Dynamic sidelink channel access

Abstract

Certain aspects of the present disclosure provide techniques for dynamic sidelink channel access. An example method for wireless communications by a user equipment (UE) includes obtaining a configuration that indicates a function associated with determination of one or more values for a set of parameters associated with sidelink channel access; and communicating via a sidelink channel based at least in part on a first set of values for the set of parameters, wherein the first set of values are according to the function.

Description

Qualcomm Ref. No.: 2405903 WO 1DYNAMIC SIDELINK CHANNEL ACCESSCROSS REFERENCE TO RELATED APPLICATION(S)

[0001] The present Application for Patent claims priority to and benefit of Greece Patent Application No. 20240100731, filed October 16, 2024, which is hereby expressly incorporated by reference herein in its entirety.INTRODUCTIONField of the Disclosure

[0002] Aspects of the present disclosure relate to wireless communications, and more particularly, to techniques for sidelink channel access.Description of Related Art

[0003] Wireless communications systems are widely deployed to provide various telecommunication services such as telephony, video, data, messaging, broadcasts, or other similar types of services. These wireless communications systems may employ multiple-access technologies capable of supporting communications with multiple users by sharing available wireless communications system resources with those users.

[0004] Although wireless communications systems have made great technological advancements over many years, challenges still exist. For example, complex and dynamic environments can still attenuate or block signals between wireless transmitters and wireless receivers. Accordingly, there is a continuous desire to improve the technical performance of wireless communications systems, including, for example: improving speed and data carrying capacity of communications, improving efficiency of the use of shared communications mediums, reducing power used by transmitters and receivers while performing communications, improving reliability of wireless communications, avoiding redundant transmissions and / or receptions and related processing, improving the coverage area of wireless communications, increasing the number and types of devices that can access wireless communications systems, increasing the ability for different types of devices to intercommunicate, increasing the number and type of wireless communications mediums available for use, and the like. Consequently, there exists a need for further improvements in wireless communications systems to overcome the aforementioned technical challenges and others.D&S Ref. No.: QCM2405903WOQualcomm Ref. No.: 2405903 WO 2SUMMARY

[0005] Certain aspects provide a method for wireless communications by a user equipment (UE). The method includes obtaining a configuration that indicates a function associated with determination of one or more values for a set of parameters associated with sidelink channel access; and communicating via a sidelink channel based at least in part on a first set of values for the set of parameters, wherein the first set of values are according to the function.

[0006] Certain aspects provide a method for wireless communications by a network node. The method includes sending, to a UE, a configuration that indicates a function for determination of one or more values for a set of parameters associated with sidelink channel access; and obtaining, from the UE, an indication of a first set of values for the set of parameters.

[0007] Other aspects provide: one or more apparatuses operable, configured, or otherwise adapted to perform any portion of any method described herein (e.g., such that performance may be by only one apparatus or in a distributed fashion across multiple apparatuses); one or more non-transitory, computer-readable media comprising instructions that, when executed by one or more processors of one or more apparatuses, cause the one or more apparatuses to perform any portion of any method described herein (e.g., such that instructions may be included in only one computer-readable medium or in a distributed fashion across multiple computer-readable media, such that instructions may be executed by only one processor or by multiple processors in a distributed fashion, such that each apparatus of the one or more apparatuses may include one processor or multiple processors, and / or such that performance may be by only one apparatus or in a distributed fashion across multiple apparatuses); one or more computer program products embodied on one or more computer-readable storage media comprising code for performing any portion of any method described herein (e.g., such that code may be stored in only one computer-readable medium or across computer-readable media in a distributed fashion); and / or one or more apparatuses comprising one or more means for performing any portion of any method described herein (e.g., such that performance would be by only one apparatus or by multiple apparatuses in a distributed fashion). By way of example, an apparatus may comprise a processing system, a device with a processing system, or processing systems cooperating over one or more networks. An apparatus may comprise one or more memories; and one or more processors configured to cause the apparatus toD&S Ref. No.: QCM2405903WOQualcomm Ref. No.: 2405903 WO 3 perform any portion of any method described herein. In some examples, one or more of the processors may be preconfigured to perform various functions or operations described herein without requiring configuration by software.

[0008] The following description and the appended figures set forth certain features for purposes of illustration.BRIEF DESCRIPTION OF DRAWINGS

[0009] The appended figures depict certain features of the various aspects described herein and are not to be considered limiting of the scope of this disclosure.

[0010] FIG. 1 depicts an example wireless communications network.

[0011] FIG. 2 depicts an example disaggregated base station architecture.

[0012] FIG. 3 depicts aspects of network entities and a user equipment (UE).

[0013] FIGS. 4A, 4B, 4C, and 4D depict various example aspects of data structures for a wireless communications network.

[0014] FIG. 5 illustrates an example artificial intelligence (Al) architecture for AI- enhanced wireless communications.

[0015] FIG. 6 illustrates an example artificial neural network.

[0016] FIG. 7 depicts an example sidelink communications system with respect to vehicular communications.

[0017] FIG. 8 depicts an example scheme for dynamic sidelink channel access.

[0018] FIG. 9 depicts an example configuration associated with dynamic sidelink channel access.

[0019] FIG. 10 illustrates an example architecture for training a machine learning model for dynamic sidelink channel access.

[0020] FIG. 11 depicts a process flow for dynamic sidelink channel access.

[0021] FIG. 12 depicts a method for wireless communications.

[0022] FIG. 13 depicts another method for wireless communications.

[0023] FIG. 14 depicts aspects of an example communications device.

[0024] FIG. 15 depicts aspects of an example communications device.D&S Ref. No.: QCM2405903WOQualcomm Ref. No.: 2405903 WO 4DETAILED DESCRIPTION

[0025] Aspects of the present disclosure provide apparatuses, methods, processing systems, and computer-readable mediums for dynamic sidelink channel access.

