channel. The reliability, accuracy, and timeliness of WTRU CSI reporting can be very important to meet Ultra-Reliable Low Latency Communications (URLLC) and / or other service requirements.

[0075] The WTRU may be configured with a CSI measurement configuration, which may include one or more CSI reporting configurations, resource configurations, and / or links between the one or more CSI reporting configurations and the one or more resource configurations.

[0076] FIG. 2 illustrates an example configuration 200 for CSI reporting configuration, resource configuration, and link.

[0077] In the CSI measurement configuration, one or more of the following configuration parameters may be provided:

[0078] (1) N≧1 CSI reporting configurations 202, 204, M≧1 resource configurations 212, 214, 216, and a CSI measurement configuration 220 linking the N CSI reporting configurations 202, 204 with the M resource configurations 212, 214, and 216.

[0079] (2) The CSI reporting configuration 202, 204 includes at least one of the following: (i) time domain behavior: e.g., aperiodic or periodic / semi-persistent, (ii) frequency granularity: at least for PMI and CQI, (iii) CSI reporting type (e.g., PMI, CQI, RI, CRI, etc.), and / or (iv) if PMI is reported, PMI type (e.g., Type I or II) and codebook configuration.

[0080] (3) The resource configurations 212, 214, 216 include at least one of the following: (i) time-domain behavior: aperiodic or periodic / semi-persistent, (ii) RS type (e.g., for channel or interference measurements), and (iii) S≧1 resource sets, where each resource set may contain Ks resources.

[0081] (4) The CSI measurement configuration 220 includes at least one of the following: (i) one CSI reporting configuration, (ii) one resource configuration, and / or (iii) in the case of CQI, a reference transmission scheme configuration.

[0082] (5) For CSI reporting for component carriers, one or more of frequency granularities may be supported, including wideband CSI, partial band CSI, and sub-band CSI.

[0083] 3 shows a basic example 300 of codebook-based precoding with feedback information 302. The feedback information 302 may include a precoding matrix index (PMI), which may be referred to as a codeword index in the example codebook 300 of FIG.

[0084] As shown in FIG. 3, the codebook may include a set of precoding vectors / matrices for each rank and number of antenna ports, with each precoding vector / matrix having its own index, so that the receiver 310 may inform the transmitter 320 of the preferred precoding vector / matrix index via feedback 302.

[0085] Codebook-based precoding may have performance degradation compared to non-codebook-based precoding due to its finite number of precoding vectors / matrices. However, the main advantage of codebook-based precoding may be lower control signaling / feedback overhead. Table 1 below shows an example of a codebook for two transmit (Tx) antennas.

[0086] [Table 1]

[0087] CSI Processing Criteria: The CSI processing unit (CPU) may be referred to as the minimum CSI processing unit, and a WTRU may support one or multiple 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 required to estimate more than N CSI feedbacks simultaneously, the WTRU may only perform the N highest priority CSI feedbacks and may not estimate the rest.

[0088] The start and end of each CPU process can be determined based on the CSI reporting type (e.g., aperiodic, periodic, semi-persistent), as in the following example:

[0089] In aperiodic CSI reporting, the CPU is occupied starting from the first OFDM symbol after the physical downlink control channel (PDCCH) trigger and until the last OFDM symbol of the PUSCH carrying the CSI report. In periodic and semi-persistent CSI reporting, the CPU is occupied starting from the first OFDM symbol of one or more associated measurement resources (not earlier than the CSI reference resource) and until the last OFDM symbol of the CSI report.

[0090] The number of CPUs occupied may vary based on the CSI measurement type (e.g., beam-based or non-beam-based). For non-beam-related reporting, it is K CPUs when there are K CSI-RS resources in the CSI-RS resource set for channel measurement. For beam-related reporting (e.g., "cri-RSRP," "ssb-Index-RSRP," or "none"), it is 1 CPU regardless of the number of CSI-RS resources in the CSI-RS resource set for channel measurement due to low CSI calculation complexity. "None" is used for P3 operation or aperiodic tracking reference signal (TRS) transmission.

[0091] For aperiodic CSI reporting using a single CSI-RS resource, 1 CPU is occupied. For CSI reporting on Ks CSI-RS resources, Ks CPUs are occupied because the WTRU needs to perform CSI measurements for each CSI-RS resource. When the number of unoccupied CPUs (N_u) is smaller than the required CPUs for CSI reporting (N_r), (i) the WTRU may drop the N_r-N_u CSI reports based on priority in the case of uplink control information (UCI) on the PUSCH without data / HARQ, and / or (ii) the WTRU may report dummy information in the Nr-Nu CSI reports based on priority to avoid rate-matching handling of the PUSCH in other cases.

[0092] Artificial intelligence (AI) can be broadly defined as the behavior exhibited by a machine. Such behavior can mimic cognitive functions, for example, to sense, reason, adapt, and act. Machine learning (ML) can refer to a type of algorithm that solves problems based on learning through experience (“data”) without being explicitly programmed (“constructing a set of rules”). Machine learning can be considered a subset of AI. Different machine learning paradigms can be envisioned based on the nature of the data or feedback available to the learning algorithm. For example, supervised learning techniques can involve learning a function that maps inputs to outputs based on labeled training examples, where each training example can be a pair consisting of an input and a corresponding output. For example, unsupervised learning techniques can involve detecting patterns in data without existing labels. For example, reinforcement learning techniques can involve performing a sequence of actions in an environment to maximize a cumulative reward. In some solutions, machine learning algorithms can be applied using a combination or interpolation of the above techniques. For example, semi-supervised learning techniques 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 lies somewhere between unsupervised learning (without labeled training data) and supervised learning (with only labeled training data).

[0093] Deep learning refers to a class of machine learning algorithms that employ artificial neural networks (e.g., DNNs specifically) loosely inspired by biological systems. Deep neural networks (DNNs) are a special class of machine learning models inspired by the human brain, in which inputs are linearly transformed and passed through nonlinear activation functions multiple times. DNNs generally consist of multiple layers, each consisting of a linear transformation and a given nonlinear activation function. DNNs can be trained using training data via the backpropagation algorithm. Recently, DNNs have demonstrated state-of-the-art performance in various domains, such as speech, vision, and natural language, as well as in various machine learning settings, supervised, unsupervised, and semi-supervised. The term AIML-based methods / processing can refer to achieving behavior and / or adapting to requirements by learning based on data without explicit configuration of a sequence of action steps. Such methods can enable learning complex behaviors that may be difficult to specify and / or implement when using legacy methods.

[0094] AI / ML-based CSI prediction,Recurrent neural networks (RNNs) are proposed for AI / ML-based,CSI prediction due to their strong time series prediction ability.,RNNs are neural networks consisting of an input layer,,an output layer, and one (or more) hidden layers, which,leverage memory of previous states to predict future samples.

[0095] 4 shows one exemplary RNN architecture 400, where the vector of hidden states 402 is a function of the current input 405 and the previous RNN output 410, where x(t) represents the vector at the RNN input 405 at time t and y(t) represents the vector at the RNN output 410 at time t. When the RNN is used for channel / CSI prediction, the input x consists of a sequence of N previous consecutive channel estimates according to Equation 1 below:

[0096]

number

[0097] The estimated channel / CSI is fed into a tapped delay line to generate the RNN input. Furthermore, depending on the RNN architecture, the input sequence of N channel estimates can be converted from matrix to vector form. The RNN output is the predicted channel / CSI at time t+L,

[0098]

number

[0099] An example of a loss function used to train an RNN is determined by Equation 2 below:

[0100]

number

[0101] where:

[0102]

number

[0103] represents the predicted channel at time t+L, H(t+L) represents the desired output of the network (the actual channel at time t+L), and the operators || || F denotes the Frobenius (Euclidean) norm. The loss function defined in this way is used to train the RNN.

[0104] One approach to reducing CSI overhead is to use the correlation characteristics of the channel in the spatial, frequency, and angular domains. For example, a scalable and flexible CSI codebook with up to 32 ports can be used. A Type II codebook utilizes several Discrete Fourier Transform (DFT) vectors to compress the spatial and frequency domains of the channel. A similar approach can be used to improve frequency domain granularity. CSI feedback overhead can be further reduced through exploiting reciprocity in frequency division duplex (FDD) operation. One use case in recent efforts is the application of CSI prediction for CSI feedback enhancement.

