Method and apparatus for prioritizing adjacent cells during cell (re)selection according to artificial intelligence / machine learning parameters

Abstract

Methods, apparatus, and procedures are disclosed for prioritizing adjacent cells during cell (re)selection according to artificial intelligence / machine learning (AI / ML) parameters. For example, a wireless transmit / receive unit (WTRU) is configured to detect one or more candidate adjacent cells for cell selection, receive information relating to AI / ML beam management associated with one or more candidate adjacent cells, receive a priority regime to be used when ranking one or more candidate adjacent cells having AI / ML capability for cell selection, rank one or more candidate adjacent cells at least based on the priority regime, and select an adjacent cell from one or more candidate adjacent cells based on the ranking of selected adjacent cells.

Description

[Technical Field]

[0001] This disclosure relates to procedures, methods, architectures, apparatus, systems, devices, and computer program products for and / or directed to prioritizing adjacent cells during cell (re)selection according to artificial intelligence / machine learning (AI / ML) parameters and processes. [Background technology]

[0002] Cross-reference of related applications This application claims priority and benefit of U.S. Provisional Patent Application No. 63 / 456,997, filed with the U.S. Patent and Trademark Office on April 4, 2023, the entirety of which is incorporated herein by reference as if it were described in its entirety below, and for all applicable purposes. [Overview of the project]

[0003] One or more embodiments disclosed herein relate to methods, apparatus, and procedures in wireless communications for prioritizing one or more adjacent cells during cell selection or cell reselection according to a set of AI / ML parameters.

[0004] In one embodiment, a method implemented by a wireless transmit and / or receive unit (WTRU) for wireless communication includes detecting one or more candidate neighbor cells for cell selection, receiving information related to AI / ML beam management associated with one or more candidate neighbor cells, and receiving a priority regime for use in ranking one or more candidate neighbor cells having AI / ML capabilities for cell selection. The method further includes ranking one or more candidate neighbor cells based at least on the priority regime and selecting a neighbor cell from one or more candidate neighbor cells based on the ranking of the selected neighbor cells. In one example, the selected neighbor cell has the highest ranking for cell selection. In some cases, the neighbor cell is re-selected from one or more candidate neighbor cells. In some examples, the received information (e.g., configuration information) indicates one or more of set B types, set B sizes, set B patterns, or AI / ML models associated with a set of reference signal resources, a set of beams, or a set of beam pairs for measuring one or more candidate neighbor cells.

[0005] In one embodiment, a wireless transmit / receive unit (WTRU) for wireless communication comprises circuitry including a processor, a transmitter, a receiver, and / or memory, and the WTRU is configured to implement and perform one or more methods discussed herein. For example, the WTRU is configured to detect one or more candidate neighbor cells for cell selection, receive information related to AI / ML beam management associated with one or more candidate neighbor cells, receive a priority regime and use it to rank one or more candidate neighbor cells having AI / ML capabilities for cell selection. The WTRU is further configured to rank one or more candidate neighbor cells at least on the priority regime and to select a neighbor cell from one or more candidate neighbor cells based on the ranking of the selected neighbor cell. In one example, the selected neighbor cell has the highest ranking for cell selection. In some cases, the neighbor cell is re-selected from one or more candidate neighbor cells. In some examples, the received information (e.g., configuration information) indicates one or more of the following: a set B type, set B size, set B pattern, or AI / ML model associated with a set of reference signal resources, a set of beams, or a set of beam pairs for measuring one or more candidate adjacent cells.

[0006] In another embodiment, a network element associated with a base station may be configured to implement and perform one or more methods discussed herein.

[0007] A more detailed understanding can be given by example from the detailed description below, along with the accompanying drawings. The figures in such drawings, as well as the detailed description, are illustrative. Therefore, the drawings and detailed description should not be considered limiting, and other equally valid examples are possible and likely. Furthermore, similar reference numerals ("ref.") in the figures ("FIG.") indicate similar elements. [Brief explanation of the drawing]

[0008] [Figure 1A] This is a system diagram showing an exemplary communication system in which one or more disclosed embodiments may be implemented. [Figure 1B] This is a system diagram showing an exemplary wireless transmit / receive unit (WTRU) that may be used in the communication system shown in Figure 1A, according to one embodiment. [Figure 1C] This is a system diagram showing an exemplary radio access network (RAN) and an exemplary core network (CN) that may be used in the communication system shown in Figure 1A according to one embodiment. [Figure 1D] This is a system diagram showing further exemplary RAN and further exemplary CN that may be used in the communication system shown in Figure 1A according to one embodiment. [Figure 2] This figure shows a scenario in one or more embodiments where a network (e.g., a base station) transmits a subset of a beam and skips the transmission of another subset of the beam. [Figure 3] This flowchart illustrates an exemplary procedure, according to one or more embodiments, for a device (e.g., WTRU) to prioritize adjacent cells for the purpose of cell (re)selection according to AI / ML parameters. [Figure 4] This flowchart illustrates an exemplary procedure, according to one or more embodiments, for a device (e.g., a WTRU) to collect and share data from adjacent cells that support an AI / ML system. [Modes for carrying out the invention]

[0009] Introduction The following detailed description includes numerous specific details to provide a full understanding of the embodiments and / or examples disclosed herein. However, it will be understood that such embodiments and examples may be carried out without some or all of the specific details described herein. In other examples, well-known methods, procedures, components, and circuits are not described in detail so as not to obscure the following description. Furthermore, embodiments and examples not specifically described herein may be carried out in place of, or in combination with, the embodiments and other examples described, disclosed, or otherwise provided expressly, implicitly, and / or essentially herein (collectively, “provided”).

[0010] Exemplary communication system The methods, procedures, apparatus, and systems provided herein are well suited to communications, including both wired and wireless networks. Outlines of various types of wireless devices and infrastructure are provided with respect to Figures 1A to 1D, and various elements of a network may utilize, operate, and be arranged according to the methods, apparatus, and systems provided herein, as well as adapt and / or configure for them.

[0011] Figure 1A shows an exemplary 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, and broadcast to multiple wireless users. The communication system 100 may enable multiple wireless users to access such content through the sharing of system resources, including wireless bandwidth. For example, the communication system 100 may use one or more channel access methods such as code division multiple access (CDMA), time division multiple access (TDMA), frequency division multiple access (FDMA), orthogonal FDMA (OFDMA), single-carrier FDMA (SC-FDMA), zero-tail unique word DFT spread OFDM (ZT UW DTS-s OFDM), unique word OFDM (UW-OFDM), resource block filtering OFDM, and filter bank multicarrier (FBMC).

[0012] As shown in FIG. 1A, the communication system 100 may include wireless transmit / receive units (WTRUs) 102a, 102b, 102c, 102d, a RAN 104 / 113, a CN 106 / 115, a public switched telephone network (PSTN) 108, the Internet 110, and other networks 112, although it should be understood that the disclosed embodiments contemplate any number of WTRUs, base stations, networks, and / or network elements. Each of the WTRUs 102a, 102b, 102c, 102d can be any type of device configured to operate and / or communicate in a wireless environment. By way of example, the WTRUs 102a, 102b, 102c, 102d may each be referred to as a “station” and / or “STA” and can be configured to transmit and / or receive wireless signals and can include user equipment (UE), mobile stations, fixed or mobile subscriber units, subscriber-based units, pagers, cellular telephones, personal digital assistants (PDA), smartphones, laptops, netbooks, personal computers, wireless sensors, hotspots or Mi-Fi devices, Internet of Things (IoT) devices, watches or other wearables, head-mounted displays (HMD), vehicles, drones, medical devices and applications (e.g., telesurgery), industrial devices and applications (e.g., robots and / or other wireless devices operating in an industrial context and / or in the context of an automated processing chain), home appliances, devices operating on commercial wireless networks and / or industrial wireless networks, etc. Any of the WTRUs 102a, 102b, 102c, and 102d may be interchangeably referred to as a UE.

[0013] The communication system 100 may also include base stations 114a and / or base stations 114b. Each of the base stations 114a and 114b may be any type of device configured to wirelessly interface with at least one of the WTRUs 102a, 102b, 102c, and 102d to facilitate access to one or more communication networks such as CN 106 / 115, the Internet 110, and / or other networks 112. For example, base stations 114a and 114b may be base transceiver stations (BTS), Node-B, eNode B, Home Node B, Home eNode B, gNB, NR Node B, site controller, access point (AP), wireless router, etc. Although base stations 114a and 114b are shown as single elements, it will be understood that base stations 114a and 114b may include any number of interconnected base stations and / or network elements.

[0014] Base station 114a may be part of RAN 104 / 113, which may also include other base stations and / or network elements (not shown) such as a base station controller (BSC), a radio network controller (RNC), a relay node, etc. Base station 114a and / or base station 114b may be configured to transmit and / or receive wireless signals on one or more carrier frequencies that may be referred to as a cell (not shown). These frequencies may be an authorized spectrum, an unlicensed spectrum, or a combination of an authorized spectrum and an unlicensed spectrum. A cell may provide coverage for wireless services in a particular geographic area that may be relatively fixed or may change over time. A cell may be further divided into cell sectors. For example, the cell associated with base station 114a may be divided into three sectors. Thus, in one embodiment, base station 114a may include three transceivers, i.e., one transceiver for each sector of the cell. In one embodiment, base station 114a may use multiple-input multiple-output (MIMO) technology and may utilize multiple transceivers for each sector of the cell. For example, beamforming may be used to transmit and / or receive signals in a desired spatial direction.

[0015] Base stations 114a, 114b can communicate with one or more of WTRUs 102a, 102b, 102c, 102d via air interface 116, which can be any suitable wireless communication link (e.g., radio frequency (RF), microwave, centimeter wave, micrometer wave, infrared (IR), ultraviolet (UV), visible light, etc.). Air interface 116 can be established using any suitable radio access technology (RAT).

[0016] More specifically, as described above, the communication system 100 may be a multiple access system and may use one or more channel access schemes such as CDMA, TDMA, FDMA, OFDMA, and SC-FDMA. For example, base stations 114a and WTRUs 102a, 102b, and 102c in RAN 104 / 113 may implement radio technologies such as Universal Mobile Communications System (UMTS) Terrestrial Radio Access (UTRA), which may establish an air interface 116 using broadband CDMA (WCDMA). WCDMA may include communication protocols such as High Speed ​​Packet Access (HSPA) and / or Advanced HSPA (HSPA+). HSPA may include High Speed ​​Downlink Packet Access (HSDPA) and / or High Speed ​​Uplink Packet Access (HSUPA).

[0017] In one embodiment, base stations 114a and WTRUs 102a, 102b, and 102c can implement radio technologies such as Advanced UMTS Terrestrial Radio Access (E-UTRA), which can establish an air interface 116 using Long-Term Evolution (LTE) and / or LTE Advanced (LTE-A) and / or LTE Advanced Pro (LTE-A Pro).

[0018] In one embodiment, base stations 114a and WTRUs 102a, 102b, and 102c can implement radio technologies such as NR radio access, which can establish an air interface 116 using New Radio (NR).

[0019] In one embodiment, base station 114a and WTRUs 102a, 102b, 102c can implement multiple radio access technologies. For example, base station 114a and WTRUs 102a, 102b, 102c may implement LTE radio access and NR radio access together, for example, using the Dual Connectivity (DC) principle. Thus, the air interface utilized by WTRUs 102a, 102b, 102c may be characterized by multiple types of radio access technologies and / or transmissions sent to / from multiple types of base stations (e.g., eNB and gNB).

[0020] In other embodiments, base stations 114a and WTRUs 102a, 102b, and 102c can implement wireless technologies such as IEEE 802.11 (i.e., Wireless Fidelity (WiFi)), IEEE 802.16 (i.e., Worldwide Interoperability for Microwave Access (WiMAX)), CDMA2000, CDMA2000 1X, CDMA2000 EV-DO, Provisional Standard 2000 (IS-2000), Provisional Standard 95 (IS-95), Provisional Standard 856 (IS-856), Global System for Mobile Communications (GSM), Enhanced Data Rates for GSM Evolution (EDGE), and GSM EDGE (GERAN).

[0021] The base station 114b in Figure 1A may be, for example, a wireless router, Home Node B, Home eNode B, or access point, and any suitable RAT can be used to facilitate wireless connectivity in local areas such as workplaces, homes, vehicles, campuses, industrial facilities, air corridors (for use by drones, for example), and roads. In one embodiment, the base station 114b and WTRU 102c, 102d can implement radio technologies such as IEEE 802.11 to establish a wireless local area network (WLAN). In one embodiment, the base station 114b and WTRU 102c, 102d can implement radio technologies such as IEEE 802.15 to establish a wireless personal area network (WPAN). In yet another embodiment, base stations 114b and WTRUs 102c, 102d can establish picocells or femtocells using cellular-based RATs (e.g., WCDMA, CDMA2000, GSM, LTE, LTE-A, LTE-A Pro, NR, etc.). As shown in Figure 1A, base station 114b may have a direct connection to the internet 110. Therefore, base station 114b may not be required to access the internet 110 via CN 106 / 115.

[0022] RAN104 / 113 can communicate with CN106 / 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 WTRU102a, 102b, 102c, and 102d. The data may have various Quality of Service (QoS) requirements, such as different throughput requirements, latency requirements, error tolerance requirements, reliability requirements, data throughput requirements, and mobility requirements. CN106 / 115 may provide call control, billing services, mobile location-based services, prepaid calls, Internet connectivity, video distribution, etc., and / or perform high-level security functions such as user authentication. Although not shown in Figure 1A, it will be understood that RAN104 / 113 and / or CN106 / 115 may communicate directly or indirectly with other RANs using the same RAT or a different RAT as RAN104 / 113. For example, in addition to being connected to RAN104 / 113, which may be using NR radio technology, CN106 / 115 may also be communicating with another RAN (not shown) using GSM, UMTS, CDMA2000, WiMAX, E-UTRA, or WiFi radio technology.

[0023] CN106 / 115 can also function as a gateway for WTRU102a, 102b, 102c, 102d to access PSTN108, the Internet 110, and / or other networks 112. PSTN108 may include a circuit-switched telephone network providing conventional telephone services (POTS). The Internet 110 may include a global system of interconnected computer networks and devices using common communication protocols such as Transmission Control Protocol (TCP), User Datagram Protocol (UDP), and / or Internet Protocol (IP) in the TCP / IP Internet Protocol Suite. Network 112 may include wired and / or wireless communication networks owned and / or operated by other service providers. For example, network 112 may include another CN connected to one or more RANs that can use the same RAT as RAN104 / 113 or a different RAT.

[0024] Some or all of the WTRUs 102a, 102b, 102c, and 102d in the communication system 100 can include multimode capability (for example, WTRUs 102a, 102b, 102c, and 102d can include multiple transceivers for communicating with different wireless networks via different wireless links). For example, WTRU 102c, shown in Figure 1A, may be configured to communicate with base station 114a, which can use cellular-based radio technology, and base station 114b, which can use IEEE 802 radio technology.

[0025] Figure 1B is a system diagram showing an exemplary WTRU 102. As shown in Figure 1B, the WTRU 102 may include, among other things, a processor 118, a transceiver 120, a transmit / receive element 122, a speaker / microphone 124, a keypad 126, a display / touchpad 128, a non-removable memory 130, a removable memory 132, a power supply 134, a Global Positioning System (GPS) chipset 136, and / or other peripherals 138. It will be understood that the WTRU 102 may include any subcombinations of the aforementioned elements while maintaining consistency with the embodiment.

[0026] The processor 118 could be a general-purpose processor, a dedicated processor, a conventional processor, a digital signal processor (DSP), multiple microprocessors, one or more microprocessors associated with a DSP core, a controller, a microcontroller, an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) circuit, any other type of integrated circuit (IC), a state machine, etc. The processor 118 can 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 can be coupled to a transceiver 120 which can be coupled to a transmit / receive element 122. Although Figure 1B shows the processor 118 and transceiver 120 as separate components, it will be understood that the processor 118 and transceiver 120 can be integrated together in an electronic package or chip.

[0027] The transmit / receive element 122 may be configured to transmit signals to or receive signals from a base station (e.g., base station 114a) via the air interface 116. For example, in one embodiment, the transmit / receive element 122 may be an antenna configured to transmit and / or receive RF signals. In one embodiment, the transmit / receive element 122 may be an emitter / detector configured to transmit and / or receive, for example, IR, UV, or visible light signals. In yet another embodiment, the transmit / receive element 122 may be configured to transmit and / or receive both RF signals and optical signals. It will be understood that the transmit / receive element 122 may be configured to transmit and / or receive any combination of wireless signals.

[0028] Although the transmit / receive element 122 is shown as a single element in Figure 1B, the WTRU 102 may include any number of transmit / receive elements 122. More specifically, the WTRU 102 can utilize MIMO technology. Thus, in one embodiment, the WTRU 102 may include two or more transmit / receive elements 122 (e.g., multiple antennas) for transmitting and receiving wireless signals via the air interface 116.

[0029] The transceiver 120 may be configured to modulate the signal to be transmitted by the transmit / receive element 122 and to demodulate the signal received by the transmit / receive element 122. As described above, the WTRU 102 may have multimode capability. Therefore, the transceiver 120 may include multiple transceivers to enable the WTRU 102 to communicate via multiple RATs, such as NR and IEEE 802.11.

