Method for channel parameter estimation

Abstract

A wireless transmit / receive unit (WTRU) may include a processor and a memory. The WTRU may receive a configuration from a network that configures the WTRU to execute an inverse deep learning model. The WTRU may receive a plurality of reference signals from the network. The WTRU may determine the number of MPCs using the inverse deep learning model and based on a plurality of reference signals. The WTRU may determine an angle of arrival (AoA), an angle of departure (AoD), and a gain associated with each MPC of the number of MPCs using the inverse deep learning model and based on a plurality of reference signals. The WTRU may transmit to the network an indication of the number of MPCs and the AoA, AoD, and gain associated with each MPC of the number of MPCs.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims priority to U.S. Provisional Patent Application No. 63 / 408,919 filed on September 22, 2022 and U.S. Provisional Patent Application No. 63 / 516,637 filed on July 31, 2023, which are incorporated herein by reference in their entirety. Background Art

[0003] In wireless communications, developing accurate signal propagation models and / or channel models that can effectively represent different environmental settings remains a fundamental task.

[0004] The propagation characteristics of these wireless channels may be affected by several different parameters associated with the transmitter configuration, such as antenna spacing, carrier frequency and / or bandwidth, etc. Different environmental conditions may affect the multiple paths that a transmitted signal propagates through before reaching a receiver. Summary of the invention

[0005] A wireless transmit / receive unit (WTRU) may receive a configuration from a network that configures the WTRU to perform an inverse deep learning model. The WTRU may receive a plurality of reference signals from the network. The WTRU may determine a number of multipath components (MPCs) using the inverse deep learning model and based on the plurality of reference signals. The WTRU may determine an angle of arrival (AoA), an angle of departure (AoD), and / or a gain associated with each of the number of MPCs using the inverse deep learning model and / or based on the plurality of reference signals. The WTRU may transmit to the network an indication of the number of MPCs and the AoA, the AoD, and the gain associated with each of the number of MPCs.

[0006] The WTRU may determine a joint probability distribution function for the number of MPCs based on the number of MPCs and the AoA, the AoD, and the gain associated with each of the number of MPCs. The indication of the number of MPCs and the AoA, the AoD, and the gain associated with each of the number of MPCs may include the joint probability distribution function for the MPCs.

[0007] The WTRU may send the joint probability distribution function of the number of MPCs to the network and / or send representative parameters associated with the joint probability distribution function of the number of MPCs to the network.

[0008] The WTRU may receive an indication from the network indicating that the WTRU is to send to the network the indication of the number of MPCs and the AoA, the AoD, and / or the gain associated with each of the number of MPCs. The WTRU may periodically transmit to the network the indication of the number of MPCs and the AoA, the AoD, and / or the gain associated with each of the number of MPCs. The number of MPCs and the AoA, the AoD, and / or the gain associated with each of the number of MPCs may be used to train a machine learning model at the network.

[0009] The WTRU may receive an indication of a path selection method from the network. The WTRU may determine a plurality of MPCs using the inverse deep learning model and based on the plurality of reference signals. The number of MPCs may be a subset of the plurality of MPCs. The WTRU may determine the AoA, the AoD, and / or the gain associated with each of the plurality of MPCs using the inverse deep learning model and based on the plurality of reference signals. The WTRU may determine the number of MPCs in the plurality of MPCs based on the path selection method, wherein the number of MPCs is a preferred MPC in the plurality of MPCs.

[0010] The path selection method may include an energy-based method, an L1 norm method, and / or a multiple-input multiple-output (MIMO) channel rank method.

[0011] The WTRU may perform quantization, entropy encoding, and / or error correction on the AoA, the AoD, and / or the gain associated with each of the number of MPCs to reduce the number of bits required to indicate the AoA, the AoD, and / or the gain associated with each of the number of MPCs. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] A more detailed understanding may be obtained from the following description given by way of example in conjunction with the accompanying drawings, in which like reference numerals indicate like elements, and in which:

[0013] Figure 1A is a system diagram illustrating an example communication system in which one or more disclosed embodiments may be implemented.

[0014] Figure 1B is an example of a method that can be used according to an embodiment of the present invention. Figure 1A A system diagram of an example wireless transmit / receive unit (WTRU) for use within an illustrated communication system.

[0015] Figure 1C is an example of a method that can be used according to an embodiment of the present invention. Figure 1ASystem diagram of an example Radio Access Network (RAN) and an example Core Network (CN) for use within an illustrated communication system.

[0016] Figure 1D is an example of a method that can be used according to an embodiment of the present invention. Figure 1A System diagram of a further example RAN and a further example CN for use within the illustrated communication system.

[0017] Figure 2 An example method of channel parameter estimation for data generation is depicted.

[0018] Figure 3 Example methods for CSI compression and feedback are described.

[0019] Figure 4 is a flowchart of an example end-to-end supervised learning framework.

[0020] Figure 5 is a diagram of an example ResNet-based architecture.

[0021] Figure 6 An example framework for multipath component estimation is depicted.

[0022] Fig. 7A is a graph depicting an example comparative error in angle of arrival / angle of departure estimation for an Orthogonal Matching Pursuit (OMP) based algorithm and a Neural Network (NN) based framework.

[0023] Figure 7B is a graph depicting example inference times for estimating multipath component parameters per-channel matrix for an OMP-based algorithm and a NN-based framework. DETAILED DESCRIPTION

[0024] Figure 1A 1 is a diagram illustrating an example communication system 100 in which one or more disclosed embodiments may be implemented. The communication system 100 may be a multiple access system that provides content such as voice, data, video, messaging, broadcast, etc. to multiple wireless users. The communication system 100 may enable multiple wireless users to access such content through sharing of system resources (including wireless bandwidth). For example, the communication system 100 may employ one or more channel access methods such as code division multiple access (CDMA), time division multiple access (TDMA), frequency division multiple access (FDMA), orthogonal FDMA (OFDMA), single carrier FDMA (SC-FDMA), zero tail unique word DFT spread OFDM (ZT UW DTS-s OFDM), unique word OFDM (UW-OFDM), resource block filtered OFDM, and filter bank multi-carrier (FBMC), etc.

[0025] like Figure 1A As shown, the communication system 100 may include wireless transmit / receive units (WTRUs) 102a, 102b, 102c, 102d, RAN 104 / 113, CN 106 / 115, public switched telephone network (PSTN) 108, Internet 110 and other networks 112, but it should be understood that the disclosed embodiments contemplate any number of WTRUs, base stations, networks, and / or network elements. Each of the WTRUs 102a, 102b, 102c, 102d may be any type of device configured to operate and / or communicate in a wireless environment. By way of example, the UEs 102a, 102b, 102c, 102d (any of which may be referred to as a “station” and / or “STA”) may be configured to send and / or receive wireless signals and may include user equipment (WTRU), a mobile station, a fixed or mobile subscriber unit, a subscription-based unit, a pager, a cellular phone, a personal digital assistant (PDA), a smart phone, a laptop, a netbook, a personal computer, a wireless sensor, a hotspot or Mi-Fi device, an Internet of Things (IoT) device, a watch or other wearable device, a head-mounted display (HMD), a vehicle, a drone, medical devices and applications (e.g., remote surgery), industrial devices and applications (e.g., robots and / or other wireless devices operating in an industrial and / or automated process chain environment), a consumer electronic device, a device operating on a commercial and / or industrial wireless network, etc. Any of the UEs 102a, 102b, 102c, and 102d may be interchangeably referred to as a WTRU.

