Prediction measurement reporting

Abstract

A WTRU may be configured to determine at least one predicted measurement. The at least one predicted measurement may include, for example, at least one predicted air interface measurement. The WTRU may determine a predicted event and a predicted time associated with the predicted event based on the at least one predicted measurement. The predicted event may include, for example, that the at least one predicted measurement is less than a threshold. The WTRU may determine to transmit a report indicative of the predicted event based on the predicted event, the predicted time, and a time offset. The WTRU may determine to transmit the report indicative of the predicted event, for example, if the difference between the current time and the predicted time is greater than the time offset. In the event that the condition is satisfied, the WTRU may transmit the report indicative of the predicted event.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims the benefit of U.S. Provisional Patent Application No. 63 / 390,057 filed on July 18, 2022 and U.S. Provisional Patent Application No. 63 / 410,812 filed on September 28, 2022, the disclosures of which are incorporated herein by reference in their entirety. Background Art

[0003] Mobile communications using wireless communications continue to evolve. The fifth generation may be referred to as 5G. Previous (legacy) mobile communications may be, for example, fourth generation (4G) Long Term Evolution (LTE). Summary of the invention

[0004] Systems, methods, and apparatus are described herein for artificial intelligence (AI) based anticipatory or predictive measurement reporting in wireless systems.

[0005] A wireless transmit and receive unit (WTRU) may be configured to determine at least one predicted measurement. The at least one predicted measurement may include, for example, at least one predicted air interface measurement. The WTRU may determine, for example, a first predicted measurement at a first predicted time and a second predicted measurement at a second predicted time.

[0006] The WTRU may determine a predicted event and a predicted time associated with the predicted event based on the at least one predicted measurement. The predicted event may include, for example, the at least one predicted measurement being less than a threshold or greater than a threshold. The predicted time may include a time interval.

[0007] The WTRU may be configured to receive configuration information including a time offset. The time offset may indicate a difference between a current time and a predicted time. The WTRU may determine to transmit a report indicating the predicted event based on the predicted event, the predicted time, and the time offset. The WTRU may determine to transmit the report indicating the predicted event, for example, if the difference between the current time and the predicted time is greater than the time offset. If the condition is met, the WTRU may transmit the report indicating the predicted event.

[0008] The WTRU may also be configured to determine a prediction confidence associated with the predicted event.The report transmitted by the WTRU may include an indication of the predicted event, the at least one predicted measurement, and the prediction confidence.

[0009] The WTRU may be configured to trigger an expected or predicted measurement report based on an AI / machine learning (ML) based prediction of a specific event or measurement occurring in the future. The WTRU may be configured to determine at least one expected or predicted measurement and evaluate the at least one predicted measurement with respect to at least one triggering condition. In response to the predicted measurement satisfying the at least one triggering condition, the WTRU may generate a measurement report and may send the measurement report to a network node. The measurement report may include the at least one predicted measurement.

[0010] The trigger for reporting the prediction may be network configured and may include several reporting options and times. The final reporting time may be a configured time before the predicted measurement event. The WTRU may receive configuration information. The configuration information may include information indicating the at least one triggering condition. The information indicating the at least one triggering condition may include information indicating at least one reporting option and information indicating at least one reporting time. The information indicating the at least one reporting time may include information indicating a length of time before the at least one predicted measurement.

[0011] The WTRU may be configured to perform filtering / averaging on predicted measurements consistent with a configuration received from a network. Configuration information received by the WTRU may include information indicating that at least one reporting option may include information indicating filtering for predicted measurements. The WTRU may apply filtering for predicted measurements. Configuration information received by the WTRU may include information indicating that at least one reporting option may include information indicating an average for predicted measurements. The WTRU may calculate an average for predicted future measurements.

[0012] The WTRU may be configured to trigger a measurement inference process multiple times and collect prediction information that may be sent to the network. The WTRU may determine multiple prediction measurements within a time period. The WTRU may receive configuration information indicating trigger factors for collecting prediction measurements. The WTRU may obtain the multiple prediction measurements in response to determining that the trigger factors have been met. The WTRU may determine at least a first prediction measurement and a second prediction measurement during the time period. The first prediction measurement may be determined by performing inference at a first time during the time period, and the second prediction measurement may be determined by performing inference at a second time during the time period. The WTRU may generate a measurement report including at least the first prediction measurement and the second prediction measurement. The WTRU may transmit the measurement report to a network node. The WTRU may transmit the prediction measurement information at the last opportunity for the WTRU to report the prediction measurement.

[0013] The WTRU may be configured to obtain multiple predicted measurement ranges within a time period. The WTRU may receive configuration information indicating a confidence value for each predicted measurement range in the multiple predicted measurement ranges in the time period. The WTRU may at least determine a first predicted value range associated with a first time in the time period and a second predicted value range associated with a second time in the time period. The first predicted value range may have a first associated confidence value, and the second predicted value range may have a second associated confidence value. If the first predicted value range and the associated first confidence value satisfy a threshold comparison, the WTRU may generate a measurement report including the first predicted value range and the associated first confidence value, and may send the measurement report to a network node.

[0014] The WTRU may be configured to override transmission of predicted measurement information based on a difference between a predicted and actual measurement value found. The WTRU may determine at least one predicted measurement and determine that the at least one predicted measurement satisfies at least one trigger condition. The WTRU may determine a measurement value and compare the at least one predicted measurement with the measurement value. In response to the difference between the predicted measurement and the measurement value, the WTRU may override transmission of the at least one predicted measurement. The configuration information received by the WTRU may include information indicating a time length before the at least one predicted measurement. The WTRU may override transmission of the at least one predicted measurement at a time corresponding to a time length before the at least one predicted measurement. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1A is a system diagram illustrating an example communication system in which one or more disclosed embodiments may be implemented.

[0016] Figure 1B is an example of a method that can be used according to an embodiment of the present invention. Figure 1A A system diagram of an example wireless transmit / receive unit (WTRU) for use within an illustrated communication system.

[0017] Figure 1C is an example of a method that can be used according to an embodiment of the present invention. Figure 1A System diagram of an example Radio Access Network (RAN) and an example Core Network (CN) for use within the illustrated communication system.

[0018] Figure 1D is an example of a method that can be used according to an embodiment of the present invention. Figure 1A System diagram of another example RAN and another example CN for use within the illustrated communication system.

[0019] Figure 2 An example measurement configuration process is depicted.

[0020] Figure 3 An example measurement report triggering event is depicted.

[0021] Figure 4 Depicted is an example occurrence of measurement reporting during a period of time.

[0022] Figure 5 Depicted is an example measurement report for a WTRU traversing a cell coverage area.

[0023] Figure 6 Depicted is an example time series generation for Reference Signal Received Power (RSRP).

[0024] Figure 7 Depicted is an example timeline of events associated with a forecast report.

[0025] Figure 8 Depicted is an example one-shot inference process window.

[0026] Fig. 9 Example periodic WTRU prediction with window reuse is depicted.

[0027] Fig.10 An example periodic WTRU prediction using a configured window setting is depicted.

[0028] Fig.11 An example WTRU moving window inference process utilizing spacing and window reuse is depicted.

[0029] Fig.12 An example WTRU moving window inference process with configurable spacing and window is depicted.

[0030] Fig.13 Example prediction bounds based on percentage confidence intervals are depicted.

[0031] Fig.14 Example signaling for WTRU measurement prediction reporting processing is depicted. DETAILED DESCRIPTION

[0032] A more detailed understanding may be obtained from the following description given by way of example in conjunction with the accompanying drawings.

[0033] Figure 1A1 is a diagram illustrating an example communication system 100 in which one or more disclosed embodiments may be implemented. The communication system 100 may be a multiple access system that provides content such as voice, data, video, messaging, broadcast, etc. to multiple wireless users. The communication system 100 may enable multiple wireless users to access such content through sharing of system resources (including wireless bandwidth). For example, the communication system 100 may employ one or more channel access methods such as code division multiple access (CDMA), time division multiple access (TDMA), frequency division multiple access (FDMA), orthogonal FDMA (OFDMA), single carrier FDMA (SC-FDMA), zero tail unique word discrete Fourier transform (DFT) spread OFDM (ZT UW DTS-s OFDM), unique word OFDM (UW-OFDM), resource block filtered OFDM, filter bank multi-carrier (FBMC), etc.

[0034] like Figure 1A As shown, the communication system 100 may include wireless transmit / receive units (WTRUs) 102a, 102b, 102c, 102d, RAN 104 / 113, CN 106 / 115, public switched telephone network (PSTN) 108, Internet 110 and other networks 112, but it should be understood that the disclosed embodiments contemplate any number of WTRUs, base stations, networks, and / or network elements. Each of the WTRUs 102a, 102b, 102c, 102d may be any type of device configured to operate and / or communicate in a wireless environment. By way of example, the WTRUs 102a, 102b, 102c, 102d (any of which may be referred to as a “station” and / or “STA”) may be configured to send and / or receive wireless signals and may include user equipment (UE), a mobile station, a fixed or mobile subscriber unit, a subscription-based unit, a pager, a cellular phone, a personal digital assistant (PDA), a smart phone, a laptop, a netbook, a personal computer, a wireless sensor, a hotspot or Mi-Fi device, an Internet of Things (IoT) device, a watch or other wearable device, a head-mounted display (HMD), a vehicle, a drone, medical devices and applications (e.g., remote surgery), industrial devices and applications (e.g., robots and / or other wireless devices operating in an industrial and / or automated process chain environment), a consumer electronic device, a device operating on a commercial and / or industrial wireless network, etc. Any of the WTRUs 102a, 102b, 102c, and 102d may be interchangeably referred to as a UE.

[0035] The communication system 100 may also include a base station 114a and / or a base station 114b. Each of the base stations 114a, 114b may be any type of device configured to wirelessly interface with at least one of the WTRUs 102a, 102b, 102c, 102d to facilitate access to one or more communication networks, such as the CN 106 / 115, the Internet 110, and / or other networks 112. By way of example, the base stations 114a, 114b may be a base transceiver station (BTS), a Node B, an evolved Node B (eNB), a Home Node B, a Home evolved Node B, a next generation Node B (gNB), an NR Node B, 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 should be appreciated that the base stations 114a, 114b may include any number of interconnected base stations and / or network elements.

