Methods for cell level measurement predictions

Abstract

A wireless transmit / receive unit (WTRU) may receive configuration information. The WTRU may determine one or more beam measurements of a first subset of beams. The WTRU may determine one or more predicted beam measurements of a second subset of beams. The WTRU may determine unfiltered cell level measurement(s) based on the first subset of beams and / or the second subset of beams. The WTRU may determine filtered cell level measurements based on the unfiltered cell level measurements. The WTRU may send a measurement report that includes an indication of the filtered cell level measurement(s).

Description

METHODS FOR CELL LEVEL MEASUREMENT PREDICTIONSCROSS-REFERENCE TO RELATED APPLICATION

[0001] This application claims priority to United States Provisional Patent Application No. 63 / 572,454 filed in the United States of America on April 1 , 2024, the entire contents of which are incorporated herein by reference.BACKGROUND

[0002] In NR, when a wireless transmit / receive unit (WTRU) is in RRC_CONNECTED state, for example, the WTRU may measure the signal level of one or more beams of a cell and / or the measurements results may be averaged to derive the cell quality. The WTRU may be configured to consider a subset of the detected beams. Filtering may take place at one or more (e.g., two) different levels: at the physical layer (L1) level to derive beam quality and / or (e.g., then) at radio resource control (RRC) (L3) level to derive cell quality from one or more (e.g., multiple) beams. Cell quality from beam measurements may be derived in the same way for the serving cell (s) and / or for the non-serving cell (s). Measurement reports may include the measurement results of the X best beams, for example, if the WTRU is configured to do so by the gNB.SUMMARY

[0003] A wireless transmit / receive unit (WTRU) may be configured to send capability information regarding beam measurement prediction. A WTRU may be configured with cell level measurement derivation and / or L3 measurement filtering configuration that considers predicted beams measured beams. A WTRU may be configured with measurement reporting configuration with triggering conditions that are related to cell level measurements that are derived considering measured and / or derived beams of serving and / or neighbor cells. A WTRU may be configured to indicate information related to predicted measurements in the measurement report. A WTRU may be configured to monitor the configured measurement triggering conditions, and / or send the measurement report, including additional information related to predicted measurements.

[0004] A wireless transmit / receive unit (WTRU) may send capability information regarding beam measurement predictions. The WTRU may receive a cell level measurement derivation and / or measurement filtering configuration applicable to the signal levels of predicted beams measurements and / or measured beams. The WTRU may receive measurement reporting configuration. The measurementreporting configuration may include one or more conditions and / or thresholds related to cell level measurement derived from one or more of: the measured beams and / or the predicted beams. The WTRU may perform measurements of beams and beam predictions. The WTRU may determine the cell level measurements. On a condition one or more measurement reporting conditions is satisfied, the WTRU may send the measurement report.

[0005] The measurement reporting configuration further comprises information to be included in the measurement report related to the predicted beam measurements. Sending the capability information may include sending the type of beam measurement prediction. The type of beam measurement prediction comprises spatial or temporal type of beam measurement. Sending the capability information may include sending additional details, wherein the additional details comprise one or more of: number of beams that can be predicted, number of beams that are to be measured to perform the predictions on other beams, confidence levels, the WTRU speed at which the beam prediction can be performed, and / or one or more frequences at which the beam prediction can be performed.

[0006] A wireless transmit / receive unit (WTRU) may receive (e.g., via a transceiver) configuration information. The configuration information may include first configuration information to perform beam measurements, second configuration information to predict beam measurements, third configuration information to derive cell level measurement(s) based on, for example, one or more performed beam measurements and / or one or more derived (e.g., predicted) beam measurements, fourth configuration to filter the unfiltered cell level measurements, and / or fifth configuration to send a measurement report. The WTRU may determine, in accordance with the first configuration information, one or more beam measurements of a first subset of beams. The WTRU may determine, in accordance with the second configuration information, one or more predicted beam measurements of a second subset of beams. The WTRU may determine, in accordance with the third configuration information, unfiltered cell level measurement(s) based on the first subset of beams and / or the second subset of beams. The WTRU may determine, in accordance with the fourth configuration information, the filtered cell level measurements based on the unfiltered cell level measurements. The WTRU may send (e.g., via the transceiver) the measurement report in accordance with the fifth configuration information. The measurement report may include an indication of the filtered cell level measurement(s).

[0007] The WTRU may send (e.g., via the transceiver) capability information. The capability information may indicate that the WTRU and / or processor is capable of predicting beam measurements. The capability information may include a beam prediction type, a number of beams that can be predicted, a number ofbeams that are to be measured to determine the second subset of beams associated with the one or more predicted beam measurements, a confidence level associated with the prediction of the second subset of beams, or one or more conditions under which an artificial intelligence I machine leaning (AI / ML) model is to operate.

[0008] The third configuration information to derive and / or determine unfiltered cell level measurement(s) may include beam consolidation criteria. The beam consolidation criteria may indicate one or more of the following: a maximum number of measured beams to be included in the consolidation, a maximum number of predicted measured beams to be included in the consolidation, a maximum total number of measured or predicted beams to be included in the consolidation, a (e.g. , signal level) threshold associated with each beam measurement of the first subset of beams, a (e.g., signal level) threshold associated with each predicted measurement of the second subset of beams, and / or a first (e.g., averaging) weighting factor associated with each predicted beam measurement (e.g., to determine the unfiltered cell level measurement(s), and / or a second (e.g., averaging weighting factor associated with each beam measurement (e.g., to determine the unfiltered cell level measurement(s). The WTRU may determine the unfiltered cel level measurement(s) based on the beam consolidation criteria.

[0009] The fourth configuration information may include spatial-based configuration information and / or temporal-based configuration information. The WTRU may determine the filtered cell level measurement(s) based on the spatial-based configuration information and / or the temporal-based configuration information. The fourth configuration information may include a first coefficient associated with the one or more beam measurements and / or a second coefficient associated with the one or more predicted beam measurements. The WTRU may use the first coefficient and / or the second coefficient to determine the filtered cell level measurements.

[0010] The WTRU may determine one or more (e.g., direct) cell level measurements based on the temporal-based configuration information. The WTRU may determine the filtered cell level measurements based on the one or more (e.g., direct) cell level measurements.

[0011] The WTRU may use an artificial intelligence / machine learning (AI / ML) model to predict the second subset of beams.

[0012] The WTRU may determine a first set of cell level measurements associated with a serving cell and / or a second set of cell level measurements associated with a target cell. The WTRU may send the measurement report based on the first set of cell measurements associated with the serving cell and / or the second set of cell level measurements associated with the target cell. The WTRU may send themeasurement report based on a comparison of the first set of cell level measurements and the second set of cell level measurements.BRIEF DESCRIPTION OF THE DRAWINGS

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

[0014] FIG. 1 B is a system diagram illustrating an example wireless transmit / receive unit (WTRU) that may be used within the communications system illustrated in FIG. 1A according to an embodiment.

[0015] FIG. 1C is a system diagram illustrating an example radio access network (RAN) and an example core network (CN) that may be used within the communications system illustrated in FIG. 1 A according to an embodiment.

[0016] FIG. 1 D is a system diagram illustrating a further example RAN and a further example CN that may be used within the communications system illustrated in FIG. 1 A according to an embodiment.

[0017] FIG. 2 depicts an example new radio (NR) measurement model.DETAILED DESCRIPTION

[0018] FIG. 1A is a diagram illustrating an example communications system 100 in which one or more disclosed embodiments may be implemented. The communications system 100 may be a multiple access system that provides content, such as voice, data, video, messaging, broadcast, etc., to multiple wireless users. The communications system 100 may enable multiple wireless users to access such content through the sharing of system resources, including wireless bandwidth. For example, the communications systems 100 may employ one or more channel access methods, such as code division multiple access (CDMA), time division multiple access (TDMA), frequency division multiple access (FDMA), orthogonal FDMA (OFDMA), single-carrier FDMA (SC-FDMA), zero-tail unique-word DFT-Spread OFDM (ZT UW DTS-s OFDM), unique word OFDM (UW-OFDM), resource block-filtered OFDM, filter bank multicarrier (FBMC), and the like.

[0019] As shown in FIG. 1A, the communications system 100 may include wireless transmit / receive units (WTRUs) 102a, 102b, 102c, 102d, a RAN 104 / 113, a CN 106 / 115, a public switched telephone network (PSTN) 108, the Internet 110, and other networks 112, though it will be appreciated that the disclosed embodiments contemplate any number of WTRUs, base stations, networks, and / or network elements. Each of the WTRUs 102a, 102b, 102c, 102d may be any type of device configured to operate and / orcommunicate in a wireless environment. By way of example, the WTRUs 102a, 102b, 102c, 102d, any of which may be referred to as a “station” and / or a “ST A”, may be configured to transmit and / or receive wireless signals and may include a user equipment (UE), a mobile station, a fixed or mobile subscriber unit, a subscription-based unit, a pager, a cellular telephone, a personal digital assistant (PDA), a smartphone, a laptop, a netbook, a personal computer, a wireless sensor, a hotspot or Mi-Fi device, an Internet of Things (loT) device, a watch or other wearable, a head-mounted display (HMD), a vehicle, a drone, a medical device and applications (e.g., remote surgery), an industrial device and applications (e.g., a robot and / or other wireless devices operating in an industrial and / or an automated processing chain contexts), a consumer electronics device, a device operating on commercial and / or industrial wireless networks, and the like. Any of the WTRUs 102a, 102b, 102c and 102d may be interchangeably referred to as a WTRU. Further, any description herein that is described with reference to a UE may be equally applicable to a WTRU (or vice versa). For example, a WTRU may be configured to perform any of the processes or procedures described herein as being performed by a UE (or vice versa).

[0020] The communications systems 100 may also include a base station 114a and / or a base station 114b. Each of the base stations 114a, 114b may be any type of device configured to wirelessly interface with at least one of the WTRUs 102a, 102b, 102c, 102d to facilitate access to one or more communication networks, such as the CN 106 / 115, the I nternet 110, and / or the other networks 112. By way of example, the base stations 114a, 114b may be a base transceiver station (BTS), a Node-B, an eNode B, a Home Node B, a Home eNode B, a gNB, a NR NodeB, a site controller, an access point (AP), a wireless router, and the like. While the base stations 114a, 114b are each depicted as a single element, it will be appreciated that the base stations 114a, 114b may include any number of interconnected base stations and / or network elements.

[0021] The base station 114a may be part of the RAN 104 / 113, which may also include other base stations and / or network elements (not shown), such as a base station controller (BSC), a radio network controller (RNC), relay nodes, etc. The base station 114a and / or the base station 114b may be configured to transmit and / or receive wireless signals on one or more carrier frequencies, which may be referred to as a cell (not shown). These frequencies may be in licensed spectrum, unlicensed spectrum, or a combination of licensed and unlicensed spectrum. A cell may provide coverage for a wireless service to a specific geographical area that may be relatively fixed or that may change over time. The cell may further be divided into cell sectors. For example, the cell associated with the base station 114a may be divided into three sectors. Thus, in one embodiment, the base station 114a may include three transceivers, i.e., one foreach sector of the cell. In an embodiment, the base station 114a may employ multiple-input multiple output (MIMO) technology and may utilize multiple transceivers for each sector of the cell. For example, beamforming may be used to transmit and / or receive signals in desired spatial directions.

[0022] The base stations 114a, 114b may communicate with one or more of the WTRUs 102a, 102b, 102c, 102d over an air interface 116, which may be any suitable wireless communication link (e.g., radio frequency (RF), microwave, centimeter wave, micrometer wave, infrared (IR), ultraviolet (UV), visible light, etc.). The air interface 116 may be established using any suitable radio access technology (RAT).

[0023] More specifically, as noted above, the communications system 100 may be a multiple access system and may employ one or more channel access schemes, such as CDMA, TDMA, FDMA, OFDMA, SC-FDMA, and the like. For example, the base station 114a in the RAN 104 / 113 and the WTRUs 102a, 102b, 102c may implement a radio technology such as Universal Mobile Telecommunications System (UMTS) Terrestrial Radio Access (UTRA), which may establish the air interface 115 / 116 / 117 using wideband CDMA (WCDMA). WCDMA may include communication protocols such as High-Speed Packet Access (HSPA) and / or Evolved HSPA (HSPA+). HSPA may include High-Speed Downlink (DL) Packet Access (HSDPA) and / or High-Speed UL Packet Access (HSUPA).