[0026] The communication system 100 may also include a base station 114a and / or a base station 114b. Each of the base stations 114a, 114b may be any type of device that is configured to interface with at least one of the WTRUs 102a, 102b, 102c, 102d to facilitate access to one or more communication networks, such as the CN 106 / 115, the Internet 110, and / or other networks 112. By way of example, the base stations 114a, 114b may be a base transceiver station (BTS), a Node B, an eNode B, a Home Node B, a Home eNode B, a gNB, an NR Node B, a site controller, an access point (AP), a wireless router, and the like. Although the base stations 114a, 114b are each depicted as a single element, it should be appreciated that the base stations 114a, 114b may include any number of interconnected base stations and / or network elements.

[0027] Base station 114a may be part of RAN 104 / 113, which may also include other base stations and / or network elements (not shown), such as base station controllers (BSC), radio network controllers (RNC), relay nodes, etc. Base station 114a and / or base station 114b may be configured to send and / or receive wireless signals on one or more carrier frequencies (which may be referred to as cells (not shown)). These frequencies may be in a licensed spectrum, an unlicensed spectrum, or a combination of a licensed spectrum and an unlicensed spectrum. A cell may provide coverage of wireless services to a specific geographic area, which may be relatively fixed or may change over time. The cell may be further divided into cell sectors. For example, the cell associated with base station 114a may be divided into three sectors. Therefore, in one embodiment, base station 114a may include three transceivers, i.e., one transceiver for each sector of the cell. In an embodiment, base station 114a may employ multiple-input multiple-output (MIMO) technology and may utilize multiple transceivers for each sector of the cell. For example, beamforming may be used to send and / or receive signals in a desired spatial direction.

[0028] 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).

[0029] More specifically, as noted above, the communication system 100 may be a multiple access system and may employ one or more channel access schemes such as CDMA, TDMA, FDMA, OFDMA, and SC-FDMA. For example, the base station 114a in the RAN 104 / 113 and the WTRUs 102a, 102b, 102c may implement a radio technology such as Universal Mobile Telecommunications System (UMTS) Terrestrial Radio Access (UTRA), which may use Wideband CDMA (WCDMA) to establish the air interface 115 / 116 / 117. WCDMA may include communication protocols such as High Speed ​​Packet Access (HSPA) and / or Evolved HSPA (HSPA+). HSPA may include High Speed ​​Downlink (DL) Packet Access (HSDPA) and / or High Speed ​​UL Packet Access (HSUPA).

[0030] In an embodiment, the base station 114a and the WTRUs 102a, 102b, 102c may implement a radio technology such as Evolved UMTS Terrestrial Radio Access (E-UTRA), which may establish the air interface 116 using Long Term Evolution (LTE) and / or Advanced LTE (LTE-A) and / or Advanced LTE Pro (LTE-A Pro).

[0031] In an embodiment, the base station 114a and the WTRUs 102a, 102b, 102c may implement a radio technology such as NR radio access, which may establish the air interface 116 using New Radio (NR).

[0032] In an embodiment, the base station 114a and the WTRUs 102a, 102b, 102c may implement multiple radio access technologies. For example, the base station 114a and the WTRUs 102a, 102b, 102c may implement LTE radio access and NR radio access together, for example using dual connectivity (DC) principles. Thus, the air interface used by the WTRUs 102a, 102b, 102c may be characterized by multiple types of radio access technologies and / or transmissions transmitted to / from multiple types of base stations (e.g., eNBs and gNBs).

[0033] In other embodiments, the base station 114a and the WTRUs 102a, 102b, 102c may implement radio technologies such as IEEE 802.11 (i.e., Wireless Fidelity (WiFi)), IEEE 802.16 (i.e., Worldwide Interoperability for Microwave Access (WiMAX)), CDMA2000, CDMA2000 1X, CDMA2000 EV-DO, Interim Standard 2000 (IS-2000), Interim Standard 95 (IS-95), Interim Standard 856 (IS-856), Global System for Mobile Communications (GSM), Enhanced Data rates for GSM Evolution (EDGE), and GSM EDGE (GERAN).

[0034] Figure 1AThe base station 114b in the may be, for example, a wireless router, a Home NodeB, a Home eNodeB, or an access point, and may utilize any suitable RAT to facilitate wireless connectivity in local areas such as commercial venues, homes, vehicles, campuses, industrial facilities, sky corridors (e.g., for use by drones), and roads. In one embodiment, the base station 114b and the WTRUs 102c, 102d may implement a radio technology such as IEEE 802.11 to establish a wireless local area network (WLAN). In an embodiment, the base station 114b and the WTRUs 102c, 102d may implement a radio technology such as IEEE 802.15 to establish a wireless personal area network (WPAN). In yet another embodiment, the base station 114b and the WTRUs 102c, 102d may utilize a cellular-based RAT (e.g., WCDMA, CDMA2000, GSM, LTE, LTE-A, LTE-A Pro, NR, etc.) to establish a picocell or a femtocell. As Figure 1A As shown, base station 114b may have a direct connection to Internet 110. Therefore, base station 114b may not need to access Internet 110 via CN 106 / 115.

[0035] The RAN 104 / 113 may be in communication with the CN 106 / 115, which may be any type of network configured to provide voice, data, applications and / or Voice over Internet Protocol (VoIP) services to one or more of the WTRUs 102a, 102b, 102c, 102d. The data may have different quality of service (QoS) requirements, such as different throughput requirements, latency requirements, error tolerance requirements, reliability requirements, data throughput requirements, mobility requirements, etc. The CN 106 / 115 may provide call control, billing services, mobile location-based services, prepaid calls, Internet connectivity, video distribution, etc., and / or perform high-level security functions, such as user authentication. Although not described in detail in the specification, the CN 106 / 115 may be configured to provide voice, data, applications and / or Voice over Internet Protocol (VoIP) services to one or more of the WTRUs 102a, 102b, 102c, 102d. Figure 1A Although not shown in the figure, it will be appreciated that the RAN 104 / 113 and / or CN 106 / 115 may be in direct or indirect communication with other RANs that employ the same RAT as the RAN 104 / 113 or a different RAT. For example, in addition to being connected to the RAN 104 / 113, which may utilize NR radio technology, the CN 106 / 115 may also be in communication with another RAN (not shown) that employs GSM, UMTS, CDMA2000, WiMAX, E-UTRA, or WiFi radio technology.

[0036] The CN 106 / 115 may also act as a gateway for the WTRUs 102a, 102b, 102c, 102d to access the PSTN 108, the Internet 110, and / or other networks 112. The PSTN 108 may include a circuit-switched telephone network that provides plain old telephone service (POTS). The Internet 110 may include a global system of interconnected computer networks and devices that use common communication protocols, such as the Transmission Control Protocol (TCP), the User Datagram Protocol (UDP), and / or the Internet Protocol (IP) in the TCP / IP Internet protocol suite. The networks 112 may include wired communication networks and / or wireless communication networks owned and / or operated by other service providers. For example, the networks 112 may include another CN connected to one or more RANs, which may employ the same RAT as the RAN 104 / 113 or a different RAT.

[0037] Some or all of the WTRUs 102a, 102b, 102c, 102d in the communication system 100 may include multi-mode capabilities (e.g., the WTRUs 102a, 102b, 102c, 102d may include multiple transceivers for communicating with different wireless networks via different wireless links). Figure 1A The illustrated WTRU 102c may be configured to communicate with the base station 114a, which may employ a cellular-based radio technology, and with the base station 114b, which may employ an IEEE 802 radio technology.