[0030] The processor 118 of the WTRU102 may be coupled to a speaker / microphone 124, a keypad 126, and / or a display / touchpad 128 (e.g., a liquid crystal display (LCD) display unit or an organic light-emitting diode (OLED) display unit), and may receive user input data from them. 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 any type of suitable memory, such as non-removable memory 130 and / or removable memory 132, and store data in them. Non-removable memory 130 may include random access memory (RAM), read-only memory (ROM), a hard disk, or any other type of memory storage device. Removable memory 132 may include a subscriber identification module (SIM) card, a memory stick, a secure digital (SD) memory card, and the like. In other embodiments, the processor 118 can access information from memory that is not physically located on the WTRU 102, such as on a server or home computer (not shown), and store data in that memory.

[0031] The processor 118 can receive power from the power supply 134 and may be configured to distribute and / or control power to other components within the WTRU 102. The power supply 134 can be any suitable device for supplying power to the WTRU 102. For example, the power supply 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.), a solar cell, a fuel cell, etc.

[0032] The processor 118 may also be coupled to a GPS chipset 136 which can be configured to provide location information (e.g., longitude and latitude) about the current location of the WTRU 102. In addition to, or instead of, the WTRU 102 may receive location information from base stations (e.g., base stations 114a, 114b) via the air interface 116 and / or determine its position based on the timing of signals received from two or more nearby base stations. It will be understood that the WTRU 102 may acquire location information by any suitable positioning method while maintaining consistency with the embodiments.

[0033] The processor 118 may be further coupled to other peripherals 138, which may include one or more software and / or hardware modules that provide additional features, functionality, and / or wired or wireless connectivity. For example, peripherals 138 may include an accelerometer, an electronic compass, a satellite transceiver, a digital camera (for photos and / or videos), a Universal Serial Bus (USB) port, a vibration device, a television transceiver, a hands-free headset, a Bluetooth® module, a frequency modulation (FM) radio unit, a digital music player, a media player, a video game player module, an internet browser, a virtual reality and / or augmented reality (VR / AR) device, an activity tracker, and the like. Peripheral 138 may include one or more sensors, which may be one or more of the following: gyroscope, accelerometer, Hall effect sensor, magnetometer, compass sensor, proximity sensor, temperature sensor, time sensor, geolocation sensor, altimeter, light sensor, touch sensor, magnetometer, barometer, gesture sensor, biometric sensor, and / or humidity sensor.

[0034] WTRU102 may include a full-duplex radio (for example, one in which the transmission and reception of some or all of a signal associated with a particular subframe for both an uplink (for example, for transmission) and a downlink (for example, for reception) may be parallel and / or simultaneous.) The full-duplex radio may include an interference management unit 139 for reducing or substantially eliminating self-interference via hardware (e.g., chokes) or via signal processing via a processor (e.g., a separate processor (not shown) or processor 118). In embodiments, WTRU102 may include a half-duplex radio for the transmission and reception of some or all of a signal (for example, one associated with a particular subframe for either an uplink (for example, for transmission) or a downlink (for example, for reception)).

[0035] Figure 1C is a system diagram showing RAN104 and CN106 according to an embodiment. As described above, RAN104 can communicate with WTRU102a, 102b, and 102c via the air interface 116 using E-UTRA wireless technology. RAN104 can also communicate with CN106.

[0036] RAN104 may include eNode-B160a, 160b, and 160c, but it will be understood that RAN104 may include any number of eNode-B while maintaining consistency with the embodiment. Each eNode-B160a, 160b, and 160c may include one or more transceivers for communicating with WTRU102a, 102b, and 102c via the air interface 116. In one embodiment, eNode-B160a, 160b, and 160c may implement MIMO technology. Thus, eNode-B160a may, for example, use multiple antennas to transmit a wireless signal to and / or receive a wireless signal from WTRU102a.

[0037] Each of the eNode-B160a, 160b, and 160c may be associated with a specific cell (not shown) and may be configured to handle wireless resource management decisions, handover decisions, user scheduling on uplink (UL) and / or downlink (DL), etc. As shown in Figure 1C, the eNode-B160a, 160b, and 160c can communicate with each other via the X2 interface.

[0038] The CN106 shown in Figure 1C may include a Mobility Management Entity (MME) 162, a Serving Gateway (SGW) 164, and a Packet Data Network (PDN) Gateway (or PGW) 166. Although each of the aforementioned elements is shown as part of CN106, it will be understood that any of these elements may be owned and / or operated by an entity other than the CN operator.

[0039] The MME162 can be connected to each of the eNode-B162a, 162b, and 162c within RAN104 via the S1 interface and can function as a control node. For example, the MME162 can be responsible for authenticating users of WTRU102a, 102b, and 102c, activating / deactivating bearers, and selecting a specific serving gateway during the initial attachment of WTRU102a, 102b, and 102c. The MME162 can provide control plane functionality for switching between RAN104 and other RANs (not shown) using other radio technologies such as GSM and / or WCDMA.

[0040] The SGW164 can be connected to each of the eNode B160a, 160b, and 160c within RAN104 via the S1 interface. The SGW164 can generally route and forward user data packets to and from WTRU102a, 102b, and 102c. The SGW164 can perform other functions such as anchoring the user plane during eNode B handovers, triggering paging when DL data is available to WTRU102a, 102b, and 102c, and managing and remembering the context of WTRU102a, 102b, and 102c.

[0041] SGW164 may be connected to PGW166, which can provide WTRU102a, 102b, and 102c with access to packet-switched networks such as the Internet 110, thereby facilitating communication between WTRU102a, 102b, and 102c and IP-enabled devices.

[0042] CN106 can facilitate communication with other networks. For example, CN106 can provide WTRU102a, 102b, and 102c with access to circuit-switched networks such as PSTN108, thereby facilitating communication between WTRU102a, 102b, and 102c and conventional land-line communication devices. For example, CN106 may include, or communicate with, an IP gateway (e.g., an IP Multimedia Subsystem (IMS) server) that acts as an interface between CN106 and PSTN108. Furthermore, CN106 can provide WTRU102a, 102b, and 102c with access to other networks 112, which may include other wired and / or wireless networks owned and / or operated by other service providers.

[0043] Although the WTRU is described as a wireless terminal in Figures 1A to 1D, in certain representative embodiments, such a terminal is intended to be able to use a wired communication interface with a communication network (for example, temporarily or permanently).

[0044] In a typical embodiment, the other network 112 may be a WLAN.

[0045] In Infrastructure Basic Service Set (BSS) mode, a WLAN may have an access point (AP) for the BSS and one or more stations (STAs) associated with the AP. The AP may have access to or interfaces with a distribution system (DS) or another type of wired / wireless network that carries traffic entering and / or leaving the BSS. Traffic originating from outside the BSS to the STA may arrive through the AP and be delivered to the STA. Traffic originating from the STA to destinations outside the BSS may be sent to the AP to be delivered to their respective destinations. Traffic between STAs within the BSS may be sent through the AP; for example, a source STA can send traffic to the AP, and the AP can deliver the traffic to the destination STA. Traffic between STAs within the BSS is considered and / or may be called peer-to-peer traffic. Peer-to-peer traffic may be sent between a source STA and a destination STA (e.g., directly between them) using a Direct Link Setup (DLS). In certain representative embodiments, the DLS may be an 802.11e DLS or an 802.11z Tunnel DLS (TDLS). A WLAN using Independent BSS (IBSS) mode may not have access points (APs), and STAs within or using IBSS (e.g., all STAs) can communicate directly with each other. The IBSS communication mode may also be referred to herein as the “ad-hoc” communication mode.

[0046] When using the 802.11ac infrastructure operating mode or a similar operating mode, an AP may transmit beacons on a fixed channel, such as a primary channel. The primary channel may be of a fixed width (e.g., a 20 MHz bandwidth) or a dynamically set width via signaling. The primary channel may be the operating channel of the BSS, which can be used by STAs to establish a connection with the AP. In certain typical embodiments, carrier sensing multiple access (CSMA / CA) with collision avoidance may be implemented, for example, in an 802.11 system. For CSMA / CA, an STA, including the AP (e.g., any STA), may sense the primary channel. If a particular STA senses / detects and / or determines that the primary channel is busy, that STA may backoff. A single STA (e.g., only one station) may transmit at any given time within a given BSS.

[0047] A high-throughput (HT) STA may use a 40MHz wide channel for communication, for example, by combining a primary 20MHz channel with adjacent or non-adjacent 20MHz channels to form a 40MHz wide channel.

[0048] Ultra-high throughput (VHT) STAs can support 20MHz, 40MHz, 80MHz, and / or 160MHz wide channels. 40MHz and / or 80MHz channels can be formed by combining consecutive 20MHz channels. 160MHz channels can be formed by combining eight consecutive 20MHz channels or two discontinuous 80MHz channels, which may be called an 80+80 configuration. For 80+80 configurations, data can be passed through a segment parser that, after channel encoding, can split the data into two streams. Inverse fast Fourier transform (IFFT) processing and time-domain processing can be performed separately for each stream. The streams can be mapped onto two 80MHz channels, and the data can be transmitted by a transmitting STA. At the receiver of a receiving STA, the above operation for the 80+80 configuration can be reversed, and the combined data can be sent to a media access control (MAC).

[0049] Sub-1GHz operating modes are supported by 802.11af and 802.11ah. The bandwidth and carrier on which the channel operates are reduced in 802.11af and 802.11ah compared to those used in 802.11n and 802.11ac. 802.11af supports 5MHz, 10MHz, and 20MHz bandwidths in the TV White Space (TVWS) spectrum, while 802.11ah supports 1MHz, 2MHz, 4MHz, 8MHz, and 16MHz bandwidths using the non-TVWS spectrum. According to a typical embodiment, 802.11ah may support meter-type control / machine-type communications, such as MTC devices in a macro coverage area. MTC devices may have limited capabilities, including support for specific bandwidths and / or limited bandwidths (e.g., support only for those bandwidths). MTC devices may include batteries with battery life exceeding a threshold (e.g., to maintain very long battery life).

[0050] WLAN systems that can support multiple channels and channel bandwidths, such as 802.11n, 802.11ac, 802.11af, and 802.11ah, include a channel that can be designated as the primary channel. The primary channel may have a bandwidth equal to the common maximum 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 mode operating at the minimum bandwidth among all STAs operating in the BSS. In the 802.11ah example, even if the AP and other STAs in the BSS support modes operating at 2MHz, 4MHz, 8MHz, 16MHz, and / or other channel bandwidths, the primary channel may be 1MHz wide for an STA (e.g., an MTC type device) that supports (e.g., only supports) the 1MHz mode. Carrier discovery settings and / or network allocation vector (NAV) settings may depend on the status of the primary channel. For example, if the primary channel is busy with an STA (which only supports a mode operating at 1MHz) transmitting to an AP, the entire available frequency band may be considered busy, even though a large portion of the frequency band remains idle and may be available.

[0051] In the United States, the available frequency band that can be used by 802.11ah is from 902 MHz to 928 MHz. In South Korea, the available frequency band is from 917.5 MHz to 923.5 MHz. In Japan, the available frequency band is from 916.5 MHz to 927.5 MHz. The total bandwidth available for 802.11ah is from 6 MHz to 26 MHz, depending on the country code.

[0052] Figure 1D is a system diagram showing RAN113 and CN115 according to an embodiment. As described above, RAN113 can communicate with WTRU102a, 102b, and 102c via the air interface 116 using NR radio technology. RAN113 can also communicate with CN115.

[0053] RAN113 may include gNB180a, 180b, and 180c, but it will be understood that RAN113 may include any number of gNBs while maintaining consistency with the embodiment. Each gNB180a, 180b, and 180c may include one or more transceivers for communicating with WTRU102a, 102b, and 102c via the air interface 116. In one embodiment, gNB180a, 180b, and 180c may implement MIMO technology. For example, gNB180a and 180b may utilize beamforming to transmit signals to and / or receive signals from gNB102a, 102b, and 102c. Therefore, gNB180a can, for example, use multiple antennas to transmit wireless signals to and / or receive wireless signals from WTRU102a. In embodiments, gNB180a, 180b, and 180c may implement carrier aggregation technology. For example, gNB180a can transmit multiple component carriers to WTRU102a (not shown). A subset of these component carriers may be on the unlicensed spectrum, while the remaining component carriers may be on the licensed spectrum. In embodiments, gNB180a, 180b, and 180c may implement coordinated multipoint (CoMP) technology. For example, WTRU102a can receive coordinated transmissions from gNB180a and gNB180b (and / or gNB180c).

[0054] WTRU102a, 102b, and 102c can communicate with gNB180a, 180b, and 180c using transmissions associated with scalable neurology. For example, OFDM symbol intervals and / or OFDM subcarrier intervals may vary for different transmissions, different cells, and / or different wireless transmission spectral portions. WTRU102a, 102b, and 102c can communicate with gNB180a, 180b, and 180c using subframes or transmit time intervals (TTIs) of varying or scalable lengths (e.g., containing varying numbers of OFDM symbols and / or sustaining varying absolute times).

[0055] The gNB180a, 180b, and 180c can be configured to communicate with WTRU102a, 102b, and 102c in standalone and / or non-standalone configurations. In a standalone configuration, WTRU102a, 102b, and 102c can communicate with the gNB180a, 180b, and 180c without accessing other RANs (e.g., eNode-B160a, 160b, and 160c). In a standalone configuration, WTRU102a, 102b, and 102c can use one or more of the gNB180a, 180b, and 180c as mobility anchor points. In a standalone configuration, WTRU102a, 102b, and 102c can communicate with the gNB180a, 180b, and 180c using signals within an unauthorized band. In a non-standalone configuration, WTRU102a, 102b, and 102c can communicate with / connect to gNB180a, 180b, and 180c, while also communicating with / connecting to other RANs such as eNode-B160a, 160b, and 160c. For example, WTRU102a, 102b, and 102c can implement the DC principle to communicate substantially simultaneously with one or more gNB180a, 180b, and 180c, and one or more eNode-B160a, 160b, and 160c. In a non-standalone configuration, eNode-B160a, 160b, and 160c can act as mobility anchors for WTRU102a, 102b, and 102c, while gNB180a, 180b, and 180c can provide additional coverage and / or throughput to service WTRU102a, 102b, and 102c.

[0056] Each of the gNB180a, 180b, and 180c may be associated with a specific cell (not shown) and may be configured to handle radio resource management decisions, handover decisions, user scheduling on uplink (UL) and / or downlink (DL), network slicing support, dual connectivity, interworking between NR and E-UTRA, routing of user plane data to user plane functions (UPF) 184a and 184b, routing of control plane information to access and mobility management functions (AMF) 182a and 182b, etc. As shown in Figure 1D, the gNB180a, 180b, and 180c can communicate with each other via the Xn interface.

[0057] The CN115 shown in Figure 1D may include at least one AMF182a, 182b, at least one UPF184a, 184b, at least one Session Management Function (SMF)183a, 183b, and optionally, a Data Network (DN)185a, 185b. Although each of the aforementioned elements is shown as part of the CN115, it will be understood that any of these elements may be owned and / or operated by entities other than the CN operator.

[0058] AMF182a and 182b can be connected to one or more of gNB180a, 180b, and 180c within RAN113 via the N2 interface and can act as control nodes. For example, AMF182a and 182b can be responsible for user authentication of WTRU102a, 102b, and 102c, support for network slicing (e.g., handling different PDU sessions with different requirements), selection of specific SMF183a and 183b, management of registration areas, termination of NAS signaling, mobility management, etc. Network slicing can be used by AMF182a and 182b to customize CN support for WTRU102a, 102b, and 102c based on the type of service being utilized. For example, different network slices may be established for different use cases such as services relying on ultra-high reliability low latency (URLLC) access, services relying on extended massive mobile broadband (eMBB) access, services for machine-type communications (MTC) access, and / or similar. AMF182a, 182b can provide control plane functionality for switching between RAN113 and other RANs (not shown) using other radio technologies such as LTE, LTE-A, LTE-A Pro, and / or non-3GPP access technologies such as WiFi.

[0059] SMF183a and 183b can be connected to AMF182a and 182b in CN115 via the N11 interface. SMF183a and 183b can also be connected to UPF184a and 184b in CN115 via the N4 interface. SMF183a and 183b can select and control UPF184a and 184b and configure the routing of traffic through UPF184a and 184b. SMF183a and 183b can perform other functions such as managing and allocating UE IP addresses, managing PDU sessions, controlling policy enforcement and QoS, and providing downlink data notifications. PDU session types can be IP-based, non-IP-based, Ethernet-based, etc.

[0060] UPF184a and 184b may be connected to one or more of gNB180a, 180b, and 180c in RAN113 via the N3 interface, which can provide WTRU102a, 102b, and 102c with access to packet-switched networks such as the Internet 110, facilitating communication between WTRU102a, 102b, and 102c and IP-enabled devices. UPF184 and 184b may perform other functions such as routing and forwarding packets, enforcing user plane policies, supporting multi-homed PDU sessions, handling user plane QoS, buffering downlink packets, and providing mobility anchoring.

