WTRU assisted proactive PHY frame reconfiguration associated with low latency applications

Abstract

Systems, methods, and instrumentalities are described herein for proactive PHY frame reconfiguration assisted by a wireless transmit / receive unit (WTRU). A device (e.g., a WTRU) may comprise a processor configured to perform one or more actions. The device may receive prediction configuration information. The prediction configuration information may indicate a prediction accuracy threshold. The device may determine a prediction accuracy associated with the prediction model to satisfy the prediction accuracy threshold. The device may determine a predicted channel metric via the prediction model based on the determination that the prediction accuracy associated with the prediction model satisfies the prediction accuracy threshold. The device may send the predicted channel metric to the network node. The device may receive an indication of resource information.

Description

WTRU ASSISTED PROACTIVE PHY FRAME RECONFIGURATION ASSOCIATED WITH LOW LATENCY APPLICATIONSCROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the benefit of U.S. Provisional Application No. 63 / 601,339, filed November 21 , 2023 the contents of which is incorporated by reference herein.BACKGROUND

[0002] Mobile communications using wireless communication continue to evolve. A fifth generation may be referred to as 5G. A previous (legacy) generation of mobile communication may be, for example, fourth generation (4G) long term evolution (LTE).

[0003] The ubiquitous deployment of 4G / 5G technology has made it a critical infrastructure for society that will facilitate the delivery and adoption of emerging applications and use cases (extended reality, automation, robotics, etc.). These new applications may require high throughput and low latency in uplink and downlink for optimal performance, while coexisting with traditional downlink-heavy consumer applications. Successfully supporting these new use cases may depend on the network allocating resources efficiently.SUMMARY

[0004] Systems, methods, and instrumentalities are described herein that may be associated with proactive PHY frame reconfiguration assisted by a wireless transmit / receive unit (WTRU). A device (e.g., a WTRU) may comprise a processor, where the WTRU may be configured to perform one or more of the following actions. The device may receive prediction configuration information. The prediction configuration information may indicate a prediction accuracy threshold. The device may determine that a prediction accuracy associated with a prediction model satisfies the prediction accuracy threshold. The device may determine a predicted channel metric via the prediction model based on the determination that the prediction accuracy associated with the prediction model satisfies the prediction accuracy threshold. The device may send the predicted channel metric to a network node. The device may receive an indication ofresource information that indicates one or more of: a slot offset value, a start symbol, an allocation length, or a start or length indicator value parameter.

[0005] In examples, the resource information may be an updated look-up table information. The device may receive look-up table information. The updated look-up table information may update the look-up table information to the updated look-up table information. The updated look-up table information may be associated with a re-configured PHY frame. The device may send, to the network node, an indication of a forecast metric associated with the prediction model. The device may send an uplink transmission using the resource information. The forecast metric associated with the prediction model may be a forecast time horizon. The prediction accuracy may be a second prediction accuracy. The device may determine that a first prediction accuracy associated with the prediction model does not satisfy the prediction accuracy threshold. The device may update the forecast time horizon to an updated forecast time horizon based on the determination that the first prediction accuracy associated with the prediction model does not satisfy the prediction accuracy threshold.

[0006] In examples, the device may send an indication of the updated forecast time horizon to the network node. The device may send an indication of the prediction accuracy to the network node. The device may maintain a historical channel metric. The device may compare the historical channel metric to the predicted channel metric. The device may receive a training time threshold. The determination that the prediction accuracy associated with the prediction model satisfies the prediction accuracy threshold may occur within the training time threshold. The training time threshold may be based on an input feature quantity or quality.

[0007] A device (e.g., a WTRU) may comprise a processor, where the WTRU may be configured to perform one or more of the following actions. The device may receive prediction configuration information that includes an indication of a prediction accuracy threshold. The device may determine forecast channel metrics, for example, based on instantaneous channel metrics and / or historical channel metrics. The device may determine whether a prediction accuracy of the forecast channel metrics is less than the prediction accuracy threshold. The device may transmit channel metrics based on the determination of whether the prediction accuracy of the forecast channel metrics is less than the prediction accuracy threshold.

[0008] If the prediction accuracy is less than the prediction accuracy threshold, the transmitted channel metrics may be the forecast channel metrics. If the prediction accuracy is equal to or greater than the prediction accuracy threshold, the transmitted channel metrics may be the instantaneous channel metrics.The device may determine the instantaneous channel metrics based on received signaling from a base station. The device may determine the prediction accuracy based on a comparison between the forecast channel metrics and the instantaneous channel metrics. The device may determine the historical channel metrics, for example, based on previous instantaneous channel metrics instances. The device may update the historical channel metrics based on the instantaneous channel metrics.

[0009] The prediction configuration information may indicate the time horizon threshold. The transmission of the channel metrics may be based on whether a time horizon threshold is satisfied. The device may modify the forecast time horizon (e.g., based on one or more conditions described herein being satisfied).BRIEF DESCRIPTION OF THE DRAWINGS

[0010] FIG. 1A is a system diagram illustrating an example communications system in which one or more disclosed embodiments may be implemented.

[0011] FIG. 1 B is a system diagram illustrating an example wireless transmit / receive unit (WTRU) that may be used within the communications system illustrated in FIG. 1 A according to an embodiment.

[0012] FIG. 1 C is a system diagram illustrating an example radio access network (RAN) and an example core network (ON) that may be used within the communications system illustrated in FIG. 1 A according to an embodiment.

[0013] FIG. 1 D is a system diagram illustrating a further example RAN and a further example ON that may be used within the communications system illustrated in FIG. 1A according to an embodiment.

[0014] FIG. 2 is an example of proposed network modules.

[0015] FIG. 3 depicts an example 5G frame structure with numerology 1 .

[0016] FIG. 4 depicts an example schematic of dynamic scheduling in 5G.

[0017] FIG. 5 depicts an example time domain resource assignment from NW to WTRU in 5G.

[0018] FIG. 6 depicts an example open air interface architecture with a WTRU connected to a gNB collocated with the core network.

[0019] FIG. 7 depicts an example uplink throughput (e.g., Iperf uplink throughput) in OAI with different PHY frame configurations.

[0020] FIG. 8 depicts example latency in transmitting data of varying sizes through OAI uplink with different PHY frame configurations.

[0021] FIG. 9 depicts an example uplink transmission process in 5G NR PHY TDD configuration with 30 KHz SCS.

[0022] FIG. 10 depicts an example dynamic PHY frame reconfiguration at NW aided by WTRU forecast of local channel and traffic metrics.

[0023] FIG. 11 depicts an example sliding window mechanism at WTRU in using historical channel metrics to predict the channel metrics of future time slots.

[0024] FIG. 12 depicts an example low diagram of a channel metric prediction framework.

[0025] FIG. 13 depicts example tensor forming, training, and testing processes in a LSTM network for channel metric prediction.

[0026] FIG. 14 depicts an example signaling message exchange between WTRU and the network for WTRU channel metric prediction.

[0027] FIG. 15 depicts an example signaling message exchange between a WTRU and the network regarding WTRU fallback to reactive channel metric estimation.

[0028] FIG. 16 depicts an example process for WTRU application traffic prediction by a LSTM neural network.

[0029] FIG. 17 depicts an example latency with proactive PHY frame configuration compared to static frame configurations in OAI.

[0030] FIG. 18 depicts an example throughput with proactive PHY frame configuration compared to static frame configurations in OAI.DETAILED DESCRIPTION

[0031] FIG. 1A is a diagram illustrating an example communications system 100 in which one or more disclosed embodiments may be implemented. The communications system 100 may be a multiple access system that provides content, such as voice, data, video, messaging, broadcast, etc., to multiple wireless users. The communications system 100 may enable multiple wireless users to access such content through the sharing of system resources, including wireless bandwidth. For example, the communications systems 100 may employ one or more channel access methods, such as code division multiple access (CDMA), time division multiple access (TDMA), frequency division multiple access (FDMA), orthogonal FDMA (OFDMA), single-carrier FDMA (SC-FDMA), zero-tail unique-word DFT-Spread OFDM (ZT UW DTS-s OFDM), unique word OFDM (UW-OFDM), resource block-filtered OFDM, filter bank multicarrier (FBMC), and the like.

[0032] As shown in FIG. 1 A, the communications system 100 may include wireless transmit / receive units (WTRUs) 102a, 102b, 102c, 102d, a RAN 104 / 113, a CN 106 / 115, a public switched telephone network (PSTN) 108, the Internet 110, and other networks 112, though it will be appreciated that the disclosed embodiments contemplate any number of WTRUs, base stations, networks, and / or network elements. Each of the WTRUs 102a, 102b, 102c, 102d may be any type of device configured to operate and / 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 a “ST A”, may be configured to transmit and / or receive wireless signals and may include a user equipment (UE), a mobile station, a fixed or mobile subscriber unit, a subscription-based unit, a pager, a cellular telephone, a personal digital assistant (PDA), a smartphone, a laptop, a netbook, a personal computer, a wireless sensor, a hotspot or Mi-Fi device, an Internet of Things (loT) device, a watch or other wearable, a head-mounted display (HMD), a vehicle, a drone, a medical device and applications (e.g., remote surgery), an industrial device and applications (e.g., a robot and / or other wireless devices operating in an industrial and / or an automated processing chain contexts), a consumer electronics device, a device operating on commercial and / or industrial wireless networks, and the like. Any of the WTRUs 102a, 102b, 102c and 102d may be interchangeably referred to as a UE.

[0033] The communications systems 100 may also include a base station 114a and / or a base station 114b. Each of the base stations 114a, 114b may be any type of device configured to wirelessly interface with at least one of the WTRUs 102a, 102b, 102c, 102d to facilitate access to one or more communication networks, such as the CN 106 / 115, the Internet 110, and / or the other networks 112. By way of example, the base stations 114a, 114b may be a base transceiver station (BTS), a Node-B, an eNode B, a Home Node B, a Home eNode B, a gNB, a NR NodeB, a site controller, an access point (AP), a wireless router, and the like. While the base stations 114a, 114b are each depicted as a single element, it will be appreciated that the base stations 114a, 114b may include any number of interconnected base stations and / or network elements.

