Timing advance update based on machine learning

Abstract

Methods, systems, and devices are described for wireless communication at a user equipment (UE). The UE may identify one or more measurement parameters for input in a machine learning (ML) model. In some cases, the measurement parameter may include a reference signal received power measurement or a past set of timing advance (TA) values for a particular node. The UE may predict the TA based on inputting the measurement parameter into the ML model. The UE may transmit uplink communications from the UE to a network entity using the TA predicted from the ML model. In some examples, the UE may send a capability report to indicate that the UE may support autonomous updates of the TA based on the prediction of the TA value generated from the ML model.

Description

[0001] Cross-references

[0002] This patent application claims the benefit of U.S. patent application No. 18 / 048,728, entitled “MACHINE LEARNINGBASED TIMING ADVANCE UPDATES,” filed by BAI et al. on October 21, 2022, which is assigned to the assignee of this application and is expressly incorporated herein by reference. Technical Field

[0003] The following relates to wireless communications including timing advance updates based on machine learning (ML). Background Art

[0004] Wireless communication systems are widely deployed to provide various types of communication content, such as voice, video, packet data, messaging, broadcasting, and the like. These systems may be able to support communication with multiple users by sharing available system resources (e.g., time, frequency, and power). Examples of such multiple access systems include fourth generation (4G) systems (such as long term evolution (LTE) systems, advanced LTE (LTE-A) systems, or LTE-A Pro systems) and fifth generation (5G) systems (which may be referred to as new radio (NR) systems). These systems may employ techniques such as code division multiple access (CDMA), time division multiple access (TDMA), frequency division multiple access (FDMA), orthogonal FDMA (OFDMA), or discrete Fourier transform spread orthogonal frequency division multiplexing (DFT-S-OFDM). A wireless multiple access communication system may include one or more base stations, each of which supports wireless communications for communication devices, which may be referred to as user equipment (UE). Summary of the invention

[0005] The described technology relates to improved methods, systems, devices and apparatuses that support technology for timing advance (TA) updates based on machine learning (ML). For example, the described technology provides a user equipment (UE) to identify one or more measurement parameters to be input into an ML model. Additionally or alternatively, the UE may receive one or more TA values ​​from a network entity. In some examples, the ML model may predict a TA value for uplink communication from the UE to the network entity based on the measurement parameters and the received TA value. The UE may send uplink communications based on the predicted TA value from the ML model. In some cases, the UE may send a capability report to the network entity, which capability report indicates the ability of the UE to support autonomous updates of the TA. In some wireless communication systems, the capability report includes which TA prediction module type the UE supports, the number of TA groups that the UE can autonomously update, and the input types that the UE supports for TA prediction.

[0006] A method for wireless communication at a UE is described. The method may include: identifying one or more measurement parameters for input to an ML model; predicting a TA value for uplink communication from the UE to a network entity based on inputting the one or more measurement parameters to the ML model; and sending the uplink communication to the network entity according to the predicted TA value.

[0007] An apparatus for wireless communication at a UE is described. The apparatus may include a processor, a memory coupled to the processor, and instructions stored in the memory. The instructions may be executable by the processor to cause the apparatus to: identify one or more measurement parameters for input to an ML model; predict a TA value for uplink communication from the UE to a network entity based on inputting the one or more measurement parameters to the ML model; and send the uplink communication to the network entity according to the predicted TA value.

[0008] Another apparatus for wireless communication at a UE is described. The apparatus may include: means for identifying one or more measurement parameters for input to an ML model; means for predicting a TA value for uplink communication from the UE to a network entity based on inputting the one or more measurement parameters to the ML model; and means for sending the uplink communication to the network entity according to the predicted TA value.

[0009] A non-transitory computer-readable medium storing code for wireless communication at a UE is described. The code may include instructions executable by a processor to: identify one or more measurement parameters for input to an ML model; predict a TA value for uplink communication from the UE to a network entity based on inputting the one or more measurement parameters to the ML model; and send the uplink communication to the network entity according to the predicted TA value.

[0010] Some examples of the methods, apparatus, and non-transitory computer-readable media described herein may also include operations, features, components, or instructions for sending a capability report to a network entity indicating the UE's ability to support autonomous updating of a TA based on a predicted TA value.

[0011] In some examples of the methods, apparatus, and non-transitory computer-readable media described herein, the capability report further indicates the UE's ability to support at least one of: TA group module type, number of TA groups for autonomous update of TA, input type for TA prediction, or a combination thereof.

[0012] Some examples of the methods, apparatus, and non-transitory computer-readable media described herein may also include operations, features, components, or instructions for receiving an indication of a set of TA values ​​from a network entity, wherein the set of TA values ​​may be based on at least one reference signal received power measurement at the UE, and training an ML model based on the set of TA values, wherein predicting the TA value may be based on inputting the one or more measurement parameters to the trained ML model.

[0013] In some examples of the methods, apparatus, and non-transitory computer-readable media described herein, identifying one or more measurement parameters may include operations, features, components, or instructions for measuring one or more reference signal received power values ​​associated with one or more channel state reference signals, wherein predicting the TA value may be based on inputting the one or more reference signal received power values ​​into an ML model.

[0014] Some examples of the methods, apparatus, and non-transitory computer-readable media described herein may also include operations, features, components, or instructions for determining that the predicted TA value corresponds to an output port associated with the ML model.

[0015] Some examples of the methods, apparatus, and non-transitory computer-readable media described herein may also include operations, features, components, or instructions for identifying a mapping between a cell identifier and an output port associated with the ML model, wherein sending the uplink communication includes sending the uplink communication associated with the cell identifier according to the predicted TA value.

[0016] Some examples of the methods, apparatus, and non-transitory computer-readable media described herein may also include operations, features, components, or instructions for identifying a mapping between a TA group identifier and an output port associated with the ML model, wherein sending the uplink communication includes sending the uplink communication associated with the TA group identifier according to the predicted TA value.

[0017] Some examples of the methods, apparatus, and non-transitory computer-readable media described herein may also include operations, features, components, or instructions for receiving a request from a network entity to suspend predicting future TA values ​​for a second uplink communication and refraining from predicting future TA values ​​for the second uplink communication based on receiving the request.

[0018] Some examples of the methods, apparatus, and non-transitory computer-readable media described herein may also include operations, features, components, or instructions for receiving a request for one or more measurement parameters input to the ML model from a network entity and sending an indication of the one or more measurement parameters to the network entity in response to the request.

[0019] Some examples of the methods, apparatus, and non-transitory computer-readable media described herein may also include operations, features, components, or instructions for sending a random access signal or a sounding reference signal, or both, to a network entity and receiving an indication of a second TA value from the network entity based on sending the random access signal or the sounding reference signal, or both.

[0020] Some examples of the methods, apparatus, and non-transitory computer-readable media described herein may also include operations, features, components, or instructions for receiving a request to update the ML model from a network entity and updating the ML model based on the request, wherein predicting the TA value may be based on inputting one or more measured parameters to the updated ML model.

[0021] In some examples of the methods, apparatus, and non-transitory computer-readable media described herein, sending uplink communications may include operations, features, components, or instructions for sending uplink communications to a network entity at a first time instance based on a predicted TA value, wherein one or more measurement parameters include a set of TA values ​​corresponding to a set of time instances prior to the first time instance.

[0022] In some examples of the methods, apparatus, and non-transitory computer-readable media described herein, the one or more measurement parameters include at least one TA value associated with the second network entity.

[0023] In some examples of the methods, apparatus, and non-transitory computer-readable media described herein, the one or more measurement parameters include at least one of: a reference signal received power, a power delay distribution associated with one or more beams, a power delay distribution associated with a network entity, a power delay distribution associated with one or more transmit and receive points, a TA value for a communication link, positioning of the UE, positioning signaling information associated with radio frequency sensing, positioning signaling information associated with a camera, positioning signaling information associated with a radar at the UE, a transmission configuration indicator identifier, or a combination thereof. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 An example of a wireless communication system supporting machine learning (ML) based timing advance (TA) update according to one or more aspects of the present disclosure is illustrated.

[0025] Figure 2 An example of a wireless communication system supporting ML-based TA update according to one or more aspects of the present disclosure is illustrated.

[0026] Figure 3A and Figure 3B An example of an ML model supporting ML-based TA updating according to one or more aspects of the present disclosure is illustrated.

[0027] Figure 4 An example of a process flow supporting ML-based TA update according to one or more aspects of the present disclosure is illustrated.

[0028] Figure 5 and Figure 6 A block diagram of a device supporting ML-based TA update according to one or more aspects of the present disclosure is shown.

[0029] Figure 7 A block diagram of a communication manager supporting ML-based TA updates according to one or more aspects of the present disclosure is shown.

[0030] Figure 8 A diagram of a system including a device supporting ML-based TA update according to one or more aspects of the present disclosure is shown.

[0031] Figures 9 to 11 A flow chart illustrating a method of supporting ML-based TA update according to one or more aspects of the present disclosure is shown. DETAILED DESCRIPTION

[0032] In some wireless communication systems, some network devices (e.g., UE, network entity) may use timing advance (TA) to predict round trip time. For example, the network entity may use TA to set expectations for timing transmission and schedule transmission with the UE (e.g., future transmission). In some examples, in order to set the TA for wireless communication, the network device may measure the TA using a random access channel (RACH) triggered by a physical downlink control channel (PDCCH) command or by one or more reference signals (RS) (e.g., a sounding reference signal (SRS) and a tracking reference signal (TRS)). In some cases, the network entity may measure the TA value associated with the SRS or RACH, and may send an indication to the UE to adjust the TA value for uplink transmission (e.g., a medium access control (MAC) control element (MAC-CE)). However, in a wireless communication system in which the network device is operating in a high mobility scenario, the TA may change frequently due to a change in the position shift or positioning of one or more UEs or when one or more service transmission reception points (TRPs) change. That is, the wireless communication system may measure and configure each TA for each beam in a set of beams and for multiple nodes. In some cases, frequently changing TAs may increase the overhead of the wireless communication system.

[0033] The described technology relates to an improved method, system, device or apparatus for supporting a technology for timing advance update based on machine learning (ML). In some wireless communication systems, one or more network devices may use ML to process multiple TAs for each transmit beam between multiple network devices. In some examples, ML may use reference signal received power (RSRP) measured in the past to predict future RSRP or determine the positioning of the UE. In some cases, an ML model (e.g., an algorithm trained by ML) may be trained to predict TA based on channel measurements. That is, the UE may use the ML model to predict the TA value and autonomously apply the predicted TA value without explicit signaling from a network entity. In some examples, TA prediction may obtain inputs of one or more RSRPs of a synchronization signal block (SSB) or a PDP of a channel state information (CSI) RS from one or more beams, apply ML prediction, and output TA to be used for one or more beams. In other examples, TAs of multiple nodes from a set (e.g., N) of TA values ​​in the past time period may be input into the ML model, and possible TA predictions of future nodes may be output. That is, the TA value output from the ML model may be applied to one or more transmissions between network devices. Certain aspects of the subject matter described herein can be implemented to achieve one or more potential advantages. The described techniques can provide for measuring and configuring multiple TAs for each beam without increasing overhead.

[0034] Aspects of the present disclosure are first described in the context of a wireless communication system. Aspects of the present disclosure are further illustrated and described by and with reference to ML models and process flows. Aspects of the present disclosure are further illustrated and described by and with reference to apparatus diagrams, system diagrams, and flow diagrams related to ML-based TA updates.

[0035] Figure 1 An example of a wireless communication system 100 supporting ML-based TA updates according to one or more aspects of the present disclosure is illustrated. The wireless communication system 100 may include one or more network entities 105, one or more UEs 115, and a core network 130. In some examples, the wireless communication system 100 may be a long term evolution (LTE) network, an advanced LTE (LTE-A) network, an LTE-A Pro network, a new radio (NR) network, or a network operating according to other systems and radio technologies, including future systems and radio technologies not explicitly mentioned herein.

[0036] The network entities 105 may be dispersed throughout a geographic area to form the wireless communication system 100, and may include devices in different forms or with different capabilities. In various examples, the network entities 105 may be referred to as network elements, mobility elements, radio access network (RAN) nodes, or network equipment, among other nomenclature. In some examples, the network entities 105 and the UE 115 may communicate wirelessly via one or more communication links 125 (e.g., radio frequency (RF) access links). For example, the network entity 105 may support a coverage area 110 (e.g., a geographic coverage area) within which the UE 115 and the network entity 105 may establish one or more communication links 125. The coverage area 110 may be an example of a geographic area within which the network entity 105 and the UE 115 may support signal communications according to one or more radio access technologies (RATs).

[0037] The UEs 115 may be dispersed throughout the coverage area 110 of the wireless communication system 100, and each UE 115 may be stationary or mobile or both stationary and mobile at different times. The UEs 115 may be devices in different forms or with different capabilities. Figure 1 Some example UEs 115 are illustrated in FIG. 1. The UEs 115 described herein may be capable of supporting communications with various types of devices, such as Figure 1 Other UEs 115 or network entities 105 are shown.