[0038] Figure 1B is a system diagram illustrating an example WTRU 102. Figure 1B As shown, the WTRU 102 may include, among other things, a processor 118, a transceiver 120, a transmit / receive element 122, a speaker / microphone 124, a keypad 126, a display / touchpad 128, non-removable memory 130, removable memory 132, a power source 134, a global positioning system (GPS) chipset 136, and / or other peripherals 138. It will be appreciated that the WTRU 102 may include any sub-combination of the foregoing elements while remaining consistent with an embodiment.

[0039] The processor 118 may be a general purpose processor, a special purpose processor, a conventional processor, a digital signal processor (DSP), a plurality of microprocessors, one or more microprocessors associated with a DSP core, a controller, a microcontroller, an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) circuit, any other type of integrated circuit (IC), a state machine, etc. The processor 118 may perform signal decoding, data processing, power control, input / output processing, and / or any other functionality that enables the WTRU 102 to operate in a wireless environment. The processor 118 may be coupled to the transceiver 120, which may be coupled to the transmit / receive element 122. Although Figure 1B The processor 118 and the transceiver 120 are depicted as separate components, but it is understood that the processor 118 and the transceiver 120 may be integrated together in an electronic package or chip.

[0040] The send / receive element 122 may be configured to send a signal to a base station (e.g., base station 114a) or receive a signal from a base station via an air interface 116. For example, in one embodiment, the send / receive element 122 may be an antenna configured to send and / or receive an RF signal. In an embodiment, the send / receive element 122 may be a transmitter / detector configured to send and / or receive, for example, an IR, UV, or visible light signal. In another embodiment, the send / receive element 122 may be configured to send and / or receive both an RF signal and an optical signal. It should be understood that the send / receive element 122 may be configured to send and / or receive any combination of wireless signals.

[0041] Although the transmit / receive element 122 Figure 1B 1 as a single element, the WTRU 102 may include any number of transmit / receive elements 122. More specifically, the WTRU 102 may employ MIMO technology. Thus, in one embodiment, the WTRU 102 may include two or more transmit / receive elements 122 (e.g., multiple antennas) for transmitting and receiving wireless signals over the air interface 116.

[0042] The transceiver 120 may be configured to modulate signals to be transmitted by the transmit / receive element 122 and to demodulate signals received by the transmit / receive element 122. As noted above, the WTRU 102 may have multi-mode capabilities. For example, the transceiver 120 may include multiple transceivers for enabling the WTRU 102 to communicate via multiple RATs, such as NR and IEEE 802.11.

[0043] The processor 118 of the WTRU 102 may be coupled to 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) and may receive user input data therefrom. The processor 118 may also output user data to the speaker / microphone 124, the keypad 126, and / or the display / touchpad 128. In addition, the processor 118 may access information from and store data in any type of suitable memory, such as a non-removable memory 130 and / or a removable memory 132. The non-removable memory 130 may include a random access memory (RAM), a read-only memory (ROM), a hard disk, or any other type of memory storage device. The removable memory 132 may include a subscriber identity module (SIM) card, a memory stick, and a secure digital (SD) memory card, among others. In other embodiments, the processor 118 may access information from and store data in a memory that is not physically located on the WTRU 102, such as on a server or a home computer (not shown).

[0044] The processor 118 may receive power from the power source 134, and may be configured to distribute and / or control power to the other components in the WTRU 102. The power source 134 may be any suitable device for powering the WTRU 102. For example, the power source 134 may include one or more dry cell batteries (e.g., nickel cadmium (NiCd), nickel zinc (NiZn), nickel metal hydride (NiMH), lithium ion (Li-ion), etc.), solar cells, fuel cells, etc.

[0045] The processor 118 may also be coupled to the GPS chipset 136, which may be configured to provide location information (e.g., longitude and latitude) regarding the current location of the WTRU 102. In addition to or in lieu of the information from the GPS chipset 136, the WTRU 102 may receive location information from a base station (e.g., base stations 114a, 114b) over the air interface 116 and / or determine its location based on the timing of signals received from two or more nearby base stations. It will be appreciated that the WTRU 102 may acquire location information by any suitable location-determination method while remaining consistent with an embodiment.

[0046] The processor 118 may also be coupled to other peripherals 138, which may include one or more software modules and / or hardware modules that provide additional features, functionality, and / or wired or wireless connectivity. For example, the peripherals 138 may include an accelerometer, an electronic compass, a satellite transceiver, a digital camera (for photos and / or video), a universal serial bus (USB) port, a vibration device, a television transceiver, a hands-free headset, module, FM radio unit, digital music player, media player, video game player module, Internet browser, virtual reality and / or augmented reality (VR / AR) device and activity tracker, etc. Peripheral device 138 may include one or more sensors, which may be one or more of the following: gyroscope, accelerometer, Hall effect sensor, magnetometer, orientation sensor, proximity sensor, temperature sensor, time sensor; geolocation sensor; altimeter, light sensor, touch sensor, magnetometer, barometer, gesture sensor, biometric sensor and / or humidity sensor.

[0047] The WTRU 102 may include a full-duplex radio for which transmission and reception of some or all signals (e.g., associated with specific subframes for both UL (e.g., for transmission) and downlink (e.g., for reception)) may be concurrent and / or simultaneous. The full-duplex radio may include an interference management unit 139 for reducing and / or substantially eliminating self-interference via signal processing performed via hardware (e.g., a choke) or via a processor (e.g., a separate processor (not shown) or via processor 118). In an embodiment, the WRTU 102 may include a half-duplex radio for which transmission and reception of some or all signals (e.g., associated with specific subframes for both UL (e.g., for transmission) or downlink (e.g., for reception)) may be concurrent and / or simultaneous.

[0048] Figure 1C 1 is a system diagram illustrating the RAN 104 and the CN 106 in accordance with an embodiment. As noted above, the RAN 104 may employ an E-UTRA radio technology to communicate with the WTRUs 102a, 102b, 102c over the air interface 116. The RAN 104 may also be in communication with the CN 106.

[0049] The RAN 104 may include evolved Node-Bs 160a, 160b, 160c, though it will be appreciated that the RAN 104 may include any number of evolved Node-Bs while remaining consistent with an embodiment. The evolved Node-Bs 160a, 160b, 160c may each include one or more transceivers for communicating with the WTRUs 102a, 102b, 102c over the air interface 116. In one embodiment, the evolved Node-Bs 160a, 160b, 160c may implement MIMO technology. Thus, the evolved Node-B 160a, for example, may use multiple antennas to transmit wireless signals to and / or receive wireless signals from the WTRU 102a.

[0050] Each of the evolved Node Bs 160a, 160b, 160c may be associated with a particular cell (not shown) and may be configured to handle radio resource management decisions, handover decisions, and scheduling of users in the UL and / or DL, among other things. Figure 1C As shown, the eNode-Bs 160a, 160b, 160c may communicate with one another via an X2 interface.

[0051] Figure 1C The illustrated CN 106 may include a mobility management entity (MME) 162, a serving gateway (SGW) 164, and a packet data network (PDN) gateway (or PGW) 166. While each of the foregoing elements is depicted as being part of the CN 106, it should be understood that any of these elements may be owned and / or operated by an entity other than the CN operator.