[0026] Certain wireless communication systems (e.g., 5G New Radio (NR) systems and / or future wireless communication technologies) allow for peer-to-peer communications where a user equipment (UE) communicates directly with other UE(s) without a network entity (e.g., a base station) relaying such communications between the UEs. Such peer-to-peer (or device-to-device (D2D)) communications are often referred to as sidelink communications. An example of sidelink communications includes vehicle to everything (V2X) communications where a vehicle may communicate with another vehicle (referred to as vehicle-to-vehicle (V2V) communications), a user equipment, a road side unit (RSU), etc. Though certain aspects may be discussed with respect to V2X communications in a V2X communications system, it should be noted that the aspects may equally apply to other suitable types of sidelink communications systems. To address the rapid increase of wireless data traffic demand especially for sidelink communications, certain wireless communication systems (e.g., 5G NR systems) allow for wireless traffic in unlicensed or shared spectrum bands (e.g., 2.4 GHz, 5 GHz, and / or 60 GHz bands) as a way to add wireless channel capacity. An unlicensed or shared spectrum refers to any frequency band(s) that are not subject to licensed use under regulatory practice, such that the frequency band(s) are open to use by any devices, and not just devices that have a license to use the particular frequency band(s). For example, unlicensed or shared spectrum may use a decentralized channel access procedure to govern access to the unlicensed or shared spectrum, as described below.

[0027] For sidelink communications via shared spectrum bands, a UE may perform certain sidelink channel access procedures to determine whether a sidelink channel is busy or idle. The sidelink channel access procedures may be referred to as listen-before- talk (LBT) procedures. Prior to transmitting via the sidelink channel, the UE may sense the channel activity (e.g., in terms of received signal energy) for a given time duration, and the UE may compare the received signal energy to an energy detection threshold (EDT). If the received signal energy is less than the EDT, the sidelink channel is considered idle, and the UE can proceed (immediately) in transmitting via the sidelink channel. If the received signal energy is higher than the EDT, the sidelink channel is considered busy, and the UE is not allowed to transmit via the sidelink channel. The UED&S Ref. No.: QCM2405903WOQualcomm Ref. No.: 2405903 WO 5 may wait until the sidelink channel becomes idle to communicate via the sidelink channel. For example, the UE may perform a subsequent LBT procedure to determine whether the sidelink channel is idle or busy.

[0028] In 5G NR systems, there are multiple types of LBT procedures such as Type 1 LBT and Type 2 LBT. In Type 1 LBT, the sensing duration is random and proportional to a randomly selected integer between 0 and a contention window (CW) value. The CW value may be any value from a list of values. In certain aspects, the list of values may be specified for a channel access priority class (CAPC). The value of the CW may be adapted according to the channel conditions, referred to herein as “CW adaptation.” When there is a high level of contention among UEs to access the channel, a high CW value may be set for LBT procedures. When there is a low level of contention among UEs, a low CW value may be set for LBT procedures. In Type 2 LBT, the sensing duration is deterministic, for example, either 25 milliseconds (for Type 2A) or 16 milliseconds (for Type 2B).

[0029] In certain aspects, CW adaptation (e.g., selection of the CW value) may be based on hybrid automatic repeat request (HARQ) feedback. The HARQ feedback may include an acknowledgement (ACK) or a negative acknowledgment (NACK). The ACK may indicate that data transmitted was successfully received or decoded. The NACK may indicate that the data transmitted was not successfully received or decoded. Suppose a UE sends a transmission via a sidelink channel. If the UE receives a NACK, the NACK may suggest that the corresponding sidelink transmission was communicated under heavy contention conditions. In response to the NACK, the UE may increase the CW value to a higher value for the determination of the sensing duration for the next LBT procedure. If the UE receives an ACK, the ACK may suggest that the sidelink transmission was communicated under a low level of contention. In response to the ACK, the UE may refrain from adjusting the CW value for the next LBT procedure.

[0030] In certain cases, sidelink communications may involve groupcast transmissions, for example, a transmission addressed to multiple UEs. A groupcast transmission may trigger multiple UEs to transmit HARQ feedback. Thus, for groupcast sidelink communications, CW adaptation may apply a ratio associated with HARQ feedback. The HARQ feedback ratio may be the ratio between the number of ACKs received for the groupcast transmission and total number of UEs to which the groupcast transmission was addressed (e.g., the total number of destination UEs). If the HARQD&S Ref. No.: QCM2405903WOQualcomm Ref. No.: 2405903 WO 6 feedback ratio is equal to or greater than a ratio threshold (e.g., the field harq-ACK- FeedbackRatioforCW-AdjustmentGC-Option2'), the UE may treat the HARQ feedback as indicating a low level of contention (e.g., an ACK case). If the HARQ feedback ratio is less than ratio threshold, the UE may treat the HARQ feedback as indicating a high level of contention (e.g., a NACK case).

[0031] Technical problems for sidelink channel access may include, for example, effective selection of values for parameters used for channel access. In certain cases, value(s) for certain parameter(s) associated with sidelink channel access may be determined by a network entity (e.g., a base station) and then configured at a UE, for example, via signaling. These parameters may include, for example, certain thresholds (such as the EDT and / or ratio threshold) that determine whether a channel is treated as being busy or idle for EBT procedures. In addition, the UE may be configured with a single, static value for such a parameter. As an example, a single, static value for the EDT may be set at a UE via a signaling from a network entity. As another example, a single, static value for the ratio threshold for group common transmissions may be set at the UE by the network entity. However, sidelink channel conditions may change over time, such as sidelink channel activity (e.g., high or low levels of contention), the locations of UEs engaged in sidelink communications (such as group common communications), the total number of UEs involved in sidelink communications (such as group common communications), and / or the like. In certain cases, the value(s) for certain parameter(s) (e.g., the EDT and / or ratio threshold) may become out-of-date with respect to the current sidelink channel conditions. This may be exacerbated by the tendency of sidelink communications or other communications in unlicensed or shared spectrum to occur in connection with moving vehicles and dynamic channel blockages and reflectors. In certain cases, the network entity may be unable to sense or measure the sidelink channel conditions, for example, in locations outside the coverage area of the network entity. Accordingly, the value(s) for certain parameter(s) may affect the performance of sidelink channel access, for example, in terms of channel access failure rate and / or latencies associated with establishing a communication link between UEs.

[0032] Aspects described herein may overcome the aforementioned technical problem(s), for example, by providing dynamic sidelink channel access. “Dynamic channel access” may refer to a UE being configured to determine a set of values associated with one or more sidelink channel access parameters. The parameter(s)D&S Ref. No.: QCM2405903WOQualcomm Ref. No.: 2405903 WO 7 assigned to dynamic channel access may include one or more threshold(s) and / or factor(s) that determine whether a channel is busy or idle with respect to LBT. Dynamic sidelink channel access may allow a UE to account for sidelink channel conditions (which may not be accessible to a network entity) in determining the set of values associated with sidelink channel access parameters. In certain aspects, the dynamic sidelink channel access may be perfomed using an artificial intelligence and / or machine learning (AI / ML) model deployed at the UE.