[0034] The base station 114a may be part of the RAN 104 / 113, which may also include other base stations and / or network elements (not shown), such as a base station controller (BSC), a radio network controller (RNC), relay nodes, etc. The base station 114a and / or the base station 114b may be configured to transmit and / or receive wireless signals on one or more carrier frequencies, which may be referred to as a cell (not shown). These frequencies may be in licensed spectrum, unlicensed spectrum, or a combination of licensed and unlicensed spectrum. A cell may provide coverage for a wireless service to a specific geographical area that may be relatively fixed or that may change over time. The cell may further be divided into cell sectors. For example, the cell associated with the base station 114a may be divided intothree sectors. Thus, in one embodiment, the base station 114a may include three transceivers, i.e., one for each sector of the cell. In an embodiment, the base station 114a may employ multiple-input multiple output (MIMO) technology and may utilize multiple transceivers for each sector of the cell. For example, beamforming may be used to transmit and / or receive signals in desired spatial directions.

[0035] 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).

[0036] More specifically, as noted above, the communications system 100 may be a multiple access system and may employ one or more channel access schemes, such as CDMA, TDMA, FDMA, OFDMA, SC-FDMA, and the like. For example, the base station 114a in the RAN 104 / 113 and the WTRUs 102a, 102b, 102c may implement a radio technology such as Universal Mobile Telecommunications System (UMTS) Terrestrial Radio Access (UTRA), which may establish the air interface 115 / 116 / 117 using wideband CDMA (WCDMA). WCDMA may include communication protocols such as High-Speed Packet Access (HSPA) and / or Evolved HSPA (HSPA+). HSPA may include High-Speed Downlink (DL) Packet Access (HSDPA) and / or High-Speed UL Packet Access (HSUPA).

[0037] In an embodiment, the base station 114a and the WTRUs 102a, 102b, 102c may implement a radio technology such as Evolved UMTS Terrestrial Radio Access (E-UTRA), which may establish the air interface 116 using Long Term Evolution (LTE) and / or LTE-Advanced (LTE-A) and / or LTE-Advanced Pro (LTE-A Pro).

[0038] In an embodiment, the base station 114a and the WTRUs 102a, 102b, 102c may implement a radio technology such as NR Radio Access , which may establish the air interface 116 using New Radio (NR).

[0039] In an embodiment, the base station 114a and the WTRUs 102a, 102b, 102c may implement multiple radio access technologies. For example, the base station 114a and the WTRUs 102a, 102b, 102c may implement LTE radio access and NR radio access together, for instance using dual connectivity (DC) principles. Thus, the air interface utilized by WTRUs 102a, 102b, 102c may be characterized by multiple types of radio access technologies and / or transmissions sent to / from multiple types of base stations (e.g., a eNB and a gNB).

[0040] In other embodiments, the base station 114a and the WTRUs 102a, 102b, 102c may implement radio technologies such as IEEE 802.11 (i.e., Wireless Fidelity (WiFi), IEEE 802.16 (i.e., WorldwideInteroperability 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), and the like.

[0041] The base station 114b in FIG. 1 A may be a wireless router, Home Node B, Home eNode B, or access point, for example, and may utilize any suitable RAT for facilitating wireless connectivity in a localized area, such as a place of business, a home, a vehicle, a campus, an industrial facility, an air corridor (e.g., for use by drones), a roadway, and the like. In one embodiment, the base station 114b and the WTRUs 102c, 102d may implement a radio technology such as IEEE 802.11 to establish a wireless local area network (WLAN). In an embodiment, the base station 114b and the WTRUs 102c, 102d may implement a radio technology such as IEEE 802.15 to establish a wireless personal area network (WPAN). In yet another embodiment, the base station 114b and the WTRUs 102c, 102d may utilize a cellular-based RAT (e.g., WCDMA, CDMA2000, GSM, LTE, LTE-A, LTE-A Pro, NR etc.) to establish a picocell or femtocell. As shown in FIG. 1 A, the base station 114b may have a direct connection to the Internet 110. Thus, the base station 114b may not be required to access the Internet 110 via the CN 106 / 115.

[0042] The RAN 104 / 113 may be in communication with the CN 106 / 115, which may be any type of network configured to provide voice, data, applications, and / or voice over internet protocol (VoIP) services to one or more of the WTRUs 102a, 102b, 102c, 102d. The data may have varying quality of service (QoS) requirements, such as differing throughput requirements, latency requirements, error tolerance requirements, reliability requirements, data throughput requirements, mobility requirements, and the like. The CN 106 / 115 may provide call control, billing services, mobile location-based services, pre-paid calling, Internet connectivity, video distribution, etc., and / or perform high-level security functions, such as user authentication. Although not shown in FIG. 1A, it will be appreciated that the RAN 104 / 113 and / or the CN 106 / 115 may be in direct or indirect communication with other RANs that employ the same RAT as the RAN 104 / 113 or a different RAT. For example, in addition to being connected to the RAN 104 / 113, which may be utilizing a NR radio technology, the CN 106 / 115 may also be in communication with another RAN (not shown) employing a GSM, UMTS, CDMA 2000, WiMAX, E-UTRA, or WiFi radio technology.

[0043] The CN 106 / 115 may also serve as a gateway for the WTRUs 102a, 102b, 102c, 102d to access the PSTN 108, the Internet 110, and / or the other networks 112. The PSTN 108 may include circuit- switched telephone networks that provide plain old telephone service (POTS). The Internet 110 may include a global system of interconnected computer networks and devices that use common communication protocols, such as the transmission control protocol (TCP), user datagram protocol (UDP)and / or the internet protocol (IP) in the TCP / IP internet protocol suite. The networks 112 may include wired and / or wireless communications networks owned and / or operated by other service providers. For example, the networks 112 may include another CN connected to one or more RANs, which may employ the same RAT as the RAN 104 / 113 or a different RAT.

[0044] Some or all of the WTRUs 102a, 102b, 102c, 102d in the communications system 100 may include multi-mode capabilities (e.g., the WTRUs 102a, 102b, 102c, 102d may include multiple transceivers for communicating with different wireless networks over different wireless links). For example, the WTRU 102c shown in FIG. 1A may be configured to communicate with the base station 114a, which may employ a cellular-based radio technology, and with the base station 114b, which may employ an IEEE 802 radio technology.

[0045] FIG. 1 B is a system diagram illustrating an example WTRU 102. As shown in FIG. 1 B, the WTRU 102 may include a processor 118, a transceiver 120, a transmit / receive element 122, a speaker / microphone 124, a keypad 126, a display / touchpad 128, non-removable memory 130, removable memory 132, a power source 134, a global positioning system (GPS) chipset 136, and / or other peripherals 138, among others. It will be appreciated that the WTRU 102 may include any sub-combination of the foregoing elements while remaining consistent with an embodiment.

[0046] The processor 118 may be a general purpose processor, a special purpose processor, a conventional processor, a digital signal processor (DSP), a plurality of microprocessors, one or more microprocessors in association with a DSP core, a controller, a microcontroller, Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) circuits, any other type of integrated circuit (IC), a state machine, and the like. The processor 118 may perform signal coding, data processing, power control, input / output processing, and / or any other functionality that enables the WTRU 102 to operate in a wireless environment. The processor 118 may be coupled to the transceiver 120, which may be coupled to the transmit / receive element 122. While FIG. 1 B depicts the processor 118 and the transceiver 120 as separate components, it will be appreciated that the processor 118 and the transceiver 120 may be integrated together in an electronic package or chip.

[0047] The transmit / receive element 122 may be configured to transmit signals to, or receive signals from, a base station (e.g., the base station 114a) over the air interface 116. For example, in one embodiment, the transmit / receive element 122 may be an antenna configured to transmit and / or receive RF signals. In an embodiment, the transmit / receive element 122 may be an emitter / detector configured to transmit and / or receive IR, UV, or visible light signals, for example. In yet another embodiment, the transmit / receive element 122 may be configured to transmit and / or receive both RF and light signals. It willbe appreciated that the transmit / receive element 122 may be configured to transmit and / or receive any combination of wireless signals.

[0048] Although the transmit / receive element 122 is depicted in FIG. 1 B as a single element, the WTRU 102 may include any number of transmit / receive elements 122. More specifically, the WTRU 102 may employ MIMO technology. Thus, in one embodiment, the WTRU 102 may include two or more transmit / receive elements 122 (e.g., multiple antennas) for transmitting and receiving wireless signals over the air interface 116.

[0049] The transceiver 120 may be configured to modulate the signals that are to be transmitted by the transmit / receive element 122 and to demodulate the signals that are received by the transmit / receive element 122. As noted above, the WTRU 102 may have multi-mode capabilities. Thus, the transceiver 120 may include multiple transceivers for enabling the WTRU 102 to communicate via multiple RATs, such as NR and IEEE 802.11, for example.

[0050] The processor 118 of the WTRU 102 may be coupled to, and may receive user input data from, the speaker / microphone 124, the keypad 126, and / or the display / touchpad 128 (e.g., a liquid crystal display (LCD) display unit or organic light-emitting diode (OLED) display unit). The processor 118 may also output user data to the speaker / microphone 124, the keypad 126, and / or the display / touchpad 128. In addition, the processor 118 may access information from, and store data in, any type of suitable memory, such as the non-removable memory 130 and / or the removable memory 132. The non-removable memory 130 may include random-access memory (RAM), read-only memory (ROM), a hard disk, or any other type of memory storage device. The removable memory 132 may include a subscriber identity module (SIM) card, a memory stick, a secure digital (SD) memory card, and the like. In other embodiments, the processor 118 may access information from, and store data in, memory that is not physically located on the WTRU 102, such as on a server or a home computer (not shown).