[0061] CN115 can facilitate communication with other networks. For example, CN115 may include, or can communicate with, an IP gateway (e.g., an IP Multimedia Subsystem (IMS) server) that acts as an interface between CN115 and PSTN108. Furthermore, CN115 can provide WTRU102a,102b,102c with access to other networks 112, which may include other wired and / or wireless networks owned and / or operated by other service providers. In one embodiment, WTRU102a,102b,102c may be connected to the local data network (DN) 185a,185b via the UPF184a,184b through an N3 interface to UPF184a,184b and an N6 interface between UPF184a,184b and DN185a,185b.

[0062] In the diagrams of Figures 1A to 1D, and the corresponding descriptions of Figures 1A to 1D, one or more or all of the functions described herein with respect to one or more of the WTRU102a to d, base stations 114a to b, eNode-B160a to c, MME162, SGW164, PGW166, gNB180a to c, AMF182a to b, UPF184a to b, SMF183a to b, DN185a to b, and / or any other devices described herein may be performed by one or more emulation devices (not shown). An emulation device may be one or more devices configured to emulate one or more or all of the functions described herein. For example, an emulation device may be used to test other devices and / or to simulate network and / or WTRU functions.

[0063] Emulation devices may be designed to implement one or more tests of other devices in a laboratory and / or operator network environment. For example, one or more emulation devices may perform one, more or all of the functions while being fully or partially implemented and / or deployed as part of a wired and / or wireless communication network to test other devices in a communication network. One or more emulation devices may perform one, more or all of the functions while being temporarily implemented / deployed as part of a wired and / or wireless communication network. Emulation devices may be directly coupled to another device for test purposes and / or may perform tests using over-the-air wireless communication.

[0064] One or more emulation devices can perform one or more functions, including all of the above, while not being implemented / deployed as part of a wired and / or wireless communication network. For example, an emulation device may be used in a test scenario in a test laboratory and / or an undeployed (e.g., test) wired and / or wireless communication network to implement testing of one or more components. One or more emulation devices may be test equipment. Direct RF coupling and / or wireless communication via RF circuitry (e.g., which may include one or more antennas) can be used by the emulation device to transmit and / or receive data.

[0065] Cell (re)selection in NR In 3GPP RAN#94-e, the RAN research item on artificial intelligence (AI) / machine learning (ML) for NR was agreed upon for air interfaces. Beam management was selected as one of the target use cases for AI / ML for air interfaces. This technology can be a crucial foundation for improving the performance and complexity of conventional beam management methods, including beam prediction in the temporal and / or spatial domains for overhead and latency reduction and improved beam selection accuracy.

[0066] In legacy NRs, a gNB can select a set of SS / PBCH blocks (SSBs) to be transmitted within an SSB burst, where the list of SSBs to be transmitted within the SSB burst may be shown via ssb-PositionsInBurst in SIB1. Transmitting all SSB beams (e.g., up to 64 in NR-Rel.17) can result in a huge payload and overhead for the gNB's performance. Reducing the number of transmitted SSBs can have a positive impact on system performance and latency. Therefore, a gNB can skip the transmission of some SSBs and transmit only a subset of SSBs, and the WTRU can predict the best beam based on the transmitted SSBs (e.g., by using an AI / ML system). An example of such a scenario is shown in Figure 2, where the gNB transmits only a subset of beams (shown as black beams) and skips the transmission of a subset of beams (shown as dashed pink beams).

[0067] During cell (re)selection, the WTRU performs cell-ranking based on cell-based RSRP measurements for the SS / PBCH block. The WTRU evaluates the RSRP (Rs for the serving cell and Rn for adjacent cells) based on the measured RSRP and one or more offset values ​​and parameters. The WTRU searches to find the strongest cell based on the evaluated RSRP, the appropriate number of beams, and the corresponding priority. In cell (re)selection, if a cell is found with a higher evaluated ranking than that of the serving cell (within the time duration), the cell is selected and cell reselection is performed.

[0068] In the NR-AI / ML system, AI / ML-capable WTRUs can prioritize selecting and connecting to different cells based on their preferences, as well as the AI / ML properties and AI / ML models of the cells. This can result in different WTRU behavior during the determination of priority levels and / or relative priorities, as well as during cell prioritization in the initial access procedure and the cell (re)selection procedure. Therefore, further investigation is needed into AI / ML-dependent prioritization enhancements during initial access and cell (re)selection.

[0069] Common terminology Hereafter, "a" and "an" and similar phrases should be interpreted as "one or more" and "at least one." Similarly, any term ending in the suffix "(s)" should be interpreted as "one or more" and "at least one." The term "may" should be interpreted as "for example, may."

[0070] The symbol " / " (e.g., a forward slash) may be used herein to represent "and / or", for example, "A / B" may mean "A and / or B".

[0071] [Artificial intelligence (AI)] Artificial intelligence can be broadly defined as the behavior exhibited by machines. Such behavior can, for example, mimic cognitive functions to perceive, reason, adapt, and act.

[0072] [Machine Learning (ML)] Machine learning can refer to a type of algorithm that solves problems based on learning through experience ("data") without being explicitly programmed ("composing a set of rules"). Machine learning can be considered a subset of AI. Different machine learning paradigms are possible depending on the nature of the data or feedback available to the learning algorithm. For example, supervised learning methods can involve learning the ability to map inputs to outputs based on labeled training examples, where each training example may be a pair consisting of an input and a corresponding output. For example, unsupervised learning methods can involve detecting patterns in data that do not already have labels. For example, reinforcement learning methods can involve performing a series of actions in an environment to maximize cumulative rewards. In some cases, it is possible to apply machine learning algorithms using combinations or interpolations of the above methods. For example, semi-supervised learning methods can use a combination of small amounts of labeled data and large amounts of unlabeled data during training. In this respect, semi-supervised learning falls between unsupervised learning (which does not use labeled training data) and supervised learning (which uses only labeled training data).

[0073] [Deep Learning (DL)] Deep learning refers to a class of machine learning algorithms that use artificial neural networks (specifically DNNs) that are broadly inspired by biological systems. A deep neural network (DNN) is a special class of machine learning models inspired by the human brain, where the input is linearly transformed and passes through a nonlinear activation function multiple times. Typically, a DNN contains multiple layers, each containing a linear transformation function and a given nonlinear activation function. DNNs can be trained using training data via a backpropagation algorithm. Recently, DNNs have demonstrated state-of-the-art performance in various domains, such as speech, vision, and natural language, as well as in various machine learning settings, including supervised, unsupervised, and semi-supervised. The term AI / ML-based methods / processes can refer to the realization and / or adaptation of behavior to requirements through data-driven learning without an explicit configuration of a set of steps or actions. Such methods can enable the learning of complex behaviors that may be difficult to specify and / or implement when using legacy methods.

[0074] [Beam Definition] A WTRU can transmit or receive a physical channel or reference signal according to at least one spatial domain filter. The term "beam" may be used to refer to a spatial domain filter.

[0075] A WTRU can transmit a physical channel or physical signal using the same spatial domain filter used to receive a reference signal (RS) (e.g., CSI-RS) or synchronization signal (SS) block. The WTRU transmission may be called the “target,” and the received RS or synchronization signal (SS) block may be called the “reference” or “source.” In such cases, the WTRU may be said to transmit a target physical channel or physical signal according to the spatial relationship referencing such RS or SS block.

[0076] A WTRU may transmit a first physical channel or physical signal according to the same spatial domain filter used to transmit a second physical channel or physical signal. The first and second transmits may be called the “target” and “reference” (or “source”), respectively. In such a case, the WTRU may be said to transmit the first (target) physical channel or physical signal according to a spatial relationship that references the second (reference) physical channel or physical signal.

[0077] Spatial relationships may be implicit, configured by RRC, or signaled by MAC CE or DCI. For example, a WTRU may implicitly transmit a physical uplink shared channel (PUSCH) and a demodulated reference signal (DM-RS) for the PUSCH, following the same spatial domain filter as the SRS, indicated by an SRI indicated within the DCI or configured by RRC. In another example, a spatial relationship may be configured by RRC for an SRS resource indicator (SRI) or signaled by MAC CE for a physical uplink control channel (PUCCH). Such spatial relationships may also be called "beam indications."

[0078] A WTRU can receive a first (target) downlink channel or signal according to the same spatial domain filter or spatial receive parameters as the second (reference) downlink channel or signal. For example, such an association may exist between a physical channel, such as a physical uplink control channel (PDCCH) or physical uplink shared channel (PDSCH), and its respective DM-RS. Such an association may exist when the WTRU is configured between corresponding antenna ports using homolocating (QCL) assumption type D, provided that at least the first and second signals are reference signals. Such an association may be configured as a TCI (Transmit Configuration Indicator) state. A WTRU may be indicated by an association between a CSI-RS or SS block and a DM-RS, indexed to a set of TCI states configured by the RRC and / or signaled by the MAC CE. Such an indication may also be called a "beam indication."

[0079] [TRP, MTRP, M-TRP] Hereinafter, a TRP (e.g., transmit and receive point) may be used interchangeably with one or more of TP (transmitter point), RP (receiver point), RRH (radio remote head), DA (distributed antenna), BS (base station), sector (of a BS), and cell (e.g., geographic cell area served by a BS), while still maintaining consistency with the present invention. Hereinafter, a multi-TRP may be used interchangeably with one or more of MTRP, M-TRP, and multiple TRPs.

[0080] [CSI component] A WTRU may report a subset of channel status information (CSI) components, where the CSI components may correspond to at least the CSI-RS resource indicator (CRI), SSB resource indicator (SSBRI), panel indications used for reception in the WTRU (such as panel identification information or group identification information), measurements such as L1-RSRP and L1-SINR taken from SSB or CSI-RS (e.g., cri-RSRP, cri-SINR, ssb-Index-RSRP, ssb-Index-SINR), and other channel status information such as at least the rank indicator (RI), channel quality indicator (CQI), precoding matrix indicator (PMI), layer index (LI), and / or similar.

[0081] [Channel and / or interferometry] [SSB]WTRUs can receive synchronous signal / physical broadcast channel (SS / PBCH) blocks. An SS / PBCH block (SSB) may include a primary synchronous signal (PSS), a secondary synchronous signal (SSS), and a physical broadcast channel (PBCH). WTRUs may monitor, receive, or attempt to decode SSBs during initial access, initial synchronization, radio link monitoring (RLM), cell discovery, cell switching, etc.

[0082] [CSI-RS]WTRU can measure and report channel status information (CSI), and the CSI for each connection mode may include or be composed of one or more of the following:

[0083] A CSI report configuration that includes one or more of the following: 〇 CSI reporting quantities, such as Channel Quality Indicator (CQI), Rank Indicator (RI), Precoding Matrix Indicator (PMI), CSI-RS Resource Indicator (CRI), and Layer Indicator (LI). 〇 CSI reporting type, e.g., aperiodic, semi-permanent, periodic. 〇 CSI reporting codebook structure, e.g., Type I, Type II, Type II port selection, etc. 〇 CSI reporting frequency. A CSI-RS resource set containing one or more of the following CSI resource settings. 〇 NZP-CSI-RS resources for channel measurement 〇 NZP-CSI-RS resources for interferometry 〇 CSI-IM resources for interferometry An NZP CSI-RS resource containing one or more of the following: 〇 NZP CSI-RS Resource ID 〇 Periodicity and offset 〇 QCL Info and TCI Status Resource mapping, such as the number of ports, port density, and code division multiplexing (CDM) type.

[0084] A WTRU can indicate, determine, or be constructed using one or more reference signals. Based on each reference signal, the WTRU can monitor, receive, and / or measure one or more parameters. For example, one or more of the following may apply. The following parameters are non-limiting examples of parameters that may be included in a reference signal measurement, and one or more of these parameters may be included. Other parameters may also be included.

[0085] SS-RSRP. The SS-RSRP (SS-RSRP) can be measured based on a synchronization signal (e.g., a demodulated reference signal (DMRS) in a PBCH or SSS). It can be defined as a linear average over the power contributions of the resource elements (REs) carrying each synchronization signal. Power scaling for the reference signal may be required when measuring RSRP. If SS-RSRP can be used for L1-RSRP, the measurement can be achieved based on a CSI reference signal in addition to the synchronization signal.

[0086] CSI-RSRP. CSI-RSRP can be measured based on a linear average of the power contributions of the resource elements (REs) carrying each CSI-RS. CSI-RSRP measurement can be configured within the measurement resources for the configured CSI-RS occasion.

[0087] SS-SINR. The SS-SINR (Signal-to-Noise Ratio) can be measured based on the synchronization signal (e.g., DMRS in a PBCH or SSS). It can be defined as a linear average over the power contributions of the resource elements (REs) carrying each synchronization signal, divided by a linear average of the noise and interference power contributions. When SS-SINR is used for L1-SINR, noise and interference power measurements can be achieved based on resources configured by higher layers.

[0088] CSI-SINR. CSI-SINR can be measured based on a linear average of the power contributions of the resource elements (REs) carrying each CSI-RS, divided by a linear average of the noise and interference power contributions. If CSI-SINR can be used for L1-SINR, noise and interference power measurements can be achieved based on resources configured by higher layers. Otherwise, noise and interference power can be measured based on the resources carrying each CSI-RS.

[0089] RSSI. The Received Signal Strength Indicator (RSSI) can be measured based on the average of the total power contributions in the configured OFDM symbol and bandwidth. Power contributions may be received from different resources (e.g., same-channel serving and non-serving cells, adjacent channel interference, thermal noise, etc.).

[0090] CLI-RSSI.The Cross-Layer Interference Received Signal Strength Indicator (CLI-RSSI) can be measured based on the average of the total power contributions in the configured OFDM symbol for the configured time and frequency resources. Power contributions may be received from different resources (e.g., cross-layer interference, same-channel serving and non-serving cells, adjacent channel interference, thermal noise, etc.).

[0091] SRS-RSRP. The sounding reference signal RSRP (SRS-RSRP) can be measured based on a linear average of the power contributions of the resource elements (REs) carrying each SRS.

[0092] SS-RSRQ. The secondary synchronization signal reference signal receive quality (SS-RSRQ) can be measured based on measurements of the reference signal receive power (SS-RSRP) and received signal strength (RSSI). In one example, SS-RSRQ may be calculated as the ratio N × SS-RSRP / NR carrier RSSI, where N may be determined based on the number of resource blocks in the corresponding NR carrier RSSI measurement bandwidth. Thus, the measurements used in the numerator and denominator can be over the same set of resource blocks.

[0093] CSI-RSRQ The CSI reference signal received quality (CSI-RSRQ) can be measured based on measurements of the reference signal received power (CSI-RSRP) and received signal strength (RSSI). In one example, SS-RSRQ can be calculated as the ratio N × CSI-RSRP / CSIRSSI, where N can be determined based on the number of resource blocks in the corresponding CSI-RSSI measurement bandwidth. Thus, the measurements to be used in the numerator and denominator can be over the same set of resource blocks.

[0094] [Beam / CSI Reporting Structure] A CSI reporting configuration (e.g., CSI-ReportConfig) can be associated with a single BWP (indicated by, for example, BWP-Id) and can consist of one or more of the following parameters: CSI-RS resources and / or CSI-RS resource sets for channel and interference measurements; CSI-RS reporting configuration types, including periodic, semi-persistent, and aperiodic; CSI-RS transmit periodicity for periodic and semi-persistent CSI reporting; CSI-RS transmit slot offsets for periodic, semi-persistent, and aperiodic CSI reporting; CSI-RS transmit slot offset lists for semi-persistent and aperiodic CSI reporting; time limits for channel and interference measurements; reporting frequency band configuration (broadband / subband CQI, PMI, etc.); thresholds and calculation modes for reported quantities (CQI, RSRP, SINR, LI, RI, etc.); codebook configuration; group-based beam reporting; CQI table; subband size; non-PMI port indication; port index; etc.

[0095] [CSI-RS Resource Configuration] A CSI-RS resource set (e.g., NZP-CSI-RS-ResourceSet) may contain one or more CSI-RS resources (e.g., NZP-CSI-RS-Resource and CSI-ResourceConfig), and a WTRU may be configured with one or more of the following in the CSI-RS resources: CSI-RS periodicity and slot offset for periodic and semi-persistent CSI-RS resources, the number, density, CDM type, OFDM symbol, and CSI-RS resource mapping for defining subcarrier occupancy, the bandwidth portion to which the configured CSI-RS is allocated, and a reference to the TCI state including the QCL source RS and the corresponding QCL type.

[0096] [RS Resource Set Configuration] One or more of the following configurations may be used for an RS resource set. A WTRU may be configured with one or more RS resource sets. An RS resource set configuration may include one or more of the following: an RS resource set ID, one or more RS resources for the RS resource set, repetition (i.e., on or off), an offset that triggers aperiodically (e.g., one of slots 0 to 6), and TRS information (e.g., true or false).

[0097] [RS Resource Configuration] One or more of the following configurations may be used for RS resources. A WTRU may be configured with one or more RS resources. An RS resource configuration may include one or more of the following: RS resources, IDResource mapping (e.g., RE in a physical resource block (PRB)), power control offset (e.g., one of -8, ..., 15), power control offset with SS (e.g., -3dB, 0dB, 3dB, 6dB), scrambling ID, periodicity and offset, and QCL information (e.g., based on TCI status).