[0033] Certain techniques for dynamic sidelink channel access described herein may provide various beneficial technical effects and / or advantages. The techniques for dynamic sidelink channel access may enable improved wireless communications performance, such as reduced latencies, improved sidelink channel usage, and / or the like. The reduced latencies and / or improved sidelink channel usage may be attributable to the UE determining the value(s) for sidelink channel access parameter(s). In certain cases, the UE may take into account sidelink channel conditions (e.g., contention level, HARQ feedback rate, sidelink access failure rate, or the like) to determine a value for the EDT and / or ratio threshold. The dynamic sidelink channel access may allow the value selection to be responsive to changes in the sidelink channel conditions encountered by UEs. As an example, when a UE detects that the sidelink channel access failure rate is high, the dynamic sidelink channel implemented at the UE may adjust the EDT to enable reliable sidelink channel access. Thus, the dynamic sidelink channel access may reduce sidelink transmissions that lead to failed access attempts, which may reduce latencies and / or improve the sidelink channel usage.Introduction to Wireless Communications Networks

[0034] The techniques and methods described herein may be used for various wireless communications networks. While aspects may be described herein using terminology commonly associated with 3G, 4G, 5G, 6G, and / or other generations of wireless technologies, aspects of the present disclosure may likewise be applicable to other communications systems and standards not explicitly mentioned herein.

[0035] FIG. 1 depicts an example of a wireless communications network 100, in which aspects described herein may be implemented.

[0036] Generally, wireless communications network 100 includes various network entities (alternatively, network elements or network nodes). A network entity is generallyD&S Ref. No.: QCM2405903WOQualcomm Ref. No.: 2405903 WO 8 a communications device and / or a communications function performed by a communications device (e.g., a user equipment (UE), a base station (BS), a component of a BS, a server, etc.). As such communications devices are part of wireless communications network 100, and facilitate wireless communications, such communications devices may be referred to as wireless communications devices. For example, various functions of a network as well as various devices associated with and interacting with a network may be considered network entities. Further, wireless communications network 100 may include terrestrial aspects, such as ground-based network entities (e.g., BSs 102), and non-terrestrial aspects (also referred to herein as non-terrestrial network entities). A non-terrestrial network entity may include satellite 140, which may be an example of an aerial or space-borne platform. In some examples, satellite 140 may include one or more network entities on-board (e.g., one or more BSs) capable of communicating with other network elements (e.g., terrestrial BSs) and UEs. For example, satellite 140 may be implemented according to a regenerative architecture (also referred to as a non-transparent architecture), and a gNB implemented at satellite 140 may implement higher-layer network functions. As another example, satellite 140 may be implemented according to a transparent architecture, and may perform a physical or other lower-layer repeater function for UEs and a network entity (such as a gateway associated with the satellite 140).

[0037] In the depicted example, wireless communications network 100 includes BSs 102, UEs 104, and one or more core networks, such as an Evolved Packet Core (EPC) 160 or a 5G Core (5GC) network 190, which interoperate to provide communications services over various communications links, including wired and wireless links. In some aspects, a core network, such as a 6G core, may implement a converged service-based architecture. In a converged service-based architecture, functions traditionally split between a core network (such as 5GC network 190) and a radio access network (RAN) (such as BS 102) may be implemented at a single network entity. For example, a mobility network entity may perform both core network functions and RAN functions related to mobility of UEs 104 attached to the wireless communications network 100. “Network entity” can refer to a BS 102, a network entity of EPC 160 or 5GC network 190, or a network entity of a converged service-based architecture.

[0038] FIG. 1 depicts various example UEs 104. UE 104 may include a cellular phone, a smart phone, a session initiation protocol (SIP) phone, a laptop, a personal digitalD&S Ref. No.: QCM2405903WOQualcomm Ref. No.: 2405903 WO 9 assistant (PDA), a satellite radio, a Global Positioning System device, a multimedia device, a video device, a digital audio player, a camera, a game console, a tablet, a smart device, a wearable device, a vehicle, an electric meter, a gas pump, a kitchen appliance, a healthcare device, an implant, a sensor / actuator, a display, an Internet of Things (loT) device, an always on (AON) device, an edge processing device, a data center, or another similar device. A UE 104 may also be referred to as a mobile device, a wireless device, a station, a mobile station, a subscriber station, a mobile subscriber station, a mobile unit, a subscriber unit, a wireless unit, a remote unit, a remote device, an access terminal, a mobile terminal, a wireless terminal, a remote terminal, a handset, and others.

[0039] BSs 102 wirelessly communicate with (e.g., transmit signals to or receive signals from) UEs 104 via communications links 120. A communications link 120 between a BS 102 and a UE 104 may include uplink (UL) (also referred to as reverse link) transmissions from a UE 104 to a BS 102 and / or downlink (DL) (also referred to as forward link) transmissions from a BS 102 to a UE 104. A communications link 120 may use multiple-input and multiple-output (MIMO) antenna technology, including spatial multiplexing, beamforming, and / or transmit diversity in various aspects.

[0040] A BS 102 may include a NodeB, an enhanced NodeB (eNB), a next generation enhanced NodeB (ng-eNB), a next generation NodeB (gNB or gNodeB), an access point, a base transceiver station, a radio base station, a radio transceiver, a transceiver function, a transmission reception point (TRP), a radio unit (RU), a distributed unit (DU), or the like. A given BS 102 may provide communications coverage for a coverage area 110, which may sometimes be referred to as a cell, and which may overlap another coverage area 110 (e.g., a small cell provided by a BS 102f) may have a coverage area 110fthat overlaps the coverage area 110 of a macro cell). A BS 102 may, for example, provide communications coverage for a macro cell (covering a relatively large geographic area), a pico cell (covering a relatively smaller geographic area, such as a sports stadium), a femto cell (covering a relatively smaller geographic area, such as a home), or another type of cell.