[0051] The processor 118 may receive power from the power source 134, and may be configured to distribute and / or control the power to the other components in the WTRU 102. The power source 134 may be any suitable device for powering the WTRU 102. For example, the power source 134 may include one or more dry cell batteries (e.g., nickel-cadmium (NiCd), nickel-zinc (NiZn), nickel metal hydride (NiMH), lithium-ion (Li-ion), etc.), solar cells, fuel cells, and the like.

[0052] The processor 118 may also be coupled to the GPS chipset 136, which may be configured to provide location information (e.g., longitude and latitude) regarding the current location of the WTRU 102. In addition to, or in lieu of, the information from the GPS chipset 136, the WTRU 102 may receive location information over the air interface 116 from a base station (e.g., base stations 114a, 114b) and / or determineits location based on the timing of the signals being received from two or more nearby base stations. It will be appreciated that the WTRU 102 may acquire location information by way of any suitable locationdetermination method while remaining consistent with an embodiment.

[0053] The processor 118 may further be coupled to other peripherals 138, which may include one or more software and / or hardware modules that provide additional features, functionality and / or wired or wireless connectivity. For example, the peripherals 138 may include an accelerometer, an e-compass, a satellite transceiver, a digital camera (for photographs and / or video), a universal serial bus (USB) port, a vibration device, a television transceiver, a hands free headset, a Bluetooth® module, a frequency modulated (FM) radio unit, a digital music player, a media player, a video game player module, an Internet browser, a Virtual Reality and / or Augmented Reality (VR / AR) device, an activity tracker, and the like. The peripherals 138 may include one or more sensors, the sensors may be one or more of a gyroscope, an accelerometer, a hall effect sensor, a magnetometer, an orientation sensor, a proximity sensor, a temperature sensor, a time sensor; a geolocation sensor; an altimeter, a light sensor, a touch sensor, a magnetometer, a barometer, a gesture sensor, a biometric sensor, and / or a humidity sensor.

[0054] The WTRU 102 may include a full duplex radio for which transmission and reception of some or all of the signals (e.g., associated with particular subframes for both the UL (e.g., for transmission) and downlink (e.g., for reception) may be concurrent and / or simultaneous. The full duplex radio may include an interference management unit to reduce and or substantially eliminate self-interference via either hardware (e.g., a choke) or signal processing via a processor (e.g., a separate processor (not shown) or via processor 118). In an embodiment, the WRTU 102 may include a half-duplex radio for which transmission and reception of some or all of the signals (e.g., associated with particular subframes for either the UL (e.g., for transmission) or the downlink (e.g., for reception)).

[0055] FIG. 1 C is a system diagram illustrating the RAN 104 and the CN 106 according to an embodiment. As noted above, the RAN 104 may employ an E-UTRA radio technology to communicate with the WTRUs 102a, 102b, 102c over the air interface 116. The RAN 104 may also be in communication with the CN 106.

[0056] The RAN 104 may include eNode-Bs 160a, 160b, 160c, though it will be appreciated that the RAN 104 may include any number of eNode-Bs while remaining consistent with an embodiment. The eNode-Bs 160a, 160b, 160c may each include one or more transceivers for communicating with the WTRUs 102a, 102b, 102c over the air interface 116. In one embodiment, the eNode-Bs 160a, 160b, 160c may implement MIMO technology. Thus, the eNode-B 160a, for example, may use multiple antennas to transmit wireless signals to, and / or receive wireless signals from, the WTRU 102a.

[0057] Each of the eNode-Bs 160a, 160b, 160c may be associated with a particular cell (not shown) and may be configured to handle radio resource management decisions, handover decisions, scheduling of users in the UL and / or DL, and the like. As shown in FIG. 1 C, the eNode-Bs 160a, 160b, 160c may communicate with one another over an X2 interface.

[0058] The CN 106 shown in FIG. 1 C may include a mobility management entity (MME) 162, a serving gateway (SGW) 164, and a packet data network (PDN) gateway (or PGW) 166. While each of the foregoing elements are depicted as part of the ON 106, it will be appreciated that any of these elements may be owned and / or operated by an entity other than the ON operator.

[0059] The MME 162 may be connected to each of the eNode-Bs 162a, 162b, 162c in the RAN 104 via an S1 interface and may serve as a control node. For example, the MME 162 may be responsible for authenticating users of the WTRUs 102a, 102b, 102c, bearer activation / deactivation, selecting a particular serving gateway during an initial attach of the WTRUs 102a, 102b, 102c, and the like. The MME 162 may provide a control plane function for switching between the RAN 104 and other RANs (not shown) that employ other radio technologies, such as GSM and / or WCDMA.

[0060] The SGW 164 may be connected to each of the eNode Bs 160a, 160b, 160c in the RAN 104 via the S1 interface. The SGW 164 may generally route and forward user data packets to / from the WTRUs 102a, 102b, 102c. The SGW 164 may perform other functions, such as anchoring user planes during inter- eNode B handovers, triggering paging when DL data is available for the WTRUs 102a, 102b, 102c, managing and storing contexts of the WTRUs 102a, 102b, 102c, and the like.

[0061] The SGW 164 may be connected to the PGW 166, which may provide the WTRUs 102a, 102b, 102c with access to packet-switched networks, such as the Internet 110, to facilitate communications between the WTRUs 102a, 102b, 102c and IP-enabled devices.

[0062] The CN 106 may facilitate communications with other networks. For example, the CN 106 may provide the WTRUs 102a, 102b, 102c with access to circuit-switched networks, such as the PSTN 108, to facilitate communications between the WTRUs 102a, 102b, 102c and traditional land-line communications devices. For example, the CN 106 may include, or may communicate with, an IP gateway (e.g., an IP multimedia subsystem (IMS) server) that serves as an interface between the CN 106 and the PSTN 108. In addition, the CN 106 may provide the WTRUs 102a, 102b, 102c with access to the other networks 112, which may include other wired and / or wireless networks that are owned and / or operated by other service providers.

[0063] Although the WTRU is described in FIGS. 1 A-1 D as a wireless terminal, it is contemplated that in certain representative embodiments that such a terminal may use (e.g., temporarily or permanently) wired communication interfaces with the communication network.

[0064] In representative embodiments, the other network 112 may be a WLAN.

[0065] A WLAN in Infrastructure Basic Service Set (BSS) mode may have an Access Point (AP) for the BSS and one or more stations (STAs) associated with the AP. The AP may have an access or an interface to a Distribution System (DS) or another type of wired / wireless network that carries traffic in to and / or out of the BSS. Traffic to STAs that originates from outside the BSS may arrive through the AP and may be delivered to the STAs. Traffic originating from STAs to destinations outside the BSS may be sent to the AP to be delivered to respective destinations. Traffic between STAs within the BSS may be sent through the AP, for example, where the source STA may send traffic to the AP and the AP may deliver the traffic to the destination STA. The traffic between STAs within a BSS may be considered and / or referred to as peer-to- peer traffic. The peer-to-peer traffic may be sent between (e.g., directly between) the source and destination STAs with a direct link setup (DLS). In certain representative embodiments, the DLS may use an 802.11e DLS or an 802.11 z tunneled DLS (TDLS). A WLAN using an Independent BSS (IBSS) mode may not have an AP, and the STAs (e.g., all of the STAs) within or using the IBSS may communicate directly with each other. The IBSS mode of communication may sometimes be referred to herein as an “ad- hoc” mode of communication.

[0066] When using the 802.11 ac infrastructure mode of operation or a similar mode of operations, the AP may transmit a beacon on a fixed channel, such as a primary channel. The primary channel may be a fixed width (e.g., 20 MHz wide bandwidth) or a dynamically set width via signaling. The primary channel may be the operating channel of the BSS and may be used by the STAs to establish a connection with the AP. In certain representative embodiments, Carrier Sense Multiple Access with Collision Avoidance (CSMA / CA) may be implemented, for example, in in 802.11 systems. For CSMA / CA, the STAs (e.g., every STA), including the AP, may sense the primary channel. If the primary channel is sensed / detected and / or determined to be busy by a particular STA, the particular STA may back off. One STA (e.g., only one station) may transmit at any given time in a given BSS.

[0067] High Throughput (HT) STAs may use a 40 MHz wide channel for communication, for example, via a combination of the primary 20 MHz channel with an adjacent or nonadjacent 20 MHz channel to form a 40 MHz wide channel.

[0068] Very High Throughput (VHT) STAs may support 20MHz, 40 MHz, 80 MHz, and / or 160 MHz wide channels. The 40 MHz, and / or 80 MHz, channels may be formed by combining contiguous 20 MHzchannels. A 160 MHz channel may be formed by combining 8 contiguous 20 MHz channels, or by combining two non-contiguous 80 MHz channels, which may be referred to as an 80+80 configuration. For the 80+80 configuration, the data, after channel encoding, may be passed through a segment parser that may divide the data into two streams. Inverse Fast Fourier Transform (IFFT) processing, and time domain processing, may be done on each stream separately. The streams may be mapped on to the two 80 MHz channels, and the data may be transmitted by a transmitting STA. At the receiver of the receiving STA, the above described operation for the 80+80 configuration may be reversed, and the combined data may be sent to the Medium Access Control (MAC).

[0069] Sub 1 GHz modes of operation are supported by 802.11af and 802.11 ah. The channel operating bandwidths, and carriers, are reduced in 802.11 af and 802.11 ah relative to those used in 802.11 n, and 802.11 ac. 802.11 af supports 5 MHz, 10 MHz and 20 MHz bandwidths in the TV White Space (TVWS) spectrum, and 802.11 ah supports 1 MHz, 2 MHz, 4 MHz, 8 MHz, and 16 MHz bandwidths using non- TVWS spectrum. According to a representative embodiment, 802.11 ah may support Meter Type Control / Machine-Type Communications, such as MTC devices in a macro coverage area. MTC devices may have certain capabilities, for example, limited capabilities including support for (e.g., only support for) certain and / or limited bandwidths. The MTC devices may include a battery with a battery life above a threshold (e.g., to maintain a very long battery life).