[0024] I n an embodiment, the base station 114a and the WTRUs 102a, 102b, 102c may implement a radio technology such as Evolved UMTS Terrestrial Radio Access (E-UTRA), which may establish the air interface 116 using Long Term Evolution (LTE) and / or LTE-Advanced (LTE-A) and / or LTE-Advanced Pro (LTE-A Pro).

[0025] I n an embodiment, the base station 114a and the WTRUs 102a, 102b, 102c may implement a radio technology such as NR Radio Access , which may establish the air interface 116 using New Radio (NR).

[0026] In an embodiment, the base station 114a and the WTRUs 102a, 102b, 102c may implement multiple radio access technologies. For example, the base station 114a and the WTRUs 102a, 102b, 102c may implement LTE radio access and NR radio access together, for instance using dual connectivity (DC) principles. Thus, the air interface utilized by WTRUs 102a, 102b, 102c may be characterized by multiple types of radio access technologies and / or transmissions sent to / from multiple types of base stations (e.g., a eNB and a gNB).

[0027] In other embodiments, the base station 114a and the WTRUs 102a, 102b, 102c may implement radio technologies such as IEEE 802.11 (i.e., Wireless Fidelity (WiFi), IEEE 802.16 (i.e., Worldwide Interoperability for Microwave Access (WiMAX)), CDMA2000, CDMA2000 1X, CDMA2000 EV-DO, InterimStandard 2000 (IS-2000), Interim Standard 95 (IS-95), Interim Standard 856 (IS-856), Global System for Mobile communications (GSM), Enhanced Data rates for GSM Evolution (EDGE), GSM EDGE (GERAN), and the like.

[0028] The base station 114b in FIG. 1 A may be a wireless router, Home Node B, Home eNode B, or access point, for example, and may utilize any suitable RAT for facilitating wireless connectivity in a localized area, such as a place of business, a home, a vehicle, a campus, an industrial facility, an air corridor (e.g., for use by drones), a roadway, and the like. In one embodiment, the base station 114b and the WTRUs 102c, 102d may implement a radio technology such as IEEE 802.11 to establish a wireless local area network (WLAN). In an embodiment, the base station 114b and the WTRUs 102c, 102d may implement a radio technology such as IEEE 802.15 to establish a wireless personal area network (WPAN). In yet another embodiment, the base station 114b and the WTRUs 102c, 102d may utilize a cellular-based RAT (e.g., WCDMA, CDMA2000, GSM, LTE, LTE-A, LTE-A Pro, NR etc.) to establish a picocell or femtocell. As shown in FIG. 1A, the base station 114b may have a direct connection to the Internet 110. Thus, the base station 114b may not be required to access the Internet 110 via the CN 106 / 115.

[0029] The RAN 104 / 113 may be in communication with the CN 106 / 115, which may be any type of network configured to provide voice, data, applications, and / or voice over internet protocol (VoIP) services to one or more of the WTRUs 102a, 102b, 102c, 102d. The data may have varying quality of service (QoS) requirements, such as differing throughput requirements, latency requirements, error tolerance requirements, reliability requirements, data throughput requirements, mobility requirements, and the like. The CN 106 / 115 may provide call control, billing services, mobile location-based services, pre-paid calling, Internet connectivity, video distribution, etc., and / or perform high-level security functions, such as user authentication. Although not shown in FIG. 1A, it will be appreciated that the RAN 104 / 113 and / or the CN 106 / 115 may be in direct or indirect communication with other RANs that employ the same RAT as the RAN 104 / 113 or a different RAT. For example, in addition to being connected to the RAN 104 / 113, which may be utilizing a NR radio technology, the CN 106 / 115 may also be in communication with another RAN (not shown) employing a GSM, UMTS, CDMA 2000, WiMAX, E-UTRA, or WiFi radio technology.

[0030] The CN 106 / 115 may also serve as a gateway for the WTRUs 102a, 102b, 102c, 102d to access the PSTN 108, the Internet 110, and / or the other networks 112. The PSTN 108 may include circuit- switched telephone networks that provide plain old telephone service (POTS). The Internet 110 may include a global system of interconnected computer networks and devices that use common communication protocols, such as the transmission control protocol (TCP), user datagram protocol (UDP)and / or the internet protocol (IP) in the TCP / IP internet protocol suite. The networks 112 may include wired and / or wireless communications networks owned and / or operated by other service providers. For example, the networks 112 may include another CN connected to one or more RANs, which may employ the same RAT as the RAN 104 / 113 or a different RAT.

[0031] Some or all of the WTRUs 102a, 102b, 102c, 102d in the communications system 100 may include multi-mode capabilities (e.g., the WTRUs 102a, 102b, 102c, 102d may include multiple transceivers for communicating with different wireless networks over different wireless links). For example, the WTRU 102c shown in FIG. 1A may be configured to communicate with the base station 114a, which may employ a cellular-based radio technology, and with the base station 114b, which may employ an IEEE 802 radio technology.

[0032] FIG. 1 B is a system diagram illustrating an example WTRU 102. As shown in FIG. 1 B, the WTRU 102 may include a processor 118, a transceiver 120, a transmit / receive element 122, a speaker / microphone 124, a keypad 126, a display / touchpad 128, non-removable memory 130, removable memory 132, a power source 134, a global positioning system (GPS) chipset 136, and / or other peripherals 138, among others. It will be appreciated that the WTRU 102 may include any sub-combination of the foregoing elements while remaining consistent with an embodiment.

[0033] The processor 118 may be a general purpose processor, a special purpose processor, a conventional processor, a digital signal processor (DSP), a plurality of microprocessors, one or more microprocessors in association with a DSP core, a controller, a microcontroller, Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) circuits, any other type of integrated circuit (IC), a state machine, and the like. The processor 118 may perform signal coding, data processing, power control, input / output processing, and / or any other functionality that enables the WTRU 102 to operate in a wireless environment. The processor 118 may be coupled to the transceiver 120, which may be coupled to the transmit / receive element 122. While FIG. 1B depicts the processor 118 and the transceiver 120 as separate components, it will be appreciated that the processor 118 and the transceiver 120 may be integrated together in an electronic package or chip.

[0034] The transmit / receive element 122 may be configured to transmit signals to, or receive signals from, a base station (e.g., the base station 114a) over the air interface 116. For example, in one embodiment, the transmit / receive element 122 may be an antenna configured to transmit and / or receive RF signals. In an embodiment, the transmit / receive element 122 may be an emitter / detector configured to transmit and / or receive IR, UV, or visible light signals, for example. In yet another embodiment, thetransmit / receive element 122 may be configured to transmit and / or receive both RF and light signals. It will be appreciated that the transmit / receive element 122 may be configured to transmit and / or receive any combination of wireless signals.

[0035] Although the transmit / receive element 122 is depicted in FIG. 1 B as a single element, the WTRU 102 may include any number of transmit / receive elements 122. More specifically, the WTRU 102 may employ MIMO technology. Thus, in one embodiment, the WTRU 102 may include two or more transmit / receive elements 122 (e.g., multiple antennas) for transmitting and receiving wireless signals over the air interface 116.

[0036] The transceiver 120 may be configured to modulate the signals that are to be transmitted by the transmit / receive element 122 and to demodulate the signals that are received by the transmit / receive element 122. As noted above, the WTRU 102 may have multi-mode capabilities. Thus, the transceiver 120 may include multiple transceivers for enabling the WTRU 102 to communicate via multiple RATs, such as NR and IEEE 802.11, for example.

[0037] The processor 118 of the WTRU 102 may be coupled to, and may receive user input data from, the speaker / microphone 124, the keypad 126, and / or the display / touchpad 128 (e.g., a liquid crystal display (LCD) display unit or organic light-emitting diode (OLED) display unit). The processor 118 may also output user data to the speaker / microphone 124, the keypad 126, and / or the display / touchpad 128. In addition, the processor 118 may access information from, and store data in, any type of suitable memory, such as the non-removable memory 130 and / or the removable memory 132. The non-removable memory 130 may include random-access memory (RAM), read-only memory (ROM), a hard disk, or any other type of memory storage device. The removable memory 132 may include a subscriber identity module (SIM) card, a memory stick, a secure digital (SD) memory card, and the like. In other embodiments, the processor 118 may access information from, and store data in, memory that is not physically located on the WTRU 102, such as on a server or a home computer (not shown).

[0038] The processor 118 may receive power from the power source 134, and may be configured to distribute and / or control the power to the other components in the WTRU 102. The power source 134 may be any suitable device for powering the WTRU 102. For example, the power source 134 may include one or more dry cell batteries (e.g., nickel-cadmium (NiCd), nickel-zinc (NiZn), nickel metal hydride (NiMH), lithium-ion (Li-ion), etc.), solar cells, fuel cells, and the like.

[0039] The processor 118 may also be coupled to the GPS chipset 136, which may be configured to provide location information (e.g., longitude and latitude) regarding the current location of the WTRU 102.In addition to, or in lieu of, the information from the GPS chipset 136, the WTRU 102 may receive location information over the air interface 116 from a base station (e.g, base stations 114a, 114b) and / or determine its location based on the timing of the signals being received from two or more nearby base stations. It will be appreciated that the WTRU 102 may acquire location information by way of any suitable locationdetermination method while remaining consistent with an embodiment.

[0040] The processor 118 may further be coupled to other peripherals 138, which may include one or more software and / or hardware modules that provide additional features, functionality and / or wired or wireless connectivity. For example, the peripherals 138 may include an accelerometer, an e-compass, a satellite transceiver, a digital camera (for photographs and / or video), a universal serial bus (USB) port, a vibration device, a television transceiver, a hands free headset, a Bluetooth® module, a frequency modulated (FM) radio unit, a digital music player, a media player, a video game player module, an Internet browser, a Virtual Reality and / or Augmented Reality (VR / AR) device, an activity tracker, and the like. The peripherals 138 may include one or more sensors, the sensors may be one or more of a gyroscope, an accelerometer, a hall effect sensor, a magnetometer, an orientation sensor, a proximity sensor, a temperature sensor, a time sensor; a geolocation sensor; an altimeter, a light sensor, a touch sensor, a magnetometer, a barometer, a gesture sensor, a biometric sensor, and / or a humidity sensor.

[0041] The WTRU 102 may include a full duplex radio for which transmission and reception of some or all of the signals (e.g., associated with particular subframes for both the UL (e.g., for transmission) and downlink (e.g., for reception) may be concurrent and / or simultaneous. The full duplex radio may include an interference management unit 139 to reduce and or substantially eliminate self-interference via either hardware (e.g., a choke) or signal processing via a processor (e.g., a separate processor (not shown) or via processor 118). In an embodiment, the WRTU 102 may include a half-duplex radio for which transmission and reception of some or all of the signals (e.g, associated with particular subframes for either the UL (e.g, for transmission) or the downlink (e.g, for reception)).

[0042] FIG. 1C is a system diagram illustrating the RAN 104 and the CN 106 according to an embodiment. As noted above, the RAN 104 may employ an E-UTRA radio technology to communicate with the WTRUs 102a, 102b, 102c over the air interface 116. The RAN 104 may also be in communication with the CN 106.

[0043] The RAN 104 may include eNode-Bs 160a, 160b, 160c, though it will be appreciated that the RAN 104 may include any number of eNode-Bs while remaining consistent with an embodiment. The eNode-Bs 160a, 160b, 160c may each include one or more transceivers for communicating with the WTRUs 102a,102b, 102c over the air interface 116. In one embodiment, the eNode-Bs 160a, 160b, 160c may implement MIMO technology. Thus, the eNode-B 160a, for example, may use multiple antennas to transmit wireless signals to, and / or receive wireless signals from, the WTRU 102a.

[0044] Each of the eNode-Bs 160a, 160b, 160c may be associated with a particular cell (not shown) and may be configured to handle radio resource management decisions, handover decisions, scheduling of users in the UL and / or DL, and the like. As shown in FIG. 1C, the eNode-Bs 160a, 160b, 160c may communicate with one another over an X2 interface.

[0045] The CN 106 shown in FIG. 1 C may include a mobility management entity (MME) 162, a serving gateway (SGW) 164, and a packet data network (PDN) gateway (or PGW) 166. While each of the foregoing elements are depicted as part of the CN 106, it will be appreciated that any of these elements may be owned and / or operated by an entity other than the CN operator.

[0046] The MME 162 may be connected to each of the eNode-Bs 162a, 162b, 162c in the RAN 104 via an S1 interface and may serve as a control node. For example, the MME 162 may be responsible for authenticating users of the WTRUs 102a, 102b, 102c, bearer activation / deactivation, selecting a particular serving gateway during an initial attach of the WTRUs 102a, 102b, 102c, and the like. The MME 162 may provide a control plane function for switching between the RAN 104 and other RANs (not shown) that employ other radio technologies, such as GSM and / or WCDMA.