[0098] [Grant or assignment characteristics] In the following, the characteristics of a grant or assignment may consist of at least one of the following: frequency allocation, mode of time allocation such as duration, priority, modulation and coding scheme, transport block size, number of spatial layers, number of transport blocks, TCI state, CRI or SRI, number of repetitions, whether the repetition scheme is type A or type B, whether the grant is a configured grant type 1, type 2, or dynamic grant, whether the assignment is a dynamic assignment or a semi-persistent scheduling (configured) assignment, configured grant index or semi-persistent assignment index, periodicity of the configured grant or assignment, channel access priority class (CAPC), and any parameters provided in DCI by MAC or RRC for scheduling the grant or assignment. In the following, DCI indications may consist of at least one of explicit indications by DCI fields used to mask or scramble the DCI's CRC or by a Radio Network Identifier (RNTI), and implicit indications by characteristics such as DCI format, DCI size, Coreset or search space, aggregation level, or a first resource element of the received DCI (e.g., the index of a first control channel element), where the mapping between characteristics and values ​​may be signaled by RRC or MAC.

[0099] Receiving or monitoring DCI using or with RNTI may mean that the DCI's CRC is masked or scrambled using RNTI.

[0100] The following signals may be used interchangeably with one or more of the following: sounding reference signal (SRS), channel state information-reference signal (CSI-RS), demodulation reference signal (DM-RS), phase tracking reference signal (PT-RS), and synchronization signal block (SSB).

[0101] The following channels may be used interchangeably with one or more of the following: physical downlink control channels (PDCCH), physical downlink sharing channels (PDSCH), physical uplink control channels (PUCCH), physical uplink sharing channels (PUSCH), and physical random access channels (PRACH).

[0102] The following signals, channels, and messages (for example, in DL signals or UL signals, channels, and messages) may be used interchangeably.

[0103] Hereinafter, RS can be used interchangeably with one or more of the following: RS resources, RS resource sets, RS ports, and RS port groups.

[0104] Hereinafter, RS may be used interchangeably with SSB, CSI-RS, SRS, and one or more of DM-RS, TRS, PRS, and PTRS.

[0105] In this specification, time instances, slots, symbols, and subframes may be used interchangeably.

[0106] In this specification, the terms SSB, SS / PBCH block, PSS, SSS, PBCH, and MIB may be used interchangeably.

[0107] In this specification, SSB, SSB beam, and SSB index may be used interchangeably.

[0108] In this specification, embodiments provided for cell (re)selection and initial access may be used interchangeably.

[0109] The embodiments proposed below for beam resource prediction can be used for beam resources belonging to a single or more cells and a single or more TRPs.

[0110] The following describes how CSI reporting can be used interchangeably with CSI measurement, beam reporting, and beam measurement.

[0111] The following RS resource sets can be used interchangeably with beam groups.

[0112] Common aspects of all embodiments [SS / PBCH blocks, MIBs, and SIBs] A WTRU can receive a physical broadcast channel (PBCH). A PBCH may be part of an SS / PBCH block (SSB). A PBCH can carry system information. A PBCH may contain or carry a master information block (MIB). The term MIB may be used to represent the content, information, payload, and / or bits carried by the PBCH. PBCH and MIB may be used interchangeably herein.

[0113] Upon detecting and / or receiving an SS / PBCH block, the WTRU can use the information in the MIB on the time and / or frequency resources to locate one or more System Information Blocks (SIBs). The term SIB can be used to represent content, information, payload, and / or bits. For example, one or more cell (re)selection parameters may be broadcast in SIBs (e.g., SIB1, SIB2, SIB3, etc.), and the WTRU can detect and / or receive them from the serving cell and / or newly detected cells.

[0114] [Select and / or reselect cells] The WTRU can perform cell selection with or without stored cell information. Cell information may include frequency and / or cell parameters. In the example, a cell may be defined as a combination of one or more uplink component carriers (CCs) and one or more downlink component carriers. The WTRU may have (previously) stored information about one or more cells, based on previously received measurement control information elements or from previously detected cells. If the WTRU has stored cell information, it can utilize it for cell selection.

[0115] If there is no stored information, or if a cell search based on stored information yields no results, the WTRU can perform an initial cell selection, where the WTRU has no prior knowledge of cell parameters. For example, the WTRU may not have knowledge of which radio frequency (RF) channels are NR frequencies. Therefore, to find a suitable cell, the WTRU can scan and / or monitor one or more RF channels from a set of RF channels (e.g., based on the synchronization raster frequency) within the NR band. For example, a synchronization raster may indicate the frequency location of a synchronization block (e.g., an SS / PBCH block) that can be used by the WTRU for system acquisition when explicit signaling of the synchronization block location is not present. Therefore, the WTRU can search to find SS / PBCH blocks corresponding to one or more cells for each frequency channel and / or raster, and the WTRU can select the strongest cell based on measuring RSSI, RSRP, RSRQ, SINR, etc. for the detected SS / PBCH blocks.

[0116] The parameters that were evaluated. Hereinafter, the term “evaluated parameter” may be used interchangeably with “evaluated RSRP,” “evaluated RSRQ,” etc., and the term “evaluated” may be interpreted as adjusted, calculated, computed, compensated, scaled, defined, determined, identified, etc. Thus, a WTRU can determine an evaluated parameter based on one or more measured values ​​together with one or more compensated and / or scaling parameters (e.g., (pre-configured) and / or indicated parameters). A WTRU can determine a corresponding evaluated parameter by calculating the addition, subtraction, multiplication, and / or division of one or more measured values ​​and one or more compensated and / or scaling parameters.

[0117] Criteria for what constitutes an appropriate cell.Once a suitable cell is found, the WTRU can select it as the serving cell. In the example, the WTRU can use one or more criteria to select a candidate cell as a suitable cell. The WTRU can determine the criteria based on one or more evaluated parameters. The WTRU may determine the evaluated parameters based on one or more of the following: measured parameters, compensation values, scaling rules, etc. For example, the WTRU can determine compensation values ​​and / or scaling rules based on one or more configured and / or indicated offsets, parameters, and / or configured values. For example, the WTRU can be configured or determined using one or more of the following parameters:

[0118] In one example, the WTRU can be constructed using or determined from the level values ​​received in the measured cell. For example, the WTRU can measure the reference signal received power (RSRP), signal-to-interference noise ratio (SINR), received signal strength indicator (RSSI), etc., for one or more SS / PBCH blocks, a reference signal, and / or channels.

[0119] In another example, WTRU can be constructed using or determined by measured cell quality values. For instance, WTRU can measure the reference signal received quality (RSRQ) for one or more SS / PBCH blocks, reference signals, and / or channels.

[0120] In another example, a WTRU can be constructed using or determined by the minimum required measured Rx level and / or quality level in a cell. For example, a WTRU can receive, determine, or construct using one or more parameters and / or offset values ​​to determine the minimum required Rx level (e.g., in dBm units) and / or minimum required quality level (e.g., in dB) in the corresponding cell.

[0121] In another example, the WTRU may be configured or determined using one or more compensation values. For example, the WTRU may receive, determine, or be configured using one or more parameters, offsets, and / or scaling values ​​that may be used, based on the indication received or the WTRU determines based on one or more operating modes, thresholds, etc.

[0122] In another example, the WTRU can be constructed using or determined by an evaluated cell (re)selection Rx level value. For example, the WTRU can calculate, evaluate, and / or compute an received level value (e.g., in dB units) based on one or more measured parameters and / or compensation and / or scaling values. In the example, the WTRU can calculate an evaluated cell (re)selection Rx level value (e.g., Srxlev) based on the level value received to the measured cell (e.g., Qrxlevmeas), the minimum required measured Rx level (e.g., Qrxlevmin and / or Qrxlevminoffset), a compensation parameter (e.g., Pcompensation), and one or more temporary offset values ​​(e.g., Qoffsettemp) (e.g., Srxlev = Qrxlevmeas - (Qrxlevmin + Qrxlevminoffset) - Pcompensation - Qoffsettemp). As described above, WTRU can select a corresponding cell as one of the suitable candidate cells if the evaluated cell (re)selection Rx level value is higher than a (pre-configured) threshold (for example, Srxlev > 0 for cell selection, or Srxlev > SintraSearchP or Srxlev > SnonIntraSearchP for cell reselection, for intra-frequency and inter-frequency, respectively).

[0123] In another example, the WTRU can be configured with or can determine an evaluated cell (re)selection quality value. For example, the WTRU can calculate, evaluate, and / or compute a received quality value (e.g., in dB) based on one or more measured parameters and / or compensation and / or scaling values. In an example, the WTRU can calculate an evaluated cell (re)selection quality value (e.g., S qual ) based on a measured cell quality value (e.g., Q qualmin ), a minimum required quality level (e.g., Q qualminoffset ), one or more temporary offset values (e.g., Q offsettemp ), etc. (e.g., S qual = Q qual - (Q qualmeas + Q qualmin ) - Q qualminoffset ). Thus, the WTRU can select the corresponding cell as one of the appropriate candidate cells when the evaluated cell (re)selection quality value is higher than a (pre)-configured threshold (e.g., for in-band and inter-band respectively, for cell reselection, etc., S offsettemp >0, or S qual >SintraSearchQ, or Squal>SnonIntraSearchQ).

[0124] ​​A WTRU may receive or be configured with one or more compensation and / or scaling parameters, values, settings, and / or rules as criteria for cell (re)selection via implicit and / or explicit indicators. Explicit indicators may be via master information blocks (MIBs), system information blocks (SIB1, SIB2, SIB3, SIB4, etc.) in the corresponding SS / PBCH block, quasi-static configurations (e.g., via RRC), dynamic indicators (e.g., via MAC-CE and / or DCI), etc. A WTRU may decide to use one or more compensation rules and / or scaling rules based on implicit indicators, for example, that are based on comparing one or more parameters with corresponding thresholds.

[0125] [Cell Ranking] Once the evaluated received power and / or evaluated quality values ​​are measured and calculated, the WTRU may perform a cell ranking for all cells that the WTRU has determined to be suitable candidate cells based on the cell selection criteria (e.g., serving cells and neighboring cells). For example, the WTRU may determine the cell ranking based on calculating the R values ​​(i.e., Rs and Rn) using the average RSRP results, where one or more of the following may apply. The following parameters are non-limiting examples of parameters that may be included in the cell ranking calculation and measurement. One or more of these parameters may be included. Other parameters may also be included. R s =Q meas,s +Q hyst -Qoffset temp R n =Q meas,n -Q offset -Qoffset temp Here, Rs and Rn correspond to the serving cell and adjacent cell, respectively. In the example above, Q hystThis can represent the mobility mode of the WTRU. Q offset Q can be constructed using different values ​​for intra-frequency and inter-frequency cell (re)selection. meas This could be the measured RSRP amount used in cell (re)selection.

[0126] WTRU can re-select a new candidate cell if an adjacent cell has a higher R value than the serving cell during a (pre-configured) time interval.

[0127] [Configuration of measurement and estimation sets] A WTRU may be composed of one or more sets of reference signal (RS) resources and / or beams (or beam pairs). Each RS resource, beam, or beam pair may be associated with transmission from a beam with specific beam parameters (e.g., beam direction and beam width). The WTRU may be composed of the associated beams and / or RS resources and beam parameters.

[0128] In the example, the WTRU may be constructed using a first set of RS resources or beams or beam pairs that can cover the entire RS resource space, beam space, or beam pair space. The WTRU can determine or select sets A and B such that their union covers the entire RS resource space, beam space, or beam pair space. In the example, sets A and B may be mutually exclusive. In the example, set B contains RS resources, on which the WTRU can perform measurements to obtain (1) direct measurements for a first set of beams or beam pairs (e.g., one-to-one mappings between RS resources and beams or beam pairs) and (2) estimated measurements for a second set of beams or beam pairs (e.g., many-to-one mappings between RS resources and beams or beam pairs, and possibly using an AI / ML estimation model).

[0129] [Set B Requirements] A WTRU may consist of one or more sets of RS resources associated with each beam. For example, a WTRU may consist of a first beam associated with two sets of RS resources, the first set containing a single RS resource and the second set containing multiple RS resources. The measurements associated with the beam can be determined through direct measurements of the RS resources in the first set or through estimations obtained from measurements of the RS resources in the second set.

[0130] For any beam to which it must obtain measurements (either directly or by estimation), the WTRU can determine the set of measurements for the RS resources (e.g., set B) such that set B includes at least one of the two sets of RS resources associated with the beam.

[0131] Set B can be used interchangeably with sets of RS resource sets, beams, beam pairs, beam RS resources, RS resources, and beam patterns.

[0132] Set A can be used interchangeably with sets of RS resource sets, beams, beam pairs, beam RS resources, RS resources, and beam patterns.

[0133] Prioritizing candidate cells based on AI / ML parameters during cell (re)selection. Methodology: Priority level determination based on AI / ML In exemplary embodiments, the WTRU may perform one or more of the following steps:

[0134] WTRU detects one or more cells for cell (re)selection or during initial access.

[0135] An AI / ML-enabled WTRU is (pre-configured) or receives one or more configurations (e.g., from a gNB, via MIB, SIB, etc.) regarding the priority level to be used for cell (re)selection.

[0136] The WTRU receives a priority level for selecting cells using AI / ML behavior.

[0137] For example, priority level 1 may indicate no priority when connecting to a cell with AI / ML functionality, meaning all cells (with or without an AI / ML system) are considered to have the same priority.

[0138] For example, priority level 2 may indicate that cells with AI / ML functionality have a higher priority than cells without AI / ML functionality.

[0139] For example, priority level 3 may indicate that cells with AI / ML behavior have a lower priority compared to cells without AI / ML behavior.

[0140] If priority level 2 is configured, the WTRU may receive, determine, or (pre-configure) using one or more priority sublevels within priority level 2 based on the AI / ML system model (e.g., in the detected cells). For example, a first priority level 2 type may include priority level 2-1, and the WTRU may consider cells with a particular set B type (e.g., fixed or random) to have the highest priority (e.g., based on the AI / ML-trained model of the WTRU).

[0141] For example, a second level 2 priority could be level 2-2, and WTRU might consider cells with a specific set B size (e.g., 16, 32 beams, etc.) to have the highest priority (e.g., based on a model trained with WTRU's AI / ML).

[0142] For example, a third level 2 priority could be level 2-3, and for example, WTRU might consider a cell having a specific ratio between set A and set B (e.g., between transmitted beams and untransmitted beams) to have the highest priority.

[0143] In another example, a second type of priority at level 2 may include priority levels 2-4, for example, WTRU using AI / ML-specific compensation values, offset values, or scaling rules (e.g., q offset or threshold) to prioritize cells that have AI / ML behavior and specific characteristics (e.g., set B type, set B size, set B ratio, etc.).

[0144] For the first type of priority, WTRU is the absolute priority of the cell, for example, Absolute priority=cellReselectPriority+CellReselectSubPriority+AI / ML_priority A priority offset can be used when calculating this.

[0145] Next, WTRU ranks the cells based on their priority level and offset value.

[0146] Finally, WTRU selects the cell with the best ranking.

[0147] [Determining the operating mode: For example, whether to use AI / ML or not] In the embodiment, the WTRU may receive, identify, or determine information regarding the operating mode of a detected cell using the received and / or detected SS / PBCH blocks, MIBs, SIBs, etc. Based on the detected information, the WTRU may determine the following operating modes: (1) AI / ML operation (e.g., the WTRU may determine whether the detected cell supports operation with or without an AI / ML system), (2) dual mode (e.g., the WTRU may determine that the dual mode is TDD, FDD, or HD-FDD), or (3) license regime mode. (4) mode (e.g., a WTRU can determine whether a detected cell operates with or without a shared spectrum, which is an operation in an unauthorized spectrum or an authorized spectrum, respectively), (5) WTRU type prohibition (e.g., prohibition of access to a specific WTRU type) (e.g., a first type of WTRU (e.g., a WTRU with limited capabilities including reduced Rx antennas, a smaller maximum bandwidth supported, and a smaller maximum transmit power) may not be permitted to access a cell if indicated so (e.g., via MIB, SIB, etc.), otherwise, a first type of WTRU may be permitted to access a cell), (6) support for a specific functionality in the network (e.g., power saving, carrier aggregation, DRX, etc.), (7) system bandwidth range, (8) use case (e.g., sidelink, Uu, NTN, etc.), and / or (9) maximum uplink transmit power, etc., can be determined.

[0148] [Parameters and settings for the operating mode] In the embodiment, upon detecting a first operating mode (e.g., having AI / ML operation) for a detected cell, the WTRU can determine information corresponding to the first operating mode (e.g., via MIB, SIB, etc.). In the example, the WTRU can determine at least some of the following information about a detected cell having the first operating mode (e.g., having AI / ML operation):

[0149] Time and frequency resources Time resources: For example, WTRU can receive and / or determine the time resources, time units, and / or time windows (e.g., symbols, slots, subframes, frames, etc.) to which a first operating mode (e.g., AI / ML operation) applies.

[0150] Time configuration: For example, the WTRU can receive and / or determine time periodicity, start time, time duration, etc. for a first operating mode (e.g., AI / ML operation).

[0151] Frequency resources: For example, a WTRU can receive and / or determine the frequency resources (e.g., carrier, BWP, subband) to which a first operating mode (e.g., AI / ML operation) applies.

