Predicted Measurement Report

Abstract

The 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, the at least one predicted measurement being less than a threshold. The WTRU may determine to send a report indicating the predicted event based on the predicted event, the predicted time, and a time offset. The WTRU may determine to send a report indicating the predicted event on the condition, for example, 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 send a report indicating the predicted event.

Description

[Technical Field]

[0001] (CROSS-REFERENCE TO RELATED APPLICATIONS) This application claims the benefit of U.S. Provisional Patent Application No. 63 / 390,057, filed July 18, 2022, and U.S. Provisional Patent Application No. 63 / 410,812, filed September 28, 2022, the disclosures of which are incorporated herein by reference in their entireties. [Background technology]

[0002] Mobile communications using radio communications continues to evolve. The fifth generation may be referred to as 5G. Previous (traditional) generations of mobile communications may be, for example, fourth generation (4G) long term evolution (LTE). Summary of the Invention

[0003] SUMMARY Described herein are systems, methods, and means for artificial intelligence (AI)-based anticipated or predicted measurement reporting in wireless systems.

[0004] 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.

[0005] The WTRU may determine a predicted event and a predicted time associated with the predicted event based on at least one predicted measurement. The predicted event may include, for example, at least one predicted measurement being less than or greater than a threshold. The predicted time may include a time interval.

[0006] The WTRU may be configured to receive configuration information including a time offset. The time offset may indicate the difference between the current time and the predicted time. The WTRU may determine to send a report indicating the predicted event based on the predicted event, the predicted time, and the time offset. The WTRU may determine to send a report indicating the predicted event on the condition, for example, 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 send a report indicating the predicted event.

[0007] The WTRU may be further configured to determine a predicted reliability associated with the predicted event. The report sent by the WTRU may include an indication of the predicted event, at least one predicted measurement, and the predicted reliability.

[0008] The WTRU may be configured to trigger expected or predicted measurement reports based on AI / machine learning (ML)-based predictions of specific events or measurements that will occur in the future. The WTRU may be configured to determine at least one expected or predicted measurement value and to evaluate the at least one predicted measurement value against at least one trigger condition. In response to the predicted measurement value satisfying the at least one trigger condition, the WTRU may generate a measurement report and may transmit the measurement report to a network node. The measurement report may include the at least one predicted measurement value.

[0009] 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 at least one trigger condition. The information indicating the at least one trigger 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.

[0010] The WTRU may be configured to perform filtering / averaging of predicted measurements that are consistent with a configuration received from the network. The configuration information received by the WTRU may include information indicating at least one reporting option may include information indicating a filter for the predicted measurements. The WTRU may apply the filter to the predicted measurements. The configuration information received by the WTRU may include information indicating at least one reporting option and may include information indicating an average of the predicted measurements. The WTRU may calculate an average of the predicted future measurements.

[0011] The WTRU may be configured to trigger a measurement inference process multiple times and to collect predicted information that may be transmitted to the network. The WTRU may determine multiple predicted measurements over a period of time. The WTRU may receive configuration information indicating a trigger for collecting predicted measurements. The WTRU may obtain multiple predicted measurements in response to determining that the trigger is met. The WTRU may determine at least a first predicted measurement and a second predicted measurement during the period. The first predicted measurement may be determined by performing inference at a first time during the period, and the second predicted measurement may be determined by performing inference at a second time during the timer period. The WTRU may generate a measurement report including at least the first predicted measurement and the second predicted measurement. The WTRU may send the measurement report to a network node. The WTRU may send the predicted measurement information at the last opportunity for the WTRU to report predicted measurements.

[0012] The WTRU may be configured to obtain multiple ranges of predicted measurement values ​​over a period of time. The WTRU may receive configuration information indicating a confidence value for each of the multiple ranges of predicted measurement values ​​for the period of time. The WTRU may determine at least a first range of predicted values ​​associated with a first time in the period of time and a second range of predicted values ​​associated with a second time in the period of time. The predicted values ​​in the first range may have a first associated confidence value, and the predicted values ​​in the second range may have a second associated confidence value. If the predicted values ​​in the first range and the associated first confidence value satisfy a threshold comparison, the WTRU may generate a measurement report including the predicted values ​​in the first range and the associated first confidence value and may transmit the measurement report to a network node.

[0013] The WTRU may be configured to override sending predicted measurement information based on a difference found between the prediction and the actual measurement. 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 measured value and compare the at least one predicted measurement with the measured value. In response to a difference between the predicted measurement and the measured value, the WTRU may override sending the at least one predicted measurement. The configuration information received by the WTRU may include information indicating a length of time before the at least one predicted measurement. The WTRU may override sending the at least one predicted measurement at a time corresponding to the length of time before the at least one predicted measurement. [Brief explanation of the drawings]

[0014] [Figure 1A] FIG. 1 is a system diagram illustrating an example communication system in which one or more disclosed embodiments may be implemented. [Figure 1B] 1B is a system diagram illustrating an exemplary wireless transmit / receive unit (WTRU) that may be used within the communication system illustrated in FIG. 1A, according to one embodiment. [Figure 1C] 1A is a system diagram illustrating an example radio access network (RAN) and an example core network (CN) that may be used within the communication system illustrated in FIG. 1A, according to one embodiment. [Figure 1D] 1B is a system diagram illustrating a further exemplary RAN and a further exemplary CN that may be used within the communication system illustrated in FIG. 1A, according to one embodiment. [Figure 2] 1 depicts an exemplary measurement configuration process. [Figure 3] 1 depicts an exemplary measurement reporting trigger event. [Figure 4] 1 depicts an exemplary generation of measurement reports over a period of time. [Figure 5]10 depicts an example measurement report of a WTRU traversing a cell coverage area. [Figure 6] 1 depicts an exemplary time series generation of reference signal received power (RSRP). [Figure 7] 1 depicts an example timeline of events associated with a forecast report. [Figure 8] 1 depicts an exemplary one-time inference procedure window. [Figure 9] 10 depicts an example periodic WTRU prediction with window reuse. [Figure 10] 10 depicts an example periodic WTRU prediction with configured window settings. [Figure 11] 10 depicts an example WTRU moving window inference process with spacing and window reuse. [Figure 12] 10 depicts an example WTRU moving window inference process with configurable spacing and window. [Figure 13] 1 depicts exemplary predicted boundaries based on percentage confidence intervals. [Figure 14] 10 depicts example signaling for WTRU measurement prediction reporting processing. DETAILED DESCRIPTION OF THE INVENTION

[0015] A more detailed understanding may be had from the following description, given by way of example in conjunction with the accompanying drawings, in which:

[0016] 1A is a diagram illustrating an example communication system 100 in which one or more disclosed embodiments may be implemented. Communication system 100 may be a multiple-access system that provides content, such as voice, data, video, messaging, broadcasts, etc., to multiple wireless users. 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 multicarrier (FBMC), etc.

[0017] 1A, communications system 100 may include wireless transmit / receive units (WTRUs) 102a, 102b, 102c, 102d, RANs 104 / 113, CNs 106 / 115, public switched telephone network (PSTN) 108, the Internet 110, and other networks 112, although it will be understood that the disclosed embodiments contemplate any number of WTRUs, base stations, networks, and / or network elements. Each of 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 transmit 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 mobile phone, a personal digital assistant (PDA), a smartphone, a laptop, a netbook, a personal computer, a wireless sensor, a hotspot or Mi-Fi device, an Internet of Things (IoT) device, a watch or other wearable device, a head-mounted display (HMD), a vehicle, a drone, a medical device and application (e.g., for remote surgery), an industrial device and application (e.g., a robot and / or other wireless device operating in an industrial and / or automated processing chain context), a consumer electronics device, a device operating on a commercial wireless network and / or an industrial wireless network, etc. Any of the WTRUs 102a, 102b, 102c, and 102d may be referred to interchangeably as a UE.

[0018] The communications 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 communications networks, such as the CN 106 / 115, the Internet 110, and / or other networks 112. By way of example, the base stations 114a, 114b may be a base transceiver station (BTS), a Node B, an eNode B (eNB), a Home Node B, a Home eNode B, a gNode B (gNB), an NR Node B, a site controller, an access point (AP), a wireless router, etc. Although the base stations 114a, 114b are each depicted as a single element, it will be appreciated that the base stations 114a, 114b may include any number of interconnected base stations and / or network elements.

[0019] The base station 114a may be part of the RAN 104 / 113, which may also include other base stations and / or network elements (not shown), such as a base station controller (BSC), a radio network controller (RNC), relay nodes, etc. The base station 114a and / or base station 114b may be configured to transmit and / or receive radio signals on one or more carrier frequencies, which may be referred to as a cell (not shown). These frequencies may be licensed spectrum, unlicensed spectrum, or a combination of licensed and unlicensed spectrum. A cell may provide wireless service coverage for a particular geographic area, which may be relatively fixed or may change over time. A cell may be further divided into cell sectors. For example, the cell associated with the base station 114a may be divided into three sectors. Thus, in one embodiment, the base station 114a may include three transceivers, i.e., one transceiver for each sector of the cell. In one embodiment, the base station 114a may employ multiple-input multiple output (MIMO) technology and may utilize multiple transceivers per sector of the cell, for example, using beamforming to transmit and / or receive signals in desired spatial directions.