[0070] WLAN systems, which may support multiple channels, and channel bandwidths, such as 802.11 n, 802.11 ac, 802.11 af, and 802.11 ah, include a channel which may be designated as the primary channel. The primary channel may have a bandwidth equal to the largest common operating bandwidth supported by all STAs in the BSS. The bandwidth of the primary channel may be set and / or limited by a STA, from among all STAs in operating in a BSS, which supports the smallest bandwidth operating mode. In the example of 802.11 ah, the primary channel may be 1 MHz wide for STAs (e.g., MTC type devices) that support (e.g., only support) a 1 MHz mode, even if the AP, and other STAs in the BSS support 2 MHz, 4 MHz, 8 MHz, 16 MHz, and / or other channel bandwidth operating modes. Carrier sensing and / or Network Allocation Vector (NAV) settings may depend on the status of the primary channel. If the primary channel is busy, for example, due to a STA (which supports only a 1 MHz operating mode), transmitting to the AP, the entire available frequency bands may be considered busy even though a majority of the frequency bands remains idle and may be available.

[0071] In the United States, the available frequency bands, which may be used by 802.11 ah, are from 902 MHz to 928 MHz. In Korea, the available frequency bands are from 917.5 MHz to 923.5 MHz. InJapan, the available frequency bands are from 916.5 MHz to 927.5 MHz. The total bandwidth available for 802.11 ah is 6 MHz to 26 MHz depending on the country code.

[0072] FIG. 1 D is a system diagram illustrating the RAN 113 and the CN 115 according to an embodiment. As noted above, the RAN 113 may employ an NR radio technology to communicate with the WTRUs 102a, 102b, 102c over the air interface 116. The RAN 113 may also be in communication with the CN 115.

[0073] The RAN 113 may include gNBs 180a, 180b, 180c, though it will be appreciated that the RAN 113 may include any number of gNBs while remaining consistent with an embodiment. The gNBs 180a, 180b, 180c may each include one or more transceivers for communicating with the WTRUs 102a, 102b, 102c over the air interface 116. In one embodiment, the gNBs 180a, 180b, 180c may implement MIMO technology. For example, gNBs 180a, 108b may utilize beamforming to transmit signals to and / or receive signals from the gNBs 180a, 180b, 180c. Thus, the gNB 180a, for example, may use multiple antennas to transmit wireless signals to, and / or receive wireless signals from, the WTRU 102a. In an embodiment, the gNBs 180a, 180b, 180c may implement carrier aggregation technology. For example, the gNB 180a may transmit multiple component carriers to the WTRU 102a (not shown). A subset of these component carriers may be on unlicensed spectrum while the remaining component carriers may be on licensed spectrum. In an embodiment, the gNBs 180a, 180b, 180c may implement Coordinated Multi-Point (CoMP) technology. For example, WTRU 102a may receive coordinated transmissions from gNB 180a and gNB 180b (and / or gNB 180c).

[0074] The WTRUs 102a, 102b, 102c may communicate with gNBs 180a, 180b, 180c using transmissions associated with a scalable numerology. For example, the OFDM symbol spacing and / or OFDM subcarrier spacing may vary for different transmissions, different cells, and / or different portions of the wireless transmission spectrum. The WTRUs 102a, 102b, 102c may communicate with gNBs 180a, 180b, 180c using subframe or transmission time intervals (TTIs) of various or scalable lengths (e.g., containing varying number of OFDM symbols and / or lasting varying lengths of absolute time).

[0075] The gNBs 180a, 180b, 180c may be configured to communicate with the WTRUs 102a, 102b, 102c in a standalone configuration and / or a non-standalone configuration. In the standalone configuration, WTRUs 102a, 102b, 102c may communicate with gNBs 180a, 180b, 180c without also accessing other RANs (e.g., such as eNode-Bs 160a, 160b, 160c). In the standalone configuration, WTRUs 102a, 102b, 102c may utilize one or more of gNBs 180a, 180b, 180c as a mobility anchor point. In the standalone configuration, WTRUs 102a, 102b, 102c may communicate with gNBs 180a, 180b, 180c using signals in an unlicensed band. In a non-standalone configuration WTRUs 102a, 102b, 102c may communicatewith / connect to gNBs 180a, 180b, 180c while also communicating with / connecting to another RAN such as eNode-Bs 160a, 160b, 160c. For example, WTRUs 102a, 102b, 102c may implement DC principles to communicate with one or more gNBs 180a, 180b, 180c and one or more eNode-Bs 160a, 160b, 160c substantially simultaneously. In the non-standalone configuration, eNode-Bs 160a, 160b, 160c may serve as a mobility anchor for WTRUs 102a, 102b, 102c and gNBs 180a, 180b, 180c may provide additional coverage and / or throughput for servicing WTRUs 102a, 102b, 102c.

[0076] Each of the gNBs 180a, 180b, 180c may be associated with a particular cell (not shown) and may be configured to handle radio resource management decisions, handover decisions, scheduling of users in the UL and / or DL, support of network slicing, dual connectivity, interworking between NR and E- UTRA, routing of user plane data towards User Plane Function (UPF) 184a, 184b, routing of control plane information towards Access and Mobility Management Function (AMF) 182a, 182b and the like. As shown in FIG. 1 D, the gNBs 180a, 180b, 180c may communicate with one another over an Xn interface.

[0077] The CN 115 shown in FIG. 1 D may include at least one AMF 182a, 182b, at least one UPF 184a, 184b, at least one Session Management Function (SMF) 183a, 183b, and possibly a Data Network (DN) 185a, 185b. While each of the foregoing elements are depicted as part of the CN 115, it will be appreciated that any of these elements may be owned and / or operated by an entity other than the CN operator.

[0078] The AMF 182a, 182b may be connected to one or more of the gNBs 180a, 180b, 180c in the RAN 113 via an N2 interface and may serve as a control node. For example, the AMF 182a, 182b may be responsible for authenticating users of the WTRUs 102a, 102b, 102c, support for network slicing (e.g., handling of different PDU sessions with different requirements), selecting a particular SMF 183a, 183b, management of the registration area, termination of NAS signaling, mobility management, and the like. Network slicing may be used by the AMF 182a, 182b in order to customize CN support for WTRUs 102a, 102b, 102c based on the types of services being utilized WTRUs 102a, 102b, 102c. For example, 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, and / or the like. The AMF 162 may provide a control plane function for switching between the RAN 113 and other RANs (not shown) that employ other radio technologies, such as LTE, LTE-A, LTE-A Pro, and / or non-3GPP access technologies such as WiFi.

[0079] The SMF 183a, 183b may be connected to an AMF 182a, 182b in the CN 115 via an N11 interface. The SMF 183a, 183b may also be connected to a UPF 184a, 184b in the CN 115 via an N4 interface. The SMF 183a, 183b may select and control the UPF 184a, 184b and configure the routing oftraffic through the UPF 184a, 184b. The SMF 183a, 183b may perform other functions, such as managing and allocating WTRU IP address, managing PDU sessions, controlling policy enforcement and QoS, providing downlink data notifications, and the like. A PDU session type may be IP-based, non-IP based, Ethernet-based, and the like.

[0080] The UPF 184a, 184b may be connected to one or more of the gNBs 180a, 180b, 180c in the RAN 113 via an N3 interface, which may provide the WTRUs 102a, 102b, 102c with access to packet- switched networks, such as the Internet 110, to facilitate communications between the WTRUs 102a, 102b, 102c and IP-enabled devices. The UPF 184, 184b may perform other functions, such as routing and forwarding packets, enforcing user plane policies, supporting multi-homed PDU sessions, handling user plane QoS, buffering downlink packets, providing mobility anchoring, and the like.

[0081] The CN 115 may facilitate communications with other networks. For example, the CN 115 may include, or may communicate with, an IP gateway (e.g., an IP multimedia subsystem (IMS) server) that serves as an interface between the CN 115 and the PSTN 108. In addition, the CN 115 may provide the WTRUs 102a, 102b, 102c with access to the other networks 112, which may include other wired and / or wireless networks that are owned and / or operated by other service providers. In one embodiment, the WTRUs 102a, 102b, 102c may be connected to a local Data Network (DN) 185a, 185b through the UPF 184a, 184b via the N3 interface to the UPF 184a, 184b and an N6 interface between the UPF 184a, 184b and the DN 185a, 185b.

[0082] In view of Figures 1A-1 D, and the corresponding description of Figures 1A-1 D, one or more, or all, of the functions described herein with regard to one or more of: WTRU 102a-d, Base Station 114a-b, eNode-B 160a-c, MME 162, SGW 164, PGW 166, gNB 180a-c, AMF 182a-b, UPF 184a-b, SMF 183a-b, DN 185a-b, and / or any other device(s) described herein, may be performed by one or more emulation devices (not shown). The emulation devices may be one or more devices configured to emulate one or more, or all, of the functions described herein. For example, the emulation devices may be used to test other devices and / or to simulate network and / or WTRU functions.

[0083] The emulation devices may be designed to implement one or more tests of other devices in a lab environment and / or in an operator network environment. For example, the one or more emulation devices may perform the one or more, or all, functions while being fully or partially implemented and / or deployed as part of a wired and / or wireless communication network in order to test other devices within the communication network. The one or more emulation devices may perform the one or more, or all, functions while being temporarily implemented / deployed as part of a wired and / or wireless communicationnetwork. The emulation device may be directly coupled to another device for purposes of testing and / or may perform testing using over-the-air wireless communications.

[0084] The one or more emulation devices may perform the one or more, including all, functions while not being implemented / deployed as part of a wired and / or wireless communication network. For example, the emulation devices may be utilized in a testing scenario in a testing laboratory and / or a non-deployed (e.g., testing) wired and / or wireless communication network in order to implement testing of one or more components. The one or more emulation devices may be test equipment. Direct RF coupling and / or wireless communications via RF circuitry (e.g., which may include one or more antennas) may be used by the emulation devices to transmit and / or receive data.

