Methods, architectures, devices and systems for data driven user equipment (UE) specific reference signal operation

Abstract

Processes, methods, architectures, devices, systems, apparatuses, and computer program products are provided for configuring, selecting, and / or feeding back data-driven UE-specific RSs. A method may include a WTRU determining and / or indicating to a network element (e.g., a gNB) data-driven UE-specific RS-related information, such as bundling type, RS pattern, RS location, RS density, RS signaling feedback overhead, and / or performance requirements.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims the benefit of U.S. Provisional Application No. 63 / 395,937, filed on August 8, 2022, the contents of which are incorporated herein by reference in their entirety. Technical Field

[0003] Example embodiments described herein may generally relate to the fields of communications, wireless systems, and / or software, including, for example, methods, architectures, devices, systems related to UE-specific Reference Signal (RS) operations. Summary of the invention

[0004] An embodiment may relate to a method which may include receiving, by a WTRU, configuration information indicating a set of parameters for determining a reference signal (RS) configuration and / or parameters to be used within the set, wherein the parameter set includes at least two bundling types. The method may also include: receiving a transmission using a first RS mode of a first RS configuration type, and performing one or more measurements on the transmission using the first RS mode. The one or more measurements may include one or more of: an effective channel matrix for one or more time slots or resource blocks, channel estimation accuracy, and / or RS signaling overhead. The method may also include: determining a bundling type associated with the RS configuration based on the parameter set and / or the one or more measurements, and transmitting a first indication of the determined bundling type.

[0005] An embodiment may relate to a wireless transmit / receive unit (WTRU) having a circuit including any of a processor, a memory, a transmitter and / or a receiver. The circuit may be configured to receive configuration information indicating a set of parameters for determining a reference signal (RS) configuration and parameters to be used within the set, wherein the parameter set includes at least two bundling types. The circuit may be configured to: receive a transmission using a first RS mode of a first RS configuration type, and perform one or more measurements on the transmission using the first RS mode. The one or more measurements may include measurements related to one or more of: an effective channel matrix for one or more time slots or resource blocks, channel estimation accuracy, and / or RS signaling overhead. The circuit may also be configured to: determine a bundling type associated with the RS configuration based on the parameter set and / or the one or more measurements, and transmit a first indication of the determined bundling type. BRIEF DESCRIPTION OF THE DRAWINGS

[0006] A more detailed understanding may be obtained from the following detailed description given by way of example in conjunction with the accompanying drawings. As with the detailed description, each of the figures in such drawings is an example. Therefore, the figures (figures) and detailed description should not be considered limiting, and other equally effective examples are possible and possible. In addition, the same reference numerals ("labels") in the various figures indicate the same elements, and wherein:

[0007] Figure 1A is a system diagram illustrating an example communication system;

[0008] Figure 1B It shows that it can be Figure 1A A system diagram of an example wireless transmit / receive unit (WTRU) for use within a communication system is shown;

[0009] Figure 1C It shows that it can be Figure 1A A system diagram of an example radio access network (RAN) and an example core network (CN) used within the illustrated communication system;

[0010] Figure 1D It shows that it can be Figure 1A A system diagram of yet another example RAN and yet another example CN used within the communication system shown;

[0011] Figure 2 An example demodulation reference signal (DM-RS) pattern is shown;

[0012] Figure 3 An example multiple-input multiple-output (MIMO) transceiver chain is shown;

[0013] Figure 4 An example of joint gNB / UE processing for data-driven UE-specific RS design is shown.

[0014] Figure 5 An example of RS selection based on AIML is shown; and

[0015] Figure 6 is an example flow chart of a method according to one embodiment. DETAILED DESCRIPTION

[0016] In the following detailed description, many specific details are set forth to provide a thorough understanding of the embodiments and / or examples disclosed herein. However, it will be understood that such embodiments and examples can be practiced without some or all of the specific details set forth herein. In other cases, well-known methods, programs, components and circuits are not described in detail to avoid blurring the following description. In addition, the embodiments and examples not specifically described herein can replace the embodiments and other examples explicitly, implicitly and / or inherently described, disclosed or otherwise provided (collectively referred to as "providing") herein to practice or practice in combination with them. Although various embodiments in which equipment, systems, devices, etc. and / or any of its elements perform operations, processes, algorithms, functions, etc. and / or any part thereof are described and / or claimed herein, it should be understood that any embodiment described and / or claimed herein assumes that any equipment, systems, devices, etc. and / or any of its elements are configured to perform any operations, processes, algorithms, functions, etc. and / or any part thereof.

[0017] Example Communication System

[0018] The methods, devices, and systems provided herein are well suited for communications involving wired and wireless networks. Figures 1A to 1D An overview of various types of wireless devices and infrastructure is provided in which various elements of the network can utilize, perform, be arranged according to, and / or be adapted and / or configured for the methods, devices, and systems provided herein.

[0019] Figure 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 multiple wireless users to access such content by sharing system resources including wireless broadband. 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 multi-carrier (FBMC), etc.

[0020] like Figure 1AAs shown, the communication system 100 may include wireless transmit / receive units (WTRUs) 102a, 102b, 102c, 102d, a radio access network (RAN) 104 / 113, a core network (CN) 106 / 115, a public switched telephone network (PSTN) 108, the Internet 110 and other networks 112, but it will be appreciated that the disclosed embodiments contemplate any number of WTRUs, base stations, networks, and / or network elements. Each of the WTRUs 102a, 102b, 102c, 102d may be any type of device configured to operate and / or communicate in a wireless environment. For example, any of the WTRUs 102a, 102b, 102c, 102d may be referred to as a “station” and / or “STA”, which 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 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 the context of an industrial and / or automated process chain), a consumer electronic device, a device operating on a commercial and / or industrial wireless network, etc. Any of the WTRUs 102a, 102b, 102c, 102d may be interchangeably referred to as a UE.

[0021] 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 configured to wirelessly interface with at least one of the WTRUs 102a, 102b, 102c, 102d, for example, to facilitate access to one or more communication networks, such as the CN 106 / 115, the Internet 110, and / or the network 112. By way of example, the base stations 114a, 114b may be a base transceiver station (BTS), a Node B (NB), an eNode-B (eNB), a master Node B (HNB), a master eNode-B (HeNB), a gNode-B (gNB), a NR Node B (NRNB), a site controller, an access point (AP), a wireless router, and the like. While the base stations 114a, 114b are each depicted as a single element, it will be appreciated that the base stations 114a, 114b may include any number of interconnected base stations and / or network elements.

[0022] The base station 114a may be part of the RAN 104 / 113, which may also include other base stations and / or network elements (not shown), such as a base station controller (BSC), a radio network controller (RNC), a relay node, etc. The base station 114a and / or the base station 114b may be configured to transmit and / or receive wireless signals on one or more carrier frequencies, and the area corresponding to the one or more carrier frequencies may be referred to as a cell (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 that may be relatively fixed or may vary over time. The cell may also be divided into cell sectors. For example, a cell associated with the base station 114a may be divided into three sectors. Therefore, in an embodiment, the base station 114a may include three transceivers, i.e., one transceiver for each sector of the cell. In an embodiment, the base station 114a may employ multiple-input multiple-output (MIMO) technology, and may utilize multiple transceivers for each or any sector of the cell. For example, beamforming may be used to transmit and / or receive signals in a desired spatial direction.

[0023] 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).

[0024] More specifically, as described 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, SC-FDMA, etc. For example, the base station 114a in the RAN 104 / 113 and the WTRUs 102a, 102b, 102c may implement a radio technology such as Universal Mobile Telecommunications System (UMTS) Terrestrial Radio Access (UTRA) which may use Wideband CDMA (WCDMA) to establish the air interface 116. WCDMA may include communication protocols such as High Speed ​​Packet Access (HSPA) and / or Evolved HSPA (HSPA+). HSPA may include High Speed ​​Downlink Packet Access (HSDPA) and / or High Speed ​​Uplink Packet Access (HSUPA).

[0025] In an embodiment, the base station 114a and the WTRUs 102a, 102b, 102c may implement a radio technology such as Evolved UMTS Terrestrial Radio Access (E-UTRA) which may establish the air interface 116 using Long Term Evolution (LTE) and / or LTE-Advanced (LTE-A) and / or LTE-Advanced Pro (LTE-APro).

[0026] In an embodiment, the base station 114a and the WTRUs 102a, 102b, 102c may implement a radio technology such as New Radio (NR) to establish NR radio access over the air interface 116.

[0027] 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 jointly implement LTE radio access and NR radio access, 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 sent to / from multiple types of base stations (e.g., eNBs and gNBs).

[0028] In an embodiment, the base station 114a and the WTRUs 102a, 102b, 102c may implement a radio 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), etc.

[0029] Figure 1AThe base station 114b in the example may be, for example, a wireless router, a master Node-B, a master eNode-B, or an access point, and may utilize any suitable RAT to facilitate wireless connectivity in a local area, such as a business location, a home, a vehicle, a campus, an industrial facility, an air corridor (e.g., for use by drones), a road, and the like. In an 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 an 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-aPro, NR, etc.) to establish any of a small cell, a pico cell, or a femto cell. As Figure 1A As shown, base station 114b may be directly connected to Internet 110. Therefore, base station 114b may not need to access Internet 110 via CN 106 / 115.

[0030] 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, fault 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 Figure 1A Not shown, but it will be appreciated that the RAN 104 / 113 and / or the CN 106 / 115 may be in direct or indirect communication with other RANs that employ the same RAT or a different RAT as the RAN 104 / 113. For example, in addition to being connected to the RAN 104 / 113, which may be utilizing an NR radio technology, the CN 106 / 115 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.

[0031] The CN 106 / 115 may also serve as a gateway for the WTRUs 102a, 102b, 102c, 102d to access the PSTN 108, the Internet 110, and / or other networks 112. The PSTN 108 may include a circuit-switched telephone network that provides plain old telephone service (POTS). The Internet 110 may include a global system of interconnected computer networks and devices that use common communication protocols such as the Transmission Control Protocol (TCP), the User Datagram Protocol (UDP), and / or the Internet Protocol (IP) in the TCP / IP Internet protocol suite. The networks 112 may include wired and / or wireless communication networks owned and / or operated by other service providers. For example, the networks 112 may include another CN connected to one or more RANs, which may use the same RAT as the RAN 104 / 114 or a different RAT.

[0032] 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.

[0033] 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 elements / 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.

[0034] 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 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. Although Figure 1B The processor 118 and the transceiver 120 are depicted as separate components, but it will be appreciated that the processor 118 and the transceiver 120 may be integrated together, such as in an electronic package or chip.

[0035] The transmit / receive element 122 may be configured to transmit or receive signals to or from a base station (e.g., base station 114a) via an air interface 116. For example, in an embodiment, the transmit / receive element 122 may be an antenna configured to transmit and / or receive RF signals. In an embodiment, the transmit / receive element 122 may be a transmitter / detector configured to transmit and / or receive, for example, IR, UV, or visible light signals. In an embodiment, the transmit / receive element 122 may be configured to transmit and / or receive both RF signals and optical signals. It will be appreciated that the transmit / receive element 122 may be configured to transmit and / or receive any combination of wireless signals.

[0036] Although the transmit / receive element 122 is Figure 1B Although depicted as a single element in the embodiment, the WTRU 102 may include any number of transmit / receive elements 122. For example, the WTRU 102 may employ MIMO technology. Thus, in an 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.

[0037] 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 described above, the WTRU 102 may have multi-mode capabilities. Thus, the transceiver 120 may include multiple transceivers for enabling the WTRU 102 to communicate via multiple RATs (e.g., such as NR and IEEE 802.11).

[0038] The processor 118 of the WTRU 102 may be coupled to and may receive user input data from: a speaker / microphone 124, a keypad 126, and / or a display / touchpad 128 (e.g., a liquid crystal display (LCD) display unit or an organic light emitting diode (OLED) display unit). The processor 118 may also output user data to the speaker / microphone 124, the keypad 126, and / or the display / touchpad 128. In addition, the processor 118 may access information from and store data in any type of suitable memory, such as a non-removable memory 130 and / or a removable memory 132. The non-removable memory 130 may include a random access memory (RAM), a read-only memory (ROM), a hard disk, or any other type of memory storage device. The removable memory 132 may include a subscriber identity module (SIM) card, a memory stick, a secure digital (SD) memory card, etc. 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).