[0020] 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).

[0021] More specifically, as noted above, the communications system 100 may be a multiple-access system, but may employ one or more channel access schemes, such as CDMA, TDMA, FDMA, OFDMA, SC-FDMA, etc. For example, the base station 114 a and the WTRUs 102 a, 102 b, 102 c in the RAN 104 / 113 may implement a radio technology such as Universal Mobile Telecommunications System (UMTS) Terrestrial Radio Access (UTRA), which may establish the air interface 115 / 116 / 117 using wideband CDMA (WCDMA). WCDMA may include communications 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 ​​Uplink Packet Access (HSUPA).

[0022] In one embodiment, the base station 114a and the WTRUs 102a, 102b, 102c may implement a radio technology such as Evolved UMTS Terrestrial Radio Access (E-UTRA), which may establish the air interface 116 using Long Term Evolution (LTE) and / or LTE-Advanced (LTE-A) and / or LTE-Advanced Pro (LTE-A Pro).

[0023] In one 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).

[0024] In one embodiment, the base station 114a and the WTRUs 102a, 102b, 102c may implement multiple radio access technologies. For example, the base station 114a and the WTRUs 102a, 102b, 102c may jointly implement LTE radio access and NR radio access, e.g., using dual connectivity (DC) principles. Thus, the air interface utilized by the WTRUs 102a, 102b, 102c may be characterized by multiple types of radio access technologies and / or transmissions sent to and from multiple types of base stations (e.g., eNBs and gNBs).

[0025] In other embodiments, the base station 114a and the WTRUs 102a, 102b, 102c may implement a wireless technology 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), GSM EDGE (GERAN), or the like.

[0026] 1A 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 a local area such as a business, a home, a vehicle, a campus, an industrial facility, an air corridor (e.g., for use by drones), a road, etc. 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 one 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 establish a picocell or a femtocell using a cellular-based RAT (e.g., WCDMA, CDMA2000, GSM, LTE, LTE-A, LTE-A Pro, NR, etc.). As shown in FIG. 1A, the base station 114b may have a direct connection to the Internet 110. Thus, the base station 114b may not need to access the Internet 110 through the CN 106 / 115.

[0027] The RAN 104 / 113 may communicate with the CN 106 / 115, which may be any type of network configured to provide voice, data, application, and / or voice over internet protocol (VoIP) services to one or more of the WTRUs 102a, 102b, 102c, 102d. The data may have various quality of service (QoS) requirements, such as different throughput, latency, error tolerance, reliability, data throughput, and mobility requirements. The CN 106 / 115 may provide call control, billing services, mobile location-based services, prepaid calling, Internet connectivity, video distribution, and / or perform high-level security functions such as user authentication. Although not shown in FIG. 1A , it will be understood that the RAN 104 / 113 and / or the CN 106 / 115 may communicate directly or indirectly with other RANs employing 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 communicate with another RAN (not shown) employing GSM, UMTS, CDMA2000, WiMAX, E-UTRA, or WiFi radio technology.

[0028] The CN 106 / 115 may also serve as a gateway for the WTRUs 102a, 102b, 102c, 102d to access the PSTN 108, the Internet 110, and / or other networks 112. The PSTN 108 may include a circuit-switched telephone network providing plain old telephone service (POTS). The Internet 110 may include a global system of interconnected computer networks and devices, which use common communication protocols such as the transmission control protocol (TCP), the user datagram protocol (UDP), and / or the internet protocol (IP) of the TCP / IP Internet protocol suite. The network 112 may include wired and / or wireless communication networks owned and / or operated by other service providers. For example, the network 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.

[0029] Some or all of the WTRUs 102a, 102b, 102c, 102d in the communications system 100 may include multi-mode capabilities (e.g., the WTRUs 102a, 102b, 102c, 102d may include multiple transceivers for communicating with different wireless networks over different wireless links.) For example, the WTRU 102c shown in FIG. 1A may be configured to communicate with a base station 114a, which may employ a cellular-based wireless technology, and a base station 114b, which may employ an IEEE 802.2 wireless technology.

[0030] 1B is a system diagram illustrating an example WTRU 102. As shown in FIG. 1B, the WTRU 102 may include, among other things, a processor 118, a transceiver 120, a transmit / receive element 122, a speaker / microphone 124, a keypad 126, a display / touchpad 128, 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 understood that the WTRU 102 may include any sub-combination of the foregoing elements while remaining consistent with an embodiment.

[0031] The processor 118 may be a general-purpose processor, a special-purpose processor, a conventional processor, a digital signal processor (DSP), multiple microprocessors, one or more microprocessors associated with a DSP core, a controller, a microcontroller, an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) circuit, any other type of integrated circuit (IC), a state machine, etc. The processor 118 may perform signal coding, data processing, power control, input / output processing, and / or any other functionality that enables the WTRU 102 to operate in a wireless environment. The processor 118 may be coupled to the transceiver 120, which may be coupled to the transmit / receive element 122. While FIG. 1B depicts the processor 118 and the transceiver 120 as separate components, it will be understood that the processor 118 and the transceiver 120 may be integrated together in an electronic package or chip.

[0032] The transmit / receive element 122 may be configured to transmit or receive signals to or from a base station (e.g., base station 114a) over the air interface 116. For example, in one embodiment, the transmit / receive element 122 may be an antenna configured to transmit and / or receive RF signals. In one embodiment, the transmit / receive element 122 may be an emitter / detector configured to transmit and / or receive IR signals, UV signals, or visible light signals, for example. In yet another embodiment, the transmit / receive element 122 may be configured to transmit and / or receive both RF signals and light signals. It will be understood that the transmit / receive element 122 may be configured to transmit and / or receive any combination of wireless signals.

[0033] 1B as a single element, the WTRU 102 may include any number of transmit / receive elements 122. More specifically, the WTRU 102 may employ MIMO technology. Thus, in one embodiment, the WTRU 102 may include two or more transmit / receive elements 122 (e.g., multiple antennas) for transmitting and receiving wireless signals over the air interface 116.

[0034] The transceiver 120 may be configured to modulate signals transmitted by the transmit / receive element 122 and demodulate signals received by the transmit / receive element 122. As noted above, the WTRU 102 may have multi-mode capabilities. Thus, the transceiver 120 may include multiple transceivers to enable the WTRU 102 to communicate via multiple RATs, such as, for example, NR and IEEE 802.11.

[0035] The processor 118 of the WTRU 102 may be coupled to and may receive user-entered data from a speaker / microphone 124, a keypad 126, and / or a display / touchpad 128 (e.g., a liquid crystal display (LCD) display unit or an organic light-emitting diode (OLED) display unit). The processor 118 may also output user data to the speaker / microphone 124, the keypad 126, and / or the display / touchpad 128. Additionally, the processor 118 may access information from and store data in any type of suitable memory, such as non-removable memory 130 and / or removable memory 132. The non-removable memory 130 may include random-access memory (RAM), read-only memory (ROM), a hard disk, or any other type of memory storage device. The removable memory 132 may include a subscriber identity module (SIM) card, a memory stick, a secure digital (SD) memory card, etc. In other embodiments, the processor 118 may access information from and store data in memory that is not physically located on the WTRU 102, such as on a server or home computer (not shown).

[0036] The processor 118 may receive power from the power source 134 and may be configured to distribute and / or control the power to other components in the WTRU 102. The power source 134 may be any suitable device for providing power to the WTRU 102. For example, the power source 134 may include one or more dry batteries (e.g., nickel-cadmium (NiCd), nickel-zinc (NiZn), nickel metal hydride (NiMH), lithium-ion (Li-ion), etc.), solar cells, fuel cells, etc.

[0037] The processor 118 may also be coupled to a 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 instead of, information from the GPS chipset 136, the WTRU 102 may receive location information from base stations (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 way of any suitable location-determination method while remaining consistent with an embodiment.

[0038] The processor 118 may further be coupled to other peripherals 138, which may include one or more software and / or hardware modules that provide additional features, functionality, and / or wired or wireless connectivity. For example, the peripherals 138 may include an accelerometer, an electronic compass, a satellite transceiver, a digital camera (for photos and / or videos), a universal serial bus (USB) port, a vibration device, a television transceiver, a hands-free headset, a Bluetooth module, a frequency modulated (FM) radio unit, a digital music player, a media player, a video game player module, an internet browser, a virtual reality and / or augmented reality (VR / AR) device, an activity tracker, etc. The peripheral device 138 may include one or more sensors, which may be one or more of a gyroscope, an accelerometer, a Hall effect sensor, a magnetometer, a direction 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.

[0039] The WTRU 102 may include a full-duplex radio where transmission and reception of some or all of the signals associated with a particular subframe (e.g., for both the UL (e.g., for transmission) and downlink (e.g., for reception)) may be parallel and / or simultaneous. The full-duplex radio may include an interference management unit to reduce and or substantially eliminate self-interference through either hardware (e.g., a choke) or signal processing via a processor (e.g., via a separate processor (not shown) or processor 118). In one embodiment, the WTRU 102 may include a half-duplex radio for transmission and reception of either some or all of the signals (e.g., associated with a particular subframe for either the UL (e.g., for transmission) or downlink (e.g., for reception)).