[0085] Systems, methods, and instrumentalities are described herein that may be associated with proactive PHY frame reconfiguration assisted by a wireless transmit / receive unit (WTRU). A device (e.g., a WTRU) may comprise a processor, where the WTRU may be configured to perform one or more of the following actions. The device may receive prediction configuration information. The prediction configuration information may indicate a prediction accuracy threshold. The device may determine that a prediction accuracy associated with a prediction model satisfies the prediction accuracy threshold. The device may determine a predicted channel metric via the prediction model based on the determination that the prediction accuracy associated with the prediction model satisfies the prediction accuracy threshold. The device may send the predicted channel metric to a network node. The device may receive an indication of resource information that indicates one or more of: a slot offset value, a start symbol, an allocation length, or a start or length indicator value parameter.

[0086] In examples, the resource information may be an updated look-up table information. The device may receive look-up table information. The updated look-up table information may update the look-up table information to the updated look-up table information. The updated look-up table information may be associated with a re-configured PHY frame. The device may send, to the network node, an indication of a forecast metric associated with the prediction model. The device may send an uplink transmission using the resource information. The forecast metric associated with the prediction model may be a forecast time horizon. The prediction accuracy may be a second prediction accuracy. The device may determine that a first prediction accuracy associated with the prediction model does not satisfy the prediction accuracy threshold. The device may update the forecast time horizon to an updated forecast time horizon based on the determination that the first prediction accuracy associated with the prediction model does not satisfy the prediction accuracy threshold.

[0087] In examples, the device may send an indication of the updated forecast time horizon to the network node. The device may send an indication of the prediction accuracy to the network node. The device may maintain a historical channel metric. The device may compare the historical channel metric to the predicted channel metric. The device may receive a training time threshold. The determination that the prediction accuracy associated with the prediction model satisfies the prediction accuracy threshold may occur within the training time threshold. The training time threshold may be based on an input feature quantity or quality.

[0088] A device (e.g., a WTRU) may comprise a processor configured to perform one or more actions. The device may receive prediction configuration information that includes an indication of a prediction accuracy threshold. The device may determine forecast channel metrics, for example, based on instantaneous channel metrics and / or historical channel metrics. The device may determine whether a prediction accuracy of the forecast channel metrics is less than the prediction accuracy threshold. The device may transmit channel metrics based on the determination of whether the prediction accuracy of the forecast channel metrics is less than the prediction accuracy threshold.

[0089] If the prediction accuracy is less than the prediction accuracy threshold, the transmitted channel metrics may be the forecast channel metrics. If the prediction accuracy is equal to or greater than the prediction accuracy threshold, the transmitted channel metrics may be the instantaneous channel metrics. The device may determine the instantaneous channel metrics based on received signaling from a base station. The device may determine the prediction accuracy based on a comparison between the forecast channel metrics and the instantaneous channel metrics. The device may determine the historical channel metrics, for example, based on previous instantaneous channel metrics instances. The device may update the historical channel metrics based on the instantaneous channel metrics.

[0090] The prediction configuration information may indicate the time horizon threshold. The transmission of the channel metrics may be based on whether a time horizon threshold is satisfied. The device may modify the forecast time horizon (e.g., based on one or more conditions described herein being satisfied).

[0091] A testbed (e.g., a 3GPP-compliant 5G testbed) may be used to analyze the limitations of legacy network time domain resource allocation methods with static physical (PHY) frame configurations and / or examine the effect on throughput and / or latency (e.g., key performance indicators). A framework may allow a proactive PHY frame configuration (e.g., as described herein) to leverage predictions of network and / or traffic metrics, which may be computed at the connected devices and / or may allocate resources (e.g., more dynamically) in the time domain (e.g., improved resource allocation). In examples, proactive PHY frameconfigurations may result in improvement(s) in meeting the latency and / or throughput requirements of applications (e.g., emerging applications such as extended reality and / or automation).

[0092] The inception of cellular networks and their subsequent technological advancements may facilitate the emergence of new applications. Applications such as extended reality (XR), teleoperations, and / or haptic communications, which may be collectively termed latency-critical applications (LCAs), may be used in industrial and / or consumer settings. The WTRUs running these applications may be unable to perform computationally intensive tasks, such as object detection, depth estimation, and / or path planning, because of resource constraints. Some WTRUs may offload computationally intensive tasks to edge servers (e.g., powerful nearby edge servers). Such offloading may present a unique set of challenges as the applications may demand high throughput and / or impose stringent requirements in terms of latency and / or reliability. Requirements of applications, for example, latency and / or reliability requirements, may be intricate to ensure wireless communication links, such as in scenarios characterized by mobile users and high user density. A portion (e.g., a significant portion) of this asymmetric behavior may be attributed to inefficient downlink and / or uplink resource scheduling executed by the network (NW). The NW may be used to collectively refer to the gNB and the core network. The NW, in an attempt to manage the (e.g., entire) bandwidth, may divide the bandwidth into (e.g., discrete) time slots, for example, apportioning some slots for downlink and other slots for uplink transmissions. This strategy may be referred to as time division duplexing (TDD).

[0093] FIG. 2 depicts example modules described herein. The proposed modules may be integrated in network modules (e.g., 3GPP compliant 5G network modules) for communications.

[0094] The cellular network may rely on channel measurements from an (e.g., each) end device(s) (e.g., WTRU) that it services to make decisions on how to (e.g., optimally) allocate network resources. A system may be reactive. In examples, the system may use (e.g., instantaneous) measurements to make allocation decisions. The efficiency of networks may be improved, for example, by making PHY frame reconfiguration and / or time domain resource allocation decisions (e.g., based on predictions of the WTRU reported local channel and / or traffic metrics). An adaptive and / or autonomous WTRU-based solution may allow the WTRU(s) to predict the device-specific channel and / or traffic metrics for future time instances. The WTRU(s) may report the device-specific channel and / or traffic metrics for future time instances to the network. This prediction may enable the network to (e.g., efficiently) configure and / or pre-allocate resources to meet the diverse set of application requirements, for example, under varying channel conditions. WTRU prediction (e.g., as described herein) may operate with network standards (e.g., the existing 3GPP standard with added enhancements shown in FIG. 2). As illustrated in FIG. 2, an exampleWTRU prediction configuration may include: deployed WTRUs with a trained machine learning (ML) module for local channel and / or traffic metric prediction; the network (NW), including the core and the base station (gNB), that receive and extract the predicted channel and traffic metrics from the WTRU uplink; and / or the standard network optimization suite (e.g., residing in the NW). The configuration may use the channel and / or traffic forecast from WTRUs to (e.g., proactively) reconfigure the PHY frame slots to (e.g., optimally) allocate the PHY frame slots to the WTRUs to meet their quality of service (QoS) requirement(s) in dynamic channel conditions.

[0095] A disparity may exist in the performance of 5G-NR. In examples, modern commercial 5G- mmWave-enabled smartphones may be capable of achieving high downlink speeds (e.g., surpassing 3.5 Gbps). The uplink throughput (e.g., consistently) may fall (e.g., significantly) behind. In examples, the uplink throughput may be constrained to an order of magnitude lower than its downlink counterpart. 5G-NR may introduce a level of flexibility that extends to a multitude of slot configurations. 5G-NR may introduce a level of flexibility that permits real-time alterations to these configurations (e.g., dynamic TDD) in response to varying traffic demands (e.g., in contrast to its predecessor LTE, which may offer a limited set of slot configurations). This adaptability may allow Latency Critical Applications (LCAs) through networks (e.g., 5G networks). Dynamic TDD implementation may be limited, with the static slot configuration tending to favor downlink traffic (e.g., making the real-time execution of LCAs a challenging endeavor).

[0096] Feature(s) associated with implementing dynamic TDD for LCAs (e.g., underpinned by an understanding of traffic patterns, channel conditions, and / or related factors) may be provided herein. These features may ensure that the pursuit of maximizing uplink throughput does not (e.g., inadvertently) compromise the downlink performance for other WTRUs (e.g., other WTRUs coexisting within the same network) and / or may enhance network performance while accommodating diverse traffic demands.

[0097] Some networks may allocate cellular resources through static PHY frame configuration, which may lead to suboptimal performance.

[0098] A proactive PHY frame configuration framework may be applied. The proactive PHY frame configuration may aid in proactive resource allocation (e.g., based on the look-ahead forecast of channel conditions and / or WTRU traffic metrics), which may improve the network QoS over the baseline reactive resource allocation approach on static PHY frames. The framework may reduce complexity may be compatible with 3GPP, and / or may isolate any additional processing at the WTRU (e.g., without affecting the NW). Example performance improvements may be achieved (e.g., as demonstrated herein), for example, in terms of latency performance and / or throughput performance.

[0099] FIG. 3 depicts an example 5G frame structure with numerology 1 .

[0100] FIG. 4 depicts an example schematic of dynamic scheduling in a network (e.g., a 5G network).

[0101] Radio link adaptation (LA) may aim at maximizing or minimizing a given key performance indicator (KPI) subject to a block error rate (BLER) threshold (e.g., to make efficient use of the channel conditions in the operating environment). Adaptive modulation and coding (AMC) may be one of the main features of LA. AMC may enable the selection of the highest modulation and coding scheme (MCS) to meet target BLER and latency thresholds while maximizing the throughput (e.g., through physical resource block (PRB) allocation) based on the estimated channel metrics. To allocate these resources for the WTRUs in the time domain, two types of scheduling in the uplink may be defined, for example, dynamic scheduling and configured scheduling (CS). Dynamic scheduling may be the mechanism in which a (e.g., each and / or every) PUSCH(s) is scheduled by DCI (e.g., DCI 0_0 or DCI 0_1 ). In CS, the PUSCH transmission may be scheduled by RRC message, for example, where the gNB may schedule PDSCH and / or PUSCH without using DCI for every transmission. The gNB may not change scheduling parameter(s) for individual transmission, which may result in some issues for LA (e.g., if the channel condition goes bad). Dynamic scheduling may be used (e.g., commonly used) in cellular networks.