[0038] As described herein, a node of the wireless communication system 100 (which may be referred to as a network node or a wireless node) may be a network entity 105 (e.g., any network entity described herein), a UE 115 (e.g., any UE described herein), a network controller, an apparatus, a device, a computing system, one or more components, or another suitable processing entity configured to perform any of the techniques described herein. For example, the node may be a UE 115. As another example, the node may be a network entity 105. As another example, a first node may be configured to communicate with a second node or a third node. In one aspect of this example, the first node may be a UE 115, the second node may be a network entity 105, and the third node may be a UE 115. In another aspect of this example, the first node may be a UE 115, the second node may be a network entity 105, and the third node may be a network entity 105. In other aspects of this example, the first node, the second node, and the third node may be different relative to these examples. Similarly, references to UE 115, network entity 105, apparatus, device, computing system, etc. may include disclosure of UE 115, network entity 105, apparatus, device, computing system, etc. as nodes. For example, a disclosure that UE 115 is configured to receive information from network entity 105 also discloses that the first node is configured to receive information from a second node.

[0039] In some examples, the network entities 105 may communicate with the core network 130 or with each other or both. For example, the network entities 105 may communicate with the core network 130 via one or more backhaul communication links 120 (e.g., according to S1, N2, N3, or other interface protocols). In some examples, the network entities 105 may communicate with each other via the backhaul communication links 120 (e.g., according to X2, Xn, or other interface protocols) directly (e.g., directly between the network entities 105) or indirectly (e.g., via the core network 130). In some examples, the network entities 105 may communicate with each other via the midhaul communication links 162 (e.g., according to the midhaul interface protocol) or the fronthaul communication links 168 (e.g., according to the fronthaul interface protocol) or any combination thereof. The backhaul communication links 120, the midhaul communication links 162, or the fronthaul communication links 168 may be or include one or more wired links (e.g., electrical links, optical fiber links), one or more wireless links (e.g., radio links, wireless optical links), etc. or various combinations thereof. UE 115 may communicate with core network 130 via communication link 155 .

[0040] One or more of the network entities 105 described herein may include or may be referred to as a base station 140 (e.g., a transceiver base station, a radio base station, an NR base station, an access point, a radio transceiver, a Node B, an evolved Node B (eNB), a next-generation Node B, or a gigabit Node B (any of which may be referred to as a gNB), a 5G NB, a next-generation eNB (ng-eNB), a home Node B, a home evolved Node B, or other suitable terminology). In some examples, the network entity 105 (e.g., a base station 140) may be implemented in an aggregated (e.g., monolithic, stand-alone) base station architecture, which may be configured to utilize a protocol stack that is physically or logically integrated within a single network entity 105 (e.g., a single RAN node, such as a base station 140).

[0041] In some examples, the network entity 105 may be implemented in a disaggregated architecture (e.g., a disaggregated base station architecture, a disaggregated RAN architecture) that may be configured to utilize a protocol stack that is physically or logically distributed between two or more network entities 105, such as an integrated access backhaul (IAB) network, an open RAN (O-RAN) (e.g., a network configuration sponsored by the O-RAN Alliance), or a virtualized RAN (vRAN) (e.g., a cloud RAN (C-RAN)). For example, the network entity 105 may include one or more of the following: a central unit (CU) 160, a distributed unit (DU) 165, a radio unit (RU) 170, a RAN intelligent controller (RIC) 175 (e.g., a near real-time RIC (near RT RIC), a non-real-time RIC (non-RT RIC)), a service management and orchestration (SMO) 180 system, or any combination thereof. The RU 170 may also be referred to as a radio head, a smart radio head, a remote radio head (RRH), a remote radio unit (RRU), or a transmit receive point (TRP). One or more components of the network entity 105 in the decomposed RAN architecture may be co-located, or one or more components of the network entity 105 may be located in distributed locations (e.g., separate physical locations). In some examples, one or more network entities 105 of the decomposed RAN architecture may be implemented as virtual units (e.g., virtual CU (VCU), virtual DU (VDU), virtual RU (VRU)).

[0042] The functional split between CU 160, DU 165, and RU 170 is flexible and can support different functions, depending on which functions (e.g., network layer functions, protocol layer functions, baseband functions, RF functions, and any combination thereof) are performed at CU 160, DU 165, or RU 170. For example, a functional split of a protocol stack can be employed between CU 160 and DU 165, such that CU 160 can support one or more layers of a protocol stack and DU 165 can support one or more different layers of a protocol stack. In some examples, CU 160 can host higher protocol layer (e.g., layer 3 (L3), layer 2 (L2)) functions and signaling (e.g., radio resource control (RRC), service data adaptation protocol (SDAP), packet data convergence protocol (PDCP)). The CU 160 may be connected to one or more DUs 165 or RUs 170, and the one or more DUs 165 or RUs 170 may host lower protocol layers, such as layer 1 (L1) (e.g., physical (PHY) layer) or L2 (e.g., radio link control (RLC) layer, medium access control (MAC) layer) functions and signaling, and may each be at least partially controlled by the CU 160. Additionally or alternatively, a functional split of the protocol stack may be employed between the DU 165 and the RU 170, such that the DU 165 may support one or more layers of the protocol stack, and the RU 170 may support one or more different layers of the protocol stack. The DU 165 may support one or more different cells (e.g., via one or more RUs 170). In some cases, the functional split between CU 160 and DU 165 or between DU 165 and RU 170 may be within a protocol layer (e.g., some functions of a protocol layer may be performed by one of CU 160, DU 165, or RU 170, while other functions of the protocol layer are performed by a different one of CU 160, DU 165, or RU 170). CU 160 may be further functionally split into CU control plane (CU-CP) and CU user plane (CU-UP) functions. CU 160 may be connected to one or more DUs 165 via midhaul communication links 162 (e.g., F1, F1-c, F1-u), and DU 165 may be connected to one or more RUs 170 via fronthaul communication links 168 (e.g., an open fronthaul (FH) interface). In some examples, midhaul communication link 162 or fronthaul communication link 168 may be implemented based on an interface (e.g., a channel) between layers of a protocol stack supported by respective network entities 105 that communicate via such communication links.

[0043] In some wireless communication systems (e.g., wireless communication system 100), infrastructure and spectrum resources for radio access may support wireless backhaul link capabilities to supplement wired backhaul connections, thereby providing an IAB network architecture (e.g., to core network 130). In some cases, in an IAB network, one or more network entities 105 (e.g., IAB nodes 104) may be partially controlled by each other. One or more IAB nodes 104 may be referred to as donor entities or IAB donors. One or more DUs 165 or one or more RUs 170 may be controlled in part by one or more CUs 160 associated with a donor network entity 105 (e.g., donor base station 140). One or more donor network entities 105 (e.g., IAB donors) may communicate with one or more additional network entities 105 (e.g., IAB nodes 104) via supported access and backhaul links (e.g., backhaul communication links 120). The IAB node 104 may include an IAB mobile terminal (IAB-MT) controlled (e.g., scheduled) by a coupled IAB donor's DU 165. The IAB-MT may include an independent set of antennas for relaying communications with the UE 115, or may share the same antennas of the IAB node 104 (e.g., of the RU 170) for access via the DU 165 of the IAB node 104 (e.g., referred to as a virtual IAB-MT (vIAB-MT)). In some examples, the IAB node 104 may include a DU 165 that supports communication links with additional entities (e.g., IAB node 104, UE 115) within a relay chain or configuration (e.g., downstream) of the access network. In such cases, one or more components of the decomposed RAN architecture (e.g., one or more IAB nodes 104 or components of the IAB node 104) may be configured to operate according to the techniques described herein.

[0044] For example, an access network (AN) or RAN may include an access node (e.g., an IAB donor), communications between an IAB node 104, and one or more UEs 115. The IAB donor may facilitate a connection between the core network 130 and the AN (e.g., via a wired or wireless connection to the core network 130). That is, the IAB donor may refer to a RAN node having a wired or wireless connection to the core network 130. The IAB donor may include a CU 160 and at least one DU 165 (e.g., and RU 170), wherein the CU 160 may communicate with the core network 130 via an interface (e.g., a backhaul link). The IAB donor and the IAB node 104 may communicate via an F1 interface according to a protocol defining a signaling message (e.g., an F1 AP protocol). Additionally or alternatively, CU 160 may communicate with the core network via an interface (which may be an example of a portion of a backhaul link) and may communicate with other CUs 160 (e.g., CU 160 associated with an alternative IAB donor) via an Xn-C interface (which may be an example of a portion of a backhaul link).

[0045] An IAB node 104 may refer to a RAN node that provides IAB functionality (e.g., access for UE 115, wireless self-backhaul capabilities, etc.). The DU 165 may act as a distributed scheduling node toward child nodes associated with the IAB node 104, and the IAB-MT may act as a scheduled node toward a parent node associated with the IAB node 104. That is, an IAB donor may be referred to as a parent node that communicates with one or more child nodes (e.g., the IAB donor may relay for transmissions to UEs through one or more other IAB nodes 104). Additionally or alternatively, depending on the relay chain or configuration of the AN, the IAB node 104 may also be referred to as a parent node or child node of other IAB nodes 104. Thus, the IAB-MT entity of the IAB node 104 may provide a Uu interface for the child IAB node 104 to receive signaling from the parent IAB node 104 , and a DU interface (eg, DU 165 ) may provide a Uu interface for the parent IAB node 104 to signal to the child IAB node 104 or the UE 115 .

[0046] For example, the IAB node 104 may be referred to as a parent node supporting communications for a child IAB node or as a child IAB node associated with an IAB donor, or both. The IAB donor may include a CU 160 having a wired or wireless connection (e.g., backhaul communication link 120) to the core network 130, and may act as a parent node of the IAB node 104. For example, the DU 165 of the IAB donor may relay the transmission to the UE 115 through the IAB node 104, or may directly signal the transmission to the UE 115, or both. The CU 160 of the IAB donor may signal the establishment of a communication link to the IAB node 104 via the F1 interface, and the IAB node 104 may schedule the transmission (e.g., the transmission relayed from the IAB donor to the UE 115) via the DU 165. That is, data may be relayed to and from the IAB node 104 via signaling via the NR Uu interface of the MT to the IAB node 104. Communications with the IAB node 104 may be scheduled by the DU 165 of the IAB donor, and communications with the IAB node 104 may be scheduled by the DU 165 of the IAB node 104 .

[0047] Where the techniques described herein are applied in the context of a decomposed RAN architecture, one or more components of the decomposed RAN architecture may be configured to support ML-based TA updates as described herein. For example, some operations described as being performed by a UE 115 or a network entity 105 (e.g., a base station 140) may additionally or alternatively be performed by one or more components of the decomposed RAN architecture (e.g., an IAB node 104, a DU 165, a CU 160, a RU 170, a RIC 175, a SMO 180).

[0048] UE 115 may include or may be referred to as a mobile device, a wireless device, a remote device, a handheld device, or a subscriber device, or some other suitable term, where a "device" may also be referred to as a unit, a station, a terminal, or a client, etc. UE 115 may also include or may be referred to as a personal electronic device, such as a cellular phone, a personal digital assistant (PDA), a tablet computer, a laptop computer, or a personal computer. In some examples, UE 115 may include or may be referred to as a wireless local loop (WLL) station, an Internet of Things (IoT) device, an Internet of Everything (IoE) device, or a machine type communication (MTC) device, etc., which may be implemented in various objects such as appliances or vehicles, meters, etc.

[0049] The UE 115 described herein may be capable of communicating with various types of devices such as other UEs 115 which may sometimes act as relays, as well as network entities 105 and network equipment including macro eNBs or gNBs, small cell eNBs or gNBs, or relay base stations, etc. Figure 1shown.

[0050] The UE 115 and the network entity 105 may wirelessly communicate with each other via one or more communication links 125 (e.g., access links) using resources associated with one or more carriers. The term "carrier" may refer to a collection of RF spectrum resources having a physical layer structure defined for supporting the communication link 125. For example, a carrier for the communication link 125 may include a portion of an RF spectrum band (e.g., a bandwidth portion (BWP)) that operates according to one or more physical layer channels for a given radio access technology (e.g., LTE, LTE-A, LTE-A Pro, NR). Each physical layer channel may carry acquisition signaling (e.g., synchronization signals, system information), control signaling for coordinating carrier operations, user data, or other signaling. The wireless communication system 100 may support communications with the UE 115 using carrier aggregation or multi-carrier operation. Depending on the carrier aggregation configuration, the UE 115 may be configured to have multiple downlink component carriers and one or more uplink component carriers. Carrier aggregation may be used for both frequency division duplex (FDD) and time division duplex (TDD) component carriers. Communication between the network entity 105 and other devices may refer to communication between these devices and any portion (e.g., entity, sub-entity) of the network entity 105. For example, the terms "send," "receive," or "communicate" when referring to the network entity 105 may refer to any portion of a network entity 105 (e.g., base station 140, CU 160, DU 165, RU 170) of a RAN communicating with another device (e.g., directly or via one or more other network entities 105).