[0047] The SGW 164 may be connected to each of the eNode Bs 160a, 160b, 160c in the RAN 104 via the S1 interface. The SGW 164 may generally route and forward user data packets to / from the WTRUs 102a, 102b, 102c. The SGW 164 may perform other functions, such as anchoring user planes during inter- eNode B handovers, triggering paging when DL data is available for the WTRUs 102a, 102b, 102c, managing and storing contexts of the WTRUs 102a, 102b, 102c, and the like.

[0048] The SGW 164 may be connected to the PGW 166, which may provide the WTRUs 102a, 102b, 102c with access to packet-switched networks, such as the Internet 110, to facilitate communications between the WTRUs 102a, 102b, 102c and IP-enabled devices.

[0049] The CN 106 may facilitate communications with other networks. For example, the CN 106 may provide the WTRUs 102a, 102b, 102c with access to circuit-switched networks, such as the PSTN 108, to facilitate communications between the WTRUs 102a, 102b, 102c and traditional land-line communications devices. For example, the CN 106 may include, or may communicate with, an IP gateway (e.g., an IP multimedia subsystem (IMS) server) that serves as an interface between the CN 106 and the PSTN 108. In addition, the CN 106 may provide the WTRUs 102a, 102b, 102c with access to the other networks 112,which may include other wired and / or wireless networks that are owned and / or operated by other service providers.

[0050] Although the WTRU is described in FIGS. 1 A-1 D as a wireless terminal, it is contemplated that in certain representative embodiments that such a terminal may use (e.g., temporarily or permanently) wired communication interfaces with the communication network.

[0051] In representative embodiments, the other network 112 may be a WLAN.

[0052] A WLAN in Infrastructure Basic Service Set (BSS) mode may have an Access Point (AP) for the BSS and one or more stations (STAs) associated with the AP. The AP may have an access or an interface to a Distribution System (DS) or another type of wired / wireless network that carries traffic in to and / or out of the BSS. Traffic to STAs that originates from outside the BSS may arrive through the AP and may be delivered to the STAs. Traffic originating from STAs to destinations outside the BSS may be sent to the AP to be delivered to respective destinations. Traffic between STAs within the BSS may be sent through the AP, for example, where the source STA may send traffic to the AP and the AP may deliver the traffic to the destination STA. The traffic between STAs within a BSS may be considered and / or referred to as peer-to- peer traffic. The peer-to-peer traffic may be sent between (e.g., directly between) the source and destination STAs with a direct link setup (DLS). In certain representative embodiments, the DLS may use an 802.11 e DLS or an 802.11 z tunneled DLS (TDLS). A WLAN using an Independent BSS (I BSS) mode may not have an AP, and the STAs (e.g., all of the STAs) within or using the IBSS may communicate directly with each other. The IBSS mode of communication may sometimes be referred to herein as an “ad- hoc” mode of communication.

[0053] When using the 802.11 ac infrastructure mode of operation or a similar mode of operations, the AP may transmit a beacon on a fixed channel, such as a primary channel. The primary channel may be a fixed width (e.g, 20 MHz wide bandwidth) or a dynamically set width via signaling. The primary channel may be the operating channel of the BSS and may be used by the STAs to establish a connection with the AP. In certain representative embodiments, Carrier Sense Multiple Access with Collision Avoidance (CSMA / CA) may be implemented, for example in in 802.11 systems. For CSMA / CA, the STAs (e.g, every STA), including the AP, may sense the primary channel. If the primary channel is sensed / detected and / or determined to be busy by a particular STA, the particular STA may back off. One STA (e.g, only one station) may transmit at any given time in a given BSS.

[0054] High Throughput (HT) STAs may use a 40 MHz wide channel for communication, for example, via a combination of the primary 20 MHz channel with an adjacent or nonadjacent 20 MHz channel to form a 40 MHz wide channel.

[0055] Very High Throughput (VHT) STAs may support 20MHz, 40 MHz, 80 MHz, and / or 160 MHz wide channels. The 40 MHz, and / or 80 MHz, channels may be formed by combining contiguous 20 MHz channels. A 160 MHz channel may be formed by combining 8 contiguous 20 MHz channels, or by combining two non-contiguous 80 MHz channels, which may be referred to as an 80+80 configuration. For the 80+80 configuration, the data, after channel encoding, may be passed through a segment parser that may divide the data into two streams. Inverse Fast Fourier Transform (IFFT) processing, and time domain processing, may be done on each stream separately. The streams may be mapped on to the two 80 MHz channels, and the data may be transmitted by a transmitting STA. At the receiver of the receiving STA, the above described operation for the 80+80 configuration may be reversed, and the combined data may be sent to the Medium Access Control (MAC).

[0056] Sub 1 GHz modes of operation are supported by 802.11 af and 802.11 ah. The channel operating bandwidths, and carriers, are reduced in 802.11 af and 802.11 ah relative to those used in 802.11 n, and 802.11ac. 802.11 af supports 5 MHz, 10 MHz and 20 MHz bandwidths in the TV White Space (TVWS) spectrum, and 802.11 ah supports 1 MHz, 2 MHz, 4 MHz, 8 MHz, and 16 MHz bandwidths using non-TVWS spectrum. According to a representative embodiment, 802.11 ah may support Meter Type Control / Machine- Type Communications, such as MTC devices in a macro coverage area. MTC devices may have certain capabilities, for example, limited capabilities including support for (e.g., only support for) certain and / or limited bandwidths. The MTC devices may include a battery with a battery life above a threshold (e.g., to maintain a very long battery life).

[0057] WLAN systems, which may support multiple channels, and channel bandwidths, such as 802.11 n, 802.11 ac, 802.11 af, and 802.11 ah, include a channel which may be designated as the primary channel. The primary channel may have a bandwidth equal to the largest common operating bandwidth supported by all STAs in the BSS. The bandwidth of the primary channel may be set and / or limited by a STA, from among all STAs in operating in a BSS, which supports the smallest bandwidth operating mode. In the example of 802.11 ah, the primary channel may be 1 MHz wide for STAs (e.g., MTC type devices) that support (e.g., only support) a 1 MHz mode, even if the AP, and other STAs in the BSS support 2 MHz, 4 MHz, 8 MHz, 16 MHz, and / or other channel bandwidth operating modes. Carrier sensing and / or Network Allocation Vector (NAV) settings may depend on the status of the primary channel. If the primary channel isbusy, for example, due to a STA (which supports only a 1 MHz operating mode), transmitting to the AP, the entire available frequency bands may be considered busy even though a majority of the frequency bands remains idle and may be available.

[0058] In the United States, the available frequency bands, which may be used by 802.11 ah, are from 902 MHz to 928 MHz. In Korea, the available frequency bands are from 917.5 MHz to 923.5 MHz. In Japan, the available frequency bands are from 916.5 MHz to 927.5 MHz. The total bandwidth available for 802.11 ah is 6 MHz to 26 MHz depending on the country code.

[0059] FIG. 1 D is a system diagram illustrating the RAN 113 and the CN 115 according to an embodiment. As noted above, the RAN 113 may employ an NR radio technology to communicate with the WTRUs 102a, 102b, 102c over the air interface 116. The RAN 113 may also be in communication with the CN 115.

[0060] The RAN 113 may include gNBs 180a, 180b, 180c, though it will be appreciated that the RAN 113 may include any number of gNBs while remaining consistent with an embodiment. The gNBs 180a, 180b, 180c may each include one or more transceivers for communicating with the WTRUs 102a, 102b, 102c over the air interface 116. In one embodiment, the gNBs 180a, 180b, 180c may implement MIMO technology. For example, gNBs 180a, 108b may utilize beamforming to transmit signals to and / or receive signals from the gNBs 180a, 180b, 180c. Thus, the gNB 180a, for example, may use multiple antennas to transmit wireless signals to, and / or receive wireless signals from, the WTRU 102a. In an embodiment, the gNBs 180a, 180b, 180c may implement carrier aggregation technology. For example, the gNB 180a may transmit multiple component carriers to the WTRU 102a (not shown). A subset of these component carriers may be on unlicensed spectrum while the remaining component carriers may be on licensed spectrum. In an embodiment, the gNBs 180a, 180b, 180c may implement Coordinated Multi-Point (CoMP) technology. For example, WTRU 102a may receive coordinated transmissions from gNB 180a and gNB 180b (and / or gNB 180c).

[0061] The WTRUs 102a, 102b, 102c may communicate with gNBs 180a, 180b, 180c using transmissions associated with a scalable numerology. For example, the OFDM symbol spacing and / or OFDM subcarrier spacing may vary for different transmissions, different cells, and / or different portions of the wireless transmission spectrum. The WTRUs 102a, 102b, 102c may communicate with gNBs 180a, 180b, 180c using subframe or transmission time intervals (TTIs) of various or scalable lengths (e.g., containing varying number of OFDM symbols and / or lasting varying lengths of absolute time).

[0062] The gNBs 180a, 180b, 180c may be configured to communicate with the WTRUs 102a, 102b, 102c in a standalone configuration and / or a non-standalone configuration. In the standalone configuration, WTRUs 102a, 102b, 102c may communicate with gNBs 180a, 180b, 180c without also accessing other RANs (e.g., such as eNode-Bs 160a, 160b, 160c). In the standalone configuration, WTRUs 102a, 102b, 102c may utilize one or more of gNBs 180a, 180b, 180c as a mobility anchor point. In the standalone configuration, WTRUs 102a, 102b, 102c may communicate with gNBs 180a, 180b, 180c using signals in an unlicensed band. In a non-standalone configuration WTRUs 102a, 102b, 102c may communicate with / connect to gNBs 180a, 180b, 180c while also communicating with / connecting to another RAN such as eNode-Bs 160a, 160b, 160c. For example, WTRUs 102a, 102b, 102c may implement DC principles to communicate with one or more gNBs 180a, 180b, 180c and one or more eNode-Bs 160a, 160b, 160c substantially simultaneously. In the non-standalone configuration, eNode-Bs 160a, 160b, 160c may serve as a mobility anchor for WTRUs 102a, 102b, 102c and gNBs 180a, 180b, 180c may provide additional coverage and / or throughput for servicing WTRUs 102a, 102b, 102c.

[0063] Each of the gNBs 180a, 180b, 180c may be associated with a particular cell (not shown) and may be configured to handle radio resource management decisions, handover decisions, scheduling of users in the UL and / or DL, support of network slicing, dual connectivity, interworking between NR and E-UTRA, routing of user plane data towards User Plane Function (UPF) 184a, 184b, routing of control plane information towards Access and Mobility Management Function (AMF) 182a, 182b and the like. As shown in FIG. 1D, the gNBs 180a, 180b, 180c may communicate with one another over an Xn interface.

[0064] The CN 115 shown in FIG. 1 D may include at least one AMF 182a, 182b, at least one UPF 184a, 184b, at least one Session Management Function (SMF) 183a, 183b, and possibly a Data Network (DN) 185a, 185b. While each of the foregoing elements are depicted as part of the CN 115, it will be appreciated that any of these elements may be owned and / or operated by an entity other than the CN operator.

[0065] The AMF 182a, 182b may be connected to one or more of the gNBs 180a, 180b, 180c in the RAN 113 via an N2 interface and may serve as a control node. For example, the AMF 182a, 182b may be responsible for authenticating users of the WTRUs 102a, 102b, 102c, support for network slicing (e.g., handling of different PDU sessions with different requirements), selecting a particular SMF 183a, 183b, management of the registration area, termination of NAS signaling, mobility management, and the like. Network slicing may be used by the AMF 182a, 182b in order to customize CN support for WTRUs 102a, 102b, 102c based on the types of services being utilized WTRUs 102a, 102b, 102c. For example, differentnetwork slices may be established for different use cases such as services relying on ultra-reliable low latency (URLLC) access, services relying on enhanced massive mobile broadband (eMBB) access, services for machine type communication (MTC) access, and / or the like. The AMF 162 may provide a control plane function for switching between the RAN 113 and other RANs (not shown) that employ other radio technologies, such as LTE, LTE-A, LTE-A Pro, and / or non-3GPP access technologies such as WiFi.