[0102] FIG. 5 depicts an example time domain resource assignment from a NW to a WTRU (e.g., in 5G, which may be used as an example herein). PHY frame configuration in time domain may be performed (e.g., in 5G). The NW may divide the (e.g., entire) operational bandwidth into time slots where, for example, some slots may be for downlink (DL), some slots may be for uplink (UL) transmissions, and some slots may be flexible (F) slots used for DL or UL. This approach of resource allocation may be referred to as time division duplexing (TDD).

[0103] FIG. 3 shows an example 5G-NR frame structure. A frame in 5G NR may be 10ms long, which may be broken down into subframes (e.g., 10 subframes). The number of slots in a subframe may vary, for example, depending on the numerology. In examples, with numerology 1 , a subframe with 30 KHz sub carrier spacing (SCS) may have two slots. In TDD, the DL-UL-periodicity may determine the time for which there may be a consecutive set of downlink and uplink slots. A (e.g., each) slot may be further broken down into a number of symbols (e.g., 14 symbols), assuming normal cyclic prefix (CP).

[0104] Time domain resource assignment may be performed. For allocating resources in the time domain (e.g., in 5G), the NW may inform the WTRU about which slots and / or symbols the data may be transmitted and / or received through, for example, by signaling of time-domain resources dynamically or semi-persistently. Dynamic scheduling in the uplink may be done using PDCCH DCI. For semi-persistent scheduling, NR may define two mechanisms: one using PDCCH DCI and the other one via RRC signaling. In NR, DCI formats 0_0 and 0_1 may allocate time-domain resources for PUSCH (e.g., dynamically). DCIformats 0_0 and 0_1 may carry a 4-bit field named ‘time domain resource assignment’ which may point to one of the rows (e.g., 16 rows) of a look-up table (e.g., where the look-up table indicates resource(s), as described herein, for the WTRU to use for or in association with WTRU transmission(s)). A row (e.g., each row) in the look-up table may provide one or more of the following parameters: slot offset K2, jointly coded start and length indicator values (SLIV), individual values for the start symbol S and the allocation length L, or PUSCH mapping type (e.g., to be applied on the PUSCH transmission). Slot offset K2 may be used to derive the slot in which PUSCH transmission occurs.

[0105] An example dynamic scheduling process and the corresponding time domain resource allocation for uplink transmission (e.g., in 5G) are shown in FIGS. 4 and 5 respectively. The WTRU may send (e.g., first send) a scheduling request (SR) in the UL or F slot, informing the NW of pending data to transmit. The WTRU may inform the NW of available data volume through the buffer status report (BSR) in the PUCCH. The NW (e.g., after receiving the request) may inform the WTRU of the assigned resources, for example, via PDCCH downlink control information (DCI). The DCI may contain the K2, S, and L values which may indicate the specific slot, start symbol, and symbol (e.g., allocation) length in a following PHY frame for PUSCH transmission. The WTRU may prepare and / or transmit data using the scheduled future UL slots. Deployments (e.g., 5G deployments) may use a “fixed” pattern which may have more DL slots than UL slots in a PHY frame. These DL heavy PHY frame configurations may enable asymmetric traffic between UL and DL demands. UL transmissions may (e.g., may likely) incur longer latency than DL.

[0106] FIG. 6 depicts an example open air interface architecture with a WTRU connected to a gNB collocated with the core network. Experiments on a testbed (e.g., a 5G-compliant testbed) may be performed to understand underlying mechanics and / or the limitations of the legacy time domain resource allocation with static PHY frame configurations for emerging delay critical applications. In examples, a WTRU connected to a single gNB with hosts running the (e.g., 3GPP compliant) Open Air Interface (OAI) 5G software stack may be used. This testbed may include a NW (e.g., gNB and core network) connected to a WTRU over the 900 MHz frequency in SISO configuration, with 40 MHz BW and 30 KHz subcarrier spacing. The gNB and the WTRU may be realized through the integration of Nl B210 SDRs with their respective host computers. With this setup, a typical uplink communication may be executed, for example, by setting up a client (e.g., an Iperf client) at the WTRU, and a server (e.g., an Iperf server) at the NW (e.g., in indoor, static, low-mobility conditions with less dynamic channels, for example, low variability). The UL data of different application packet sizes (e.g., 100 KB, 450KB, 1 MB) may be transmitted to understand the uplink performance of 5G. The OAI code base may allow for recording the logs from (e.g., all) the layers of the stack (e.g., 5G stack) on the NW and WTRU. The throughput and / or latency in UL may bemeasured with (e.g., three) different PHY frame configurations, for example, 7 DL-2 UL-1 F (7-2-1); 6 DL-3 UL-1 F (6-3-1); and 8 DL-1 UL-1 F (8-1-1).

[0107] FIG. 7 depicts example Iperf uplink throughput in OAI with different PHY frame configurations (e.g., 7-2-1 ; 6-3-1; 8-1-1). There may be dynamic interplay between various downlink (DL) and uplink (UL) slot configurations. In tests (e.g., Iperf tests), a throughput of ~8 Mbps for the 7DL-2UL-1 F slot configuration, ~12 Mbps for the 6DL-3UL-1 F configuration, and ~4.5 Mbps for the 8DL, 1 UL, 1 F slot configuration were observed, as illustrated in FIG. 7. An inverse relationship may exist between downlinkheavy slot configurations and uplink throughput. In the example, the 8-1-1 slot configuration exhibited the lowest uplink throughput, while the 6-3-1 configuration demonstrated the highest throughput (e.g., among the considered PHY frame configurations).

[0108] FIG. 8 depicts example latency in transmitting data of varying sizes through OAI uplink with different PHY frame configurations (e.g., 7-2-1 ; 6-3-1; 8-1-1). As illustrated in FIG. 8, a series of experiments with varying packet sizes and evaluating the latency across multiple downlink-heavy slot configurations within the context of the 5G physical (PHY) frame was performed. For a 100 KB packet size, a latency of 0.09 seconds with the 6-3-1 frame configuration, 0.16 seconds with the 7-2-1 configuration, and 0.22 seconds with the 8-1-1 configuration was measured. As the packet size increased to 459 KB, latency grew to 0.3 seconds for the 6-3-1 configuration, 0.48 seconds for the 7-2-1 configuration, and 0.8 seconds for the 8-1-1 configuration. With a 1 MB packet size, a latency of 0.7 seconds for the 6-3-1 configuration, 1.1 seconds for the 7-2-1 configuration, and 1.9 seconds for the 8-1-1 configuration were observed. The adverse effect of downlink-heavy slot configurations on uplink latency may be seen from these results, with the 8-1-1 configuration demonstrating the highest latency and the 6-3-1 configuration achieving the lowest latency (e.g., among the considered PHY frame configurations).

[0109] FIG. 9 depicts an example uplink transmission process in 5G NR PHY TDD configuration with 30 KHz SCS. The process of UL slot configuration determining the UL latency in 5G is shown in FIG. 9 with the uplink transmission methodology in TDD (e.g., taken from the OAI logs). In a flexible (F) slot, the WTRU may (e.g., first) send a scheduling request (SR) to the NW indicating that the WTRU has data to send. The NW, in the next DL DCI slot (e.g., in the same frame or the next one), may schedule the next UL slot for the WTRU to send the data. This process may repeat, for example, if the WTRU has a non-empty BSR. The WTRU (e.g., based on NW indication) may wait for the next available UL slot to transmit data in the uplink. This process may increase the WTRU UL latency more (e.g., much more) in worst-case scenarios of high WTRU density in a network with bad channel conditions. Over-the-air experimentation may show that a one-way UL latency of ~16 milliseconds (ms) from WTRU to the NW increases the false positives by~31% in edge assisted object detection, which may be an emerging application use case. These findings may offer insights into optimizing network performance (e.g., 5G network performance) and / or balancing downlink and uplink resource allocation for enhanced efficiency and quality of service.

[0110] Feature(s) associated with dynamic PHY frame reconfiguration are provided herein. The NW may (e.g., in 5G NR) rely on WTRU reports of channel conditions (e.g., estimated channel conditions) and / or the current WTRU traffic in buffer, to determine the slot (e.g., optimal slot) and symbols to allocate to the WTRU, in the current PHY frame configuration, which may be sent via DCI command. In examples where the channel conditions for the WTRU degrade, the NW may identify the WTRU from the following WTRU report on channel estimates and / or may determine whether to update the PHY frame configuration and / or notify the WTRU in the following DCI. This reactive mechanism may be slow to converge to a (e.g., optimal) PHY frame configuration for the WTRU in dynamic channels with high variance in channel metrics like SINR, RSRP, RSSI, etc. and may / or increase the probability of overshooting the QoS and / or QoE thresholds for LCAs. Dynamic PHY frame configuration for these emerging applications may be enabled through proactive understanding of the WTRU traffic pattern, channel conditions, etc. (e.g., to address the QoS and / or QoE needs of the emerging applications in dynamic channel conditions).

[0111] FIG. 10 depicts an example dynamic PHY frame reconfiguration at the NW aided by the WTRU forecast of local channel and traffic metrics. PHY frame configuration selection may be made to be proactive with WTRU assistance (e.g., active WTRU assistance). An example overall system diagram for this process is depicted in FIG. 10. The WTRU, for example while reporting the legacy current channel estimates to the NW, may predict (e.g., continuously predict) and / or forecast the channel related metrics such as SINR, RSRP, RSSI, etc., through local artificial intelligence (Al) and / or machine learning (ML) model(s) (e.g., a prediction model), e.g., by using historical channel metric data as input (e.g., and may compare the historical channel metric data to the predicted channel metric(s)). The WTRU may have application specific requirement(s) that give an indication of how much data to send to and / or receive from the NW and / or the QoS expectation of the application. The WTRU may compute resource allocation metric(s) (e.g., preferred resource allocation metrics (e.g., slot and / or symbol, MCS, etc.)), and / or may transmit this combined forecast of channel metric(s), traffic characteristic(s), and / or future allocation (e.g., preferred future allocation) to the NW. The NW, e.g., on receiving this information from multiple WTRUs, may (e.g., proactively) configure future frames (e.g., with slots and time and / or frequency allocation that cater to the needs of all the WTRUs, minimize interference to neighbor cells, and / or optimize the overall performance of the network). If the NW reconfigures the future PHY frame(s), the future PHY frame(s) may be communicated to the WTRU, for example, via legacy time domain resource allocation.