[0040] 1C is a system diagram illustrating the RAN 104 and the CN 106, according to one embodiment. As noted above, the RAN 104 may employ E-UTRA radio technology to communicate with the WTRUs 102a, 102b, 102c over the air interface 116. The RAN 104 may also communicate with the CN 106.

[0041] The RAN 104 may include eNodeBs 160a, 160b, and 160c, although it will be understood that the RAN 104 may include any number of eNodeBs while remaining consistent with an embodiment. The eNodeBs 160a, 160b, and 160c may each include one or more transceivers for communicating with the WTRUs 102a, 102b, and 102c over the air interface 116. In an embodiment, the eNodeBs 160a, 160b, and 160c may implement MIMO technology. Thus, the eNodeB 160a may, for example, use multiple antennas to transmit wireless signals to and / or receive wireless signals from the WTRU 102a.

[0042] Each of the eNodeBs 160a, 160b, 160c may be associated with a particular cell (not shown) and may be configured to handle radio resource management decisions, handover decisions, scheduling of users in the UL and / or DL, etc. As shown in FIG. 1C, the eNodeBs 160a, 160b, 160c may communicate with one another via an X2 interface.

[0043] 1C may include a mobility management entity (MME) 162, a serving gateway (SGW) 164, and a packet data network (PDN) gateway (or PGW) 166. Although each of the foregoing elements is depicted as part of the CN 106, it will be understood that any of these elements may be owned and / or operated by an entity other than the CN operator.

[0044] The MME 162 may be connected to each of the eNodeBs 162a, 162b, 162c in the RAN 104 via an S1 interface and may function as a control node. For example, the MME 162 may be responsible for authenticating users of the WTRUs 102a, 102b, 102c, activating / deactivating bearers, selecting a particular serving gateway during initial attach of the WTRUs 102a, 102b, 102c, etc. The MME 162 may provide a control plane function for switching between the RAN 104 and other RANs (not shown) that employ other radio technologies such as GSM and / or WCDMA.

[0045] The SGW 164 may be connected to each of the eNodeBs 160a, 160b, 160c in the RAN 104 via an S1 interface. The SGW 164 may generally route and forward user data packets to and from the WTRUs 102a, 102b, 102c. The SGW 164 may perform other functions, such as anchoring the user plane during inter-eNodeB handovers, triggering paging when DL data is available to the WTRUs 102a, 102b, 102c, and managing and storing the context of the WTRUs 102a, 102b, 102c.

[0046] The SGW 164 may be connected to a 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.

[0047] 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 landline communications devices. For example, the CN 106 may include or communicate with an IP gateway (e.g., an IP multimedia subsystem (IMS) server) that serves as an interface between the CN 106 and the PSTN 108. Additionally, the CN 106 may provide the WTRUs 102a, 102b, 102c with access to other networks 112, which may include other wired and / or wireless networks owned and / or operated by other service providers.

[0048] Although the WTRU is illustrated in FIGS. 1A-1D as a wireless terminal, it is contemplated that in certain representative embodiments, such a terminal may use a wired communication interface (e.g., temporarily or permanently) with the communication network.

[0049] In a representative embodiment, the other network 112 may be a WLAN.

[0050] A WLAN in infrastructure Basic Service Set (BSS) mode may have an access point (AP) of the BSS and one or more stations (STAs) associated with the AP. The AP may have access to or interface with a Distribution System (DS) or another type of wired / wireless network that carries traffic into and / or out of the BSS. Traffic originating from outside the BSS to a STA may arrive through the AP and be delivered to the STA. Traffic originating from a STA to a destination outside the BSS may be sent to the AP to be delivered to the respective destination. Traffic between STAs within a BSS may be sent, for example, through the AP, where the source STA may send traffic to the AP, and the AP may deliver the traffic to the destination STA. Traffic between STAs within a BSS may be considered and / or referred to as peer-to-peer traffic. Peer-to-peer traffic may be sent between (e.g., directly between) a source STA and a destination STA using a direct link setup (DLS). In certain representative embodiments, the DLS may use 802.11e DLS or 802.11z tunneled DLS (TDLS). A WLAN using an Independent BSS (IBSS) mode may not have an AP, and STAs within or using the IBSS (e.g., all of the STAs) may communicate directly with each other. The IBSS mode of communication may be referred to herein as an "ad hoc" communication mode.

[0051] When using the 802.11ac infrastructure mode of operation or a similar mode of operation, an AP may transmit 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 that is dynamically set via signaling. The primary channel may be the operating channel of the BSS, but may also be used by STAs to establish a connection with the AP. In certain representative embodiments, for example, in an 802.11 system, Carrier Sense Multiple Access with Collision Avoidance (CSMA / CA) may be implemented. With CSMA / CA, STAs (e.g., all STAs), including the AP, may sense the primary channel. If a particular STA senses / detects and / or determines that the primary channel is busy, the particular STA may back off. One STA (e.g., only one station) may transmit in a given BSS at any given time.

[0052] High Throughput (HT) STAs may use 40 MHz wide channels for communication, which may be formed, for example, through a combination of a primary 20 MHz channel and adjacent or non-adjacent 20 MHz channels.

[0053] A Very High Throughput (VHT) STA may support 20 MHz, 40 MHz, 80 MHz, and / or 160 MHz wide channels. A 40 MHz and / or 80 MHz channel may be formed by combining contiguous 20 MHz channels. A 160 MHz channel may be formed by combining eight contiguous 20 MHz channels or by combining two non-contiguous 80 MHz channels, which may be referred to as an 80+80 configuration. For the 80+80 configuration, after channel encoding, the data may pass through a segment parser that may split the data into two streams. Inverse Fast Fourier Transform (IFFT) processing and time-domain processing may be performed separately on each stream. The streams may be mapped to two 80 MHz channels, and the data may be transmitted by the transmitting STA. At the receiver of the receiving STA, the operations described above for the 80+80 configuration may be reversed, and the combined data may be sent to Medium Access Control (MAC).

[0054] Sub-1 GHz operating modes are supported by 802.11af and 802.11ah. Channel operating bandwidths and carriers are reduced in 802.11af and 802.11ah compared to those used in 802.11n and 802.11ac. 802.11af supports 5 MHz, 10 MHz, and 20 MHz bandwidths in the TV White Space (TVWS) spectrum, while 802.11ah supports 1 MHz, 2 MHz, 4 MHz, 8 MHz, and 16 MHz bandwidths using non-TVWS spectrum. According to representative embodiments, 802.11ah may support meter-type control / machine-type communications, such as MTC devices, within a macro coverage area. MTC devices may have limited capabilities, including, for example, support for (e.g., only support for) certain specific and / or limited bandwidths. MTC devices may include batteries with above-threshold battery life (e.g., to maintain very long battery life).

[0055] WLAN systems that can support multiple channels and channel bandwidths, such as 802.11n, 802.11ac, 802.11af, and 802.11ah, include a channel that can be designated as a primary channel. The primary channel can have a bandwidth equal to the maximum common operating bandwidth supported by all STAs in the BSS. The bandwidth of the primary channel can be configured and / or limited by a STA from among all STAs operating in the BSS that support the minimum bandwidth operating mode. In an 802.11ah embodiment, the primary channel can be 1 MHz wide for STAs (e.g., MTC-type devices) that support (e.g., only) the 1 MHz mode, even if the AP and other STAs in the BSS support 2 MHz, 4 MHz, 8 MHz, 16 MHz, and / or other channel bandwidth operating modes. Carrier sensing and / or Network Allocation Vector (NAV) configuration can depend on the status of the primary channel. For example, if the primary channel is busy due to STAs (that support (e.g., only) 1 MHz operating mode) transmitting to the AP, the entire available frequency band may be considered busy, even though most of the frequency band may remain idle and available.

[0056] In the United States, the available frequency band that can be used by 802.11ah is 902MHz to 928MHz. In South Korea, the available frequency band is 917.5MHz to 923.5MHz. In Japan, the available frequency band is 916.5MHz to 927.5MHz. The total bandwidth available for 802.11ah is 6MHz to 26MHz depending on the country code.

[0057] 1D is a system diagram illustrating the RAN 113 and the CN 115, according to one embodiment. As mentioned 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 communicate with the CN 115.

[0058] The RAN 113 may include gNBs 180a, 180b, and 180c, although it will be understood that the RAN 113 may include any number of gNBs while remaining consistent with an embodiment. The gNBs 180a, 180b, and 180c may each include one or more transceivers for communicating with the WTRUs 102a, 102b, and 102c over the air interface 116. In an embodiment, the gNBs 180a, 180b, and 180c may implement MIMO technology. For example, the gNB 180a, 180b may transmit signals to and / or receive signals from the gNBs 180a, 180b, and 180c using beamforming. Thus, the gNB 180a may transmit and / or receive wireless signals to and / or from the WTRU 102a using, for example, multiple antennas. In one embodiment, the gNBs 180a, 180b, 180c may implement carrier aggregation technology. For example, the gNB 180a may transmit multiple component carriers to the WTRU 102a (not shown). A subset of these component carriers may be on unlicensed spectrum, while the remaining component carriers may be on licensed spectrum. In one embodiment, the gNBs 180a, 180b, 180c may implement Coordinated Multi-Point (CoMP) technology. For example, the WTRU 102a may receive coordinated transmissions from the gNBs 180a and 180b (and / or 180c).