[0066] The SMF 183a, 183b may be connected to an AMF 182a, 182b in the CN 115 via an N11 interface. The SMF 183a, 183b may also be connected to a UPF 184a, 184b in the CN 115 via an N4 interface. The SMF 183a, 183b may select and control the UPF 184a, 184b and configure the routing of traffic through the UPF 184a, 184b. The SMF 183a, 183b may perform other functions, such as managing and allocating WTRU IP address, managing PDU sessions, controlling policy enforcement and QoS, providing downlink data notifications, and the like. A PDU session type may be IP-based, non-IP based, Ethernet-based, and the like.

[0067] The UPF 184a, 184b may be connected to one or more of the gNBs 180a, 180b, 180c in the RAN 113 via an N3 interface, which may provide the WTRUs 102a, 102b, 102c with access to packet-switched networks, such as the Internet 110, to facilitate communications between the WTRUs 102a, 102b, 102c and IP-enabled devices. The UPF 184, 184b may perform other functions, such as routing and forwarding packets, enforcing user plane policies, supporting multi-homed PDU sessions, handling user plane QoS, buffering downlink packets, providing mobility anchoring, and the like.

[0068] The CN 115 may facilitate communications with other networks. For example, the CN 115 may include, or may communicate with, an IP gateway (e.g. , an IP multimedia subsystem (IMS) server) that serves as an interface between the CN 115 and the PSTN 108. In addition, the CN 115 may provide the WTRUs 102a, 102b, 102c with access to the other networks 112, which may include other wired and / or wireless networks that are owned and / or operated by other service providers. In one embodiment, the WTRUs 102a, 102b, 102c may be connected to a local Data Network (DN) 185a, 185b through the UPF 184a, 184b via the N3 interface to the UPF 184a, 184b and an N6 interface between the UPF 184a, 184b and the DN 185a, 185b.

[0069] In view of Figures 1A-1 D, and the corresponding description of Figures 1A-1 D, one or more, or all, of the functions described herein with regard to one or more of: WTRU 102a-d, Base Station 114a-b, eNode-B 160a-c, MME 162, SGW 164, PGW 166, gNB 180a-c, AMF 182a-ab, UPF 184a-b, SMF 183a-b, DN 185a-b, and / or any other device(s) described herein, may be performed by one or more emulation devices (not shown). The emulation devices may be one or more devices configured to emulate one ormore, or all, of the functions described herein. For example, the emulation devices may be used to test other devices and / or to simulate network and / or WTRU functions.

[0070] The emulation devices may be designed to implement one or more tests of other devices in a lab environment and / or in an operator network environment. For example, the one or more emulation devices may perform the one or more, or all, functions while being fully or partially implemented and / or deployed as part of a wired and / or wireless communication network in order to test other devices within the communication network. The one or more emulation devices may perform the one or more, or all, functions while being temporarily implemented / deployed as part of a wired and / or wireless communication network. The emulation device may be directly coupled to another device for purposes of testing and / or may performing testing using over-the-air wireless communications.

[0071] The one or more emulation devices may perform the one or more, including all, functions while not being implemented / deployed as part of a wired and / or wireless communication network. For example, the emulation devices may be utilized in a testing scenario in a testing laboratory and / or a non-deployed (e.g., testing) wired and / or wireless communication network in order to implement testing of one or more components. The one or more emulation devices may be test equipment. Direct RF coupling and / or wireless communications via RF circuitry (e.g., which may include one or more antennas) may be used by the emulation devices to transmit and / or receive data.

[0072] When a wireless transmit / receive unit (WTRU) is in RRC_CONNECTED state, for example, in NR, the WTRU may measure the signal level of one or more beams of a cell and / or the measurements results may be averaged to derive the cell quality. The WTRU may be configured to consider a subset of the detected beams. Filtering may take place at one or more (e.g., two) different levels: at the physical layer (L1) level to derive beam quality and / or (e.g., then) at radio resource control (RRC) (L3) level to derive cell quality from one or more (e.g., multiple) beams. Cell quality from beam measurements may be derived in the same way for the serving cell (s) and / or for the non-serving cell (s). Measurement reports may include the measurement results of the X best beams, for example, if the WTRU is configured to do so by the gNB.

[0073] FIG. 2 depicts an example new radio (NR) measurement model 200. A (e.g., high-level) measurement model may be described herein. A WTRU may perform measurements of serving and / or neighbor cells based on a configuration received by the gNB. The WTRU can be configured to report the measurements periodically and / or when certain events are fulfilled (e.g., A3 event, where a neighbor cell’s signal quality becomes better (e.g., stronger) than the serving cell by more than a certain threshold). The WTRU may be configured with a conditional handover (CHO) configuration regarding a certain neighborcell, which may include a handover (HO) command and / or associated measurement event. When the measurement event conditions get fulfilled, for example, the WTRU may execute the HO command associated with the event (e.g. , instead of sending a measurement report).

[0074] Artificial intelligence (Al) and / or machine learning (ML) for NR may be described herein. AI / ML mobility enhancements may include network triggered L3-based handover (e.g., handover triggered by the network based on information received by the UE, such as measurement reports). Cell level measurement predictions of serving and / or neighbor cells may be included.

[0075] AI / ML enhancements for NR air interface may include beam management (BM), where (e.g., both) spatial BM (e.g., prediction of the signal level of a certain beams using the actual measurements of other beams) and / or temporal BM (e.g., prediction of the signal level of a beam at a future time based on current measurements) may be standardized.

[0076] One or more (e.g, current) mobility procedures may be reactive and / or may be based on (e.g, rely on) current measurements available at the WTRU (e.g, measurement reported to the gNB and / or gNB sending the HO command, and / or the WTRU executing a CHO when the CHO conditions are fulfilled). This may come with a big overhead on (e.g, both) the WTRU and / or the network (NW) side. For example, WTRUs may (e.g, must) continuously perform measurements of neighbor cells and / or evaluate measurement reporting and / or CHO conditions. In the case of CHO, for example, resources on one or more (e.g, several) candidate neighbor cells may (e.g, must) be reserved in anticipation of the WTRU handing over to one of these cells.

[0077] If a WTRU has a capability to do measurement prediction (e.g, based on an AI / ML model) a (e.g, more) proactive approach can be taken to performing WTRU mobility that has advantages (e.g, both) for the WTRU and / or the network. For example, the WTRU may not (e.g, need) to perform measurements (e.g, all the time), the network may not (e.g, need) to transmit on one or more (e.g, all) the beams (e.g, all the time) and / or (e.g, also) can allocate resources (e.g, for CHO) on a timely manner.

[0078] With the advancement of AI / ML mechanisms, it may be anticipated that models can be trained that can predict the cell level measurements. A model can be trained to infer cell level measurements directly and / or if the WTRU is able to do beam predictions, the beam level predictions, along with actual beam measurements, can be used to derive the cell level measurements. The latter may have advantages over the former in that the network can have (e.g, more) control in configuring the WTRU on how cell level measurement predictions are performed.

[0079] Systems, methods, and / or apparatuses described herein may relate to how to perform cell level measurement prediction and / or derivation and / or L3 measurement filtering based on predicted beam measurements and / or actual measured beams.

[0080] A WTRU may be configured to send capability information regarding beam measurement prediction. A WTRU may be configured with cell level measurement derivation and / or L3 measurement filtering configuration that considers predicted beams measured beams. A WTRU may be configured with measurement reporting configuration with triggering conditions that are related to cell level measurements that are derived considering measured and / or derived beams of serving and / or neighbor cells. A WTRU may be configured to indicate information related to predicted measurements in the measurement report. A WTRI may be configured to monitor the configured measurement triggering conditions, and / or send the measurement report, including additional information related to predicted measurements.

[0081] Methods for deriving cell measurements that utilizes the combination of the signal levels of predicted beams and / or measured beams, based on a combining / filtering configuration received from the network, may be described herein.

[0082] A WTRU may send capability information regarding beam measurement predictions (e.g., based on AI / ML model). Capability may include the type of beam measurement prediction (e.g., spatial, temporal) and / or additional details (e.g., number of beams that can be predicted, number of beams that need to be measured to perform the predictions on others, confidence level, other additional conditions like supported WTRU speed, frequencies, etc.). The WTRU may send (e.g., via the transceiver) capability information. The capability information may indicate that the WTRU and / or processor is capable of predicting beam measurements. The capability information may include a beam prediction type, a number of beams that can be predicted, a number of beams that are to be measured to determine the second subset of beams associated with the one or more predicted beam measurements, a confidence level associated with the prediction of the second subset of beams, or one or more conditions under which an artificial intelligence I machine leaning (AI / ML) model is to operate.

[0083] The WTRU may use spatial beam prediction to predict one or more beams. The beam consolidation procedure may select one or more of the measured beams and / or may determine the cell level measurements. The WTRU may apply filtering to the one or more predicted beams to determine a subset of predicted beams. The WTRU may use temporal prediction at cell level and / or at beam level.

[0084] With respect to temporal beam prediction, the WTRU may be measuring one or more (e.g., all) of the beams and / or the WTRU may predict one or more beam measurements in time. For example, theWTRU may measure the beam(s) for a first time period (e.g., Xms). The WTRU may, based on the measured beams for Xms, for example, predict the beams for a second time period (e.g., Yms). The WTRU may be configured to perform one or more measurements every x ms, where x may represent the measurement periodicity. If the WTRU has an AI / ML model for predicting the measurement(s), for example, the WTRU may not (e.g., need to) perform one or more measurements (e.g., all the time). For example, the WTRU may perform one measurement, predict one measurement, and so on. In example, the WTRU may measure a certain number of samples; the WTRU may use these samples to predict a certain number of (e.g., future) samples (and / or may skip from measuring them). For example, if the periodicity for measurement is 50ms (e.g., WTRU may take a sample every 50ms), the WTRU may be configured with an observation window of 400ms and / or prediction window of 400ms; the WTRU may measure eight (e.g., consecutive) samples and / or may use the sample(s) to predict (e.g., the next) eight samples, and / or may skip from performing the measurements at these (e.g., next) eight time instances, and so on. For example, the WTRU may perform (e.g., actual) beam measurements during the first time period (e.g., at each measurement periodicity). For example, the WTRU may predict beam(s) during the second time period (e.g., Yms). Additionally or alternatively, the WTRU may employ an (e.g., AIML) model to predict cell level measurements). For example, during the second time duration (e.g., Yms), the (e.g., AIML) model may predict cell level measurements) (e.g., directly, at each measurement periodicity). The WTRU may perform beam level measurements) and / or may consolidate the beam level measurement(s) (e.g., linear average of the top n beams) to determine the cell level measurements). If the cell has (e.g., only) one beam, for example, the cell level measurement and beam level measurement may be the same; the WTRU may determine that the cell level measurement and beam level measurement are the same. With respect to a WTRU configured to predict one or more measurements, for example, the WTRU may include an AI / ML that can be trained to perform beam level and / or cell level prediction (e.g., measure one or more beams, predict one or more beams, and / or may consolidate the one or more measured beams and / or the one or more predicted beams). Additionally or alternatively, the WTRU may use an AI / ML model to use (e.g., current) measurement(s) and / or other (e.g, historical) cell level measurement(s) to (e.g, directly) output one or more predicted cell level measurement(s).

[0085] A WTRU may receive a cell level measurement derivation and / or measurement filtering configuration applicable to predicted (e.g, non-transmitted) beam measurements and / or transmitted (e.g, actual) beam measurements (e.g, multiple / different beam consolidation thresholds for predicted and / or measured beams, maximum / minimum number of predicted and / or measured beams to consolidate,different L3 filtering coefficients / parameters that are related to measured beams, etc.). For example, the WTRU may receive (e.g., via the transceiver) configuration information. The configuration information may include first configuration information to perform beam measurements, second configuration information to predict beam measurements, third configuration information to derive cell level measurement(s) based on, for example, one or more performed beam measurements and / or one or more derived (e.g., predicted) beam measurements, fourth configuration to filter the unfiltered cell level measurements, and / or fifth configuration information to send a measurement report. The third configuration information to derive and / or determine unfiltered cell level measurement(s) may include beam consolidation criteria. The beam consolidation criteria may indicate one or more of the following: a maximum number of measured beams to be included in the consolidation, a maximum number of predicted measured beams to be included in the consolidation, a maximum total number of measured or predicted beams to be included in the consolidation, a (e.g., signal level) threshold associated with each beam measurement of the first subset of beams, a (e.g., signal level) threshold associated with each predicted measurement of the second subset of beams, and / or a first (e.g., averaging) weighting factor associated with each predicted beam measurement (e.g., to determine the unfiltered cell level measurement(s), and / or a second (e.g., averaging weighting factor associated with each beam measurement (e.g., to determine the unfiltered cell level measurement(s). The fourth configuration information may include spatial-based configuration information and / or temporal-based configuration information. The fourth configuration information may include a first coefficient associated with the one or more beam measurements and / or a second coefficient associated with the one or more predicted beam measurements.