[0052] The MME 162 may be connected to each of the evolved Node-Bs 162a, 162b, 162c in the RAN 104 via an S1 interface and may serve as a control node. For example, the MME 162 may be responsible for authenticating users of the WTRUs 102a, 102b, 102c, bearer activation / deactivation, and selecting a particular serving gateway during an initial attach of the WTRUs 102a, 102b, 102c. The MME 162 may provide a control plane function for switching between the RAN 104 and other RANs (not shown) that employ other radio technologies, such as GSM and / or WCDMA.

[0053] The SGW 164 may be connected to each of the evolved Node-Bs 160a, 160b, 160c in the RAN 104 via an S1 interface. The SGW 164 may generally route and forward user data packets to / from the WTRUs 102a, 102b, 102c. The SGW 164 may perform other functions, such as anchoring the user plane during an inter-evolved Node-B handover, triggering paging when DL data is available for the WTRUs 102a, 102b, 102c, and managing and storing the context of the WTRUs 102a, 102b, 102c.

[0054] The SGW 164 may be connected to the PGW 166, which may provide the WTRUs 102a, 102b, 102c with access to packet-switched networks, such as the Internet 110, to facilitate communications between the WTRUs 102a, 102b, 102c and IP-enabled devices.

[0055] The CN 106 may facilitate communications with other networks. For example, the CN 106 may provide the WTRUs 102a, 102b, 102c with access to circuit-switched networks, such as the PSTN 108, to facilitate communications between the WTRUs 102a, 102b, 102c and traditional land-line communications devices. For example, the CN 106 may include, or may be in communication with, an IP gateway (e.g., an IP Multimedia Subsystem (IMS) server) that acts as an interface between the CN 106 and the PSTN 108. In addition, the CN 106 may provide the WTRUs 102a, 102b, 102c with access to other networks 112, which may include other wired networks and / or wireless networks owned and / or operated by other service providers.

[0056] Although the WTRU Figures 1A to 1D Although described as wireless terminals, it is contemplated that in certain representative embodiments, such terminals may (eg, temporarily or permanently) use a wired communication interface with a communication network.

[0057] In a representative embodiment, the other network 112 may be a WLAN.

[0058] A WLAN in infrastructure basic service set (BSS) mode may have an access point (AP) for a BSS and one or more stations (STAs) associated with the AP. The AP may have access or an interface to a distribution system (DS) or another type of wired / wireless network that carries traffic to and / or carries traffic away from the BSS. Traffic originating from outside the BSS and destined for the STA may arrive through the AP and may be delivered to the STA. Traffic originating from the STA and destined for a destination outside the BSS may be transmitted to the AP to be delivered to the corresponding destination. Traffic between STAs within the BSS may be transmitted through the AP, for example, wherein the source STA may transmit traffic to the AP, and the AP may deliver traffic to the destination STA. Traffic between STAs within the BSS may be considered and / or referred to as point-to-point traffic. Point-to-point traffic may be transmitted between the source STA and the destination STA (e.g., directly between them) using a direct link setup (DLS). In certain representative embodiments, the DLS may use 802.11e DLS or 802.11z tunnel DLS (TDLS). A WLAN using an independent BSS (IBSS) mode may not have an AP, and STAs within or using the IBSS (eg, all STAs in the STA) may communicate directly with each other. The IBSS communication mode may sometimes be referred to herein as an "ad hoc" communication mode.

[0059] When using the 802.11ac infrastructure operation mode or a similar operation mode, the AP may send beacons on a fixed channel, such as a primary channel. The primary channel may be a fixed width (e.g., a 20 MHz wide bandwidth) or a width dynamically set via signaling. The primary channel may be an operating channel of the BSS and may be used by the STA to establish a connection with the AP. In certain representative embodiments, carrier sense multiple access / collision avoidance (CSMA / CA) may be implemented, for example, in an 802.11 system. For CSMA / CA, a STA (e.g., each STA) (including the AP) may sense the primary channel. If the primary channel is sensed / detected by a particular STA and / or determined to be busy, the particular STA may back off. One STA (e.g., only one station) may transmit at any given time in a given BSS.

[0060] High throughput (HT) STAs may communicate using a 40 MHz wide channel (eg, via a combination of a primary 20 MHz channel and adjacent or non-adjacent 20 MHz channels) to form a 40 MHz wide channel.

[0061] Very high throughput (VHT) STA can support 20MHz, 40MHz, 80MHz and / or 160MHz wide channels. 40MHz channels and / or 80MHz channels can be formed by combining continuous 20MHz channels. 160MHz channels can be formed by combining 8 continuous 20MHz channels, or by combining two non-continuous 80MHz channels (this can be called 80+80 configuration). For 80+80 configuration, after channel coding, the data can pass through a segment parser that can divide the data into two streams. Each stream can be processed by inverse fast Fourier transform (IFFT) and time domain processing separately. These streams can be mapped to two 80MHz channels, and the data can be sent by the transmitter STA. At the receiver of the receiver STA, the above-mentioned operation for the 80+80 configuration can be reversed, and the combined data can be transmitted to the medium access control (MAC).

[0062] 802.11af and 802.11ah support operating modes below 1GHz. Channel operating bandwidths and carriers are reduced in 802.11af and 802.11ah relative to those used in 802.11n and 802.11ac. 802.11af supports 5MHz, 10MHz, and 20MHz bandwidths in the TV White Space (TVWS) spectrum, and 802.11ah supports 1MHz, 2MHz, 4MHz, 8MHz, and 16MHz bandwidths using non-TVWS spectrum. According to a representative embodiment, 802.11ah may support instrument type control / machine type communications, such as MTC devices in macro coverage areas. MTC devices may have certain capabilities, such as limited capabilities, including support for (e.g., only support for) certain bandwidths and / or limited bandwidths. MTC devices may include batteries with battery life above a threshold (e.g., to maintain very long battery life).

[0063] WLAN systems that can support multiple channels and channel bandwidths (such as 802.11n, 802.11ac, 802.11af, and 802.11ah) include channels that can be designated as primary channels. The primary channel may have a bandwidth equal to the maximum common operating bandwidth supported by all STAs in the BSS. The bandwidth of the primary channel may be set and / or limited by a STA (which supports the minimum bandwidth operating mode) from all STAs operating in the BSS. In the example of 802.11ah, for STAs (e.g., MTC-type devices) that support (e.g., only support) a 1MHz mode, the primary channel may be 1MHz wide, even if the AP and other STAs in the BSS support 2MHz, 4MHz, 8MHz, 16MHz, and / or other channel bandwidth operating modes. Carrier sensing and / or network allocation vector (NAV) settings may depend on the state of the primary channel. If the primary channel is busy, for example, because a STA (supporting only a 1MHz operating mode) is transmitting to the AP, the entire available band may be considered busy even if most of the band remains idle and may be available.

[0064] In the United States, the available frequency band for 802.11ah is 902MHz to 928MHz. In South Korea, the available frequency band is 917.5MHz to 923.5MHz. In Japan, the available frequency band is 916.5MHz to 927.5MHz. The total bandwidth available for 802.11ah is 6MHz to 26MHz, depending on the country code.

[0065] Figure 1D1 is a system diagram illustrating the RAN 113 and the CN 115 in accordance with an embodiment. As noted above, the RAN 113 may employ NR radio technology to communicate with the WTRUs 102a, 102b, 102c over the air interface 116. The RAN 113 may also be in communication with the CN 115.