[0059] The WTRUs 102a, 102b, 102c may communicate with the gNBs 180a, 180b, 180c using transmissions associated with scalable numerology. For example, the OFDM symbol spacing and / or OFDM subcarrier spacing may vary for different transmissions, different cells, and / or different portions of the wireless transmission spectrum. The WTRUs 102a, 102b, 102c may communicate with the gNBs 180a, 180b, 180c using subframes or transmission time intervals (TTIs) of different or scalable lengths (e.g., including different numbers of OFDM symbols and / or lasting different lengths of absolute time).

[0060] The gNBs 180a, 180b, 180c may be configured to communicate with the WTRUs 102a, 102b, 102c in a standalone configuration and / or a non-standalone configuration. In a standalone configuration, the WTRUs 102a, 102b, 102c may communicate with the gNBs 180a, 180b, 180c without accessing another RAN (e.g., eNodeBs 160a, 160b, 160c, etc.). In a standalone configuration, the WTRUs 102a, 102b, 102c may utilize 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 unlicensed bands. In a non-standalone configuration, the WTRUs 102a, 102b, 102c may communicate with and connect to gNBs 180a, 180b, 180c while also communicating with and connecting to another RAN, such as eNodeBs 160a, 160b, 160c. For example, the WTRUs 102a, 102b, 102c may implement DC principles 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 eNodeBs 160a, 160b, 160c may act as mobility anchors for the WTRUs 102a, 102b, 102c, and the gNBs 180a, 180b, 180c may provide additional coverage and / or throughput for serving the WTRUs 102a, 102b, 102c.

[0061] Each of the gNBs 180a, 180b, 180c may be associated with a particular cell (not shown) and may be configured to handle radio resource management decisions, handover decisions, scheduling of users in the UL and / or DL, support for network slicing, dual connectivity, interworking between NR and E-UTRA, routing of user plane data to User Plane Functions (UPFs) 184a, 184b, routing of control plane information to Access and Mobility Management Functions (AMFs) 182a, 182b, etc. As shown in FIG. 1D, the gNBs 180a, 180b, 180c may communicate with each other via an Xn interface.

[0062] 1D may include at least one AMF 182a, 182b, at least one UPF 184a, 184b, at least one Session Management Function (SMF) 183a, 183b, and possibly a Data Network (DN) 185a, 185b. While each of the foregoing elements is depicted as part of the CN 115, it will be understood that any of these elements may be owned and / or operated by an entity other than the CN operator.

[0063] The AMF 182a, 182b may be connected to one or more of the gNBs 180a, 180b, 180c in the RAN 113 via an N2 interface and may function as a control node. For example, the AMF 182a, 182b may be responsible for authenticating users of the WTRUs 102a, 102b, 102c, supporting network slicing (e.g., handling different PDU sessions with different requirements), selecting a particular SMF 183a, 183b, managing registration areas, terminating NAS signaling, mobility management, etc. Network slicing may be used by the AMF 182a, 182b to customize the CN support of the WTRUs 102a, 102b, 102c based on the type of service utilizing the WTRUs 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 massive mobile broadband (eMBB) access, services for machine type communication (MTC) access, etc. The AMF 162 may provide a control plane function for switching between the RAN 113 and other RANs (not shown) that employ other radio technologies, such as LTE, LTE-A, LTE-A Pro, and / or non-3GPP access technologies, such as WiFi.

[0064] The SMFs 183a and 183b may be connected to the AMFs 182a and 182b in the CN 115 via an N11 interface. The SMFs 183a and 183b may also be connected to the UPFs 184a and 184b in the CN 115 via an N4 interface. The SMFs 183a and 183b may select and control the UPFs 184a and 184b and configure the routing of traffic through the UPFs 184a and 184b. The SMFs 183a and 183b may perform other functions such as managing and assigning UE IP addresses, managing PDU sessions, controlling policy enforcement and QoS, providing downlink data notification, etc. The PDU session type may be IP-based, non-IP-based, Ethernet-based, etc.

[0065] The UPFs 184a, 184b may be connected to one or more of the gNBs 180a, 180b, 180c in the RAN 113 via an N3 interface, which may provide the WTRUs 102a, 102b, 102c with access to packet-switched networks such as the Internet 110 to facilitate communications between the WTRUs 102a, 102b, 102c and IP-enabled devices. The UPFs 184, 184b may perform other functions such as routing and forwarding packets, enforcing user plane policy, supporting multi-homed PDU sessions, handling user plane QoS, buffering downlink packets, providing mobility anchoring, etc.

[0066] The CN 115 may facilitate communication with other networks. For example, the CN 115 may include or 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 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 through the UPFs 184a, 184b via an N3 interface to the UPFs 184a, 184b and an N6 interface between the UPFs 184a, 184b and the DNs 185a, 185b.

[0067] 1A-1D and their corresponding descriptions, one or more or all of the functions described herein with respect to one or more of the WTRUs 102a-d, base stations 114a-b, eNodeBs 160a-c, MME 162, SGW 164, PGW 166, gNBs 180a-c, AMFs 182a-b, UPFs 184a-b, SMFs 183a-b, DNs 185a-b, and / or any other devices described herein may be performed by one or more emulation devices (not shown). The emulation devices may be one or more devices configured to emulate one or more or all of the functions described herein. For example, the emulation devices may be used to test other devices and / or simulate network and / or WTRU functions.

[0068] The emulation devices may be designed to implement one or more tests of other devices in a lab environment and / or an operator network environment. For example, one or more emulation devices may perform one or more or all functions while fully or partially implemented and / or deployed as part of a wired and / or wireless communication network to test other devices in the communication network. One or more emulation devices may perform one or more or all functions while temporarily implemented / deployed as part of a wired and / or wireless communication network. The emulation devices may be directly coupled to another device for testing purposes and / or may use terrestrial wireless communication to perform the tests.

[0069] One or more emulation devices may perform one or more functions, inclusive, while not being implemented / deployed as part of a wired and / or wireless communication network. For example, the emulation devices may be utilized in test scenarios in a test lab and / or in an undeployed (e.g., test) wired and / or wireless communication network to implement testing of one or more components. One or more emulation devices may be test equipment. Direct RF coupling and / or wireless communication via RF circuitry (which may include, e.g., one or more antennas) may be used by the emulation devices to transmit and / or receive data.

[0070] An implementation for predicted measurement reporting is disclosed. The 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 and / or associated with the predicted measurement based on the at least one predicted measurement. The predicted event may include, for example, at least one predicted measurement being below a threshold. The WTRU may determine to send a report indicating the predicted event based on the predicted event, the predicted time, and a time offset. The WTRU may determine to send a report indicating the predicted event on the condition, for example, 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 send the report indicating the predicted event. The WTRU may further be configured to determine a predicted reliability associated with the predicted event. The report may include an indication of the predicted event, the at least one predicted measurement, and the predicted reliability.

[0071] 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. Reporting may be triggered based on criteria that compare the signal levels of the serving cell and / or neighboring cells to absolute or relative thresholds, e.g., A1-A6 events. Measurement reports may be reactive by design. The WTRU may perform measurements and report them to the network (NW) at specific reporting times or when certain event criteria are triggered. The network may decide to perform an action based on the received measurements. For example, the network may send a handover command, configure the WTRU with conditional HO (CHO), or take some other action. Such reactive processing of measurements may result in less-than-desirable processing. For example, the network may send a HO command too late, causing the WTRU to move out of coverage of the serving cell after sending measurements to the network but before receiving the HO command from the network. In the example of a network orchestrating CHO, unnecessary network resource utilization may occur as the network may reserve resources in neighboring cells for a significant amount of time to prevent a premature HO.

[0072] Disclosed herein is a method for configuring a WTRU to preemptively send measurement reports inferred from an artificial intelligence (AI) / machine learning (ML)-based predictive model, which may enable the network to perform / prepare mobility procedures (e.g., preemptively and optimally), allocate resources, activate other features, etc.

[0073] When the WTRU predicts that the trigger condition for a given measurement event may be met in the future, e.g., in the near future, it may send a predicted measurement report. The measurement report may include the predicted signal levels of the serving cell and / or neighboring cells and other relevant details of the event, e.g., the predicted time range, the error of the prediction, etc.

[0074] The WTRU may receive a measurement configuration. The measurement configuration may be received, for example, from a network node. Figure 2 depicts an example measurement configuration. A WTRU, which may be in RRC_Connected mode, may be configured by the network to perform measurements and report them back to the network according to a configuration, for example, a specific configuration. The configuration may be provided by dedicated signaling. For example, RRCReconfiguration or RRCResume may be used to provide the configuration information to the WTRU.

[0075] The network may configure the WTRU to report the following measurement information based on the SS / PBCH block: measurement results per SS / PBCH block, measurement results per cell based on the SS / PBCH block, and / or SS / PBCH block index.

[0076] The network may configure the WTRU to report the following measurement information based on the CSI-RS resources: measurement results per CSI-RS resource, measurement results per cell based on the CSI-RS resources, and / or CSI-RS resource measurement identifiers.