[0086] A WTRU may receive measurement reporting configuration (e.g., fifth configuration information), where the configuration may include conditions and / or thresholds related to cell level measurement derived from transmitted beam measurements (e.g., only), conditions and / or thresholds related to cell level measurements derived from predicted beam measurements (e.g., only), and / or conditions / thresholds related to cell level measurements derived from transmitted beam measurements and / or predicted beam measurements. Configuration may (e.g., further) include information to be included in the measurement report related to predicted measurements (e.g., predicted measurements, indication if predicted beams were considered, indication of (each) predicted beams if beam measurements are included in the report, etc.,).

[0087] A WTRU may perform measurements of beams and / or beam predictions and / or may derive the cell level measurements according to the configuration received (e.g., as described herein), and / or may monitormeasurement reporting conditions received (e.g.., as described herein). For example, the WTRU may determine, in accordance with the second configuration information, one or more predicted beam measurements of a second subset of beams. The WTRU may determine, in accordance with the third configuration information, unfiltered cell level measurement(s) based on the first subset of beams and / or the second subset of beams. For example, the first subset of beams may be and / or include measured beam(s); the WTRU may use the measure beam(s) to predict the second subset of beams. For example, the WTRU may input one or more measured beams and / or the fist subset of beams into an AI / ML model to output the second subset of beams. The WTRU may determine, in accordance with the fourth configuration information, the filtered cell level measurements based on the unfiltered cell level measurements. The WTRU may determine the unfiltered cel level measurement(s) based on the beam consolidation criteria. The WTRU may determine the filtered cell level measurement(s) based on the spatial-based configuration information and / or the temporal-based configuration information. The WTRU may use the first coefficient and / or the second coefficient to determine the filtered cell level measurements. The WTRU may use an artificial intelligence I machine learning (AI / ML) model to predict the second subset of beams.

[0088] The WTRU may determine one or more (e.g., direct) cell level measurements based on the temporal-based configuration information. The WTRU may determine the filtered cell level measurements based on the one or more (e.g., direct) cell level measurements.

[0089] When the measurement reporting conditions are fulfilled, for example, the WTRU may send the measurement report (e.g., including the information related to predicted measurements configured to be included as described herein). For example, the WTRU may send (e.g., via the transceiver) the measurement report in accordance with the fifth configuration information. The measurement report may include an indication of the filtered cell level measurement(s). The WTRU may send the measurement report based on a comparison of the first set of cell level measurements and the second set of cell level measurements.

[0090] Systems, methods, and / or apparatuses described herein may enable cell level measurement predictions and / or derivations for a WTRU that is capable of beam level measurement predictions, giving the network control on how to consolidate the measured and / or derived beam measurements. Based on these consolidated cell level measurements, for example, (e.g., more) proactive and / or optimal mobility decisions can be made.

[0091] The terms AI / ML and AIML may be used interchangeably. The terms data, measurements, report, and / or results may be used interchangeably. The terms starting conditions and validity conditions may beused interchangeably. The terms indication, information, and message may be used interchangeably. The terms serving cell and source cell may be used interchangeably. The terms target cell and candidate cell may be used interchangeably.

[0092] The term Ax may be used to refer to one or more (e.g., any) of the events A1 , A2, A3, A4, A5, and / or A6. The events may be referred to as: Event A1 (Serving becomes better than threshold); Event A2 (Serving becomes worse than threshold); Event A3 (Neighbor becomes offset better than SpCell, where SpCell may be the Primary Cell, PCell, and / or the Primary Secondary Cell, PSCell, in the case of dual connectivity); Event A4 (Neighbor becomes better than threshold). Event A5 (SpCell becomes worse than thresholdl and neighbor becomes better than threshold2); and / or Event A6 (Neighbor becomes offset better than SCell, where an SCell is a Secondary Cell in the case of carrier aggregation).

[0093] The term Bx may be used to refer to one or more (e.g., any) of the events B1 , B2, where the events may be referred to as: Event B1 (Inter radio access technology (RAT) neighbor becomes better than threshold); and / or Event B2 (PCell becomes worse than thresholdl and inter RAT neighbor becomes better than threshold2).

[0094] Systems, methods, and / or apparatuses described herein may relate to (e.g., both) beam prediction based on AI / ML models and / or one or more (e.g., any) other forms of prediction that doesn’t use AIML (e.g. time series forecasting, interpolation methods, etc.,)

[0095] One or more (e.g., all) the systems, methods, and / or apparatuses described herein may be agnostic to the kind of Al. ML model / technique used by the WTRU (e.g., the algorithm used, the mechanism such as neural network and / or what kind of neural network, e.g., depth and parameters / weights of the network, etc.,), the origins of the model (e.g., WTRU vendor, operator, network vendor, etc.), and / or how and / or where the training of the model is done (e.g., the input data used for the training, where the training is performed, if the training is performed offline and / or online, etc.). The model may be trained based on historical observation of one or more UEs’ actual measurements in different WTRU and / or network conditions (e.g., during certain time durations of the day, during certain days of the week, at different locations, different WTRU mobility patterns / speeds, under different network conditions that are visible to the WTRU such as frequency / bandwidth, etc., under different network configurations, which may be visible to the WTRU just as a network configuration index that is provided by the network at the time of training and / or data collection for the training, etc.,).

[0096] One or more of the following may be determined (e.g., assumed). There may be one or more (e.g., some) WTRU capability(ies) communication between the WTRU and the network about AI / ML capability(e.g., where the WTRU can indicate to the network the supported AIML models / fu notions, confidence level of predictions, time horizon of predictions (how far along in the future are the prediction being made, etc.). The WTRU may support one or more (e.g., several) AI / ML models for a certain functionality (e.g., with different prediction time horizons, prediction confidence levels, processing requirements, trained under / for operation in different frequencies / cells / location / times of day, etc.). A given AI / ML model can operate in different modes (e.g., with different levels of prediction confidence levels at different prediction time horizons, at different locations, frequencies, WTRU mobility pattern / speed, etc.). The WTRU may choose the AI / ML model to use for a certain functionality (e.g., network may decide for which functionalities the WTRU can use AI / ML based operation, and / or the WTRU may choose the AI / ML model to use), and / or the network may (e.g., explicitly) control this (e.g., WTRU may provide details of AI / ML models and / or their capabilities, the network may determine which model to activate for a particular functionality). The AI / ML models can be available at the WTRU already trained, and / or the WTRU may be provided with an untrained AI / ML model and / or may perform the training by itself. The AI / ML model may be available at the WTRU already trained, and / or the WTRU may be enabled and / or configured to perform further training (e.g., for different conditions such as freq uencies / cells / location / times of day, for the same conditions as the initial training but for increasing the level of confidence and / or the prediction time horizon, for different WTRU speeds, etc.). The AI / ML model may be available at the WTRU but not trained (e.g., at all) and / or (e.g., only) trained for certain WTRU / network conditions, and / or the WTRU may be configured to train the model (e.g. for the conditions that it is not trained for).

[0097] Systems, methods, and / or apparatuses described herein may not relate to the Life Cycle Management (LCM) of the beam / cell measurement prediction models / functionality. It may be determined (e.g., assumed) that the WTRU is using a certain model for beam prediction that has been trained and / or performance tested for the current WTRU and / or network conditions. One or more systems, methods, and / or apparatuses described herein can be used to enable one or more (e.g., some) LCM aspects. For example, the measurement results the WTRU provides that include actual measurement, predicted measurements, and / or combined measurements that are derived and / or calculated considering (e.g., both) actual measurements and / or predicted measurements, according to one or more (e.g., any) of the systems, methods, and / or apparatuses described herein, can be collected, and / or used for performance monitoring and / or model retraining. In examples, the WTRU can (e.g., also) be configured to do actual measurements of one or more (e.g., some) of the beams in parallel with using the AI / ML model to predict the beams (e.g., in temporal and / or spatial manner), compare the actual measurements and the predicted ones, and / ordecide to switch from one model to another and so on, for example, based on this comparison. A WTRU may be configured to predict beam measurement(s) (e.g., to save battery). For example, a certain beam can (e.g., actually) be transmitted (e.g., at long periodicity) and / or the WTRU may be configured to predict beam measurements in between the transmissions.

[0098] A WTRU may indicate beam measurement prediction capability. In examples, a WTRU may indicate to the network that the WTRU is capable of predicting beam level measurements. The capability may include one or more of the following. The capability may include beam prediction type. For example, beam prediction type may include temporal prediction (e.g., prediction of the signal level of a beam at a future time instance based on, for example, current and / or historical signal levels of the beam). For example, beam prediction type may include spatial prediction (e.g., prediction of the signal level of one beam based on the signal level of another beam, for example, beam of the same cell, beam of a different cell, etc.). The capability may include the number of beams that can be predicted. The capability may include the number of beams that need to be measured to do the predictions. The capability may include the confidence level of the predictions. The capability may include conditions under which the AI / ML functionality and / or models can operate at (e.g., the models and / or functionality were trained under the indicated conditions, where the performance of the models and / or functionality has been tested and / or shown to work properly, etc.), where the conditions may include WTRU and / or network conditions (e.g., WTRU mobility state, WTRU location, cells / frequencies, time of day, network configuration index, etc.).

[0099] The capability may be provided at a functionality level (e.g., WTRU not explicitly indicating the number / identity of the models it is using, but simply providing the overall capability of the one or more models for the beam prediction capability), and / or it can be model level (e.g., WTRU providing explicit information about each model it has for the beam prediction functionality and / or associated capability information for each model).

[0100] The capability information may be provided autonomously by the WTRU (e.g., upon connection setup / resume, upon handover, upon detecting that the WTRU has entered a new cell / region / RAT where the capability regarding beam prediction is different from previously reported capability, etc.), and / or based on an explicit request from the network.

[0101] If capability information is requested from the network, for example, the request may be a generic request (in which case WTRU may provide one or more (e.g., all) its capabilities), and / or it can be a more granular request. For example, the WTRU may receive a request from the network to determine whether the WTRU supports beam prediction at a certain frequency layer. The WTRU may respond with one ormore of the following. The WTRU may respond with an indication that it does not support that, or the WTRU may respond with an indication that the WTRU supports that and / or detailed information about the capability regarding prediction of beam at the frequency layer (e.g., summarized information at functionality level, detailed information for each AI / ML model that supported beam prediction at that frequency layer, etc.).

[0102] Configuration on how to select predicted beams for cell level measurement consolidation may be described herein. One or more different systems, methods, and / or apparatuses may be described for configuring the WTRU with cell level measurement derivation based on measured and / or predicted beams at a given cell level measurement derivation instance.

[0103] When it comes to which beams are selected for cell level measurement derivation (e.g., in NR) one or more of the following information elements (lEs) may be defined for each measurement object (e.g., each NR frequency being measured). For example, nrofCSI-RS-ResourcesToA verage may refer to the maximum number of measurement results per beam based on CSI-RS resources to be averaged. For example, nrofSS-BlocksToAverage may refer to the maximum number of measurement results per beam based on SS / PBCH blocks to be averaged. For example, absThreshCSI-RS-Consolidation may refer to an absolute threshold for the consolidation of measurement results per CSI-RS resource(s) from L1 filter(s). For example, absThreshSS-BlocksConsolidation may refer to an absolute threshold for the consolidation of measurement results per SS / PBCH block(s) from L1 filter(s).

[0104] A WTRU may perform the cell level derivation as described herein.

[0105] If the cell measurement was configured to be derived based on measured synchronization signal (SS) blocks, and the nrofSS-BlocksToAverage and / or absThreshSS-BlocksConsolidation is not configured, and / or if the highest measured beam based on SS is below the absThreshSS-BlocksConsolidation, the WTRU may consider the highest SS beam measurement as the cell level measurement. Otherwise, the WTRU may derive the cell level measurement as the linear power average of the SS beams with a value above the absThreshSS-BlocksConsolidation (but considering not more than nrofSS-BlocksToAverage beams).

[0106] If the cell measurement was configured to be derived based on measured CSI-RS, and if the nrofCSI-RS-ResourcesToAverage and / or absThreshCSI-RS-Consolidation is not configured, and / or if the highest measured beam based on CSI-RS is below the absThreshCSI-RS-Consolidation, the WTRU may consider the highest CSI-RS beam measurement as the cell level measurement. Otherwise, the WTRU may derive the cell level measurement as the linear power average of the CSI-RS beams with a valueabove the absThreshCSI-RS-Consolidation (but considering not more than nrofCSI-RS- ResourcesTo Average beams).