[0066] The RAN 113 may include gNBs 180a, 180b, 180c, though it will be appreciated that the RAN 113 may include any number of gNBs while remaining consistent with an embodiment. The gNBs 180a, 180b, 180c may each include one or more transceivers for communicating with the WTRUs 102a, 102b, 102c over the air interface 116. In one embodiment, the gNBs 180a, 180b, 180c may implement MIMO technology. For example, the gNBs 180a, 180b may utilize beamforming to send signals to and / or receive signals from the gNBs 180a, 180b, 180c. Thus, the gNB 180a may, for example, use multiple antennas to send wireless signals to and / or receive wireless signals from the WTRU 102a. In an embodiment, the gNBs 180a, 180b, 180c may implement carrier aggregation technology. For example, the gNB 180a may transmit multiple component carriers to the WTRU 102a (not shown). A subset of these component carriers may be on an unlicensed spectrum, while the remaining component carriers may be on a licensed spectrum. In an embodiment, the gNBs 180a, 180b, 180c may implement coordinated multi-point (CoMP) technology. For example, the WTRU 102a may receive coordinated transmissions from the gNB 180a and the gNB 180b (and / or the gNB 180c).

[0067] The WTRUs 102a, 102b, 102c may communicate with the gNBs 180a, 180b, 180c using transmissions associated with scalable parameter sets. For example, OFDM symbol spacing and / or OFDM subcarrier spacing may vary for different transmissions, different cells, and / or different portions of the wireless transmission spectrum. The WTRUs 102a, 102b, 102c may communicate with the gNBs 180a, 180b, 180c using subframes or Transmission Time Intervals (TTIs) of varying or scalable lengths (e.g., containing varying numbers of OFDM symbols and / or varying absolute time lengths over time).

[0068] The gNBs 180a, 180b, 180c may be configured to communicate with the WTRUs 102a, 102b, 102c in a standalone configuration and / or a non-standalone configuration. In a standalone configuration, the WTRUs 102a, 102b, 102c may communicate with the gNBs 180a, 180b, 180c while not accessing other RANs (e.g., such as the eNodeBs 160a, 160b, 160c). In a standalone configuration, the WTRUs 102a, 102b, 102c may use one or more of the gNBs 180a, 180b, 180c as mobility anchor points. In a standalone configuration, the WTRUs 102a, 102b, 102c may communicate with the gNBs 180a, 180b, 180c using signals in an unlicensed band. In a non-standalone configuration, the WTRUs 102a, 102b, 102c may communicate / connect with the gNB 180a, 180b, 180c while also communicating / connecting with another RAN, such as the 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 eNodeB 160a, 160b, 160c may act as a mobility anchor for the WTRUs 102a, 102b, 102c, and the gNB 180a, 180b, 180c may provide additional coverage and / or throughput for serving the WTRUs 102a, 102b, 102c.

[0069] Each of the gNBs 180a, 180b, 180c may be associated with a specific cell (not shown) and may be configured to handle radio resource management decisions, handover decisions, scheduling of users in UL and / or DL, support of network slicing, dual connectivity, interworking between NR and E-UTRA, routing of user plane data towards a user plane function (UPF) 184a, 184b, routing of control plane information towards an access and mobility management function (AMF) 182a, 182b, etc. Figure 1D As shown, gNBs 180a, 180b, and 180c may communicate with each other via an Xn interface.

[0070] Figure 1DThe illustrated CN 115 may include at least one AMF 182a, 182b, at least one UPF 184a, 184b, at least one session management function (SMF) 183a, 183b, and possible data networks (DNs) 185a, 185b. Although each of the foregoing elements is depicted as part of the CN 115, it should be understood that any of these elements may be owned and / or operated by an entity other than the CN operator.

[0071] The AMF 182a, 182b may be connected to one or more of the gNBs 180a, 180b, 180c in the RAN 113 via the N2 interface and may act as a control node. For example, the AMF 182a, 182b may be responsible for authenticating users of the WTRU 102a, 102b, 102c, support of network slicing (e.g., handling of different PDU sessions with different requirements), selecting a specific SMF 183a, 183b, management of registration areas, termination of NAS signaling and mobility management, etc. The AMF 182a, 182b may use network slicing to customize CN support for the WTRU 102a, 102b, 102c based on the type of services utilized by the WTRU 102a, 102b, 102c. For example, different network slices may be established for different use cases, such as services relying on Ultra Reliable Low Latency (URLLC) access, services relying on enhanced Mobile Broadband (eMBB) access, services for Machine Type Communication (MTC) access, etc. The AMF 162 may provide a control plane function for switching between the RAN 113 and other RANs (not shown) that employ other radio technologies, such as LTE, LTE-A, LTE-A Pro, and / or non-3GPP access technologies, such as WiFi.

[0072] The SMF 183a, 183b may be connected to the AMF 182a, 182b in the CN 115 via the N11 interface. The SMF 183a, 183b may also be connected to the UPF 184a, 184b in the CN 115 via the N4 interface. The SMF 183a, 183b may select and control the UPF 184a, 184b, and configure traffic routing through the UPF 184a, 184b. The SMF 183a, 183b may perform other functions such as managing and allocating UE IP addresses, managing PDU sessions, controlling policy enforcement and QoS, providing downlink data notification, etc. The PDU session type may be IP-based, non-IP-based, Ethernet-based, etc.

[0073] The UPF 184a, 184b may be connected to one or more of the gNBs 180a, 180b, 180c in the RAN 113 via the N3 interface, which may provide the WTRUs 102a, 102b, 102c with access to packet-switched networks, such as the Internet 110, to facilitate communications between the WTRUs 102a, 102b, 102c and IP-enabled devices. The UPF 184, 184b may perform other functions, such as routing and forwarding packets, enforcing user plane policies, supporting multi-homed PDU sessions, handling user plane QoS, buffering downlink packets, providing mobility anchoring, etc.

[0074] The CN 115 may facilitate communications with other networks. For example, the CN 115 may include or may communicate with an IP gateway (e.g., an IP Multimedia Subsystem (IMS) server) that acts as an interface between the CN 115 and the PSTN 108. In addition, the CN 115 may provide the WTRUs 102a, 102b, 102c with access to other networks 112, which may include other wired networks and / or wireless networks owned and / or operated by other service providers. In one embodiment, the WTRUs 102a, 102b, 102c may be connected to local data networks (DNs) 185a, 185b via the UPF 184a, 184b via the N3 interface to the UPF 184a, 184b and the N6 interface between the UPF 184a, 184b and the DN 185a, 185b.

[0075] Given that Figures 1A to 1D as well as Figures 1A to 1D Corresponding to the description of the present invention, one or more or all of the functions described herein for one or more of the following items may be performed by one or more simulation devices (not shown): WTRU102a-102d, base station 114a-114b, evolved Node B 160a-160c, MME 162, SGW 164, PGW 166, gNB 180a-180c, AMF 182a-182ab, UPF 184a-184b, SMF 183a-183b, DN 185a-185b and / or any other device described herein. The simulation device may be one or more devices configured to mimic one or more or all of the functions described herein. For example, the simulation device can be used to test other devices and / or simulate network and / or WTRU functions.

[0076] The simulation device may be designed to implement one or more tests of other devices in a laboratory environment and / or in an operator network environment. For example, the one or more simulation devices may perform one or more functions or all functions while being fully or partially implemented and / or deployed as part of a wired communication network and / or a wireless communication network in order to test other devices within the communication network. The one or more simulation devices may perform one or more functions or all functions while being temporarily implemented / deployed as part of a wired communication network and / or a wireless communication network. The simulation device may be directly coupled to another device for testing purposes and / or may use over-the-air wireless communications to perform testing.