[0077] The network may configure the WTRU to perform the following types of measurements on the NR sidelink and the V2X sidelink: CBR measurements.

[0078] The network may configure the WTRU to report the following CLI measurement information based on the SRS resource: measurement results per SRS resource and / or SRS resource index.

[0079] 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.

[0080] The WTRU may be configured with information that defines the manner of reporting measurements. The WTRU may be configured with information that may define what information the WTRU may report to a network node, how, and when. The configuration information, which may be referred to as a measurement report configuration, may include the following: reporting criteria, RS type, and / or report format. The reporting criteria may be a criterion that triggers the WTRU to send 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 report format may include per-cell and per-beam quantities that the WTRU may include in a measurement report (e.g., RSRP) and other associated information, such as, for example, the maximum number of cells to report and the maximum number of beams per cell.

[0081] WTRU measurement reporting may be configured by the network to be triggered, for example, based on one or more of the following: periodic reporting and / or event triggered. 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 further rely on criteria for triggering reporting. The criteria for triggering reporting may include the occurrence of an event in the network. Figure 3 illustrates exemplary measurement report trigger events. The WTRU may be configured with information specifying the trigger events, for example, as illustrated in Figure 3.

[0082] A measurement report may be triggered, for example, in response to an event criterion being met. For example, the occurrence of Event A3 as depicted in FIG. 3 (e.g., a neighbor cell being better offset than the SPCell) may trigger the generation of a measurement report. A measurement report may be triggered in response to the expiration of a ReportInterval time amount. FIG. 4 illustrates exemplary occurrences of measurement reports during a period. Each vertical line on the time continuum may correspond to the generation of a measurement report. Vertical marks, e.g., purple vertical marks, on the top timeline represent periodic measurement reports. As shown in FIG. 4, the time between occurrences of measurement reports may be referred to as a reporting interval. Referring to the bottom timeline of FIG. 4, a vertical mark, e.g., green vertical mark 410, may correspond to an event-triggered report, which may have been triggered, for example, by the occurrence of Event 1 in FIG. 3. A vertical mark, e.g., green vertical mark 412, may correspond to a second event trigger, which may be, for example, the occurrence of Event A3 in FIG. 3.

[0083] The WTRU may be configured to perform filtering at Layer 3. 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 equation to average measurement data points received from the physical layer for measurement report trigger evaluation: F n =(1-a) * F n-1 +a * M n

[0084] In this formula, M n F may be the latest received measurement result from the physical layer. n F may be an updated filtered measurement result that may be used for evaluation of reporting criteria or for measurement reporting. n-1may be the old filtered measurement, where F may be set to M when the first measurement from the physical layer is received, and a=½ for MeasObjectNR. (ki / 4) where k i may be the filterCoefficient for the corresponding measurement quantity of the ith QuantityConfigNR in the quantityConfigNR list, where i may be indicated by the quantityConfigIndex in MeasObjectNR, and for other measurements, a=½ k / 4 where k may be the filterCoefficient for the corresponding measurement received by quantityConfig, and a=½ for UTRA-FDD. (k / 4) where k may be the filterCoefficient for the corresponding measurement received by the UTRA-FDD in QuantityConfig.

[0085] M n may correspond to measurement data points that can be substituted into the equation influenced by the weighting parameter a. This means that spikes in the most recent measurements are n This may be done to avoid situations that may significantly change the value of

[0086] F n may represent the average measurement value for any of the measured quantities (RSRP, RSRQ, SINR) that can be evaluated for the purpose of triggering a measurement report to the gNB or to verify that a conditional reconfiguration configuration is met. The parameters nrofSS-BlocksToAverage and nrofCSI-RS-ResourcesToAverage are the number of samples (F 2、 F3, F 4、 F 5、 ... 、 F max ) that the WTRU averages before evaluating / verifying the measurement reports and conditional reconfiguration triggers.

[0087] The proposed IEs, e.g., all of the proposed 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, e.g., all information, needed from a time domain perspective to perform measurements defined by the gNB.

[0088] The WTRU may measure the air interface using the SMTC window configuration. It may sample the air interface every x ms according to this configuration. The parameters nrofSS-BlocksToAverage and nrofCSI-RS-ResourcesToAverage may be used by the WTRU to select how many of the samples are used in the averaging process before delivering the results to the network.

[0089] The machine learning (“ML”) model employed to generate the prediction may have embedded statistical aspects. The estimation accuracy of the machine learning model may vary depending on the time horizon associated with the generated prediction. The machine learning model may be able to forecast or predict future values ​​of a given quantity, among other capabilities. A prediction may have a timestamp value associated with it. A predicted value may have an associated timestamp and / or a short interval (e.g., a few ms) within which the predicted value may be considered valid. The timestamp may be generalized as a future time step. With each predicted value, there may (e.g., always) be an estimate of the error or deviation associated with the predicted value. This error may be calculated recursively, and the longer the time horizon associated with the prediction, the more inaccurate, e.g., worse, the estimate may be expected to be. For example, a machine learning model outputting a sample at time step t+4 may have estimated values ​​of the sample / prediction and estimated values ​​of the error that may be more accurate than the estimated values ​​of the prediction and error estimate at a later time, such as t+7.

[0090] In predictive inference, a prediction interval may be an estimate of the interval into which a future observation may fall with a certain probability, given what may have been observed. A machine learning model may output an interval based on an associated probability P. If the probability may be P=0.95 or 95%, the model may output a window of values, where it may be estimated that the predicted value for any future time step may fall within a given window with a certain probability, for example, 95%. Different values ​​of the confidence probability may cause the machine learning model to generate different prediction windows.

[0091] Using AI / ML techniques, network-configured events can be predicted at the WTRU. However, with conventional mechanisms, the network may not receive this predicted information in advance and may not be able to take advantage of anticipated knowledge of the event.

[0092] FIG. 5 illustrates exemplary measurement reports by a WTRU as it moves through multiple cell coverage areas. 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"). A mobile WTRU across the coverage of three cells is shown for illustrative purposes. With reference to FIG. 5, the mobile 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 within the coverage of cell A, cell B, and cell C, each of whose coverage areas may be depicted using an ellipse. The "handover (HO) area," which may be marked by an ellipse 514, e.g., an orange ellipse, occurs where there may be overlapping coverage of cells A and B. In the HO area, the signal from cell A may decrease in power and the signal from cell B may increase in power as it follows the WTRU trajectory. In this area, the WTRU may also receive a conditional handover (CHO) configuration since the network may be uncertain as to which cell (either B or C) may be best for the WTRU.

[0093] 5 also depicts a timeline. The timeline indicates instances at which measurement reports may be generated. Periodic measurement reports may be represented on the timeline using vertical marks 508, which may be purple marks. The periodic measurement reports represented by vertical marks 508 may be sent by the WTRU at "ReportInterval" time intervals. Event-triggered measurement reports may be represented at vertical marks 510 and 512, which may be green marks. The WTRU may send event-triggered measurement reports when a network-configured event may be detected by the WTRU.

[0094] The measurement reporting method may be reactive, which may result in delayed processing by the network, as illustrated from the processing associated with the "HO area" and "CHO ​​area." With respect to the movement of a WTRU through an HO area, the WTRU may trigger a measurement report message with measurement information upon detection of the event. Based on the received measurement report, the network may take some action, such as communicating a handover command. However, since the network may not send the handover command until the WTRU has substantially passed the HO area, and the WTRU may not receive the handover command, it may be too late for the network to send a command, e.g., for the WTRU to connect to another cell. In such a scenario, the network may configure the WTRU in a CHO configuration and provide the WTRU with two cell options for handing over to cells B and C. This may result in longer WTRU configuration and longer resource reservation for the network nodes radiating these cells.

[0095] Leveraging AI / ML techniques, models can be trained to predict measurements and, consequently, network-configured events. A method that allows the network to receive predicted events before the WTRU actually experiences the event may be desirable, which may allow the network to act proactively and prevent service interruptions.

[0096] The WTRU may be configured to make predictions. The WTRU may be pre-trained with an AI / ML model that may be capable of generating predictions of air interface measurements (RSRP, RSRQ, SINR, etc.) of the serving cell and / or neighboring cells. The predictions may be compared to measurement trigger conditions. The WTRU may send a measurement report when the AI / ML model indicates that the trigger conditions may be met in the near future.

[0097] Disclosed herein is an implementation for reporting predicted measurements when a WTRU detects that a trigger condition for reporting a configured measurement is expected to be met in the future. The network may receive the predicted measurements and take any suitable action based on the anticipation of a particular measurement.

[0098] The WTRU may receive configuration data that enables it to trigger reporting of measurements with predicted information. Reports with predicted information may be generated before reports employed in other measurement reporting procedures. The WTRU may receive configuration on how to perform prediction, how to reuse or not reuse configurations, and how to report to the network. When configured conditions are met, the WTRU may send prediction-related information to the network to anticipate the event, thus providing the network time to take relevant actions.