[0051] In some examples, such as in a carrier aggregation configuration, a carrier may also have acquisition signaling or control signaling that coordinates the operation of other carriers. A carrier may be associated with a frequency channel (e.g., an Evolved Universal Mobile Telecommunications System Terrestrial Radio Access (E-UTRA) Absolute RF Channel Number (EARFCN)) and may be identified according to a channel raster for discovery by UE 115. A carrier may operate in a standalone mode, in which case initial acquisition and connection may be performed by UE 115 via the carrier, or a carrier may operate in a non-standalone mode, in which case the connection is anchored using a different carrier (e.g., a different carrier of the same or different radio access technology).

[0052] The communication link 125 shown in the wireless communication system 100 may include downlink transmissions (e.g., forward link transmissions) from the network entity 105 to the UE 115, uplink transmissions (e.g., return link transmissions) from the UE 115 to the network entity 105, or both, among other transmission configurations. A carrier may carry either downlink communications or uplink communications (e.g., in an FDD mode), or may be configured to carry both downlink communications and uplink communications (e.g., in a TDD mode).

[0053] The signal waveform transmitted via the carrier may include multiple subcarriers (e.g., using multi-carrier modulation (MCM) techniques, such as orthogonal frequency division multiplexing (OFDM) or discrete Fourier transform spread OFDM (DFT-S-OFDM)). In a system using MCM technology, a resource element may refer to a symbol period (e.g., the duration of a modulation symbol) and a resource of a subcarrier, in which case the symbol period and the subcarrier spacing may be inversely related. The number of bits carried by each resource element may depend on the modulation scheme (e.g., the order of the modulation scheme, the coding rate of the modulation scheme, or both), so that a relatively high number of resource elements (e.g., in the transmission duration) and a relatively high order modulation scheme may correspond to a relatively high rate of communication. Wireless communication resources may refer to a combination of RF spectrum resources, time resources, and spatial resources (e.g., spatial layers or beams), and the use of multiple spatial resources may increase the data rate or data integrity used for communication with UE 115.

[0054] One or more parameter sets for a carrier may be supported, and the parameter sets may include subcarrier spacing (Δf) and cyclic prefixes. A carrier may be divided into one or more BWPs with the same or different parameter sets. In some examples, a UE 115 may be configured with multiple BWPs. In some examples, a single BWP for a carrier may be active at a given time, and communications of a UE 115 may be constrained to one or more active BWPs.

[0055] The time interval for the network entity 105 or the UE 115 may be expressed as a multiple of a basic time unit, which may be, for example, a sampling period T s =1 / (Δf max ·N f ) seconds, for which Δf max It can represent the supported subcarrier spacing, and N f The supported discrete Fourier transform (DFT) size may be indicated. The time intervals of the communication resources may be organized according to radio frames each having a specified duration (e.g., 10 milliseconds (ms)). Each radio frame may be identified by a system frame number (SFN) (e.g., ranging from 0 to 1023).

[0056] Each frame may include multiple consecutively numbered subframes or time slots, and each subframe or time slot may have the same duration. In some examples, the frame may be divided into subframes (e.g., in the time domain), and each subframe may be further divided into a certain number of time slots. Alternatively, each frame may include a variable number of time slots, and the number of time slots may depend on the subcarrier spacing. Each time slot may include a certain number of symbol periods (e.g., depending on the length of the cyclic prefix appended in front of each symbol period). In some wireless communication systems 100, the time slot may be further divided into a plurality of micro time slots associated with one or more symbols. Excluding the cyclic prefix, each symbol period may be associated with one or more (e.g., N f The duration of a symbol period may depend on the subcarrier spacing or the operating frequency band.

[0057] A subframe, slot, mini-slot, or symbol may be the smallest scheduling unit (e.g., in the time domain) of the wireless communication system 100 and may be referred to as a transmission time interval (TTI). In some examples, the TTI duration (e.g., the number of symbol periods in a TTI) may be variable. Additionally or alternatively, the smallest scheduling unit of the wireless communication system 100 may be dynamically selected (e.g., in a burst of a shortened TTI (sTTI)).

[0058] According to various techniques, physical channels may be multiplexed using carriers for communication. Physical control channels and physical data channels may be multiplexed for signaling via downlink carriers, for example, using one or more of a time division multiplexing (TDM) technique, a frequency division multiplexing (FDM) technique, or a hybrid TDM-FDM technique. A control region (e.g., a control resource set (CORESET)) of a physical control channel may be defined by a set of symbol periods and may extend across a system bandwidth of a carrier or a subset of that system bandwidth. One or more control regions (e.g., CORESETs) may be configured for a set of UEs 115. For example, one or more UEs in UE 115 may monitor or search a control region to obtain control information according to one or more search space sets, and each search space set may include one or more control channel candidates in one or more aggregation levels arranged in a cascaded manner. The aggregation level of a control channel candidate may refer to the amount of control channel resources (e.g., control channel elements (CCEs)) associated with coded information for a control information format having a given payload size. The search space sets may include a common search space set configured for transmitting control information to multiple UEs 115 , and a UE-specific search space set for transmitting control information to a specific UE 115 .

[0059] The network entity 105 may provide communication coverage via one or more cells (e.g., macro cells, small cells, hot spots, or other types of cells, or any combination thereof). The term "cell" may refer to a logical communication entity used to communicate with the network entity 105 (e.g., using a carrier), and may be associated with an identifier used to distinguish adjacent cells (e.g., a physical cell identifier (PCID), a virtual cell identifier (VCID), or other cell identifier). In some examples, a cell may also refer to a coverage area 110 or a portion of a coverage area 110 (e.g., a sector) on which a logical communication entity operates. Depending on various factors such as the capabilities of the network entity 105, such cells may range from smaller areas (e.g., structures, subsets of structures) to larger areas. For example, a cell may be or may include a building, a subset of a building, or an external space between or overlapping coverage areas 110, and the like.

[0060] In some examples, the network entities 105 (e.g., base stations 140, RUs 170) may be mobile and thus provide communication coverage for mobile coverage areas 110. In some examples, different coverage areas 110 associated with different technologies may overlap, but the different coverage areas 110 may be supported by the same network entity 105. In some other examples, overlapping coverage areas 110 associated with different technologies may be supported by different network entities 105. The wireless communication system 100 may include, for example, a heterogeneous network in which different types of network entities 105 provide coverage for various coverage areas 110 using the same or different radio access technologies.

[0061] The wireless communication system 100 may support synchronous or asynchronous operation. For synchronous operation, the network entities 105 (e.g., base stations 140) may have similar frame timing, and transmissions from different network entities 105 may be approximately aligned in time. For asynchronous operation, the network entities 105 may have different frame timing, and in some examples, transmissions from different network entities 105 may not be aligned in time. The techniques described herein may be used for either synchronous operation or asynchronous operation.

[0062] Some UEs 115, such as MTC or IoT devices, may be low-cost or low-complexity devices and may provide automated communication between machines (e.g., via machine-to-machine (M2M) communication). M2M communication or MTC may refer to data communication technology that allows devices to communicate with each other or with a network entity 105 (e.g., base station 140) without human intervention. In some examples, M2M communication or MTC may include communication from devices that integrate sensors or meters to measure or acquire information and relay such information to a central server or application that uses the information or presents the information to a person interacting with the application. Some UEs 115 may be designed to collect information or implement automated behavior of machines or other devices. Examples of applications for MTC devices include: smart metering, inventory monitoring, water level monitoring, equipment monitoring, health care monitoring, wildlife monitoring, weather and geographic event monitoring, queue management and tracking, remote security sensing, physical access control, and transaction-based commercial charging.

[0063] Some UEs 115 may be configured to employ an operating mode that reduces power consumption, such as half-duplex communication (e.g., a mode that supports unidirectional communication via transmission or reception but does not transmit and receive concurrently). In some examples, half-duplex communication may be performed at a reduced peak rate. Other energy-saving techniques for UE 115 include entering a power-saving deep sleep mode when not engaged in active communications, operating using limited bandwidth (e.g., according to narrowband communications), or a combination of these techniques. For example, some UEs 115 may be configured to operate using a narrowband protocol type that is associated with a defined portion or range (e.g., a set of subcarriers or resource blocks (RBs)) within a carrier, within a guard band of a carrier, or outside a carrier.

[0064] The wireless communication system 100 may be configured to support ultra-reliable communication or low-latency communication or various combinations thereof. For example, the wireless communication system 100 may be configured to support ultra-reliable low-latency communication (URLLC). UE 115 may be designed to support ultra-reliable or low-latency or critical functions. Ultra-reliable communication may include private communication or group communication, and may be supported by one or more services (such as push-to-talk, video, or data). Support for ultra-reliable, low-latency functions may include prioritization of services, and such services may be used for public safety or general commercial applications. The terms ultra-reliable, low-latency, and ultra-reliable low-latency may be used interchangeably herein.

[0065] In some examples, the UE 115 may be configured to support communication directly with other UEs 115 via a device-to-device (D2D) communication link 135 (e.g., according to a peer-to-peer (P2P), D2D, or sidelink protocol). In some examples, one or more UEs 115 in a group that are performing D2D communication may be within a coverage area 110 of a network entity 105 (e.g., a base station 140, a RU 170), which may support aspects of such D2D communication configured (e.g., scheduled) by the network entity 105. In some examples, one or more UEs 115 in such a group may be outside of the coverage area 110 of the network entity 105, or may otherwise be unable or not configured to receive transmissions from the network entity 105. In some examples, a group of UEs 115 communicating via D2D communication may support a one-to-many (1:M) system, in which each UE 115 transmits to each of the other UEs 115 in the group. In some examples, network entity 105 may facilitate scheduling of resources for D2D communications. In some other examples, D2D communications may be performed between UEs 115 without involving network entity 105.

[0066] The core network 130 may provide user authentication, access authorization, tracking, Internet Protocol (IP) connectivity, and other access, routing, or mobility functions. The core network 130 may be an evolved packet core (EPC) or a 5G core (5GC), which may include at least one control plane entity (e.g., a mobility management entity (MME), an access and mobility management function (AMF)) for managing access and mobility and at least one user plane entity (e.g., a serving gateway (S-GW), a packet data network (PDN) gateway (P-GW), or a user plane function (UPF)) for routing packets or interconnecting to an external network. The control plane entity may manage non-access stratum (NAS) functions such as mobility, authentication, and bearer management of UE 115 served by a network entity 105 (e.g., a base station 140) associated with the core network 130. User IP packets may be delivered through the user plane entity, which may provide IP address allocation and other functions. The user plane entity may be connected to the IP service 150 of one or more network operators. IP services 150 may include access to the Internet, an intranet, an IP Multimedia Subsystem (IMS), or packet-switched streaming services.

[0067] The wireless communication system 100 may operate using one or more frequency bands that may be in the range of 300 megahertz (MHz) to 300 gigahertz (GHz). Generally speaking, the region from 300 MHz to 3 GHz is referred to as the ultra-high frequency (UHF) region or decimeter band because the wavelength ranges from about one decimeter to one meter in length. UHF waves may be blocked or redirected by buildings and environmental features (which may be referred to as clusters), but these waves may be sufficient to penetrate structures so that macro cells provide services to UEs 115 located indoors. Communications using UHF waves may be associated with smaller antennas and shorter ranges (e.g., less than 100 kilometers) compared to communications using lower frequencies and longer waves in the high frequency (HF) or very high frequency (VHF) portions of the spectrum below 300 MHz.

[0068] The wireless communication system 100 may also operate using a super high frequency (SHF) region (also known as a centimeter band) that may be in the range of 3 GHz to 30 GHz or an extremely high frequency (EHF) region (e.g., 30 GHz to 300 GHz) (also known as a millimeter band) using a spectrum. In some examples, the wireless communication system 100 may support millimeter wave (mmW) communications between UE 115 and network entity 105 (e.g., base station 140, RU 170), and the EHF antenna of the corresponding device may be smaller and closer than the UHF antenna. In some examples, such technology may facilitate the use of antenna arrays within the device. However, the propagation of EHF transmissions may be affected by greater attenuation and shorter range than SHF or UHF transmissions. The technology disclosed herein may be adopted across transmissions using one or more different frequency regions, and the use of frequency bands specified across these frequency regions may vary by country or regulatory agency.

[0069] The wireless communication system 100 can utilize licensed and unlicensed RF spectrum bands. For example, the wireless communication system 100 can use unlicensed bands (such as the 5 GHz Industrial, Scientific, and Medical (ISM) band) to adopt License Assisted Access (LAA), LTE Unlicensed (LTE-U) radio access technology, or NR technology. When operating using unlicensed RF spectrum bands, devices such as network entity 105 and UE 115 can use carrier sensing for conflict detection and avoidance. In some examples, operations using unlicensed bands can be based on carrier aggregation configuration (e.g., LAA) in combination with component carriers operating using licensed bands. Operations using unlicensed spectrum can include downlink transmissions, uplink transmissions, P2P transmissions, or D2D transmissions, and the like.