[0039] The processor 118 may receive power from the power source 134 and may be configured to distribute the power to and / or control 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 cell batteries (e.g., nickel-cadmium (NiCd), nickel-zinc (NiZn), nickel-metal hydride (NiMH), lithium-ion (Li-ion), etc.), solar cells, fuel cells, etc.

[0040] 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 receiving signals 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.

[0041] The processor 118 may also be coupled to other components / 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 components / peripherals 138 may include an accelerometer, an electronic compass, a satellite transceiver, a digital camera (e.g., for photos and / or videos), a universal serial bus (USB) port, a vibration device, a television transceiver, a hands-free headset, Module, frequency modulation (FM) radio unit, digital music player, media player, video game player module, Internet browser, virtual reality and / or augmented reality (VR / AR) device, activity tracker, etc. Components / peripherals 138 may include one or more sensors, which may be gyroscopes, accelerometers, Hall effect sensors, magnetometers, orientation sensors, proximity sensors, temperature sensors, time sensors; geolocation sensors; altimeters, light sensors, touch sensors, magnetometers, barometers, gesture sensors, biometric sensors, and / or humidity sensors.

[0042] The WTRU 102 may include a full-duplex radio, in which transmission and reception of some or all of the signals (e.g., associated with specific subframes for both uplink (e.g., for transmission) and downlink (e.g., for reception)) may be concurrent and / or simultaneous. The full-duplex radio may include an interference management unit to reduce and or substantially eliminate self-interference via hardware (e.g., choke) or signal processing via a processor (e.g., a separate processor (not shown) or via the processor 118). In an embodiment, the WTRU 102 may include a half-duplex radio, in which transmission and reception of some or all of the signals (e.g., associated with specific subframes for both uplink (e.g., for transmission) or downlink (e.g., for reception)) are performed.

[0043] Figure 1C 1 is a system diagram illustrating the RAN 104 and the CN 106 in accordance with an embodiment. As described above, the RAN 104 may employ an 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.

[0044] The RAN 104 may include eNode-Bs 160a, 160b, 160c, though it will be appreciated that the RAN 104 may include any number of eNode-Bs while remaining consistent with an embodiment. The eNode-Bs 160a, 160b, 160c may each include one or more transceivers for communicating with the WTRUs 102a, 102b, 102c over the air interface 116. In an embodiment, the eNode-Bs 160a, 160b, 160c may implement MIMO technology. Thus, for example, the eNode-B 160a may use multiple antennas to transmit wireless signals to, and receive wireless signals from, the WTRU 102a.

[0045] Each of the eNode-Bs 160a, 160b, and 160c may be associated with a particular cell (not shown) and may be configured to handle radio resource management decisions, handover decisions, user scheduling in uplink (UL) and / or downlink (DL), etc. Figure 1C As shown, eNode-Bs 160a, 160b, 160c may communicate with each other via an X2 interface.

[0046] 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 (PGW) 166. While each of the foregoing elements are depicted as part of the CN 106, it will be appreciated that any of these elements may be owned and / or operated by an entity other than the CN operator.

[0047] The MME 162 may be connected to each of the eNode-Bs 160a, 160b, and 160c in the RAN 104 via an S1 interface and may serve as a control node. For example, the MME 162 may be responsible for authenticating users of the WTRUs 102a, 102b, 102c, bearer activation / deactivation, selecting a particular serving gateway during an initial attach of the WTRUs 102a, 102b, 102c, 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.

[0048] The SGW 164 may be connected to each of the eNode Bs 160a, 160b, 160c in the RAN 104 via an S1 interface. The SGW 164 may generally route and forward user data packets to or from the WTRUs 102a, 102b, 102c. The SGW 164 may perform other functions, such as anchoring the user plane during inter-eNode-B handovers, triggering paging when DL data is available for the WTRUs 102a, 102b, 102c, managing and storing the context of the WTRUs 102a, 102b, 102c, and the like.

[0049] 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.

[0050] The CN 106 may facilitate communications with other networks. For example, the CN 106 may provide the WTRUs 102a, 102b, 102c with access to circuit-switched networks, such as the PSTN 108, to facilitate communications between the WTRUs 102a, 102b, 102c and traditional land-line communications devices. For example, the CN 106 may include or may communicate with an IP gateway (e.g., an IP Multimedia Subsystem (IMS) server) as an interface between the CN 106 and the PSTN 108. In addition, the CN 106 may provide the WTRUs 102a, 102b, 102c with access to other networks 112, which may include other wired and / or wireless networks owned and / or operated by other service providers.

[0051] Although the WTRU Figures 1A to 1D Although described as a wireless terminal, it is contemplated that in certain representative embodiments such a terminal may (eg, temporarily or permanently) use a wired communication interface with a communication network.

[0052] In a representative embodiment, other network 112 may be a WLAN.

[0053] A WLAN in infrastructure basic service set (BSS) mode may have an access point (AP) for the BSS and one or more stations (STA) associated with the AP. The AP may access a distribution system (DS) or another type of wired / wireless network that loads traffic into and / or out of the BSS or has an interface thereto. Traffic originating from outside the BSS to the STA may be reached by the AP and may be delivered to the STA. Traffic from the STA to a destination outside the BSS may be sent to the AP to be delivered to the corresponding destination. Traffic between STAs within the BSS may be sent by the AP, for example, where the source STA may send traffic to the AP, and the AP may deliver the traffic to the destination STA. Traffic between STAs within the BSS may be considered and / or referred to as point-to-point traffic. Point-to-point traffic may be sent between the source STA and the destination STA using a direct link setup (DLS) (e.g., directly sent between them). In certain representative embodiments, the DLS may use 802.11e DLS or 802.11z tunnel DLS (TDLS). A WLAN using an independent BSS (IBSS) mode may not have an AP, and STAs (eg, all STAs) within or using the IBSS may communicate directly with each other. The IBSS communication mode may sometimes be referred to herein as an "ad hoc" communication mode.

[0054] When using the 802.11ac infrastructure mode of operation or a similar mode of operation, the AP can send 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 an operating channel of the BSS and can be used by STAs to establish a connection with the AP. In certain representative embodiments, carrier sense multiple access-collision avoidance (CSMA / CA) can be implemented, for example, in an 802.11 system. For CSMA / CA, STAs (e.g., each STA) including the AP can sense the primary channel. If the primary signal is sensed / detected by a particular STA and / or is determined to be busy, the particular STA can back off. One STA (e.g., only one station) can transmit in a given BSS at any given time.

[0055] A high throughput (HT) STA may communicate using a 40 MHz wide channel, for example, via a combination of a primary 20 MHz channel and adjacent or non-adjacent 20 MHz channels to form a 40 MHz wide channel.

[0056] Very high throughput (VHT) STA can support 20MHz, 40MHz, 80MHz and / or 160MHz wide channels. 40MHz and / or 80MHz channels can be formed by combining continuous 20MHz channels. A 160MHz channel can be formed by combining 8 continuous 20MHz channels, or by combining two discontinuous 80MHz channels, which can be referred to as an 80+80 configuration. For the 80+80 configuration, the data can be passed through a fragment parser after channel coding, which can divide the data into two streams. Inverse Fast Fourier Transform (IFFT) processing and time domain processing can be performed on each stream respectively. The stream can be mapped to two 80MHz channels, and the data can be transmitted by the transmitting STA. At the receiver of the receiving STA, the above-mentioned operation of the 80+80 configuration can be reversed, and the combined data can be sent to a media access control (MAC) layer, an entity, etc.

[0057] 802.11af and 802.11ah support operating modes below 1 GHz. The channel operating bandwidth and carrier 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 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 can support meter type control / machine type communication (MTC), such as MTC devices in macro coverage. MTC devices may have certain capabilities, for example, limited capabilities, including support for (e.g., only support for) certain and / or limited bandwidths. MTC devices may include batteries with battery life above a threshold (e.g., to maintain very long battery life).

[0058] A WLAN system that can support multiple channels and channel bandwidths (such as 802.11n, 802.11ac, 802.11af, and 802.11ah) includes a channel that can be designated as a primary channel. 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 the STA that supports the minimum bandwidth operating mode among 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, due to a STA (which only supports a 1MHz operating mode) transmitting to the AP, the entire available band may be considered busy even if most of the band remains idle and may be available.

[0059] In the United States, the available frequency band that can be used by 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.

[0060] Figure 1D1 is a system diagram showing the RAN 113 and the CN 115 according to an embodiment. As described 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.

[0061] 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 an embodiment, the gNBs 180a, 180b, 180c may implement MIMO technology. For example, the gNBs 180a, 180b may utilize beamforming to transmit signals to and / or receive signals from the WTRUs 102a, 102b, 102c. Thus, for example, the gNB 180a may use multiple antennas to transmit wireless signals to and / or receive wireless signals from the WTRU 102a. In an embodiment, the gNBs 180a, 180b, 180c may implement carrier aggregation technology. For example, the gNB 180a may transmit multiple component carriers to the WTRU 102a (not shown). A subset of these component carriers may be located on an unlicensed spectrum, while the remaining component carriers may be located 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 gNB 180b (and / or gNB 180c).

[0062] The WTRUs 102a, 102b, 102c may communicate with the gNBs 180a, 180b, 180c using transmissions associated with scalable parameter sets. For example, the OFDM symbol spacing and / or OFDM subcarrier spacing may vary for different transmissions, different cells, and / or different portions of the wireless transmission spectrum. The WTRUs 102a, 102b, 102c may communicate with the gNBs 180a, 180b, 180c using subframes or transmission time intervals (TTIs) of varying or scalable lengths (e.g., including different numbers of OFDM symbols and / or lasting different lengths of absolute time).

[0063] 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 without also accessing other RANs (e.g., such as the eNode Bs 160a, 160b, 160c). In a standalone configuration, the WTRUs 102a, 102b, 102c may use one or more of the gNBs 180a, 180b, 180c as mobility anchors. In a standalone configuration, the WTRUs 102a, 102b, 102c may communicate with the gNBs 180a, 180b, 180c using signals in an unlicensed band. In a non-standalone configuration, the WTRU 102a, 102b, 102c may communicate / connect with the gNB 180a, 180b, 180c while also communicating / connecting with another RAN, such as the eNode-B 160a, 160b, 160c. For example, the WTRU 102a, 102b, 102c may implement the DC principle to communicate with one or more gNBs 180a, 180b, 180c and one or more eNode-Bs 160a, 160b, 160c substantially simultaneously. In a non-standalone configuration, the eNode-B 160a, 160b, 160c may serve as a mobility anchor for the WTRU 102a, 102b, 102c, and the gNB 180a, 180b, 180c may provide additional coverage and / or throughput to serve the WTRU 102a, 102b, 102c.

[0064] 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, user scheduling in UL and / or DL, support for 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.

[0065] 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 at least one data network (DN) 185a, 185b. Although each of the foregoing elements is described as part of the CN 115, it will be appreciated that any of these elements may be owned and / or operated by an entity other than the CN operator.

[0066] 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 serve as a control node. For example, the AMF 182a, 182b may be responsible for authenticating users of the WTRU 102a, 102b, 102c, supporting network slicing (e.g., handling different protocol data unit (PDU) sessions with different requirements), selecting a specific SMF 183a, 183b, managing registration areas, terminating NAS signaling, mobility management, etc. The AMF 182a, 182b may use network slicing, for example, to customize CN support for the WTRU 102a, 102b, 102c based on the type of service the WTRU 102a, 102b, 102c is utilizing. 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 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-APro) and / or non-3GPP access technologies (such as Wi-Fi).

[0067] The SMF 183a, 183b may be connected to the AMF 182a, 182b in the CN 115 via the N11 interface. The SMF 183a, 183b may also be connected to the UPF 184a, 184b in the CN 115 via the N4 interface. The SMF 183a, 183b may select and control the UPF 184a, 184b, and configure the routing of traffic 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.