[0099] The WTRU may be configured to predict future measurements based on current and / or past measurements. The WTRU may be configured with a trained AI / ML model that may be capable of generating predictions of air interface radio signal levels. The AI / ML model in the WTRU may be implementation-based. The AI / ML model in the WTRU may obtain the AI / ML model from the network. The AI / ML model may be configured to take current and / or past RSRP measurements as input. The AI / ML model may be configured to take additional inputs such as WTRU location information, WTRU mobility information, etc. The AI / ML model may be configured to generate a single-value prediction, e.g., 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, through t+n. Predictions with time series outputs may be more beneficial than single-value predictions because it may be difficult to match a prediction with a configured measurement event that has a single prediction point.

[0100] To generate a meaningful or potentially useful prediction, the WTRU may perform the prediction in a time series manner. From the moment the WTRU prediction is triggered, the WTRU may generate several prediction outputs over a future time span at a certain granularity or time step. Figure 6 depicts an example time series generation of reference signal received power (RSRP). Referring to Figure 6, at time t, the WTRU may predict one RSRP prediction point for each time step from time t+1 to t+n.

[0101] The WTRU may be configured to perform prediction and generate predicted values. The predicted values ​​may have an associated timestamp and / or a short interval (e.g., a few ms) within 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 index, an estimated confidence, a precision, an estimated accuracy, and / or any other error measurement technique. This may be specific to the ML algorithm. The further away in time the prediction is made (e.g., the longer the value of n), the higher the error of the prediction may be. It may be appropriate to define the duration of the time span of the prediction, e.g., a maximum duration (e.g., the value of n time steps), which may be defined in various forms, including the number of time steps (e.g., a value for n), the granularity between time steps (e.g., the time value between each prediction point), and / or a threshold for the error of the prediction.

[0102] The AI / ML models resident in the WTRU may have been distributed by the network, and thus the network may have access to specific characteristics, e.g., all model-specific characteristics. Thus, the network may configure the WTRU with suitable, e.g., necessary, parameterization. This may be done, for example, when the models are distributed to the WTRU, or at any time after the models are distributed via any message or SIB broadcast. A group of AI / ML models may have been distributed to the WTRU, in which case the network may provide the WTRU with relevant information for model selection, e.g., via a model ID.

[0103] Another option for the origin of the AI / ML model may be that it may be proprietary and vendor-specific and may already be available in 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 enable the WTRU to determine which set of parameters to use.

[0104] In connection with measuring the air interface, the WTRU may use the parameters nrofSS-BlocksToAverage and nrofCSI-RS-ResourcesToAverage to select how many samples may be used in the averaging process before delivering the results to the network.

[0105] Given a predicted time span of size n (e.g., having n time steps), predicting samples at the same rate as the SMTC window (e.g., every 5 ms at the finest granularity) may reduce the actual time length of n, making the inference process more computationally intensive. Increasing it too much may result in a longer n time window, but may affect, e.g., greatly affect, the error associated with the prediction, especially towards the end of the time window.

[0106] If the WTRU may be configured to perform prediction, the prediction granularity may take into account the averaging process.

[0107] The WTRU may be configured to use the parameters nrofSS-BlocksToAverage and / or nrofCSI-RS-ResourcesToAverage (e.g., which may be explicitly configured or fixed in a standard) as parameters defining the time step granularity of the prediction.

[0108] The WTRU may be configured to use different averaging parameters for the predicted measurements compared to the normal measurements, e.g., the WTRU may be configured to use nrofSS-BlocksToAverage=n1 for the normal measurements and nrofSS-BlocksToAverage=n2 for the predicted measurements.

[0109] The WTRU may be configured to use different granularity for the time steps configured for different parts of the window (t; t+n), for example, four samples for (t; t+x) and two samples for (t+x; t+n). This option may be relevant to take into account that the error in 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 averaging samples in an attempt to make the last samples less important to the final result.

[0110] The WTRU may be configured to use different granularity for the time steps configured for different portions of the window (e.g., t; t+n), according to different rates of change for each sample, e.g., two samples for (e.g., t; t+x, where the rate of change in this sub-window may be higher), and four samples for (e.g., t+x; t+n, where the rate of change in this window may be lower).

[0111] The WTRU may be configured to perform predictive reporting. FIG. 7 depicts a timeline showing example WTRU prediction and reporting. Referring to FIG. 7, reference numerals 1 through 5 are shown on the timeline and may correspond to events and / or actions. At time t, the WTRU may perform AI / ML inference and generate predictions of air interface measurements in a time-series manner. The WTRU may generate a predicted estimate and associated error for time t+1, another error at time t+2, etc., where each time may comprise a time interval. At t+n_evt time steps after the prediction is generated, at n_evt time steps, the WTRU may predict that a trigger condition for the predicted measurement report may be met. By way of example, the WTRU may be configured with an A2 event or an event similar to A2, e.g., predEventA2 for a particular cell (during which the serving cell may be expected to deteriorate below a threshold).

[0112] The WTRU may be configured to send prediction-related information before t+n_evt. The amount of time before t+n_evt at which the WTRU sends prediction-related information may be configurable and may correspond to the "preconfigured time offset" depicted in FIG. 7. The bidirectional arrow 710 in FIG. 7, e.g., the green bidirectional arrow, may represent a time span over which inference / prediction may be made. If the WTRU triggers prediction at time t, the WTRU may generate n prediction points spaced by a time step value or granularity until time t+n. The green arrow 710 may represent the period from t to t+n.

[0113] 7, at reference numeral 1, the WTRU may indicate its prediction capabilities to the network. These capabilities may be specific to the AI / ML model used by the WTRU and may include, for example, parameters as described herein.

[0114] 7, at reference numeral 2, corresponding to time t, the WTRU may receive an inference configuration where the model output prediction is configured by the network. The configuration may, for example, be fully or partially fixed in a standard, or may include parameters and specific occasions for the WTRU to trigger inference / prediction. The trigger may be configured by the network to have some restrictions on the areas / locations / times where predicted measurements may be sent or where WTRU measurement prediction and reporting may be active.

[0115] Specific air interface measurement quantities may be used to trigger prediction. When specific air interface measurement quantities may be used to trigger prediction, the configuration may include, for example: specific cells that the WTRU may be able to detect (e.g., trigger prediction if cell X can be detected via measurements), a list of cells that the WTRU may be able to detect (e.g., trigger prediction if cells in a given set of cells, e.g., all cells, are detected via measurements), specific values ​​for air interface measurement quantities for specific cells (e.g., RSRQ may be below a threshold for cellID=x), specific values ​​for air interface measurement quantities for a set of cells (e.g., RSRQ may be below a threshold for cells in a given set of cells, e.g., all cells, or SINR may be below a threshold for cellID=x and RSRP may be higher than a threshold for cellID=y), specific air interface measurement quantities for specific cells. intervals for a particular air interface measurement quantity for a group of cells (e.g., a≦RSRP≦b for cellID=X, c≦RSRP≦d for cellID=Y), a particular beam level that the WTRU may be able to detect (e.g., that may trigger a prediction if beam level X can be detected via measurements), a list of beam levels that the WTRU may be able to detect (e.g., beam levels within a given set of beam levels, e.g., that may trigger a prediction if all beam levels are detected via measurements), a particular value for the air interface measurement quantity for a particular beam level, a particular value for the air interface measurement quantity for a set of beam levels, as well as other triggers that may be similar to existing triggers in the measurement procedure, such as comparing the quality of a measurement to a threshold or comparing quality between cells.Other triggers may include, for example: an immediate trigger of a prediction, a specific timestamp for triggering a prediction, a timer for triggering a prediction, where the timer counts from a timestamp when a configuration may have been received, a timer, a specific geolocation for triggering a prediction, and / or a specific geoarea for triggering a prediction.

[0116] The WTRU may perform inference at time t (e.g., immediately after receiving configuration) and up to t+n. The WTRU may have obtained estimates for prediction points at t+1, t+2, (...), t+n_evt, t+n (e.g., points may refer only to times, since the model may output time steps, e.g., RSRP and RSRQ simultaneously for all time steps). The WTRU may have predicted that the air interface quantities may be such that a measurement event, e.g., a conventionally configured measurement event, may occur at t+n_evt, n_evt time steps after the prediction is triggered.

[0117] Since the WTRU may be predicting a network-configured event, the WTRU may report the prediction immediately (if the WTRU may be instructed to do so) or may report the prediction using the next periodic measurement report (according to a measurement configuration from the network that may have been received before the prediction trigger). The WTRU may hold the measurement prediction without sending it and wait for time t+4, which corresponds to reference number 4 in the timeline of Figure 7, where the prediction may be sent regardless of before the event, e.g., t+n_evt, actually occurs.

[0118] Referring to reference numeral 3 in FIG. 7 , at time t, the WTRU may predict the occurrence of a measurement at time t+n_evt. Time step n_evt represents a time that may be near the end of a prediction / inference window of size n time steps. Due to the nature of AI / ML algorithms, the further away in time a prediction may be made (e.g., the longer the value of n), the higher the error in the prediction may be. A prediction at time n_evt may be more likely to have a higher error value, e.g., significantly higher, than a prediction made for time t+1 or t+2. For this reason, the WTRU may have the option to re-run the inference process at any time before the last opportunity to send a prediction, e.g., at the time corresponding to reference numeral 4 on the timeline. The WTRU may be configured with different options for inference re-run, e.g., from time t to time t+4. One option for inference re-run may be the WTRU triggering inference re-run every time step until the time corresponding to reference numeral 4. The WTRU may trigger inference re-run at a specific time step or times. The WTRU may trigger inference re-run for a delta number of time steps from the initial event prediction.