[0070] The network entity 105 (e.g., base station 140, RU 170) or UE 115 may be equipped with multiple antennas that can be used to employ technologies such as transmit diversity, receive diversity, multiple-input multiple-output (MIMO) communications, or beamforming. The antennas of the network entity 105 or UE 115 may be located in one or more antenna arrays or antenna panels, which may support MIMO operations or transmit or receive beamforming. For example, one or more base station antennas or antenna arrays may be co-located at an antenna assembly, such as an antenna tower. In some examples, antennas or antenna arrays associated with the network entity 105 may be located at different geographical locations. The network entity 105 may include an antenna array having a collection of multiple rows and columns of antenna ports that the network entity 105 can use to support beamforming for communications with the UE 115. Similarly, the UE 115 may include one or more antenna arrays that may support various MIMO or beamforming operations. Additionally or alternatively, the antenna panel may support RF beamforming for signals sent via the antenna ports.

[0071] Beamforming (which may also be referred to as spatial filtering, directional transmission, or directional reception) is a signal processing technique that may be used at a transmitting device or a receiving device (e.g., a network entity 105, a UE 115) to shape or direct an antenna beam (e.g., a transmit beam, a receive beam) along a spatial path between the transmitting device and the receiving device. Beamforming may be achieved by combining signals conveyed via antenna elements of an antenna array so that some signals propagating along a particular direction relative to the antenna array experience constructive interference, while other signals experience destructive interference. Adjustments to signals conveyed via antenna elements may include the transmitting device or the receiving device applying an amplitude offset, a phase offset, or both to signals carried via antenna elements associated with the device. Adjustments associated with each of these antenna elements may be defined by a set of beamforming weights associated with a particular direction (e.g., relative to the antenna array of the transmitting device or the receiving device or relative to some other direction).

[0072] The network entity 105 or UE 115 may use beam scanning techniques as part of a beamforming operation. For example, the network entity 105 (e.g., base station 140, RU 170) may use multiple antennas or antenna arrays (e.g., antenna panels) to perform beamforming operations for directional communication with UE 115. Some signals (e.g., synchronization signals, reference signals, beam selection signals, or other control signals) may be sent multiple times by the network entity 105 along different directions. For example, the network entity 105 may send signals according to different sets of beamforming weights associated with different transmission directions. Transmission along different beam directions may be used to identify (e.g., by a transmitting device (such as network entity 105), or by a receiving device (such as UE 115)) the beam direction for later transmission or reception by the network entity 105.

[0073] Some signals, such as data signals associated with a particular receiving device, may be sent by a sending device (e.g., sending network entity 105, sending UE 115) along a single beam direction (e.g., a direction associated with a receiving device (such as receiving network entity 105 or receiving UE 115)). In some examples, the beam direction associated with the transmission along the single beam direction may be determined based on signals sent along one or more beam directions. For example, UE 115 may receive one or more of the signals sent by network entity 105 along different directions, and may report to network entity 105 an indication of the signal received by UE 115 with the highest signal quality or other acceptable signal quality.

[0074] In some examples, transmission by a device (e.g., by network entity 105 or UE 115) may be performed using multiple beam directions, and the device may use a combination of digital precoding or beamforming to generate a combined beam for transmission (e.g., from network entity 105 to UE 115). UE 115 may report feedback indicating precoding weights for one or more beam directions, and the feedback may correspond to a configured set of beams across a system bandwidth or one or more subbands. Network entity 105 may send a reference signal (e.g., a cell-specific reference signal (CRS), a channel state information reference signal (CSI-RS)), which may be precoded or non-precoded. UE 115 may provide feedback for beam selection, which may be a precoding matrix indicator (PMI) or codebook-based feedback (e.g., a multi-panel codebook, a linear combination codebook, a port selection codebook). Although these techniques are described with reference to signals sent along one or more directions by a network entity 105 (e.g., base station 140, RU 170), UE 115 may use similar techniques to send signals multiple times along different directions (e.g., to identify a beam direction for subsequent transmission or reception by UE 115), or to send signals along a single direction (e.g., to send data to a receiving device).

[0075] A receiving device (e.g., UE 115) may perform receiving operations according to multiple receiving configurations (e.g., directional listening) when receiving various signals (such as synchronization signals, reference signals, beam selection signals, or other control signals) from a receiving device (e.g., network entity 105). For example, the receiving device may perform reception according to multiple receiving directions by: receiving via different antenna subarrays, processing received signals according to different antenna subarrays, receiving according to different receiving beamforming weight sets (e.g., different directional listening weight sets) applied to signals received at multiple antenna elements of an antenna array, or processing received signals according to different receiving beamforming weight sets applied to signals received at multiple antenna elements of an antenna array, any of which may be referred to as "listening" according to different receiving configurations or receiving directions. In some examples, the receiving device may use a single receiving configuration to receive along a single beam direction (e.g., when receiving a data signal). A single receive configuration may be aligned along a beam direction determined based on listening according to different receive configuration directions (e.g., a beam direction determined to have the highest signal strength, highest signal-to-noise ratio (SNR), or other acceptable signal quality based on listening according to multiple beam directions).

[0076] The wireless communication system 100 may be a packet-based network that operates according to a layered protocol stack. In the user plane, communications at the bearer or PDCP layer may be IP-based. The RLC layer may perform packet segmentation and reassembly to communicate via logical channels. The MAC layer may perform priority processing and multiplexing of logical channels into transport channels. The MAC layer may also implement error detection techniques, error correction techniques, or both to support retransmission to improve link efficiency. In the control plane, the RRC layer may provide the establishment, configuration, and maintenance of an RRC connection that supports a radio bearer for user plane data between the UE 115 and the network entity 105 or the core network 130. The PHY layer may map a transport channel to a physical channel.

[0077] UE 115 and network entity 105 may support retransmission of data to increase the likelihood that the data is successfully received. Hybrid automatic repeat request (HARQ) feedback is a technique for increasing the likelihood of correctly receiving data via a communication link (e.g., communication link 125, D2D communication link 135). HARQ may include a combination of error detection (e.g., using a cyclic redundancy check (CRC)), forward error correction (FEC), and retransmission (e.g., automatic repeat request (ARQ)). HARQ can improve the throughput at the MAC layer under poor radio conditions (e.g., low signal-to-noise ratio conditions). In some examples, the device may support same-slot HARQ feedback, in which case the device may provide HARQ feedback in a specific time slot for data received in a previous symbol in the time slot. In some other examples, the device may provide HARQ feedback in a subsequent time slot or according to some other time interval.

[0078] In some wireless communication systems 100, some network devices such as one or more network entities 105 and one or more UEs 115 may use TA to predict round trip time. For example, the network entity 105 may use TA to set expectations for timing transmission and schedule transmission with UE 115. In some examples, in order to set the TA for one or more wireless communications, the network device may measure TA using RACH triggered by a PDCCH command or by one or more RSs (such as one or more SRSs and TRSs). In some cases, the network entity 105 may measure the TA value and send an indication to the UE 115 to adjust the TA value for uplink transmission. However, in a wireless communication system 100 in which the network device is operating in a high mobility scenario, the TA may change frequently due to position shift or positioning of one or more UEs 115 or when one or more service TRPs change for a specific UE 115. That is, the wireless communication system 100 may measure and configure each TA for each beam in a set of beams and for multiple nodes. In some cases, the frequently changing TA may increase the overhead of the wireless communication system 100.

[0079] In some implementations, the wireless communication system 100 may use ML to process multiple TAs for each beam between multiple network devices. In some examples, ML may use past measured RSRP to predict future RSRP or determine the positioning of UE 115. In some cases, the ML model may be trained to predict TA based on channel measurements. That is, UE 115 may use the ML model to predict TA values ​​and autonomously apply the predicted TA values ​​without explicit signaling from the network entity 105. In some examples, the ML model that performs TA prediction may receive input of one or more RSRPs or PDPs of CSI-RS for SSBs from one or more beams, apply ML prediction, and output TA for one or more beams. In other examples, the TAs of multiple nodes from a set of TA values ​​over a past time period may be input into the ML model, and possible TA predictions for future nodes may be output. That is, the TA value output from the ML model may be applied to one or more transmissions between network devices.

[0080] Figure 2 An example of a wireless communication system 200 that supports ML-based TA updates according to one or more aspects of the present disclosure is illustrated. In some examples, the wireless communication system 200 may implement aspects of the wireless communication system 100. The wireless communication system 200 may include a UE 115-a, a network entity 105-a, and a network entity 105-b, which may be as described herein, including reference Figure 1 Examples of corresponding devices described.

[0081] In some specific implementations, the wireless communication system 200 may support network devices such as UE 115-a, network entity 105-a, and network entity 105-b. In some examples, the round trip time may include the total time period between two network devices. For example, the round trip time may include the time it takes for UE 115-a to send a message to network entity 105-a and the additional time it takes for network entity 105-a to send a response message back to UE 115-a. In some cases, the round trip time may depend on the propagation environment. In some aspects, the network device may consider the round trip time for scheduling transmission. For example, UE 115-a may use TA to send uplink transmission before network entity 105-a sends uplink reception. In such examples, UE 115-a may consider the round trip time (e.g., half of the round trip time) it takes to receive a message at network entity 105-a. UE 115-a may send a message that takes into account the travel time so that network entity 105-a receives the message before sending the uplink reception.

[0082] In some examples, to set the TA for wireless communication, the network device may measure the TA using a RACH triggered by a PDCCH command or through one or more RSs (such as one or more SRSs and TRSs). In some cases, the network entity 105 may measure the TA value using the SRS or RACH and send an indication (e.g., MAC-CE) to adjust the TA value for uplink transmission to the UE 115. In some examples, the network entity 105-a may access the TA value and the UE 115-a may receive the TA value.

[0083] In some wireless communication networks (e.g., millimeter wave (mmW) networks), UE 115-a and network entity 105-a can perform directional beamforming 205 to form a beamformed channel. In this example, UE 115-a can use a set of directional beams 205-a to communicate with a set of directional beams 205-a associated with network entity 105-a, and a set of directional beams 205-b associated with network entity 105-b. In some examples, UE 115-a can use access links to one or more network entities 105 via different beams 205 or multiple network entities 105 or TRPs. In some cases, each beamformed link between UE 115-a and another device can use a different TA.

[0084] In some implementations, the wireless communication system 200 may allow network devices to operate in high mobility scenarios. In some examples, the TA may change frequently due to position shift or positioning of the UE 115-a or when one or more service TRPs change. That is, the wireless communication system 200 may measure and configure each TA for each beam in the set of beams 205 and for multiple nodes. In some cases, the frequently changing TA may increase the overhead of the wireless communication system 200.

[0085] In some examples, TA values ​​may be grouped together. In some aspects, beam management for the wireless communication system 200 may include multiple-input multiple-output (MIMO) enhancements for a frequency band (e.g., FR2) and how to configure the UE 115-a to have multiple (e.g., different) cells or TRPs with multiple TA groups. In some cases, multiple cells and TRPs may include different propagation distances to the UE 115-a, and different TA values ​​may be used. In some cases, the signaling design may be intended to separate the configuration signaling for the RS or channel from different cells or TRPs. In some examples, multiple TA groups may be configured for TRPs and cells, each of which may indicate a different TA value. In some cases, the TAs of different TA groups may be updated via separate signaling (by a network entity). In some aspects, dividing the TA value into multiple TA groups may increase overhead because the wireless communication system 200 may track and configure each TA in the TA group for uplink communication. In some high-speed scenarios, the TA sent by the uplink may need to be updated faster, which is similar to the Doppler compensation scenario.

[0086] Artificial intelligence (AI) and ML techniques may be used in some wireless communication systems 200 (e.g., radio access networks (RANs)) for air interface communications. In some cases, the wireless communication system 200 may implement AI techniques and ML techniques for CSI feedback enhancement (e.g., overhead reduction, improved accuracy, prediction), beam management (e.g., beam prediction in time, and / or spatial domain overhead and latency reduction, beam selection, accuracy improvement), and positioning accuracy enhancement for different scenarios (e.g., scenarios with non-line-of-sight (NLOS) conditions). In some examples, the wireless communication system 200 may use AI techniques and ML techniques, and may implement an AI or ML framework including terminology, capability indication, configuration procedures (e.g., training, inference), verification and testing procedures, data management, and AI or ML models. In some examples, the wireless communication system 200 may introduce ML techniques in the air interface design to utilize past measured RSRP for beam prediction (e.g., predicting channel metrics for future beams) or use the UE's position (e.g., positioning) for TA. In some cases, ML techniques may allow the wireless communication system 200 to measure RSRP to predict future RSRP for the location of the UE.