[0036] Base station 114a may be part of RAN 104 / 113, which may also include other base stations and / or network elements (not shown), such as base station controllers (BSC), radio network controllers (RNC), relay nodes, etc. Base station 114a and / or base station 114b may be configured to send and / or receive wireless signals on one or more carrier frequencies (which may be referred to as cells (not shown)). These frequencies may be in a licensed spectrum, an unlicensed spectrum, or a combination of licensed and unlicensed spectrum. A cell may provide coverage of wireless services to a specific geographic area, which may be relatively fixed or may change over time. A cell may be further divided into cell sectors. For example, a cell associated with base station 114a may be divided into three sectors. Therefore, in one embodiment, base station 114a may include three transceivers, i.e., one transceiver for each sector of the cell. In an embodiment, base station 114a may employ multiple-input multiple-output (MIMO) technology and may utilize multiple transceivers for each sector of the cell. For example, beamforming may be used to send and / or receive signals in a desired spatial direction.

[0037] 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).

[0038] More specifically, as noted above, the communication system 100 may be a multiple access system and may employ one or more channel access schemes such as CDMA, TDMA, FDMA, OFDMA, and SC-FDMA, among others. For example, the base station 114a in the RAN 104 / 113 and the WTRUs 102a, 102b, 102c may implement a radio technology such as Universal Mobile Telecommunications System (UMTS) Terrestrial Radio Access (UTRA), which may use Wideband CDMA (WCDMA) to establish the air interface 115 / 116 / 117. WCDMA may include communication protocols such as High Speed ​​Packet Access (HSPA) and / or Evolved HSPA (HSPA+). HSPA may include High Speed ​​Downlink (DL) Packet Access (HSDPA) and / or High Speed ​​UL Packet Access (HSUPA).

[0039] In an embodiment, the base station 114a and the WTRUs 102a, 102b, 102c may implement a radio technology such as Evolved UMTS Terrestrial Radio Access (E-UTRA), which may establish the air interface 116 using Long Term Evolution (LTE) and / or Advanced LTE (LTE-A) and / or Advanced LTE Pro (LTE-A Pro).

[0040] In an embodiment, the base station 114a and the WTRUs 102a, 102b, 102c may implement a radio technology such as NR radio access, which may establish the air interface 116 using New Radio (NR).

[0041] 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 together implement LTE radio access and NR radio access, for example using the dual connectivity (DC) principle. Thus, the air interface utilized by the WTRUs 102a, 102b, 102c may be characterized by multiple types of radio access technologies and / or transmissions transmitted to / from multiple types of base stations (e.g., eNBs and gNBs).

[0042] In other embodiments, the base station 114a and the WTRUs 102a, 102b, 102c may implement radio technologies such as IEEE 802.11 (i.e., Wireless Fidelity (WiFi)), IEEE 802.16 (i.e., Worldwide Interoperability for Microwave Access (WiMAX)), CDMA2000, CDMA2000 1X, CDMA2000 EV-DO, Interim Standard 2000 (IS-2000), Interim Standard 95 (IS-95), Interim Standard 856 (IS-856), Global System for Mobile communications (GSM), Enhanced Data rates for GSM Evolution (EDGE), and GSM EDGE (GERAN).

[0043] Figure 1A The base station 114b in the may be, for example, a wireless router, a Home NodeB, a Home eNodeB, or an access point, and may utilize any suitable RAT to facilitate wireless connectivity in local areas such as commercial locations, homes, vehicles, campuses, industrial facilities, sky corridors (e.g., for use by drones), roads, 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 a femtocell. As Figure 1A As shown, base station 114b may have a direct connection to Internet 110. Therefore, base station 114b may not need to access Internet 110 via CN 106 / 115.

[0044] The RAN 104 / 113 may be in communication with the CN 106 / 115, which may be any type of network configured to provide voice, data, applications and / or Voice over Internet Protocol (VoIP) services to one or more of the WTRUs 102a, 102b, 102c, 102d. The data may have different quality of service (QoS) requirements, such as different throughput requirements, latency requirements, error tolerance requirements, reliability requirements, data throughput requirements, mobility requirements, etc. The CN 106 / 115 may provide call control, billing services, mobile location-based services, prepaid calling, Internet connectivity, video distribution, etc., and / or perform high-level security functions, such as user authentication. Although not described in detail in the text and video, the CN 106 / 115 may be configured to provide voice, data, applications and / or Voice over Internet Protocol (VoIP) services to one or more of the WTRUs 102a, 102b, 102c, 102d. The data may have different quality of service (QoS) requirements, such as different throughput requirements, latency requirements, error tolerance requirements, reliability requirements, data throughput requirements, mobility requirements, etc. The CN 106 / 115 may provide call control, billing services, mobile location-based services, prepaid calling, Internet connectivity, video distribution, etc., and / or perform high-level security functions, such as user authentication. Figure 1AAlthough not shown in the figure, 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 utilize NR radio technology, the CN 106 / 115 may also be in communication with another RAN (not shown) that employs GSM, UMTS, CDMA2000, WiMAX, E-UTRA, or WiFi radio technology.

[0045] The CN 106 / 115 may also act as a gateway for the WTRUs 102a, 102b, 102c, 102d to access the PSTN 108, the Internet 110, and / or other networks 112. The PSTN 108 may include a circuit-switched telephone network that provides plain old telephone service (POTS). The Internet 110 may include a global system of interconnected computer networks and devices that use common communication protocols, such as the Transmission Control Protocol (TCP), the User Datagram Protocol (UDP), and / or the Internet Protocol (IP) in the TCP / IP Internet protocol suite. The networks 112 may include wired communication networks and / or wireless communication networks owned and / or operated by other service providers. For example, the networks 112 may include another CN connected to one or more RANs, which may employ the same RAT as the RAN 104 / 113 or a different RAT.

[0046] Some or all of the WTRUs 102a, 102b, 102c, 102d in the communication system 100 may include multi-mode capabilities (e.g., the WTRUs 102a, 102b, 102c, 102d may include multiple transceivers for communicating with different wireless networks via different wireless links). Figure 1A The illustrated WTRU 102c may be configured to communicate with the base station 114a, which may employ a cellular-based radio technology, and with the base station 114b, which may employ an IEEE 802 radio technology.

[0047] Figure 1B is a system diagram illustrating an example WTRU 102. Figure 1B As shown, the WTRU 102 may include, among other things, a processor 118, a transceiver 120, a transmit / receive element 122, a speaker / microphone 124, a keypad 126, a display / touchpad 128, non-removable memory 130, removable memory 132, a power source 134, a global positioning system (GPS) chipset 136, and / or other peripherals 138. It will be appreciated that the WTRU 102 may include any sub-combination of the foregoing elements while remaining consistent with an embodiment.

[0048] The processor 118 may be a general purpose processor, a special purpose processor, a conventional processor, a digital signal processor (DSP), a plurality of microprocessors, one or more microprocessors associated with a DSP core, a controller, a microcontroller, an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) circuit, any other type of integrated circuit (IC) and state machine, etc. The processor 118 may perform signal decoding, data processing, power control, input / output processing, and / or any other functionality that enables the WTRU 102 to operate in a wireless environment. The processor 118 may be coupled to the transceiver 120, which may be coupled to the transmit / receive element 122. Although Figure 1B The processor 118 and the transceiver 120 are depicted as separate components, but it is understood that the processor 118 and the transceiver 120 may be integrated together in an electronic package or chip.

[0049] The send / receive element 122 may be configured to send a signal to a base station (e.g., base station 114a) or receive a signal from the base station via an air interface 116. For example, in one embodiment, the send / receive element 122 may be an antenna configured to send and / or receive an RF signal. In an embodiment, the send / receive element 122 may be a transmitter / detector configured to send and / or receive, for example, IR, UV or visible light signals. In another embodiment, the send / receive element 122 may be configured to send and / or receive both RF signals and optical signals. It should be understood that the send / receive element 122 may be configured to send and / or receive any combination of wireless signals.

[0050] Although the transmit / receive element 122 Figure 1B Although depicted as a single element in the figure, 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.

[0051] The transceiver 120 may be configured to modulate signals to be transmitted by the transmit / receive element 122 and to demodulate signals received by the transmit / receive element 122. As noted above, the WTRU 102 may have multi-mode capabilities. For example, the transceiver 120 may include multiple transceivers to enable the WTRU 102 to communicate via multiple RATs, such as NR and IEEE 802.11.

[0052] The processor 118 of the WTRU 102 may be coupled to a speaker / microphone 124, a keypad 126, and / or a display / touchpad 128 (e.g., a liquid crystal display (LCD) display unit or an organic light emitting diode (OLED) display unit) and may receive user input data therefrom. The processor 118 may also output user data to the speaker / microphone 124, the keypad 126, and / or the display / touchpad 128. In addition, the processor 118 may access information from and store data in any type of suitable memory, such as a non-removable memory 130 and / or a removable memory 132. The non-removable memory 130 may include a random access memory (RAM), a read-only memory (ROM), a hard disk, or any other type of memory storage device. The removable memory 132 may include a subscriber identity module (SIM) card, a memory stick, and a secure digital (SD) memory card, among others. In other embodiments, the processor 118 may access information from and store data in a memory that is not physically located on the WTRU 102, such as on a server or a home computer (not shown).

[0053] The processor 118 may receive power from the power source 134, and may be configured to distribute and / or control power to the other components in the WTRU 102. The power source 134 may be any suitable device for powering the WTRU 102. For example, the power source 134 may include one or more dry cell batteries (e.g., nickel-cadmium (NiCd), nickel-zinc (NiZn), nickel-metal hydride (NiMH), lithium-ion (Li-ion), etc.), solar cells, fuel cells, etc.

[0054] The processor 118 may also be coupled to the GPS chipset 136, which may be configured to provide location information (e.g., longitude and latitude) regarding the current location of the WTRU 102. In addition to or in lieu of the information from the GPS chipset 136, the WTRU 102 may receive location information from a base station (e.g., base stations 114a, 114b) over the air interface 116 and / or determine its location based on the timing of signals received from two or more nearby base stations. It will be appreciated that the WTRU 102 may acquire location information by any suitable location-determination method while remaining consistent with an embodiment.

[0055] The processor 118 may also be coupled to other peripherals 138, which may include one or more software modules and / or hardware modules that provide additional features, functionality, and / or wired or wireless connectivity. For example, the peripherals 138 may include an accelerometer, an electronic compass, a satellite transceiver, a digital camera (for photos and / or video), a universal serial bus (USB) port, a vibration device, a television transceiver, a hands-free headset, module, a frequency modulation (FM) radio unit, a digital music player, a media player, a video game player module, an Internet browser, a virtual reality and / or augmented reality (VR / AR) device, and an activity tracker, etc. The peripheral device 138 may include one or more sensors, which may be one or more of the following: 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.