[0112] WTRU channel metric and / or traffic prediction may be used in proactive time domain resource allocation, resulting in a dynamic PHY frame configuration. FIG. 11 depicts an example sliding window mechanism at the WTRU for using historical channel metrics (e.g., ’’Historical data” in FIG. 11) to predict the channel metrics of future time slots (e.g., “Predicted metrics of next 3 slots” in FIG. 11). FIG. 12 depicts an example diagram of a channel metric prediction framework, where one or more of the illustrated actions may be performed. FIGs. 11-12 illustrate example channel metric prediction action(s). A (e.g., each) WTRU may keep a record of the historical channel metric data in a buffer (e.g., local buffer).

[0113] At t1 (e.g., the beginning of time t1), the WTRU may use this historical channel metric data as input for the channel metric prediction module, which may forecast channel metrics for a (e.g., desired) number of time slots in the future, e.g., by leveraging, for example, Long Short Term Memory (LSTM) neural networks, a type of recurrent neural network (RNN) specifically designed to capture and / or analyze sequential data. This forecast data may be shared with the NW by a (e.g., each) WTRU through the uplink (e.g., channel). The NW may use this forecast knowledge to allocate (e.g., proactively allocate) and / or send network resources (e.g., look-up table information, as described herein) in the time and / or frequency domains to the WTRU(s) (e.g., a WTRU may receive an indication of resource(s), which may be via look-up table information or updated look-up table information (e.g., an update at t2 to the information at t1 )). This resource allocation information may be communicated back to the WTRU(s) through the downlink (e.g., channel).

[0114] At time instant t2, to predict channel metrics (e.g., a next batch of channel metrics), the WTRU(s) may follow a sliding window mechanism, as shown in FIG. 11 . The sliding window mechanism may allow the WTRU(s) to use recent historical data (e.g., the most recent historical data) for the prediction module. At the next cycle of channel metric prediction (e.g., time t2), this window may move forward by the number of time slots for which the channel metrics are predicted (e.g., 3 slots in FIG. 11 which is the forecast time horizon). At a time slot (e.g., each time slot), the WTRU(s) may verify how far the predicted channel metric values fall from the actual estimated value for that time slot. This prediction accuracy may be useful, for example, since lower accuracy may be associated with false channel metric prediction, which may affect the WTRU QoS. The WTRUs may revert back to reactive resource allocation if the predicted value crosses a predefined accuracy threshold metric (e.g., as shown in FIG. 12).

[0115] A neural network may be operated for channel metric prediction, for example, LSTM based prediction. FIG. 13 depicts example tensor forming, training, and testing processes in a LSTM network for channel metric prediction. The SINR, RSRP, and RSRQ sequences are divided into training and testing sets, which may be used in the LSTM network to predict future channel metrics.

[0116] FIG. 14 depicts an example signaling message exchange between a WTRU and the network for WTRU channel metric prediction which may meet the network provided prediction accuracy and / or forecast time horizon thresholds. FIG. 15 depicts an example signaling message exchange between a WTRU and the network regarding WTRU fallback to reactive channel metric estimation, for example, while re-training local ML model to predict channel metrics which may meet the network provided prediction accuracy and forecast time horizon thresholds.

[0117] The capability of deep learning models to identify complex, non-linear patterns in data may be used for time series forecasting. Long Short-Term Memory (LSTM) may be applied in a range of different domains, including aspects of wireless network infrastructure management, but may come at a cost of high computational complexity.

[0118] An example LSTM architecture for channel metric prediction is depicted in Fig. 13. Given an input historical time series or sequence (t-m to t) of channel metric data, the LSTM network may forecast the channel metric values of future time steps t + 1 to t + n, with t + n being the forecast time horizon. The signaling message exchange between the WTRU and the network for channel metric prediction which may meet the network provided prediction accuracy threshold and forecast time horizon threshold is shown in FIG. 14. The WTRU may send (e.g., start by sending) an indication to the network, e.g., via MAC CE, UCI, or L3 messaging, for (e.g., locally) predicting the channel metrics (e.g., WTRU informs the NW that the WTRU intends to provide channel metric prediction information). The network may acknowledge this request and / or may provide configuration(s) (e.g., a set of configuration(s)) to the WTRU regarding (e.g., indicating or including) a prediction threshold and / or a required forecast time horizon (e.g., the WTRU may receive prediction configuration information, which may indicate or include one or more of a prediction accuracy threshold or a forecast time horizon). If the local ML model meets these threshold condition(s) (e.g., if a prediction accuracy associated with the prediction model is determined to satisfy the prediction accuracy threshold), the WTRU may start inference of the ML model (e.g., determine a predicted channel metric) and / or may transmit the predicted channel metric(s) to the network (e.g., via MAC CE, UCI, or L3 messaging). If the WTRU local ML model cannot meet these threshold condition(s), the WTRU may modify (e.g., may first modify) the input data length to the ML model and / or may modify the forecast time horizon to meet the network mandated prediction accuracy threshold. If modification results in the ML model meeting the prediction accuracy threshold, the WTRU may send an indication to the network regarding the update in forecast time horizon (e.g., if any). The WTRU may (e.g., following a network acknowledgement) continue with the prediction. If updating the input data length and / or forecast time horizon does not improve the prediction accuracy (e.g., prediction accuracy is still below network mandated threshold) the WTRUmay send an indication to the network regarding fallback to legacy channel metric estimation (e.g., via new MAC CE, UCI, or L3 messaging). The WTRU may (e.g., following the network acknowledgment) perform reactive channel metric estimation for data communication with the network and / or may invoke re-training of the local ML model with the most recent historical channel metric data, as illustrated in FIG. 15. If the training results meet the network provided thresholds for prediction accuracy and / or forecast time horizon, the WTRU may send an indication to the network for performing channel metric prediction. After the network acknowledgement, the WTRU may start the inference process on the local ML model for channel metric prediction and / or may transmit the predicted channel metrics to the network (e.g., via new MAC CE, UCI, or L3 messaging).

[0119] A prediction accuracy measurement may be determined by the WTRU. The mismatch between the actual and predicted channel metric (e.g., SINR) may cause performance degradation, for example, since the decision about modulation and coding scheme (MCS) selection is contaminated by the SINR prediction offset. This may be because the estimated and / or predicted SINR values are mapped to channel quality indicator (CQI) metrics, which may be used to select the highest MCS possible that leads to an achieved block error rate (BLER) that is less than the target BLER threshold. The CQI to modulation order, code rate, and / or spectral efficiency (SE) mapping tables for different target BLER values may be available (e.g., in 3GPP technical specifications). If a WTRU predicts a higher SINR than the actual value, the prediction may result in the wrong choice of high MCS, which may lead to BLER crossing the target threshold and / or the throughput being lowered. If the WTRU predicts a lower SINR than the actual value, an MCS with a lower value than the actual may be chosen, which may lower the spectral efficiency and / or (e.g., eventually) the throughput.

[0120] The accuracy of the predictions may be measured through Root Mean Square Error (RMSE) E, which may show how far the predictions fall from measured true values using Euclidean distance, which is expressed as:

[0121] Where N may be the number of data points, xtmay be the measurement of time t, and xtmay be its corresponding prediction.

[0122] A WTRU may predict application traffic. Predicting user application traffic may be an aspect (e.g., a critical aspect) of network management and resource allocation in telecommunications systems. The power of Long Short-Term Memory (LSTM) neural networks may be used (e.g., to predict user applicationtraffic). User application traffic may be predicted by utilizing historical application traffic data, for example, by considering the temporal dependencies and / or patterns inherent in network usage. LSTM networks may be used to capture and / or model long-range dependencies in time series data.

[0123] Historical application traffic data may be collected and preprocessed, for example, to ensure that the dataset is (e.g., appropriately) structured for training and / or evaluation. Each data point in the sequence may represent an observation at a specific time, such as network traffic data collected over hours, days, or weeks. The LSTM architecture may be employed to learn from this temporal data (e.g., as it may excel in preserving contextual information and accounting for variations over time). During the training phase (e.g., a training time threshold received by the WTRU based on an input feature quantity or quality, during which the determination may be made that a prediction accuracy associated with the prediction model satisfies the prediction accuracy threshold), the LSTM network may be fed with sequential data, learning to model the (e.g., complex) relationships and / or patterns in the application traffic. This approach may adapt to dynamic changes in user behavior and traffic patterns. By analyzing the historical data, the LSTM neural network may learn to make predictions about future application traffic, aiding network operators and service providers in optimizing resource allocation, managing network congestion, and / or enhancing the quality of service for users.

[0124] FIG. 16 depicts an example process for WTRU application traffic prediction by a LSTM neural network. FIG 16 illustrates example tensor forming, training, and testing processes in the LSTM network for traffic metric prediction. The application traffic data generated by the WTRU sensors may be divided into training and / or testing sets, which may be used in the LSTM network to predict future application traffic arrival rates.

[0125] Proactive time domain resource allocation may be performed. An algorithm may be designed to address the proactive time domain resource allocation, aided by WTRU channel and traffic metric prediction, in a (e.g., 5G) communication system with (e.g., stringent) latency requirements. The algorithm may initialize critical parameters, for example, including the total number of slots allocated to fulfill latency constraints. These slots may be distributed across a predetermined number of frames, each containing a fixed number of slots.