[0105] As mentioned above, in a MIMO system, as the number of antenna ports increases, the overhead for transmitting and providing feedback on CSI also increases in both the uplink and downlink. This increase in overhead poses practical challenges to obtaining accurate CSI, since it relies on reference signals sent to the WTRU for measurement and subsequent reporting to the gNB. As the number of antennas is expected to continue to increase in future standards, this overhead is expected to increase accordingly. As disclosed herein, accurate CSI can be achieved with reduced overhead based on one or more of the following:

[0106] Embodiments for a WTRU that perform CSI prediction to reduce the number of transmission opportunities and thereby reduce CSI overhead or improve CSI accuracy

[0107] An embodiment for a WTRU to evaluate and indicate the accuracy of predictions to request and achieve reduced CSI-RS transmissions, if applicable;

[0108] Embodiments for reporting the accuracy of CSI prediction and for monitoring and supervising CSI prediction operations; and

[0109] An embodiment for leveraging the accuracy of prediction and the variability of each CSI component or set of components to obtain reduced CSI feedback.

[0110] An example procedure for CSI prediction will now be described. Referring to FIG. 5, an example method 500 for a WTRU to perform CSI prediction and validation is shown. In one embodiment, the WTRU is configured for CSI prediction with validation 505. In this example, the WTRU configuration may include configuring at least one of: (i) one or more sets of reference signals to be input to a CSI predictor, where a first set may be used to train the CSI predictor model, a second set may be used for CSI prediction, and / or a third set may be used to validate the predicted CSI; (ii) one or more prediction (e.g., look-ahead) window lengths (L) and sets of resources; (iii) prediction accuracy threshold(s); and / or (iv) one or more sets of resources for CSI reporting.

[0111] The WTRU performs measurements on the received RS (e.g., configured from the second set of reference signals) and determines predicted CSI for at least one resource of one or more prediction windows 510. The WTRU may also determine preferred CSI prediction parameters, preferably as a function of the RS measurements and the configured prediction accuracy 515. In one example, the preferred CSI prediction parameters may include one or more prediction window lengths (L) and one or more numbers of RS resources for predicted verification measurements (e.g., from the third set (K) used for verification of the predicted CSI in a subsequent look-ahead window).

[0112] The WTRU then reports the determined preferred CSI prediction parameters to the gNB 520. The WTRU also reports the predicted CSI 525. The report may include predicted CSI values ​​for one (or more) subsequent prediction windows and / or CSI validation / adjustment information for previously reported predicted CSI. Detailed examples are described further below.

[0113] In one embodiment, a WTRU that performs CSI prediction is configured to monitor and report CSI prediction accuracy (e.g., to enable adaptation of CSI prediction parameters). The configuration for monitoring and reporting CSI prediction accuracy may include: (i) a type of accuracy to be monitored (e.g., current behavior or time behavior), (ii) a minimum accuracy threshold or a set of accuracy values ​​(e.g., above the minimum threshold), (iii) one or more prediction window resources, and / or (iv) one or more sets of reference signals (RSs), K, where the set of RSs K may be received in the resources of the i-th prediction window.

[0114] In an example embodiment, the WTRU receives a first set of RSs and determines predicted CSI. If the WTRU is configured to monitor current accuracy, the WTRU determines the accuracy of the predicted CSI in the first prediction window based on the K1 reference signals (RSs) transmitted in the first prediction window. If the current accuracy is higher than a configured threshold, the WTRU determines predicted CSI for the second prediction window (e.g., from measurements performed on the K1 RSs and / or previously predicted CSI for the first window).

[0115] If the WTRU is configured to monitor the time behavior of the CSI prediction accuracy, the WTRU determines the prediction accuracy in a first prediction window and a second prediction window, where the first prediction window and the second prediction window are temporally consecutive prediction windows. The WTRU determines a preferred number (K3) of RSs for validation / tracking in a third prediction window (e.g., occurring after the first prediction window and the second prediction window) as a function of the determined prediction accuracy in the first prediction window and the second window or as a function of the slope or change of the prediction accuracy in the first prediction window and the second prediction window. The WTRU reports the measured CSI prediction accuracy of at least one of the first prediction window or the second prediction window and the preferred number K3 of RSs for validation / tracking in the third prediction window. A detailed example of this procedure is described further below.

[0116] For dynamic reporting of specific predicted CSI components, in an exemplary embodiment, a WTRU that performs CSI prediction can be configured to report reduced predicted CSI feedback (e.g., a subset of predicted CSI components). The WTRU is configured with one or more prediction windows (each made up of one or more prediction instances) and one or more sets of reference signals (RSs). The WTRU receives RSs from one or more sets of RSs in a first prediction window to determine one or more predicted CSI components for a second prediction window. The WTRU then determines the prediction accuracy of each predicted CSI component and, preferably, the rate of change of the value of the predicted CSI component (e.g., from two or more instances of the prediction window).

[0117] In this example, the WTRU determines a subset of predicted CSI components to include in a reduced predicted CSI feedback report based on the prediction accuracy and / or the rate of change of the predicted components. As an example, a predicted CSI component may be excluded from reporting if its rate of change is below a configured threshold and / or if its prediction accuracy is greater than a minimum accuracy. The WTRU then reports the subset of predicted CSI components for the second prediction window.

[0118] Referring to FIG. 6, a timing diagram 600 for DL ​​RS(s) 610 and WTRU CSI reporting 620 includes aspects relating to various embodiments as shown, where the following terms are defined as follows:

[0119] Look-ahead window: A CSI prediction window, typically of size L or Li. L1, L2, L3, and L4, as shown in Figure 6.

[0120] Reference Signals: Includes reference signals used to train AI / ML for prediction, reference signals used as input for CSI predictor to obtain predicted CSI, and reference signals associated with look-ahead windows, defined as K or Ki, K1, K2, K3, and K4, as shown in Figure 6.

[0121] Prediction accuracy: The prediction accuracy of the CSI prediction, which can be measured using prediction accuracy measurement techniques such as cosine similarity and normalized mean square error (NMSE).

[0122] Tracking and testing: The process of tracking the accuracy of a CSI prediction using a reference signal received during a look-ahead window or set of look-ahead windows.

[0123] Validation: The process of verifying the accuracy of a prediction. In this disclosure, this term is used interchangeably with tracking and testing.

[0124] Component-wise accuracy: The prediction accuracy of a single predicted CSI component.

[0125] Component-wise variability: The variability of a single CSI component between successive prediction windows.

[0126] Various benefits of the proposed CSI prediction accuracy embodiments may include, among others, reduced feedback opportunities by reporting predicted CSI for one or a set of subsequent prediction windows; improved CSI prediction by verifying and adjusting predicted CSI reported in previous windows; joint monitoring of the CSI prediction process by providing the network with prediction accuracy measured at the WTRU of previously predicted CSI; reducing the number of reference signals, where the WTRU can report a preferred number of RSs for verification / tracking in the next look-ahead window; and / or reducing feedback overhead by excluding a set of CSI components from predicted CSI reporting for the next set of subsequent prediction windows.

[0127] A detailed example of CSI prediction will now be described. In various exemplary embodiments, the WTRU may be configured to perform CSI prediction with or without verification. To perform CSI prediction, the WTRU may be configured with an AI / ML model. In one example, the WTRU may be configured to train an AI / ML model to perform CSI prediction.

[0128] The WTRU may receive AI / ML configuration information via semi-static signaling (e.g., radio resource control (RRC)), or through dynamic signaling (e.g., MAC CE or DCI), or through some combination of these WTRU configuration methods. The WTRU may be activated / deactivated to use CSI prediction in various manners or for specified time periods. In one example, the WTRU may be configured with CSI prediction with or without verification and may only perform CSI prediction and / or verification upon receiving an activation command.

[0129] In some embodiments, the WTRU may be configured with CSI prediction and reporting, with or without validation, and may also be configured with legacy CSI reporting. In another embodiment, the WTRU may only be expected to report one of CSI prediction or legacy CSI reporting for the reference resource.

[0130] WTRU Configuration for CSI Prediction: In one example, the WTRU may be configured to determine one or more of the following factors, with or without verification:

[0131] - Whether validation is configured.

[0132] Configuration for a first set of reference signals (RSs). The WTRU may perform measurements on RSs from the first set of RSs to train AI / ML for prediction. The configuration may include start resources (e.g., time) and end resources (e.g., time) on which the first set of RSs will be transmitted by a network access station, e.g., a gNB.

[0133] - Configuration for the second set of RSs. The WTRU may use measurements performed on the second set of RSs as input for a CSI predictor (e.g., an AI / ML model). For example, the CSI predictor may be used to obtain CSI values ​​for reference resources other than those of the measured RSs. The configuration may include start resources (e.g., time) and end resources (e.g., time) on which the second set of RSs will be transmitted by the gNB.