[0056] 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 specific subframes for 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 for reducing and / or substantially eliminating self-interference via signal processing performed via hardware (e.g., a choke) or via a processor (e.g., a separate processor (not shown) or via processor 118). In an embodiment, the WRTU 102 may include a half-duplex radio for which transmission and reception of some or all of the signals (e.g., associated with specific subframes for UL (e.g., for transmission) or downlink (e.g., for reception)) may be concurrent and / or simultaneous.

[0057] Figure 1C 1 is a system diagram illustrating the RAN 104 and the CN 106 in accordance with an embodiment. As noted above, the RAN 104 may employ an E-UTRA radio technology to communicate with the WTRUs 102a, 102b, 102c over the air interface 116. The RAN 104 may also be in communication with the CN 106.

[0058] The RAN 104 may include evolved Node-Bs 160a, 160b, 160c, though it will be appreciated that the RAN 104 may include any number of evolved Node-Bs while remaining consistent with an embodiment. The evolved Node-Bs 160a, 160b, 160c may each include one or more transceivers for communicating with the WTRUs 102a, 102b, 102c over the air interface 116. In one embodiment, the evolved Node-Bs 160a, 160b, 160c may implement MIMO technology. Thus, the evolved Node-B 160a, for example, may use multiple antennas to transmit wireless signals to and / or receive wireless signals from the WTRU 102a.

[0059] Each of the evolved Node Bs 160a, 160b, 160c may be associated with a particular cell (not shown) and may be configured to handle radio resource management decisions, handover decisions, and scheduling of users in the UL and / or DL, among other things. Figure 1C As shown, the eNode-Bs 160a, 160b, 160c may communicate with one another via an X2 interface.

[0060] Figure 1C The illustrated CN 106 may include a mobility management entity (MME) 162, a serving gateway (SGW) 164, and a packet data network (PDN) gateway (or PGW) 166. While each of the foregoing elements is depicted as part of the CN 106, it should be understood that any of these elements may be owned and / or operated by an entity other than a CN operator.

[0061] The MME 162 may be connected to each of the evolved Node-Bs 162a, 162b, 162c in the RAN 104 via an S1 interface and may serve as a control node. For example, the MME 162 may be responsible for authenticating users of the WTRUs 102a, 102b, 102c, bearer activation / deactivation, and selecting a particular serving gateway during an initial attach of the WTRUs 102a, 102b, 102c. The MME 162 may provide a control plane function for switching between the RAN 104 and other RANs (not shown) that employ other radio technologies, such as GSM and / or WCDMA.

[0062] The SGW 164 may be connected to each of the evolved Node-Bs 160a, 160b, 160c in the RAN 104 via an S1 interface. The SGW 164 may generally route and forward user data packets to / from the WTRUs 102a, 102b, 102c. The SGW 164 may perform other functions, such as anchoring the user plane during an inter-evolved Node-B handover, triggering paging when DL data is available for the WTRUs 102a, 102b, 102c, and managing and storing the context of the WTRUs 102a, 102b, 102c.

[0063] 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.

[0064] The CN 106 may facilitate communications with other networks. For example, the CN 106 may provide the WTRUs 102a, 102b, 102c with access to circuit-switched networks, such as the PSTN 108, to facilitate communications between the WTRUs 102a, 102b, 102c and traditional land-line communications devices. For example, the CN 106 may include, or may be in communication with, an IP gateway (e.g., an IP Multimedia Subsystem (IMS) server) that acts as an interface between the CN 106 and the PSTN 108. In addition, the CN 106 may provide the WTRUs 102a, 102b, 102c with access to other networks 112, which may include other wired networks and / or wireless networks owned and / or operated by other service providers.

[0065] Although the WTRU Figures 1A to 1D Although described as wireless terminals, it is contemplated that in certain representative embodiments, such terminals may (eg, temporarily or permanently) use a wired communications interface with a communications network.

[0066] In a representative embodiment, the other network 112 may be a WLAN.

[0067] A WLAN in infrastructure basic service set (BSS) mode may have an access point (AP) for a BSS and one or more stations (STAs) associated with the AP. The AP may have access or an interface to a distribution system (DS) or another type of wired / wireless network that carries traffic to and / or out of the BSS. Traffic originating from outside the BSS and destined for the STA may be reached by the AP and may be delivered to the STA. Traffic originating from the STA and destined for a destination outside the BSS may be transmitted to the AP to be delivered to the corresponding destination. Traffic between STAs within the BSS may be transmitted by the AP, for example, wherein the source STA may transmit traffic to the AP, and the AP may deliver traffic to the destination STA. Traffic between STAs within the BSS may be considered and / or referred to as point-to-point traffic. Direct link establishment (DLS) may be utilized to transmit point-to-point traffic between the source STA and the target STA (e.g., directly between them). In certain representative embodiments, the DLS may use 802.11e DLS or 802.11z tunnel DLS (TDLS). A WLAN using an independent BSS (IBSS) mode may not have an AP, and STAs within or using the IBSS (eg, all STAs in the STA) may communicate directly with each other. The IBSS communication mode may sometimes be referred to herein as an "ad hoc" communication mode.

[0068] When using the 802.11ac infrastructure operation mode or a similar operation mode, the AP may send beacons on a fixed channel (such as a primary channel). The primary channel may be a fixed width (e.g., a 20 MHz wide bandwidth) or a width dynamically set via signaling. The primary channel may be an operating channel of the BSS and may be used by the STA to establish a connection with the AP. In certain representative embodiments, carrier sense multiple access / collision avoidance (CSMA / CA) may be implemented, for example, in an 802.11 system. For CSMA / CA, a STA (e.g., each STA) (including the AP) may sense the primary channel. If the primary channel is sensed / detected 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.

[0069] High throughput (HT) STAs may communicate using a 40 MHz wide channel, for example, via a combination of a primary 20 MHz channel and adjacent or non-adjacent 20 MHz channels to form a 40 MHz wide channel.

[0070] Very high throughput (VHT) STA can support 20MHz, 40MHz, 80MHz and / or 160MHz wide channels. 40MHz channels and / or 80MHz channels can be formed by combining continuous 20MHz channels. 160MHz channels can be formed by combining 8 continuous 20MHz channels, or by combining two non-continuous 80MHz channels (this can be called 80+80 configuration). For 80+80 configuration, after channel coding, the data can pass through a segment parser that can divide the data into two streams. Each stream can be processed by inverse fast Fourier transform (IFFT) and time domain processing separately. These streams can be mapped to two 80MHz channels, and data can be sent by the transmitter STA. At the receiver of the receiver STA, the above-mentioned operation for the 80+80 configuration can be reversed, and the combined data can be transmitted to the medium access control (MAC).

[0071] 802.11af and 802.11ah support operating modes below 1GHz. Channel operating bandwidths and carriers are reduced in 802.11af and 802.11ah relative to those used in 802.11n and 802.11ac. 802.11af supports 5MHz, 10MHz, and 20MHz bandwidths in the TV White Space (TVWS) spectrum, and 802.11ah supports 1MHz, 2MHz, 4MHz, 8MHz, and 16MHz bandwidths using non-TVWS spectrum. According to a representative embodiment, 802.11ah may support instrument type control / machine type communications, such as MTC devices in macro coverage areas. MTC devices may have certain capabilities, such as limited capabilities, including support for (e.g., only support for) certain bandwidths and / or limited bandwidths. MTC devices may include batteries with battery life above a threshold (e.g., to maintain very long battery life).

[0072] WLAN systems (which may support multiple channels) and channel bandwidths (such as 802.11n, 802.11ac, 802.11af, and 802.11ah) include channels that may be designated as primary channels. The primary channel may have a bandwidth equal to the maximum common operating bandwidth supported by all STAs in the BSS. The bandwidth of the primary channel may be set and / or limited by a STA (which supports the minimum bandwidth operating mode) from all STAs operating in the BSS. In the example of 802.11ah, for STAs (e.g., MTC-type devices) that support (e.g., only support) a 1MHz mode, the primary channel may be 1MHz wide, even if the AP and other STAs in the BSS support 2MHz, 4MHz, 8MHz, 16MHz, and / or other channel bandwidth operating modes. Carrier sensing and / or network allocation vector (NAV) settings may depend on the state of the primary channel. If the primary channel is busy, for example, because a STA (supporting (supporting only) a 1MHz operating mode) is sending to the AP, the entire available band may be considered busy even if most of the band remains idle and may be available.

[0073] In the United States, the available frequency band for 802.11ah is 902MHz to 928MHz. In South Korea, the available frequency band is 917.5MHz to 923.5MHz. In Japan, the available frequency band is 916.5MHz to 927.5MHz. The total bandwidth available for 802.11ah is 6MHz to 26MHz, depending on the country code.

[0074] Figure 1D 1 is a system diagram illustrating the RAN 113 and the CN 115 in accordance with an embodiment. As noted above, the RAN 113 may employ NR radio technology to communicate with the WTRUs 102a, 102b, 102c over the air interface 116. The RAN 113 may also be in communication with the CN 115.

[0075] The RAN 113 may include gNBs 180a, 180b, 180c, though it will be appreciated that the RAN 113 may include any number of gNBs while remaining consistent with an embodiment. The gNBs 180a, 180b, 180c may each include one or more transceivers for communicating with the WTRUs 102a, 102b, 102c over the air interface 116. In one embodiment, the gNBs 180a, 180b, 180c may implement MIMO technology. For example, the gNBs 180a, 180b may utilize beamforming to send signals to and / or receive signals from the gNBs 180a, 180b, 180c. Thus, the gNB 180a may, for example, use multiple antennas to send wireless signals to and / or receive wireless signals from the WTRU 102a. In an embodiment, the gNBs 180a, 180b, 180c may implement carrier aggregation technology. For example, gNB 180a may transmit multiple component carriers to 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, 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).

[0076] The WTRUs 102a, 102b, 102c may communicate with the gNBs 180a, 180b, 180c using transmissions associated with scalable parameter sets. For example, OFDM symbol spacing and / or OFDM subcarrier spacing may vary for different transmissions, different cells, and / or different portions of the wireless transmission spectrum. The WTRUs 102a, 102b, 102c may communicate with the gNBs 180a, 180b, 180c using subframes or Transmission Time Intervals (TTIs) of varying or scalable lengths (e.g., containing varying numbers of OFDM symbols and / or varying absolute time lengths over time).