[0119] For the WTRU to re-run the inference process may be an extra computational burden that may be alleviated by configuration. If inference re-run may be triggered at time t+2, the WTRU may, for example, perform one or more of the following: re-run the inference only up to the initial time t_evt when the event was originally predicted, re-run the inference using the same time span (e.g., if the WTRU re-runs the inference at t+2, the WTRU may predict values ​​up to t+n+2), and / or re-run the inference until the event can be predicted again, be it at time t_evt or some other later or earlier time (as a result of a new prediction).

[0120] If the inference can be performed multiple times, there may be multiple predictions and associated errors for each time step. The WTRU may be configured to transmit predictions during this window and before the time corresponding to reference numeral 4 using conventional measurement reporting or using other configurable options. If there can be one, e.g., only one, periodic measurement report (based on the measurement configuration), the WTRU may send predictions, e.g., all predictions, and error values ​​for time steps, e.g., all time steps, resulting from the inference runs, e.g., all inference runs. If there can be one, e.g., only one, periodic measurement report (based on the measurement configuration), the WTRU may send predictions, e.g., all predictions, for a particular subset of time steps (e.g., x time steps before reference numeral 4). If there can be one, e.g., only one periodic measurement report (based on the measurement configuration), the WTRU may send a subset of predictions for time steps, e.g., all time steps (e.g., it may send, e.g., only the prediction corresponding to the lowest error, or only the predictions whose error is below a threshold). If there can be one, e.g., only one, periodic measurement report (based on the measurement configuration), the WTRU may send a subset of predictions for a particular time step (e.g., it may send, e.g., only, the prediction corresponding to the lowest error, or it may send, e.g., only, the prediction whose error is below a threshold, but this time only for a particular time step). If there are multiple periodic measurement reports within the window, the WTRU may send the prediction values ​​to the network and spread the results across multiple measurement reports (e.g., it may send a prediction for a delta number of time steps closer to the latest t_evt in the first measurement report, then another delta number of time steps before that in the next measurement report).

[0121] 7, at reference numeral 4, the WTRU may trigger an expected or predicted measurement report a time interval "Pre-configured time offset" before the predicted event. The network may configure the WTRU with one or more of the following: a) a pre-configured time offset (reference numeral 5) counted from the timestamp of the predicted event, b) a geolocation associated with the geographic distance of t_evt, c) a distance from t_evt, d) the number of predictions that the WTRU predicted the event would occur at t_evt (e.g., after 5 predictions, the event occurs at t_evt and a prediction is sent), e) UL and / or DL ​​volume, f) air interface radio volume drop / increase delta, e.g., RSRP delta, g) the time at which the WTRU detected a drop / increase in air interface radio volume below a threshold at t_evt. h) items d or g, but considering consecutive predictions; and / or i) a preconfigured time offset may be associated with other options (e.g., if item d, g, or h occurs before the preconfigured time offset, the WTRU may send a prediction; otherwise, the WTRU may send a prediction at the preconfigured time offset, regardless of whether other criteria may be met).

[0122] The time corresponding to reference numeral 4 may be considered the last opportunity for the WTRU to send predicted measurement information to the network. Thereafter, due to WTRU-network message latency, the prediction procedure may be too late, so that the prediction may not be meaningful and may not produce the desired result. The WTRU may override the transmission of the prediction based on one or more conditions, which 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 send a prediction based on a state transition change; the WTRU may not send a prediction based on a set of detectable cell changes; the WTRU may not send a prediction based on a configured set of measurement cell changes; the WTRU may not send a prediction based on the error of the prediction not meeting a trigger criterion (e.g., configuration item g above); and / or the WTRU may not send a prediction based on the predicted measurements not falling under a trigger condition, where inference may be re-run.

[0123] 7, reference numeral 5 corresponds to the time at which the WTRU may predict that a particular event may occur. It may be marked at time t_evt, although it may be taken into account that t_evt may change each time the WTRU performs inference. Hence the configuration options discussed herein in relation to reference numerals 3 and 4 in FIG. 7.

[0124] There may be several options for how to define the signaling for configuring the reporting and actually report the predicted measurements: The functionality for predicting measurements may be incorporated into the existing WTRU measurement procedure. This may be the default option. There may be a separate predicted measurement procedure that may have the same structure as the existing measurement procedure with all or a subset of the existing events or new defined events in the new measurement prediction procedure. Another existing or new procedure may be defined that allows for the configuration and reporting of events. In any of these options, the procedure may configure the measurement parameters for the calculation of the predicted measurements, e.g., all of the measurement parameters, 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 be applied.

[0125] The processes described herein apply equally well when predicted events are configured and reported in existing measurement procedures, in predicted measurement procedures modeled after existing measurement procedures, or embedded in other existing or new procedures.

[0126] The predictive reports may be employed to detect an event, and the inference process may be re-run to detect that particular event, e.g., to confirm or re-evaluate a previous prediction. The WTRU may receive configuration that enables it to use the event detection and inference process multiple times, if needed.

[0127] The WTRU may be configured to perform the inference procedure once. The WTRU may be configured to perform the inference process periodically. The WTRU may be configured to trigger the inference process in a moving window manner, for example, as described in connection with FIG. 11 .

[0128] The WTRU may be configured with different procedure options for reuse for different cells as required by the network. The WTRU may be configured to perform one or more of the following: perform the procedure once for a specific cell, perform the procedure once for a set of cells, perform the procedure once for cells in the measurement configuration, e.g., all cells, and / or perform the procedure once for detectable cells, e.g., all detectable cells.

[0129] The WTRU may be configured with different options related to the recursive inference procedure. The WTRU may be configured to perform one or more of the following: recursively perform the procedure for a specific cell, recursively perform the procedure for a set of cells, recursively perform the procedure for cells in the measurement configuration, e.g., all cells, and / or recursively perform the procedure for detectable cells, e.g., all detectable cells.

[0130] 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: 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 the measurement configuration, e.g., all cells, and / or perform the moving window procedure for detectable cells, e.g., all detectable cells.

[0131] The WTRU may be configured to perform a one-time inference procedure. The WTRU may perform the inference procedure once, e.g., only once. Figure 8 depicts an example one-time inference procedure window. As shown, the time inference procedure window may be simple and may include a period of time from t to t+n.

[0132] The WTRU may be configured to perform a recursive inference procedure. The WTRU may perform the recursive inference procedure based on network configuration and / or based on a fixed, e.g., standard-defined format or configuration. Figure 9 depicts an example periodic WTRU prediction with window reuse. As shown, for a particular cell, the WTRU may be configured with a value for n and may trigger prediction periodically at multiples of n.

[0133] In connection with performing recursive inference processing, the WTRU may be configured with one or more back-off timers. These timers may cause the TRU to refrain from performing inference for a certain amount of time. The network may have an estimate of when WTRU prediction support may be needed and, in this way, may limit the WTRU's battery and computational resource usage. FIG. 10 depicts an example periodic WTRU prediction with configured window settings. As shown in FIG. 10, different back-off 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.).

[0134] The WTRU may be configured to perform a moving window inference procedure. The WTRU may perform inference in a moving window manner. The WTRU may be configured to perform moving window processing, e.g., specially configured, and / or the WTRU may be configured to perform moving window processing, e.g., fixed, as defined in a standard. Multiple, e.g., two exemplary approaches may be implemented. FIG. 11 illustrates an exemplary WTRU moving window inference process with spacing and window reuse. There may be a fixed spacing configuration y, after which the WTRU may re-run the inference process (starting at time t). The inference process window may be a fixed size n.

[0135] In an exemplary approach, both the spacing y and the time span n of the prediction may be configured for different values. Figure 12 illustrates an exemplary WTRU moving window inference process with configurable spacing and window. Such a process may be useful where the network can take into account internal predictions and estimates, for example, that an event may occur around time n. The WTRU may perform inference up to t+n. If an event is not predicted, the WTRU may re-perform inference at time t+y. If an event is predicted, the WTRU may re-perform inference at time t+y so that the newer prediction has a lower associated error.

[0136] The network may have prediction results that conclude that no event will occur around time t+n+y. For this reason, it may be beneficial to relieve the WTRU from extra computation and introduce y2 with a high degree of confidence that the likelihood of the event is low, e.g., very low.

[0137] The WTRU may be configured to allow the possibility to adjust the inference window by 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 will experience the configured measurement event may be used to adjust the value of n for different occasions. If the network can be confident that the event will not be detected or predicted, the window may be longer. Otherwise, the window may be shorter.

[0138] A WTRU may be configured to employ predictive sensing. The AI / ML model may be implementation- or vendor-specific or fetched by the WTRU from the network and / or delivered to the WTRU by the network. When the AI / ML model may be implementation- or vendor-specific, the prediction intervals and time spans of prediction-related capability exchanges may be relevant. This may also apply when the AI / ML model is received from the network, for example, equally apply, as using the same trained model may be suitable or not suitable for generating predictions for the same WTRU under different situations.