[0152] Reference signal (RS) resources and / or beams (or beam pairs) Set A. For example, a WTRU may receive a configuration relating to a first set (e.g., Set A) of RS resources (e.g., SSB, CSI-RS, etc.) or beams or beam pairs that can cover an entire RS resource space or beam space or beam pair space.

[0153] Set B, Measurement Resources: In the example, the WTRU may receive a configuration for a second set of RS resources (e.g., SSB, CSI-RS, etc.), or for a beam or beam pair that may actually be transmitted (e.g., measurement resources and / or set B). The WTRU may receive information about one or more parameters for set B in the detected cell.

[0154] Set B type: For example, WTRU may receive information on the supported Set B types in the detected cell (e.g., fixed type, random type, etc., via AI / ML-BeamResourceSet-type).

[0155] Set B size: For example, the WTRU may receive information on the supported set B size in the detected cell (e.g., via AI / ML-BeamResourceSet-size, e.g., the number of transmitted reference signals included in the corresponding set B, e.g., 8, 16, 32, 64 beams, etc.).

[0156] Set B Pattern: For example, WTRU may receive information about the supported Set B patterns in the detected cell (for example, via AI / ML-model-location-support, if the supported Set B patterns in the detected cell are the first pattern, the second pattern, etc.).

[0157] AI / ML Models: For example, WTRU can receive the AI / ML models used in the detected cells. WTRU can receive one or more indicator indexes to one or more lists or tables of AI / ML models, patterns, etc. (e.g., via AI / ML-operation-status).

[0158] Parameters, thresholds, and / or scaling rules (e.g., AI / ML specific) Priority Level: For example, a WTRU may receive, identify, or determine an indication for one or more cell (re)selection priority levels. The WTRU may determine a first priority level for a first operating mode (e.g., with AI / ML operation), a second priority level for a second operating mode (e.g., without AI / ML operation), and so on. Indications may be based on:

[0159] Explicit Indications: For example, a WTRU can receive explicit indications (e.g., from a gNB) via MIB, SIB, DCI, MAC-CE, RRC, etc.

[0160] Implicit indications: WTRU Capabilities. In the example (for example, if the WTRU has not received an explicit indication of a priority level), the WTRU may determine a priority level based on one or more WTRU capabilities (e.g., an AI / ML-enabled WTRU) and / or operating modes. For example, the WTRU may determine a higher priority for a first operating mode (e.g., one with AI / ML operation) and a lower priority for a second operating mode (e.g., one without AI / ML operation). Alternatively, the WTRU may determine a lower priority for a first operating mode (e.g., one with AI / ML operation) and a higher priority for a second operating mode (e.g., one without AI / ML operation).

[0161] In another example, a WTRU can determine a priority level based on at least one of the following criteria: latency, coverage, or mobility. A WTRU can determine a priority level based on one or more thresholds. For example, a WTRU can determine that the (expected) latency is higher than the corresponding threshold (for example, a WTRU can determine a request based on each mobility parameter). Thus, a WTRU can decide to use and / or consider a higher priority for a cell that has a first operating mode that reduces latency (e.g., has AI / ML operation) compared to a cell that has an operating mode that has potentially higher latency (e.g., does not have AI / ML operation).

[0162] Thresholds: For example, a WTRU may receive, identify, determine, or be configured with one or more threshold values ​​for a first operating mode (e.g., having AI / ML operation). A WTRU can use thresholds to determine one or more limits, levels, ranges, and corresponding actions. A WTRU may receive one or more thresholds indicating minimum and maximum limits for one or more values. In an example, a WTRU may receive thresholds for latency, mobility, RSRP meter, the difference between detected RSRP and predicted RSRP, accuracy level, etc.

[0163] Compensation and / or scaling rules and / or values: For example, a WTRU may receive, identify, determine, or construct using one or more compensation and / or scaling values. A WTRU may use each value that is added to, subtracted from, multiplied, and / or divided by one or more configured, indicated and / or determined parameters.

[0164] [Determining the priority level for the operating mode] In embodiments, the WTRU may receive, identify, determine, or provide one or more priority levels for cells having one or more operating modes (e.g., having an AI / ML operation for a first operating mode). For example, at least one of the following may apply: There is no priority for cells having a first operating mode for priority level 1. For example, the WTRU may determine that cells having a first operating mode (e.g., having an AI / ML operation) have the same priority level for cell (re)selection as other cells (e.g., not having an AI / ML operation).

[0165] For priority level 2, cells with a first operating mode (e.g., AI / ML operation) have a higher priority than cells with other operating modes (e.g., cells without AI / ML operation). For example, if a WTRU supports a first operating mode (e.g., an AI / ML-capable WTRU), the WTRU can consider cell (re)selection candidate frequencies that cannot receive the first operating mode (e.g., using AI / ML operation) to have the lowest priority.

[0166] For priority level 3, cells with a first operating mode (e.g., AI / ML operation) have a lower priority compared to cells with other operating modes (e.g., cells without AI / ML operation). For example, if a WTRU supports a first operating mode (e.g., an AI / ML-capable WTRU), the WTRU can consider cell (re)selection candidate frequencies that are capable of receiving the first operating mode (e.g., using AI / ML operation) to have the lowest priority.

[0167] In an embodiment, a WTRU may receive an indication to which one of the priority levels is selected to be applied (for example, for cell (re)selection). In an example, a WTRU may receive start and / or end times, as well as / or time duration (e.g., time units, symbols, slots, etc.) to apply one or more priority levels. The indication may be explicit. For example, the indication may be received via MIB, SIB, DCI, MAC-CE, and / or RRC indications.

[0168] Alternatively, the indication may be implicit. For example, it may be based on the operating state. For instance, a WTRU may determine one or more events to trigger, enable, set up, or permit the WTRU to consider priority levels for cell (re)selection from cells having a first operating mode (e.g., AI / ML operation). Thus, a WTRU may determine one or more states or events as criteria for determining priority levels (e.g., the WTRU's mobility state has low and / or normal mobility, medium mobility, or high mobility).

[0169] For example, if the WTRU determines that it is in a first state (e.g., a high mobility state), it may decide to consider a first priority level (e.g., priority level 3). If the WTRU determines that it is in a second state (e.g., a moderate mobility state), it may decide to consider a second priority level (e.g., priority level 1). If the WTRU determines that it is in a third state (e.g., a normal and / or low mobility state), it may decide to consider a third priority level (e.g., priority level 2). Mobility states can be used interchangeably with other events or states, such as coverage states, latency states, etc.

[0170] [Determining the priority level for a first operating mode (e.g., one with AI / ML operation)] The WTRU may determine or be configured to use a first priority level (e.g., priority level 2, as described herein) that gives higher priority to cells having a first operating mode (e.g., having AI / ML operation) compared to cells having other operating modes (e.g., not having AI / ML operation).

[0171] In embodiments, the WTRU may implement and / or perform initial access and / or cell (re)selection based on one or more subtypes of priority. Examples may include one or more of the following:

[0172] The first type of prioritization. In embodiments, the WTRU may determine or be configured with respect to a first type of priority, and the WTRU may determine or be configured with respect to one or more priority levels to be considered. The WTRU may receive the configuration of the priority type and priority levels via signaling (e.g., from a gNB, e.g., via MIB, SIB, DCI, MAC-CE, RRC, etc.). In the example, one or more of the following priority levels may be determined and / or configured:

[0173] Set B type. For example, WTRU may decide or be configured to consider cells having at least one specific Set B type (e.g., fixed, random, etc.) as having the highest priority. In the example, WTRU may determine specific Set B types based on a model trained in WTRU (e.g., in AI / ML).

[0174] Set B size. For example, WTRU may decide or be configured to consider cells having at least one specific set B size (e.g., 8, 16, 32, etc.) as having the highest priority. In one example, WTRU may determine specific set B sizes based on a model trained in WTRU (e.g., in AI / ML).

[0175] The ratio of Set A to Set B. For example, WTRU may decide or be configured to consider cells having the highest priority if they have at least one specific ratio between the number of beams in Set A and the number of beams in Set B (e.g., between transmitted beams and untransmitted beams). In the example, WTRU may determine a specific ratio between Set A and Set B based on a model trained in WTRU (e.g., in AI / ML).

[0176] Set B patterns. For example, WTRU may decide or be configured to consider cells in Set B that have at least one specific pattern as having the highest priority. In the example, WTRU may determine specific Set B patterns based on a model trained in WTRU (e.g., in AI / ML).

[0177] Therefore, in the first type of prioritization, WTRU may consider cells that follow the determined and / or configured priority levels as having the highest priority (e.g., the highest in the list of cell rankings). WTRU may consider (other) cells that do not follow the determined and / or configured priority levels as having a lower priority (e.g., the lower and / or lowest in the list of cell rankings).

[0178] For example, the WTRU may decide to use a prioritization parameter (e.g., AI / ML_priority) determined and / or (pre-configured) (e.g., via MIB, SIB, DCI, MAC-CE, RRC, etc.) to estimate, calculate, compute, and / or evaluate the absolute priority of the corresponding detected cell. The prioritization parameter may have different values ​​for different priority levels. For example, the WTRU may use the corresponding priority for a first operating mode (e.g., AI / ML_priority), the (pre-configured) and / or determined priority of the detected cell (e.g., cellReselectPriority), the (pre-configured) and / or determined sub-priority of the detected cell (e.g., cellReselectSubPriority), etc., to determine the absolute priority of the cell, e.g., Absolute priority=cellReselectPriority+CellReselectSubPriority+AI / ML_priority This can be evaluated.

[0179] WTRU can use the priority evaluated in initial access and / or cell ranking to select the cell with the highest ranking. For example, WTRU can use determined priority levels, prioritization parameters (e.g., AI / ML_priority), evaluated absolute priority (e.g., Absolute priority) to determine the relative priority to be used in cell ranking. For example, UE may decide that cells with an evaluated absolute priority higher than a first value should be considered prioritized (e.g., at the top of the list of cell rankings based on evaluated RSRP, RSRQ, etc.). In another example, WTRU may decide that cells with an evaluated absolute priority lower than a second value should be considered lower priority (e.g., at the bottom of the list of cell rankings based on evaluated RSRP, RSRQ, etc.).

[0180] The second type of prioritization. In another embodiment, the WTRU may be configured to determine or use one or more compensation values, offset values, or scaling rules (e.g., q offset or threshold) (e.g., AI / ML specific, SetB type specific, SetB size specific, etc.) to prioritize cells having a first operating mode (e.g., having AI / ML operation) and / or specific characteristics (e.g., SetB type, SetB size, etc.).

[0181] In the example, the WTRU may be configured to determine or (re)evaluate quality parameters based on one or more measured quality parameters (e.g., RSRP) for the detected cell (e.g., based on the detected SSB). The WTRU may receive scaling parameters based on (pre-configured) parameters and / or via signaling (e.g., from gNB, via MIB, SIB, DCI, MAC-CE, RRC, etc.). The WTRU may (re)evaluate quality parameters based on one or more offset values, compensation values, and / or scaling parameters (e.g., (pre-configured) and / or indicated parameters). The WTRU may determine the corresponding (re)evaluated parameters by calculating addition, subtraction, multiplication, and / or division of one or more measured values ​​using one or more compensation and / or scaling parameters.

[0182] For example, the WTRU may decide to use scaling parameters specific to a first operating mode (e.g., AI / ML specific scaling parameters, e.g., Qoffset-AI / ML) to estimate, calculate, compute, and / or evaluate parameters (e.g., received power, signal strength, etc.) for cell ranking during (e.g., cell (re)selection, initial access, etc.). For example, the WTRU may use the corresponding offset (e.g., Qoffset-AI / ML) together with the level value received by the measured cell (e.g., Qrxlevmeas), the minimum required measured Rx level (e.g., Qrxlevmin and / or Qrxlevminoffset), compensation parameters (e.g., Pcompensation), one or more temporary offset values ​​(e.g., Qoffsettemp), etc. (e.g., Srxlev = Qrxlevmeas - (Qrxlevmin + Qrxlevminoffset) - Pcompensation - Qoffsettemp + Qoffset-AI / ML).

[0183] In embodiments, the WTRU may decide or be configured to use different scaling parameters (e.g., Qoffset-AI / ML) for different priority levels. That is, for example, if the WTRU decides or is configured to use a first priority level (e.g., priority level A), the WTRU may use the first scaling parameter (e.g., Qoffset-AI / ML-A) for cells that satisfy the required criteria (e.g., have a fixed set of type B).

[0184] Alternatively, if the WTRU determines or is configured using a second priority level (e.g., priority level B), the WTRU may use a second scaling parameter (e.g., Qoffset-AI / ML-B) for cells that satisfy the required criteria (e.g., set B size).

[0185] Alternatively, if the WTRU determines or is configured using a third priority level (e.g., priority level C), the WTRU may use a third scaling parameter (e.g., Qoffset-AI / ML-C) for cells that satisfy the required criteria (e.g., the ratio of set A to set B).

[0186] WTRU can use the cell ranking and / or (re)evaluated parameters in initial access to select the cell with the best ranking.

[0187] Prioritizing candidate cells during cell (re)selection. In exemplary embodiments, the WTRU may perform one or more of the following:

[0188] The WTRU can perform cell reselection procedures (e.g., periodic cell search) to detect one or more candidate neighboring cells with acceptable RSRP / RSRQ values ​​(i.e., valid cells). Based on the received SSB, the WTRU can measure RSRP, RSRQ, Line of Sight (LOS) probability, etc.

[0189] The WTRU can receive information about the AI / ML beam management system supported by the detected candidate cell. For example, the WTRU can receive the Set B type, or the number of beams in Set B, from the cell to which the WTRU is currently camped, for example, via SIB2, SIB3, etc.

[0190] AI / ML-enabled WTRUs can receive priority levels for using cells with AI / ML behavior based on criteria such as set B, the trained dataset, latency, coverage, LOS probability, or mobility.

[0191] WTRU can rank detected cells based on factors such as RSRP, RSRQ, and the number of beams.

[0192] WTRU can perform separate cell rankings to prioritize AI / ML cells (for example, for priority levels 2-1, 2-2, and 2-3 as described herein) (for the first type of priority). WTRU can perform cell rankings in separate lists for cells with different priority levels, for example, a first list: prioritized AI / ML cells, a second list: unprioritized AI / ML cells (for the first type of priority) (cells that do not satisfy the conditions for priority levels 2-1, 2-2, and 2-3), a third list: legacy cells (cells that do not support AI / ML), and so on. WTRU can sequentially perform cell rankings and cell selections based on the order of cells in the first, second, and third lists.

[0193] Alternatively, WTRU can perform cell ranking together by using scaling offsets and thresholds for AI / ML cells (for example, for priority levels 2-4 as described herein) (for the second type of priority). For example, WTRU may use one or more offset values ​​and thresholds (e.g., Qoffset-AI / ML) to measure / calculate cell rankings for AI / ML cells (e.g., RSRP, RSRQ, etc.), e.g., Rn=Qmeas,n+Qoffset-AI / ML-Qoffset-Qoffsettemp. WTRU can determine offset values ​​and thresholds based on different use cases, AI / ML models, or system scenarios. For example, the offset value or threshold may be more conservative (e.g., a smaller offset so that the gap between measured and compensated values ​​is narrower for RSRP) when cell (re)selection is based on predicted beams rather than legacy transmitted beams. WTRU can make a final decision about cell (re)selection based on the ranking of joint cells between AI-ML cells and non-AI / ML cells, and the calculated cell ranking values ​​(e.g., RSRP, RSRQ, etc.).

[0194] WTRU selects the cell with the highest ranking and initiates initial access to the corresponding cell (for example, by sending a PRACH preamble).

[0195] If a WTRU configured to prioritize AI / ML cells (e.g., priority level 2 as described herein) is unable to connect to any AI / ML cells, the WTRU may decide to use a priority level that prioritizes cells that do not have an AI / ML system / operation (e.g., priority level 3 as described herein).

[0196] Typical procedure The WTRU can perform a cell (re)selection procedure (e.g., periodic cell search) when it detects one or more SSBs from one or more candidate adjacent cells. The WTRU can measure one or more quality parameters (e.g., RSRP, RSRQ, etc.) based on the SSBs detected from the detected cells. The WTRU can determine that one or more of the detected cells can be considered valid candidate cells (e.g., having acceptable RSRP, RSRQ, etc. values). The WTRU can determine the priority level at which the valid cells will be considered in cell ranking and further cell (re)selection procedures.

[0197] In embodiments, the WTRU may be configured, or may decide to do so, to perform prioritization of valid cells having different priority levels separately, together, etc., based on requirements such as latency, coverage, mobility, etc., one or more of the following may apply:

[0198] Separate cell (re)selection for cells with different operating modes (e.g., with or without AI / ML) In embodiments, the WTRU can divide and / or classify detected cells into different lists based on determined and / or (pre-)configured priority levels. That is, the WTRU may be considered to have a first list containing a set of detected cells that support and / or provide a configuration indicated by a first determined and / or configured priority level (e.g., the highest priority), a second list containing a set of detected cells that support and / or provide a configuration indicated by a second determined and / or configured priority level (e.g., the second highest priority), and so on.

[0199] In the example, a WTRU configured with priority level A (for example, to prioritize cells with a fixed set B) may consider two or more lists of the cells found. The WTRU may determine that the first list contains cells that support a prioritized configuration indicated via priority level A (e.g., a fixed set B), and the second list contains cells that support a lower priority configuration indicated via priority level A (e.g., a random set B), and so on.