[0077] The one or more simulation devices can perform one or more (including all) functions without being implemented / deployed as a part of a wired communication network and / or a wireless communication network. For example, the simulation device can be used in a test scenario in a test laboratory and / or a non-deployed (e.g., testing) wired communication network and / or wireless communication network to implement the test of one or more components. The one or more simulation devices can be test equipment. Direct RF coupling and / or wireless communication performed via RF circuits (e.g., which can include one or more antennas) can be used by the simulation device to send and / or receive data.

[0078] Channel models in different environments may range from different indoor settings in offices and / or halls to multiple outdoor settings in urban and / or semi-urban environments. Different simulation tools (e.g., COST2100 and / or NYUSIM) may allow channel simulation in these predefined settings. Random channel models may be used (e.g., in 3GPP). Such models may use a subset of these channel models to represent some line-of-sight (LOS) and / or non-line-of-sight (NLOS) settings for evaluating the efficiency of a wireless communication system.

[0079] This paper describes a deep learning-based inverse modeling framework that can be used to extract the underlying multipath components from the channel matrix. Knowing such multipath components can be useful for generating simulated data that matches the characteristics of airborne data collected in the field.

[0080] The methods herein describe inverse modeling of a channel to identify parameters associated with the underlying multipath components; how to train a machine learning (ML) model to solve the inverse problem associated with identifying multipath component parameters given an original channel matrix; and / or use the inverse modeling framework to perform data generation for training ML-based solutions and / or to perform channel state information (CSI) compression.

[0081] Figure 2 An example method 200 for channel parameter estimation for data generation is depicted. Figure 2As depicted, the method 200 described herein may be used at a WTRU in a downlink (DL) communication setting and / or at a gNB in ​​an uplink (UL) communication setting. Taking into account the DL setting, the WTRU and / or the network may be configured to perform different functions. At 202, the WTRU may receive a configuration from the network that configures the WTRU to perform an inverse deep learning model. For example, the WTRU may be configured to receive, configure and / or utilize an inverse deep learning model (e.g., by the network). At 204, the WTRU may receive multiple reference signals (RS) from the network. The RS may be suitable for channel estimation and / or parameter estimation using an inverse deep learning model. The WTRU may perform channel estimation to estimate a multiple-input multiple-output (MIMO) channel.

[0082] At 206, the WTRU may estimate the channel. For example, the WTRU may determine one or more channel-related parameters and properties based on the RS and / or using an inverse deep learning model. The parameters may include any combination of the number of multipath components (MPCs) and / or multipath clusters, an azimuth of arrival (AoA) and / or an elevation of arrival (AoA) and / or an azimuth of departure (AoD) and / or an elevation of departure (AoD) associated with each of the MPCs and / or multipath clusters, and / or a gain associated with each of the MPCs and / or multipath clusters. The WTRU may transmit an indication of the number of MPCs and the AoA, AoD, and gain associated with each of the number of MPCs to the network.

[0083] At 208, over time and / or through multiple channel estimates, the WTRU may further estimate individual distributions associated with each of these parameters and / or may construct a joint distribution of these parameters. For example, the WTRU may transmit to the network an indication of the number of MPCs and / or the AoA, AoD, and / or gain associated with each of the number of MPCs. Alternatively or additionally, the WTRU may send a joint probability distribution function to the gNB. For example, the WTRU may determine a joint probability distribution function for the number of MPCs based on the number of MPCs and / or the AoA, AoD, and / or gain associated with each of the number of MPCs, and transmit the joint probability distribution function to the network. If the joint distribution is modeled as a Gaussian mixture model (GMM), the variance and / or mean of the GMM may adequately represent the distribution. In some examples, the WTRU may then send representative parameters associated with these distributions. The WTRU may periodically send an indication of the number of MPCs and / or the AoA, AoD, and / or gain associated with each of the number of MPCs to the network, or the WTRU may transmit an indication of the number of MPCs and / or the AoA, AoD, and / or gain associated with each of the number of MPCs to the network in response to a request received from the network. The WTRU may receive an indication from the network that the WTRU is to send an indication of the number of MPCs and the AoA, AoD, and gain associated with each of the number of MPCs to the network.

[0084] At 212, the gNB may sample the received joint distribution, which may allow the gNB to create valid channel data that can then be used for ML tasks. At 214, the gNB may train the model using the large data set (e.g., online or offline). The machine learning tasks may include, but are not limited to, online and / or offline model training, model evaluation and / or retraining triggers, etc. Additionally or alternatively, the WTRU may send MPC parameters associated with each channel separately.

[0085] Figure 3 An example method 300 for CSI compression and feedback is depicted. Figure 3 As depicted, the method 300 described herein may be performed by a combination of a WTRU and / or a gNB. Considering a DL setting, the WTRU and / or the network may be configured to perform different functions. For example, at 302, the WTRU may be configured to receive, configure, and / or utilize an inverse deep learning model. For example, the WTRU may receive a configuration from the network that configures the WTRU to perform an inverse deep learning model.

[0086] At 304, the WTRU may be configured with one or more methods for dominant path selection via the network. Thus, the WTRU may receive an indication of the path selection method from the network. For example, the WTRU may be configured with energy-based dominant path selection. In this configuration, the multipaths may be sorted based on the squared value of the gain / loss associated therewith. In addition, the WTRU may select the path with the maximum gain whose cumulative sum is less than a pre-specified threshold. The WTRU may be configured to select paths up to a threshold based on a function of multipath gain and / or loss. An example of such a function may be the Lp norm. In this configuration, the WTRU may select multipaths up to a threshold based on the Lp norm of the gain and / or loss. Wherein, the Lp norm v of the N1-dimensional vector: = [v1, v2,…, v N1 ]Depend on Given. In addition, the WTRU may select a path based on the cosine similarity or squared generalized cosine similarity (SGCS) of the paths, where multiple paths with strong cosine similarity in the overall path response (or just the angle) may be grouped together. If there is a threshold gap in cosine similarity in the overall response associated with a path, the path may be considered different and / or reported separately. In addition, the WTRU configuration may be rank-based (e.g., a multiple-input multiple-output multiple-output (MIMO) channel rank method). In this configuration, the paths may be ordered based on any of the previous methods, but a subset of the paths (e.g., only R paths) are selected for transmission (e.g., where R is the rank of the MIMO channel).

[0087] The WTRU may use the inverse model to receive one or more RS suitable for channel estimation and / or parameter estimation at 306. The WTRU may perform channel estimation to estimate the MIMO channel.

[0088] At 308, the WTRU may estimate the channel based on the RS to determine channel-related parameters and / or properties associated with the channel, such as, but not limited to: the number of MPCs and / or multipath clusters; the AoA and / or AoD associated with each of the MPCs or multipath clusters; and / or the gain associated with each of the MPCs or multipath clusters. For example, the WTRU may determine the number of MPCs based on multiple RSs using an inverse deep learning model. The WTRU may determine the AoA, AoD, and / or gain associated with each MPC based on multiple RSs using an inverse deep learning model. The MPC may have all the information contained in the channel matrix. As described herein, the WTRU may use the MPC for CSI feedback. The WTRU may transmit an indication of the number of MPCs and the AoA, AoD, and gain associated with each of the number of MPCs to the network.

[0089] At 310, the WTRU may identify a subset of paths (e.g., M dominant paths) and / or MPCs (e.g., dominant MPCs) based on a method specified for path selection (e.g., energy based, L1 / Lp based, and / or rank based, etc.). The WTRU may send post-processing information corresponding to the dominant MPCs to the gNB to the network as CSI feedback. The WTRU may periodically transmit the dominant MPCs to the network as CSI feedback, or the WTRU may transmit the dominant MPCs to the network as CSI feedback in response to a request received from the network. The dominant MPCs may include AoA, AoD, and / or gain (e.g., indications of the above) associated with each of the dominant MPCs.