[0077] The gNBs 180a, 180b, 180c may be configured to communicate with the WTRUs 102a, 102b, 102c in a standalone configuration and / or a non-standalone configuration. In a standalone configuration, the WTRUs 102a, 102b, 102c may communicate with the gNBs 180a, 180b, 180c while not accessing other RANs (e.g., such as the eNodeBs 160a, 160b, 160c). In a standalone configuration, the WTRUs 102a, 102b, 102c may use one or more of the gNBs 180a, 180b, 180c as mobility anchor points. In a standalone configuration, the WTRUs 102a, 102b, 102c may communicate with the gNBs 180a, 180b, 180c using signals in an unlicensed band. In a non-standalone configuration, the WTRUs 102a, 102b, 102c may communicate / connect with the gNB 180a, 180b, 180c while also communicating / connecting with another RAN, such as the eNodeB 160a, 160b, 160c. For example, the WTRUs 102a, 102b, 102c may implement the DC principle to communicate with one or more gNBs 180a, 180b, 180c and one or more eNodeBs 160a, 160b, 160c substantially simultaneously. In a non-standalone configuration, the eNodeB 160a, 160b, 160c may serve as a mobility anchor point for the WTRUs 102a, 102b, 102c, and the gNB 180a, 180b, 180c may provide additional coverage and / or throughput for serving the WTRUs 102a, 102b, 102c.

[0078] 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 UL and / or DL, support for network slicing, dual connectivity, interworking between NR and E-UTRA, routing of user plane data towards a user plane function (UPF) 184a, 184b and routing of control plane information towards an access and mobility management function (AMF) 182a, 182b, etc. Figure 1D As shown, gNBs 180a, 180b, and 180c may communicate with each other via an Xn interface.

[0079] Figure 1DThe CN 115 shown may include at least one AMF 182a, 182b, at least one UPF 184a, 184b, at least one session management function (SMF) 183a, 183b, and possible data networks (DNs) 185a, 185b. Although each of the foregoing elements is depicted as part of the CN 115, it should be understood that any of these elements may be owned and / or operated by an entity other than a CN operator.

[0080] The AMF 182a, 182b may be connected to one or more of the gNBs 180a, 180b, 180c in the RAN 113 via the N2 interface and may act as a control node. For example, the AMF 182a, 182b may be responsible for authenticating users of the WTRU 102a, 102b, 102c, support of network slicing (e.g., handling of different PDU sessions with different requirements), selecting a specific SMF 183a, 183b, management of registration areas, termination of NAS signaling, mobility management, etc. The AMF 182a, 182b may use network slicing in order to customize CN support for the WTRU 102a, 102b, 102c based on the type of services utilized by the WTRU 102a, 102b, 102c. For example, different network slices may be established for different use cases, such as services relying on ultra-reliable low latency (URLLC) access, services relying on enhanced mobile broadband (eMBB) access, and / or services for machine type communication (MTC) access, etc. The AMF 162 may provide a control plane function for switching between the RAN 113 and other RANs (not shown) that employ other radio technologies, such as LTE, LTE-A, LTE-A Pro, and / or non-3GPP access technologies, such as WiFi.

[0081] The SMF 183a, 183b may be connected to the AMF 182a, 182b in the CN 115 via the N11 interface. The SMF 183a, 183b may also be connected to the UPF 184a, 184b in the CN 115 via the N4 interface. The SMF 183a, 183b may select and control the UPF 184a, 184b, and configure the service routing performed by the UPF 184a, 184b. The SMF 183a, 183b may perform other functions, such as managing and allocating UE IP addresses, managing PDU sessions, controlling policy implementation and QoS, and providing downlink data notifications. The PDU session type may be IP-based, non-IP-based, Ethernet-based, etc.

[0082] The UPF 184a, 184b may be connected to one or more of the gNBs 180a, 180b, 180c in the RAN 113 via the N3 interface, which may provide the WTRUs 102a, 102b, 102c with access to packet-switched networks, such as the Internet 110, to facilitate communications between the WTRUs 102a, 102b, 102c and IP-enabled devices. The UPF 184, 184b may perform other functions, such as routing and forwarding packets, enforcing user plane policies, supporting multi-homed PDU sessions, handling user plane QoS, buffering downlink packets, and providing mobility anchoring, etc.

[0083] The CN 115 may facilitate communications with other networks. For example, the CN 115 may include or may communicate with an IP gateway (e.g., an IP Multimedia Subsystem (IMS) server) that acts as an interface between the CN 115 and the PSTN 108. In addition, the CN 115 may provide the WTRUs 102a, 102b, 102c with access to other networks 112, which may include other wired networks and / or wireless networks owned and / or operated by other service providers. In one embodiment, the WTRUs 102a, 102b, 102c may be connected to local data networks (DNs) 185a, 185b via the UPF 184a, 184b via the N3 interface to the UPF 184a, 184b and the N6 interface between the UPF 184a, 184b and the DN 185a, 185b.

[0084] Given that Figures 1A to 1D as well as Figures 1A to 1D Corresponding to the description of the present invention, one or more or all of the functions described herein with reference to one or more of the following may be performed by one or more simulation devices (not shown): WTRU102a-102d, base station 114a-114b, evolved Node B 160a-160c, MME 162, SGW 164, PGW 166, gNB 180a-180c, AMF 182a-182b, UPF 184a-184b, SMF 183a-183b, DN 185a-185b and / or any other device described herein. The simulation device may be one or more devices configured to emulate one or more or all of the functions described herein. For example, the simulation device may be used to test other devices and / or simulate network and / or WTRU functions.

[0085] The simulation device may be designed to implement one or more tests of other devices in a laboratory environment and / or in an operator network environment. For example, one or more simulation devices may perform one or more functions or all functions while being fully or partially implemented and / or deployed as part of a wired and / or wireless communication network in order to test other devices within the communication network. One or more simulation devices may perform one or more functions or all functions while being temporarily implemented / deployed as part of a wired and / or wireless communication network. The simulation device may be directly coupled to another device for testing purposes and / or may use over-the-air wireless communications to perform testing.

[0086] One or more simulation devices can perform one or more (including all) functions without being implemented / deployed as part of a wired and / or wireless communication network. For example, the simulation device can be used in a test scenario in a test lab and / or a non-deployed (e.g., testing) wired and / or wireless communication network to implement testing of one or more components. One or more simulation devices can be test equipment. Direct RF coupling and / or wireless communication via RF circuits (e.g., which can include one or more antennas) can be used by the simulation device to send and / or receive data.

[0087] Specific implementations for predictive measurement reporting are disclosed. A WTRU may be configured to determine at least one predicted measurement. The at least one predicted measurement may include, for example, at least one predicted air interface measurement. The WTRU may determine a predicted event and a predicted time based on the at least one predicted measurement, the predicted time being associated with the predicted event and / or associated with the predicted measurement. The predicted event may include, for example, the at least one predicted measurement being less than a threshold. The WTRU may determine to transmit a report indicating the predicted event based on the predicted event, the predicted time, and a time offset. The WTRU may determine to transmit the report indicating the predicted event, for example, under the condition that the difference between the current time and the predicted time is greater than the time offset. If the condition is met, the WTRU may transmit the report indicating the predicted event. The WTRU may also be configured to determine a prediction confidence associated with the predicted event. The report may include an indication of the predicted event, the at least one predicted measurement, and the prediction confidence.

[0088] Measurement reports may be generated periodically. Measurement reports may be reported every x amount of time, where x refers to the reporting interval. Measurement reports may be event-triggered. Reports may be triggered based on criteria (e.g., A1 to A6 events) that compare the serving and / or neighboring cell signal levels to absolute or relative thresholds. Depending on the design, measurement reports may be reactive. The WTRU may perform measurements and report these measurements to the network (NW) at a specific reporting time or when a certain event criterion is triggered. The network may determine the execution action based on the received measurements. For example, the network may transmit a handover command, may configure the WTRU with conditional HO (CHO), or may take some other action. This reactive processing of measurements may result in less than ideal processing. For example, in the case where the WTRU moves out of the coverage of the serving cell after transmitting measurements to the network but before receiving a HO command from the network, the network may transmit a HO instruction too late. In instances of network-coordinated CHO, unnecessary network resource utilization may result because the network may reserve resources for neighboring cells for a considerable time in order to prevent HO from being too late.

[0089] Disclosed herein is a method of configuring a WTRU to proactively transmit measurement reports inferred from an artificial intelligence (AI) / machine learning (ML) based predictive model. The measurement reports may allow the network to perform / prepare (e.g., proactively and optimally perform / prepare) mobility procedures, allocate resources, activate other features, etc.

[0090] When it is predicted that the triggering condition for a given measurement event may be met in the future (e.g., in the near future), the WTRU may transmit a predictive measurement report. The measurement report may include the predicted signal levels of the serving and / or neighboring cells and other relevant details of the event, such as, for example, the prediction time horizon, the prediction error, etc.

[0091] The WTRU may receive a measurement configuration. The measurement configuration data may be received, for example, from a network node. Figure 2 An example measurement configuration is depicted. A WTRU that may be in RRC_Connected mode may be configured by the network to perform measurements and report these measurements back to the network according to the configuration (e.g., a specific configuration). The configuration may be provided by means of dedicated signaling. For example, RRCReconfiguration or RRCResume may be used to provide configuration information to the WTRU.

[0092] The network may configure the WTRU to report the following measurement information based on SS / PBCH blocks: measurement results per SS / PBCH block; per cell measurement results based on SS / PBCH blocks; and / or SS / PBCH block index.

[0093] The network may configure the WTRU to report the following measurement information based on the CSI-RS resource: measurement results per CSI-RS resource; per-cell measurement results based on the CSI-RS resource; and / or a CSI-RS resource measurement identifier.

[0094] The network may configure the WTRU to perform the following types of measurements for the NR side link and the V2X side link: CBR measurements.

[0095] The network may configure the WTRU to report the following CLI measurement information based on SRS resources: measurement results per SRS resource; and / or SRS resource index.

[0096] The network may configure the WTRU to report the following CLI measurement information based on the CLI-RSSI resource: measurement results per CLI-RSSI resource; and / or CLI-RSSI resource index.