[0126] For each frame within the predefined range, a local frame configuration may be initialized to capture the slot allocation for that specific frame. The algorithm may iterate over each WTRU, for example, evaluating whether their global communication requirements are met. If a WTRU’s requirements are satisfied, the algorithm may exit the current frame and may proceed to the next. If the WTRU(s) do not meet their global requirements, the algorithm may calculate the average data requirement per frame and / ormay iterate through different rank levels. For a (e.g., each) rank, the WTRU(s) may calculate the transport block size (TBS) and may check if the available slots are empty. If an empty slot is found, the empty slot may be assigned to the respective WTRU. The allocated TBS may be deducted from the global total bits required and / or the WTRU’s average transmission request for the frame. The algorithm may check whether the WTRU’s transmission request is met and, if so, the algorithm may exit the loop. This process may continue until the frame’s slots are (e.g., fully) allocated. In examples where slots remain unallocated in a frame, the algorithm may redistribute the slots among the WTRUs based on the proportion of the remaining data requirements of the (e.g., each) WTRU(s) (e.g., considering the total bits required for the WTRUs, for example, all the WTRUs). The algorithm may employ this proportion to allocate slots, ensuring that each WTRU may receive a share (e.g., an equitable share) based on its data requirements. This algorithm may be used to manage slot allocation and / or latency requirements in 5G networks, optimize resource utilization, and / or ensure that latency-sensitive applications receive (e.g., enough) bandwidth (e.g., the necessary bandwidth) while maintaining fairness and / or efficiency in the allocation process.

[0127] Dynamic and proactive PHY frame configuration may serve emerging low-latency applications in next generation cellular networks. The configuration may be scaled across different traffic models and different numbers of users in different dynamic channel conditions.

[0128] Modifications may be introduced to the OAI code, for example, to enhance both WTRU and NW components. If receiving the WTRU’s prediction of channel and / or traffic metrics in the uplink, the NW may be equipped to (e.g., proactively) allocate the WTRU(s) in (e.g., the best) possible slots based on their channel conditions and / or traffic requirements, ensuring the WTRU(s) may complete their transmission within the required latency bounds. Control and / or data channel logs from various layers within the WTRU and / or the NW may be (e.g., systematically) extracted to evaluate the performance enhancements achieved by dynamic and / or proactive PHY frame configuration. For the latency and / or throughput performance, (e.g., 3) WTRUs may be connected with the NW. In examples, a first WTRU (WTRU 0) may transmit UL data of application packet size 90 KB, a second WTRU (WTRU 1) may transmit UL data with application packet size of 200 KB, and a third WTRU (WTRU 2) may receive DL data of application packet size 400 KB.

[0129] FIG. 17 depicts an example latency with proactive PHY frame configuration compared to static frame configurations in OAI. FIG. 18 depicts an example throughput with proactive PHY frame configuration compared to static frame configurations in OAI.

[0130] The latency deadline for each WTRU was kept at 200 ms and the example latency performance is illustrated in FIG. 17. If employing a static DL heavy configuration of 8-1-1 (8 DL-2 UL in FIG. 17), theprobability of achieving a threshold may be 250 ms and 450 ms respectively for the WTRU 0 and the WTRU 1 . The DL WTRU (WTRU 2) may be able to meet the latency deadline. Using the 7-2-1 (7 DL-3 UL in FIG. 17) and 6-3-1 (6 DL-4 UL in FIG. 17) PHY frame configurations, WTRU 0 may be able to meet the latency deadline. The WTRU 1 may overshoot the latency bound (e.g., because of large data packets). The 6-3-1 configuration resulted in the DL WTRU 2 not meeting the latency deadline because of a smaller number of DL slots. With the proactive PHY frame configuration (dynamic configuration in FIG. 17), the WTRU(s) (e.g., all the WTRUs) were able to meet the latency deadline of 200 ms. The average latency with the proposed approach was -150 ms. This is possible because the NW had accurate forecasts of WTRU traffic and channel conditions which helped the NW to optimize the slots and allocate the WTRUs accordingly in each frame such that all the WTRUs met their respective latency deadlines. In static frame configurations, even with the forecast of channel and traffic metrics, the NW may not allocate UL WTRUs to DL slots, resulting in reduced performance.

[0131] For throughput performance, the (e.g., same 3) WTRUs with similar traffic characteristics (e.g., same traffic characteristics) were used, and the results are shown in FIG. 18. With static frame configurations, the UL WTRUs (WTRU 0 and WTRU 1) suffered the most compared to DL WTRU (WTRU 2). This may be because the static frame configurations were all DL heavy. With the proposed approach (dynamic configuration), the WTRUs (e.g., all the WTRUs) were able to experience better (e.g., significantly better) throughput than the static PHY frame configuration. With the proposed approach, the NW may allocate (e.g., optimally allocate) the WTRUs to (e.g., the best) possible slots based on the WTRU reported local channel and traffic metrics, achieving better throughput results compared to static PHY configurations, where even with the WTRU predictions of channel and traffic metrics, the NW may not be able to allocate UL WTRUs to DL slots.

[0132] Look-ahead forecasting of WTRU channel and traffic metrics may enhance network performance (e.g., significantly) over other models, by enabling proactive PHY frame configuration by the NW to allocate the WTRUs time domain resources based on their QoS requirements (e.g., without any additional computation requirements at the gNB or core network). Particular implementations may be (e.g., dynamically) chosen from several implemented algorithms in a way that balances the NW needs and algorithm complexity.

[0133] Although features and elements described above are described in particular combinations, each feature or element may be used alone without the other features and elements of the preferred embodiments, or in various combinations with or without other features and elements.

[0134] Although the implementations described herein may consider 3GPP specific protocols, it is understood that the implementations described herein are not restricted to this scenario and may be applicable to other wireless systems. For example, although the solutions described herein consider LTE, LTE-A, New Radio (NR) or 5G specific protocols, it is understood that the solutions described herein are not restricted to this scenario and are applicable to other wireless systems as well.

[0135] The processes described above may be implemented in a computer program, software, and / or firmware incorporated in a computer-readable medium for execution by a computer and / or processor. Examples of computer-readable media include, but are not limited to, electronic signals (transmitted over wired and / or wireless connections) and / or computer-readable storage media. Examples of computer- readable storage media include, but are not limited to, a read only memory (ROM), a random access memory (RAM), a register, cache memory, semiconductor memory devices, magnetic media such as, but not limited to, internal hard disks and removable disks, magneto-optical media, 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

CLAIMSWhat is claimed is:1 . A wireless transmit / receive unit (WTRU) comprising: a processor configured to: receive prediction configuration information, wherein the prediction configuration information indicates a prediction accuracy threshold; determine that a prediction accuracy associated with a prediction model satisfies the prediction accuracy threshold; determine, based on the determination that the prediction accuracy associated with the prediction model satisfies the prediction accuracy threshold, a predicted channel metric via the prediction model; send the predicted channel metric to a network node; and receive an indication of resource information that indicates one or more of: a slot offset value, a start symbol, an allocation length, or a start or length indicator value parameter.

2. The WTRU of claim 1 , wherein the resource information is updated look-up table information, and wherein the processor is further configured to: receive look-up table information, wherein the updated look-up table information updates the lookup table information to the updated look-up table information, and wherein the updated look-up table information is associated with a re-configured PHY frame.

3. The WTRU of claim 1 , wherein the processor is further configured to: send, to the network node, an indication of a forecast metric associated with the prediction model; and send an uplink transmission using the resource information.

4. The WTRU of claim 3, wherein the forecast metric associated with the prediction model is a forecast time horizon.

5. The WTRU of claim 4, wherein the prediction accuracy is a second prediction accuracy, and wherein the processor is further configured to:determine that a first prediction accuracy associated with the prediction model does not satisfy the prediction accuracy threshold; and update the forecast time horizon to an updated forecast time horizon based on the determination that the first prediction accuracy associated with the prediction model does not satisfy the prediction accuracy threshold.

6. The WTRU of claim 5, wherein the processor is further configured to: send an indication of the updated forecast time horizon to the network node.

7. The WTRU of claim 1 , wherein the processor is further configured to: send an indication of the prediction accuracy to the network node.

8. The WTRU of claim 1 , wherein the processor is further configured to: maintain a historical channel metric; and compare the historical channel metric to the predicted channel metric.

9. The WTRU of claim 1 , wherein the processor is further configured to: receive a training time threshold, wherein the determination that the prediction accuracy associated with the prediction model satisfies the prediction accuracy threshold occurs within the training time threshold.

10. The WTRU of claim 9, wherein the training time threshold is based on an input feature quantity or quality.

11. A method implemented in a wireless transmit / receive unit (WTRU) comprising: receiving prediction configuration information, wherein the prediction configuration information indicates a prediction accuracy threshold; determining that a prediction accuracy associated with a prediction model satisfies the prediction accuracy threshold; determining, based on the determination that the prediction accuracy associated with the prediction model satisfies the prediction accuracy threshold, a predicted channel metric via the prediction model;sending the predicted channel metric to a network node; and receiving an indication of resource information that indicates one or more of: a slot offset value, a start symbol, an allocation length, or a start or length indicator value parameter.

12. The method of claim 11 , wherein the resource information is updated look-up table information, and further comprising: receiving look-up table information, wherein the updated look-up table information updates the look-up table information to the updated look-up table information, and wherein the updated look-up table information is associated with a re-configured PHY frame.

13. The method of claim 11 , further comprising: sending, to the network node, an indication of a forecast metric associated with the prediction model; and sending an uplink transmission using the resource information.

14. The method of claim 13, wherein the forecast metric associated with the prediction model is a forecast time horizon.

15. The method of claim 14, wherein the prediction accuracy is a second prediction accuracy, and further comprising: determining that a first prediction accuracy associated with the prediction model does not satisfy the prediction accuracy threshold; and updating the forecast time horizon to an updated forecast time horizon based on the determination that the first prediction accuracy associated with the prediction model does not satisfy the prediction accuracy threshold.

16. The method of claim 15, further comprising: sending an indication of the updated forecast time horizon to the network node.

17. The method of claim 11 , further comprising: sending an indication of the prediction accuracy to the network node.

18. The method of claim 11 , further comprising: maintaining a historical channel metric; and comparing the historical channel metric to the predicted channel metric.

19. The method of claim 11 , further comprising: receiving a training time threshold, wherein the determination that the prediction accuracy associated with the prediction model satisfies the prediction accuracy threshold occurs within the training time threshold.

20. The method of claim 19, wherein the training time threshold is based on an input feature quantity or quality.

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