[0087] In some implementations, the wireless communication system 200 may use ML to process multiple TAs for each beam between multiple network devices. In some examples, the UE 115-a may use ML by implementing an ML model 245 (e.g., an ML module). In some examples, the ML model 245 may implement a neural network function (NNF), where a function (e.g., Y=F(x)) is supported by a neural network (NN) model 255. In some cases, each NNF is identified by a standardized NNF identifier (ID). In other cases, a private extension of the NN model 255 may use a non-standardized ID for the NNF. In some aspects, the NNF may include an input 250 (e.g., X) and an output 260 (e.g., Y) standardized for each NNF in the NNF. In some examples, the standardized input 250 and output 260 may include an information element (IE) for internal operation between vendors, and other optional IEs may be used for flexible implementation of the NNF. In some examples, the NNF may be supported by multiple models (e.g., vendor-specific implementations).

[0088] In some implementations, the NN model 255 may include a model structure and a parameter set. In some examples, the model structure may include a model ID that is unique in the network and associated with the NNF. In some cases, the model structure includes a default parameter set, where the parameter set includes weights and other configuration parameters (such as positioning) of the NN model 255 or is a configuration specific to the NN model 255. In some cases, the NN model 255 may be defined by an operator, an infrastructure provider, or a third party (e.g., an original equipment manufacturer (OEM)).

[0089] In some embodiments of the present disclosure, a UE 115-a may be connected to one or more nodes with varying TA values. In some examples, configuring multiple TAs from different nodes may utilize more overhead compared to a single TA group case. In some aspects, TA values ​​may be updated frequently to keep up with conditions in high-speed scenarios, and current TA configuration methods may not meet the requirements for supporting multiple TAs (e.g., signaling updates are slow, and frequent updates to TAs increase overhead levels). Additionally or alternatively, the TA sent in the uplink is related to one or more UEs 115 in a cell and may be less dynamic than the RSRP of different beams. In some cases, the ML model 245 may be suitable for UE 115-a positioning and beam prediction, while predicting the TA based on the same inputs provided for UE 115-a positioning and beam prediction (e.g., RSRP, PDP of different beams over time). That is, the wireless communication system 200 may benefit from ML for multiple TAs.

[0090] As described herein, the wireless communication system 200 may implement an ML model 245 to predict a TA value. In some examples, the TA value is related to the propagation environment and channel measurements (e.g., RSRP measurements) and can be used to represent the conditions of the environment. In some cases, channel measurements (e.g., past RSRP measurements, positioning information) can be used to train the ML model 245 to predict TA. For example, the network entity 105-a may transmit the RSRP 220 of the SSB to the set 205-c of beams at the UE 115-a via the beam 205-a and the downlink channel 210-a. In other examples, the network entity 105-a may transmit the PDP 225 of the possible CSI-RS to the UE 115-a via the beam 205-a and the downlink channel 210-a. In some cases, the RSRP 220 and the PDP 225 may be input measurement parameters to be used for the ML model 245. The RSRP 220 or the PDP 225 may be input into the NNF as an input 250 to generate a TA prediction. In other examples, the input 250 for the NNF may include RSRP, certain beams, PDP of a network entity or TRP, TA of some other link, UE location measured by a global positioning system (GPS), positioning signaling, sensing such as RF sensing, a camera, a radar at the UE, a network entity, a TRP, or a transmission control information (TCI) ID, or a link to be predicted. In some cases, when the input 250 is input into the NN model 255, the TA prediction is the output 260. In some examples, the TA prediction may represent the TA of a non-measured or non-configured link. The UE 115-a may use the set 205-c of beams to send an uplink transmission 230-a via an uplink channel 215-a based on the TA prediction.

[0091] Additionally or alternatively, the UE 115-a may use the ML model 245 to predict the TA and autonomously apply the predicted TA value to the communication without signaling from one or more network entities 105. For example, the network entity 105-b may transmit a set of past TAs 235 to a set of beams 205-c at the UE 115-a via beams 205-a and downlink channels 210-b. In some cases, the past TAs 235 may be used as input measurement parameters to be used for the ML model 245. The past TAs 235 may be input as input 250 into the NNF to output a TA prediction for one or more future TA values. In some cases, the output 260 may be generated by or may include the TA prediction of the input 250 input into the NN model 255. In some implementations, the UE 115-a may use the set of beams 205-c to send an uplink transmission 230-b based on the future TA prediction via the uplink channel 215-b.

[0092] In some cases, the UE 115-a may run the ML model 245 to predict a TA for the link. In some cases, the predicted TA may correspond to a future time or for a link where the TA is not configured by the network entity 105-a. In some examples, the predicted TA may correspond to a TA group. In some implementations, the UE 115-a may autonomously apply the predicted TA to the corresponding uplink at the corresponding time.

[0093] In some aspects, the ML model 245 may be configured by a network entity (e.g., network entity 105-a or network entity 105-b) or a third-party server. In some examples, the network entity 105-a may collect a set of TA values ​​based on RSRP measured at the UE 115-a. In some cases, the network entity 105-a may send the TA values ​​to train the ML model 245 at the UE 115-a (e.g., train the ML module offline). In some examples, the network entity 105-a may send one or more parameters or parameter updates to the UE 115-a. In some cases, the parameter updates may be used in the ML model 245 to predict the TA value.

[0094] In some implementations, the network entity 105-a may configure the RS to measure input 250 (e.g., configure the CSI-RS to measure RSRP input). In some cases, the network entity 105-a may identify that RSRP may be a measurement parameter that may be used as an input 250 for the ML model 245. In some examples, the association between the input port and the measured RS may be pre-configured by the network entity 105-a. In some aspects, the UE 115-a may measure RSRP associated with the CSI-RS as a possible input 250 for predicting the TA value.

[0095] In some examples, the ML model 245 may include multiple outputs 260, where each output port may correspond to a link or multiple links that may share the same TA prediction. In such cases, the UE 115-a may determine that the predicted TA value corresponds to an output port associated with the ML model 245. In some examples, the UE 115-a may identify an association between a link and a TA output that may be preconfigured by the network entity 105-a (e.g., TA port X corresponds to cell ID Y or TA group X). The UE 115-a may identify a mapping between a cell ID or TA group identifier and an output port associated with the ML model 245 that outputs the predicted TA value.

[0096] In some implementations of the present disclosure, the ML model 245 may determine future TA predictions for uplink transmissions without assistance from the network entity 105-a. In some examples, large estimation errors may occur, and the network entity 105-a may trigger explicit TA measurements that request the UE 115-a to suppress the use of autonomous TA predictions for additional uplink transmissions. In such examples, the network entity 105-a may request to suspend TA prediction as a fallback solution. In some cases, the network entity 105-a may require the UE 115-a to send one or more inputs 250 to the ML model 245 corresponding to the error situation to collect data for refining the ML model 245. Additionally or alternatively, the network entity 105-a may trigger the UE 115-a to send a RACH or SRS so that the network entity 105-a can measure the TA value. In some cases, the network entity 105-a may update the ML model 245 using the TA measurements and feedback sent from the UE 115-a.

[0097] In some aspects, the input 250 of the ML model 245 may include measured channel metrics (e.g., RSRP, channel impulse response (CIR), or PDP of SBS or CSI-RS) for multiple beams 205 and other configured TAs for other links. For example, the UE 115-a may configure a TA using a first TRP at an initial time (e.g., time 0) and may be used to predict a TA to the first TRP at a future time. In some examples, the UE 115-a may send an uplink communication 230 at a first time instance based on a predicted TA value output when one or more measurement parameters include a set of TA values ​​corresponding to previous TA values ​​before the first time instance. In other examples, the predicted TA value may use measurement parameters associated with another network entity 105 (e.g., a second network entity). For example, in a high-speed scenario, the TA of the first TRP configured by the network entity 105-a may be a function of the TA of the second TRP, which is calculated based on the calculated configured function. In some examples, input 250 to ML model 245 may include positioning information from positioning reference signal (PRS) or geographic routing protocol (GRP) measurements. In some cases, UE 115-a may measure the speed and orientation of UE 115-a.

[0098] In some aspects, the UE 115-a may send a capability report to the network entity 105-a indicating that the UE 115-a may support autonomous updating of TA values. In some examples, the UE 115-a may autonomously update the TA value based on a prediction of the TA value without assistance from the network entity 105-a. In some cases, the capability report may include information about whether the UE 115-a may support a TA group module type, a number of TA groups for autonomous updating, and an input type for TA prediction.

[0099] Additionally or alternatively, some wireless communication systems 200 may implement an integrated access and backhaul (IAB) network, in which a distributed unit (DU) of a child node may change the uplink receive timing for UE 115-a based on the configuration of a parent node. In some cases, the parent node indicates instructions or configurations for the DU, which may be a child node of the parent node. In such cases, the uplink timing change of the DU may result in an update of the TA for UE 115-a (e.g., by an incremental value for all links connected to the DU node). In some examples, the DU may update a prediction module or alternatively broadcast an adjustment value to one or more service UEs 115.

[0100] FIG. 3 illustrates an example of ML models 300a and 300-b that support ML-based TA updates according to one or more aspects of the present disclosure. The ML models 300-a and 300-b may respectively implement reference Figure 1 and Figure 2 Various aspects of the described wireless communication systems 100 and 200 may be implemented by various aspects of these wireless communication systems, respectively.

[0101] In some aspects of the present disclosure, such as Figure 3A As depicted in , UE 115 may implement ML model 300-a to output TA values. In some examples, ML model 300-a may represent a NNF that takes input and produces output. In some cases, ML model 300-a may use input 305 for NN model 310. In some cases, input 305 may include one or more measurement parameters sent from network entity 105 via a beam (e.g., one or more RSRPs of SBS, PDP of CSI-RS). In some cases, NN model 310 may include a model structure with a model ID and a parameter set with one or more weights and other configuration parameters of NN model 310. In some examples, input 305 is used for NN model 310 to provide output 315 that may include TA for one or more beams. In some cases, output 315 may include a TA for each respective beam and may be applied to a beam for uplink communication from UE 115 to network entity 105.

[0102] In some aspects, such as Figure 3B As depicted, the UE 115 may implement an ML model 300-b to predict a TA value for a particular node (e.g., a UE, a network entity, a DU, a TRP, etc.). In some cases, the ML model 300-b may represent an NNF that takes an input and produces an output. In some cases, the ML model 300-b may use an input 320 in the form of a TA value for a time period for a node. In some cases, the input 320 may represent a set of TA values ​​that have occurred in the past for the node. In some examples, the NN model 325 may include a model structure with a model ID and a parameter set with one or more weights and other configuration parameters of the NN model 325. In some examples, the input 320 is used for the NN model 325 to provide an output 330 that can predict an upcoming TA value for a node. In some cases, the output 330 provides a future TA value that the node can use for uplink communications from the UE 115 to the network entity 105.

[0103] Figure 4 An example of a process flow 400 for supporting ML-based TA updates according to one or more aspects of the present disclosure is illustrated. In some examples, the process flow 400 can implement aspects of the wireless communication systems 100 and 200 and the ML model 300. The process flow 400 may include a UE 115-b and a network entity 105-c, which may be as described herein, including reference Figure 1 An example of a corresponding device described to FIG. 3 .

[0104] At 405, the network entity 105-c may send one or more TA values. In some examples, the TA value may be based on one or more RSRP measurements at the UE 115-b.

[0105] At 410, UE 115-b may identify measurement parameters for input into the ML model. In some cases, UE 115-b may identify the measurement parameters by measuring one or more RSRP values ​​associated with one or more CSI-RS. Additionally or alternatively, the measurement parameters for the ML model may include a PDP associated with one or more beams, a PDP associated with network entity 105-c, a PDP associated with one or more TRPs, a TA value for a communication link, positioning of UE 115-b, positioning signaling information associated with RF sensing, positioning signaling information associated with a camera, positioning signaling information associated with a radar at UE 115-b, a TCIID, or a combination of the listed measurement parameters.

[0106] At 415, UE 115-b may train an ML model based on the TA value and the measured parameters for the input. In some cases, the ML model may include an NNF that uses the input, applies a model structure and parameter set to the input using a NN model, and produces an output.

[0107] At 420, the UE 115-b may employ the output of the ML model to predict one or more TA values ​​for one or more beams or future TA values ​​for a particular node. In some examples, the output TA values ​​may be applied to communications between the UE 115-b and the network entity 105-c.

[0108] At 425, UE 115-b may send an uplink communication to network entity 105-c. In some examples, the one or more TA values ​​predicted at 420 may be implemented into the uplink communication to enable UE 115-b to account for the time it takes for the transmission to travel to network entity 105-c.

[0109] At 430, the UE 115-b may send an additional capability report to the network entity 105-c indicating the ability of the UE 115-b to support autonomous updating of the TA. In some examples, at 420, the UE 115-b may support autonomous updating of the TA based on the predicted TA. In some cases, the capability report may include the ability of the UE 115-b to support at least one TA group module type, the number of TA groups for autonomously updating the TA, the input type for TA prediction, or a combination of capabilities. In some cases, the capability report indicates the ability of the UE 115-b to update the TA value without assistance from the network entity 105-c.