[0200] In an alternative embodiment, the WTRU may determine or receive a configuration relating to the order of priorities in which different priority levels are considered. For example, priority level A is the highest priority, priority level B is the second highest priority, and so on.

[0201] As described above, WTRU can perform separate cell prioritization procedures (e.g., during initial access and / or cell re-selection) with respect to separate determined lists of detected cells. In the example, performing separate cell ranking during cell (re)selection, WTRU can determine one or more of the highest-ranking cells in the first list (e.g., cells with determined and / or configured set B type), one or more of the highest-ranking cells in the second list (e.g., cells with determined and / or configured set B size), and so on.

[0202] In embodiments, a WTRU may receive an indication (e.g., via MIB, SIB, DCI, MAC-CE, RRC, etc., e.g. configuration) that, based on the indication, the WTRU may selectively consider and / or apply a first list (e.g., select only the highest-ranking cell in the first list) or a second list (e.g., select only the highest-ranking cell in the second list), and / or be required to do so. This can provide flexibility and / or efficiency benefits in that a network (e.g., gNB) can (e.g., dynamically) choose a cell ranking procedure based on either the first or second list, depending on, for example, changing traffic conditions, WTRU congestion patterns per cell, load balancing purposes, etc.

[0203] Furthermore, a WTRU may determine or receive an indication (explicit or implicit) that it may consider and / or apply two or more lists for cell ranking (e.g., configuration via MIB, SIB, DCI, MAC-CE, RRC, etc.). That is, for example, when a WTRU performs cell ranking and selects the final highest one (or more) cells across two lists, it may consider both a first list and a second list, each having one or more pre-selected highest-ranking cells.

[0204] In another example, WTRU can perform cell ranking in separate lists of cells having different priority levels. For example, the first list may include cells that support a prioritized first mode of operation (e.g., having AI / ML operation), where each cell satisfies at least one of the priority levels (e.g., priority levels A, B, etc.). The second list may include cells that support a prioritized first mode of operation (e.g., having AI / ML operation), where each cell does not satisfy any of the (pre-configured) priority levels (e.g., priority levels A, B, etc.). The third list may include cells that do not support a prioritized first mode of operation (e.g., cells without AI / ML operation, and / or legacy cells), etc. WTRU can sequentially perform cell ranking and cell selection based on the order of cells in the first list, the second list, the third list, etc.

[0205] Joint cell (re)selection for cells with different operating modes (e.g., with or without AI / ML) In the embodiment, the WTRU can select the best cell (e.g., the appropriate and / or strongest cell) for cell (re)selection based on joint optimization and / or selection among cells having different operating modes (e.g., having and not having AI / ML operation) and / or different priority types and levels. For example, if several cells having similar characteristics satisfy the cell (re)selection criteria, the WTRU can decide to use one or more offsets, compensations, and / or scaling rules, as well as parameters, to calculate, evaluate, and / or determine the cell ranking for one or more cells supporting a first operating mode (e.g., having AI / ML operation).

[0206] In embodiments, the WTRU may decide to use one or more compensation and / or scaling rules to enhance the ranking of cells for prioritizing cells having a first operating mode (e.g., AI / ML operation). For example, if the WTRU is configured or decides to apply a second type of prioritization, the WTRU may decide to use one or more (pre-configured) offset values, compensation parameters and / or scaling rules based on one or more thresholds and / or configurations for the first operating mode (e.g., AI / ML operation). Based on the modified ranking after applying the determined offset values, compensation parameters and / or scaling rules, the WTRU may (re)evaluate the received power and / or intensity (e.g., RSRP, RSSI, SINR, etc.) and / or received signal quality (e.g., RSRQ) for cells having the first operating mode (e.g., AI / ML operation).

[0207] The WTRU can perform cell (re)selection for one or more detected cells, where one or more preferred SSBs are based on one or more predicted beams (e.g., based on predicted RSRP, RSRQ, etc., via an AI / ML system). In one embodiment, if the WTRU selects a cell based on predicted SSBs (e.g., instead of legacy transmitted SSBs), it may decide to use a different set of scaling rules, compensation parameters, and / or offset values. For example, one or more scaling rules for one or more parameters (e.g., RSRP) may have lower gaps and / or offsets (e.g., more conservative) for cell (re)selection based on predicted SSBs compared to scaling rules used for cell (re)selection based on measured detected SSBs.

[0208] WTRU makes a final decision about cell (re)selection based on the ranking of a joined cell between cells having a first operating mode (e.g., using AI / ML operation), cells having a second operating mode (e.g., not using AI / ML operation), and cells having calculated and / or (re)evaluated cell ranking values ​​(e.g., RSRP, RSRQ, etc.).

[0209] Cell (re)selection In the example, the WTRU may decide to (re)evaluate the received signal power, intensity, and / or quality (e.g., RSRP, RSSI, SINR, RSRQ, etc.) of a first cell having a first operating mode (e.g., having AI / ML operation) based on the measured parameters, as well as the respective configured and / or determined compensation and / or scaling values.

[0210] WTRU can perform cell ranking based on the (re)evaluated parameters. WTRU can determine that the cell ranking based on the (re)evaluated parameters results in the first cell having the highest and / or strongest cell ranking. Therefore, WTRU can select the first cell as the serving cell.

[0211] Once a first cell is selected, the WTRU can initiate the initial access procedure to the selected first cell (e.g., sending a PRACH preamble) and connect to the selected first cell.

[0212] In the example, a WTRU configured with a first priority level to prioritize cells that support a first operating mode (e.g., AI / ML operation) may not be able to connect to any of the cells that have the first operating mode (e.g., AI / ML operation). Therefore, the WTRU may decide to use a second priority level to prioritize cells that have a second operating mode (e.g., not AI / ML operation).

[0213] Inter-frequency cell reselection The WTRU can perform cell reselection across multiple NR frequencies and / or radio access technology (RAT) frequencies. The WTRU determines and applies priorities for each of at least one of the frequencies and applies the priorities and applicable measurement results (e.g., S) between at least one frequency. rxlev S qual Cell reselection can be performed based on ).

[0214] Priority of frequencies having a first operating mode In some embodiments, the WTRU can determine frequency priority based on whether a configuration for a first operating mode (e.g., using AI / ML operation) is provided for that frequency. The WTRU can obtain the above configuration, for example, from system information or from RRC connection release. In an example, the WTRU can determine that if a configuration for a first operating mode (e.g., using AI / ML operation) is provided for that frequency, then this frequency is the highest priority frequency. The WTRU can only make this determination on the condition that the WTRU supports the first operating mode (e.g., using AI / ML operation). In another embodiment, the WTRU can determine that if a configuration for a first operating mode (e.g., using AI / ML operation) is provided for a frequency, and the WTRU does not support the first operating mode (e.g., using AI / ML operation), then this frequency is the lowest priority frequency.

[0215] Prioritizing candidate cells during initial access A WTRU can detect one or more SSBs from one or more detected cells (for example, during initial access). For example, a WTRU can decode one or more parameters from the detected SSBs (e.g., PSS, SSS, PBCH, and MIB). For example, a WTRU (e.g., AI / ML-enabled) can determine (e.g., via the MIB) whether the detected cell supports a first operating mode (e.g., using AI / ML operation).

[0216] In an embodiment, the WTRU can prioritize one or more of the detected cells for a detected cell that supports a first operating mode (e.g., using AI / ML operation). For example, the WTRU can determine the multiplicity of the prioritized candidate cells such that the prioritized candidate cells satisfy a performance level relative to the selected best prioritized cell. For example, the WTRU can determine that the difference between the measured RSRP of a candidate cell and the measured RSRP of the best cell should be less than a (pre-configured) threshold.

[0217] In an embodiment, the WTRU may attempt to find and decode one or more information constructs (e.g., SIB1) for one or more more detailed information (e.g., information about AI / ML characteristics, models, or priorities) of one or more prioritized cells. In an example, the WTRU may attempt to find more information (e.g., SIB1) for cells that support a first operating mode (e.g., AI / ML operation). In another example, the WTRU may decide to find more information (e.g., SIB1) based on the WTRU's determined and / or (pre-)configured priority level for the first operating mode (e.g., AI / ML operation). The WTRU may determine the priority level based on the WTRU's preferences, the WTRU's configuration, and / or the WTRU's operating state (e.g., required latency, coverage, mobility, etc.).

[0218] If the WTRU decides to detect more information about one or more of the detected cells, the WTRU may receive one or more configuration pieces of information (e.g., set B type, size, and / or pattern from SIB1) about a first operating mode (e.g., using AI / ML operation) (e.g., via SIB1). Alternatively, the WTRU may select a first cell as the best cell based on the WTRU's preferences, for example, based on measured quality parameters (e.g., RSRP, RSRQ, etc.), based on (re)evaluated parameters (see Section 4.3.1 as described herein), based on support for the first operating mode (e.g., AI / ML operation).

[0219] In the example, the following exemplary characteristics are possible: WTRU can select cells whose one or more operating parameters match preferred cell selection criteria; WTRU can select cells having AI / ML-model-location-support that matches preferred AI / ML-model-location-support of WTRU; WTRU can select cells whose AI / ML-BeamResourceSet-size is equal to, smaller than, or larger than preferred AI / ML-BeamResourceSet-size of WTRU; WTRU's AI / ML-BeamResourceSet-type is W WTRU may select a particular cell for a first operating mode if at least one of the following is supported in the selected cell: that it can select a cell that matches a superset of TRU's preferred AI / ML-BeamResourceSet-type (where the random type is a superset of both the fixed and random types) and is of WTRU's preferred AI / ML-BeamResourceSet-type, or that is a subset of WTRU's preferred AI / ML-BeamResourceSet-type (where the fixed type is a subset of both the random and fixed types).

[0220] The WTRU can initiate initial access to the selected cell (for example, by sending a PRACH preamble). The WTRU can receive more detailed information about the first operating mode (e.g., AI / ML operation), such as the Set B type or size, as part of the messages received during the initial access (e.g., via Random Access Response (RAR) PDSCH, Msg4, and / or MsgB).

[0221] In the example, the WTRU may determine that the selected cell satisfies the requirements for operation in the first operating mode (e.g., AI / ML operation) based on the information received from the selected cell and its priority level. The WTRU remains connected to the cell, i.e., for example, the WTRU initiates initial access to the corresponding cell (e.g., sends a PRACH preamble), and / or the WTRU continues sending Msg3s.

[0222] Otherwise, in another example, the WTRU might determine that the selected cell satisfies fewer characteristics (e.g., none) than all of the characteristics desired for it to operate in a first operating mode (e.g., AI / ML operation). In such a case, the WTRU might reject the cell and attempt to find another one.

[0223] In another example, the WTRU may determine that none of the detected cells having a first operating mode (e.g., AI / ML operation) satisfy the configured and / or determined characteristics for the first operating mode. Therefore, in the example, the WTRU may switch to a fallback mode and prioritize cells that do not have the first operating mode (i.e., cells that have a second operating mode and do not have AI / ML operation).

[0224] Intercellular data collection supporting AI / ML systems In an exemplary embodiment, the WTRU may perform one or more of the following actions:

[0225] A WTRU can receive requests from a gNB and perform automatic network relationship (ANR) acquisition from one or more neighboring cells.

[0226] WTRU can receive configuration information about one or more AI / ML parameters to be obtained from adjacent cells, such as AI / ML support, set B type, set B size, and set B pattern.

[0227] WTRU can receive one or more preferred criteria for detecting and reporting cells (e.g., reporting only cells that have (or do not have) AI / ML behavior).

[0228] WTRU can attempt to search for and find SSB bursts in neighboring cells.

[0229] Upon detecting an SSB of an adjacent cell, the WTRU can decode the MIB and SIB1 to determine the configured AI / ML parameters. For example, based on the MIB, the WTRU can determine whether the cell supports AI / ML. In another example, the WTRU may decide to decode the SIB1 only if the AI / ML support determined from the MIB satisfies the preference indicated by the gNB.

[0230] WTRU can report the determined AI / ML parameters as part of the ANR report.

[0231] overview The WTRU can receive configuration and / or indications (e.g., via RRC messages, MAC-CE, and / or DCI) to perform automated neighbor cell association (ANR) acquisition procedures. The WTRU can receive one or more configuration pieces of information (e.g., from serving cells and / or camp-on cells, e.g., via RRC, MAC-CE, and / or DCI) to measure and / or acquire signal and / or beam quality measurements (e.g., RSRP, SINR, RSSI, CQI, etc.) from one or more neighbor cells. The WTRU may receive one or more thresholds for one or more of the configured parameters.

[0232] A WTRU can detect SSBs associated with one or more neighboring cells. The WTRU can perform measurements on one or more configured parameters (e.g., RSRP) of the detected SSBs of one or more detected neighboring cells. Based on the measurements, the WTRU can determine the signal quality of one or more detected neighboring cells. In one example, the WTRU can decode the MIB associated with the detected SSBs of the detected neighboring cells. In another example, the WTRU can determine one or more physical cell IDs (PCIDs) of the detected neighboring cells based on the decoded MIB. The WTRU may decide to report, or be configured to report, one or more of the measured parameters (e.g., RSRP) and / or information obtained from one or more of the detected neighboring cells (e.g., PCIDs).

[0233] Exemplary Embodiments To obtain information In an embodiment, the WTRU may receive configuration information and obtain one or more settings, parameters, and / or capabilities from the detected neighboring cell. The WTRU may be configured to report information obtained from the detected neighboring cell (e.g., gNB, serving cell, etc.). For example, the WTRU may receive one or more thresholds to determine information to be obtained and / or reported from the detected neighboring cell. In another example, the WTRU may be configured to decode information provided in the MIB and / or SIB. That is, the WTRU may be configured to detect and decode additional information from the detected neighboring cell that is in the corresponding SIB. In an example, the WTRU may be configured to obtain one or more (e.g., AI / ML) parameters.

[0234] For example, a WTRU can be configured to acquire an operating mode. For instance, a WTRU can determine (e.g., via an MIB, SIB, etc.) whether an adjacent cell supports or does not support one or more operating modes (e.g., operation with or without an AI / ML system). In another example, a WTRU can acquire information about the AI / ML model used in the detected adjacent cell.

[0235] For example, WTRU can be configured to acquire set B types. For instance, for a detected neighboring cell that supports an AI / ML operation, WTRU can determine the set B types supported within that detected neighboring cell. That is, WTRU can determine and report whether the set B types supported within the detected neighboring cell are fixed types, random types, etc.

[0236] For example, a WTRU may be configured to acquire a set B size. For instance, for a detected neighboring cell that supports operation with AI / ML, the WTRU can determine the supported set B size within the detected neighboring cell. For example, the WTRU can determine and report the number of transmitted reference signals included in the corresponding set B (e.g., 8, 16, 32, or 64 beams).

[0237] For example, a WTRU can be configured to acquire a set B pattern. For instance, for a detected neighboring cell that supports an AI / ML operation, the WTRU can determine the supported set B pattern within the detected neighboring cell. In this example, the WTRU can determine and report whether the supported set B pattern within the detected neighboring cell is the first pattern, the second pattern, and so on.

[0238] Report information In an embodiment, the WTRU may receive one or more parameters for determining whether to report a detected adjacent cell as part of an ANR report, and / or one or more thresholds, maximum and / or minimum limits, and / or ranges for parameters that should be reported as part of an ANR report for a detected adjacent cell. The WTRU may be configured with time and frequency resources to report an ANR report (e.g., ANR PUCCH and / or PUSCH resources).

[0239] For example, one or more of the following may apply:

[0240] WTRU can only report the PCID for the detected adjacent cell.

[0241] The WTRU can report the PCID in addition to one or more measured quality parameters (e.g., RSRP) for the detected SSB in the detected adjacent cell.

[0242] WTRU may be configured to decide to report an operating mode (e.g., operation with or without an AI / ML model) only for adjacent cells where one or more of the measured quality parameters (e.g., RSRP) are detected to be higher than the corresponding threshold. WTRU may indicate the operating mode via a flag indication, where a first value (e.g., 1) may indicate a first operating mode (e.g., operation with AI / ML), and a second value (e.g., 0) may indicate a second operating mode (e.g., operation without AI / ML).

[0243] The WTRU may decide to report parameters (e.g., PCID) of one or more detected neighboring cells that support a first operating mode (e.g., operation with AI / ML capability), the WTRU may be configured to report only parameters (e.g., PCID) of one or more detected neighboring cells that support a second operating mode (e.g., operation without AI / ML, e.g., legacy operation), the WTRU may be configured to report only parameters (e.g., PCID) of one or more detected neighboring cells that support both the first and second operating modes (e.g., operation with and without AI / ML), and so on.

[0244] The WTRU may determine, or be configured to determine, that the measured RSRP is higher than the corresponding threshold and that the detected neighboring cell reports a parameter (e.g., PCID) for one or more detected neighboring cells that support a first operating mode (e.g., operation with AI / ML).