[0134] A first set of CSI reporting resources. The WTRU may use at least one resource in the first set of CSI reporting resources to report legacy CSI (i.e., CSI related to a reference resource associated with at least one transmitted RS). For example, the WTRU may report legacy CSI during AI / ML model training or during accumulation of measurements to be used for CSI prediction.

[0135] A second set of CSI reporting resources: The WTRU may report the predicted CSI on at least one resource in the second set of CSI reporting resources.

[0136] - A set of values ​​L = [L1, L2, ..., Ln]. The WTRU can determine the predicted CSI for a set of up to n Lx reference resources. The value x can be considered as an index of the look-ahead window. The WTRU can be configured with a second set of CSI reporting resources of size n, such that the xth reporting resource (where 1 <= x <= n) is used to transmit CSI values ​​for the Lx reference resources. The report on the predicted CSI related to the number of reference resources Lx can be of a size less than, equal to, or greater than Lx.

[0137] A set of values ​​K=[K1, K2, ..., Kn]. The WTRU may be configured with n third sets of RSs. The xth third set of RSs may be associated with Kx RS resources. The xth Kx RS resource may be received on resources associated with the Lx reference resources. The WTRU may perform measurements on the xth third set of RSs to verify the predicted CSI for the xth set of Lx reference resources. The WTRU may determine the predicted CSI for the (x+1)th set of L(x+1) reference resources based on the measurements performed on the xth third set of RSs.

[0138] Resources that the WTRU may request to switch to legacy CSI reporting. The WTRU may assume that the first or second set of RSs is transmitted until receiving an indication from the gNB indicating that the set is no longer transmitted or is not active. In one example, the WTRU may indicate to the gNB when it no longer requires RSs from the first or second set of RSs. For example, the WTRU may indicate to the gNB when its CSI predictor AI / ML model is sufficiently trained. In another example, the WTRU may indicate to the gNB when it has received enough RSs from the second set to generate a potentially configurable number of predicted CSIs, possibly with configurable prediction accuracy.

[0139] According to various embodiments, the WTRU may be configured with one or more prediction accuracy thresholds and may determine that the predicted CSI is ready to be reported when it achieves an accuracy greater than or equal to the threshold(s). The WTRU may report the number of predicted CSI values ​​and / or the accuracy of the predicted CSI values.

[0140] In some embodiments, the WTRU determines various CSI prediction parameters. The WTRU may determine, for example, based on measurements performed on the first or second set of RSs as described herein, that it has obtained a set of predicted CSI that satisfies one or more accuracy criteria.

[0141] In one embodiment, the WTRU may be configured with a value L as described above, and may determine that it has a valid set of predicted CSI when it obtains a set of Lx CSI predictions (or a set of CSI predictions associated with Lx reference resources) that meets an accuracy criterion.

[0142] In another embodiment, the WTRU may determine a value L at which it can obtain a set of predicted CSI that meets an accuracy criterion. The WTRU may determine a set of values ​​K that indicates the required number of RS resources needed for validation of the prediction of each set of Lx reference resources.

[0143] Next, WTRU reporting of CSI prediction parameters will be described. The WTRU may be configured with resources for reporting the CSI prediction parameters to the gNB, and / or the WTRU may request resources for reporting a new set of CSI prediction parameters. For example, the WTRU may be configured with resources for requesting resources for reporting the new set of CSI prediction parameters. In another example, the WTRU may report a request for resources for reporting the new set of CSI prediction parameters in reporting resources of a first set of reporting resources (e.g., used for legacy CSI feedback reporting).

[0144] The reporting of the desired set of CSI prediction parameters may be associated with an indication that the AI / ML model training is complete or that the AI / ML inference is complete. According to various embodiments, the reporting of the CSI prediction parameters may include one or more of the following:

[0145] - A set L of values ​​determined by the WTRU. The WTRU may also indicate the index or identification (e.g., time stamp) of the Lx reference resources associated with the xth set of reference resources. The WTRU may also indicate the number of predicted CSI values ​​associated with the xth set of reference resources. For example, in some cases, the WTRU may report a single CSI feedback report value that is applicable to the Lx reference resources. In another example, the WTRU may report Lx CSI feedback report values, each associated with one of the Lx reference resources.

[0146] - A set of values ​​K determined by the WTRU. The WTRU may report a desired or requested distribution of the Kx RS resources associated with the xth Lx reference resource. For example, the WTRU may request that the Kx RS resources should span the entire Lx reference resource. In another example, the WTRU may request that the Kx RS resources be configured in bursts. The WTRU may explicitly report the desired distribution or may report an index of a pre-configured distribution.

[0147] Measurements that can be predicted (e.g., RI, CQI, PMI, LI, CRI, SINR, RSRP, RSRQ, RSSI, Doppler spread, Angle of Arrival (AoA), Angle of Departure (AoD), delay spread, average delay). The WTRU may use a previously configured (e.g., configured by the gNB) set of prediction parameters until it receives an acknowledgement that the desired set of prediction parameters has been configured.

[0148] Predicted CSI Feedback Report. The WTRU may receive configuration information indicating CSI prediction parameters to use for subsequent CSI prediction. In one embodiment, the configuration may specify the timing of the look-ahead window. In another embodiment, the WTRU may report the timing (e.g., start time, end time, duration) of the look-ahead window in a predicted CSI feedback report.

[0149] The WTRU may receive an indication to activate (or deactivate) one or more third sets of RS resources (Kx) and the second set of CSI reporting resources. The WTRU may receive an indication to deactivate (or activate) the first or second set of RS resources and the first set of CSI reporting resources.

[0150] The WTRU may report a first set of predicted CSI values ​​associated with a first set of reference resources L1. The WTRU may report the first set of predicted CSI values ​​in reporting resources from a second set of reporting resources. The WTRU may indicate in the feedback report whether it should continue to report legacy feedback for the Lx reference resources using reporting resources from the first set of reporting resources.

[0151] The WTRU can receive from the gNB an indication that reporting resources in a first set of reporting resources are deactivated. The WTRU can be configured with resources for requesting activation of reporting resources in a first set of reporting resources. In some cases, resources in a second set of reporting resources may overlap with resources in a first set of reporting resources. In such cases, in some embodiments, the WTRU can multiplex predicted CSI and legacy CSI in one reporting resource (e.g., in either the first set or the second set of reporting resources). In another embodiment, the WTRU can be configured with a priority order and can drop one of two reports (e.g., the WTRU can drop a legacy CSI feedback report).

[0152] In some embodiments, the WTRU can use measurements regarding a combination of RSs in a first set, or a second set, or any i-th third set (where i < m) that occurred before the m-th look-ahead window to generate predicted CSI for Lm reference resources of the m-th look-ahead window. The WTRU can use measurements regarding a combination of RSs in a first set, or a second set, or any i-th third set (where i <= m) to verify previously reported predicted CSI feedback for the Lm reference resources of the m-th look-ahead window.

[0153] WTRU reporting of CSI prediction and CSI prediction verification. According to some embodiments, the WTRU can report predicted CSI, or predicted CSI verification information, in a reporting format that can include one or more of the following.

[0154] A set of predicted CSI values ​​for one or more subsequent look-ahead windows. For example, the WTRU may report predicted CSI values ​​for look-ahead windows 1, 2, and 3 associated with sets of reference resources L1, L2, and L3.

[0155] - Validation of previously reported predicted CSI reports. For example, the WTRU may report whether the predicted CSI associated with the xth look-ahead window is valid (e.g., achieves the required accuracy) prior to or following the xth look-ahead window.

[0156] - Adjustment to previously reported CSI feedback reports. For example, the WTRU may send adjustment values ​​for one or more previously reported predicted CSI feedback reports. The adjustment values ​​may be added to previously reported values ​​or may be entirely new CSI report values.

[0157] - A request to switch to legacy CSI reporting: For example, if the WTRU determines that the predicted CSI does not meet the accuracy requirements or cannot be adjusted to meet the accuracy requirements, the WTRU may request to switch to legacy CSI reporting, i.e., without CSI prediction.

[0158] Legacy CSI reporting: For example, the WTRU may report one or more legacy CSI reports associated with one or more reference resources or RSs included in one or more preceding look-ahead windows.

[0159] In one embodiment, the WTRU is configured with n look-ahead windows and reports predicted CSI feedback for the x look-ahead window prior to (e.g., immediately before) the x look-ahead window. During the x look-ahead window, the WTRU receives K reference signals and performs measurements to verify the predicted CSI feedback. The WTRU may report the accuracy or validity of the predicted CSI for the x window following the x look-ahead window to the gNB.