[0107] In examples, a WTRU may be configured to consider measured and / or predicted beams equally in the cell level measurement determination. For example, consider the case where the WTRU measures n1 beams and predicts n2 beams. At a given cell level measurement derivation instance, the WTRU may perform one or more of the following. The WTRU may arrange the beams in order of beam value (e.g. , disregarding if the beams were measured or predicted). The WTRU may select the beams that have values above the consolidation threshold (e.g., and / or may consider not more than the maximum beams that can be consolidated). The WTRU may derive the cell level measurement as the linear average of the selected beams.

[0108] In examples, the WTRU may be configured with different consolidation thresholds and / or resources / blocks to average when it comes to predicted beams. For example, the WTRU may be configured with nrofPredictedCSI-RS-ResourcesToAverage, nrofPredictedSS-BlocksToAverage, absThreshPredictedCSI-RS-Consolidation, and / or absThreshPredictedSS-BlocksConsolidation.

[0109] In examples, (e.g., instead of different values for the predicted ones), the WTRU may be configured a scaling factor for deriving the number of resources to average and / or the thresholds to consider for predicted beams based on the values corresponding the measured beams. For example, this could be configured to be the same for one or more (e.g., all) frequencies (e.g., the WTRU may not need to receive the configuration with each measurement object configuration corresponding to the different frequencies to reduce the required signaling).

[0110] In examples, the WTRU may be configured to derive the cell measurements based on measured and / or predicted CSI-RS beams independently and / or calculate the cell level measured as the linear average of the two. For example, the WTRU may derive the following intermediate values to derive the cell measurements. The WTRU may derive measured_value as: Measured_value = linear power average of the measured CSI-RS beams with a value above the absThreshCSI-RS-Consolidation (but considering not more than nrofCSI-RS-ResourcesToAverage beams). The WTRU may derive predicted value as: Predicted_Value = linear power average of the predicted CSI-RS beams with a value above the absThreshPredictedCSI-RS-Consolidation (but considering not more than nrofPredictedCSI-RS- ResourcesToAverage beams).

[0111] The cell measurement may be derived as the linear average of measured_value and predicted_value.

[0112] In examples, the WTRU may be configured to perform a weighted averaging of the measured_value and the predicted_val ue. The weighting may consider the number of measured and / or predicted beams in the calculation of the measured_value and predicted_value. For example, if n1 beams were considered in deriving the measured_value (e.g., there were n1 measured beams that have value above absThreshCSI-RS-Consolidation, if measurement were based on CSI-RS, and / or absThreshSS- BlocksConsol / dat / on, if measurements were based on SS), and / or n2 beams were considered in deriving the predicted_value (e.g., there were n2 predicted beams that have value above absThreshPredictedCSI- RS-Consolidation, if measurement were based on CSI-RS, and / or absThreshPredictedSS- BlocksConsolidation, if measurements were based on SS), (e.g., then) the cell measurement may be derived as: Cell measurement = (n1*measured_value + n2*predicted_value) / (n1 +n2). Since measured_value = sum of the n1 measured beams divided by n1 , and predicted_value is equal to sum of the n2 predicted beams divided by n2, the above equation may simplify to: Cell measurements = (sum of the values of the measured beams with values above the measured beams consolidation threshold + sum of the values of the predicted beams with values above the predicted beams consolidation threshold) I (number of measured and predicted beams considered in the consolidation).

[0113] In examples, the WTRU may be configured with an explicit weighting factor to use when determining the cell measurement from measured and / or predicted beams. For example, the WTRU may be configured with a weighing factor of alp ha_1 , and may determine the cell measurements as: Cell measurement = alpha_1*measured_value + (1-alpaha_1)*predicted_value.

[0114] In examples, the WTRU may be configured to consider (e.g., both) an explicit weighting factor and / or the number of measured and / or predicted beams that were considered in the cell level derivation. For example, the WTRU may derive the cell measurement as: Alpha_2= alpha_1 * n1 / (n1 +n2) (e.g., putting more weight on measured beams if there were more measured beams than predicted beams). Additionally or alternatively, the WTRU may derive cell measurement as: Cell measurement = alpha_2*measured_value + (1-alpaha_2)*predicted_value.

[0115] In examples, the WTRU may consider (e.g., only) measured beams in the cell measurement derivation if the number of the measured beams that fulfill the measured beam consolidation are greater than or equal to the maximum number of measured beams that can be considered for consolidation. For example, the WTRU may not consider predicted beams in the cell level measurement derivation if the number of measured beams that have values above absThreshCSI-RS-Consolidation is greater than or equal to nrofCSI-RS-ResourcesToAverage (e.g., in the case of measurements based on CSI-RS), and / orthe number of beams that have values above absThreshSS-BlocksConsolidation is greater than or equal to nrofSS-BlocksToA verage (e.g., in the case of measurements based on SS).

[0116] In examples, if the WTRU determines that the number of measured beams that fulfill the consolidation threshold for the measured beams is less than the maximum number of measured beams that can be considered for consolidation, the WTRU can consider predicted beams in the consolidation. For example, if N1 = (nrofCSI-RS-ResourcesToAverage) - (number of measured beams that have values above absThreshCSI-RS-Consolidation), (e.g., then) the WTRU may consider up to N1 predicted beams for the cell level measurement consolidation (e.g., among the predicted beams that fulfill the predicted beam consolidation threshold). A similar behavior can be applied for SS based measurements.

[0117] In one or more (e.g., most) of the systems, methods, and / or apparatuses described herein, the focus may be related (e.g., mainly) to spatial beam prediction (e.g., WTRU predicting some beams based on other beams and / or other input such as WTRU mobility, location, etc.). Additionally or alternatively, another beam prediction type may be temporal prediction. For example, for a given cell, the WTRU may perform measurements of n1 beams, and / or the prediction may be the measurements of these n1 beams at one or more future time instances.

[0118] In examples, the WTRU may be configured to perform the beam measurements at certain time intervals and / or may use predicted measurements at certain instances between two actual measurements. One example is shown herein: At TO, the WTRU may perform beam measurements and / or may predict the measurements at TO+dt, T0+2dt and T0+3dt ; At T1 (e.g., T0+4dt), the WTRU may perform beam measurements and / or may predict the measurements at T1 +dt, T1 +2dt, T 1 +3dt ; and / or so on.

[0119] In such a case of temporal beam prediction, for example, the WTRU may be configured to derive the cell level measurements at the instances where actual measurements are performed (e.g., TO, T1 , etc., in the example herein), using the configured beam consolidation threshold and / or configured number of beams to consolidate for measured beams, as described herein.

[0120] Similarly, at the instances where one or more (e.g., all) the beams were being predicted (e.g., TO+dt, T0+2dt, T0+3dt, T1 +dt, etc.,), the WTRU may be configured to derive the cell level measurements using the configured beam consolidation threshold and / or the configured number of beams to consolidate for predicted beams, as described herein.

[0121] In examples, when deriving the cell level measurements that consider measured beams and / or predicted beams, according to one or more (e.g., any) of the systems, methods, and / or apparatuses described herein, the WTRU may be configured to consider the prediction confidence (e.g., scaling factorsdepending on confidence levels, different values and / or thresholds for different confidence levels and / or range of confidence levels, etc.).

[0122] In examples, the WTRU may be configured to consider a predicted beam for consolidation in the cell level measurement according to one or more (e.g., any) of the systems, methods, and / or apparatuses described herein, (e.g., only) if the confidence level of the beam prediction is above a certain threshold.

[0123] In examples, the WTRU may be configured with different parameters (e.g., any of the parameters described herein) that is associated with different beam prediction confidence values or range of values. The WTRU may be configured with one or more of the following. For example, the WTRU may be configured with different values for the number of predicted beams that can be considered in the cell level derivation (e.g., n1 if confidence is below thresholdl, n2>n1 : if the confidence is above thresholdl but below threshold2, n3: if the confidence level is above threshold2, etc.). For example, the WTRU may be configured with different values for the predicted beam consolidation threshold (e.g., threshold_a: if confidence is below thresholdl, threshold_b: if the confidence is above thresholdl but below threshold2, threshold^: if the confidence level is above threshold2, etc.). For example, the WTRU may be configured with different weighting factors for different confidence levels (e.g., if predication confidence is high, the weight attributed for predicted values can be higher than the case where prediction confidence is low).

[0124] The confidence level of the beam prediction can be beam specific and / or the same for one or more (e.g., all) predicted beams (e.g., at a given prediction instance). The systems, methods, and / or apparatuses described herein regarding the confidence level consideration can (e.g., also) be beam specific instead of being the same for one or more (e.g., all) beams. For example, at a given prediction instance, the WTRU may determine different prediction confidence values for the different predicted beams and / or consider (e.g., only) the beams that have associated predicted confidence level above the configured threshold level for cell level measurements. In examples, the WTRU may be configured to categorize the predicted beams into different groups based on confidence levels and / or confidence level ranges, and / or may apply a weighting factor that is specific for each group, etc.

[0125] Configuration on how to perform L3 filtering of cell level measurements that are based on measured and / or predicted beams may be described herein. In examples, the WTRU may apply L3 filtering of cell level measurements that were derived based on measured and / or predicted beams, according to one or more (e.g., any) of the systems, methods, and / or apparatuses described herein, using other (e.g., legacy) L3 filtering coefficients provided for L3 filtering. That is, Filteredjneasurement = (1- alpha)*old_filtered_measurement + alpha *current_measurement. For example, for one or more (e.g.,every) measurement filtering instances, the WTRLI may take the current measurements and / or the WTRU may perform L3 filtering (e.g., V1 = alpha*current_measurement+(1-alpha)*v0). At the next measurement filtering instance, for example, the WTRU may perform L3 filtering (e.g., V2 = alpha*current_measurement+(1-alpha)*V1), and so on. The term current jneasurement may be the cell level measurement derived according to the methods, apparatuses, and / or systems described herein considering measured and / or predicted beams. The term old_filtered_measurement may be the last (e.g., previous) filtered measurement. The term filteredjneasurement may be the current L3 filtered measurement.

[0126] Alpha may be a weighting factor that is derived from one or more filtered coefficients the WTRU is configured with (e.g., according to the derivation specified in 3GPP).

[0127] In examples, the WTRU may be configured with two different sets of coefficients. For example one set of coefficients may be to derive the alpha value for cells whose cell level derivation is based (e.g., only) on measured beams (e.g., as in legacy). For example, another set of coefficients may relate to deriving the alpha to be used for cells whose cell level derivation is based on measured and / or predicted beams.

[0128] In examples, the WTRU may be configured to do the cell level measurement derivation separately for measured beams and predicted beams, and / or (e.g., then) combine them during L3 filtering. For example, the WTRU may calculate Filteredjneasured using alphal and Filtered_predicted using alpha2 and / or (e.g., then) combine the two (e.g., Filteredjneasured and Filtered predicted) using a configured weighted averaging factor. For example the combination of Filteredjneasured and Filtered Dredicted may include: L3 filtered result = w1*filtered_measured + (1 -w1 )*filtered_predicted.

[0129] In the case of temporal beam prediction, for example, the WTRU may be configured to apply different alphas (e.g., configured with different coefficients that results in different alphas), if the L3 filtering is being done at the time instance where the beam measurements were actually being performed and / or the instances the beam measurements were being predicted. Referring again to the example described herein, for example, at TO: the WTRU may perform beam measurements and / or may predict the measurements at TO+ t, T0+2dt and T0+3dt. At T1 (e.g., T0+4dt), the WTRU may perform beam measurements and / or may predict the measurements at T1+dt, T1 +2dt, T1+3dt, and so on.

[0130] At TO, since beams were being measured, the WTRU may use the alpha that is associated with actual measurements. For example, Filteredjneasurement |T0] = (1-alpha)*old_filtered_measurement + alpha *measurement|T0].

[0131] At TO-Kit, since the measurement at that time is predicted measurement, the filtering may be done using the alpha value associated with predicted measurements. For example, Filteredjneasurement |TO+dt] = (1-alpha1)*old_filtered_measurement + alphal *predicted_measurement|TO+dt].

[0132] The measurement [TO] may be the cell level measurement that is the consolidation of actual measured beams, while the measurement TO-nit] is the cell level measurement that may be the consolidation of predicted beams (e.g., according to the configured beam consolidation threshold and number of beams to consolidate for measured and predicted beams, as described herein).