[0097] The WTRU may be configured with information that defines reporting aspects of the measurements. The WTRU may be configured with information that defines what, how, and when the WTRU may report information to a network node. Configuration information, which may be referred to as a measurement report configuration, may include the following: reporting criteria; RS type; and / or reporting format. The reporting criteria may be a criterion that triggers the WTRU to transmit a measurement report. The criterion may define a periodic trigger and / or a single event trigger description. The RS type may be a reference signal (RS) that the WTRU uses for beam and cell measurement results. The RS type may be, for example, an SS / PBCH block and / or a CSI-RS. The reporting format may include the per-cell and per-beam quantities (e.g., RSRP) that the WTRU may include in the measurement report, as well as other associated information, such as, for example, the maximum number of cells to be reported and the maximum number of beams per cell.

[0098] The WTRU measurement reports may be configured by the network to be triggered based on, for example, one or more of: periodic reporting and / or event triggered reporting. For periodic reporting, the reporting interval may be specified as follows: ReportInterval::=ENUMERATED{ms120,ms240,ms480,ms640,ms1024,ms2048,ms5120,ms10240,ms20480,ms40960,min1,min6,min12,min30}. For event triggered reporting, the reporting interval may be specified as follows: ReportInterval::=ENUMERATED{ms120,ms240,ms480,ms640,ms1024,ms2048,ms5120,ms10240,ms20480,ms40960,min1,min6,min12,min30}. Event triggered reporting may also depend on the criteria used to trigger the reporting. Criteria for triggering a report may include the occurrence of an event in the network. Figure 3 An example measurement report triggering event is depicted. A WTRU may be configured with a specified triggering event (such as, for example Figure 3 The triggering events described in

[0099] The measurement report may be triggered in response to, for example, an event criterion being met. Figure 3 The occurrence of event A3 depicted in (eg, the offset of the neighboring cell becomes better than the SPCell) may trigger the generation of a measurement report. The measurement report may be triggered in response to a reporting interval time amount having expired. Figure 4 An example occurrence of measurement reports during a period of time is illustrated. Each vertical line on the time continuum may correspond to the generation of a measurement report. A vertical mark (e.g., a purple vertical mark) on the upper timeline represents a periodic measurement report. Figure 4 As noted, the time between occurrences of measurement reports may be referred to as a reporting interval. Figure 4 In the lower timeline of FIG. 1 , a vertical marker (eg, a green vertical marker) 410 may correspond to an event-triggered report, which may be triggered by, for example, Figure 3 The vertical mark (eg, a green vertical mark) 412 may correspond to a second event triggering factor, which may be, for example, Figure 3 The occurrence of event A3.

[0100] The WTRU may be configured to perform layer 3 filtering. When the WTRU measures the air interface, whether it may be measuring a cell, beam, and / or sidelink and whether it may be measuring RSRP, RSRQ, and / or SINR quantities, the WTRU may use the following formula to average the measurement data points received from the physical layer for use in evaluating a measurement report trigger:

[0101] F n =(1–a)*F n-1 +a*M n

[0102] In this formula, M n This can be the most recently received measurement result from the physical layer. n It may be an updated filtered measurement result, which can be used to evaluate reporting criteria or for measurement reporting. n-1 may be an old filtered measurement result, wherein when a first measurement result from the physical layer may be received, F 0 Set to M 1 ; and for MeasObjectNR, a=1 / 2 (ki / 4) , where k i It can be the filterCoefficient of the corresponding measurement quantity for the i-th QuantityConfigNR in quantityConfigNR-List, and i can be indicated by quantityConfigIndex in MeasObjectNR; for other measurements, a=1 / 2 (k / 4) , where k can be the filterCoefficient of the corresponding measurement quantity received by quantityConfig; for UTRA-FDD, a=1 / 2 (k / 4) , where k may be the filterCoefficient for the corresponding measurement quantity received by quantityConfigUTRA-FDD in QuantityConfig.

[0103] M n may correspond to a measured data point that can be inserted into the formula affected by the weight parameter a. This may be done to avoid peaks in recent measurements that may significantly change F n The value of .

[0104] F nThe parameters nrofSS-BlocksToAverage and nrofCSI-RS-ResourcesToAverage define the number of samples (F 2 、F 3 、F 4 、F 5、 ……、F max ), the WTRU averages this number of samples before evaluating / verifying the measurement report and conditional reconfiguration trigger.

[0105] The presented IEs (e.g., all presented IEs) may be passed from the gNB to the WTRU via an RRC reconfiguration message. With the described information, the WTRU may have the information required to perform measurements defined by the gNB from a time domain perspective, e.g., all information.

[0106] The WTRU may use the SMTC window configuration to measure the air interface. The WTRU may sample the air interface every x ms according to the configuration. The WTRU may use the parameters nrofSS-BlocksToAverage and nrofCSI-RS-ResourcesToAverage to select how many of those samples will be used in the averaging process before delivering the results to the network.

[0107] A machine learning ("ML") model used to generate predictions may have embedded statistical aspects. The accuracy of the estimates of the machine learning model may vary depending on the time range associated with the generated prediction. Among other functions, the machine learning model is also able to forecast or predict the future value of a given quantity. The prediction may have a timestamp value associated with it. The predicted value may have an associated timestamp and / or a small interval (e.g., a few ms) during which the predicted value may be considered valid. The timestamp may be summarized as a future time step. For each predicted value, there may be (e.g., there may always be) an estimate of the error or bias associated with the predicted value. The error may be calculated recursively, and the longer the time range associated with the prediction, the less accurate (e.g., worse) the estimate may be expected. For example, a machine learning model that outputs a sample at time step t+4 may have an estimate and error estimate for the sample / prediction that may be more accurate than the estimate and error estimate for the prediction at a later time (such as, for example, t+7).

[0108] In predictive inference, a prediction interval can be an estimate of the interval that future observations may fall into with a certain probability, given the data that may be observed. The machine learning model can output the interval based on the associated probability P. If the probability can be P = 0.95 or 95%, the model can output a window of values ​​where it can be estimated that the predicted value for any future time step can fall within the given window with a certain probability (e.g., 95%). Different values ​​of the confidence probability can cause the machine learning model to generate different prediction windows.

[0109] Using AI / ML techniques, network-configured events can be predicted at the WTRU. However, using traditional mechanisms, the network may not receive this prediction information in advance and may not be able to utilize the expected knowledge of the event.

[0110] Figure 5 Example measurement reports performed by a WTRU as it moves through multiple cell coverage areas are illustrated. In the depicted example, the WTRU may generate reports as it moves through a handover area ("HO area") and a conditional handover area ("CHO area"). For illustration purposes, a WTRU is shown in motion across the coverage of three cells. Figure 5 , the moving WTRU may be designated as a UE. The trajectory followed by the WTRU may be marked by a black line 516. The WTRU may be illustrated as moving in the coverage of cell A, cell B, and cell C, the coverage area of ​​each of these cells being depicted using an ellipse. A "handover (HO) area," which may be marked by an ellipse 514 (e.g., an orange ellipse), occurs where the coverage of cell A and cell B may overlap. In the HO area, following the WTRU trajectory, the power of the signal from cell A may decrease, and the power of the signal from cell B may increase. In this area, the WTRU may also receive a conditional handover (CHO) configuration because the network may not be sure which cell (B or C) may be the best for the WTRU.

[0111] Figure 5 A timeline is also depicted. The timeline indicates instances at which measurement reports may be generated. Periodic measurement reports may be indicated on the timeline by vertical marker 508, which may be a purple marker. The periodic measurement reports indicated by vertical marker 508 may be transmitted by the WTRU at "reporting interval" time intervals. Event-triggered measurement reports may be indicated at vertical markers 510 and 512, which may be green markers. When the WTRU may detect a network configured event, the WTRU may transmit an event-triggered measurement report.

[0112] The measurement reporting method may be reactive, which may result in network processing delays, as illustrated from the processing associated with the "HO area" and "CHO ​​area". For the movement of the WTRU in the HO area, the WTRU may trigger a measurement report message with measurement information when an event is detected. Based on the received measurement report, the network may take some actions, such as, for example, conveying a handover command. However, because the network may not transmit the handover command and the WTRU may not receive the handover command until the WTRU may be substantially through the HO area, the network may transmit the command (e.g., for the WTRU to connect to another cell) too late. In this case, the network may configure the WTRU with a CHO configuration, thereby providing the WTRU with two cell options for handover to cell B and cell C. This may result in longer WTRU configuration and longer resource reservation for the network nodes radiating these cells.

[0113] Using AI / ML techniques, models can be trained to predict measurements and therefore predict network configured events. A method that enables the network to receive predicted events before the WTRU actually experiences the event may be desirable and can enable the network to act proactively and prevent service disruptions.

[0114] The WTRU may be configured to make predictions. The WTRU may be pre-trained with an AI / ML model that is capable of generating predictions of air interface measurements (RSRP, RSRQ, SINR, etc.) for the serving cell and / or neighboring cells. The predictions may be compared to measurement trigger conditions. When the AI / ML model indicates that the trigger conditions may be met in the near future, the WTRU may transmit a measurement report.

[0115] Specific implementations are disclosed herein for a WTRU to report predicted measurements upon detecting that a trigger condition for reporting configured measurements is expected to be met in the future. The network may receive the predicted measurements and take any suitable action based on the expectation of certain measurements.

[0116] The WTRU may receive configuration data that enables the WTRU to trigger measurement reports with prediction information. Reports with prediction information may be generated before reports used in other measurement reporting processes. The WTRU may receive configuration on how to perform predictions, how to reuse or not reuse the configuration, and how to report predictions back to the network. When the configured conditions are met, the WTRU may transmit prediction related information to the network, thereby anticipating the event and thus providing time for the network to take relevant actions.

[0117] The WTRU may be configured to predict future measurements based on current and / or historical measurements. The WTRU may be configured with a trained AI / ML model that is capable of generating predictions for radio signal levels at a radio interface. The AI / ML model at the WTRU may be implementation specific. The AI / ML model at the WTRU may obtain the AI / ML model from the network. The AI / ML model may be configured to take current and / or historical RSRP measurements as input. The AI / ML model may be configured to obtain additional inputs, such as WTRU location information, WTRU mobility information, etc. The AI / ML model may be configured to generate a prediction of a signal value, such as RSRP, at a future time instance t. The AI / ML model may be configured to predict a series of RSRP values ​​corresponding to future time instances t+1, t+2, etc. until t+n. Predictions with a time series output may be more beneficial than single value predictions because it may be difficult to match the predictions to configured measurement events using a single prediction point.