[0160] In another embodiment, the WTRU is configured with n look-ahead windows and reports predicted CSI feedback for two or more look-ahead windows (e.g., for y look-ahead windows, where y<=n). For example, prior to y look-ahead windows, the WTRU reports predicted CSI feedback for the y look-ahead windows. During a first window in the set y windows, the WTRU receives Ki RS resources. The WTRU may use measurements on the Ki RS resources to verify the CSI prediction for the first look-ahead window. The WTRU may use measurements on the Ki RS resources to verify or determine adjustments for other (e.g., subsequent) look-ahead windows in set y. During a second look-ahead window in the set y window, the WTRU receives Kj RS resources. The WTRU may use measurements on the Ki or Kj RS resources to verify the CSI prediction for the second window. The WTRU may use measurements on the Ki or Kj RS resources to verify or determine adjustments for other (e.g., subsequent) look-ahead windows in set y. This may continue until the last look-ahead window in the set of y windows. The WTRU may report validity or adjustments at the end of any or all look-ahead windows in the set of y windows.

[0161] Referring to FIG. 7, a timing diagram 700 shows the L i report the predicted CSIs, then i Diagram 700 shows an example process for receiving DL CSI-RS. Diagram 700 shows two example configurations for receiving DL CSI-RS: a first configuration 710 in which K CSI-RS are transmitted by the base station during windows of length L1, L2, and / or L3 based on WTRU reporting, and a second configuration 715 for periodic CSI-RS in which the number of CSI-RS transmitted by the base station does not change, but the WTRU can sample / measure less than all transmitted CSI-RS. The WTRU reports CSI 720 based on the received CSI-RS, as shown.

[0162] In both the example configurations 710 and 715 of FIG. 7 , the WTRU is configured with two look-ahead windows with L1 and L2 reference resources K1 and K2, respectively. That is, the WTRU is configured with K1 and K2 RS resources in the look-ahead windows of L1 and L2, respectively. Prior to the first look-ahead window, the WTRU may report legacy CSI and receive additional RS(s) for CSI prediction. Just before the look-ahead window L1, the WTRU may report predicted CSI for the first look-ahead window L1 based on the received RS 722. In this example, during the first look-ahead window L1, the WTRU may perform measurements on the K1 RS resources, and just before the second look-ahead window (having duration L2), the WTRU may decide to report predicted CSI for the second look-ahead window 724. In one example, this reporting may be as a function of the accuracy of the predicted CSI for the first look-ahead window. If and / or when the accuracy of the predicted CSI falls below a configured threshold accuracy, the WTRU may request to switch to legacy CSI reporting 726. In one example, the WTRU may revert to CSI prediction by reporting 728 the predicted L3 samples for the next look-ahead window L3.

[0163] 8 shows an example method 800 of signal / messaging flow between a network and a WTRU for reporting and configuring CSI prediction parameters, as well as determining predicted CSI values ​​and reporting the predicted CSI, according to one embodiment. In method 800, the network can optionally request L (look-ahead window length) and K RS resources to be used by the WTRU for CSI prediction, and the WTRU replies 810 with the requested information. At 815, the network can confirm or modify these parameters and provide an indication thereof to the WTRU 815. The base station then sends 820 N CSI-RS samples to the WTRU to build a historical CSI sample model for CSI prediction. In this example, the WTRU can request an UL grant 825, and the base station can provide UL resources 830 for the WTRU to send 835 a predicted CSI report, as described in embodiments herein. The base station may acknowledge 840 the predicted CSI report and transmit 845 data to the WTRU based on the predicted CSI parameters using L (the look-ahead window duration).

[0164] In various embodiments, a WTRU that performs CSI prediction is configured with CSI prediction parameters. Example configurations may include the following: a look-ahead window length (Li), a CSI-RS configuration during the look-ahead window including the number of CSI-RSs (Ki), an accuracy threshold for the predicted CSI, a number of consecutive windows for determining the behavior of the predicted CSI accuracy, a length of historical CSI samples (N), and / or an accuracy grade, where each accuracy grade corresponds to a specific number (K) of CSI-RSs in the look-ahead window.

[0165] The WTRU can be semi-statically configured, e.g., via RRC configuration. Some of the parameters (e.g., the number of CSI-RS in the next look-ahead window) can be dynamically changed, e.g., via dedicated UL and DL signaling.

[0166] In one solution, the WTRU receives one or more sets K of reference signals, where set K may be received in the resources of the i prediction window. The WTRU may use the reference signals received during one look-ahead window to determine the accuracy of the CSI predicted in the previous window and to determine a preferred number of CSI-RS for verification and tracking in the third look-ahead window.

[0167] WTRU measurements to determine the behavior of CSI prediction accuracy / precision. Length L i During look-ahead window i, the WTRU i The received CSI-RS (0≦K i ≦L i ) to K i CSIs are measured, where 0 indicates no CSI-RS transmission, and L i denotes the total CSI-RS transmission during the look-ahead window.

[0168] The WTRU uses the reference signal received during the ith look-ahead window to calculate K i For a received CSI-RS, the CSI prediction accuracy α can be determined by calculating the relative difference between the measured CSI-RS and the predicted CSI.

[0169] The WTRU determines and reports the required CSI-RS for the next look-ahead window based on the current measured accuracy. In one embodiment, the WTRU compares the current measured CSI prediction accuracy in look-ahead window i with a configured prediction accuracy threshold to determine the required number K of CSI-RS in the next look-ahead window i+1. i+1 Determine.

[0170] 9, an example signal flow diagram 900 is shown when the CSI prediction accuracy is better than a configured threshold, i.e., α>threshold. As shown in FIG. 9, the WTRU adjusts the look-ahead window L i Between K i 905, and receives the corresponding K CSI-RSs. i The WTRU then measures K CSIs 906. i The WTRU calculates 907 a prediction accuracy α using the K measured CSIs and compares it to a configured prediction accuracy threshold 908. Then, in one example, the WTRU i The measured CSI and L i The predicted CSIs are combined to form a matrix of length L i+1 The WTRU may then predict the CSI for the next look-ahead window i+1 with a prediction accuracy α and then send this to the gNB 920. As mentioned above, the WTRU may request 910 and receive 915 an UL grant before sending 920 the predicted CSI report with prediction accuracy α to the base station. In one solution, i+1 The value of may be calculated based on the accuracy level of the previously predicted CSI. In particular, if the determined prediction accuracy is high, the number of CSI-RS transmissions may be reduced in the next look-ahead window. If the determined prediction accuracy is low, the WTRU may request, or the base station may determine, that an increased number of reference signals be sent for the next look-ahead window to improve the prediction accuracy in the next look-ahead window, or set of look-ahead windows. In this example, the base station may acknowledge 925 receipt of the predicted CSI.

[0171] Referring to FIG. 10, an example signal flow diagram 1000 is shown, which is similar to FIG. 9, but when the CSI prediction is lower than a configured threshold. Note that similar steps / signaling will not be described separately. In step 1008, if α<threshold, the WTRU determines that the prediction accuracy calculated using reference signals received during the i-th look-ahead window is below a certain threshold. In one embodiment of this case, the WTRU may request to fall back to legacy CSI reporting to report one or a set of CSI in the i-th window. In another embodiment of this case, the WTRU may report CSI measured using reference signals received during the i-th window. Furthermore, the WTRU may report CSI measured using reference signals received during the i-th window, while reporting legacy CSI until the predicted CSI is accurate enough to be reported. i+1 The WTRU may request 1012 that a new set of reference signals (e.g., triggered by DCI or MAC-CE) be sent by the base station 1014 to construct a new historical CSI data model for predicting the CSI for the next look-ahead window i+1 or set of look-ahead windows with ΔN=NK i In another example, the WTRU may request N reference signals when the i-th received K reference signals are not sufficient to use in prediction operations 1012. In one option, ΔN may not be limited to N−Ki, and ΔN may be determined as a function of relevant parameters such as N, K, α, L, and SINR.

[0172] In both cases of Figures 9 and 10, the WTRU may incorporate the CSI measured through the K CSI-RSs and the CSI prediction accuracy α into the predicted CSI report, which keeps the gNB updated regarding the accuracy of the prediction process.

[0173] 11 , a diagram 1100 of graded accuracy levels versus time is shown. In some embodiments, the WTRU may define or be configured to define one or more grades of prediction accuracy 1110 (e.g., varying grades of accuracy above a prediction accuracy threshold 1115), each grade resulting in a particular value of K. In some embodiments, the accuracy grade values ​​may be reported to the network, and either the network determines the number of CSI-RSs, or the WTRU maps the accuracy levels to particular values ​​of K. The accuracy grade levels may be used for various purposes, such as increasing / reducing the number of RSs used for CSI prediction.