[0133] The confidence levels of predictions could (e.g., also) be used to modify the L3 filtering behavior, as in the case of cell level measurement derivation described herein. For example, in the temporal prediction case described above, the WTRU may be configured with different alphal values (e.g., coefficients that lead to those alpha values) corresponding to different prediction confidence levels and / or scaling factors that are used to increase and / or decrease the alphal values depending on the prediction coefficient. Since the prediction cell level measurement at a given instance is a combination of one or more (e.g., several) predicted beams at that time instance, for example, the WTRU may be configured to derive the confidence level of the cell level measurement from the confidence level of the individual beam predictions (e.g., average / minimum / maximum / mean, etc., of the individual beam level prediction confidence levels, etc.,), and, based on this determined confidence level, for example, may choose and / or determine the alphal .

[0134] In examples, where the beam derivation is spatial (e.g., at a given filtering instance, the current value may be a combination of measured and / or predicted beams, for example, according to any of the solutions described herein), then the cell level measurement derivation may (e.g., then) be associated with the confidence level for that measurement according to how many of the consolidated beams on deriving that particular cell level measurement were predicted, and / or the confidence level of these measurements.

[0135] In examples, if none of the derived beams were considered in the cell level derivation at a given cell level derivation instance, for example, because there were more measured beams than the required number of beams for consolidation that fulfilled the beam consolidation threshold, the confidence level of that derived cell measurement can be considered to be 100%.

[0136] In examples, if none of the measured beams were considered in the cell level derivation at a given cell level derivation instance, for example, because the (e.g., only) beams that fulfilled the beam consolidation threshold were predicted beams, (e.g., then) the confidence level of that derived cellmeasurement can be considered to be the average / minimum / maximum, etc. of the predicted beams that were considered in the cell level derivation.

[0137] Measurement reporting configurations may be described herein. In examples, the WTRU may be configured to report cell level measurements that consider predicted and / or measured beams (e.g., cell level measurements and / or filtered cell level measurements, according to any of the methods, systems, and / or apparatuses described herein), periodically.

[0138] In examples, the WTRU may be configured with different periodicity configurations depending on if the cell measurements consider predicted beams or not. For example, the WTRU may be configured with a measurement object of a certain frequency, and / or there may be 2 cells operating at that frequency (e.g., cell A and cell B), where the WTRU may be configured to measure one or more (e.g., all) of the beams of cell A, and measure (e.g., only) a fraction of the beams of cell B. The reporting configuration associated with the measurement object for that frequency (e.g., applicable to both cells A and B) may include different reporting periodicities for cell A and cell B. For example, the WTRU may report measurements of cell A more frequently than measurements of cell B, or vice versa. In examples, the WTRU may be configured with one periodicity, to be used for cells whose cell measurement is based (e.g., only) on actual measurements, and / or a scaling factor to be applied to determine the periodicity for reporting cell measurements that are based on actual measured and / or predicted values.

[0139] In examples, the WTRU may be configured to trigger a measurement report based on cell level measurements that considered (e.g., only) actual measured beams. For example, the WTRU may be performing cell level measurement derivation and / or filtering (e.g., as in legacy, for example, based on measured beams / cells) while also performing the cell level measurement derivation and / or filtering that considers predicted values according to one or more (e.g., any) of the methods, systems, and / or apparatuses described herein. The WTRU may be configured with measurement events (Ax / Bx, etc,), and / or may check if the filtered measurements that considered (e.g, only) actual measurements fulfill the triggering conditions and / or thresholds. When the conditions get fulfilled, for example, the WTRU may send a measurement report.

[0140] In examples, the WTRU may be configured to trigger a measurement report based on cell level measurements that consider actual and / or predicted measurements. The WTRU may be configured with measurement events (Ax / Bx, etc,), and / or may check if the filtered measurements that considered (e.g, both) actual measurements and / or predicted measurements fulfill the triggering conditions and / or thresholds. When the conditions get fulfilled, for example, the WTRU may send a measurement report.

[0141] In examples, the WTRU may be configured to trigger a measurement report based on cell level measurements that (e.g., also) consider (e.g., only) predicted measurements (e.g, if the WTRU was doing the cell level derivation from the predicted beams only separately and / or doing the filtering of the predicted cell level measurements separately, etc.,). The WTRU may be configured with measurement events (Ax / Bx, etc.,), and / or may check if the filtered measurements that considered (e.g., only) predicted measurements fulfill the triggering conditions and / or thresholds. When the conditions get fulfilled, for example, the WTRU may send a measurement report.

[0142] In examples, the WTRU may be configured to trigger a measurement report based on the comparison of one or more of the following. For example, the WTRU may be configured to trigger a measurement report based on filtered cell level measurements that considered (e.g., only) actual measured values is lower / higher than filtered cell level measurement that considered (e.g., only) predicted measurements by more than a certain threshold. For example, the WTRU may be configured to trigger a measurement report based on filtered cell level measurements that considered (e.g., only) actual measured values is lower / higher than filtered cell level measurement that considered (e.g., both) actual and / or predicted measurements by more than a certain threshold. For example, the WTRU may be configured to trigger a measurement report based on filtered cell level measurements that considered (e.g., only) predicted measurements is lower / higher than filtered cell level measurement that considered (e.g., both) actual and / or predicted measurements by more than a certain threshold.

[0143] Contents of the measurement report may be described herein. The measurement report triggered according to one or more of the systems, methods, and / or apparatuses described herein may include the filtered cell level measurements that considered (e.g., only) actual measurements. The measurement report triggered according to one or more of the systems, methods, and / or apparatuses described herein may include the filtered cell level measurements that considered (e.g, only) predicted measurements. The measurement report triggered according to one or more of the systems, methods, and / or apparatuses described herein may include the filtered cell level measurements that considered (e.g, both) actual and / or predicted measurements.

[0144] In examples, the WTRU may be configured to include in the report (e.g, only) the filtered beam measurements of actual measured beams (e.g, in the case of spatial beam prediction).

[0145] In examples, the WTRU may be configured to include in the report (e.g, only) the filtered beam measurements of predicted beams.

[0146] In examples, the WTRU may be configured to include in the report the filtered beam measurements of (e.g., both) measured and / or predicted beams.

[0147] In examples, the WTRU may be configured with the maximum number of total beams to include in the report (e.g., as in legacy maxNrofRS-lndexesToReport IE in ReportConfigNR), and / or if the WTRU was configured to include L3 filtered results of actual measured and / or predicted beams, the WTRU may sort one or more (e.g., all) of the L3 beam measurements (e.g., regardless of the measurement being actual or predicted) and / or may include the top maxNrOfRS-lndexesToReport number of beams (e.g., if they have values above the beam consolidation threshold). For example, the WTRU may include results of beams x, y and z, where x and y are actual measured beams and z is a predicted beam.

[0148] In examples, the WTRU may be configured with separate maximum number of actual measured beams and / or maximum number of predicted beams to include in the measurement report.

[0149] If the WTRU is configured to include beam measurements in the measurement report, the WTRU (e.g., also) may apply L3 beam filtering of the beam measurements (e.g., the beam measurements that are included in the measurement report may be one shot beam measurements at the time of measurement reporting, but the filtered measurements of the individual beams that are being measurements). One or more (e.g., all) the systems, methods, and / or apparatuses for the L3 filtering of cell level measurements described herein can be equally applied for the beam level measurements (e.g., possibly with different filtering configurations, such as filtering coefficients, to be used for the beam filtering as compared to the cell level measurement filtering).

[0150] One or more (e.g., all) of the systems, methods, and / or apparatuses described herein for the cell level measurement derivation that consider measured and / or predicted beams can be configured at a cell / frequency level. That may mean the cell level derivation configuration for the source and / or target cells can be different. The WTRU may derive the cell level measurements for the serving cell according to the cell level measurement derivation configuration for the serving cell, and / or the cell level measurements for the target cell according to the cell level measurement derivation configuration for the target cell. For example, the WTRU may determine a first set of cell level measurements associated with a serving cell and / or a second set of cell level measurements associated with a target cell. The WTRU may determine the cell level measurement(s) based on the predicted and / or measured beam(s) (e.g., for the serving cell, and / or for one or more neighbor cells). For example, the WTRU may determine and / or derive the cell level measurement(s) of the target cell independently from one or more neighbor cells; the determination of the different cells may not be dependent on each other. The WTRU may send the measurement report basedon the first set of cell measurements associated with the serving cell and / or the second set of cell level measurements associated with the target cell. The WTRU may apply L3 filtering for both cells (e.g., also according to the filtering configuration associated with the source and target cell, which can be different) and / or (e.g., only then) may use the two filtered cell level measurements when evaluating the conditions for the measurement events for measurement reporting and / or CHO execution.

[0151] In examples, the WTRU may be configured to consider (e.g., only) measured beams in the cell level measurement derivation of the serving cell and / or may consider one of the following for the cell level derivation of the target cell: (e.g., only) measured beams; (e.g., only) predicted beams; or (e.g., both) measured and / or predicted beams.

[0152] In examples, the WTRU may be configured to consider (e.g., only) predicted beams in the cell level measurement derivation of the serving cell and / or may consider one of the following for the cell level derivation of the target cell: (e.g, only) measured beams; (e.g, only) predicted beams; or (e.g, both) measured and / or predicted beams.

[0153] In examples, the WTRU may be configured to consider both measured and predicted beams in the cell level measurement derivation of the serving cell and / or may consider one of the following for the cell level derivation of the target cell: (e.g, only) measured beams; (e.g, only) predicted beams; or (e.g, both) measured and / or predicted beams.

[0154] The different combinations of considering measured and / or predicted beams for servin g / target cells described herein can be configured within a single measurement event configuration. For example, there could be one measurement event (e.g, a modified A3 event) that includes up to the 9 A3 thresholds (e.g, for the 9 possible combinations described herein).

[0155] In examples (e.g, one variant of the methods described herein), where a single measurement event is provided with one or more (e.g, multiple) thresholds, the WTRU may be (e.g, further) configured to consider the measurement event conditions to be fulfilled if one or more (e.g, any) one of the thresholds are fulfilled.

[0156] In examples (e.g, one variant of the methods described herein), where a single measurement event is provided with one or more (e.g, multiple) thresholds, the WTRU may be (e.g, further) configured to consider the measurement event conditions to be fulfilled (e.g, only) if one or more (e.g, all) the thresholds are fulfilled.

[0157] In examples (e.g, one variant of the methods described herein), where a single measurement event is provided with one or more (e.g, multiple) thresholds, the WTRU may be (e.g, further) configuredto consider the measurement event conditions to be fulfilled (e.g., only) if a certain number and / or percentage of (e.g., at least 2, at least half of, etc.,) of one or more (e.g., all) the thresholds are fulfilled.

[0158] In examples (e.g., one variant of the methods described herein), where a single measurement event is provided with one or more (e.g., multiple) thresholds, the WTRU may be (e.g., further) configured to consider the measurement event conditions to be fulfilled (e.g., only) if the threshold that is associated with the actual measurements of the serving cell and the actual measurements of the neighbor cell are fulfilled, and / or a certain number or percentage of (e.g., at least 2, at least half of, etc.,) of the other thresholds are fulfilled (e.g., at least the threshold associated with the predicted measurements of the serving cell and the predicted measurements of the target cell, etc.).

[0159] Embodiments (e.g., as described herein) may relate to cell level measurement derivation and / or measurement filtering that considers actual measured beams and / or predicted beams. Methods for deriving cell level measurements that utilizes the combination of the signal levels of predicted beams and measured beams, based on a combining and / or filtering configuration received from the network, may be described herein.

[0160] A WTRU may send capability information regarding beam measurement predictions (e.g., based on AI / ML model). Capability may include the type of beam measurement prediction (e.g., spatial, temporal) and / or additional details (e.g., number of beams that can be predicted, number of beams that need to be measured to perform the predictions on others, confidence level, other additional conditions like supported WTRU speed, frequencies, etc.,). The WTRU may send (e.g., via the transceiver) capability information. The capability information may indicate that the WTRU and / or processor is capable of predicting beam measurements. The capability information may include a beam prediction type, a number of beams that can be predicted, a number of beams that are to be measured to determine the second subset of beams associated with the one or more predicted beam measurements, a confidence level associated with the prediction of the second subset of beams, or one or more conditions under which an artificial intelligence I machine leaning (AI / ML) model is to operate.