[0245] In the embodiment, the WTRU may receive a configuration indicating one or more PCIDs (e.g., via RRC, MAC-CE, DCI, etc.) (based on the reported PCIDs of detected neighboring cells and / or the AI / ML capabilities of the reported cells), and an indication that the WTRU should report one or more parameters relating to a first operating mode (e.g., AI / ML operation, e.g., AI / ML-operation-status, AI / ML-BeamResourceSet-type, AI / ML-BeamResourceSet-size, and / or AI / ML-model-location-support) of the cell associated with the indicated PCID (e.g., for cells having AI / ML-operation-capability). Furthermore, the WTRU may receive a configuration of preferred criteria for cell selection (e.g., preferred AI / ML-BeamResourceSet-type, AI / ML-BeamResourceSet-size, and / or AI / ML-model-location-support).

[0246] In the example, the WTRU can detect the SSB and decode the MIB and / or SIB of the cell associated with the indicated PCID. Based on the decoded information from the MIB and / or SIB, the WTRU can determine the status of a first operating mode (e.g., AI / ML operation) and / or one or more of the other operating parameters from the detected adjacent cell.

[0247] For example, a WTRU can determine the status of a first operating mode (e.g., AI / ML operation) that can be enabled or disabled in the detected cell based on the decoded information from the MIB associated with that cell. Based on the determined operating status, the WTRU can decode the SIB1. For example, if the first operating mode is enabled in the cell, the WTRU can decode the cell's SIB.

[0248] For example, the WTRU can determine a set B type (e.g., AI / ML-BeamResourceSet-type) for a detected cell (e.g., a fixed or random beam resource set) based on the decoded information from the SIB.

[0249] For example, the WTRU can determine, based on the decoded information from the SIB, a set B size (e.g., AI / ML_BeamResourceSet-size) for the detected cell to be, for example, N beam RS resources or the maximum number of K_max beam RS resources in a beam resource set.

[0250] For example, the WTRU can determine an AI / ML-model-location-support (e.g., gNB-side AI / ML model or WTRU-side AI / ML model, or both) based on the decoded information from the SIB.

[0251] In an embodiment, the WTRU can report an operation status of a first operation mode for a cell associated with a shown PCID (e.g., via PUCCH and / or PUSCH, e.g., via an ANR report regarding PUCCH and / or PUSCH) based on the received configuration (i.e., AI / ML reporting parameters and the shown PCID) and the decoded information of the MIB of the cell associated with the shown PCID. Further, the WTRU can report one or more of the cell operation parameters for the first operation mode based on the decoded information from the SIB and / or the detected operation status of the cell associated with the shown PCID (e.g., when activated).

[0252] For example, the WTRU can report an operation status indication via a flag indication where a first value (e.g., 0) indicates activation and a second value (e.g., 1) indicates deactivation.

[0253] For example, a WTRU can report an indicator that shows the SIB (e.g., SIB1) decoding status (e.g., SIB1_status) via a flag indicator, where a first value (e.g., 0) may indicate that it was successfully decoded, and a second value (e.g., 1) may indicate that it could not be decoded.

[0254] For example, a WTRU can report an AI / ML-model-location-support indicator (e.g., based on whether the decoded SIB_status was successfully decoded) via an indicator (e.g., a 2-bit indicator) which consists of a first value indicated on the WTRU side (e.g., 0), a second value indicated on the gNB and / or network side (e.g., 1), and a third value indicated on both the WTRU and gNB sides (e.g., 2).

[0255] For example, WTRU can report an AI / ML-BeamResourceSet-type indicator via an indicator (e.g., based on successful decoding of SIB_status), where a first value (e.g., 0) could indicate a first set B type (e.g., fixed), a second value (e.g., 1) could indicate a second set B type (e.g., random), a third value (e.g., 2) could indicate a third set B type (e.g., all supported types), and so on.

[0256] For example, WTRU can report AI / ML-BeamResourceSet-size as N RS resources and / or the maximum number of supported max_K RS resources (for example, based on successful decoding of SIB_status).

[0257] In the embodiment, the WTRU can report the PCID of the detected adjacent cell based on the received configuration, preferred cell selection criteria received from the serving cell, information decoded from the SIB, priority level, and operational status (e.g., enabled) for a first operating mode (e.g., AI / ML operation).

[0258] In the embodiment, the WTRU may report the PCID of a cell whose one or more operating parameters match a preferred cell selection criterion.

[0259] For example, a WTRU may report the PCID of a cell whose AI / ML-model-location-support matches the preferred AI / ML-model-location-support of the gNB.

[0260] For example, a WTRU may report PCIDs for cells whose AI / ML-BeamResourceSet-size is equal to, smaller than, or larger than the preferred AI / ML-BeamResourceSet-size for the gNB.

[0261] For example, WTRU may report that the PCID of a cell whose AI / ML-BeamResourceSet-type matches is either a superset of gNB's preferred AI / ML-BeamResourceSet-type (where the random type is a superset of both the fixed and random types) or a subset of gNB's preferred AI / ML-BeamResourceSet-type (where the fixed type is a subset of both the random and fixed types).

[0262] Figure 3 is a flowchart illustrating an exemplary process by which a WTRU prioritizes adjacent cells for the purpose of cell (re)selection according to AI / ML parameters, according to an embodiment.

[0263] In step 301, the WTRU initiates a cell (re)selection procedure to detect one or more candidate neighboring cells. The cell (re)selection procedure may be initiated, for example, by periodic or impending handovers. The WTRU can select appropriate candidate neighboring cells based on parameters such as the probabilities of RSRP, RSRQ, and LOS.

[0264] In step 303, the WTRU receives information about the AI / ML beam management system supported by the detected candidate cell. This information may be received via the cell to which the WTRU is currently camped, or via SIBs, e.g., SIB2, SIB3. The information may consist of a set B type indicating the number of beams in set B.

[0265] In step 305, the WTRU receives a priority regime to be used when ranking candidate adjacent cells for the purpose of (re)selection.

[0266] In step 307, the WTRU ranks candidate adjacent cells according to at least the assigned priority regime. Ranking may be based further on other parameters such as RSRP and RSRQ.

[0267] In step 309, WTRU selects the candidate adjacent cell with the highest ranking for cell (re)selection and initiates initial access (and may initiate initial access using the highest-ranked candidate adjacent cell).

[0268] In an embodiment, the information regarding the AI / ML beam management system supported by the detected candidate cell includes at least one of a set B type, a set B size, a set B pattern, and an AI / ML model.

[0269] In an embodiment, the priority regime is based on any one or more of the set B parameters of candidate neighboring cells, the trained data set, latency, coverage, probability of LOS, and mobility conditions.

[0270] In an embodiment, the candidate neighboring cell ranking is performed separately for candidate neighboring cells that are AI / ML capable in a first ranked list and candidate neighboring cells that are not AI / ML capable in a second ranked list.

[0271] In an embodiment, the candidate neighboring cells in the first list are ranked higher than the candidate neighboring cells in the second list.

[0272] In an alternative embodiment, candidate neighboring cells with AI / ML capabilities and candidate neighboring cells without AI / ML capabilities are ranked in a single list, but compensation values, offset values, and / or scaling rules are applied as a function of the AI / ML capabilities of the candidate neighboring cells and are applied in the ranking.

[0273] Typical procedure for prioritizing adjacent cells during cell selection or cell re-selection according to a set of AI / ML parameters In one embodiment, a method for wireless communication (implemented by WTRU) includes detecting one or more candidate neighbor cells for cell selection, receiving information related to AI / ML beam management associated with one or more candidate neighbor cells, and receiving a priority regime for use in ranking one or more candidate neighbor cells having AI / ML capabilities for cell selection. The method further includes ranking one or more candidate neighbor cells based at least on the priority regime and selecting a neighbor cell from one or more candidate neighbor cells based on the ranking of selected neighbor cells. In an example, the selected neighbor cell has the highest ranking for cell selection. In some cases, the neighbor cell is re-selected from one or more candidate neighbor cells. In some examples, the received information (e.g., configuration information) indicates one or more of set B types, set B sizes, set B patterns, or AI / ML models associated with a set of reference signal resources, a set of beams, or a set of beam pairs for measuring one or more candidate neighbor cells.

[0274] In some examples, the received information indicates the type associated with a set of reference signal resources for measurement, a set of beams, or a set of beam pairs.

[0275] In some examples, the received information indicates the size or number of beams associated with a set of reference signal resources, a set of beams, or a set of beam pairs for measuring one or more candidate adjacent cells.

[0276] In some examples, the received information shows a pattern associated with a set of reference signal resources, a set of beams, or a set of beam pairs for measuring one or more candidate adjacent cells.

[0277] In some examples, the received information indicates an AI / ML model associated with a set of reference signal resources, a set of beams, or a set of beam pairs for measuring one or more candidate adjacent cells.

[0278] In some examples, the received information indicates the time and / or frequency resources needed to measure one or more candidate neighboring cells.

[0279] In the example, the method may also include determining at least a subset of reference signal resources associated with one or more beams or beam pairs based on received information related to AI / ML beam management and for measuring one or more candidate adjacent cells.

[0280] In some examples, the priority regime is based on one or more sets of parameters for candidate adjacent cells, the trained dataset, latency, coverage, line of sight (LOS) probability, and / or mobility conditions.

[0281] In some examples, the method may further include ranking candidate adjacent cells that are AI / ML-capable in a first list and ranking candidate adjacent cells that are not AI / ML-capable in a second list. In the example, candidate adjacent cells in the first list are given higher priority than candidate adjacent cells in the second list.

[0282] In some examples, the method may also include ranking candidate adjacent cells with AI / ML capabilities against candidate adjacent cells without AI / ML capabilities within a third list.

[0283] In some examples, the method may also include applying at least one of a compensation value, an offset value, or a scaling rule when ranking one or more candidate adjacent cells, depending on the AI / ML capabilities of one or more candidate adjacent cells in a first list, a second list, or a third list.

[0284] In one embodiment, a WTRU for wireless communication is provided, comprising circuitry including a processor, transmitter, receiver, and memory. The WTRU is configured to detect one or more candidate neighbor cells for cell selection, receive information related to AI / ML beam management associated with one or more candidate neighbor cells, and receive a priority regime to use in ranking one or more candidate neighbor cells having AI / ML capabilities for cell selection. The WTRU is further configured to rank one or more candidate neighbor cells based at least on the priority regime and to select a neighbor cell from one or more candidate neighbor cells based on the ranking of the selected neighbor cells. In an example, the selected neighbor cell has the highest ranking for cell selection. In some cases, the neighbor cell is re-selected from one or more candidate neighbor cells. In some examples, the received information (e.g., configuration information) indicates one or more of set B types, set B sizes, set B patterns, or AI / ML models associated with a set of reference signal resources, a set of beams, or a set of beam pairs for measuring one or more candidate neighbor cells.

[0285] In one embodiment, the method (implemented in WTRU) includes initiating a cell (re)selection procedure, detecting candidate neighboring cells, receiving information about the AI / ML beam management system supported by the detected candidate cells, receiving a priority regime for use in ranking candidate neighboring cells having AI / ML capabilities for (re)selection, ranking the candidate neighboring cells at least based on the received priority regime, and selecting the candidate neighboring cell having the highest ranking for cell (re)selection.

[0286] In some examples, information about the AI / ML beam management system supported by the detected candidate cell includes at least one of the set B type, set B size, set B pattern, and AI / ML model. The priority regime is based on at least one of the set B parameters of the candidate neighbor cell, the trained dataset, latency, coverage, LOS probability, and mobility conditions. Ranking the candidate neighbor cells involves separately ranking AI / ML-capable candidate neighbor cells in a first ranking list and non-AI / ML-capable candidate neighbor cells in a second ranking list. Candidate neighbor cells in the first list are prioritized over candidate neighbor cells in the second list.

[0287] In some examples, candidate adjacent cells with AI / ML capabilities and those without are ranked in a single list, and at least one of the following—compensation value, offset value, and scaling rule—is applied according to the AI / ML capabilities of the candidate adjacent cell in that rank.

[0288] In one embodiment, referring to Figure 4, a flowchart is provided illustrating exemplary procedures for a WTRU to collect and share data from adjacent cells that support an AI / ML system.

[0289] In step 401, the WTRU receives a request from its serving gNB to perform ANR acquisition from one or more neighboring cells to acquire one or more capabilities from the detected neighboring cells, such as settings, parameters, and / or the cell's AI / ML capability.

[0290] In step 403, the WTRU further receives from the gNB configurations for one or more AI / ML parameters that should be obtained from neighboring cells.

[0291] In step 405, the WTRU receives further indications from the gNB regarding preferred criteria for which adjacent cells to obtain such information from. For example, the WTRU may receive indications that only AI / ML-capable cells should be considered, or only cells that do not possess AI / ML capabilities should be considered.

[0292] Accordingly, in step 407, the WTRU attempts to detect the SSB burst of the indicated adjacent cell.

[0293] In step 409, the WTRU decodes the MIB and SSB for each indicated adjacent cell to determine the indicated settings, parameters, and / or capabilities, for example, the settings, parameters, and / or capabilities indicated in AI / ML.

[0294] In step 411, the WTRU reports the determined configuration, parameters, and / or capabilities to its serving gNB.

[0295] In this embodiment, the settings, parameters, and / or capabilities of adjacent cells include AI / ML capabilities, set B type, set B size, set B pattern, and so on.

[0296] In this embodiment, a preferred criterion for determining which adjacent cells to acquire such information from might be an indication that only AI / ML-capable cells should be considered.

[0297] In this embodiment, a preferred criterion for determining which adjacent cells to acquire such information from might be an indication that only cells without AI / ML capabilities should be considered.

[0298] In the embodiment, the WTRU may be configured to decode SIB1 or decide to do so only if the AI / ML support determined from decoding the MIB satisfies the preference indicated by the gNB (for example, only if the adjacent cell is AI / ML compatible).

[0299] conclusion While features and elements are provided above in specific combinations, those skilled in the art will understand that each feature or element can be used alone or in any combination with other features and elements. This disclosure should not be limited to the specific embodiments described in this application, which are intended as examples of various aspects. As will be apparent to those skilled in the art, many modifications and variations can be made without departing from the spirit and scope of the invention. Elements, actions, or commands used in the description of this application should not be construed as important or essential to the invention unless expressly provided so. In addition to those enumerated herein, functionally equivalent methods and apparatus within the scope of this disclosure will be apparent to those skilled in the art from the foregoing description. Such modifications and variations are intended to fall within the scope of the appended claims. This disclosure should be limited only by the terms of the appended claims and the entire scope of equivalents to which such claims are entitled. It should be understood that this disclosure is not limited to any particular method or system.

[0300] For simplicity, the embodiments described above have been discussed in terms of the terminology and structure of infrared-capable devices, i.e., infrared emitters and receivers. However, the embodiments discussed are not limited to these systems and may be applied to other systems using other forms of non-electromagnetic waves, such as electromagnetic waves or sound waves.

[0301] Furthermore, it should be understood that the terminology used herein is for the sole purpose of describing specific embodiments and is not intended to limit them. When used herein, the terms “video” or “imagery” may mean a snapshot, a single image, and / or multiple images displayed on a time basis. As another example, when referred herein, the terms “user equipment” and its abbreviation “UE,” the terms “remote” and / or the terms “head-mounted display” or its abbreviation “HMD” may mean or include (i) a wireless transmit and / or receive unit (WTRU), (ii) any of several embodiments of a WTRU, (iii) a wirelessly and / or wired (e.g., tetherable) device configured using some or all of the structure and functionality of a WTRU, (iii) a wirelessly and / or wired device configured using all or less of the structure and functionality of a WTRU, or (iv) similar. Details of exemplary WTRUs that can represent any WTRU listed herein are provided herein with respect to Figures 1A to 1D. As another example, the various embodiments disclosed herein above and below are described as utilizing a head-mounted display. Those skilled in the art will recognize that devices other than head-mounted displays may be used, and that some or all of the present disclosure and various disclosed embodiments can be modified accordingly without excessive experimentation. Examples of such other devices may include drones or other devices configured to stream information for providing an adapted reality experience.

[0302] Furthermore, the methods provided herein may be implemented in computer programs, software, or firmware embedded in computer-readable media for execution by a computer or processor. Examples of computer-readable media include electronic signals (transmitted via wired or wireless connections) and computer-readable storage media. Examples of computer-readable storage media include, but are not limited to, read-only memory (ROM), random access memory (RAM), registers, cache memory, semiconductor memory devices, magnetic media such as internal hard disks and removable disks, magneto-optical media, and optical media such as CD-ROM disks and digital multipurpose disks (DVDs). Processors associated with software may be used to implement radio frequency transceivers for use in WTRUs, UEs, terminals, base stations, RNCs, MMEs, EPCs, AMFs, or any host computer.

[0303] Modifications of the methods, apparatus, and systems provided above are possible without departing from the scope of the present invention. It should be understood that the embodiments shown are merely examples and should not be considered limiting to the following claims, in view of the wide range of embodiments to which they may be applied. For example, embodiments provided herein include a handheld device which may include, or may be used with, any suitable voltage source, such as a battery, that provides any suitable voltage.

[0304] Furthermore, the embodiments provided above describe other devices, including processing platforms, computing systems, controllers, and processors. These devices may include at least one central processing unit ("CPU") and memory. In accordance with the practices of those skilled in computer programming, references to acts and symbolic representations of actions or instructions may be performed by various CPUs and memories. Such acts and actions or instructions may be referred to as "executed," "computer-executed," or "CPU-executed."