[0041] The term “cell” may refer to a portion, partition, or segment of wireless communication coverage served by a network entity within a wireless communications network 100. A cell may have geographic characteristics, such as a geographic coverage area, as well as radio frequency characteristics, such as time and / or frequency resources dedicated to the cell. For example, a specific geographic coverage area may be coveredD&S Ref. No.: QCM2405903WOQualcomm Ref. No.: 2405903 WO 10 by multiple cells employing different frequency resources (e.g., bandwidth parts) and / or different time resources. As another example, a specific geographic coverage area may be covered by a single cell. In some contexts (e.g., a carrier aggregation scenario and / or multi-connectivity scenario), the terms “cell” or “serving cell” may refer to or correspond to a specific carrier frequency (e.g., a component carrier) used for wireless communications, and a “cell group” may refer to or correspond to multiple carriers used for wireless communications. As examples, in a carrier aggregation scenario, a UE may communicate on multiple component carriers corresponding to multiple (serving) cells in the same cell group, and in a multi-connectivity (e.g., dual connectivity) scenario, a UE may communicate on multiple component carriers corresponding to multiple cell groups.

[0042] While BSs 102 are depicted in various aspects as unitary communications devices, BSs 102 may be implemented in various configurations. For example, one or more components of a base station may be disaggregated, including a central unit (CU), one or more DUs, one or more RUs, a Near-Real Time (Near-RT) RAN Intelligent Controller (RIC), or aNon-Real Time (Non-RT) RIC, to name a few examples. In another example, various aspects of a base station may be virtualized. A base station (e.g., BS 102) may include components that are located at a single physical location or components located at various physical locations. In examples in which a base station includes components that are located at various physical locations, the various components may each perform functions such that, collectively, the various components achieve functionality that is similar to a base station that is located at a single physical location. Implementing a base station in this fashion may provide efficiency gains by enabling cloud-based implementation of certain (e.g., non-time-sensitive) higher-layer functions while physical-layer or other lower-layer functions can be implemented at or in proximity to a geographic coverage area of a corresponding cell. In some aspects, a base station including components that are located at various physical locations may be referred to as having a disaggregated RAN architecture, such as an Open RAN (O-RAN) or Virtualized RAN (VRAN) architecture. FIG. 2 depicts and describes an example disaggregated RAN architecture.

[0043] Different BSs 102 within wireless communications network 100 may also be configured to support different radio access technologies, such as 3G, 4G, 5G, and / or 6G. For example, BSs 102 configured for 4G ETE (collectively referred to as Evolved Universal Mobile Telecommunications System (UMTS) Terrestrial Radio AccessD&S Ref. No.: QCM2405903WOQualcomm Ref. No.: 2405903 WO 11Network (E-UTRAN)) may interface with the EPC 160 through first backhaul links 132 (e.g., an SI interface). BSs 102 configured for 5G (e.g., 5G NR or Next Generation RAN (NG-RAN)) may interface with 5GC 190 through second backhaul links 184. BSs 102 may communicate directly or indirectly (e.g., through the EPC 160 or the 5GC 190) with each other over third backhaul links 134 (e.g., an X2 or XN interface), which may be wired or wireless.

[0044] Wireless communications network 100 may subdivide the electromagnetic spectrum into various classes, bands, channels, or other features. In some aspects, the subdivision is provided based on wavelength and frequency, where frequency may also be referred to as a carrier, a subcarrier, a frequency channel, a tone, or a subband. For example, the Third Generation Partnership Project (3 GPP) currently defines Frequency Range 1 (FR1) as including 410 MHz - 7125 MHz, which is often referred to (interchangeably) as “Sub-6 GHz”. Similarly, 3GPP currently defines Frequency Range 2 (FR2) as including 24,250 MHz - 71,000 MHz, which is sometimes referred to (interchangeably) as a “millimeter wave” (“mmW” or “mmWave”). In some cases, FR2 may be further defined in terms of sub-ranges, such as a first sub-range FR2-1 including 24,250 MHz - 52,600 MHz and a second sub-range FR2-2 including 52,600 MHz - 71,000 MHz. A base station configured to communicate using mmWave / near mmWave radio frequency bands (e.g., a mmWave base station such as BS 180) may utilize beamforming (e.g., 182) with a UE (e.g., 104) to improve path loss and range.

[0045] A communications links 120 may be through one or more carriers, which may have different bandwidths (e.g., 5 MHz, 10 MHz, 15 MHz, 20 MHz, 100 MHz, 400 MHz, and / or other bandwidths), and which may be aggregated in various aspects. Carriers may or may not be adjacent to each other. Allocation of carriers may be asymmetric with respect to DL and UL (e.g., more or fewer carriers may be allocated for DL than for UL).

[0046] Communications using higher frequency bands may have higher path loss and a shorter range compared to lower frequency communications. Accordingly, certain base stations (e.g., base station 180 in FIG. 1) may utilize beamforming (indicated by reference number 182) with a UE 104 to improve path loss and range. For example, BS 180 and the UE 104 may each include a plurality of antennas, such as antenna elements, antenna panels, and / or antenna arrays to facilitate the beamforming. In some cases, BS 180 may transmit a beamformed signal to UE 104 in one or more transmit directions 182'. UE 104 may receive the beamformed signal from the BS 180 in one or more receive directionsD&S Ref. No.: QCM2405903WOQualcomm Ref. No.: 2405903 WO 12182". UE 104 may also transmit a beamformed signal to the BS 180 in one or more transmit directions 182". BS 180 may also receive the beamformed signal from UE 104 in one or more receive directions 182f. BS 180 and UE 104 may perform beam training to determine suitable receive and transmit directions for each of BS 180 and UE 104. Notably, the transmit and receive directions for BS 180 may or may not be the same. Similarly, the transmit and receive directions for UE 104 may or may not be the same.

[0047] Wireless communications network 100 may include a Wi-Fi access point (AP) 150 in communication with Wi-Fi stations (STAs) 152 via communications links 154 in, for example, a 2.4 GHz and / or 5 GHz unlicensed frequency spectrum.

[0048] Certain UEs 104 may communicate with each other using device-to-device (D2D) communications link 158. In some examples, D2D communications link 158 may use one or more sidelink channels, such as a physical sidelink broadcast channel (PSBCH), a physical sidelink discovery channel (PSDCH), a physical sidelink shared channel (PSSCH), a physical sidelink control channel (PSCCH), and / or a physical sidelink feedback channel (PSFCH). D2D communications link 158 may be implemented using a variety of technologies, such as a radio access technology (e.g., 5G, ProSe sidelink), a WiFi technology, a Bluetooth technology, or the like.