[0068] 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, for example, 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.

[0069] 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) as an interface between the CN 115 and the PSTN 108. In addition, the CN 115 may provide the WTRUs 102a, 102b, 102c with access to other networks 112, which may include other wired and / or wireless networks owned and / or operated by other service providers. In an embodiment, the WTRUs 102a, 102b, 102c may be connected to a local data network (DN) 185a, 185b via an N3 interface to the UPF 184a, 184b and an N6 interface between the UPF 184a, 184b and the DN 185a, 185b through the UPF 184a, 184b.

[0070] Given that Figures 1A to 1D and Figures 1A to 1D 102a to 102d, base stations 114a to 114b, eNode-Bs 160a to 160c, MMEs 162, SGWs 164, PGWs 166, gNBs 180a to 180c, AMFs 182a to 182b, UPFs 184a to 184b, SMFs 183a to 183b, DNs 185a to 185b, and / or any other elements / devices described herein. A simulation device may be one or more devices configured to simulate one or more or all of the functions described herein. For example, a simulation device may be used to test other devices and / or simulate network and / or WTRU functions.

[0071] The simulation device can be designed to implement one or more tests of other devices in a laboratory environment and / or an operator network environment. For example, one or more simulation devices can perform one or more or all functions when fully or partially implemented and / or deployed as part of a wired and / or wireless communication network to test other devices within the communication network. One or more simulation devices can perform one or more or all functions when temporarily implemented / deployed as part of a wired and / or wireless communication network. The simulation device can be directly coupled to another device for testing purposes, and / or can use over-the-air wireless communication to perform testing.

[0072] One or more simulation devices can perform one or more (including all) functions when not implemented / deployed as part of a wired and / or wireless communication network. For example, the simulation device can be used for testing scenarios in a test laboratory and / or a wired and / or wireless communication network that is not deployed (e.g., testing) to implement testing of one or more components. One or more simulation devices can be test equipment. The simulation device can use direct RF coupling and / or wireless communication via RF circuits (e.g., which can include one or more antennas) to transmit and / or receive data.

[0073] Introduction

[0074] As will be discussed in detail below, certain example embodiments may at least provide a process for configuring, selecting, and / or feeding back artificial intelligence (AI) / machine learning (ML)-based data-driven UE-specific RS, e.g., including flexible modes, density, and / or bundling across time / frequency / space. Some example embodiments may be applicable to RS selection for both DL and / or UL transmissions.

[0075] Overview

[0076] In current systems, composite channel estimation (CEST) and coherent demodulation of precoded / beamformed signals at the receiver are facilitated by the transmission of RSs, such as demodulation reference signals (DM-RS) and phase tracking reference signals (PTRS). RS operation in today's NR is static, whereby there are several predefined options for the pattern (uniform / equidistant) and density of RSs based on the physical channel type, which are configured using scheduling (based on DCI) and higher layer configurations to cater to different use cases and UE capabilities.

[0077] Flexible RS operation (such as data-driven UE-specific RS design and feedback based on AIML) allows many benefits, including reduced RS density, reduced RS feedback, reduced receiver (CEST) complexity, improved CEST performance, or a combination of these. In the context of artificial intelligence / machine learning (AIML) for the NR air interface in Rel-18, RS density reduction using AIML is being studied as one of the use cases.

[0078] AIML enables configuration, selection, and feedback of data-driven UE-specific RS operations such as non-uniform RS patterns and dynamic bundling across time and / or frequency. The AIML model (e.g., by exploiting channel characteristics such as inherent time / frequency / spatial correlation between REs, symbols, and antennas) can predict the preferred RS for subsequent DL transmissions and / or uplink transmissions. AIML processing can be done on the UE side, or jointly between the UE and the base station / gNB, whereby compressed preferred RS reports or inter-node data are exchanged between the UE (encoder) and the gNB (decoder). Under this framework, pilot-less transmissions can be supported as a special case.

[0079] Some example embodiments may include a process for determining UE capabilities (e.g., AIML models) relative to data-driven UE-specific RS. Certain embodiments may also provide a process for configuring an AIML-capable UE based on UE capabilities and system performance requirements (e.g., signaling overhead, channel estimation accuracy, RS overhead, etc.) to implement AIML-based data-driven UE-specific RS. Another embodiment may include a process for transmitting a default RS configuration for AIML model training. Some embodiments may also include a process for determining and / or feedback of AIML-based data-driven UE-specific RS operations, which may include joint AIML model processing between gNB / UE. Additionally, certain embodiments may provide processes for AIML model / channel performance monitoring and triggers for model retraining and recovery to traditional operations.

[0080] Coherent demodulation of signals transmitted over the radio interface typically requires knowledge of the (precoded) wireless channel. The channel estimation (CEST) process at the receiver in NR relies on the transmission of the physical channel together with an accompanying reference signal (RS) or pilot.

[0081] RS is generated using a pseudo-random sequence based on system parameters known to the receiver. Parameters used to control sequence generation include scrambling identity, symbol position, number of OFDM symbols in a slot, etc. RS operation in today's NR is static, whereby there are several predefined options for RS pattern (uniform / equidistant) and density based on the physical channel, which are configured using scheduling (based on DCI) and higher layer configurations to meet different use cases and UE capabilities.

[0082] The configuration of DM-RS may include density and pattern in the resource grid, duration, starting symbol (e.g., front-loaded DM-RS), and / or cover codes to distinguish antenna ports sharing the same time / frequency resources (for single-user and multi-user MIMO cases). The set of parameters for DM-RS may differ depending on the physical channel and UE capabilities, e.g., for the physical downlink shared channel (PDSCH) DM-RS, there are configuration types 1 or 2, mapping types A or B, starting symbols for mapping type A, single-symbol vs. dual-symbol DM-RS, DM-RS additional locations, and duration. DM-RS may also be grouped over several resource blocks, where the precoder is constant so that the receiver can perform wideband channel estimation.

[0083] The specific selection of DM-RS may be performed by both higher layer configuration and dynamic (DCI based) signaling, but there may also be cases where a default configuration is appropriate. Figure 2 An example NR DM-RS single symbol configuration type 1 for 4×4 MIMO is shown. More specifically, Figure 2 An example DM-RS pattern with configuration type 1, mapping type A, and starting symbol 3 using downlink antenna ports 1000 to 1003 on one symbol and one resource block in NR is described, where CDM is grouped across frequency and code domains.

[0084] After selecting the DM-RS setting, the base station can signal the selection to the terminal (e.g., UE) (e.g., using RRC, MAC-CE, or PDCCH / DCI). The terminal can then utilize the DM-RS for CEST and coherent demodulation of the corresponding physical channel. This can be achieved by a specific receiver filter implementation (e.g., least squares, minimum mean square error, etc.) that broadly estimates the composite channel by mapping the transmitted layers to the receive antennas of the scheduled resource blocks.

[0085] More specifically, the CEST process is as follows: (i) the receiver first determines an estimate of the channel for the pilot symbols based on the known positions of the pilot symbols in the receive time slot, where an averaging window is typically used to minimize the effects of noise; (ii) multidimensional interpolation and extrapolation operations are then used to estimate the missing values ​​in the channel estimation grid; (iii) noise power estimation can be performed to improve performance by comparing direct and average channel estimates; (iv) using the channel estimate, the terminal then performs coherent OFDM demodulation of the precoded / beamformed physical channel.

[0086] In current NR systems, reference signal design and CEST are designed and operated separately, such as Figure 3 In particular, Figure 3 A conventional MIMO transceiver chain is described, where the DM-RS is inserted at the transmitter side before the MIMO precoding step.

[0087] RS configuration and receiver implementation dictate CEST performance: (i) Higher density increases CEST accuracy but also increases overhead, reduces spectral efficiency, and for MU-MIMO (multi-user multiple input multiple output), reduces the scope of spatial multiplexing; (ii) CEST across a larger number of physical resource blocks (PRBs) improves performance, but this "bundling" reduces the resolution of frequency selective precoding; (iii) MU-MIMO requires code division multiplexing (CDM) to distinguish antenna ports sharing the same REs (resource elements), and to increase CDM capabilities for higher-order MIMO, pilot REs need to be added across additional symbols; (iv) For the same density, the location of the pilots on the resource grid affects the receiver computational complexity.

[0088] Artificial intelligence (AI) can be broadly defined as behavior exhibited by a machine. Such behavior can, for example, mimic cognitive functions to perceive, reason, adapt, and / or act.

[0089] Machine learning (ML) may refer to a type of algorithm that solves problems based on learning from experience (“data”) without being explicitly programmed (“configured rule sets”). 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 methods may 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 methods may involve detecting patterns in data without pre-existing labels. For example, reinforcement learning methods may involve performing a series of actions in an environment to maximize the cumulative reward. In some solutions, machine learning algorithms can be applied using a combination or interpolation of the above methods. For example, semi-supervised learning methods 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 is between unsupervised learning (no labeled training data) and supervised learning (only labeled training data).

[0090] Deep learning (DL) refers to a class of machine learning algorithms that use artificial neural networks (e.g., deep neural networks) loosely inspired by biological systems. Deep neural networks (DNNs) are a special class of machine learning models inspired by the human brain, in which the input is linearly transformed and passed through a nonlinear activation function multiple times. DNNs typically include multiple layers, each of which consists of a linear transformation and a given nonlinear activation function. For example, DNNs can be trained via a backpropagation algorithm using training data. Recently, DNNs have shown state-of-the-art performance in a variety of fields (e.g., speech, vision, natural language, etc.) and in various machine learning settings (supervised, unsupervised, and semi-supervised). The term AIML-based method or process may refer to achieving behavior and / or compliance with requirements by learning based on data without explicitly configuring the order of action steps. Such methods can enable learning of complex behaviors that may be difficult to specify and / or implement when using traditional methods.

[0091] An autoencoder (AE) is a specific class of DNNs that arises in the context of unsupervised machine learning settings, where a DNN-based encoder is used to nonlinearly transform high-dimensional data into a low-dimensional latent vector, and the low-dimensional latent vector is then used to regenerate the high-dimensional data using a nonlinear decoder. The encoder is denoted as E(x; W e ), where x is high-dimensional data and W e Denotes the parameters of the encoder. The decoder is represented by D(z; W d ), where z is the low-dimensional latent representation and W d represents the parameters of the decoder. In addition, using the training data {x1, ..., x N}, the autoencoder can be trained by solving the following optimization problem

[0092]

[0093] The above problem can be approximately solved using the back-propagation algorithm. Can be used to compress high-dimensional data, and the decoder is trained Can be used to decompress the latent representation.

[0094] The terms artificial intelligence (AI), machine learning (ML), deep learning (DL), and DNN may be used interchangeably herein. The methods described herein may be exemplified based on learning in a wireless communication system. However, the methods described herein are not limited to such scenarios, systems, and services, and may be applicable to any type of transmission, communication system, and / or service, etc.

[0095] RS operation in NR may be considered suboptimal, for example, due to static (non-adaptive) configuration of RS and, more importantly, a limited set of options in terms of pattern (uniform / equidistant) and density in time / frequency / space. Even with the limited options available, determining the optimal RS configuration is non-trivial. As a result, these aspects lead to under- or over-utilization of radio resources used for control. This problem becomes more challenging for more flexible RS patterns, e.g., identifying the optimal density and location of RS in time / frequency / space across multiple resource blocks and multiple time slots. Achieving a more flexible RS design is challenging due to several factors, including control signaling feedback overhead, lack of model-based optimal solutions (NP-hard problems), and / or UE receiver complexity. In addition, there are currently no / limited features in NR to enable pilot-free transmission of physical channels.