[0139] In relation to prediction-related capability exchanges, the WTRU may signal to the network a time value and / or a range representing the duration of a prediction value that may or may not be greater than a particular threshold of a prediction window. For example, the prediction value may not be accurate enough to fall within a reasonable range, such as 95 < RSRP < -97 instead of an unreasonable range, such as -95 < RSRP < -120, for the same confidence percentage, e.g., 0.95 or 95%. The threshold may be communicated to the WTRU in network signaling configuration information and / or broadcast via SIB.

[0140] Estimation / prediction may become less accurate over time. The estimated value may initially be high, may become low, and may become high again. The change in the estimated value may be related to the quality of the data used to train the model, and in certain situations, more data points may have been available for training. Then, when making inferences, the model may output predictions within a smaller range again if the model can consider the input data to be in the same or similar situations. For this reason, as referred to above, the information that the WTRU may signal to the network may be in the form of a value or a range.

[0141] Adopting a prediction interval may include adding additional information to the WTRU configuration information for generating predictions that may be delivered to the network as part of a capability exchange, e.g., in an expected measurement report. The additional configuration information may include, for example, information associated with the prediction interval and a percentage confidence value associated with that interval. The WTRU may employ the configuration information to determine a predicted value within the prediction interval with an associated percentage confidence value.

[0142] Figure 13 illustrates an example implementation of prediction intervals. In Figure 13, RSRP bounds based on percentage (x%) confidence intervals are depicted. The upper RSRP bound for a given interval is depicted by line 1310, and the lower RSRP bound for that interval is depicted by line 1312. Line 1314 depicts the WTRU predicted upper RSRP bound at a defined percentage (x%) confidence level over the time series range t to t+n. Line 1316 depicts the WTRU predicted lower RSRP bound at a defined percentage (x%) confidence level over the time series range t to t+n. As shown, line 1314 depicts the predicted upper RSRP bound at the percentage confidence level, and line 1316 depicts the lower RSRP bound at the percentage confidence level change due to estimated variability over time.

[0143] 13 depicts an example illustrating how prediction boundaries (confidence intervals) can remain fixed over time (as depicted by lines 1310 and 1312) or can change (as depicted by lines 1314 and 1316). The upper and lower boundaries can be further separated, as the boundaries can change differently over time. The time scale can be general and can be represented over several time slots from t to t+n.

[0144] The use of confidence levels in generating AI / ML results can impact processing and outcomes.

[0145] As described herein, the network may configure the WTRU to generate a range of values, e.g., 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 range of predictions based on a confidence value, the value may vary from time step to time step.

[0146] As described herein, the WTRU may be configured with parameterization. The configuration information may be adapted to take into account the confidence value. The network may configure the WTRU with a different value for the confidence value of the prediction for each time step if a range of prediction values ​​applies.

[0147] As described herein, the WTRU may send an expected measurement report when one or more of a number of conditions, e.g., several, are met. The WTRU may be further configured to send the expected measurement report based on additional and / or different rules based on the reliability report.

[0148] For example, the WTRU may be configured to send a report at time t_evt if, after making several predictions, the WTRU is likely predicting that an event will be within a prediction range for a given confidence percentage value. For example, after determining a number of predictions, e.g., five predictions, with a determined percentage (x%) confidence that the predictions are within range, the WTRU may send the predictions.

[0149] The WTRU may be configured to send a report if, at time t_evt, after a number of predictions, the WTRU is likely to predict that the event will fall below an upper boundary of a prediction range associated with a confidence percentage value. After determining the number of predictions, e.g., five predictions, with a determined percentage (x%) confidence that the prediction will fall below the upper boundary of the prediction range, the WTRU may send the prediction.

[0150] The WTRU may be configured to send a report if, at time t_evt, after a number of predictions, the WTRU is likely to predict that the event will exceed the lower boundary of the prediction range for a given confidence percentage value. For example, the WTRU may send a prediction after determining a number of predictions, e.g., five predictions, with a determined percentage (x%) confidence that the prediction exceeds the lower boundary range.

[0151] The WTRU may be configured to send a report including a prediction after considering predictions associated with successive time steps. For example, the WTRU may send a prediction if, after making several predictions in successive time steps, the WTRU is likely to predict that the event will be within a prediction range for a given confidence percentage value. The WTRU may be configured to send a prediction if, after making several predictions in successive time steps, the WTRU is likely to predict that the event will be below an upper boundary of a prediction range associated with a confidence percentage value. The WTRU may be configured to send a prediction if, after making several predictions in successive time steps, the WTRU is likely to predict that the event will be above a lower boundary of a prediction range for a given confidence percentage value.

[0152] The WTRU may be configured to send a report including a prediction after considering predictions associated with various distributions of time steps. The distributions of time steps may be provided to the WTRU by the network, for example. The distributions may be any suitable distribution, and the time steps associated with the distributions may be contiguous or non-contiguous. The WTRU may send a prediction if, after making several predictions at time steps across a defined distribution, the WTRU is likely to predict that the event will be within a prediction range for a given confidence percentage value. The WTRU may be configured to send a prediction if, after making several predictions at time steps across a defined distribution, the WTRU is likely to predict that the event will be below an upper boundary of a prediction range associated with a confidence percentage value. The WTRU may be configured to send a prediction if, after making several predictions at time steps across a defined distribution, the WTRU is likely to predict that the event will be above a lower boundary of a prediction range for a given confidence percentage value.

[0153] 14 depicts example signaling for a WTRU measurement prediction reporting process. As shown, a Layer 3 message diagram can be used to illustrate the configuration of the WTRU and the transmission of predicted measurements or events back to the network.

[0154] Referring to Figure 14, in Reference 1, the network node may perform coverage assessment using an internal database and prepare an inference procedure for the WTRU. The network may derive cells on which the WTRU may perform inference and may determine WTRU configuration information consistent with the discussion herein. In Reference 2, the network node may send a configuration to the WTRU. In Reference 3, the WTRU may perform the inference process as described herein. In Reference 4, the WTRU may report a prediction to the network node using a reporting configuration.

[0155] Although the features and elements described herein are described in particular combinations, each feature or element may be used alone without other features and elements of the preferred embodiments, or may be used in various combinations with or without other features and elements.

[0156] The descriptions herein may be provided for illustrative purposes and in no way limit the applicability of the described systems, methods, and means to other radio technologies and / or radio technologies using different principles, when applicable. The term network in this disclosure may refer to one or more gNBs that may be associated with one or more Transmission / Reception Points (TRPs) and / or any other nodes in a radio access network.

[0157] While the implementations described herein may consider 3GPP-specific protocols, it will be understood that the implementations described herein are not limited to this scenario and may be applicable to other wireless systems. For example, while the solutions described herein consider LTE, LTE-A, New Radio (NR), or 5G-specific protocols, it will be understood that the solutions described herein are not limited to this scenario and may be applicable to other wireless systems.

[0158] The processes described herein may be implemented in a computer program, software, and / or firmware embodied 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 (transmitted 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, magneto-optical media, such as, but not limited to, internal hard disks and removable disks, and / or optical media, such as compact disc (CD)-ROM disks and / or digital versatile disks (DVDs). A processor in association with software may be used to implement a radio frequency transceiver for use in a WTRU, terminal, base station, RNC, and / or any host computer.

Claims

1. 1. A wireless transmit and receive unit (WTRU), comprising: a processor, the processor 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 send a report indicating the predicted event; and a wireless transmit and receive unit (WTRU) configured to send the report indicating the predicted event.

2. The WTRU of claim 1 , wherein the predicted time comprises 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 includes the at least one measurement value being less than a threshold.

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

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

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

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

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

10. the processor configured to determine the at least one predicted measurement is configured to determine a first predicted measurement and a second predicted measurement; 4. The WTRU of claim 3, wherein the processor configured to determine the predicted event and the predicted time associated with the predicted event based on the at least one predicted measurement is further configured to determine the predicted event and the predicted time associated with the predicted event based on the second predicted measurement.

11. 1. 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 send a report indicating the predicted event; sending the report indicating the predicted event.

12. The method of claim 11 , wherein the predicted time comprises a time interval.

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

14. determining a predicted confidence associated with the predicted event; The method of claim 11 , wherein the report indicating the predicted event includes an indicator of the predicted event, the at least one measurement, and the predicted confidence level.

15. 12. The method of claim 11 , wherein determining to send the report indicating the predicted event based on the predicted event, the predicted time, and the time offset further comprises sending the report indicating the predicted event on the condition that a difference between a current time and the predicted time is greater than the time offset.

16. determining the at least one measurement value includes determining a first measurement value and a second measurement value; 12. The method of claim 11 , wherein determining the predicted event and the predicted time associated with the predicted event based on the at least one measurement value comprises determining the predicted event and the predicted time associated with the predicted event based on the second measurement value.

17. 1. A wireless transmit and receive unit (WTRU), comprising: a processor, the processor comprising: Determining the multiple measurements; determining a 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, and a predicted time associated with the predicted event; determining a difference between a current time and the predicted time; and and a wireless transmit and receive unit (WTRU) further configured to: send a report to a network node indicating the predicted event, on condition that the difference between the current time and the predicted time is greater than a predetermined time offset.

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

19. The WTRU of claim 17 , wherein the predicted time comprises a time interval.

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

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