[0118] In order to generate meaningful predictions or predictions that may be useful, the WTRU may perform predictions in a time series manner. From the moment the WTRU prediction is triggered, the WTRU may generate several prediction outputs within a future time span at a specific granularity or time step. Figure 6 Depicted is an example time series generation for reference signal received power (RSRP). Figure 6 , at time t, the WTRU may predict one RSRP prediction point per time step from time t+1 to t+n.

[0119] The WTRU may be configured to perform a prediction and generate a predicted value. The predicted value may have an associated timestamp and / or a small interval (e.g., a few ms) during which the predicted value may be considered valid. For each prediction point, at each time step, there may be an error value associated with the prediction. The error value may include an error, an estimated error, a confidence indication, an estimated confidence, an accuracy, an estimated accuracy, and / or any other error measurement technique. This may be inherent to the ML algorithm. The further apart in time the predictions are made (e.g., the larger the value of n), the larger the error of the prediction may be. The duration (e.g., a maximum duration) of the time span of the prediction (e.g., a value of n time steps) may be appropriately defined, and the duration may include various forms of definition of: the number of time steps (e.g., the value of n); the granularity between time steps (e.g., the time value between each prediction point); and / or a threshold value for the error of the prediction.

[0120] The AI / ML model resident at the WTRU may have been delivered by the network, and thus the network may have access to the intrinsic properties, e.g., all model intrinsic properties. Thus, the network may be able to configure the WTRU with appropriate (e.g., necessary) parameterization. This may be done, for example, via any message or SIB broadcast when the model may be delivered to the WTRU or at any point in time after the model may be delivered. A set of AI / ML models may have been delivered to the WTRU, in which case the network may provide the WTRU with relevant information for model selection, e.g., via a model ID.

[0121] Another option for the source of the AI / ML model may be that the AI / ML model may be proprietary and vendor specific and may already be available at the WTRU. The WTRU may signal its model capabilities to the network so that the network may take the WTRU capabilities into account when configuring the WTRU and / or may allow the WTRU to decide which parameter set to use.

[0122] In conjunction with measuring the air interface, the WTRU may use the parameters nrofSS-BlocksToAverage and nrofCSI-RS-ResourcesToAverage to select how many of the samples may be used in the averaging process before delivering the results to the network.

[0123] Considering a prediction time span of size n (e.g., having n time steps), predicting samples at the same rate as the SMTC window (e.g., at the finest granularity, every 5ms) can make the inference process more computationally intensive because it can reduce the actual time length of n. Increasing this rate too much can result in longer n time windows, but can affect (e.g., greatly affect) the error associated with the prediction, especially near the end of the time window.

[0124] When the WTRU may be configured to perform prediction, the prediction granularity may take into account an averaging process.

[0125] The WTRU may be configured to use parameters nrofSS-BlocksToAverage and / or nrofCSI-RS-ResourcesToAverage (eg, which may be explicitly configured or fixed in the standard) as parameters defining the time step granularity of the prediction.

[0126] The WTRU may be configured to use different averaging parameters for predicted measurements compared to normal measurements.The WTRU may be configured to use, for example, nrofSS-BlocksToAverage=n1 for normal measurements and nrofSS-BlocksToAverage=n2 for predicted measurements.

[0127] The WTRU may be configured to use different granularities for the time steps configured for different parts of the window (t; t+n), e.g., 4 samples in (t; t+x) and 2 samples in (t+x; t+n). This option may be relevant considering that the error in the prediction may be higher towards the end of the window (e.g., time step n). For this reason, the network may configure different numbers of samples for averaging in an attempt to make the last sample less important for the final result.

[0128] The WTRU may be configured to use different granularities for time steps configured for different parts of a window (e.g., t; t+n) (e.g., 2 samples in (e.g., t; t+x, where the rate of change in that subwindow may be higher) and 4 samples in (e.g., t+x; t+n, where the rate of change in that window may be lower)) based on different rates of change of each sample.

[0129] The WTRU may be configured to perform predictive reporting. Figure 7 A timeline showing example WTRU predictions and reports is depicted. Figure 7 , reference numbers 1-5 are shown on a timeline and may correspond to events and / or processes. At time t, the WTRU may perform AI / ML inference and generate predictions for air interface measurements in a time series manner. The WTRU may generate a predicted estimate and associated error for time t+1, another predicted estimate and associated error at time t+2, and so on, where each time may include a time interval. At t+n_evt (n_evt time steps after the prediction was generated), the WTRU may predict that a trigger condition for a predicted measurement report may be met. As an example, the WTRU may be configured with an A2 event or an event similar to A2, such as predEventA2 for a particular cell (during which the serving cell may be expected to become inferior to a threshold).

[0130] The WTRU may be configured to transmit prediction related information before t+n_evt. The amount of time before t+n_evt that the WTRU transmits prediction related information may be configurable and may correspond to Figure 7 The “preconfigured time offset” depicted in . Figure 7 The double-headed arrow 710 in FIG. 1 (e.g., a green double-headed arrow) may represent a time span over which inference / prediction may be performed. If the WTRU triggers a prediction at time t, then until time t+n, the WTRU may generate n prediction points spaced apart by a time step value or granularity. The green arrow 710 may represent a time span from t to t+n.

[0131] refer to Figure 7, at reference numeral 1, the WTRU may indicate prediction capabilities to the network. These capabilities may be inherent to the AI / ML model used by the WTRU and may include, for example, parameters as described herein.

[0132] refer to Figure 7 , at reference numeral 2 corresponding to time t, if the model output prediction is configured by the network, the WTRU may receive an inference configuration. The configuration may be fully or partially fixed, for example, by a standard, or may include parameters and specific times when the WTRU triggers the inference / prediction. The triggers may be configured by the network to impose some restrictions on the areas / locations / times of the day where the prediction measurements may be transmitted or the WTRU measurement predictions and reports may be active.

[0133] A specific air interface measurement quantity may be used to trigger a prediction. If a specific air interface measurement quantity may be used to trigger a prediction, the configuration may include, for example, the following: a specific cell that the WTRU is capable of detecting (e.g., if cell X is detectable via measurement, a prediction is triggered); a list of cells that the WTRU is capable of detecting (e.g., if a cell in a given set of cells (e.g., all cells) is detected via measurement, a prediction is triggered); a specific value of the air interface measurement quantity for a specific cell (e.g., for cell ID=x, the RSRQ may be below a threshold); a specific value of the air interface measurement quantity for a set of cells (e.g., for cells in a given set of cells (e.g., all cells), the RSRQ may be below a threshold, or for cell ID=x, the SINR may be below a threshold, and for cell ID=y, the RSRP may be above a threshold); an interval of specific air interface measurement quantities for specific cells (e.g., for cells in a given set of cells (e.g., all cells), the RSRQ may be below a threshold, or for cell ID=x, the SINR may be below a threshold, and for cell ID=y, the RSRP may be above a threshold); a specific air interface measurement quantity for a specific cell ... For example, for cell ID=X, a≤RSRP≤b); different intervals of specific air interface measurement quantities for a set of cells (e.g., for cell ID=X, a≤RSRP≤b, and for cell ID=Y, c≤RSRP≤d); a specific beam level that the WTRU is capable of detecting (e.g., if beam level X can be detected via measurement, then the prediction is triggered); a list of beam levels that the WTRU is capable of detecting (e.g., if a beam level in a given set of beam levels (e.g., all beam levels) is detected via measurement, then the prediction is triggered); a specific value of the air interface measurement quantity for a specific beam level; a specific value of the air interface measurement quantity for a set of beam levels; and other triggers, which may be similar to existing triggers in the measurement process, comparing the quality of the measurement to a threshold, or comparing the quality between cells. Other triggers may include, for example, the following: an immediate triggering of the prediction; a specific timestamp that triggers the prediction; a timer that triggers the prediction, where the timer starts counting from the timestamp when the configuration may have been received; a specific geographic location that triggers the prediction; and / or a specific geographic area that triggers the prediction.

[0134] The WTRU may perform extrapolation at time t (e.g., immediately after configuration reception) until t+n. The WTRU may have obtained estimates for prediction points at t+1, t+2, (...), t+n_evt, t+n (e.g., where the points may refer only to time, as the model may output both RSRP and RSRQ for time steps (e.g., all time steps)). The WTRU may have predicted that the air interface quantity may be such that a measurement event (e.g., a conventional configured measurement event) may occur at t+n_evt (i.e., n_evt time steps after the prediction is triggered).

[0135] Since the WTRU may have predicted the event configured by the network, the WTRU may report the prediction immediately (if the WTRU may be instructed to do so) or the WTRU may report the prediction using the next periodic measurement report (depending on the measurement configuration from the network, which may have been received before the prediction was triggered). The WTRU may keep the measurement prediction without transmitting it and may wait for the prediction to be sent to the WTRU. Figure 7 The time t+4 corresponding to reference number 4 in the timeline of is when the prediction may be transmitted regardless of before the event (eg, t+n_evt) actually occurs.

[0136] refer to Figure 7 Referring to reference numeral 3 in the timeline, at time t, the WTRU may predict the occurrence of a measurement at time t+n_evt. The time step n_evt represents a time that may be close to the end of a prediction / inference window of size n time steps. Due to the nature of AI / ML algorithms, the further apart the times at which predictions may be made (e.g., the larger the value of n), the larger the error in the predictions may be. The prediction at time n_evt may (e.g., may be highly likely) have a higher error value than the predictions made for time t+1 or t+2. To this end, the WTRU may have an option for rerunning the inference process at any point in time before the last opportunity to transmit a prediction (e.g., at a time corresponding to reference numeral 4 of the timeline). For example, the WTRU may be configured with different options for rerunning the inference between time t and time t+4. One option for rerunning the inference may be for the WTRU to trigger a rerun of the inference at each time step until the time corresponding to reference numeral 4. The WTRU may trigger a rerun of the inference at a specific time step or steps. The WTRU may trigger a rerun of the inference for a certain incremental number of time steps since the first event prediction.

[0137] Re-running the inference process may be an additional computational burden for the WTRU, which may be reduced by configuration. If an inference re-run may be triggered at time t+2, the WTRU may perform one or more of the following operations, for example: re-run the inference until (e.g., only until) the initial time t_evt at which the event was first predicted; re-run the inference using the same time span (e.g., if the WTRU re-runs the inference at t+2, it may predict values ​​until t+n+2); and / or re-run the inference until the event can be predicted again, either at time t_evt or at another later or earlier time (due to a new prediction).