[0110] Figure 5 A block diagram 500 of a device 505 supporting ML-based TA updates according to one or more aspects of the present disclosure is shown. The device 505 may be an example of aspects of the UE 115 as described herein. The device 505 may include a receiver 510, a transmitter 515, and a communication manager 520. The device 505 may also include a processor. Each of these components may communicate with each other (e.g., via one or more buses).

[0111] The receiver 510 may provide means for receiving information such as packets associated with various information channels (e.g., control channels, data channels, information channels related to ML-based TA updates), user data, control information, or any combination thereof. The information may be communicated to other components of the device 505. The receiver 510 may utilize a single antenna or a collection of multiple antennas.

[0112] The transmitter 515 may provide means for transmitting signals generated by other components of the device 505. For example, the transmitter 515 may transmit information such as packets associated with various information channels (e.g., control channels, data channels, information channels related to ML-based TA updates), user data, control information, or any combination thereof. In some examples, the transmitter 515 may be co-located with the receiver 510 in a transceiver module. The transmitter 515 may utilize a single antenna or a collection of multiple antennas.

[0113] The communication manager 520, the receiver 510, the transmitter 515, or various combinations thereof, or various components thereof, may be examples of means for performing various aspects of ML-based TA updates as described herein. For example, the communication manager 520, the receiver 510, the transmitter 515, or various combinations thereof, or components thereof, may support methods for performing one or more of the functions described herein.

[0114] In some examples, the communication manager 520, the receiver 510, the transmitter 515, or various combinations or components thereof may be implemented in hardware (e.g., in a communication management circuit). The hardware may include a processor, a digital signal processor (DSP), a central processing unit (CPU), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic device, a microcontroller, discrete gate or transistor logic, discrete hardware components, or any combination thereof that is configured to or otherwise supports components for performing the functions described in the present disclosure. In some examples, the processor and a memory coupled to the processor may be configured to perform one or more of the functions described herein (e.g., by executing instructions stored in the memory by the processor).

[0115] Additionally or alternatively, in some examples, the communication manager 520, the receiver 510, the transmitter 515, or various combinations or components thereof may be implemented in code executed by a processor (e.g., as communication management software or firmware). If implemented in code executed by a processor, the functionality of the communication manager 520, the receiver 510, the transmitter 515, or various combinations or components thereof may be performed by a general purpose processor (e.g., configured as or otherwise supporting components for performing the functions described in the present disclosure), a DSP, a CPU, an ASIC, an FPGA, a microcontroller, or any combination of these or other programmable logic devices.

[0116] In some examples, communication manager 520 can be configured to perform various operations (e.g., receive, obtain, monitor, output, send) using or otherwise cooperating with receiver 510, transmitter 515, or both. For example, communication manager 520 can receive information from receiver 510, transmit information to transmitter 515, or be integrated with receiver 510, transmitter 515, or both to obtain information, output information, or perform various other operations as described herein.

[0117] According to examples as disclosed herein, the communication manager 520 may support wireless communications at a UE. For example, the communication manager 520 may be configured to or otherwise support components for identifying one or more measurement parameters for input to an ML model. The communication manager 520 may be configured to or otherwise support components for predicting a TA value for uplink communications from the UE to a network entity based on inputting the one or more measurement parameters to the ML model. The communication manager 520 may be configured to or otherwise support components for sending uplink communications to the network entity based on the predicted TA value.

[0118] By including or configuring a communication manager 520 according to the examples described herein, a device 505 (e.g., a processor controlling or otherwise coupled to a receiver 510, a transmitter 515, a communication manager 520, or a combination thereof) may support techniques for reducing processing, reducing power consumption, and more efficiently utilizing communication resources.

[0119] Figure 6 A block diagram 600 of a device 605 supporting ML-based TA updates according to one or more aspects of the present disclosure is shown. The device 605 may be an example of aspects of the device 505 or UE 115 as described herein. The device 605 may include a receiver 610, a transmitter 615, and a communication manager 620. The device 605 may also include a processor. Each of these components may communicate with each other (e.g., via one or more buses).

[0120] The receiver 610 may provide means for receiving information such as packets associated with various information channels (e.g., control channels, data channels, information channels related to ML-based TA updates), user data, control information, or any combination thereof. The information may be communicated to other components of the device 605. The receiver 610 may utilize a single antenna or a collection of multiple antennas.

[0121] The transmitter 615 may provide means for transmitting signals generated by other components of the device 605. For example, the transmitter 615 may transmit information such as packets associated with various information channels (e.g., control channels, data channels, information channels related to ML-based TA updates), user data, control information, or any combination thereof. In some examples, the transmitter 615 may be co-located with the receiver 610 in a transceiver module. The transmitter 615 may utilize a single antenna or a collection of multiple antennas.

[0122] The device 605 or its various components may be examples of components for performing various aspects of ML-based TA updates as described herein. For example, the communication manager 620 may include a measurement parameter component 625, a TA value component 630, an uplink communication component 635, or any combination thereof. The communication manager 620 may be an example of various aspects of the communication manager 520 as described herein. In some examples, the communication manager 620 or its various components may be configured to perform various operations (e.g., receive, obtain, monitor, output, send) using or otherwise cooperating with the receiver 610, the transmitter 615, or both. For example, the communication manager 620 may receive information from the receiver 610, transmit information to the transmitter 615, or be integrated with the receiver 610, the transmitter 615, or both to obtain information, output information, or perform various other operations as described herein.

[0123] According to examples as disclosed herein, the communication manager 620 can support wireless communications at a UE. The measurement parameter component 625 can be configured to or otherwise support components for identifying one or more measurement parameters for input to an ML model. The TA value component 630 can be configured to or otherwise support components for predicting a TA value for uplink communications from the UE to a network entity based on inputting the one or more measurement parameters to the ML model. The uplink communication component 635 can be configured to or otherwise support components for sending uplink communications to the network entity based on the predicted TA value.

[0124] Figure 7A block diagram 700 of a communication manager 720 supporting ML-based TA updates according to one or more aspects of the present disclosure is shown. The communication manager 720 can be an example of aspects of the communication manager 520, the communication manager 620, or both as described herein. The communication manager 720 or its various components can be examples of components for performing various aspects of ML-based TA updates as described herein. For example, the communication manager 720 can include a measurement parameter component 725, a TA value component 730, an uplink communication component 735, a capability reporting component 740, an ML training component 745, a pause prediction component 750, a measurement parameter request component 755, a measurement parameter indication component 760, a signaling component 765, an ML request component 770, an ML mapping component 775, or any combination thereof. Each of these components can communicate with each other directly or indirectly (e.g., via one or more buses).

[0125] According to examples as disclosed herein, the communication manager 720 can support wireless communications at a UE. The measurement parameter component 725 can be configured to or otherwise support components for identifying one or more measurement parameters for input to an ML model. The TA value component 730 can be configured to or otherwise support components for predicting a TA value for uplink communications from the UE to a network entity based on inputting the one or more measurement parameters to the ML model. The uplink communication component 735 can be configured to or otherwise support components for sending uplink communications to the network entity based on the predicted TA value.

[0126] In some examples, capability reporting component 740 may be configured or otherwise support means for sending a capability report to a network entity indicating the UE's ability to support autonomous updating of a TA based on a predicted TA value.

[0127] In some examples, the capability report further indicates the UE's ability to support at least one of: a TA group module type, a number of TA groups for autonomous update of the TA, an input type for TA prediction, or a combination thereof.

[0128] In some examples, TA value component 730 can be configured or otherwise support means for receiving an indication of a set of TA values ​​from a network entity, wherein the set of TA values ​​is based on at least one reference signal received power measurement at the UE. In some examples, ML training component 745 can be configured or otherwise support means for training an ML model based on the set of TA values, wherein predicting the TA value is based on inputting one or more measurement parameters to the trained ML model.

[0129] In some examples, to support identifying one or more measurement parameters, measurement parameter component 725 may be configured as or otherwise support components for measuring one or more reference signal received power values ​​associated with one or more channel state reference signals, wherein predicting a TA value is based on inputting the one or more reference signal received power values ​​into an ML model.

[0130] In some examples, TA value component 730 can be configured or otherwise support means for determining that the predicted TA value corresponds to an output port associated with the ML model. In some examples, ML mapping component 775 can be configured or otherwise support means for identifying a mapping between a cell identifier and an output port associated with the ML model, wherein sending the uplink communication includes sending the uplink communication associated with the cell identifier according to the predicted TA value.

[0131] In some examples, the ML mapping component 775 can be configured to or otherwise support means for identifying a mapping between a TA group identifier and an output port associated with the ML model, wherein sending the uplink communication includes sending the uplink communication associated with the TA group identifier according to the predicted TA value.

[0132] In some examples, pause prediction component 750 can be configured as or otherwise support means for receiving a request from a network entity to pause predicting future TA values ​​for the second uplink communication. In some examples, TA value component 730 can be configured as or otherwise support means for refraining from predicting future TA values ​​for the second uplink communication based on receiving the request.

[0133] In some examples, measurement parameter request component 755 can be configured or otherwise support means for receiving a request for one or more measurement parameters input to the ML model from a network entity. In some examples, measurement parameter indication component 760 can be configured or otherwise support means for sending an indication of one or more measurement parameters to a network entity in response to the request.

[0134] In some examples, signaling component 765 can be configured or otherwise support means for sending a random access signal or a sounding reference signal, or both, to a network entity. In some examples, TA value component 730 can be configured or otherwise support means for receiving an indication of a second TA value from a network entity based on sending a random access signal or a sounding reference signal, or both.

[0135] In some examples, ML request component 770 can be configured or otherwise support means for receiving a request to update the ML model from a network entity. In some examples, TA value component 730 can be configured or otherwise support means for updating the ML model based on the request, wherein the predicted TA value is based on inputting one or more measured parameters to the updated ML model.

[0136] In some examples, to support sending uplink communications, the uplink communications component 735 may be configured as or otherwise support components for sending uplink communications to a network entity at a first time instance based on a predicted TA value, wherein one or more measurement parameters include a set of TA values ​​corresponding to a set of time instances prior to the first time instance.

[0137] In some examples, measurement parameter component 725 may be configured or otherwise support means for one or more measurement parameters including at least one TA value associated with the second network entity.

[0138] In some examples, the one or more measurement parameters include at least one of: a reference signal received power, a power delay distribution associated with one or more beams, a power delay distribution associated with a network entity, a power delay distribution associated with one or more transmitting and receiving points, a TA value for a communication link, positioning of the UE, positioning signaling information associated with radio frequency sensing, positioning signaling information associated with a camera, positioning signaling information associated with a radar at the UE, a transmission configuration indicator identifier, or a combination thereof.

[0139] Figure 8 A diagram of a system 800 including a device 805 supporting ML-based TA updates according to one or more aspects of the present disclosure is shown. The device 805 may be an example of a device 505, a device 605, or a UE 115 as described herein, or include components thereof. The device 805 may communicate (e.g., wirelessly) with one or more network entities 105, one or more UEs 115, or any combination thereof. The device 805 may include components for two-way voice and data communications, including components for sending and receiving communications, such as a communication manager 820, an input / output (I / O) controller 810, a transceiver 815, an antenna 825, a memory 830, a code 835, and a processor 840. These components may be in electronic communication or otherwise coupled (e.g., operatively, communicatively, functionally, electronically, electrically) via one or more buses (e.g., a bus 845).

[0140] I / O controller 810 can manage input signals and output signals of device 805. I / O controller 810 can also manage peripheral devices that are not integrated into device 805. In some cases, I / O controller 810 can represent a physical connection or port to an external peripheral device. In some cases, I / O controller 810 can utilize an operating system, such as or another known operating system. Additionally or alternatively, I / O controller 810 may represent or interact with a modem, keyboard, mouse, touch screen, or similar device. In some cases, I / O controller 810 may be implemented as part of a processor (such as processor 840). In some cases, a user may interact with device 805 via I / O controller 810 or via hardware components controlled by I / O controller 810.

[0141] In some cases, the device 805 may include a single antenna 825. However, in some other cases, the device 805 may have more than one antenna 825, which may be capable of concurrently transmitting or receiving multiple wireless transmissions. The transceiver 815 may communicate bidirectionally via one or more antennas 825, wired or wireless links as described herein. For example, the transceiver 815 may represent a wireless transceiver and may communicate bidirectionally with another wireless transceiver. The transceiver 815 may also include a modem for: modulating packets; providing the modulated packets to one or more antennas 825 for transmission; and demodulating packets received from one or more antennas 825. The transceiver 815 or the transceiver 815 and one or more antennas 825 may be examples of transmitters 515, transmitters 615, receivers 510, receivers 610, or any combination thereof or components thereof as described herein.