[0305] Those skilled in the art will understand that actions and symbolically represented operations or instructions involve the manipulation of electrical signals by the CPU. The electrical system represents data bits that, as a result of the electrical signals, can be transformed or reduced, and maintained at memory locations within the memory system, thereby causing the CPU's operations and other processing of signals to be reconfigured or otherwise altered. The memory locations where the data bits are maintained are physical locations having specific electrical, magnetic, optical, or organic properties corresponding to or representing the data bits. It should be understood that the embodiments are not limited to the platforms or CPUs described above, and other platforms and CPUs may support the methods provided.

[0306] Data bits may also be maintained on computer-readable media including magnetic disks, optical disks, and any other volatile (e.g., Random Access Memory (RAM)) or non-volatile (e.g., Read-Only Memory (ROM)) mass storage systems readable by the CPU. Computer-readable media may include collaborative or interconnected computer-readable media distributed among multiple interconnected processing systems, which may reside exclusively on a processing system or be local or remote to a processing system. It should be understood that embodiments are not limited to the memory described above, and other platforms and memories may support the methods provided.

[0307] In exemplary embodiments, any of the operations, processes, etc., described herein may be implemented as computer-readable instructions stored on a computer-readable medium. These computer-readable instructions may be executed by the processors of mobile units, network elements, and / or any other computing devices.

[0308] There is little distinction left between hardware and software implementations of a system configuration. The use of hardware or software is generally (but not always, in that in certain situations the choice between hardware and software can be important) a design choice representing a cost-effectiveness trade-off. Various means (e.g., hardware, software, and / or firmware) may exist by which the processes and / or systems and / or other technologies described herein can be produced, and the preferred means may vary depending on 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 paramount, the implementer may choose primarily hardware and / or firmware means. If flexibility is paramount, the implementer may choose primarily software implementation. Alternatively, the implementer may choose any combination of hardware, software, and / or firmware.

[0309] The detailed description above illustrates various embodiments of devices and / or processes using block diagrams, flowcharts, and / or examples. To the extent that such block diagrams, flowcharts, 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, flowcharts, or examples can be implemented individually and / or collectively by a wide range of hardware, software, firmware, or virtually any combination thereof. In embodiments, some parts of the subject matter described herein may be implemented via application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), digital signal processors (DSPs), and / or other integrated formats. However, a person skilled in the art will recognize that some aspects of the embodiments disclosed herein can be uniformly implemented on an integrated circuit, in whole or in part, 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 substantially any combination thereof, and that designing circuits and / or writing code for software and / or firmware is well within the scope of the art of a person skilled in the art in light of this disclosure. Furthermore, a person skilled in the art will understand that the mechanisms of the subject matter described herein can be distributed as various forms of programmed products, and that the exemplary embodiments of the subject matter described herein apply regardless of the particular type of medium carrying the signals used to actually perform the distribution.Examples of media that carry signals include, but are not limited to, recordable media such as floppy disks, hard disk drives, CDs, DVDs, digital tapes, computer memory, etc., as well as transmission media such as digital communication media and / or analog communication media (e.g., fiber optic cables, waveguides, wired communication links, wireless communication links, etc.).

[0310] Those skilled in the art will recognize that it is common practice in the industry to describe devices and / or processes in the manner described herein and then integrate such described devices and / or processes into data processing systems using engineering practices. That is, at least some of the devices and / or processes described herein can be integrated into data processing systems through a reasonable amount of experimentation. Those skilled in the art will recognize that a typical data processing system may generally include one or more of the following: a system unit housing, video display devices, memory such as volatile and non-volatile memory, processors such as microprocessors and digital signal processors, computing entities such as operating systems, drivers, graphical user interfaces, and application programs, one or more interaction devices such as touchpads or screens, and / or control systems including feedback loops and control motors (e.g., feedback for sensing position and / or velocity, control motors for moving and / or adjusting components and / or quantities). A typical data processing system may be implemented using any suitable commercially available components, such as those typically found in data computing / communication and / or network computing / communication systems.

[0311] The subject matter described herein may include different components that are contained within or connected to other different components. Such illustrated architectures are merely examples, and it should be understood that many other architectures can be implemented to achieve the same function. Conceptually, any arrangement of components to achieve the same function is effectively “associated” in such a way that the desired function can be achieved. Thus, any two components combined herein to achieve a particular functionality, regardless of architecture or intermediate components, can be considered “associated” with each other in such a way that the desired function can be achieved. Similarly, any two components thus associated can be considered “operably connected” or “operably coupled” with each other in such a way that the desired functionality can be achieved, and any two components that can be associated in such a way can also be considered “operably coupled” with each other in such a way that the desired functionality can be achieved. Specific examples of operable coupling include, but are not limited to, physically matable and / or physically interacting components, as well as / or wirelessly interactable and / or wirelessly interacting components, as well as / or logically interacting and / or logically interactable components.

[0312] With regard to the use of substantially any plural and / or singular terms herein, those skilled in the art can convert from plural to singular and / or singular to plural as appropriate to the context and / or use. Various singular / plural permutations may be explicitly stated herein for clarity.

[0313] In general, the terms used herein, in particular in the appended claims (e.g., the body of the appended claims), are generally "open" terms (e.g., the term "including" should be interpreted as "including but not limited to", the term "having" should be interpreted as "at least having", the term "includes" should be interpreted as "including but not limited to", etc.) and / or "permissible" terms (e.g., the terms "is" and / or "are" may be interpreted as "may" and / or "might", the term "refer" may be interpreted as "may" and / or "might refer", the term "receive" may be interpreted as "may" and / or "might receive", the term "support" may be interpreted as "may" and / or "might support", the term "interface ( The term "interface)" may be interpreted as "can interface" and / or "may interface", the term "transmit" may be interpreted as "can interface" and / or "may interface", "can send" and / or "may send", the term "send" may be interpreted as "can send" and / or "may send", the term "do not refer" (and / or similar) may be interpreted as "do not refer" and / or "may not refer", the term "do not receive" (and / or similar) may be interpreted as "do not receive" and / or "may not receive", the term "do not support" (and / or similar) may be interpreted as "do not support" and / or "may not support", the term "do not interface" (and / or similar) may be interpreted as "do not interface" and / or "may not interface",It will be understood by those skilled in the art that the term “not send” (and / or similar) may be interpreted as “may not send” and / or “may not send,” and the term “not send” (and / or similar) may be interpreted as “may not send” and / or “may not send,” etc. It will be understood by those skilled in the art that if a particular number of items described in an introduced claim are intended, such intent is explicitly stated in the claim, and if such statement is absent, such intent does not exist. For example, if only one item is intended, the term “single” or a similar expression may be used. For the sake of understanding, the following appended claims and / or description herein may include the use of introductory phrases “at least one” and “one or more” to introduce the description of a claim. However, the use of such phrases should not be interpreted as suggesting that the introduction of a claim by the indefinite article "a" or "an" limits any particular claim containing such introduced claim content to only one embodiment containing such content (for example, "a" and / or "an" should be interpreted as meaning "at least one" or "one or more"). The same applies to the use of the definite article used to introduce a claim content. Furthermore, even if a specific number of introduced claim content is explicitly stated, a person skilled in the art will recognize that such a statement should be interpreted as meaning at least the number stated (for example, the mere statement "two descriptions" without other modifiers means at least two descriptions, or two or more descriptions). Furthermore, where conventions similar to "at least one of A, B, and C, etc." are used, such configurations are generally intended in the sense that a person skilled in the art would understand the convention (for example, "a system having at least one of A, B, and C" includes systems having only A, only B, only C, A and B together, A and C together, B and C together, and / or systems having A, B and C together, etc.).(and not limited to these). Where conventions similar to “at least one of A, B, or C, etc.” are used, such configurations are generally intended in a sense that a person skilled in the art would understand the convention to mean (for example, “a system having at least one of A, B, or C” includes, but is not limited to, a system having only A, only B, only C, A and B together, A and C together, B and C together, and / or a system having A, B and C together, etc). It will further be understood by a person skilled in the art that substantially any disjunct word and / or disjunct phrase presenting two or more alternative terms should be understood in the specification, claims, or drawings as contingent on the possibility of including one of the terms, either of the terms, or both of the terms. For example, the phrase “A or B” will be understood to include the possibilities of “A” or “B” or “A and B”. Furthermore, when used herein, the term “any” preceding a list of multiple items and / or categories of multiple items is intended to include, individually or in conjunction with other items and / or categories of other items, “any,” “any combination of,” “any multiple,” and / or “any combination of multiple.” Furthermore, when used herein, the term “set” is intended to include any number of items, including zero. Furthermore, when used herein, the term “number” is intended to include any number, including zero. Also, when used herein, the term “multiple” is intended to be synonymous with “a plurality.”

[0314] Furthermore, if any feature or aspect of this disclosure is described in relation to the Markush Group, a person skilled in the art will recognize that this disclosure also describes any individual member or subgroup of a member of the Markush Group.

[0315] As will be understood by those skilled in the art, for any and all purposes, including providing a specification, all scopes disclosed herein also encompass any and all possible subscopes and combinations thereof. Any named scope can be readily recognized as sufficient to explain and enable that scope may be divided into at least equal halves, thirds, quarters, fifths, tenths, etc. As a non-limiting example, each scope discussed herein can be readily decomposed into a lower third, a middle third, an upper third, etc. As will also be understood by those skilled in the art, all expressions such as “up to,” “at least,” “greater than,” “less than,” etc., include the named number and refer to a scope that can be substantially decomposed into the subscopes described above. Finally, as will be understood by those skilled in the art, a scope includes its individual members. Thus, for example, a group having 1 to 3 cells refers to a group having 1, 2, or 3 cells. Similarly, a group having 1 to 5 cells refers to a group having 1, 2, 3, 4, or 5 cells, and so on.

[0316] Furthermore, the claims should not be read as being limited to the order or elements provided unless stated to that effect. Moreover, the use of the term “means for” in any claim is intended to require the means-plus-function claim form under 112(6) of the U.S. Patent Act, and no claim without the term “means for” is intended to do so.

[0317] Appropriate processors include, for example, general-purpose processors, dedicated processors, conventional processors, digital signal processors (DSPs), multiple microprocessors, one or more microprocessors associated with a DSP core, controllers, microcontrollers, application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), field-programmable gate array (FPGA) circuits, any other type of integrated circuit (IC), and / or state machines.

[0318] WTRU may be used with modules implemented in hardware and / or software, including Software Defined Radio (SDR), as well as other components such as cameras, video camera modules, video phones, speakerphones, vibration devices, speakers, microphones, television transceivers, hands-free headsets, keyboards, Bluetooth® modules, frequency modulation (FM) radio units, near-field communication (NFC) modules, liquid crystal display (LCD) display units, organic light-emitting diode (OLED) display units, digital music players, media players, video game player modules, internet browsers, and / or any wireless local area network (WLAN) or ultra-wideband (UWB) modules.

[0319] Although various embodiments have been described with respect to communication systems, it is intended that the systems may be implemented in software on a microprocessor / general-purpose computer (not shown). In certain embodiments, one or more functions of the various components may be implemented in software that controls the general-purpose computer.

[0320] Furthermore, although the present invention is illustrated and described herein with reference to specific embodiments, the present invention is not intended to be limited to the details shown. Rather, various modifications may be made in detail without departing from the present invention, within the scope and range of equivalence of the claims.

Claims

1. A method implemented in a wireless transmit / receive unit (WTRU) for wireless communication, To detect one or more candidate adjacent cells for cell selection, Receiving information related to artificial intelligence / machine learning (AI / ML) beam management associated with one or more candidate adjacent cells, Receiving a priority regime used when ranking one or more candidate adjacent cells that have AI / ML capabilities for cell selection, Ranking one or more candidate adjacent cells based at least on the aforementioned priority regime, Selecting an adjacent cell from the one or more candidate adjacent cells based on the order of the selected adjacent cells. A method characterized by comprising:

2. The method according to claim 1, characterized in that selecting the adjacent cell from the one or more candidate adjacent cells includes re-selecting the adjacent cell from the one or more candidate adjacent cells.

3. The method according to 1 or 2, characterized in that the information indicates a type associated with a set of reference signal resources, a set of beams, or a set of beam pairs for measurement.

4. The method according to 1 or 2, characterized in that the information indicates the size or number of beams associated with a set of reference signal resources, a set of beams, or a set of beam pairs for measuring one or more candidate adjacent cells.

5. The method according to 1 or 2, characterized in that the information indicates a pattern associated with a set of reference signal resources, a set of beams, or a set of beam pairs for measuring the one or more candidate adjacent cells.

6. The method according to 1 or 2, characterized in that the information indicates an AI / ML model associated with a set of reference signal resources, a set of beams, or a set of beam pairs for measuring one or more candidate adjacent cells.

7. The method according to 1 or 2, characterized in that the information indicates time and / or frequency resources for measuring one or more candidate adjacent cells.

8. Based on the received information relating to AI / ML beam management, determine at least a subset of reference signal resources associated with one or more beams or beam pairs in order to measure the one or more candidate adjacent cells. The method according to 1 or 2, further comprising:

9. The method according to any one of claims 1 to 8, characterized in that the priority regime is based on any one of the following: a set of parameters for one or more candidate adjacent cells, a trained dataset, latency, coverage, line of sight (LOS) probability, and / or mobility conditions.

10. Assigning the aforementioned order to one or more candidate adjacent cells is, In the first list, the candidate adjacent cells that are capable of AI / ML are ranked, In the second list, the candidate adjacent cells that are not AI / ML-compatible are ranked accordingly. The method according to any one of claims 1 to 9, characterized by including the following:

11. The method according to 10, characterized in that the candidate adjacent cell in the first list is given a higher priority than the candidate adjacent cell in the second list.

12. Assigning the aforementioned order to one or more candidate adjacent cells is, In the third list, rank candidate adjacent cells that have AI / ML capabilities against candidate adjacent cells that do not. The method according to any one of claims 1 to 11, characterized by including the following:

13. When ranking the one or more candidate adjacent cells, at least one of a compensation value, an offset value, or a scaling rule is applied according to the AI / ML capability of the one or more candidate adjacent cells in the first list, the second list, or the third list. The method according to any one of claims 10 to 12, further comprising:

14. A wireless transceiver unit (WTRU) for wireless communication, comprising a circuit including a transmitter, receiver, processor, and memory, wherein the WTRU Detect one or more candidate adjacent cells for cell selection, Information related to artificial intelligence / machine learning (AI / ML) beam management associated with one or more candidate adjacent cells is received. The system receives a priority regime used when ranking one or more candidate adjacent cells that have AI / ML capabilities for cell selection, The one or more candidate adjacent cells are ranked according to at least the priority regime, Select an adjacent cell from the one or more candidate adjacent cells based on the order of the selected adjacent cells. A WTRU characterized by being configured in such a way.

15. The WTRU according to claim 14, characterized in that when selecting an adjacent cell from the one or more candidate adjacent cells, the WTRU is further configured to re-select the adjacent cell from the one or more candidate adjacent cells.

16. The WTRU according to claim 14 or 15, characterized in that the information indicates a type associated with a set of reference signal resources, a set of beams, or a set of beam pairs for measurement.

17. The WTRU according to claim 14 or 15, characterized in that the information indicates the size or number of beams associated with a set of reference signal resources, a set of beams, or a set of beam pairs for measuring one or more candidate adjacent cells.

18. The WTRU according to claim 14 or 15, characterized in that the information indicates a pattern associated with a set of reference signal resources, a set of beams, or a set of beam pairs for measuring one or more candidate adjacent cells.

19. The WTRU according to claim 14 or 15, characterized in that the information indicates an AI / ML model associated with a set of reference signal resources, a set of beams, or a set of beam pairs for measuring one or more candidate adjacent cells.

20. The WTRU according to claim 14 or 15, characterized in that the information indicates time and / or frequency resources for measuring one or more candidate adjacent cells.

21. The aforementioned WTRU is Based on the received information relating to AI / ML beam management, determine at least a subset of reference signal resources associated with one or more beams or beam pairs in order to measure the one or more candidate adjacent cells. The WTRU according to claim 14 or 15, further characterized by being configured as follows.

22. The WTRU according to any one of claims 14 to 21, characterized in that the priority regime is based on any one of the following: a set of parameters for one or more candidate adjacent cells, a trained dataset, latency, coverage, line of sight (LOS) probability, and / or mobility conditions.

23. The aforementioned WTRU is In the first list, the candidate adjacent cells that are capable of AI / ML are ranked, In the second list, rank the candidate adjacent cells that are not AI / ML-compatible. The WTRU according to any one of claims 14 to 22, further characterized by being configured as follows.

24. The WTRU according to claim 23, characterized in that the candidate adjacent cell in the first list is given a higher priority than the candidate adjacent cell in the second list.

25. The WTRU according to any one of claims 14 to 24, further configured to rank candidate adjacent cells with AI / ML capability and candidate adjacent cells without AI / ML capability in a third list.

26. The aforementioned WTRU is When ranking the one or more candidate adjacent cells, at least one of a compensation value, an offset value, or a scaling rule is applied according to the AI / ML capability of the one or more candidate adjacent cells in the first list, the second list, or the third list. The WTRU according to any one of claims 23 to 25, further characterized by being configured as follows.

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