[0096] Flexible RS operation that exploits inherent channel characteristics such as cross-time / frequency / spatial correlation between subcarriers and antennas may provide reduced density, reduced feedback, reduced receiver complexity, improved performance, or a combination of these. There is a debate on whether data-driven (AIML-based) solutions make the UE or joint gNB / UE ( Figure 4 ) The problem of being able to select and feedback data-driven UE-specific RS design, the optimal mode possibly being combined with / optimized using the CEST process. Figure 4 An example of joint gNB / UE processing for data-driven UE-specific RS design is shown. The problem can be viewed as a feature selection task with both prediction and compression based on channel characteristics such as correlation in time and frequency (leveraging data-driven learning, e.g., NN layers, NN as classifier). Under this framework, pilot-less transmission can be supported as a special case, leading to increased frequency efficiency and reduced signaling.

[0097] In view of the foregoing, some example embodiments may provide a process for configuring a UE with one or a set of attributes for data-driven UE-specific RS selection and feedback, including RS patterns across time / frequency / space, density and bundling characteristics, RS control overhead, RS signaling overhead, etc. Configuration options may include use of non-deterministic pilot patterns and use of superimposed pilots (where energy is distributed between data / pilot symbols).

[0098] Some example embodiments may provide a process for determining data-driven UE-specific RS AIML model processing, where the input may be a valid channel matrix from a resource grid and the output is a flexible RS design based on configurations involving one or any combination and implementation of design attributes (including location / pattern in frequency and time resources, bundling across multiple frequency and time sources, etc.).

[0099] Some example embodiments may provide a process for providing feedback to the UE, such as a process for providing feedback of data-driven UE-specific RS design (including the case without pilot transmission) either explicitly, implicitly, or using inter-node data exchange (for AIML joint processing case).

[0100] Some example embodiments may provide a process for UE-specific RS design (AIML model) performance monitoring by the gNB, UE, or both. Performance monitoring may include monitoring various performance evaluation metrics, for example, if below a certain threshold, AIML model retraining may be triggered, and in some cases including reverting to conventional RS operation.

[0101] It should be noted that reference signal (RS) patterns may be used interchangeably with UL or DL ​​DM-RS patterns herein. In addition, RS patterns or RS configurations may be used interchangeably. In addition, RS patterns or parameters of RS patterns may be used interchangeably. A UE-specific RS configuration may refer to an RS pattern or configuration specific to a single UE.

[0102] In accordance with certain embodiments, an AIML model (or alternative data-driven learning) may be trained and used to determine UE-specific RS. For example, the gNB may configure attributes (and restrictions) for RS design and feedback, including RS pattern selection parameters, RS control overhead, and RS signaling overhead across time, frequency, and / or space. For the AIML model, the input may be an effective channel matrix from a resource grid, and the output may be a data-driven UE-specific RS design based on a gNB configuration involving one or any combination and implementation of design attributes (including location / pattern in frequency and time resources, bundling across multiple frequency and time resources, etc.). The UE may feedback data-driven UE-specific RS design (including the case of no pilot transmission) explicitly, implicitly, or using inter-node data exchange (for AIML joint processing cases).

[0103] According to certain embodiments, different implementations of RS design may be considered based on UE capabilities, system requirements, etc. These may affect UE behavior in terms of configuration, processing, and signaling. According to certain embodiments, examples of RS designs that may be used may include one or more of the following: position / pattern in the frequency domain, dynamic resource block bundling, position / pattern in the time domain, multi-slot bundling, MU-MIMO, CDM and legacy operation, non-deterministic pilot patterns, and / or non-orthogonal pilots.

[0104] The location / pattern in the frequency domain may refer to flexibly allocating REs for pilots in the frequency domain, resulting in different densities, different locations, different ranges for frequency reuse / CDM. In one example, there is an additional DM-RS configuration type (in addition to type 1 and type 2), for example, flexible configuration type X (AIML) for PDSCH / PUSCH, where, for example, in one instance, the first RE and the last RE in the RS symbol are allocated to pilots, resulting in a density of 16.6%, compared to 50% in type 1 and 33.3% in type 2, where if orthogonal cover codes (OCC) are used for CDM, up to 4 antenna ports (for single symbol transmission) are supported, which is the same as NR type 1. In one example, the gNB may specify the maximum and / or minimum frequency density for pilot transmission, where AIML processing at the UE or joint UE / gNB is used to identify a specific location on the resource grid.

[0105] Dynamic resource block bundling may refer to dynamically configuring PRB bundling based on a trade-off between frequency selective precoding and CEST performance. Increasing the PRB bundling size is more suitable for reciprocity-based transmission (no need to quantize CSI feedback into subbands), where the performance of the time domain channel estimation algorithm is also improved, while reducing the PRB bundling size is more suitable for codebook-based transmission. In one example, the AIML model can be used to flexibly decide the choice of PRB bundling, for example, using a delay spread indication, where the effective channel is estimated by filtering / averaging the DM RS (in NR, there are options for two and four PRB bundlings configured through higher layer signaling as well as wideband).

[0106] The position / pattern in the time domain may refer to flexibly allocating REs for pilots in the time domain, resulting in different densities, different positions, different ranges for frequency reuse / CDM. In one example, for a given configuration type (i.e., frequency domain setting), a multi-symbol configuration is added to support increasing the number of available antenna ports towards higher level (including MU-MIMO) transmissions. In another example, an additional mapping type X is included (in addition to types A and B), where the AIML model determines the position of the pilots in the time domain, which is a different approach from the frequency-centric configuration in NR. In one example, the gNB may specify a maximum and / or minimum frequency density for pilot transmission, where AIML processing at the UE or joint UE / gNB is used to identify a specific location on the resource grid.

[0107] Multi-slot bundling may refer to enabling CHEST across multiple time slots, where the precoder remains fixed across the time slots. This approach may be suitable for low-variance channels with large time coherence intervals, where the signaling overhead (REs for pilots) may be reduced, thereby providing more resources for the transmission of data symbols. In one example, the AIML model dynamically determines the number of time slots for bundling, for example using a Doppler spread indication. This approach also increases the applicability of the time domain-based CEST algorithm.

[0108] MU-MIMO, CDM and legacy operation may refer to that for MU-MIMO cases, flexible UE-specific RS configuration may be enabled, in which case CDM grouping must be configured at the gNB after specific feedback from the UE. In one example, the AIML model may utilize a broadcast message to notify the UE of CDM grouping / muting. In another example, the UE may signal muting mode to neighboring UEs that are not aware of the grouping. In one example, the scheduler separates legacy UEs from UEs with flexible RS capabilities for MU-MIMO operation.

[0109] The non-deterministic pilot pattern may refer to a random pilot pattern. It has been shown that for MMSE receivers, random pilot patterns (across time and frequency) are statistically optimal, although they result in high complexity, but the UE can signal the ability of random pilot patterns. According to this method, the UE can continue to perform CEST and generate adaptive feedback about the next configuration (for example, in one solution, increase or decrease the time / frequency density).

[0110] Non-orthogonal pilots may refer to using superimposed pilots that are different from the conventional orthogonality between pilot and data symbols. In one example, the energy of REs is allocated between pilot symbols and data symbols, and an end-to-end AIML model is used to learn the amount of energy that should be allocated to the pilots (i.e., determine the pilot allocation matrix).

[0111] Representative procedure for configuring UE-specific RS

[0112] According to an embodiment, the gNB may configure the UE with attributes (and possibly limitations) of AIML-based data-driven UE-specific RS selection and feedback, including, for example, RS pattern and density characteristics across time / frequency / space, RS control overhead, RS signaling overhead, etc. The gNB may transmit a default RS configuration for AIML training.

[0113] In some embodiments, the UE may support data-driven UE-specific RS processing (i.e., flexible RS operation) based on AIML, for example, using an autoencoder (AE). Different types of AIML models (e.g., AE) may be used for flexible RS operation. For example, a dedicated AIML model may be designed and trained to predict preferred RS patterns and densities across time / frequency / space, and dedicated AIML may be used to compress RS selection. Generalized models may also be designed to predict and compress preferred RS selection. In another example, AIML processing may be distributed between UE / gNB, where an encoder model is designed and trained on the UE side, and then using some inter-node data exchange, the decoder at the gNB may extract the preferred RS selection.

[0114] In an embodiment, the UE may report its AIML-based data-driven UE-specific RS processing capabilities to the gNB and may report the configured AIML model. The parameters describing the UE RS processing model may include one or more of the following:

[0115] i. RS attributes in the frequency domain - location, resource block bundling, equal spacing, non-equal spacing, uniform, non-uniform, etc.

[0116] ii. RS attributes in the time domain - location, multi-slot bundling, equidistant, non-equidistant, uniform, non-uniform, etc.

[0117] iii. RS properties in the spatial domain—orthogonal cover codes, spatial multiplexing, MU-MIMO.

[0118] iv. RS joint gNB / UE processing;

[0119] v. Non-deterministic RS processing capabilities; and / or

[0120] vi. Non-orthogonal RS processing capability.

[0121] In an embodiment, the UE may also indicate its CEST processing type to the gNB. The CEST processing type may include one or more of the following:

[0122] i. Time domain CEST;

[0123] ii. Frequency domain CEST; and / or

[0124] iii. Time domain and frequency domain CEST.

[0125] In an embodiment, the UE may also indicate its CEST algorithm type to the gNB. The CEST algorithm type may include one or more of the following: MMSE, LS, AIML-based, etc.

[0126] According to certain embodiments, the data-driven UE-specific RS design attributes required for selection and feedback may be configured by the gNB (based on high-layer configuration or scheduling) for AIML-capable UEs. For example, the configuration and feedback may be used to select a flexible non-uniform RS pattern across multiple frequency and time resources, or to select a subset of attributes, such as an equidistant or non-equidistant uniform RS pattern.

[0127] In some embodiments, the gNB configuration may select degrees of freedom for selection based on UE capabilities, performance requirements, MU-MIMO scenarios, etc. For example, the gNB may set a specific minimum / maximum RS density for RS selection in time, frequency, or both. The gNB may set a range of resource blocks and / or time slots for frequency and time domain bundling for the UE to select from. The gNB may set a specific (maximum) signaling load for feedback of UE-specific RS design.

[0128] According to an embodiment, the gNB may transmit a default user-specific RS for CEST, coherent demodulation, and / or AIML model training, where the default design may be a legacy (e.g., NR types 1 and 2), a new RS pattern including additional NR types (e.g., type X), or a new non-uniform option.

[0129] In one embodiment, the default configuration may be a new non-deterministic (random) RS pattern that may be close to optimal for certain receiver types (e.g., MMSE). This may be a low overhead option where an AIML model may be used to indicate a higher / lower density of the random RS pattern (rather than selecting a specific location). In one example, the gNB may calculate the random RS pattern and configure the UE with the exact location / pattern / density of the RS in time / frequency / space using higher layer signaling (RRC) or dynamic signaling (MAC-CE, DCI, etc.). In one example, the UE may perform CEST with a random RS pattern where an iterative process may be used to determine the location of the pilots prior to the interpolation / extrapolation operation. In one example, the gNB may calculate the random RS pattern and signal the seed value or equivalent statistical value used to the UE. The UE may utilize this information to determine the location of the pilots (via an iterative process), after which it proceeds with the CEST operation.

[0130] Embodiments may utilize non-orthogonal RS patterns where the default configuration is a new overlay RS where the energy for REs is allocated between pilot symbols and data symbols. In this example, an AIML model (e.g., gNB-based processing, UE-based processing, or joint UE / gNB-based processing) may be used to learn the amount of energy that should be allocated to the pilots (i.e., determine the pilot allocation matrix). In one example, end-to-end learning may be used where the gNB computes the overlay pilot allocation matrix based on received CSI and feeds the matrix back as part of the input to the CESTAIML model at the UE to maximize certain performance (error rate, throughput, etc.). In one example, end-to-end learning may be used where the UE may compute the overlay pilot allocation matrix using an iterative receiver or equivalent and feed the matrix back as part of the CSI compression report where the pilot allocation matrix is ​​used as input to the CESTAIML model at the gNB to maximize certain performance (error rate, throughput, etc.). In one example, the UE may be configured with an AIML-based model that is trained to compute the compressed inter-node data required to compute the overlay pilot allocation matrix at the decoder (gNB).