[0174] In various embodiments, the WTRU determines and reports the CSI-RS for the next look-ahead window based on the time behavior of the prediction accuracy. In some embodiments, when accurate prediction is maintained over consecutive windows, e.g., two or three windows, the WTRU may request a reduction in CSI-RS transmission in the next window. The number of CSI-RS during look-ahead window "i" is generally 0≦K. i ≦L i Therefore, K i By reducing , the number of CSI-RS transmissions is reduced during the look-ahead window. When the accuracy of the prediction decreases over successive windows, the WTRU may request an increase in the number of CSI-RS transmissions from the base station.

[0175] Referring to FIG. 12 , a diagram 1200 is shown for monitoring prediction accuracy behavior, e.g., using slope accuracy. In some exemplary embodiments, the WTRU may calculate and store the accuracy of the CSI prediction to determine the accuracy behavior, i.e., whether it is improving or decreasing in successive windows. For example, the WTRU may calculate the accuracy gradient 1210, 1212 between successive windows. As shown in FIG. 12 , the sign of the gradient 1210, 1212 determines its direction, with a positive gradient 1210 indicating increasing prediction accuracy and a negative gradient 1212 indicating decreasing prediction accuracy. In some examples, the gradient value (positive or negative) indicates the steepness of the accuracy change (improvement or degradation) and can be used in predictive CSI operations. For example, in one process, the WTRU may request a reduction in the value of K in the next window when a positive gradient 1210 is captured. In contrast, the WTRU may request that the value of K in the next window be increased when a negative slope value 1212 is captured. In either case, the value of the slope (positive or negative) may be used to determine the change (increase or decrease) in K.

[0176] As an example, the gNB may request a fallback to legacy CSI reporting to abort the current window and construct N new historical CSI samples to perform prediction for the next look-ahead window. In some embodiments, this may be detected based on several collected measurements, such as detecting a large number of NACKs, or based on an accuracy level reported by the WTRU.

[0177] Next, an embodiment for dynamic reporting of specific predicted CSI components will be described.

[0178] Dynamic CSI Control Using Per-Component Accuracy. In an example embodiment, the WTRU may be configured, indicated, or requested to indicate, for each CSI component, the accuracy of a particular CSI component or set of CSI components, as well as the number of CSI-RS needed for the next look-ahead window. In one example, the WTRU receives Ki CSI-RSs during the i-th look-ahead window and performs measurements on the CSI-RSs to evaluate the accuracy of the prediction for each CSI component (e.g., CQI, RI, PMI) in the reported CSI reporting (i-1) window for per-CSI component validation. In some embodiments, the WTRU may calculate the prediction accuracy of at least one of the following CSI components:

[0179] Overall accuracy (overall CSI report): α, or component accuracy: CQI: α CQI , RI:α RI , PMI:α PMI , CRI:α CRI , LI:α LI , L1-RSRP:α L1RSRP , coherence bandwidth: α CB , coherence time: α CT ,

[0180]

number

[0181] Furthermore, in some embodiments, the WTRU may decompose a CSI component having multiple parameters to track the prediction accuracy of each parameter in the corresponding CSI component, given that some parameters fluctuate more frequently than others in one CSI quantity. For example, the PMI includes multiple components (e.g., W1, W2), and therefore, it may have the following accuracy:

[0182]

number

[0183] can be further decomposed into

[0184] According to further embodiments, the WTRU may report the prediction accuracy of each CSI component, or a "grade" of accuracy for each CSI component or set of CSI components, based on a predefined accuracy grading system. The WTRU may evaluate the accuracy grade based on the prediction accuracy of each CSI component, and the accuracy grade may be reported instead of reporting the accuracy. An example of a grading system for the accuracy of W2 is shown in FIG. 13 , diagram 1300. As shown, the prediction accuracy of W2 is measured on a scale of four grades 1310, all of which are above a predefined minimum accuracy threshold 1315. In this case, each grade may correspond to a particular value of K, and as the accuracy grade, and therefore the accuracy, improves, the value of K may be decreased.

[0185] In some embodiments, the WTRU may report one or more of the following:

[0186] - Predicted CSI for the next look-ahead window or next set of look-ahead windows.

[0187] - A list of prediction accuracies for the set of CSI components in the predicted CSI report for the target look-ahead window. For example, the WTRU may report whether the predicted CSI component or set of CSI components in the i-th look-ahead window reaches a certain criterion (e.g., exceeds a certain threshold). The list of prediction accuracies in some embodiments may be one or more of the following: prediction accuracies for the set of CSI components or all CSI components in the predicted CSI report, information about the measured accuracy level per component (e.g., maximum, minimum, average, etc.), and / or accuracy grades for the set of CSI components or all CSI components in the predicted CSI report.

[0188] - Adjustments to previously reported CSI components (e.g., based on CSI component prediction accuracy). For example, the WTRU may transmit revised values ​​for one or more previously reported CSI components.

[0189] - Adjustments to the set of K values ​​and their distribution for the corresponding look-ahead window.

[0190] A request to switch to legacy CSI reporting. For example, if the WTRU determines that the predicted CSI does not meet the accuracy requirements or cannot be adjusted to meet the accuracy requirements, the WTRU may request to switch to legacy CSI reporting.

[0191] A request to fall back to legacy CSI reporting. For example, when a WTRU performs measurements on at least one CSI component, it may request to fall back to legacy if either the prediction accuracy per measured component does not reach a certain criterion (e.g., a prediction accuracy threshold) or a CSI component or set of CSI components is adjusted but still does not reach a certain level of accuracy.

[0192] Legacy CSI reporting: For example, the WTRU may report one or more legacy CSI reports associated with one or more reference resources or RSs included in one or more preceding look-ahead windows.

[0193] The network can use the reported per-component accuracy and / or the preferred value of K to increase or decrease the number of CSI-RS transmissions during the look-ahead window. The network can also use the recommended number of CSI-RS transmissions reported by the WTRU. In one embodiment, the number of CSI-RS transmissions utilized in the next look-ahead window may be equal to the number of CSI-RS transmissions required for the CSI component with the lowest predicted accuracy.

[0194] 14, a method 1400 for determining the number of CSI-RS transmissions during the next look-ahead window based on per-component accuracy is shown. In this embodiment, a process for determining a number K is shown, where the WTRU iterates through P CSI components 1402 to determine 1420 K, and for each CSI component p 1404, K p are needed CSI-RS 1415. After evaluating 1422 all available CSI components, K can be determined 1425 to be equal to the maximum value from Equation 3 below. K p (For example, max{K p}∀p in P) Equation 3

[0195] In this approach, the WTRU may be configured by the network (e.g., through DCI) to track per-component accuracy during the i-th look-ahead window or set of look-ahead windows. The WTRU may be requested to calculate the accuracy for all CSI components or a specific set of CSI components. Furthermore, in some embodiments, the WTRU may be configured to report selected per-component accuracy values ​​based on two options: (i) reporting per-component predicted accuracy for each CSI-RS received before the next look-ahead window, and / or (ii) reporting per-component predicted accuracy measured using all Ki reference signals after the current look-ahead window.

[0196] Relying on the K CSI-RS transmitted during the look-ahead window, the WTRU calculates the CSI samples and obtains precision 1406, 1410, 1412 for each CSI component p. The WTRU can use these calculations to determine / adjust a suitable number K of reference signals for the next look-ahead window.

[0197] 15, an example method 1500 for signaling / messaging flow for per-component accuracy reporting is shown. In one example embodiment, the WTRU may be configured 1505, indicated, or requested to dynamically report a particular CSI component and indicate the number of CSI-RS needed for the next look-ahead window. Upon receiving the K CSI-RS sent 1510 from the base station, the WTRU may report the accuracy α for the configured CSI parameter p. p Calculate 1512 and determine the accuracy α pThe WTRU may determine a best value K for the CSI-RS in the future window based on K 1514. The WTRU may determine a predicted CSI for the next window or set of windows 1516. The WTRU may send a predicted CSI report 1520, as in other embodiments.

[0198] In some embodiments, the WTRU may use the predicted CSI, the best K, and the per-parameter accuracy α p and sending 1520 a reduced CSI report including the CSI component selection signal and the CSI component selection signal. In one example, the WTRU may select a set of CSI components based on the CSI component variation (e.g., per-component variation) and / or per-component accuracy. In one example, the WTRU receives Ki reference signals during the ith look-ahead window and performs measurements to evaluate the variation of each CSI component, or the CSI component selection, as well as the per-component predicted accuracy. Based on the variation requirement (e.g., variation within a certain range), the WTRU may exclude a particular CSI component or set of CSI components from the next CSI report if its accuracy level is above a certain threshold.