[0090] In some examples, at 312, the WTRU may utilize post-processing methods to reduce the number of bits required to specify the gain, AOA, and / or AOD corresponding to the MPC. The post-processing methods may include, but are not limited to, different forms of quantization. In an example, scalar quantization and / or vector quantization methods may be utilized to reduce the number of bits representing each parameter. Parameters corresponding to the most dominant path may be quantized using more bits. The post-processing methods may also include entropy coding. The post-processing methods may also include error correction. Different error correction bits and / or redundancy may be added to each parameter, depending on its importance.

[0091] At 314, the gNB may receive the dominant MPCs. The gNB may use the AoA, AoD, and / or gain associated with each dominant MPC to reconstruct the CSI using an inverse deep learning model.

[0092] The WTRU may be configured to identify parameters associated with the underlying MPC (e.g., using an inverse deep learning model). The WTRU may be configured to identify parameters associated with each multipath in different ways. For example, the WTRU may use MUSIC, ESPIRIT, and / or SAGE to estimate AoA and / or AoD estimates. The WTRU may be configured to determine (e.g., solve) the gain associated with the multipath as a least squares problem, for example, after estimating the angle using one or more methods described herein. The WTRU may be configured to apply an orthogonal matching pursuit (OMP) method for simultaneous angle and gain estimation. In an example, OMP may be used as a baseline for experiments. Additionally or alternatively, a tensor decomposition method may solve the parameter estimation problem.

[0093] Figure 4A flow chart 400 is depicted of an example end-to-end supervised learning framework that may be implemented by a WTRU and / or by a network element. The WTRU may be configured to transform a channel matrix 404 using a discrete Fourier transform (DFT) 408. The WTRU may be configured to feed the channel matrix into a ResNet18-like architecture 412 in an end-to-end architecture to generate MPC parameters 416. The flow chart 400 depicts an end-to-end supervised learning framework for estimating multipath component parameters (MPC) given a channel matrix, which may be implemented, for example, by a WTRU.

[0094] Figure 5 An example ResNet-based architecture 500 is illustrated. Figure 4 The inverse function approximator block shown can be represented as Figure 5 The WTRU may train the model using the mean squared error between the predicted multipath component parameters and / or the ground truth multipath component parameters. The model may primarily employ residual network style blocks and / or fully connected (FC) layers. Convolutional neural network (CNN) layers may be stacked with batch normalization layers, non-linear activations (e.g., ReLu and / or LeakyReLu), and / or skip connections to form a ResNet block. Multiple ResNet blocks may be stacked with CNN and / or FC layers to generate Figure 5 The architecture presented in .

[0095] The WTRU may be configured to apply a DFT transform to the input raw channel matrix, for example using a supervised framework. The WTRU may be configured to pre-process the channel matrix using the DFT to generate a sparse representation of the channel matrix. The sparse representation of the channel matrix may provide a better estimate of AoA / AoD (e.g., compared to an OMP-based algorithm).

[0096] The WTRU may construct a data set. For example, the WTRU may be configured to sort the parameters associated with the MPC based on the gain value of each of the multipaths to correspond to a given channel matrix. The WTRU may sort the parameters associated with the MPC because, for example, if the parameters associated with the MPC are not sorted, the multipaths may result in a one-to-many mapping that may be difficult to learn through a neural network.

[0097] Figure 6 A framework for multipath component estimation is depicted. In addition, Figure 6 End-to-end estimation of MPC using the proposed deep learning framework is described. The DFT-transformed channel matrix 602 can be fed to a classification model 630 to estimate the number of multipaths p, and then fed to Np regression models (one for each value of p) to estimate the MPC parameters.

[0098] Figure 6 A graphical representation of the proposed DL-based solution is shown. Assuming a setting where there may be a maximum of Np MPCs, a classification model 630 may be trained that predicts the number of multipath components in the input channel matrix. Np different regression models 660 may also be trained, one for each potential number (e.g., p) of multipath components that may be encountered. Thus, a first regression model 660 may explicitly predict the parameters of a channel containing only p=1 MPCs (4 predicted parameters: real and imaginary gain, AoA, and / or AoD), while a second model predicts the parameters of a channel with exactly p=2 MPCs (2 times 4 predicted parameters). p The model can predict and have N p The N corresponding to the multipath channels p × 4 parameters. The classification model 630 and / or the regression model 660 may be trained using a ResNet-18 architecture.

[0099] When using the classification model 630, the multipath detector (MPD) 604 head of the classification model may be trained using an end-to-end architecture, e.g. Figure 6 As shown. The input to MPD 604 may be a 2D-DFT transformed channel matrix. Since the transformed channel may be a complex valued matrix, the real matrix and / or the imaginary matrix may be concatenated to form R 2×Nr×Nt Tensor. The first convolutional layer can increase the number of input dimensions at the beginning of the architecture. Each ResNet block can consist of a convolutional layer and / or a batch normalization layer and a Leaky ReLu activation function. Fully connected layers can exist near the end of the network architecture. Since the maximum possible number N of multipaths can be assumed p The prior knowledge of , so the output layer size can be fixed to N p ×4. In order to <N p The channel matrix of the present invention may be labeled, and multipath component parameters with zero gain values ​​and / or out-of-range angle values ​​may be attached. The MPD 604 using the mean square error between the ground truth multipath features and / or the predicted multipath features may be trained. Next, the MPC classification features 606 may be generated based on the DFT transformed data. A supervised classification scheme (e.g., LDA and / or linear discriminant analysis) may be used to estimate the number of multipath components in the channel.

[0100] When using regression model 660, N p Multipath regression (MPR) models, one of which corresponds to the range [1...N p] Each multipath in the MPR 622 model. Similar to the MPD 604 model mentioned above, each of the MPR 622 models utilizes a DFT-transformed channel matrix 620 that is regressed to a corresponding set of MPC parameters 624. In general, N p There are different regression models 660, each with a different number of output units in the last layer. The number of output units can be determined based on the number of multipaths used to generate the channel matrix. In other words, the pth model has a size of 4×p (p is in [1...N p ] is the output layer of the .

[0101] MPC parameter estimation can be formulated as a sparse recovery problem. The WTRU can be configured to use the OMP algorithm to solve the sparse recovery problem. The WTRU can be configured to use the OMP algorithm to benchmark the performance of the proposed framework (e.g., the neural network (NN) based framework described herein). In an example, the WTRU can use a grid with a resolution of 1 degree and a certain range (e.g., [-60,60]) because the OMP based algorithm can be an exhaustive search algorithm. For the supervised framework, 70% of the data is used for training and the remaining 30% of the data is used for validation. The WTRU can test the performance of the NN based framework and the OMP based algorithm. For example, the WTRU can generate (e.g., randomly generate) multiple channel matrices (e.g., 10,000 channel matrices) using the parameters mentioned above. The WTRU can evaluate the error in the AoD / AoA estimation.

[0102] Fig. 7A is a graph 700 of an example performance comparison of an OMP-based algorithm 710 and a NN-based framework 720 for errors in angle estimation. Fig. 7A The curve 700 depicts an example comparative error in AoD / AoA estimation for the OMP-based algorithm 710 and the NN method 720 for different numbers of multipath component parameters. The number of multipath component parameters in the channel matrix may increase as the error in the AoA / AoD estimation increases (e.g., as Fig. 7A shown).