[0131] Representative procedure for determining UE-specific RS

[0132] In an embodiment, the UE may be configured to determine and report a new RS pattern based on PDSCH performance, or receiver complexity (ie, the amount of available computational resources), or signaling overhead, or any combination thereof.

[0133] For the AIML model, the input can be the effective channel matrix from the resource grid, and the output can be a data-driven UE-specific RS design based on the gNB configuration involving one or any combination of design attributes and implementations (including location / pattern in frequency and time resources, bundling across multiple frequency and time resources, etc.). For example, the AIML model can be trained offline or online with the following attributes:

[0134] a) Input: A set of effective channel matrices; from one or more time slots, resource blocks, etc. The input may also include specific design attribute details, such as the maximum allowed density of time and frequency, the maximum allowed signaling overhead, etc.

[0135] b) Output: bundling type and / or location, pattern and / or density of UE-specific RS patterns based on performance objectives such as improved CEST accuracy, reduced signaling overhead, reduced receiver complexity, etc.

[0136] As an example of pilot pattern design, consider a time-frequency grid of 12 subcarriers and 14 symbols, where the maximum overhead of (orthogonal) RSs is 16. Therefore, there is pilot pattern combinations (this is a huge search space). The data-driven approach here can be used to obtain a near-optimal pattern for a particular channel model. The input is the estimated effective channel (using the default RS configuration), where a DNN with a multi-stage process is present to: (1) extract features related to the preferred location of the pilots (e.g., time / frequency-based correlations) and / or (2) predict / estimate CEST performance with the selected pattern.

[0137] According to some embodiments, the UE may be configured to determine a new RS pattern based on PDSCH performance. For example, if the block error rate (BLER) exceeds a certain preconfigured threshold, the UE may recommend an RS pattern, such as a pattern with a higher density or the same density but with a different position. The UE may also determine a new RS pattern based on receiver complexity. For example, based on the amount of available computational resources, the UE may determine and recommend a pattern that reduces receiver complexity, such as a pattern with a lower density that substantially requires a small amount of effective channel matrix calculation. Therefore, adding less weight to the effective channel matrix calculation and adding more weight to the number of interpolation / extrapolation steps is computationally less expensive relative to the effective channel matrix estimate, thereby reducing receiver complexity overall. The UE may also determine a pattern that can be signaled with minimal uplink signaling overhead.

[0138] In an embodiment, the UE may use an AI / ML model to determine a preferred UE-specific RS (e.g., DM-RS) configuration. The preferred UE-specific RS configuration may include a preferred RS pattern, a preferred density (in the time domain and / or frequency domain), a preferred OCC and / or bundling type, etc. The AI / ML model may be trained offline and deployed to the UE, for example, for inference during online operation. The AI / ML model may also be retrained online. When the UE is configured with a UE-specific RS configuration (e.g., a default RS configuration or a previously indicated preferred RS configuration), the UE estimates the effective channel using the RS received at the configured RE positions. The UE may then perform interpolation to determine the full effective channel matrix at all RE positions within a PRB in the allocated transmission bandwidth. The full channel matrix / full effective channel matrix may be vectorized or reshaped before being applied to the input of the AI / ML model, for example, to determine a preferred UE-specific RS configuration. The UE may report the determined preferred RS configuration to the gNB.

[0139] Alternatively, in an embodiment, the input to the AI / ML model may also be an array comprising a tuple of channel estimates formed using RSs received at configured RE positions and the coordinates of the RE positions in a grid. The array may be zero-padded to obtain a predefined maximum length corresponding to the maximum RS pattern, or may be viewed as a sequence for a recurrent neural network (RNN) style AIML model.

[0140] According to certain embodiments, the UE may use an AI / ML model to determine a preferred UE-specific configuration. In one example, the UE may use a default RS configuration to determine a preferred RS configuration. The default RS configuration may refer to: an RS pattern in the time / frequency domain, or a new DM-RS pattern. For example, the RS pattern in the time / frequency domain may be a traditional DM-RS configuration (type 1 / type 2, mapping type A / mapping type B, duration, and number of additional DM-RS positions).

[0141] The preferred RS configuration may be one of the existing DM-RS configurations or a completely new RS pattern. For example, the UE may determine a new RS pattern from the existing pattern based on channel measurements (e.g., delay spread and Doppler spread) and signal back the new parameters (e.g., the number of additional DM-RS positions) required to use the pattern in the next transmission slot.

[0142] When configured to use a default RS configuration to determine a preferred UE-specific RS configuration, the timing of the transmission of the default RS configuration may be indicated to the UE. For example, the default RS configuration may be periodic, semi-static, or aperiodic. The RS configuration (which may include timing configuration and periodicity) may be signaled to the UE, for example, via RRC signaling. Alternatively, the presence of the default RS configuration may be indicated via downlink control information (DCI).

[0143] In another example, the UE may use the current RS configuration (eg, which may be a previously determined and configured preferred RS configuration) to determine a new or updated preferred RS configuration.

[0144] In an embodiment, the input to the AI / ML model may be a full channel matrix or a full effective channel matrix, where "full" refers to the channel determined for all RE positions within a PRB in the allocated transmission bandwidth, and "effective" refers to the channel experienced by the UE receiver, which includes Tx precoding when precoding is applied.

[0145] According to certain embodiments, the UE may use the received RS (e.g., a default RS configuration or a selected preferred RS configuration) to perform channel estimation using the received RS and then perform channel interpolation to determine a full effective channel matrix. The full effective channel matrix may be further vectorized and reshaped and then applied to the input of the AI / ML model.

[0146] In some embodiments, in addition to the full effective channel matrix, other channel characteristics / parameters may also be applied to the AI / ML model inputs. For example, the channel coherence bandwidth and / or the channel coherence time may be used as additional inputs that may select, for example, the density of the preferred RS configuration in the frequency domain and / or time domain.

[0147] Alternatively, only the estimated channel at the RS location and the coordinates of the RE corresponding to the RS can be used as input to the AI / ML model.Such input may be particularly beneficial for sequence-based AI / ML models.

[0148] The output of the AI / ML model may be a means for determining a preferred UE-specific RS configuration. For example, the AI / ML model output may be an index into a predefined table of RS configurations. Here, the overall problem may be converted into a classification problem for identifying the next RS configuration. The AI / ML model may consist of two sequential models, whereby the first model uses the measured coherence time / coherence bandwidth and outputs an indication of the preferred density (in the time domain and / or frequency domain), and the second model outputs an index into a predefined RS configuration table of the time / frequency density indicated by the first model. The AI / ML model output may be an indicator for increasing or decreasing the time and / or frequency density of the RS pattern. Different sparsity-based formulas may also be specifically used to reduce the density of the RS pattern. The AI / ML model may also output a segmentation mask of the same size as the channel dimension, thereby clustering / grouping REs that may have highly correlated channel measurements within the channel tensor, thereby effectively indicating that only a single RS symbol per grouping / clustering is needed to estimate the area of ​​the overall channel on the RS within the group. The AI / ML model may also output a set of clusters / groups of variable size, possibly in an unsupervised manner, where the number of clusters represents the RS pattern length (number of reserved REs), and each cluster has its associated representative RE whose effective channel matrix is ​​strongly correlated with other REs in the same cluster. Additionally or alternatively, in some embodiments, the output of the AI / ML model may include an indication of at least one bundling type.

[0149] In one example, pruning techniques may be used to further reduce pilot signals while maintaining a specific CEST performance. In an embodiment, sparse regularization techniques may be used by the DNN to further reduce pilot signals while achieving a specific tradeoff between sparsity and MSE. The AI / ML model may provide an indication of bundling across time and / or frequency.

[0150] Figure 5 An example of RS selection based on an AIML model according to an embodiment is shown. Figure 5 In the example of , the data-driven UE-specific RS is selected as a non-equidistant non-uniform pattern, where bundling is performed across time slots (here two).

[0151] According to some embodiments, the AI / ML model can be trained offline and deployed to the UE, for example, for inference during runtime. The AI / ML model using the full effective channel matrix as input can be trained offline, for example, with a synthetic training data set obtained by applying a predefined (e.g., traditional) precoding to the generated channel samples. The corresponding output (e.g., an RS pattern or an index into a predefined pattern table) can be determined according to an optimization criterion. For example, the optimization criterion can be to minimize the MSE between the interpolated channel and the true channel given a maximum RS overhead.

[0152] In some embodiments, the AI / ML model can be retrained online. For example, the UE can be configured with a labeled output corresponding to a specific training RS configuration (for the AI / ML model). When the gNB configures the UE for online training, the gNB can transmit the training RS configuration, and the UE can use the received RS to perform channel estimation, interpolation, and use the interpolated channel in conjunction with the configured labeled output to retrain the model.

[0153] Representative procedure for indicating UE-specific RS

[0154] According to an embodiment, the UE may indicate or determine the configuration (e.g., pattern, position, etc.) of the DM-RS configuration and may then report it to the gNB. The UE may feedback the DM-RS configuration implicitly, explicitly, or using inter-node exchange, i.e., data-driven UE-specific RS design, including the case of no pilot transmission (for x upcoming slots or the first x transmissions in time).

[0155] The UE may receive an indication to configure data-driven UE-specific RS, which may be included in, for example, DCI, MAC control element (CE), or RRC. Upon receiving the indication, the UE may indicate to the gNB a new model type or a modified model for handling data-driven UE-specific RS based on gNB configuration involving one or any combination of design attributes and implementations, including location / pattern in frequency and time resources, bundling across multiple frequency and time resources.

[0156] The UE can decode the resource grid using the default pilot and can then perform additional processing by feeding the estimated channel into the AI / ML encoder. The output of the encoder can have information about the preferred position for the pilot within the resource grid. In an example embodiment, the UE can construct a bitmap describing the non-uniform pilot allocation, which can be a matrix of zeros and ones (e.g., a time-frequency grid representing 12 subcarriers and 14 symbols). In this embodiment, the position of a one in the grid can indicate the position of the pilot in the next resource grid. In another embodiment, the UE can construct an index string that includes the symbol index and subcarrier index of the preferred position of the pilot.

[0157] The UE may use such an index string along with the DM-RS position of the OFDM symbol index, subcarrier index, antenna port, etc. to report or indicate the construction pattern of REs for one or more RBs / time slots. The indication may be a recommended number of RBs and / or time slots to be bundled in time / frequency. Resource bundling may also be extended to pilotless transmission, where the UE may indicate the option of pilotless transmission to the gNB. In this case, the UE may use an AIML model to predict the effective channel characteristics. For resource bundling (with both pilot or pilotless transmission), the UE may indicate the characteristics of the user-specific RS bundling. For example, the characteristics of the user-specific RS bundling may include: the number of transmissions (other than N RBs); the effective duration of the constructed pattern; the ports to which the constructed pattern applies; and / or the transmission type (other than N consecutive RBs / time slots or N non-contiguous RBs / time slots).

[0158] In one example, the UE may receive an indication requesting dynamic resource block bundling. The UE may determine the conditions to be applied to dynamic RB bundling based on an input effective channel matrix of S subcarriers (e.g., multiple groups of 12 OFDM subcarriers). In an example embodiment, the UE may determine a set of physical resource blocks that can be bundled together for CEST. For example, after detecting correlation between multiple channels, the UE may assign a reduced number of pilots in a reduced number of subbands. In another example, the UE may receive an indication requesting dynamic timeslot bundling. The UE may determine the conditions for applying dynamic timeslot bundling based on an input effective channel across multiple (time) time slots. For example, after detecting correlation between channels across multiple time slots, the UE may assign a reduced number of pilots in a reduced number of time slots. In another solution, the UE determines dynamic bundling across frequency and time so as to perform RS selection towards CEST at a future determined time instance.

[0159] Data-driven user-specific RS can be explicitly reported via PUCCH or PUSCH based on the configured time domain behavior (periodically, semi-periodically or periodically). UE-specific RS reporting is applicable to any AI / ML model, where the AI / ML model can be configured by the network or specific to each UE. The UE can be configured to report specific AI / ML models such as NN architecture and weights.