[0142] The memory 830 may include random access memory (RAM) and read-only memory (ROM). The memory 830 may store computer-readable, computer-executable code 835 including instructions that, when executed by the processor 840, cause the device 805 to perform various functions described herein. The code 835 may be stored in a non-transitory computer-readable medium (such as system memory or another type of memory). In some cases, the code 835 may not be directly executable by the processor 840, but may (e.g., when compiled and executed) cause the computer to perform the functions described herein. In some cases, the memory 830 may include, among other things, a basic I / O system (BIOS) that may control basic hardware or software operations, such as interaction with peripheral components or devices.

[0143] The processor 840 may include an intelligent hardware device (e.g., a general-purpose processor, a DSP, a CPU, a microcontroller, an ASIC, an FPGA, a programmable logic device, a discrete gate or transistor logic component, a discrete hardware component, or any combination thereof). In some cases, the processor 840 may be configured to operate a memory array using a memory controller. In some other cases, the memory controller may be integrated into the processor 840. The processor 840 may be configured to execute computer-readable instructions stored in a memory (e.g., a memory 830) to cause the device 805 to perform various functions (e.g., functions or tasks that support ML-based TA updates). For example, the device 805 or a component of the device 805 may include a processor 840 and a memory 830 coupled to or coupled to the processor 840, and the processor 840 and the memory 830 are configured to perform the various functions described herein.

[0144] According to examples as disclosed herein, the communication manager 820 may support wireless communications at a UE. For example, the communication manager 820 may be configured to or otherwise support components for identifying one or more measurement parameters for input to an ML model. The communication manager 820 may be configured to or otherwise support components for predicting a TA value for uplink communications from the UE to a network entity based on inputting the one or more measurement parameters to the ML model. The communication manager 820 may be configured to or otherwise support components for sending uplink communications to the network entity based on the predicted TA value.

[0145] By including or configuring an example communication manager 820 as described herein, the device 805 can support techniques for improving communication reliability, reducing latency, improving user experience related to reduced processing, reducing power consumption, more efficiently utilizing communication resources, and improving coordination between devices.

[0146] In some examples, the communication manager 820 may be configured to perform various operations (e.g., receive, monitor, transmit) using or otherwise cooperating with the transceiver 815, one or more antennas 825, or any combination thereof. Although the communication manager 820 is illustrated as a separate component, in some examples, one or more functions described with reference to the communication manager 820 may be supported or performed by the processor 840, the memory 830, the code 835, or any combination thereof. For example, the code 835 may include instructions that are executable by the processor 840 to cause the device 805 to perform various aspects of ML-based TA updates as described herein, or the processor 840 and the memory 830 may be otherwise configured to perform or support such operations.

[0147] Fig. 91 shows a flowchart illustrating a method 900 for supporting ML-based TA updates according to one or more aspects of the present disclosure. The operations of the method 900 may be implemented by a UE or a component thereof as described herein. For example, the operations of the method 900 may be implemented by a UE or a component thereof as described in reference to Figures 1 to 8 The described UE 115 may be performed. In some examples, the UE may execute an instruction set to control the functional elements of the UE to perform the described functions. Additionally or alternatively, the UE may use dedicated hardware to perform various aspects of the described functions.

[0148] At 905, the method may include identifying one or more measurement parameters for input to the ML model. The operations of 905 may be performed according to examples as disclosed herein. In some examples, aspects of the operations of 905 may be performed as described in reference to Figure 7 The described measurement parameter component 725 is performed.

[0149] At 910, the method may include predicting a TA value for uplink communication from the UE to the network entity based on inputting one or more measurement parameters into the ML model. The operations of 910 may be performed according to examples as disclosed herein. In some examples, aspects of the operations of 910 may be performed as described in reference to Figure 7 The TA value component 730 is used to perform.

[0150] At 915, the method may include sending an uplink communication to a network entity based on the predicted TA value. The operations of 915 may be performed according to examples as disclosed herein. In some examples, aspects of the operations of 915 may be performed as described in reference to Figure 7 The described uplink communication component 735 is performed.

[0151] Fig.10 1 is a flowchart illustrating a method 1000 for supporting ML-based TA updates according to one or more aspects of the present disclosure. The operations of the method 1000 may be implemented by a UE or a component thereof as described herein. For example, the operations of the method 1000 may be implemented by a UE or a component thereof as described in reference to Figures 1 to 8 The described UE 115 may be performed. In some examples, the UE may execute an instruction set to control the functional elements of the UE to perform the described functions. Additionally or alternatively, the UE may use dedicated hardware to perform various aspects of the described functions.

[0152] At 1005, the method may include identifying one or more measurement parameters for input to the ML model. The operations of 1005 may be performed according to examples as disclosed herein. In some examples, aspects of the operations of 1005 may be performed as described in reference to Figure 7 The described measurement parameter component 725 is performed.

[0153] At 1010, the method may include predicting a TA value for uplink communication from the UE to the network entity based on inputting one or more measurement parameters into the ML model. The operations of 1010 may be performed according to examples as disclosed herein. In some examples, aspects of the operations of 1010 may be performed as described in reference to Figure 7 The TA value component 730 is used to perform.

[0154] At 1015, the method may include sending an uplink communication to a network entity according to the predicted TA value. The operations of 1015 may be performed according to examples as disclosed herein. In some examples, aspects of the operations of 1015 may be performed as described in reference to Figure 7 The described uplink communication component 735 is performed.

[0155] At 1020, the method may include sending a capability report to a network entity, the capability report indicating the UE's ability to support autonomous update of the TA based on the predicted TA value. The operations of 1020 may be performed according to examples as disclosed herein. In some examples, aspects of the operations of 1020 may be performed as described in reference to Figure 7 The capability reporting component 740 is used to execute.

[0156] Fig.11 1 is a flowchart illustrating a method 1100 for supporting ML-based TA updates according to one or more aspects of the present disclosure. The operations of the method 1100 may be implemented by a UE or a component thereof as described herein. For example, the operations of the method 1100 may be implemented by a UE or a component thereof as described in reference to Figures 1 to 8 The described UE 115 may be performed. In some examples, the UE may execute an instruction set to control the functional elements of the UE to perform the described functions. Additionally or alternatively, the UE may use dedicated hardware to perform various aspects of the described functions.

[0157] At 1105, the method may include receiving an indication of a set of TA values ​​from a network entity, wherein the set of TA values ​​is based on at least one reference signal received power measurement at the UE. The operations of 1105 may be performed according to examples as disclosed herein. In some examples, aspects of the operations of 1105 may be performed by Figure 7 The TA value component 730 is used to perform.

[0158] At 1110, the method may include identifying one or more measurement parameters for input to the ML model. The operations of 1110 may be performed according to examples as disclosed herein. In some examples, aspects of the operations of 1110 may be performed as described in reference to Figure 7 The described measurement parameter component 725 is performed.

[0159] At 1115, the method may include training an ML model based on the set of TA values, wherein the predicted TA value is based on inputting one or more measured parameters into the trained ML model. The operations of 1115 may be performed according to examples disclosed herein. In some examples, aspects of the operations of 1115 may be performed as described in reference to Figure 7 The ML training component 745 is used to perform.

[0160] At 1120, the method may include predicting a TA value for uplink communication from the UE to the network entity based on inputting one or more measurement parameters into the ML model. The operations of 1120 may be performed according to examples as disclosed herein. In some examples, aspects of the operations of 1120 may be performed as described in reference to Figure 7 The TA value component 730 is used to perform.

[0161] At 1125, the method may include sending an uplink communication to a network entity based on the predicted TA value. The operations of 1125 may be performed according to examples as disclosed herein. In some examples, aspects of the operations of 1125 may be performed as described in reference to Figure 7 The described uplink communication component 735 is performed.

[0162] The following provides an overview of various aspects of the disclosure:

[0163] Aspect 1: A method for wireless communication at a UE, the method comprising: identifying one or more measurement parameters for input to an ML model; predicting a TA value for uplink communication from the UE to a network entity based at least in part on inputting the one or more measurement parameters to the ML model; and sending the uplink communication to the network entity according to the predicted TA value.

[0164] Aspect 2: According to the method of aspect 1, the method also includes: sending a capability report to the network entity, the capability report indicating the capability of the UE to support autonomous update of the TA based at least in part on predicting the TA value.

[0165] Aspect 3: The method according to aspect 2, wherein the capability report further indicates the capability of the UE to support at least one of the following: a TA group module type, a number of TA groups for autonomous update of the TA, an input type for TA prediction, or a combination thereof.

[0166] Aspect 4: According to any one of Aspects 1 to 3, the method also includes: receiving an indication of a set of TA values ​​from the network entity, wherein the set of TA values ​​is based at least in part on at least one reference signal received power measurement at the UE; and training the ML model based at least in part on the set of TA values, wherein predicting the TA value is based at least in part on inputting the one or more measurement parameters into the trained ML model.

[0167] Aspect 5: A method according to any one of Aspects 1 to 4, wherein identifying the one or more measurement parameters also includes: measuring one or more reference signal received power values ​​associated with the one or more channel state reference signals, wherein predicting the TA value is at least partially based on inputting the one or more reference signal received power values ​​into the ML model.

[0168] Aspect 6: The method according to any one of Aspects 1 to 5, further comprising: determining that the predicted TA value corresponds to an output port associated with the ML model.

[0169] Aspect 7: According to the method according to Aspect 6, the method also includes: identifying a mapping between a cell identifier and the output port associated with the ML model, wherein sending the uplink communication includes sending the uplink communication associated with the cell identifier according to the predicted TA value.

[0170] Aspect 8: According to the method described in any one of Aspects 6 to 7, the method further includes: identifying a mapping between a TA group identifier and the output port associated with the ML model, wherein sending the uplink communication includes sending the uplink communication associated with the TA group identifier according to the predicted TA value.

[0171] Aspect 9: According to the method described in any one of Aspects 1 to 8, the method also includes: receiving a request from the network entity to suspend prediction of future TA values ​​for second uplink communication; and suppressing prediction of the future TA value for the second uplink communication based at least in part on receiving the request.

[0172] Aspect 10: According to the method described in any one of Aspects 1 to 9, the method further includes: receiving a request for the one or more measurement parameters input to the ML model from the network entity; and sending an indication of the one or more measurement parameters to the network entity in response to the request.

[0173] Aspect 11: According to any one of Aspects 1 to 10, the method further includes: sending a random access signal or a sounding reference signal or both to the network entity; and receiving an indication of a second TA value from the network entity at least partially based on sending the random access signal or the sounding reference signal or both.

[0174] Aspect 12: According to the method of any one of Aspects 1 to 11, the method also includes: receiving a request to update the ML model from the network entity; and updating the ML model based at least in part on the request, wherein predicting the TA value is based at least in part on inputting the one or more measurement parameters into the updated ML model.

[0175] Aspect 13: A method according to any one of Aspects 1 to 12, wherein sending the uplink communication further comprises: sending the uplink communication to the network entity at a first time instance based on the predicted TA value, wherein the one or more measurement parameters include a set of TA values ​​corresponding to a set of time instances before the first time instance.

[0176] Aspect 14: The method according to any one of Aspects 1 to 13, the method further comprising: the one or more measurement parameters comprising at least one TA value associated with the second network entity.

[0177] Aspect 15: A method according to any one of Aspects 1 to 14, wherein the one or more measurement parameters include at least one of the following: a reference signal received power, a power delay distribution associated with one or more beams, a power delay distribution associated with the network entity, a power delay distribution associated with one or more transmitting and receiving points, a TA value for a communication link, the positioning of the UE, positioning signaling information associated with radio frequency sensing, the positioning signaling information associated with a camera, the positioning signaling information associated with a radar at the UE, a transmission configuration indicator identifier, or a combination thereof.

[0178] Aspect 16: An apparatus for performing wireless communications at a UE, the apparatus comprising: a processor; a memory coupled to the processor; and instructions stored in the memory and executable by the processor to cause the apparatus to perform a method according to any one of Aspects 1 to 15.

[0179] Aspect 17: An apparatus for wireless communication at a UE, the apparatus comprising at least one component for performing the method according to any one of aspects 1 to 15.

[0180] Aspect 18: A non-transitory computer-readable medium storing a code for wireless communication at a UE, the code comprising instructions executable by a processor to perform a method according to any one of aspects 1 to 15.

[0181] It should be noted that the methods described herein describe possible implementations, and that the operations and steps may be rearranged or otherwise modified and other implementations are possible. In addition, aspects from two or more methods may be combined.

[0182] Although aspects of LTE, LTE-A, LTE-A Pro, or NR systems may be described for example purposes, and LTE, LTE-A, LTE-A Pro, or NR terminology may be used in much of the description, the techniques described herein may also be applicable to networks other than LTE, LTE-A, LTE-A Pro, or NR networks. For example, the techniques described may be applicable to various other wireless communication systems, such as Ultra Mobile Broadband (UMB), Institute of Electrical and Electronics Engineers (IEEE) 802.11 (Wi-Fi), IEEE 802.16 (WiMAX), IEEE 802.20, Flash-OFDM, and other systems and radio technologies not explicitly mentioned herein.