[0049] EPC 160 may include various functional components, such as a Mobility Management Entity (MME) 162, other MMEs 164, a Serving Gateway 166, a Multimedia Broadcast Multicast Service (MBMS) Gateway 168, a Broadcast Multicast Service Center (BM-SC) 170, and / or a Packet Data Network (PDN) Gateway 172. MME 162 may be in communication with a Home Subscriber Server (HSS) 174. MME 162 is a control node that processes signaling between the UEs 104 and the EPC 160. Generally, MME 162 provides bearer and connection management.

[0050] Generally, user Internet protocol (IP) packets are transferred through Serving Gateway 166. Serving gateway 166 is connected to PDN Gateway 172. PDN Gateway 172 provides UE IP address allocation as well as other functions. PDN Gateway 172 and BM-SC 170 are connected to IP Services 176, which may include, for example, the Internet, an intranet, an IP Multimedia Subsystem (IMS), a Packet Switched (PS) streaming service, and / or other IP services.

[0051] BM-SC 170 may provide functions for MBMS user service provisioning and delivery. BM-SC 170 may serve as an entry point for content provider MBMSD&S Ref. No.: QCM2405903WOQualcomm Ref. No.: 2405903 WO 13 transmission, may be used to authorize and initiate MBMS Bearer Services within a public land mobile network (PLMN), and / or may be used to schedule MBMS transmissions. MBMS Gateway 168 may be used to distribute MBMS traffic to the BSs 102 belonging to a Multicast Broadcast Single Frequency Network (MBSFN) area broadcasting a particular service, and / or may be responsible for session management (start / stop) and for collecting eMBMS related charging information.

[0052] 5GC 190 may include various functional components, such as an Access and Mobility Management Function (AMF) 192, other AMFs 193, a Session Management Function (SMF) 194, and a User Plane Function (UPF) 195. AMF 192 may be in communication with Unified Data Management (UDM) 196.

[0053] AMF 192 is a control node that processes signaling between UEs 104 and the 5GC 190. AMF 192 provides, for example, quality of service (QoS) flow and session management.

[0054] IP packets are transferred through UPF 195, which is connected to the IP Services 197. UPF 195 may provide UE IP address allocation as well as other functions for 5GC 190. IP Services 197 may include, for example, the Internet, an intranet, an IMS, a PS streaming service, and / or other IP services.

[0055] In various aspects, a network entity or network node can be implemented as an aggregated base station, as a disaggregated base station, a component of a base station, an integrated access and backhaul (TAB) node, a relay node, a core network entity, or a sidelink node, to name a few examples.

[0056] FIG. 2 depicts an example disaggregated base station 200 architecture. The disaggregated base station 200 architecture may include one or more CUs 210 that can communicate directly with a core network 220 or other CUs 210 via a backhaul link (such as backhaul link 134), or indirectly with the core network 220 through one or more disaggregated base station units (such as a Near-Real Time (Near-RT) RAN Intelligent Controller (RIC) 225 via an E2 link, aNon-Real Time (Non-RT) RIC 215 associated with a Service Management and Orchestration (SMO) Framework 205, or both). A CU 210 may communicate with one or more DUs 230 via respective midhaul links, such as an Fl interface. The DUs 230 may communicate with one or more RUs 240 via respective fronthaul links. The RUs 240 may communicate with respective UEs 104 via one or moreD&S Ref. No.: QCM2405903WOQualcomm Ref. No.: 2405903 WO 14 radio frequency (RF) access links (such as communication link 120). In some implementations, a UE 104 may be simultaneously served by multiple RUs 240.

[0057] Each of the units, e.g., the CUs 210, the DUs 230, the RUs 240, as well as the Near-RT RICs 225, the Non-RT RICs 215 and the SMO Framework 205, may include one or more interfaces or be coupled to one or more interfaces configured to receive or transmit signals, data, or information (collectively, signals) via a wired or wireless transmission medium. Each of the units, or a processor or controller providing instructions to the interfaces of the units, can be configured to communicate with one or more of the other units via the transmission medium. For example, the units can include a wired interface configured to receive or transmit signals over a wired transmission medium to one or more of the other units. Additionally or alternatively, the units can include a wireless interface, which may include a receiver, a transmitter, or a transceiver (such as a RF transceiver), configured to receive or transmit signals, or both, over a wireless transmission medium.

[0058] In some aspects, the CU 210 may host one or more higher layer control functions. Such control functions can include radio resource control (RRC), packet data convergence protocol (PDCP), service data adaptation protocol (SDAP), or the like. Each control function can be implemented with an interface configured to communicate signals with other control functions hosted by the CU 210. The CU 210 may be configured to handle user plane functionality (e.g., Central Unit - User Plane (CU-UP)), control plane functionality (e.g., Central Unit - Control Plane (CU-CP)), or a combination thereof. In some implementations, the CU 210 can be logically split into one or more CU-UP units and one or more CU-CP units. The CU-UP unit can communicate bidirectionally with the CU-CP unit via an interface, such as the El interface when implemented in an O-RAN configuration. The CU 210 can be implemented to communicate with the DU 230 for network control and signaling.

[0059] The DU 230 may be or correspond to a logical unit that includes one or more base station functions to control the operation of one or more RUs 240. In some aspects, the DU 230 may host one or more of a radio link control (REC) layer, a medium access control (MAC) layer, and one or more high physical (PHY) layers (such as modules for forward error correction (FEC) encoding and decoding, scrambling, modulation and demodulation, or the like) depending, at least in part, on a functional split, such as those defined by the 3rdGeneration Partnership Project (3GPP). In some aspects, the DU 230D&S Ref. No.: QCM2405903WOQualcomm Ref. No.: 2405903 WO 15 may further host one or more low PHY layers. Each layer (or module) can be implemented with an interface configured to communicate signals with other layers (and modules) hosted by the DU 230, or with the control functions hosted by the CU 210.