[0160] The UE may receive an indication to perform joint processing between the gNB and the UE. Upon receiving the indication, the UE may utilize the AIML model (encoder) to process certain evaluations of the channel and / or identify optimized RS characteristics for subsequent transmissions, and may feed this inter-node data back to the gNB. In one example, the UE may extract channel characteristics such as inherent time / frequency / spatial correlations between REs, symbols, and antennas that may be used to predict a preferred RS. The gNB may use this information to determine a data-driven user-specific RS. After determining the user-specific RS, the gNB indicates the obtained pilot pattern to the UE.

[0161] Representative procedures for performance monitoring and triggering for UE-specific RS

[0162] According to some embodiments, the UE may monitor the performance of an RS pattern (e.g., a DL or UL DM-RS pattern) or a parameter of an RS pattern. The UE may determine an absolute performance (e.g., a granular performance level) or a relative performance. The relative performance may be compared to a threshold (e.g., a configurable threshold). For example, if the performance is greater than the threshold, the UE may consider the RS pattern or the parameter of the RS pattern to be valid, or sufficient, or accurate, or appropriate. If the performance is lower than the threshold, the UE may consider the RS pattern or the parameter of the RS pattern to be invalid, or insufficient, or inaccurate, or inappropriate. The UE may determine the threshold based on a gNB indication. The threshold may be determined based on at least one of: an associated data type, a priority level of the associated data, a frequency allocation, a time allocation, an RS pattern density, and / or a UE capability (e.g., a channel estimation capability).

[0163] In an embodiment, the UE may be triggered to determine and / or report the performance of the RS pattern or the parameters of the RS pattern. The trigger may include at least one of the following:

[0164] i. Indication received from the gNB. The indication can be PHY layer signaling (e.g., DCI or MAC CE) or higher layer signaling (e.g., RRC);

[0165] ii. When configuring or reconfiguring a new RS mode;

[0166] iii. At specific time instances. For example, the UE may monitor or report performance at periodic times or time slots or subframes or symbols;

[0167] iv. When a timer expires. For example, the UE may maintain a timer, and when the timer expires, the UE may monitor or report the performance of the RS mode or parameters of the RS mode. The UE may (re)start the timer when a new RS mode is configured or when reporting performance. The UE may (re)start the timer when a specific instance of the RS mode is considered valid or invalid. For example, the UE may (re)start the timer when a transmission is confirmed (ACK) or negatively confirmed (NACK). For one or more associated transmissions, the UE may (re)start the timer when the performance of the RS mode is considered valid or invalid.

[0168] v. Based on a counter. When the counter reaches a possibly configurable value N, the UE may determine or report the performance of the RS pattern. The UE may increment the counter when a particular instance of the RS pattern is considered invalid or valid. For example, the UE may increment the counter when a transmission is ACKed or NACKed. The UE may reset the counter when the timer expires; and / or

[0169] vi. Changes to BWP.

[0170] In some embodiments, the UE may monitor and / or determine the performance of the RS pattern. The performance may be determined by at least one of the following:

[0171] i. Performance of a channel estimator (CHEST). For example, the UE may determine the performance of CHEST in a first RS mode based on the output of a second (potentially denser) RS mode. For example, the UE may determine the statistical performance of CHEST in RS mode. For example, the UE may determine the mean error (e.g., MMSE) of CHEST in RS mode. In an example, the UE may report the convergence of CHEST or statistics (e.g., mean or variance) of CHEST;

[0172] ii. BLER. For example, the UE may determine the performance of the RS pattern based on the BLER of the associated data transmission. If the BLER is below a threshold, the RS pattern may be considered valid, and if the BLER is above a threshold, it may be considered invalid;

[0173] iii. HARQ-ACK statistics of the associated data transmissions. For example, the UE may determine the performance of the RS pattern as invalid if the percentage of NACKs is greater than a threshold over a possibly configurable period of time.

[0174] If the percentage of NACKs within a possibly configurable time period is less than a threshold, the UE may determine the performance of the RS pattern as valid. In another example, the performance of the RS pattern may be the percentage of NACKs or ACKs within a possibly configurable time period;

[0175] iv. Based on measurement. For example, the UE may determine the performance of the RS pattern based on at least one of the following:

[0176] RSRP, RSSI, RSRQ, CO, CQI, PMI, RI, LI, CRI, Doppler spread, Doppler shift, average delay, delay spread, LOS or NLOS, LOS probability; and / or

[0177] v. Based on the performance of the transmission. For example, the UE may determine the performance of the RS pattern based on the throughput, latency, spectral efficiency, and time / frequency allocation of one or more transmissions.

[0178] In an embodiment, the UE may report the performance of the RS pattern or a parameter of the RS pattern. The report may include absolute performance or relative performance (e.g., relative to a threshold). The report may include at least one of the following:

[0179] i. Results of the above methods (e.g., CHEST performance, BLER, HARQ-ACK statistics, measurements).

[0180] The report may include the actual value or the result of the comparison with the threshold value (e.g., valid or invalid). The report may include the threshold value used (e.g., the index of the threshold value used);

[0181] ii. Request for a new pattern. For example, if the UE determines that the RS pattern is not suitable, it can request a new pattern from the gNB. The request may include a reason. The reason may include the measurement or statistics that led to the determination that the RS pattern is not suitable;

[0182] iii. Request to increase or decrease RS density. The request may be granular. For example, the UE may indicate where the density should be increased or decreased (e.g., what time occasion or which subcarrier / frequency region); iv. Request to retrain CHESTAIML model. For example, the UE may request to retrain the CHEST model for this RS pattern or for a new RS pattern, or to determine resources or signals for a new RS pattern;

[0183] v. Request for legacy-based RS. For example, the UE may request to fall back to the legacy DM-RS mode; vi. Request to start using a UE-specific (e.g., AIML-based) RS mode;

[0184] vii. Report indicating performance of no pilot transmission. In this context, no pilot transmission may be considered as RS mode; and / or

[0185] viii. A report indicating the performance of a random pilot transmission or RS pattern. The report may include the UE's determination or assumption of a random RS pattern for one or more transmissions. For example, the UE may receive a transmission with a random RS pattern, and the UE may determine or assume an RS pattern or a parameter of an RS pattern (e.g., the density of RSs or the location of RSs). The UE may report a detected or assumed RS pattern or a parameter of an RS pattern. The UE may also report the performance of a detected or assumed RS pattern.

[0186] According to some embodiments, the UE may be configured with one or more resources on which to report the performance of one or more RS patterns. The resources may be periodic, semi-persistent (e.g., activated or deactivated via the reporting triggers listed above), or aperiodic (e.g., indicated in the reporting trigger mechanism). The reporting resources may include at least one of the following:

[0187] i. Dedicated UL resources. For example, the UE may be configured with PUCCH resources to report the performance of the RS pattern;

[0188] ii. UCI in PUSCH. For example, the UE may report the performance of the RS pattern in PUSCH transmission; iii. UL MAC CE; and / or

[0189] iv. Part of HARQ feedback. For example, the UE may report the performance of the RS pattern in the HARQ feedback report. The UE may report the performance of the RS pattern associated with a set of transmissions (e.g., associated with a set of HARQ-ACK values). For example, for a set of ACK-NACK values, the UE may report the performance of the associated RS pattern. In an example, the UE may report the performance of the RS pattern only for ACK transmissions or only for NACK transmissions. In an example, the type of performance reported may be determined based on whether the associated transmission is ACKed or NACKed.

[0190] In one embodiment, if the number of NACKs in the HARQ-ACK report is greater than a threshold, the UE may include the performance of the RS pattern in the HARQ-ACK report or subsequent / associated reporting resources. For example, the threshold may be an absolute number or a ratio of NACKs to ACKs.

[0191] According to some embodiments, the UE may determine that the RS pattern is valid based on the performance of the RS pattern. The UE may continue to use the RS pattern for UL transmission, or may expect to receive DL transmission using the RS pattern. The UE may select or perform at least one of the following actions, possibly based on the performance of the RS pattern (e.g., determining that the RS pattern is invalid):

[0192] i. Retraining the AIML model. The UE may retrain the AIML model at the UE. The UE may transmit a signal to enable the gNB to retrain the AIML model at the gNB;

[0193] ii. Receive or transmit one or more reference signals. For example, after determining or reporting the performance of the RS pattern, the UE may start receiving and measuring the AIML model retraining RS. The UE may use the measurements obtained on the AIML model retraining RS to retrain the AIML model. The AIML model retraining RS may be encoded as an RS (e.g., encoded with a specific beam or precoding matrix). The encoding of a specific instance of an RS may be known at the UE or indicated in a transmission associated with the RS transmission. For example, the encoding may reuse the precoding used for the most recent RS or data transmission. For example, the encoding may be determined by the timing of the RS transmission. For example, the encoding may be determined by a predetermined frequency hopping pattern. The AIML model retraining RS may be transmitted without associated data;

[0194] iii. Switch to legacy RS behavior and mode. For example, the UE may start to transmit UL DM-RS using the legacy RS mode. In another example, the UE may expect subsequent DL transmissions to use the legacy RS mode;

[0195] iv. Switching from one type of RS pattern to another type of RS pattern. For example, if the UE is operating with pilot-free transmission, the UE can switch to pilot-based transmission. In another example, a UE operating with a first (e.g., lower) density RS pattern can switch to a second (e.g., higher) density RS pattern;

[0196] v. Retransmit TB using traditional DM-RS;

[0197] vi. monitor for changes to RS patterns or indications of new RS patterns from the gNB; and / or

[0198] vii. Transmit the desired RS mode or RS mode parameters.

[0199] Example Implementation

[0200] According to an example embodiment, a UE or WTRU (such as a UE / WTRU with data-driven UE / WTRU-specific RS design capabilities) may be configured to indicate its capabilities to a network element (such as a base station or gNB) (e.g., as part of UE capability signaling). The UE / WTRU may be configured to receive configuration information related to an AIML RS design process (e.g., if not legacy) from a network element (e.g., a base station, a gNB, etc.), the configuration information including one or any combination of attributes. For example, the attributes may include one or more of the following: location / pattern in frequency or / and time resources; bundling across multiple frequency or / and time resources; maximum allowed density in time or / and frequency; MU-MIMO settings (e.g., CDM muting); maximum allowed signaling feedback; and / or performance requirements (e.g., CEST accuracy threshold, receiver processing complexity, etc.).

[0201] In an embodiment, the UE / WTRU may be configured to receive a default RS transmission from a network element (e.g., base station, gNB, etc.) based on any of the following options. For example, the received default RS transmission may be based on one or more options, which may include: NR legacy DM-RS (e.g., types 1 and 2, mapping types A or B, etc.); new uniform RS pattern (e.g., type X with different frequency resource RS insertion); new (deterministic) non-uniform RS pattern; new (random) non-uniform RS pattern; and / or new non-orthogonal RS pattern (superimposed pilot).

[0202] According to an embodiment, the UE / WTRU may be configured to determine a data-driven UE-specific RS design based on the signaled attributes by using an AIML model. In some embodiments, the AIML model may be trained, for example, with at least the following labeled data:

[0203] • Input: effective channel matrix from one or more time slots, resource blocks, etc.; properties of RS design choices (mode, maximum bundling value, etc.).

[0204] ● Output: bundling type (e.g., an indication of at least one bundling type) and / or location, pattern and / or density of UE-specific RS patterns based on performance objectives such as improving CEST accuracy, reducing signaling overhead, reducing receiver complexity.

[0205] In an embodiment, the UE / WTRU may be configured to send feedback (periodically or semi-periodically) to a network element (e.g., base station, gNB, etc.) implicitly, explicitly, or using inter-node data exchange (e.g., for AIML joint processing cases) for UE-specific RS design for use in subsequent payloads or transmissions (e.g., future transmissions of the indicated RS will begin within X time units from a reference time point). As an example, the triggering of the (non-periodic) indication may include evaluating CEST performance accuracy against a specific threshold and / or evaluating channel statistics (coherence interval) against a specific threshold, etc. For example, in some embodiments, the sending of feedback may be triggered when the CEST performance meets a specific threshold and / or when the channel statistics meet a specific threshold.