[0103] In some examples, when the channel matrix consists of 1 or 2 multipath component parameters, the NN-based framework 720 may outperform the OMP-based algorithm 710 in estimating MPC parameter values. In addition, in some examples (e.g., for 3 multipaths), the OMP-based algorithm 710 may slightly outperform the NN-based framework.

[0104] Figure 7B is a graph 750 of an example performance comparison of an OMP-based algorithm 730 and a NN-based framework 740 for inference time for estimating multipath component parameters per channel matrix. Figure 7B The graph 750 of FIG. 750 shows that the inference time of the NN-based algorithm may be approximately 100 times faster than the inference time of the OMP-based algorithm (e.g., regardless of the number of multipath component parameters). In an example, the OMP-based algorithm 730 may be an iterative exhaustive search algorithm. In an example, the inference time may increase as the number of multipath component parameters increases.

[0105] In an example, the WTRU may use an encoder-decoder-like architecture to correct the unsupervised AoA / AoD estimates. The ground truth labels of the channel parameters or MPC parameters may be unknown (e.g., in the scenario of an OTA communication pipeline). Therefore, in some examples, it may be challenging to build a supervised regression model for real OTA data due to the unavailability of ground truth channel parameters. Therefore, the WTRU may generate and / or implement an unsupervised model architecture similar to an encoder-decoder. For example, the encoder portion of the model may encode the channel matrix into multipath components. The decoder may be an analytical SV model that generates the channel matrix given a channel model. According to the supervised framework, the WTRU may use (e.g., feed) a sparse representation of the channel matrix, which may, for example, lead to better estimates of the channel parameters. In the unsupervised framework, the WTRU may estimate the loss as the mean square error loss between the ground truth channel matrix and / or the predicted channel matrix generated using the SV model applied to the encoded multipath parameters, for example, when a DFT transformed channel matrix is ​​applied as input.

Claims

1. A wireless transmit / receive unit (WTRU), the wireless transmit / receive unit (WTRU) comprising: A processor and a memory, the processor being configured to: receiving a configuration from a network, the configuration configuring the WTRU to execute an inverse deep learning model; receiving a plurality of reference signals from the network; determining a number of multipath components (MPCs) using the inverse deep learning model and based on the plurality of reference signals; determining an angle of arrival (AoA), an angle of departure (AoD), and a gain associated with each of the number of MPCs using the inverse deep learning model and based on the plurality of reference signals; and An indication of the number of MPCs and the AoA, the AoD, and the gain associated with each of the number of MPCs is transmitted to the network.

2. The WTRU of claim 1 , wherein the processor is configured to: A joint probability distribution function of the number of MPCs is determined based on the number of MPCs and the AoA, the AoD, and the gain associated with each of the number of MPCs, and wherein the indication of the number of MPCs and the AoA, the AoD, and the gain associated with each of the number of MPCs comprises the joint probability distribution function of the MPCs.

3. The WTRU of claim 2, wherein the processor is configured to: The joint probability distribution function of the number of MPCs is sent to the network, or representative parameters associated with the joint probability distribution function of the number of MPCs are sent to the network.

4. The WTRU of any one of claims 1 to 3, wherein the processor is configured to: receiving an indication from the network, the indication indicating that the WTRU is to send the indication of the number of MPCs and the AoA, the AoD, and the gain associated with each of the number of MPCs to the network.

5. The WTRU of any one of claims 1 to 4, wherein the processor is configured to: The indication of the number of MPCs and the AoA, the AOD, and the gain associated with each of the number of MPCs is periodically transmitted to the network.

6. The WTRU of any one of claims 1 to 5, wherein the indication of the number of MPCs and the AoA, the AoD, and the gain associated with each of the number of MPCs are used to train a machine learning model at the network.

7. The WTRU of any one of claims 1 to 6, wherein the processor is configured to: receiving an indication of a path selection method from the network; determining a plurality of MPCs using the inverse deep learning model and based on the plurality of reference signals, wherein the number of MPCs is a subset of the plurality of MPCs; determining the AoA, the AoD, and the gain associated with each of the plurality of MPCs using the inverse deep learning model and based on the plurality of reference signals; and The number of MPCs among the plurality of MPCs is determined based on the path selection method, wherein the number of MPCs are preferred MPCs among the plurality of MPCs.

8. The WTRU of claim 7, wherein the path selection method comprises an energy-based method, an L1-norm method, or a multiple-input multiple-output (MIMO) channel rank method.

9. The WTRU of any one of claims 1 to 7, wherein the processor is configured to: Quantization, entropy encoding, or error correction is performed on the AoA, the AoD, and the gain associated with each of the number of MPCs to reduce a number of bits required to indicate the AoA, the AoD, and the gain associated with each of the number of MPCs.

10. A method performed by a wireless transmit / receive unit (WTRU), the method comprising: receiving a configuration from a network, the configuration configuring the WTRU to execute an inverse deep learning model; receiving a plurality of reference signals from the network; determining a number of multipath components (MPCs) using the inverse deep learning model and based on the plurality of reference signals; determining an angle of arrival (AoA), an angle of departure (AoD), and a gain associated with each of the number of MPCs using the inverse deep learning model and based on the plurality of reference signals; and An indication of the number of MPCs and the AoA, the AoD, and the gain associated with each of the number of MPCs is transmitted to the network.

11. The method according to claim 10, further comprising: A joint probability distribution function of the number of MPCs is determined based on the number of MPCs and the AoA, the AoD, and the gain associated with each of the number of MPCs, and wherein the indication of the number of MPCs and the AoA, the AoD, and the gain associated with each of the number of MPCs comprises the joint probability distribution function of the MPCs.

12. The method according to claim 10, further comprising: The joint probability distribution function of the number of MPCs is sent to the network, or representative parameters associated with the joint probability distribution function of the number of MPCs are sent to the network.

13. The method according to any one of claims 10 to 12, further comprising: receiving an indication from the network, the indication indicating that the WTRU is to send the indication of the number of MPCs and the AoA, the AoD, and the gain associated with each of the number of MPCs to the network.

14. The method according to any one of claims 10 to 13, further comprising: The indication of the number of MPCs and the AoA, the AOD, and the gain associated with each of the number of MPCs is periodically transmitted to the network.

15. A method according to any one of claims 10 to 14, wherein the indication of the number of MPCs and the AoA, the AoD and the gain associated with each of the number of MPCs is used to train a machine learning model at the network.

16. The method according to any one of claims 10 to 15, further comprising: receiving an indication of a path selection method from the network; determining a plurality of MPCs using the inverse deep learning model and based on the plurality of reference signals, wherein the number of MPCs is a subset of the plurality of MPCs; determining the AoA, the AoD, and the gain associated with each of the plurality of MPCs using the inverse deep learning model and based on the plurality of reference signals; and The number of MPCs among the plurality of MPCs is determined based on the path selection method, wherein the number of MPCs are preferred MPCs among the plurality of MPCs.

17. The method of claim 16, wherein the path selection method comprises an energy-based method, an L1 norm method, or a Multiple Input Multiple Output (MIMO) channel rank method.

18. The method according to any one of claims 10 to 16, further comprising: Quantization, entropy encoding, or error correction is performed on the AoA, the AoD, and the gain associated with each of the number of MPCs to reduce a number of bits required to indicate the AoA, the AoD, and the gain associated with each of the number of MPCs.

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