[0199] Tracking Per-Component Variations. In some embodiments, the WTRU may perform additional measurements of CSI components, such as per-component variations (tracking changes in each CSI component) between two or more consecutive windows. If there are frequent changes in the measured CSI components, the WTRU may include them in the next CSI report. However, when a CSI component, or set of CSI components, remains relatively stable (based on a particular threshold / range) over a particular set of windows, the WTRU may omit these CSI components in the next CSI report.

[0200] In one embodiment, the WTRU may track changes in at least one CSI component over a certain number of look-ahead windows by comparing values ​​in previous intervals with the current predicted value. This may be achieved by obtaining at least one variation of the accuracy Δ, where Δ p indicates the change in CSI component p between the currently predicted window and the previous window. Examples of possible variations may include one or more of the following: overall accuracy: Δ, or change in CQI: Δ CQI , RI:Δ RI , PMI:Δ PMI , CRI:Δ CRI , LI:Δ LI , L1-RSRP:Δ L1RSRP , coherence bandwidth: Δ CB , coherence time: Δ CT ,

[0201]

number

[0202] Furthermore, in some embodiments, the WTRU may decompose a CSI quantity having multiple CSI components to track variations per component. For example, the PMI may be further decomposed into changes in:

[0203]

number

[0204] CSI component variation Δ p may be a number indicating the amount of change of a particular CSI component between two windows. The value of Δ may be obtained based on the type of CSI component as follows:

[0205] When a CSI component p is defined as an integer, such as RI, Δ p teeth,

[0206]

number

[0207] where i denotes the window index. When a CSI component p is defined as a vector or a matrix, such as W1, Δ p is a norm operation, e.g.,

[0208]

number

[0209] can be obtained using

[0210] According to some embodiments, the WTRU may be configured to track the amount of change in at least one CSI component over a particular number of windows I, where Δ p remains below a certain threshold (say |Δ p |< threshold) or falls within a certain range (for example, threshold2<|Δ p If |<threshold 1), the WTRU may exclude the CSI component from the next CSI report.

[0211] FIG. 16 shows a diagram of a window spanning multiple windows 1605.

[0212]

number

[0213] 16 is a timing diagram 1600 illustrating an example embodiment of the variation of W2. In FIG. 16, it can be seen that the variation of W2 is relatively stable in the highlighted box 1610, indicating that W2 has not changed significantly over the last few windows 1606.

[0214] In another embodiment, the WTRU may categorize the CSI components into groups of interdependent components, such that if one CSI component in the group is included in a CSI report, all other components in the group should also be included.

[0215] For example, if the precoding matrix W2 has a large change between the current window and the previous I-1 windows,

[0216]

number

[0217] , the precoding matrix W2 will be included in the next CSI report. To capture the precoder vectors for the entire set of all FD units, f When considering that both W1 and W2 are necessary, they can be grouped together. On the other hand, W1 is independent from W2 and generally experiences less frequent changes, so W1 can be placed in a separate group and will not need to be included in the CSI feedback message if it has not undergone any changes in the past I windows.

[0218] In implementations of the disclosed embodiments, the WTRU may be configured to report a reduced CSI report based on a particular set of conditions, such as the regularity of change of a particular CSI component or set of components over a particular set of windows. Upon obtaining a reduced CSI report request (where at least one CSI component is excluded from the requested CSI report), the WTRU may report to the network indicating that reduced CSI reporting is available, and the WTRU may report back to the network indicating that reduced CSI reporting has remained relatively stable (e.g., Δ p<threshold) components in the CSI report. In various embodiments, the WTRU then uses, e.g., reports via, a PUSCH a reduced CSI report corresponding to the predicted look-ahead window.

[0219] Dynamic reporting and CSI-RS control based jointly on per-component accuracy and per-component variation. In an example embodiment, the WTRU may be configured to dynamically report predicted CSI and recommend the number of CSI-RS transmissions to utilize during one or more look-ahead windows. Furthermore, the WTRU may be allocated resources that allow the WTRU to request / fall back to legacy CSI reporting to report CSI components with accuracy levels below a certain threshold. In this example, the WTRU monitors per-component accuracy and per-component variation to further reduce CSI overhead.

[0220] The configuration for reduced CSI prediction with or without verification may include a configuration for reporting a reduced CSI report after obtaining predicted CSI for window L. The WTRU may report the reduced predicted CSI in at least one resource. The WTRU may perform measurements on a set of RSs to verify the predicted CSI for the set of Li reference resources. Further, the WTRU may perform measurements to obtain one or both of the prediction accuracy and variance of each CSI component. The WTRU may determine the predicted CSI for the (i+1)th set of L(i+1) reference resources based on the measurements performed on the set of RSs during window Li. The WTRU then includes / excludes CSI components in the L(i+1) windows using the measurements of per-component accuracy and per-component variance.

[0221] Alternatively or additionally, the configuration for reduced CSI prediction, with or without validation, may include resources that may request the WTRU to fall back to legacy CSI reporting, which may be invoked when prediction accuracy of at least one element is obtained.

[0222] During the look-ahead window Li, the WTRU receives Ki reference signals and performs measurements on each CSI component to verify the predicted CSI feedback and obtain per-component accuracy and per-component variance. In some embodiments, the WTRU may also report the verification results to the gNB.

[0223] In an example embodiment, the WTRU may report to the gNB the per-component accuracy and per-component variation of the predicted CSI for the i-th window. Further, the WTRU may use measurements on the Ki RS resources to determine an adjustment for at least one CSI component for a subsequent look-ahead window. The WTRU may adjust and report the number K of reference signals to utilize for the next look-ahead window. For example, based on the determined per-component accuracy and per-component variation, the WTRU may adjust the number K of reference signals that may be needed for the next look-ahead window.

[0224] In some embodiments, the WTRU may exclude a set of predicted CSI components from the predicted CSI. For example, after obtaining the predicted CSI for the next look-ahead window, the WTRU may exclude a set of CSI components if it determines that the variations of these components meet certain requirements (e.g., the variations of the CSI components are below a certain threshold), provided that the prediction accuracy of the excluded CSI components meets a certain threshold (e.g., the prediction accuracy of the CSI components is below a certain threshold).

[0225] In some embodiments, the WTRU may request to fall back to legacy CSI reporting when it determines that the prediction accuracy of a particular CSI component or set of components for the predicted CSI falls below or does not meet the accuracy requirement. In one example, the WTRU may report a request for resources to report a new set of CSI components in reporting resources for the components that did not meet the accuracy requirement (e.g., did not meet or exceed the prediction accuracy threshold).

[0226] According to various embodiments, the WTRU may report at least one of the following:

[0227] - Predicted CSI for the next look-ahead window or next set of look-ahead windows.

[0228] Reduced CSI reporting for the next look-ahead window or next set of look-ahead windows (e.g., the WTRU may filter out CSI components that meet accuracy and variance requirements).

[0229] - A list of prediction accuracies for the set of CSI components in the predicted CSI report for the target look-ahead window.

[0230] - Adjustments to previously reported CSI components (e.g., based on CSI component prediction accuracy).

[0231] - Adjustment to the number K of reference signals and their distribution for the target look-ahead window.

[0232] - A request to switch to legacy CSI reporting, and / or

[0233] Legacy CSI reporting (e.g., if the WTRU determines that the predicted CSI components do not meet the accuracy requirements or cannot be adjusted to meet the accuracy requirements, the WTRU may switch to legacy CSI reporting and request to report at least the set of components that did not meet the accuracy requirements).

[0234] Example Embodiment of Joint Variation and Accuracy Processing. In one example, the WTRU may perform measurements on Ki reference signals received in the ith look-ahead window. The measurements may include determining component-by-component prediction accuracy and component-by-component variance.

[0235] 17, an example method 1700 for a WTRU to include / exclude a CSI component p in a CSI report is shown. In this example, accuracy requirements may be defined that include multiple accuracy thresholds. In one embodiment, a base accuracy threshold (α p ), a high accuracy threshold (e.g., a particular range or level of accuracy above the basic accuracy), and a low accuracy threshold (e.g., a particular range of accuracy above the basic accuracy but below the high accuracy threshold). In one embodiment, a variability requirement is also defined (e.g., a variability threshold Δp).

[0236] Once these thresholds are defined, the method 1700 may include the following steps.

[0237] The WTRU initializes K=Ki and defines a list S={} containing all CSI components to be excluded from CSI reporting. The WTRU then calculates the precision and variance of each given CSI parameter p in the P CSI components as α p , Δ p Decided as 1705.