[0060] Lower-layer functionality can be implemented by one or more RUs 240. In some deployments, an RU 240, controlled by a DU 230, may correspond to a logical node that hosts RF processing functions, or low-PHY layer functions (such as performing fast Fourier transform (FFT), inverse FFT (iFFT), digital beamforming, physical random access channel (PRACH) extraction and filtering, or the like), or both, based at least in part on the functional split, such as a lower layer functional split. In such an architecture, the RU(s) 240 can be implemented to handle over the air (OTA) communications with one or more UEs 104. In some implementations, real-time and non-real-time aspects of control and user plane communications with the RU(s) 240 can be controlled by the corresponding DU 230. In some scenarios, this configuration can enable the DU(s) 230 and the CU 210 to be implemented in a cloud-based RAN architecture, such as a vRAN architecture.

[0061] The SMO Framework 205 may be configured to support RAN deployment and provisioning of non- virtualized and virtualized network elements. For non- virtualized network elements, the SMO Framework 205 may be configured to support the deployment of dedicated physical resources for RAN coverage requirements which may be managed via an operations and maintenance interface (such as an 01 interface). For virtualized network elements, the SMO Framework 205 may be configured to interact with a cloud computing platform (such as an open cloud (O-Cloud) 290) to perform network element life cycle management (such as to instantiate virtualized network elements) via a cloud computing platform interface (such as an 02 interface). Such virtualized network elements can include, but are not limited to, CUs 210, DUs 230, RUs 240 and Near-RT RICs 225. In some implementations, the SMO Framework 205 can communicate with a hardware aspect of a 4G RAN, such as an open eNB (O-eNB) 211, via an 01 interface. Additionally, in some implementations, the SMO Framework 205 can communicate directly with one or more DUs 230 and / or one or more RUs 240 via an 01 interface. The SMO Framework 205 also may include aNon-RT RIC 215 configured to support functionality of the SMO Framework 205.

[0062] The Non-RT RIC 215 may be configured to include a logical function that enables non-real-time control and optimization of RAN elements and resources, ArtificialD&S Ref. No.: QCM2405903WOQualcomm Ref. No.: 2405903 WO 16Intelligence / Machine Learning (AI / ML) workflows including model training and updates, or policy-based guidance of applications / features in the Near-RT RIC 225. The Non-RT RIC 215 may be coupled to or communicate with (such as via an Al interface) the Near-RT RIC 225. The Near-RT RIC 225 may be configured to include a logical function that enables near-real-time control and optimization of RAN elements and resources via data collection and actions over an interface (such as via an E2 interface) connecting one or more CUs 210, one or more DUs 230, or both, as well as an O-eNB, with the Near-RT RIC 225.

[0063] In some implementations, to generate AI / ML models to be deployed in the Near-RT RIC 225, the Non-RT RIC 215 may receive parameters or external enrichment information from external servers. Such information may be utilized by the Near-RT RIC 225 and may be received at the SMO Framework 205 or the Non-RT RIC 215 from nonnetwork data sources or from network functions. In some examples, the Non-RT RIC 215 or the Near-RT RIC 225 may be configured to tune RAN behavior or performance. For example, the Non-RT RIC 215 may monitor long-term trends and patterns for performance and employ AI / ML models to perform corrective actions through the SMO Framework 205 (such as reconfiguration via 01) or via creation of RAN management policies (such as Al policies).

[0064] FIG. 3 depicts aspects of network entities 300 and 302 and a UE 304.

[0065] FIG. 3 includes a first network entity 300 and a second network entity 302. In some examples, first network entity 300 may be an example of a CU 210 or a DU 230. In some examples, second network entity 302 may be an example of a DU 230 or an RU 240. First network entity 300 and second network entity 302 may communicate with one another via a communications link, such as a midhaul link. In some examples, first network entity 300 and second network entity 302 may be implemented at a same BS (e.g., BS 102). For example, first network entity 300 and second network entity 302 may be co-located. In some other examples, first network entity 300 may be implemented separately from second network entity 302. For example, first network entity 300 may be implemented as a function (e.g., one or more processes) running on a server, such as in a cloud (e.g., a public or private cloud). As another example, first network entity 300 may be implemented as a virtual computing instance (e.g., virtual machine, container, etc.) or as a physical server.D&S Ref. No.: QCM2405903WOQualcomm Ref. No.: 2405903 WO 17

[0066] First network entity 300 and second network entity 302 each include a processing system 306, illustrated as “processing system 306a” at first network entity 300 and “processing system 306b” at second network entity 302. For example, first network entity 300 and second network entity 302 may include one or more chips, system-on- chips (SoCs), system-in-packages (SiPs), chipsets, packages, or devices that individually or collectively constitute or comprise a processing system 306. A processing system 306 includes one or more processors 308 (illustrated as “processor(s) 308a” and “processor(s) 308b”) and one or more memories 310 (illustrated as “memory(ies) 310a” and “memory(ies) 310b”) coupled to the one or more processors 308. The one or more processors 308 may include one or multiple processors, microprocessors, processing units (such as central processing units (CPUs), graphics processing units (GPUs), neural processing units (NPUs) (also referred to as neural network processors or deep learning processors (DLPs)) and / or digital signal processors (DSPs)), processing blocks, application-specific integrated circuits (ASIC), programmable logic devices (PLDs) (such as field programmable gate arrays (FPGAs)), or other discrete gate or transistor logic or circuitry (any one or more of which may be generally referred to herein individually as a “processor” or collectively as “the processor” or “the processor circuitry”). One or more of the processors may be individually or collectively configurable or configured to perform various functions or operations described herein. A group of processors collectively configurable or configured to perform a set of functions may include a first processor configurable or configured to perform a first function of the set and a second processor configurable or configured to perform a second function of the set. In some other examples, each of a group of processors may be configurable or configured to perform a same set of functions.

[0067] In some aspects, the processing system 306 may perform processing (such as digital signal processing) of data, control information, or signals received or transmitted by a network entity. For example, the processing system 306 may include a coder, a decoder, a multiplexer, a demultiplexer, a transmit MIMO processor, a transmit processor, a receive processor, a receive MIMO detector, an automatic gain control component, or the like.

[0068] The one or more memories 310 may include one or more memory devices, memory blocks, memory elements or other discrete gate or transistor logic or circuitry, each of which may include tangible storage media such as random-access memoryD&S Ref. No.: QCM2405903WOQualcomm Ref. No.: 2405903 WO 18(RAM) or read-only memory (ROM), or combinations thereof (all of which may be generally referred to herein individually as “memories” or collectively as “the memory” or “the memory circuitry”). The one or more memories 310 may store data and program code for first network entity 300 and / or second network entity 302.