[0138] If the extrapolation may be performed multiple times, there may be multiple prediction values ​​and associated errors for each time step. During this window and before the time corresponding to reference number 4, the WTRU may be configured to send the prediction values ​​using traditional measurement reports or using other configurable options. If there may be one (e.g., only one) periodic measurement report (based on the measurement configuration), the WTRU may transmit the predictions (e.g., all predictions) and error values ​​for the time steps (e.g., all time steps) generated by the extrapolation execution (e.g., all extrapolation executions). If there may be one (e.g., only one) periodic measurement report (based on the measurement configuration), the WTRU may transmit the predictions (e.g., all predictions) for a subset of time steps (e.g., x time steps before reference number 4). If there may be one (e.g., only one) periodic measurement report (based on the measurement configuration), the WTRU may transmit a subset of the predictions for the time steps (e.g., all time steps) (e.g., transmit (e.g., transmit only) the predictions corresponding to the lowest error, or transmit only the predictions with errors below a threshold). If there may be one (e.g., only one) periodic measurement report (based on the measurement configuration), the WTRU may transmit a subset of predictions for a particular time step (e.g., transmit (e.g., only transmit) the prediction corresponding to the lowest error, or transmit (e.g., only transmit) the predictions with an error below a threshold, but this time only for a particular time step). If there are multiple periodic measurement reports in the window, the WTRU may send the prediction values ​​to the network and spread the results across the multiple measurement reports (e.g., transmit predictions for an incremental number of time steps closer to the most recent t_evt in the first measurement report, and then transmit predictions for another incremental number of time steps before that incremental number of time steps in the next measurement report).

[0139] refer to Figure 7, at reference numeral 4, the WTRU may trigger an expected or predicted measurement report at a time interval "preconfigured time offset" before the predicted event. The network may configure the WTRU with one or more of the following: a) a preconfigured time offset (reference numeral 5) counting from the timestamp of the predicted event; b) a geographical location associated with the geographical distance of t_evt; c) a distance from t_evt; d) a predicted number of times at t_evt the WTRU has predicted that the event will occur (e.g., after 5 predictions, the event occurs at t_evt, then the prediction is transmitted); e) a UL and / or DL ​​quantity; f) an air interface radio quantity drop / increase increment, such as an RSRP increment; g) where the number of predictions that the WTRU has predicted that the event will occur at t_evt with a certain error below / above a threshold (e.g., after 5 predictions, the event occurs at t_evt with a certain error value below the threshold, then the prediction is transmitted); h) item d or g, but considering continuous predictions; and / or i) a preconfigured time offset may be associated with other options (e.g., if item d, g or h occurs before a preconfigured time offset, the WTRU may transmit the prediction; otherwise, the WTRU may transmit the prediction at the preconfigured time offset, regardless of whether other criteria may be met).

[0140] The time corresponding to reference numeral 4 may be considered the last opportunity for the WTRU to transmit predicted measurement information to the network. Thereafter, due to the latency of the WTRU-network messages, the anticipation of the process may not make sense because it may be too late and may not produce the desired results. The WTRU may override the sending of the prediction based on one or more conditions that may be configured by the network. The one or more conditions may include, for example, one or more of the following: the WTRU may not transmit the prediction based on a state transition change; the WTRU may not transmit the prediction based on a set of detectable cell changes; the WTRU may not transmit the prediction based on a configured set of measured cell changes; the WTRU may not transmit the prediction based on the predicted error not meeting the trigger criteria (e.g., configuration item g above); and / or when the inference can be rerun, the WTRU may not transmit the prediction based on the predicted measurement value falling outside the trigger condition.

[0141] refer to Figure 7 , reference numeral 5 corresponds to the time at which the WTRU may have predicted that a certain event may occur. This time may be marked at time t_evt, but it is considered that t_evt may change each time the WTRU performs an inference. Therefore, this article combines Figure 7 Reference numerals 3 and 4 discuss configuration options.

[0142] There may be multiple options for how to define the signaling for configuring reporting and actually reporting predicted measurements. The functionality of predictive measurements may be embedded in an existing WTRU measurement procedure. This may be the default option. There may be a separate predictive measurement procedure that may have the same structure as the existing measurement procedure, with all or a subset of the existing events or newly defined events in the new measurement prediction procedure. Another existing or new procedure may be defined that allows for configuration and event reporting. In any of these options, the procedure may configure measurement parameters (e.g., all measurement parameters) for calculation of the predicted measurements, or some or all of the parameters may be fixed values, and other aspects of the measurement (such as layer 3 filtering) may or may not apply.

[0143] The processing described herein applies in a similar manner to situations where predicted events are configured and reported in an existing measurement process, in a predicted measurement process modeled after an existing measurement process, or embedded in other existing or new processes.

[0144] Prediction reporting may be employed to detect an event, and the inference process may be rerun to detect that particular event, for example to confirm or re-evaluate a previous prediction.The WTRU may receive a configuration that allows the WTRU to use the event detection and inference process multiple times as needed.

[0145] The WTRU may be configured to perform the inference process once. The WTRU may be configured to perform the inference process periodically. The WTRU may be configured to perform the inference process, for example, in conjunction with Fig.11 The described moving window approach triggers the inference process.

[0146] The WTRU may be configured with different procedure options for reuse for different cells based on network request and / or need. The WTRU may be configured to perform one or more of the following operations: perform the procedure once for a specific cell; perform the procedure once for a set of cells; perform the procedure once for cells in a measurement configuration (e.g., all cells); and / or perform the procedure once for detectable cells (e.g., all detectable cells).

[0147] The WTRU may be configured with different options related to the iterative inference process. The WTRU may be configured to perform one or more of the following operations: repeatedly perform the process for a specific cell; repeatedly perform the process for a set of cells; repeatedly perform the process for cells in a measurement configuration (e.g., all cells); and / or repeatedly perform the process for detectable cells (e.g., all detectable cells).

[0148] The WTRU may be configured with different options related to the moving window procedure. The WTRU may be configured to perform one or more of the following operations: perform the moving window procedure for a specific cell; perform the moving window procedure for a set of cells; perform the moving window procedure for cells in a measurement configuration (e.g., all cells); and / or perform the moving window procedure for detectable cells (e.g., all detectable cells).

[0149] The WTRU may be configured to perform a one-time inference procedure. The WTRU may perform a one-time inference procedure, for example, only once. Figure 8 An example one-shot inference process window is depicted. As shown, the inference process window can be direct and can include a period between t and t+n.

[0150] The WTRU may be configured to perform an iterative inference process.The WTRU may perform an iterative inference process based on a network configuration and / or based on a fixed (eg, standard defined) form or configuration. Fig. 9 An example periodic WTRU prediction with window reuse is depicted. As shown, for a particular cell, the WTRU may be configured with a value of n and prediction may be triggered periodically at multiples of n.

[0151] In conjunction with performing an iterative inference process, the WTRU may be configured with one or more back-off timers. These timers may prevent the WTRU from performing inferences for a certain amount of time. The network may have an estimate of when the WTRU's prediction support may be needed, and in this way may limit the WTRU's battery and computing resource usage. Fig.10 An example periodic WTRU prediction using a configured window setting is depicted. Fig.10 As shown, different backoff timers may be set before the WTRU performs inference, and when this occurs, the WTRU may be configured with windows of different sizes (n, n2, etc.).

[0152] The WTRU may be configured to perform a moving window inference process. The WTRU may perform inference in a moving window manner. The WTRU may be configured to (e.g., specifically configured to) perform moving window processing and / or the WTRU may be configured to perform (e.g., fixed) moving window processing as defined in a standard. Multiple (e.g., two) example methods may be performed. Fig.11 An example WTRU moving window inference process with spacing and window reuse is illustrated. There may be a fixed spacing configuration y after which the WTRU (starting at time t) may rerun the inference process. The inference process window may have a fixed size n.

[0153] In an example approach, both the spacing y and the predicted time span n may be configured to different values. Fig.12 Illustrates an example WTRU moving window inference process that utilizes configurable spacing and windows. This process may be useful in situations where the network can consider internal predictions and estimate, for example, that an event may occur near time n. The WTRU can perform inferences up to t + n. If no event is predicted, the WTRU can re-run the inference at time t + y. If an event is predicted, the WTRU can re-run the inference at time t + y such that the updated prediction has a lower associated error.

[0154] The network can have a prediction result that concludes that no event will occur near time t + n + y. For this, it may be beneficial to exempt the WTRU from additional calculations and introduce a high confidence y2 where the likelihood of having an event is low (e.g., very low).

[0155] The WTRU can be configured to allow the inference window to be adjustable by potentially adjusting n (e.g., n, n2, n3, etc.). The longer the window, the higher the error associated with the prediction may be. The likelihood that the WTRU experiences a configured measurement event can be used to adjust the value of n for different timings. If the network is confident that no event will be detected or predicted, the window can be longer. Otherwise, the window can be shorter.

[0156] The WTRU can be configured to adopt a prediction interval. The AI / ML model can be specific to a particular implementation or vendor, or can be obtained by the WTRU from the network and / or delivered to the WTRU by the network. If the AI / ML model can be specific to a particular implementation or vendor, the time span of the prediction interval and prediction-related capability exchange can become relevant. This can also apply if the AI / ML model is received from the network, e.g., equally apply because using the same trained model may or may not be suitable for generating predictions for the same WTRU in different situations.

[0157] In conjunction with the prediction-related capability exchange, the WTRU can signal to the network a time value and / or range representing the duration of the prediction value during which the prediction window can be greater than or not greater than a specific threshold. For example, the prediction value can be accurate enough and not within an unreasonable range, e.g., for the same confidence percentage (e.g., 0.95 or 95%), the reasonable range is 95 < RSRP < -97 instead of -95 < RSRP < -120. This threshold can be conveyed to the WTRU in network signaling configuration information and / or can be broadcast on the SIB.

[0158] The estimate / prediction may become less accurate over time. The estimate may start out higher, may become lower, and may become higher again. The change in the estimate may be related to the quality of the data used to train the model, where in certain cases more data points may be available for training. If subsequently, when performing inference, the model may take as input data belonging to the same or similar cases, the model may again output a prediction within a smaller range. To this end, the information that the WTRU may signal to the network may be in the form of a value or a range, as described above.

[0159] Employing a prediction interval may involve adding additional information for generating a prediction to WTRU configuration information that may be delivered to the network as part of a capability exchange, such as in an expected measurement report. The additional configuration information may include, for example, information associated with the prediction interval and a percentage confidence associated with the interval. The WTRU may employ the configuration information to determine a prediction value within a prediction interval having an associated percentage confidence value.