[0238] For (each p in P CSI components): The accuracy of p is below a certain threshold (e.g., α p < threshold) 1710, the WTRU falls back to legacy and requests 1712 to report p and determines a new set n of reference signals for predicting p for the next look-ahead window; otherwise, The accuracy of p is above a certain threshold (e.g., α p > the threshold) 1710, the WTRU determines 1715 whether the accuracy of is high accuracy or low accuracy. Low accuracy level (e.g., R1 low <α p <R2 low ), the WTRU configures a higher number of Kp reference signals for the next look-ahead window, or High accuracy level (e.g., R1 high <α p <R2 high ), the WTRU configures a lower number of Kp reference signals for the next look-ahead window. The WTRU then determines 1720 whether the variation Δ of the CSI parameter p meets a variation threshold, threshold Δp. Variation requirements are not met (e.g., Δ p > threshold Δ p If so, the WTRU may request a specific RS for Kp (e.g., to continue receiving and / or increase the RS) 1722 and include the relevant information in the CSI report for the next look-ahead window 1724; or Variation requirements are met (e.g., Δ p <threshold Δ p If p is 1720, the WTRU may add parameter p to list S (CSI components to be excluded 1728 from CSI reporting). In this case, the WTRU may also request specific RSs for Kp (e.g., to eliminate or reduce the number of RSs related to parameter p) 1726.

[0239] The WTRU repeats this process for all parameters p for P CSI components to find the maximum number of K, Ki=max(K p ∀P) 1730 and then remove all CSI components p in S. Finally, the WTRU determines the largest (K imax,i 1732 and prepares a list of parameters for CSI reporting.

[0240] The results of the above process analogy are summarized in Table 2 below.

[0241] [Table 2]

[0242] Common CSI Prediction Components. In the previously described embodiments, the processes and features relate to a WTRU configured by a gNB to perform adaptive CSI prediction using selected components and that dynamically reports one or more of the following, or any combination of the following:

[0243] - one or more sets of reference signals for training, predicting, monitoring performance (accuracy), and validating the model;

[0244] WTRU-based adaptation of reference signals in one or any combination of density and pattern in the time domain, frequency domain, and spatial domain, and for a specific time window (e.g., X time units);

[0245] - one or a set of CSI prediction windows, all or any combination of CSI components for prediction, one or a set of prediction accuracy thresholds, type of accuracy to be monitored,

[0246] - one or more sets of radio resources for CSI prediction reporting based on all or any combination of the CSI components;

[0247] - One or more sets of radio resources for WTRU-based adaptive reference signal (e.g., CSI-RS) indication.

[0248] In an embodiment for a WTRU to perform adaptive CSI prediction and performance monitoring (accuracy) for a configured selected CSI component and a selected window (including length L) using one or more received sets of reference signals, the process may include one or more of the following, or any combination of the following:

[0249] the WTRU determining predicted CSI for all or any combination of CSI components for the selected window;

[0250] - the WTRU calculating the accuracy of CSI prediction for all or selected CSI components for the next look-ahead window, or for several windows;

[0251] - WTRU selection of one or more preferred CSI components for the next look-ahead window, or for several windows;

[0252] - WTRU selection of preferred accuracy thresholds for gNB-configured or preferred CSI components;

[0253] WTRU selection of a preferred (adaptive) reference signal configuration for a particular time window (e.g., X time units);

[0254] In all WTRU processing embodiments herein, the WTRU may utilize predictions from previous measurements along with the received set or sets of reference signals to perform adaptive CSI prediction and performance monitoring.

[0255] The WTRU may utilize dynamic reporting related to adaptive CSI prediction with the selected components using the previously configured set or sets of reserved radio resources, which may include one or a combination of the following procedures.

[0256] - the WTRU reporting predicted CSI (all or any combination of CSI components) for a selected time window or windows;

[0257] - the WTRU reporting a preferred CSI component subset for future CSI prediction in a subsequent time window;

[0258] the WTRU reporting a preferred prediction accuracy threshold based on an initial gNB configured configuration or a WTRU selected preferred configuration for the CSI component;

[0259] the WTRU reporting a preferred reference signal configuration, including any pattern and density in the time, frequency, and spatial domains, for CSI prediction in components for the next look-ahead window or for several windows;

[0260] - The WTRU reports any adjustments or validations of a previously predicted CSI report or reports.

[0261] 18, a method 1800 for CSI prediction according to an example embodiment is shown. The method generally includes configuring 1805 a WTRU with one or more sets of reference signals (RS), one or more CSI prediction parameters including one or more prediction window lengths (L) and sets of resources, one or more CSI prediction accuracy thresholds, and one or more sets of resources for CSI reporting. In various embodiments, one set of configured reference signals (RS) may be used for legacy CSI measurements and AI / ML model training. Another set of configured RSs may be used for CSI prediction in subsequent prediction windows (e.g., look-ahead), and yet another set of RSs may be used to verify or adjust previously predicted CSI.

[0262] The WTRU then receives 1810 a CSI-RS associated with the set of configured RSs and determines a predicted CSI value for at least one resource in the configured prediction window based on those measurements. As with other example embodiments described herein, while CSI-RS may be shown or described, other types of reference signals or combinations of RS types may be utilized, and the embodiments are not limited to any particular type of RS. The WTRU may further determine 1815 preferred CSI prediction parameters as a function of the RS measurements and the configured prediction accuracy threshold, according to any one or combination of previously described embodiments. In some embodiments, the preferred CSI prediction parameters may include a prediction window length (L) and / or a number of RS resources for validation of the predicted CSI in a subsequent look-ahead window.

[0263] Using the configured set or sets of resources, the WTRU may report 1820 the determined preferred CSI prediction parameters and predicted CSI values ​​for one or more subsequent prediction windows.

[0264] In some optional embodiments, the method 1800 may continue with the WTRU receiving 1825 additional CSI-RS associated with the RS configuration set, and the WTRU may use corresponding measurements to verify or adjust the determined preferred CSI prediction parameters for the prediction window. Finally, the WTRU may report verification or adjustment information for the previously reported predicted CSI based on the received additional CSI-RS 1830.

[0265] 19, an example method 1900 of a WTRU providing CSI prediction information to a base station is shown. The WTRU is configured with look-ahead window length(s) (Li), a CSI-RS (Ki), one or more accuracy thresholds, and a CSI reporting resource via RRC 1905. The WTRU receives K CSI-RS from the base station 1910, measures CSI based on the received CSI-RS(s) 1915, and determines an accuracy (α) of the prediction for a CSI component and / or component parameter 1920. If the determined accuracy is greater than an associated configured threshold 1925, the WTRU combines the new K CSI samples with existing CSI prediction information (if any) to predict CSI for the next look-ahead window (L) and reports the predicted CSI and, optionally, the determined accuracy (α) to the base station 1935. On the other hand, if the determined accuracy is less than the associated configured threshold 1925, the WTRU may request additional CSI-RS to reconstruct historical CSI samples 1940 and / or fall back to legacy CSI reporting. The WTRU may use the received CSI-RS(s) to predict CSI for the next look-ahead window (L) 1945 and report the predicted CSI to the base station 1950.

[0266] 20 , a method 2000 for dynamic reporting of a particular predicted CSI component is shown, according to an example embodiment. In the method 2000, a WTRU using AI / ML modeling for predicted CSI may be configured 2005, indicated, or requested to indicate, per CSI component accuracy threshold for a particular CSI component, sub-component, or set of CSI components, the number of CSI-RS needed for the next look-ahead window and CSI reporting resources. The WTRU receives 2010 Ki CSI-RSs during the i-th look-ahead window and performs measurements 2015 to determine the accuracy α (e.g., α ) of the prediction for each particular CSI (sub)component in the previous predicted CSI reporting (i−1) window for per-CSI component verification. CQI , α RI , α PMI ) in 2020. Additionally or alternatively, the accuracy of specific parameters (or subcomponents) of specific CSI components (e.g., PMI subcomponents W1, W2, W f , W d →α W1 , α W2 , α Wf , α Wd ) may be determined and used in predicting the CSI, validating the CSI prediction, and / or modifying the selection of reference signals for CSI measurement. Optionally, any of the above specific CSI component or sub-component precisions α may be evaluated for a "grade" for CSI prediction purposes, similar to that described previously. The WTRU may report one or more precisions α, or grades of α, to validate the predicted CSI, determine adjustments in the prediction, and / or correct the number of K CSI-RSs to receive in one or more next look-ahead windows (e.g., i+1...i+2) 2025.