[0069] As further shown, second network entity 302 includes one or more transceivers 312 (illustrated as “transceiver(s) 312”). The one or more transceivers 312 may perform processing related to implementing physical layer (e.g., radio, air interface) communication with other devices such as UE 304. The one or more transceivers 312 may include one or more radio frequency (RF) components, such as an RF transceiver, a front-end module (e.g., an RF front-end (RFFE)), or the like. For example, the one or more transceivers 312 may include a transmit path (also referred to as a transmit chain), a receive path (also referred to as a receive chain), and / or an interface with one or more antennas 314.

[0070] The one or more antennas 314 may perform wireless transmission and reception of signals. The one or more antennas 314 may include, or may be included within, one or more antenna panels, one or more antenna groups, one or more sets of antenna elements, or one or more antenna arrays, among other examples. An antenna panel, an antenna group, a set of antenna elements, or an antenna array may include one or more antenna elements (within a single housing or multiple housings), a set of coplanar antenna elements, a set of non-coplanar antenna elements, or one or more antenna elements coupled with one or more transmission or reception components, such as one or more components of FIG. 3.

[0071] UE 304 may be an example of UE 104. As shown, UE 304 includes a processing system 316. For example, UE 304 may include one or more chips, SoCs, SiPs, chipsets, packages, or devices that individually or collectively constitute or comprise a processing system 316. A processing system 316 includes one or more processors 318, and one or more memories 320 coupled to the one or more processors 318. Further, UE 304 includes one or more antennas 322, one or more transceivers 324, and / or other components that enable wireless transmission and reception of data.

[0072] The one or more processors 318 may include one or multiple processors, microprocessors, processing units (such as CPUs, GPUs, NPUs (also referred to as neural network processors or DEPs) and / or DSPs), processing blocks, ASICs, PLDs (such asD&S Ref. No.: QCM2405903WOQualcomm Ref. No.: 2405903 WO 19FPGAs), or other discrete gate or transistor logic or circuitry (any one or more of which may be generally referred to herein individually as a “processor” or collectively as “the processor” or “the processor circuitry”). One or more of the processors may be individually or collectively configurable or configured to perform various functions or operations described herein. In some aspects, the processing system 316 may perform processing (such as digital signal processing) of data, control information, or signals received or transmitted by a network entity. For example, the processing system 316 may include a coder, a decoder, a multiplexer, a demultiplexer, a transmit MIMO processor, a transmit processor, a receive processor, a receive MIMO detector, an automatic gain control component, or the like.

[0073] As shown, in some examples, the one or more processors 318 may include one or more modems 326, one or more application processors (APs) 328, one or more Al processors 330, a combination thereof, and / or another form of processor.

[0074] The one or more modems 326 may include a digital signal processor that converts information into a waveform for analog signal transmission (e.g., via modulation) and / or converts the waveform of a received signal into information (e.g., via demodulation). The one or more modems 326 may process information or waveforms in connection with signal transmission or reception. For example, the one or more modems 326 may include a coder, a decoder, a multiplexer, a demultiplexer, a transmit MIMO processor, a transmit processor, a receive processor, a receive MIMO detector, an automatic gain control component, or the like.

[0075] The one or more APs 328 may perform processing relating to an operating system and / or a higher layer application of the UE 304. For example, the one or more APs 328 may provide a higher-level operating system (HEOS), software, audio or video processing, graphics processing, or the like. In some examples, the one or more APs 328 may be a data source (e.g., for transmissions) or a data sink (e.g., for receptions).

[0076] The one or more transceivers 324 may perform processing related to implementing physical layer (e.g., radio, air interface) communication with other devices such as other UEs 304 or second network entity 302. The one or more transceivers 324 may include one or more RF components, such as an RF transceiver, a front-end module (e.g., an RFFE), or the like. For example, the one or more transceivers 324 may includeD&S Ref. No.: QCM2405903WOQualcomm Ref. No.: 2405903 WO 20 a transmit path (also referred to as a transmit chain), a receive path (also referred to as a receive chain), and / or an interface with one or more antennas 322.

[0077] The one or more antennas 322 may perform wireless transmission and reception of signals. The one or more antennas 322 may include, or may be included within, one or more antenna panels, one or more antenna groups, one or more sets of antenna elements, or one or more antenna arrays, among other examples. An antenna panel, an antenna group, a set of antenna elements, or an antenna array may include one or more antenna elements (within a single housing or multiple housings), a set of coplanar antenna elements, a set of non-coplanar antenna elements, or one or more antenna elements coupled with one or more transmission or reception components, such as one or more components of FIG. 3.

[0078] For an example downlink transmission by second network entity 302, the processing system 306 (e.g., a transmit processor) may receive data and / or control information. The control information may be for the physical broadcast channel (PBCH), physical control format indicator channel (PCFICH), physical hybrid automatic repeat request (HARQ) indicator channel (PHICH), physical downlink control channel (PDCCH), group common PDCCH (GC PDCCH), and / or others. The data may be for the physical downlink shared channel (PDSCH), in some examples.

[0079] The processing system 306 (e.g., a transmit processor) may process (e.g., encode and symbol map) the data and control information to obtain data symbols and control symbols, respectively. The processing system 306 may also generate reference symbols, such as for the primary synchronization signal (PSS), secondary synchronization signal (SSS), PBCH demodulation reference signal (DMRS), or channel state information reference signal (CSI-RS).

[0080] The processing system 306 (e.g., a TX MIMO processor) may perform spatial processing (e.g., precoding) on the data symbols, the control symbols, and / or the reference symbols, if applicable, and may provide output symbol streams to one or more modulators of the processing system 306. The one or more modulators may process one or more respective output symbol streams to obtain an output sample stream. The one or more transceivers 312 may process (e.g., convert to analog, amplify, fdter, and upconvert) the output sample stream to obtain a downlink signal. Second network entity 302 may transmit the downlink signal via the one or more antennas 314.D&S Ref. No.: QCM2405903WOQualcomm Ref. No.: 2405903 WO 21

[0081] In order to receive the downlink transmission at UE 304 (or a sidelink transmission from another UE), the one or more antennas 322 may receive the downlink signal and may provide received signals to the one or more transceivers 324. The one or more transceivers 324 may condition (e.g., fdter, amplify, downconvert, and digitize) the received signals to obtain input samples. The one or more transceivers 324 and / or the processing system 316 may further process the input samples to obtain received symbols.