[0183] The information and signals described herein may be represented using any of a variety of different technologies and methods. For example, data, instructions, commands, information, signals, bits, symbols, and chips mentioned throughout the specification may be represented by voltage, current, electromagnetic waves, magnetic fields or magnetic particles, optical fields or optical particles, or any combination thereof.

[0184] The various illustrative blocks and components described in conjunction with the disclosure herein may be implemented or performed using a general purpose processor, DSP, ASIC, CPU, FPGA or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general purpose processor may be a microprocessor, but in an alternative, the processor may be any processor, controller, microcontroller, or state machine. The processor may also be implemented as a combination of computing devices (e.g., a combination of a DSP and a microprocessor, multiple microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration).

[0185] The functions described herein can be implemented using hardware, software executed by a processor, firmware, or any combination thereof. When implemented using software executed by a processor, the functions can be stored as one or more instructions or codes of a computer-readable medium, or sent using one or more instructions or codes of a computer-readable medium. Other examples and specific implementations are within the scope of the present disclosure and the appended claims. For example, due to the nature of software, the functions described herein can be implemented using software executed by a processor, hardware, firmware, hard wiring, or a combination of any of these items. Features that implement the functions can also be physically located at different locations, including being distributed so that the various parts of the functions are implemented at different physical locations.

[0186] Computer-readable medium includes both non-transient computer storage medium and communication medium, and it includes any medium that facilitates computer program to be transmitted from one position to another position.Non-transient storage medium can be any available medium that can be accessed by general or special-purpose computer.By way of example and not limitation, non-transient computer-readable medium can include RAM, ROM, electrically erasable programmable ROM (EEPROM), flash memory, compact disc (CD) ROM or other optical disc storage device, disk storage device or other magnetic storage device or can be used for carrying or storing desired program code parts and any other non-transient medium that can be accessed by general or special-purpose computer or general or special-purpose processor in the form of instruction or data structure.Moreover, any connection is appropriately referred to as computer-readable medium.For example, if software is sent from website, server or other remote source using coaxial cable, fiber optic cable, twisted pair, digital subscriber line (DSL) or wireless technology such as infrared, radio and microwave, then coaxial cable, fiber optic cable, twisted pair, DSL or wireless technology such as infrared, radio and microwave are included in the definition of computer-readable medium. Disks and optical disks used herein include CDs, laser optical disks, optical optical disks, digital versatile disks (DVDs), floppy disks, and Blu-ray disks. Disks can reproduce data magnetically, while optical disks can reproduce data optically using lasers. Combinations of the above are also included within the scope of computer-readable media.

[0187] As used herein (including in the claims), "or" used in a list of items (e.g., a list of items followed by a phrase such as "at least one of" or "one or more of") indicates an inclusive list, so that, for example, a list of at least one of A, B, or C means A or B or C or AB or AC or BC or ABC (i.e., A and B and C). In addition, as used herein, the phrase "based on" should not be interpreted as a reference to a closed set of conditions. For example, an example step described as "based on condition A" can be based on both condition A and condition B without departing from the scope of the present disclosure. In other words, as used herein, the phrase "based on" should be interpreted in the same manner as the phrase "based at least in part on."

[0188] The term "determining" encompasses a variety of actions, and thus, "determining" may include calculating, computing, processing, deriving, investigating, looking up (such as via looking up in a table, database or other data structure), ascertaining, and the like. Furthermore, "determining" may include receiving (e.g., receiving information), accessing (e.g., accessing data stored in a memory), etc. Additionally, "determining" may include parsing, obtaining, selecting, choosing, establishing, and other such similar actions.

[0189] In the drawings, similar components or features may have the same reference label. In addition, various components of the same type may be distinguished by following the reference label with a dash and a second label to distinguish between similar components. If only the first reference label is used in the specification, the description may apply to any of the similar components having the same first reference label, regardless of the second or other subsequent reference labels.

[0190] The descriptions set forth herein in conjunction with the accompanying drawings describe example configurations and do not represent all examples that may be implemented or within the scope of the claims. The term "example" as used herein means "used as an example, instance, or illustration," rather than "preferred" or "advantageous over other examples." The specific implementation includes specific details to provide an understanding of the described techniques. However, these techniques may be practiced without these specific details. In some cases, known structures and devices are shown in block diagram form to avoid obscuring the concepts of the described examples.

[0191] The description herein is provided to enable one of ordinary skill in the art to implement or use the present disclosure. Various modifications to the present disclosure will be apparent to one of ordinary skill in the art, and the general principles defined herein may be applied to other variations without departing from the scope of the present disclosure. Thus, the present disclosure is not limited to the examples and designs described herein, but should be granted the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A device for wireless communication at a user equipment (UE), the device include: processor; a memory coupled to the processor; and instructions stored in the memory and executable by the processor to cause the apparatus to: identifying one or more measurement parameters for input to the machine learning model; predicting a timing advance value for uplink communications from the UE to a network entity based at least in part on inputting the one or more measurement parameters into the machine learning model; as well as The uplink communication is sent to the network entity according to the predicted timing advance value.

2. The apparatus of claim 1 , wherein the instructions are further executable by the processor to cause the apparatus to: A capability report is sent to the network entity, the capability report indicating a capability of the UE to support autonomous updating of timing advance based at least in part on predicting the timing advance value.

3. The apparatus of claim 2, wherein the capability report further indicates a capability of the UE to support at least one of: a timing advance group module type, a number of timing advance groups for autonomous update of the timing advance, an input type for timing advance prediction, or a combination thereof.

4. The apparatus of claim 1, wherein the instructions are further executable by the processor to cause the apparatus to: receiving an indication of a set of timing advance values ​​from the network entity, wherein the set of timing advance values ​​is based at least in part on at least one reference signal received power measurement at the UE; and The machine learning model is trained based at least in part on the set of timing advance values, wherein predicting the timing advance value is based at least in part on inputting the one or more measured parameters into the trained machine learning model.

5. The apparatus of claim 1 , wherein the instructions for identifying the one or more measurement parameters are further executable by the processor to cause the apparatus to: measuring one or more reference signal received power values ​​associated with one or more channel state reference signals, wherein predicting the timing advance value is based at least in part on inputting the one or more reference signal received power values ​​into the machine learning model.

6. The apparatus of claim 1, wherein the instructions are further executable by the processor to cause the apparatus to: Determine whether the predicted timing advance value corresponds to an output port associated with the machine learning model.

7. The apparatus of claim 6, wherein the instructions are further executable by the processor to cause the apparatus to: A mapping between a cell identifier and the output port associated with the machine learning model is identified, wherein sending the uplink communication comprises sending the uplink communication associated with the cell identifier according to the predicted timing advance value.

8. The apparatus of claim 6, wherein the instructions are further executable by the processor to cause the apparatus to: Identify a mapping between a timing advance group identifier and the output port associated with the machine learning model, wherein sending the uplink communication includes sending the uplink communication associated with the timing advance group identifier based on the predicted timing advance value.

9. The apparatus of claim 1, wherein the instructions are further executable by the processor to cause the apparatus to: receiving a request from the network entity to suspend predicting future timing advance values ​​for a second uplink communication; and Based at least in part on receiving the request, refraining from predicting the future timing advance value for the second uplink communication.

10. The apparatus of claim 1, wherein the instructions are further executable by the processor to cause the apparatus to: receiving, from the network entity, a request for the one or more measurement parameters to be input to the machine learning model; and An indication of the one or more measurement parameters is sent to the network entity in response to the request.

11. The apparatus of claim 1 , wherein the instructions are further executable by the processor to cause the apparatus to: sending a random access signal or a sounding reference signal or both to the network entity; and An indication of a second timing advance value is received from the network entity based at least in part on sending the random access signal or the sounding reference signal, or both.

12. The apparatus of claim 1, wherein the instructions are further executable by the processor to cause the apparatus to: receiving a request from the network entity to update the machine learning model; and The machine learning model is updated based at least in part on the request, wherein predicting the timing advance value is based at least in part on inputting the one or more measurement parameters into the updated machine learning model.

13. The apparatus of claim 1, wherein the instructions for sending the uplink communication are further executable by the processor to cause the apparatus to: The uplink communication is sent to the network entity at a first time instance according to the predicted timing advance value, wherein the one or more measurement parameters include a set of timing advance values ​​corresponding to a set of time instances prior to the first time instance.

14. The apparatus of claim 1, wherein the instructions are further executable by the processor to cause the apparatus to: The one or more measurement parameters include at least one timing advance value associated with the second network entity.

15. The apparatus of claim 1 , wherein the one or more measurement parameters comprise at least one of: a reference signal received power, a power delay profile associated with one or more beams, a power delay profile associated with the network entity, a power delay profile associated with one or more transmitting and receiving points, a timing advance value for a communication link, the positioning of the UE, positioning signaling information associated with radio frequency sensing, the positioning signaling information associated with a camera, the positioning signaling information associated with a radar at the UE, a transmission configuration indicator identifier, or a combination thereof.

16. A method for wireless communication at a user equipment (UE), the method include: identifying one or more measurement parameters for input to the machine learning model; predicting a timing advance value for uplink communications from the UE to a network entity based at least in part on inputting the one or more measurement parameters into the machine learning model; as well as The uplink communication is sent to the network entity according to the predicted timing advance value.

17. The method according to claim 16, further comprising: include: A capability report is sent to the network entity, the capability report indicating a capability of the UE to support autonomous updating of timing advance based at least in part on predicting the timing advance value.

18. The method of claim 17, wherein the capability report further indicates the capability of the UE to support at least one of: a timing advance group module type, a number of timing advance groups for autonomous update of the timing advance, an input type for timing advance prediction, or a combination thereof.

19. The method according to claim 16, further comprising: include: receiving, from the network entity, an indication of a set of timing advance values, wherein the set of timing advance values ​​is based at least in part on at least one reference signal received power measurement at the UE; as well as The machine learning model is trained based at least in part on the set of timing advance values, wherein predicting the timing advance value is based at least in part on inputting the one or more measured parameters into the trained machine learning model.

20. The method of claim 16, wherein identifying the one or more measurement parameters further comprises: include: measuring one or more reference signal received power values ​​associated with one or more channel state reference signals, wherein predicting the timing advance value is based at least in part on inputting the one or more reference signal received power values ​​into the machine learning model.

21. The method according to claim 16, further comprising: include: Determine whether the predicted timing advance value corresponds to an output port associated with the machine learning model.

22. The method according to claim 21, further comprising: include: A mapping between a cell identifier and the output port associated with the machine learning model is identified, wherein sending the uplink communication comprises sending the uplink communication associated with the cell identifier according to the predicted timing advance value.

23. The method according to claim 21, further comprising: include: Identify a mapping between a timing advance group identifier and the output port associated with the machine learning model, wherein sending the uplink communication includes sending the uplink communication associated with the timing advance group identifier based on the predicted timing advance value.

24. The method according to claim 16, further comprising: include: receiving a request from the network entity to suspend prediction of future timing advance values ​​for a second uplink communication; as well as Based at least in part on receiving the request, refraining from predicting the future timing advance value for the second uplink communication.

25. The method according to claim 16, further comprising: include: receiving, from the network entity, a request for the one or more measurement parameters to be input to the machine learning model; as well as An indication of the one or more measurement parameters is sent to the network entity in response to the request.

26. The method according to claim 16, further comprising: include: sending a random access signal or a sounding reference signal or both to the network entity; as well as An indication of a second timing advance value is received from the network entity based at least in part on sending the random access signal or the sounding reference signal, or both.

27. The method according to claim 16, further comprising: include: receiving a request from the network entity to update the machine learning model; as well as The machine learning model is updated based at least in part on the request, wherein predicting the timing advance value is based at least in part on inputting the one or more measurement parameters into the updated machine learning model.

28. The method of claim 16, wherein sending the uplink communication further comprises: include: The uplink communication is sent to the network entity at a first time instance according to the predicted timing advance value, wherein the one or more measurement parameters include a set of timing advance values ​​corresponding to a set of time instances prior to the first time instance.

29. An apparatus for wireless communication at a user equipment (UE), the apparatus include: means for identifying one or more measurement parameters for input to a machine learning model; means for predicting a timing advance value for uplink communications from the UE to a network entity based at least in part on inputting the one or more measurement parameters into the machine learning model; and Means for sending the uplink communication to the network entity based on the predicted timing advance value.

30. A non-transitory computer-readable medium storing code for wireless communication at a user equipment (UE), the code comprising instructions executable by a processor to: identifying one or more measurement parameters for input to the machine learning model; predicting a timing advance value for uplink communications from the UE to a network entity based at least in part on inputting the one or more measurement parameters into the machine learning model; and The uplink communication is sent to the network entity according to the predicted timing advance value.

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