[0267] In one solution, the WTRU receives K CSI-RSs during the i-th look-ahead window and performs measurements to evaluate the accuracy of the prediction for each CSI component (e.g., CQI, RI, PMI) in the reported CSI reporting (i-1) window for per-CSI-component validation. The WTRU may calculate and optionally report a list of prediction accuracies for the set of CSI components in the predicted CSI reporting for the target look-ahead window. When K meets one or more accuracy thresholds, a further reduced set K of RSs may be requested and sampled / measured by the WTRU to validate future look-ahead windows. The predicted CSI parameters L and K, as well as CSI measurements for at least K, are provided to the gNB.

[0268] Although features and elements have been described above in particular combinations, those skilled in the art will understand that each feature or element may be used alone or in any combination with the other features and elements. Furthermore, the methods described herein may be implemented in a computer program, software, or firmware embodied in a computer-readable medium for execution by a computer or processor. Examples of computer-readable media include electronic signals (transmitted via wired or wireless connections) 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 internal hard disks and removable disks, magneto-optical media, and optical media such as CD-ROM disks and digital versatile disks (DVDs). A processor in association with software may be used to implement a radio frequency transceiver for use in a WTRU, UE, terminal, base station, RNC, or any host computer.

Claims

1. 1. A method for a wireless transmit / receive unit (WTRU), comprising: receiving, from a base station, configuration information including a first set of reference signals (RSs) for channel state information (CSI) prediction, one or more CSI prediction parameters including one or more prediction window lengths (L) and sets of resources, one or more prediction accuracy thresholds, and one or more sets of resources for CSI reporting; receiving, from the base station, one or more RSs associated with a configured first set of RSs and determining a predicted CSI value for at least one resource of one or more configured prediction windows; determining preferred CSI prediction parameters as a function of RS measurements and a configured prediction accuracy threshold; reporting, to the base station, the determined preferred CSI prediction parameters and the determined predicted CSI values ​​for one or more subsequent prediction windows using the configured one or more sets of resources; A method for providing the above.

2. the configuration information further includes a second set of RSs for verifying the predicted CSI; receiving, from the base station, one or more RSs associated with a configured second set of RSs, and verifying or adjusting the determined preferred CSI prediction parameter; reporting CSI verification information or CSI adjustment information to the base station based on the received one or more RSs associated with the second set of RSs; The method of claim 1 further comprising:

3. 3. The method of claim 2, wherein the determined preferred CSI prediction parameter is validated when measurements of the one or more RSs associated with the configured second set of RSs meet or exceed an accuracy criterion.

4. requesting a reduced number of RSs for the one or more subsequent prediction windows when the accuracy criterion is exceeded by a predetermined amount; or requesting an increased number of RSs for the one or more subsequent prediction windows when the accuracy criterion is exceeded by less than a predetermined amount. The method of claim 3 further comprising:

5. 2. The method of claim 1, wherein the determined predicted CSI value relates to one or more of a rank indicator (RI), a channel quality index (CQI), a precoding matrix indicator (PMI), a layer indicator (LI), a CSI-RS resource indicator (CRI), a signal-to-interference-to-noise ratio (SINR), a reference signal received power (RSRP), a reference signal received quality (RSRQ), a received signal strength indicator (RSSI), Doppler spread, an angle of arrival (AoA), an angle of departure (AoD), a delay spread, or an average delay.

6. The method of claim 1 , wherein the determined preferred CSI prediction parameters include one or more of a length of a prediction window or a number of RS resources desired for a prediction window.

7. the configuration information includes a third set of RSs; receiving, from the base station prior to receiving the one or more RSs associated with the configured first set of RSs, one or more RSs associated with a configured third set of RSs; training an artificial intelligence machine learning (AIML) model for CSI prediction based on the received RSs associated with the configured third set of RSs while the WTRU performs legacy CSI reporting; The method of claim 1 further comprising:

8. 1. A wireless transmit / receive unit (WTRU), comprising: a processor, and a transceiver in communication with the processor Equipped with The processor and the transceiver receive, from a base station, configuration information including a first set of reference signals (RSs) for channel state information (CSI) prediction, one or more CSI prediction parameters including one or more prediction window lengths (L) and sets of resources, one or more prediction accuracy thresholds, and one or more sets of resources for CSI reporting; receiving, from the base station, one or more RSs associated with a configured first set of RSs; and determining a predicted CSI value for at least one resource of one or more configured prediction windows; determining preferred CSI prediction parameters as a function of the received one or more RS measurements and a configured prediction accuracy threshold; reporting, to the base station, the determined preferred CSI prediction parameters and the determined predicted CSI values ​​for one or more subsequent prediction windows using the configured one or more sets of resources; A WTRU configured as follows:

9. The configuration information further includes a second set of RSs for verifying the predicted CSI, and the processor and the transceiver: receiving, from the base station, one or more RSs associated with a configured second set of RSs; and verifying or adjusting the determined preferred CSI prediction parameter; reporting CSI verification information or CSI adjustment information to the base station based on the received one or more RSs associated with the configured second set of RSs; The WTRU of claim 8 further configured to:

10. 10. The WTRU of claim 9, wherein the determined preferred CSI prediction parameters are validated when measurements of the one or more RSs associated with the configured second set of RSs meet or exceed an accuracy criterion.

11. The processor and the transceiver requesting a reduced number of RSs for the one or more subsequent prediction windows when the accuracy criterion is exceeded by a predetermined amount; or When the accuracy criterion is exceeded by less than a predetermined amount, requesting an increased number of RSs for the one or more subsequent prediction windows. The WTRU of claim 10 further configured to:

12. 9. The WTRU of claim 8, wherein the determined predicted CSI value relates to one or more of a rank indicator (RI), a channel quality index (CQI), a precoding matrix indicator (PMI), a layer indicator (LI), a CSI-RS resource indicator (CRI), a signal-to-interference-to-noise ratio (SINR), a reference signal received power (RSRP), a reference signal received quality (RSRQ), a received signal strength indicator (RSSI), a Doppler spread, an angle of arrival (AoA), an angle of departure (AoD), a delay spread, or an average delay.

13. 10. The WTRU of claim 8, wherein the determined preferred CSI prediction parameters include one or more of a length of a prediction window or a number of RS resources desired for a prediction window.

14. the configuration information includes a third set of RSs, and the processor and the transceiver: receiving, from the base station, one or more RSs associated with a configured third set of RSs prior to receiving the one or more RSs associated with the configured first set of RSs; training an artificial intelligence machine learning (AIML) model for CSI prediction based on the received RSs associated with the configured third set of RSs while the WTRU performs CSI reporting without prediction; The WTRU of claim 8 further configured to:

15. 1. A method for a base station, comprising: sending, to a wireless transmit / receive unit (WTRU), configuration information including a first set of reference signals (RSs) for channel state information (CSI) prediction, one or more CSI prediction parameters including one or more prediction window lengths (L) and sets of resources, one or more prediction accuracy thresholds, and one or more sets of resources for CSI reporting; sending, to the WTRU, one or more RSs associated with a configured first set of RSs for the WTRU to determine a predicted CSI value for at least one resource of one or more configured prediction windows; receiving predicted CSI values ​​and preferred CSI prediction parameters for one or more subsequent prediction windows from the WTRU via one or more configured sets of resources; A method for providing the above.

16. the configuration information further includes a second set of RSs for verifying the predicted CSI; sending, to the WTRU, one or more RSs associated with a configured second set of RSs to enable the WTRU to verify or adjust the preferred CSI prediction parameter; receiving CSI validation information or CSI adjustment information from the WTRU based on the received one or more RSs associated with the configured second set of RSs; The method of claim 15 further comprising:

17. 17. The method of claim 16, wherein the preferred CSI prediction parameter is verified when measurements of the one or more RSs associated with the configured second set of RSs meet or exceed one or more configured prediction accuracy thresholds.

18. receiving a request for a reduced number of RSs for the one or more subsequent prediction windows when the configured one or more prediction accuracy thresholds are exceeded by a predetermined amount; or receiving a request for an increased number of RSs for the one or more subsequent prediction windows when the configured one or more prediction accuracy thresholds are exceeded by less than the predetermined amount.

20. The method of claim 17 further comprising:

19. 16. The method of claim 15, wherein the received predicted CSI values ​​relate to one or more of a rank indicator (RI), a channel quality index (CQI), a precoding matrix indicator (PMI), a layer indicator (LI), a CSI-RS resource indicator (CRI), a signal-to-interference-to-noise ratio (SINR), a reference signal received power (RSRP), a reference signal received quality (RSRQ), a received signal strength indicator (RSSI), Doppler spread, an angle of arrival (AoA), an angle of departure (AoD), a delay spread, or an average delay.

20. 16. The method of claim 15, wherein the received preferred CSI prediction parameters include one or more of a length of a prediction window or a number of RS resources desired for a prediction window.

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