[0206] According to an embodiment, if the UE / WTRU determines that the requested data-driven UE / WTRU-specific RS design has not been activated within the configured time window, it may be interpreted as a decoding failure at the network element (e.g., base station, gNB, etc.) and the indication may be retransmitted. In one example, the UE / WTRU may indicate to the network element (e.g., base station, gNB, etc.) an option for pilot-free transmission, where the AIML model is used to predict the effective channel characteristics.

[0207] According to some embodiments, data-driven UE-specific transmissions may continue if / until performance falls below a threshold, where the AIML model may benefit from retraining, or the channel quality is so poor that it would benefit from switching to conventional operation.

[0208] Example Method

[0209] Figure 6 is an example flow chart illustrating an example method 600 of configuring, selecting and / or feeding back UE-specific RS information according to some example embodiments. Figure 6 The exemplary methods and the disclosures attached hereto may be considered as summaries or integrations of the various disclosures discussed above. For the sake of simplicity and ease of explanation, for example, reference may be made to FIG. 1 or Figure 4 Describe the architecture Figure 6 However, Figure 6 The example methods described in can also be implemented using different architectures. According to some embodiments, Figure 6 The method may be implemented by a UE or a WTRU (such as the WTRU 102 described in the foregoing). It should be noted that Figure 6 The methods and / or blocks of the present invention may be modified to include or be replaced by any one or more of the processes or blocks discussed elsewhere herein. Therefore, it will be understood by those skilled in the art that Figure 6It is provided as an example and may be modified while remaining within the scope of certain example embodiments.

[0210] like Figure 6 As shown in the example of , method 600 may include receiving configuration information by the WTRU at 605, the configuration information may include or indicate any of the following: a set of parameters for determining a reference signal (RS) configuration, and optionally parameters to be used within the set. As an example, the parameter set may include at least two bundling types. In another example, the parameter set may additionally or alternatively include any one or more of the following: RS position, RS mode, maximum allowed RS density, minimum allowed RS density, multi-user multiple input multiple output (MU-MIMO) settings, RS configuration signaling feedback overhead, and performance requirements.

[0211] In some embodiments, the method 600 may also include receiving a transmission using a first RS pattern of a first RS configuration type at 610. For example, the first RS configuration type may include one or more of: a new radio (NR) legacy demodulation reference signal (DM-RS), a uniform RS pattern, a deterministic non-uniform RS pattern, a random non-uniform RS pattern, and / or a non-orthogonal RS pattern.

[0212] According to some embodiments, method 600 may then include performing one or more measurements on the transmission using the first RS pattern at 615. For example, the one or more measurements may include measurements related to one or more of: an effective channel matrix for one or more time slots or resource blocks, channel estimation accuracy, and / or RS signaling overhead.

[0213] According to an embodiment, method 600 may include determining, at 620, a bundling type associated with the RS configuration based at least in part on the parameter set and / or one or more measurements. In one example, determination 620 of the bundling type may include determining or selecting the bundling type from the at least two bundling types included in the parameter set. In an embodiment, determination 620 of the bundling type may include determining or selecting the bundling type based on other criteria, such as measurements performed on received transmissions. At 625, method 600 may include transmitting a first indication of the determined bundling type. As an example, in one embodiment, the bundling type may indicate a resource bundling characteristic across one or more of time and / or frequency.

[0214] In some embodiments, the determination 620 of the bundling type may be performed using an AI / ML model as discussed in detail above.

[0215] In some embodiments, although Figure 6Although not shown in the example of , method 600 may include determining one or more of a second RS mode and a second RS configuration type based at least on the parameter set and / or one or more measurements, and transmitting a second indication of the determined one or more of the second RS mode and the second RS configuration type. According to example embodiments, an AI / ML model may be used to determine one or more of the second RS mode and / or the second RS configuration type.

[0216] In some example embodiments, the AI / ML model may be trained with labeled data including inputs and outputs. For example, the inputs may include one or more of: (i) an effective channel matrix for one or more time slots or resource blocks and / or (ii) the set of parameters for determining the RS configuration. The outputs may include one or more of a bundling type (e.g., an indication of at least one bundling type) and / or a location, pattern, and / or density of a second RS pattern based on a performance objective.

[0217] According to some embodiments, the transmission of the first indication and / or the second indication may include: implicitly transmitting one or more of the first indication and the second indication; explicitly transmitting one or more of the first indication and the second indication; using inter-node data exchange to transmit one or more of the first indication and the second indication; periodically transmitting one or more of the first indication and the second indication; and / or semi-periodically transmitting one or more of the first indication and the second indication.

[0218] According to example embodiments, the transmission of the second indication may be triggered based on one or more of: the channel estimation performance accuracy satisfies at least one threshold and / or the channel statistics satisfies at least one threshold.

[0219] in conclusion

[0220] The contents of each of the following references are incorporated herein by reference in their entirety: (1) 3GPP TS 38.214, "Physical layer procedures for data"; (2) 3GPP TS 38.213, "Physical layer procedures for control"; (3) 3GPP TS 38.212, "Multiplexing and channel coding"; (4) 3GPP TS 38.211, "Physical Channels and Modulation"; (5) 3GPP TS 38.331, "Radio Resource Control (RRC) protocol specification"; (6) 3GPP TS 38.321, "Medium Access Control (MAC) protocol specification"; and (7) 3GPP TS 38.215, “Physical layer measurements”.

[0221] Although features and elements are provided above in specific combinations, it will be understood by those skilled in the art that each feature or element can be used alone or in any combination with other features and elements. The present disclosure is not limited in terms of the specific embodiments described in the present application, which are intended to be illustrations of various aspects. Without departing from its spirit and scope, many modifications and changes can be made, as will be apparent to those skilled in the art. Any element, action or instruction used in the description of the present application should not be interpreted as being critical or necessary to the present invention unless explicitly provided as such. In addition to those listed herein, functionally equivalent methods and devices within the scope of the present disclosure are apparent to those skilled in the art from the foregoing description. Such modifications and changes are intended to fall within the scope of the appended claims. The present disclosure is limited only by the terms of the appended claims and the full scope of equivalents given by such claims. It should be understood that the present disclosure is not limited to specific methods or systems herein.

[0222] For simplicity, the foregoing embodiments are discussed with respect to terminology and structure of devices with infrared capabilities (i.e., infrared transmitters and receivers). However, the embodiments discussed are not limited to these systems, but can be applied to other systems using other forms of electromagnetic waves or non-electromagnetic waves (such as sound waves).

[0223] It will also be understood that the terms used herein are used only for the purpose of describing specific embodiments and are not intended to be limiting. As used herein, the term "video" or the term "image" may mean any of a snapshot, a single image, and / or a plurality of images displayed over a certain time basis. As another example, when referred to herein, the term "user equipment" and its abbreviation "UE", the term "remote" and / or the term "head-mounted display" or its abbreviation "HMD" may mean or include (i) a wireless transmit and / or receive unit (WTRU); (ii) any of many embodiments of a WTRU; (iii) a device with wireless capabilities and / or wired capabilities (e.g., wearable) that is configured with some or all of the structure and functionality of a WTRU; (iii) a device with wireless capabilities and / or wired capabilities that is configured with less than all of the structure and functionality of a WTRU; or (iv) a similar device. References herein to Figures 1A to 1D Details of an example WTRU are provided, which may be representative of any WTRU set forth herein. As another example, various disclosed embodiments herein are described above and below as utilizing a head mounted display. Those skilled in the art will recognize that devices other than a head mounted display may be utilized, and some or all of the present disclosure and various disclosed embodiments may be modified accordingly without undue experimentation. Examples of such other devices may include drones or other devices configured to stream information to provide an adapted reality experience.

[0224] In addition, the methods provided herein may be implemented in a computer program, software or firmware that is incorporated into a computer-readable medium for execution by a computer or processor. Examples of computer-readable media include electronic signals (transmitted via a wired or wireless connection) and computer-readable storage media. Examples of non-transitory 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), processors associated with software may be used to implement a radio frequency transceiver for a WTRU, UE, terminal, base station, RNC or any host computer.

[0225] Variations of the methods, devices, and systems provided above are possible without departing from the scope of the present invention. In view of the various embodiments that may be applied, it should be understood that the embodiments shown are merely examples and should not be considered to limit the scope of the appended claims. For example, the embodiments provided herein include handheld devices that may include or be used with any suitable voltage source (such as a battery, etc.) that provides any suitable voltage.

[0226] In addition, in the embodiments provided above, processing platforms, computing systems, controllers, and other devices including processors are mentioned. These devices may include at least one central processing unit ("CPU") and memory. According to the practice of those skilled in the art of computer programming, references to actions and symbolic representations of operations or instructions may be performed by various CPUs and memories. Such actions and operations or instructions may be referred to as "execution," "computer execution," or "CPU execution."

[0227] Those of ordinary skill in the art will appreciate that the actions and symbolic representations of operations or instructions include manipulation of electrical signals by the CPU. The electrical system represents data bits, which may cause the final conversion or reduction of electrical signals and maintain the data bits in memory locations in the storage system, thereby reconfiguring or otherwise changing the operation of the CPU and other processing of signals. The memory locations where the data bits are maintained are physical locations having specific electrical, magnetic, optical, or organic properties that correspond to or represent the data bits. It should be understood that the embodiments are not limited to the above-mentioned platforms or CPUs, and other platforms and CPUs may support the provided methods.

[0228] The data bits may also be maintained on a computer-readable medium, including a magnetic disk, an optical disk, and any other volatile (e.g., random access memory (RAM)) or non-volatile (e.g., read-only memory (ROM)) mass storage system readable by a CPU. The computer-readable medium may include cooperating or interconnected computer-readable media that reside only on a processing system or distributed among multiple interconnected processing systems that may be local or remote to the processing system. It should be understood that the embodiments are not limited to the above-mentioned memories, and other platforms and memories may support the provided methods.

[0229] In an illustrative embodiment, any operations, processes, etc. described herein may be implemented as computer-readable instructions stored on a computer-readable medium. The computer-readable instructions may be executed by a processor of a mobile unit, a network element, and / or any other computing device.

[0230] There is little difference between hardware and software implementations of various aspects of the system. The use of hardware or software is usually (but not always, as in some cases, the choice of hardware or software may become important) a design choice that represents a cost-efficiency trade-off. There may be various vehicles (e.g., hardware, software, and / or firmware) with which the processes and / or systems and / or other technologies described herein can be implemented, and the preferred vehicle may change with the context in which the processes and / or systems and / or other technologies are deployed. For example, if the implementer determines that speed and accuracy are most important, then the implementer may select a primary hardware and / or firmware vehicle. If flexibility is most important, then the implementer may select a primary software implementation. Alternatively, the implementer may select some combination of hardware, software, and / or firmware.

[0231] The foregoing detailed description has been described various embodiments of the apparatus and / or process by using block diagrams, flow charts and / or examples. Insofar as such block diagrams, flow charts and / or examples include one or more functions and / or operations, it will be understood by those skilled in the art that each function and / or operation within such block diagrams, flow charts or examples can be implemented individually and / or collectively by a wide range of hardware, software, firmware or indeed any combination thereof. In an embodiment, several parts of the subject matter described herein may be implemented via an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a digital signal processor (DSP) and / or other integrated formats. However, those skilled in the art will recognize that all or part of some aspects of the embodiments disclosed herein may be equivalently implemented in an integrated circuit, as one or more computer programs running on one or more computers (e.g., as one or more programs running on one or more computer systems), as one or more programs running on one or more processors (e.g., as one or more programs running on one or more microprocessors), as firmware or almost as any combination thereof, and according to the present disclosure, designing circuit systems and / or writing codes for software and / or firmware will be completely within the skill range of those skilled in the art. In addition, those skilled in the art will appreciate that the mechanisms of the subject matter described herein can be distributed as a program product in a variety of forms, and that the illustrative examples of the subject matter described herein apply regardless of the particular type of signal-bearing medium used to actually carry out the distribution. Examples of signal-bearing media include, but are not limited to, the following: recordable media such as floppy disks, hard drives, CDs, DVDs, digital tapes, computer memory, etc.; and transmission media such as digital and / or analog communication media (e.g., fiber optic cables, waveguides, wired communication links, wireless communication links, etc.).