[0160] Fig.13 An example implementation of a prediction interval is illustrated. Fig.13 , RSRP boundaries based on percentage (x%) confidence intervals are depicted. The upper RSRP boundary for a given interval is depicted by line 1310, and the lower RSRP boundary for the interval is depicted by line 1312. Line 1314 depicts the upper RSRP boundary of the WTRU prediction at a defined percentage (x%) confidence level within the time series range t to t+n. Line 1316 depicts the lower RSRP boundary of the WTRU prediction at a defined percentage (x%) confidence level within the time series range t to t+n. As shown in the figure, line 1314 depicting the upper RSPR boundary of the prediction at the percentage confidence level and line 1316 depicting the lower RSRP boundary at the percentage confidence level change over time due to estimation variability.

[0161] Fig.13 An example is depicted illustrating how the prediction bounds (confidence intervals) may remain fixed over time (as depicted by lines 1310 and 1312) or how the prediction bounds may change (as depicted by lines 1314 and 1316). The upper and lower bounds may be further separated because the bounds may change differently over time. The time scale may be general and may be represented by multiple time slots from t to t+n.

[0162] The confidence levels used when generating AI / ML results can impact processing and results.

[0163] As explained herein, the network may configure the WTRU to generate a range of values, such as a range of RSRP values, for each time step, for any one of the time steps, and / or for any subset of the time steps within the prediction window. If the WTRU may be configured to generate a prediction range based on a confidence value, the value may be different for each time step.

[0164] As explained herein, the WTRU may be configured with a parameterized approach. The configuration information may be adjusted to take into account the confidence value. If a range of predicted values ​​is applied, the network may configure the WTRU with a different value for the predicted confidence value for each time step.

[0165] As explained herein, the WTRU may transmit an expected measurement report when one or more of a plurality (eg, several) of conditions are met. The WTRU may also be configured to transmit an expected measurement report based on additional and / or different rules based on a confidence report.

[0166] For example, the WTRU may be configured to transmit a report if, at time t_evt, after making multiple predictions, the WTRU may have predicted that the event will fall within the prediction range for a given confidence percentage value. For example, after determining multiple predictions (e.g., five predictions) with a certain percentage (x%) confidence that the prediction falls within the range, the WTRU may transmit the prediction.

[0167] The WTRU may be configured to transmit a report if, at time t_evt, after multiple predictions, the WTRU may have predicted that the event will fall below the upper boundary of the prediction range associated with the confidence percentage value. After determining multiple predictions (e.g., five predictions) with a determined confidence percentage (x%) that the prediction falls below the upper boundary of the prediction range, the WTRU may transmit the prediction.

[0168] The WTRU may be configured to transmit a report if, at time t_evt, after multiple predictions, the WTRU may have predicted that the event will fall above the lower boundary of the prediction range for a given confidence percentage value. For example, after determining multiple predictions (e.g., five predictions) with a certain confidence percentage (x%) that the prediction falls above the lower boundary range, the WTRU may transmit the prediction.

[0169] The WTRU may be configured to transmit a report including a prediction after considering predictions associated with consecutive time steps. For example, if after making multiple predictions at consecutive time steps, the WTRU may have predicted that the event will fall within the prediction range for a given confidence percentage value, the WTRU may transmit the prediction. The WTRU may be configured to transmit the prediction if after making multiple predictions at consecutive time steps, the WTRU may have predicted that the event will fall below the upper boundary of the prediction range associated with the confidence percentage value. The WTRU may be configured to transmit the prediction if after making multiple predictions at consecutive time steps, the WTRU may have predicted that the event will fall above the lower boundary of the prediction range for a given confidence percentage value.

[0170] The WTRU may be configured to transmit a report including a prediction after considering predictions associated with various distributions of time steps. The distribution of time steps may be provided to the WTRU by, for example, a network. The distribution may be any suitable distribution and the time steps associated with the distribution may or may not be continuous. The WTRU may transmit the prediction if, after making multiple predictions at time steps across a defined distribution, the WTRU may have predicted that the event will fall within a prediction range for a given confidence percentage value. The WTRU may be configured to transmit the prediction if, after making multiple predictions at time steps across a defined distribution, the WTRU may have predicted that the event will fall below an upper boundary of the prediction range associated with the confidence percentage value. The WTRU may be configured to transmit the prediction if, after making multiple predictions at time steps across a defined distribution, the WTRU may have predicted that the event will fall above a lower boundary of the prediction range for a given confidence percentage value.

[0171] Fig.14 Depicted is an example signaling for a WTRU measurement prediction reporting process. As shown, a layer 3 message diagram may be used to illustrate the configuration of a WTRU and the sending of predicted measurements or events back to the network.

[0172] refer to Fig.14 , at reference 1, the network node may use an internal database to perform coverage assessment and may prepare an inference procedure for the WTRU. The network may derive which cells the WTRU may perform inference for and may determine WTRU configuration information consistent with the discussion herein. At reference 2, the network node may transmit the configuration to the WTRU. At reference 3, the WTRU may perform an inference procedure as described herein. At reference 4, the WTRU may use a reporting configuration to report the prediction back to the network node.

[0173] Although the features and elements described herein are described in particular combinations, each feature or element may be used alone without the other features and elements of the preferred embodiments, or in various combinations with or without the other features and elements.

[0174] The description herein is provided for exemplary purposes and does not in any way limit the applicability of the described systems, methods, and apparatuses to other wireless technologies and / or wireless technologies using different principles where applicable. The term network in the present disclosure may refer to one or more gNBs (which in turn may be associated with one or more transmit / receive points (TRPs)), or may refer to any other node in a radio access network.

[0175] Although the implementation described herein may consider 3GPP specific protocols, it should be understood that the implementation described herein is not limited to such scenarios and is applicable to other wireless systems. For example, although the solution described herein considers LTE, LTE-A, New Radio (NR) or 5G specific protocols, it should be understood that the solution described herein is not limited to such scenarios and is also applicable to other wireless systems.

[0176] The processes described herein may be implemented in a computer program, software, and / or firmware incorporated in a computer-readable medium for execution by a computer and / or processor. Examples of computer-readable media include, but are not limited to, electronic signals (sent via wired and / or wireless connections) and / or computer-readable storage media. Examples of computer-readable storage media include, but are not limited to, read-only memory (ROM), random access memory (RAM), registers, cache memory, semiconductor memory devices, magnetic media (such as, but not limited to, built-in hard disks and removable disks), magneto-optical media, and / or optical media (such as compact disk (CD)-ROM disks and / or digital versatile disks (DVDs)). A processor associated with the software may be used to implement a radio frequency transceiver for a WTRU, a terminal, a base station, an RNC, and / or any host computer.

Claims

1. A wireless transmit and receive unit (WTRU), the wireless transmit and receive unit (WTRU) comprising: A processor, the processor being configured to: determining at least one measurement; determining a predicted event and a predicted time associated with the predicted event based on the at least one measurement; determining, based on the predicted event, the predicted time and a time offset, to transmit a report indicating the predicted event; as well as The report is transmitted indicating the predicted event.

2. The WTRU of claim 1, The predicted time includes a time interval.

3. The WTRU of claim 1 , Wherein the at least one measurement comprises at least one predicted measurement.

4. The WTRU of claim 1 , Wherein the predicted event comprises the at least one measurement being less than a threshold value.

5. The WTRU of claim 1, wherein the time offset indicates a difference between a current time and the predicted time.

6. The WTRU of claim 1 , wherein the processor is further configured to determine a prediction confidence associated with the predicted event; Wherein the report indicating the predicted event comprises an indication of the predicted event, the at least one measurement, and the prediction confidence.

7. The WTRU of claim 1 , The processor configured to determine to transmit the report indicating the predicted event based on the predicted event, the predicted time and the time offset is further configured to transmit the report indicating the predicted event under the condition that the difference between the current time and the predicted time is greater than the time offset.

8. The WTRU of claim 1 , The processor is further configured to receive configuration information, wherein the configuration information includes information identifying the time offset.

9. The WTRU of claim 8, The configuration information also includes information identifying the predicted event.

10. The WTRU of claim 3, wherein the processor configured to determine the at least one predictive measure is configured to determine a first predictive measure and a second predictive measure; and Wherein the processor configured to determine the predicted event and the predicted time associated with the predicted event based on the at least one prediction measure is further configured to determine the predicted event and the predicted time associated with the predicted event based on the second prediction measure.

11. A method comprising: determining at least one measurement; determining a predicted event and a predicted time associated with the predicted event based on the at least one measurement; determining, based on the predicted event, the predicted time and a time offset, to transmit a report indicating the predicted event; as well as The report is transmitted indicating the predicted event.

12. The method according to claim 11, The predicted time includes a time interval.

13. The method according to claim 11, wherein the at least one measurement comprises at least one predicted measurement; wherein the predicted event comprises the at least one predicted measurement being less than a threshold; and The time offset indicates the difference between the current time and the predicted time.

14. The method of claim 11, further comprising determining a prediction confidence associated with the predicted event; Wherein the report indicating the predicted event comprises an indication of the predicted event, the at least one measurement, and the prediction confidence.

15. The method according to claim 11, Wherein determining to transmit the report indicating the predicted event based on the predicted event, the predicted time and the time offset further comprises: The report indicating the predicted event is transmitted on the condition that a difference between a current time and the predicted time is greater than the time offset.

16. The method according to claim 11, wherein determining the at least one measurement comprises determining a first measurement and a second measurement; and Wherein determining the predicted event and the predicted time associated with the predicted event based on the at least one measurement comprises: The predicted event and the predicted time associated with the predicted event are determined based on the second measurement.

17. A wireless transmit and receive unit (WTRU), the wireless transmit and receive unit (WTRU) comprising: A processor, the processor being configured to: Determine multiple measurements; determining a predicted event and a predicted time associated with the predicted event based on one of the plurality of measurements, the predicted event being associated with the one of the plurality of measurements that satisfies a threshold; determining a difference between a current time and the predicted time; as well as A report indicating the predicted event is transmitted to a network node on the condition that the difference between the current time and the predicted time is greater than a predetermined time offset.

18. The WTRU of claim 17, wherein the processor is further configured to determine a prediction confidence associated with the predicted event; Wherein the report indicating the predicted event comprises an indication of the predicted event, the one of the plurality of measurements, and the prediction confidence.

19. The WTRU of claim 17, The predicted time includes a time interval.

20. The WTRU of claim 17, Wherein the one measurement of the plurality of measurements comprises at least one predicted measurement.

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