[0161] The WTRU may use spatial beam prediction to predict one or more beams. The beam consolidation procedure may select one or more of the measured beams and / or may determine the cell level measurements. The WTRU may apply filtering to the one or more predicted beams to determine a subset of predicted beams. The WTRU may use temporal prediction at cell level and / or at beam level.

[0162] With respect to temporal beam prediction, the WTRU may be measuring one or more (e.g., all) of the beams and / or the WTRU may predict one or more beam measurements in time. For example, theWTRU may measure the beam(s) for a first time period (e.g., Xms). The WTRU may, based on the measured beams for Xms, for example, predict the beams for a second time period (e.g., Yms). The WTRU may be configured to perform one or more measurements every x ms, where x may represent the measurement periodicity. If the WTRU has an AI / ML model for predicting the measurement(s), for example, the WTRU may not (e.g., need to) perform one or more measurements (e.g., all the time). For example, the WTRU may perform one measurement, predict one measurement, and so on. In example, the WTRU may measure a certain number of samples; the WTRU may use these samples to predict a certain number of (e.g., future) samples (and / or may skip from measuring them). For example, if the periodicity for measurement is 50ms (e.g., WTRU may take a sample every 50ms), the WTRU may be configured with an observation window of 400ms and / or prediction window of 400ms; the WTRU may measure eight (e.g., consecutive) samples and / or may use the sample(s) to predict (e.g., the next) eight samples, and / or may skip from performing the measurements at these (e.g., next) eight time instances, and so on. For example, the WTRU may perform (e.g., actual) beam measurements during the first time period (e.g., at each measurement periodicity). For example, the WTRU may predict beam(s) during the second time period (e.g., Yms). Additionally or alternatively, the WTRU may employ an (e.g., AIML) model to predict cell level measurements). For example, during the second time duration (e.g., Yms), the (e.g., AIML) model may predict cell level measurements) (e.g., directly, at each measurement periodicity). The WTRU may perform beam level measurements) and / or may consolidate the beam level measurement(s) (e.g., linear average of the top n beams) to determine the cell level measurements). If the cell has (e.g., only) one beam, for example, the cell level measurement and beam level measurement may be the same; the WTRU may determine that the cell level measurement and beam level measurement are the same. With respect to a WTRU configured to predict one or more measurements, for example, the WTRU may include an AI / ML that can be trained to perform beam level and / or cell level prediction (e.g., measure one or more beams, predict one or more beams, and / or may consolidate the one or more measured beams and / or the one or more predicted beams). Additionally or alternatively, the WTRU may use an AI / ML model to use (e.g., current) measurement(s) and / or other (e.g, historical) cell level measurement(s) to (e.g, directly) output one or more predicted cell level measurement(s).

[0163] A WTRU may receive a cell level measurement derivation and / or measurement filtering configuration applicable to predicted (e.g, non-transmitted) beam measurements and / or transmitted (e.g, actual) beam measurements (e.g, m ultiple / different beam consolidation thresholds for predicted and / or measured beams, maximum / minimum number of predicted and / or measured beams to consolidate, different L3 filteringcoefficients / parameters that are related to measured beams, etc.). For example, the WTRU may receive (e.g., via the transceiver) configuration information. The configuration information may include first configuration information to perform beam measurements, second configuration information to predict beam measurements, third configuration information to derive cell level measurement(s) based on, for example, one or more performed beam measurements and / or one or more derived (e.g., predicted) beam measurements, fourth configuration to filter the unfiltered cell level measurements, and / or fifth configuration information to send a measurement report. The third configuration information to derive and / or determine unfiltered cell level measurement(s) may include beam consolidation criteria. The beam consolidation criteria may indicate one or more of the following: a maximum number of measured beams to be included in the consolidation, a maximum number of predicted measured beams to be included in the consolidation, a maximum total number of measured or predicted beams to be included in the consolidation, a (e.g., signal level) threshold associated with each beam measurement of the first subset of beams, a (e.g., signal level) threshold associated with each predicted measurement of the second subset of beams, and / or a first (e.g., averaging) weighting factor associated with each predicted beam measurement (e.g., to determine the unfiltered cell level measurement(s), and / or a second (e.g., averaging weighting factor associated with each beam measurement (e.g., to determine the unfiltered cell level measurement(s). The fourth configuration information may include spatial-based configuration information and / or temporal-based configuration information. The fourth configuration information may include a first coefficient associated with the one or more beam measurements and / or a second coefficient associated with the one or more predicted beam measurements.

[0164] A WTRU may receive measurement reporting configuration, where the configuration may include conditions and / or thresholds related to cell level measurement derived from transmitted beam measurements (e.g., only), conditions and / or thresholds related to cell level measurements derived from predicted beam measurements (e.g., only), and / or conditions / thresholds related to cell level measurements derived from transmitted beam measurements and / or predicted beam measurements. Configuration may further include information to be included in the measurement report related to predicted measurements (e.g., predicted measurements, indication if predicted beams were considered, indication of (each) predicted beams if beam measurements are included in the report, etc.,)

[0165] A WTRU may perform measurements of beams and / or beam predictions and / or may derive the cell level measurements according to the configuration received (e.g., as described herein), and / or may monitor measurement reporting conditions received (e.g.., as described herein). For example, the WTRU maydetermine, in accordance with the second configuration information, one or more predicted beam measurements of a second subset of beams. The WTRU may determine, in accordance with the third configuration information, unfiltered cell level measurement(s) based on the first subset of beams and / or the second subset of beams. For example, the first subset of beams may be and / or include measured beam(s); the WTRU may use the measure beam(s) to predict the second subset of beams. For example, the WTRU may input one or more measured beams and / or the fist subset of beams into an AI / ML model to output the second subset of beams. The WTRU may determine, in accordance with the fourth configuration information, the filtered cell level measurements based on the unfiltered cell level measurements. The WTRU may determine the unfiltered cel level measurement(s) based on the beam consolidation criteria. The WTRU may determine the filtered cell level measurement(s) based on the spatial-based configuration information and / or the temporal-based configuration information. The WTRU may use the first coefficient and / or the second coefficient to determine the filtered cell level measurements. The WTRU may use an artificial intelligence I machine learning (AI / ML) model to predict the second subset of beams.

[0166] The WTRU may determine one or more (e.g., direct) cell level measurements based on the temporal-based configuration information. The WTRU may determine the filtered cell level measurements based on the one or more (e.g., direct) cell level measurements.

[0167] When the measurement reporting conditions are fulfilled, for example, the WTRU may send the measurement report (e.g., including the information related to predicted measurements configured to be included as described herein). For example, the WTRU may send (e.g., via the transceiver) the measurement report in accordance with the fifth configuration information. The measurement report may include an indication of the filtered cell level measurement(s). The WTRU may send the measurement report based on a comparison of the first set of cell level measurements and the second set of cell level measurements.

Claims

CLAIMS:1 . A wireless transmit / receive unit (WTRU) comprising: a transceiver; and a processor configured to: receive, via the transceiver, configuration information, wherein the configuration information comprises first configuration information to perform beam measurements, second configuration information to predict beam measurements, third configuration information to determine unfiltered cell level measurements, fourth configuration information to filter the unfiltered cell level measurements, and fifth configuration information to send a measurement report; determine, in accordance with the first configuration information, one or more beam measurements of a first subset of beams; determine, in accordance with the second configuration information, one or more predicted beam measurements of a second subset of beams; determine, in accordance with the third configuration information, unfiltered cell level measurements based on the first subset of beams and the second subset of beams; determine, in accordance with the fourth configuration information, the filtered cell level measurements based on the unfiltered cell level measurements; and send, via the transceiver, the measurement report in accordance with the fifth configuration information, wherein the measurement report comprises an indication of the filtered cell level measurements.

2. The WTRU of claim 1 , wherein the processor is configured to send, via the transceiver, capability information, wherein the capability information indicates that the processor is capable of predicting beam measurements, wherein the capability information comprises a beam prediction type, a number of beams that can be predicted, a number of beams that are to be measured to determine the second set of beams associated with the predicted beam measurements, a confidence level associated with the prediction of the second subset of beams, or one or more conditions under which an artificial intelligence / machine learning (AI / ML) model is capable to operate.

3. The WTRU of claim 1 , wherein the third configuration information comprises beam consolidation criteria indicating one or more of the following: a maximum number of measured beams to beincluded in the consolidation, a maximum number of predicted beams to be included in the consolidation, a maximum total number of measured or predicted beams to be included in the consolidation, the threshold associated with each beam measurement of the first subset of beams, a threshold associated with each predicted measurement of the second subset of beams, a first weighting factor associated with each predicted beam measurement, or a second weighting factor associated with each beam measurement, and wherein the processor is configured to determine the unfiltered cell level measurements based on the beam consolidation criteria.

4. The WTRU of claim 1 , wherein the fourth configuration information comprises spatial-based configuration information or temporal-based configuration information, and wherein the processor is configured to determine the filtered cell level measurements based on the spatial-based configuration information or the temporal-based configuration information.

5. The WTRU of claim 4, wherein the fourth configuration information comprises a first coefficient associated with the one or more beam measurements and a second coefficient associated with the one or more predicted beam measurements, and wherein the processor is configured to use the first coefficient or the second coefficient to determine the filtered cell level measurements.

6. The WTRU of claim 1 , wherein the processor is configured to use an artificial intelligence / machine learning (AI / ML) model to predict the second subset of beams.

7. The WTRU of claim 1 , wherein the processor is configured to determine a first set of cell level measurements associated with a serving cell and a second set of cell level measurements associated with a target cell.

8. The WTRU of claim 7, wherein the measurement report is sent based on the first set of cell level measurements associated with the serving cell or the second set of cell level measurements associated with the target cell.

9. The WTRU of claim 7, wherein the measurement report is sent based on a comparison of the first set of cell level measurements and the second set of cell level measurements.

10. A method performed by a wireless transmit / receive unit (WTRU), the method comprising: receiving configuration information, wherein the configuration information comprises first configuration information to perform beam measurements, second configuration information to predict beam measurements, third configuration information to determine unfiltered cell level measurements, fourth configuration information to filter the unfiltered cell level measurements, and fifth configuration information to send a measurement report; determining, in accordance with the first configuration information, one or more beam measurements of a first subset of beams; determining, in accordance with the second configuration information, one or more predicted beam measurements of a second subset of beams; determining, in accordance with the third configuration information, unfiltered cell level measurements based on the first subset of beams and the second subset of beams; determining, in accordance with the fourth configuration information, the filtered cell level measurements based on the unfiltered cell level measurements; and sending, via the transceiver, the measurement report in accordance with the fifth configuration information, wherein the measurement report comprises an indication of the filtered cell level measurements.

11. The method of claim 10, further comprising sending capability information, wherein the capability information indicates that the WTRU is capable of predicting beam measurements, wherein the capability information comprises a beam prediction type, a number of beams that can be predicted, a number of beams that are to be measured to determine the second set of beams associated with the predicted beam measurements, a confidence level associated with the prediction of the second subset of beams, or one or more conditions under which an artificial intelligence I machine learning (AI / ML) model is capable to operate.

12. The method of claim 10, wherein the third configuration information comprises beam consolidation criteria indicating one or more of the following: a maximum number of measured beams to beincluded in the consolidation, a maximum number of predicted beams to be included in the consolidation, a maximum total number of measured or predicted beams to be included in the consolidation, the threshold associated with each beam measurement of the first subset of beams, a threshold associated with each predicted measurement of the second subset of beams, a first weighting factor associated with each predicted beam measurement, or a second weighting factor associated with each beam measurement, and wherein the unfiltered cell level measurements are determined based on the beam consolidation criteria.

13. The method of claim 10, wherein the fourth configuration information comprises spatial-based configuration information or temporal-based configuration information, and wherein the filtered cell level measurements are determined based on the spatial-based configuration information or the temporal-based configuration information.

14. The method of claim 13, wherein the fourth configuration information comprises a first coefficient associated with the one or more beam measurements and a second coefficient associated with the one or more predicted beam measurements, and wherein the filtered cell level measurements are determined based on the first coefficient or the second coefficient.

15. The method of claim 10, further comprising using an artificial intelligence / machine learning (AI / ML) model to predict the second subset of beams.

16. The method of claim 10, further comprising determining a first set of cell level measurements associated with a serving cell and a second set of cell level measurements associated with a target cell.

17. The method of claim 16, wherein the measurement report is sent based on the first set of cell level measurements associated with the serving cell or the second set of cell level measurements associated with the target cell.

18. The method of claim 16, wherein the measurement report is sent based on a comparison of the first set of cell level measurements and the second set of cell level measurements.

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