[0232] Those skilled in the art will recognize that it is common in the art to describe devices and / or processes in the manner set forth herein, and thereafter use engineering practices to integrate such described devices and / or processes into data processing systems. That is, at least a portion of the devices and / or processes described herein can be integrated into a data processing system by a reasonable amount of experimentation. Those skilled in the art will recognize that a typical data processing system can typically include a system unit housing, a video display device, a memory such as volatile and non-volatile memory, a processor such as a microprocessor and a digital signal processor, a computing entity such as an operating system, a driver, a graphical user interface, and an application, one or more interactive devices such as a touch pad or screen, and / or a control system including a feedback loop and a control motor (e.g., feedback for sensing position and / or velocity; a control motor for moving and / or adjusting components and / or quantities). A typical data processing system can be implemented using any suitable commercially available components, such as components typically found in data computing / communication and / or network computing / communication systems.

[0233] The subject matter described herein sometimes illustrates different parts that are included in different other parts or connected with different other parts.It should be understood that the architecture of such description is only an example, and many other architectures that realize the same function can actually be implemented.In a conceptual sense, any arrangement of parts for realizing the same function is effectively "associated" so that the desired function can be realized.Therefore, any two parts that are combined to realize a specific function in this article can be regarded as "associated" with each other so that no matter how the architecture or the intermediate parts realize the desired function.Similarly, any two parts that are so associated can also be regarded as "operably connected" or "operably coupled" to realize the desired function, and any two parts that can be so associated can also be regarded as "operably coupled" to realize the desired function.The specific example of operable coupling includes (but is not limited to) parts that can be matched physically and / or physically interact, and / or parts that can be wirelessly interacted and / or wirelessly interacted, and / or parts that can be logically interacted and / or parts that can be logically interacted.

[0234] With respect to the use of substantially any plural and / or singular terms herein, those skilled in the art can translate from the plural to the singular and / or from the singular to the plural where appropriate to the context and / or application. For clarity, the various singular / plural arrangements may be expressly set forth herein.

[0235] Those skilled in the art will understand that, in general, the terms used herein and particularly in the appended claims (e.g., the bodies of the appended claims) are generally intended to be "open" terms (e.g., the term "including" should be interpreted as "including but not limited to," the term "having" should be interpreted as "having at least," the term "includes" should be interpreted as "including but not limited to," etc.). Those skilled in the art will further understand that if a specific number of an introduced claim statement is intended, such intent will be expressly stated in the claim, and in the absence of such a statement, such intent is not present. For example, where only one item is intended, the term "single" or similar language may be used. To aid understanding, the following appended claims and / or the description herein may include the use of the introductory phrases "at least one" and "one or more" to introduce multiple claim statements. However, the use of such phrases should not be interpreted as implying that a claim recitation introduced by the indefinite article "a" or "an" limits any particular claim that includes such introduced claim recitation to embodiments that include only one such recitation, even if the same claim includes the introductory phrases "one or more" or "at least one" and an indefinite article such as "a" or "an" (e.g., "a" and / or "an" should be interpreted as meaning "at least one" or "one or more"). The same is true for the use of definite articles to introduce claim recitations.

[0236] In addition, even if a specific number of an introduced claim recitation is explicitly recited, one skilled in the art will recognize that such recitation should be interpreted as meaning at least the recited number (e.g., merely reciting "two recitations" without other modifiers means at least two recitations or two or more recitations). In addition, in those cases where a convention similar to "at least one of A, B, and C, etc." is used, generally, such construction is intended to represent the convention that one skilled in the art will understand (e.g., "a system having at least one of A, B, and C" would include but is not limited to systems having only A, only B, only C, both A and B, both A and C, both B and C, and / or both A, B, and C, etc.). In those cases where a convention similar to "at least one of A, B, or C, etc." is used, generally, such meaning is intended in the sense that one skilled in the art will understand the convention (e.g., "a system having at least one of A, B, or C" would include but is not limited to systems having only A, only B, only C, both A and B, both A and C, both B and C, and / or both A, B, and C, etc.). Those skilled in the art will further understand that any transitional words and / or phrases (whether in the specification, claims or drawings) that actually give two or more alternatives should be understood as the possibility of expecting to include one of the items, any one of the items or two items. For example, the phrase "A or B" will be understood to include the possibility of "A" or "B" or "A and B". In addition, the term "any" used herein is followed by a list of items of multiple items and / or multiple categories, and is intended to include "any", "any combination", "any multiple" and / or "any multiple combination" that are either alone or in combination with other items and / or other categories of items. In addition, as used herein, the term "set" is intended to include any number of items, including zero. Additionally, as used herein, the term "number" is intended to include any number, including zero. And the term "multiple" used herein is intended to be synonymous with "multiple".

[0237] In addition, where features or aspects of the disclosure are described in terms of Markush groups, those skilled in the art will recognize that the disclosure is also thereby described in terms of any individual member or subgroup of members of the Markush group.

[0238] As will be understood by those skilled in the art, for any and all purposes, such as in providing a written description, all ranges disclosed herein also encompass any and all possible sub-ranges and combinations of sub-ranges thereof. Any listed range can be easily identified as fully describing and capable of decomposing the same range into at least equal halves, one-third, one-quarter, one-fifth, one-tenth, etc. As a non-limiting example, each range discussed herein can be easily decomposed into a lower third, a middle third, and an upper third, etc. As will be understood by those skilled in the art, all languages ​​such as "at most", "at least", "greater than", "less than", etc. include the enumerated numbers, and refer to the ranges that can be subsequently subdivided into sub-ranges as discussed above. Finally, as will be understood by those skilled in the art, the range includes each individual member. Therefore, for example, a group with 1 to 3 units refers to a group with 1, 2 or 3 units. Similarly, a group with 1 to 5 units refers to a group with 1, 2, 3, 4 or 5 units, and so on.

[0239] Furthermore, the claims should not be read as limited to the order or elements provided unless otherwise stated. In addition, the use of the term "means for..." in any claim is intended to invoke 35 USC § 112, 6 or means-plus-function claim format, and any claim without the term "means for..." is not intended to be so.

Claims

1. A method comprising: Receiving, by a wireless transmit / receive unit (WTRU), configuration information indicating: (1) a set of parameters for determining a reference signal (RS) configuration, and (2) parameters to be used within the set, wherein the set of parameters includes at least two bundling types; receiving a transmission using a first RS mode of a first RS configuration type; performing one or more measurements on the transmission using the first RS pattern, wherein the one or more measurements include one or more of: an effective channel matrix for one or more time slots or resource blocks, channel estimation accuracy, and RS signaling overhead; determining a bundling type associated with the RS configuration based on (i) the set of parameters and (ii) the one or more measurements; as well as A first indication of the determined bundling type is transmitted. 2 . The method of claim 1 , wherein the bundling type indicates a resource bundling characteristic across one or more of time and frequency.

3. The method of any one of claims 1 to 2, wherein the determination of the bundling type is performed using an artificial intelligence / machine learning (AI / ML) model.

4. The method according to any one of claims 1 to 3, wherein the parameter set also includes one or more of the following: RS position, RS mode, maximum allowed RS density, minimum allowed RS density, multi-user multiple input multiple output (MU-MIMO) setting, RS configuration signaling feedback overhead and performance requirements.

5. The method according to any one of claims 1 to 4, comprising: determining one or more of a second RS mode and a second RS configuration type based on (i) the set of parameters and (ii) the one or more measurements; and A second indication of the determined one or more of the second RS pattern and the second RS configuration type is transmitted. 6 . The method of claim 5 , wherein one or more of the second RS mode and the second RS configuration type are determined using an artificial intelligence / machine learning (AI / ML) model.

7. The method according to claim 6, wherein: The AI / ML model is trained with labeled data including input and output, The input includes one or more of: (i) the effective channel matrix for one or more time slots or resource blocks and (ii) the set of parameters for determining the RS configuration, and The output includes one or more of: the bundling type, location, pattern, and density of the second RS pattern based on performance objectives.

8. The method according to any one of claims 1 to 7, wherein the first RS configuration type comprises one or more of the following: New Radio (NR) legacy demodulation reference signal (DM-RS); Uniform RS mode; Deterministic non-uniform RS pattern; Random non-uniform RS patterns; and Non-orthogonal RS mode.

9. The method according to any one of claims 1 to 8, wherein transmitting the first indication or the second indication comprises: implicitly transmitting one or more of the first indication and the second indication; explicitly transmitting one or more of the first indication and the second indication; transmitting one or more of the first indication and the second indication using an inter-node data exchange; periodically transmitting one or more of the first indication and the second indication; or One or more of the first indication and the second indication are transmitted semi-periodically.

10. The method according to any one of claims 1 to 9, wherein the transmission of the second indication is triggered based on one or more of the following: (i) channel estimation performance accuracy meets at least one threshold and (ii) channel statistics meet at least one threshold.

11. A wireless transmit / receive unit (WTRU), comprising: A circuit, the circuit comprising any one of a processor, a memory, a transmitter and a receiver, the circuit being configured to: receiving configuration information indicating: (1) a set of parameters for determining a reference signal (RS) configuration, and (2) parameters to be used within the set, wherein the set of parameters includes at least two bundling types; receiving a transmission using a first RS mode of a first RS configuration type; performing one or more measurements on the transmission using the first RS pattern, wherein the one or more measurements include one or more of: an effective channel matrix for one or more time slots or resource blocks, channel estimation accuracy, and RS signaling overhead; determining a bundling type associated with the RS configuration based on (i) the set of parameters and (ii) the one or more measurements; as well as A first indication of the determined bundling type is transmitted.

12. The WTRU of claim 11, wherein the bundling type indicates a resource bundling characteristic across one or more of time and frequency.

13. The WTRU of any one of claims 11 to 12, wherein an artificial intelligence / machine learning (AI / ML) model is used to determine the bundling type.

14. A WTRU according to any one of claims 11 to 13, wherein the parameter set also includes one or more of the following: RS position, RS mode, maximum allowed RS density, minimum allowed RS density, multi-user multiple input multiple output (MU-MIMO) settings, RS configuration signaling feedback overhead and performance requirements.

15. The WTRU of any one of claims 11 to 14, wherein the circuit is configured to: determining one or more of a second RS mode and a second RS configuration type based on (i) the set of parameters and (ii) the one or more measurements; and A second indication of the determined one or more of the second RS pattern and the second RS configuration type is transmitted.

16. The WTRU of claim 15, wherein the circuit is configured to determine one or more of the second RS mode and the second RS configuration type using an artificial intelligence / machine learning (AI / ML) model.

17. The WTRU of claim 16 wherein: The AI / ML model is trained with labeled data including input and output, The input includes one or more of: (i) the effective channel matrix for one or more time slots or resource blocks and (ii) the set of parameters for determining the RS configuration, and The output includes one or more of: the bundling type, location, pattern, and density of the second RS pattern based on performance objectives.

18. The WTRU of any one of claims 11 to 17, wherein the first RS configuration type comprises one or more of: New Radio (NR) legacy demodulation reference signal (DM-RS); Uniform RS mode; Deterministic non-uniform RS pattern; Random non-uniform RS patterns; and Non-orthogonal RS mode.

19. The WTRU of any one of claims 11 to 18, wherein the circuit is configured to: implicitly transmitting one or more of the first indication and the second indication; explicitly transmitting one or more of the first indication and the second indication; transmitting one or more of the first indication and the second indication using an inter-node data exchange; periodically transmitting one or more of the first indication and the second indication; or One or more of the first indication and the second indication are transmitted semi-periodically.

20. The WTRU of any one of claims 11 to 19, wherein transmitting the second indication is triggered based on one or more of: (i) channel estimation performance accuracy meets at least one threshold and (ii) channel statistics meets at least one threshold.

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