[0082] The processing system 316 (e.g., modem 326, an RX MIMO detector) may obtain the received symbols, perform MIMO detection on the received symbols if applicable, and provide detected symbols. The processing system 316 (e.g., a modem 326, a receive processor) may process (e.g., de-interleave and decode) the detected symbols. The processing system 316 may provide decoded data for the UE 304 (e.g., to an AP 328) and / or decoded control information (e.g., to a controller / processor of the processing system 316).

[0083] For an example uplink transmission or a sidelink transmission from UE 304, the processing system 316 (e.g., modem 326, a transmit processor) may receive and process data and / or control information to obtain a set of symbols for transmission. The data may be for the physical uplink shared channel (PUSCH), and may be received from a data source such as the AP 328. The control information may be for the physical uplink control channel (PUCCH), and may be received, for example, from a controller / processor of the processing system 316. The processing system 316 (e.g., a modem 326, the transmit processor) may also generate reference symbols for a reference signal (e.g., for a sounding reference signal (SRS), a demodulation reference signal, a phase tracking reference signal, or the like). In some examples, the symbols and / or reference signals may be precoded by the processing system 316 (e.g., modem 326, a TX MIMO processor), further processed by the one or more transceivers 324 (e.g., for SC-FDM), and transmitted to second network entity 302.

[0084] At second network entity 302, the uplink signals from UE 304 may be received by the one or more antennas 314, conditioned by the one or more transceivers 312 (e.g., fdtered, amplified, downconverted, and digitized), detected (e.g., by the processing system 306b such as a modem and / or an RX MIMO detector), and further processed by the processing system 306b (e.g., a modem and / or a receive processor) to obtain decoded data and control information sent by UE 304. The processing system 306b may provideD&S Ref. No.: QCM2405903WOQualcomm Ref. No.: 2405903 WO 22 the decoded data and the decoded control information (such as to a controller / processor of the processing system 306b, an AP, first network entity 300, or another entity).

[0085] In various aspects, a wireless communication device, such as first network entity 300, second network entity 302, BS 102, UE 104, or UE 304 may be described as sending, transmitting, obtaining, or receiving various types of data associated with the methods described herein. In these contexts, “transmitting” or “sending” may refer to various mechanisms of outputting data, such as outputting data from a processing system, one or more memories, one or more transceivers, one or more antennas, and / or other aspects described herein. For example, “sending” or “transmitting” by a device may include sending (such as wirelessly, via a wired connection, or both) to a recipient directly or via another device. As another example, “sending” or “transmitting” may include sending internally to a device (such as the UE 304, first network entity 300, or second network entity 302) by a process to memory. “Receiving” or “obtaining” may refer to various mechanisms of obtaining data, such as obtaining data from the processing system, one or more memories, one or more transceivers, one or more antennas, and / or other aspects described herein. For example, “receiving” or “obtaining” by a device may include obtaining (such as wirelessly, via a wired connection, or both) from a recipient directly or via another device. As another example, “receiving” or “obtaining” may include obtaining internally to a device (such as the UE 304, first network entity 300, or second network entity 302) by a process from memory. As used herein, “communicating” by a device may include sending, obtaining, receiving, and / or transmitting a communication. “Communicating” can refer to communication with another device or internal communication of the device.

[0086] In various aspects, the processing system 306 or the processing system 316 may include one or more Al processors (such as Al processor 330 of the processing system 316). An Al processor may perform Al processing. The Al processor may include Al accelerator hardware or circuitry such as one or more neural processing units (NPUs), one or more neural network processors, one or more tensor processors, one or more deep learning processors, etc. As an example, the Al processor may perform Al-based beam management, Al-based channel state feedback (CSF), Al-based antenna tuning, and / or Al-based positioning (e.g., non-line of sight positioning prediction). In some cases, at the UE 104, the Al processor may process feedback generated by the UE 304 (e.g., CSF) using hardware accelerated Al inferences and / or Al training. In some cases, at the secondD&S Ref. No.: QCM2405903WOQualcomm Ref. No.: 2405903 WO 23 network entity 302, the Al processor may decode compressed CSF from the UE 304, for example, using a hardware accelerated Al inference associated with the CSF. In certain cases, the Al processor may perform certain RAN-based functions including, for example, network planning, network performance management, energy-efficient network operations, etc.

[0087] FIGS. 4A, 4B, 4C, and 4D depict aspects of data structures for a wireless communications network, such as wireless communications network 100 of FIG. 1.

[0088] FIG. 4A is a diagram 400 illustrating an example of a first subframe within a 5G (e.g., 5G NR) frame structure, FIG. 4B is a diagram 430 illustrating an example of DL channels within a 5G subframe, FIG. 4C is a diagram 450 illustrating an example of a second subframe within a 5G frame structure, and FIG. 4D is a diagram 480 illustrating an example of UL channels within a 5G subframe.

[0089] Wireless communications systems may utilize orthogonal frequency division multiplexing (OFDM) with a cyclic prefix (CP) on the uplink and downlink. Such systems may also support half-duplex operation using time division duplexing (TDD). OFDM and single-carrier frequency division multiplexing (SC-FDM) partition the system bandwidth (e.g., as depicted in FIGS. 4B and 4D) into multiple orthogonal subcarriers. One or more subcarriers may be modulated with data. Modulation symbols may be sent in the frequency domain with OFDM and / or in the time domain with SC-FDM.

[0090] In some examples, a wireless communications frame structure may be implemented using frequency division duplexing (FDD). In FDD, some subcarriers may be configured for DL communication, and other subcarriers (which may overlap in time with the DL subcarriers) may be configured for UL communication. In some other examples, wireless communications frame structures may be implemented using time division duplexing (TDD). In TDD, for a particular set of subcarriers, some subframes are configured for DL communication and other subframes are configured for UL communication.

[0091] In FIGs. 4A and 4C, the wireless communications frame structure is implemented using TDD. “D” indicates DL time resources, “U” indicates UL time resources, and “X” indicates flexible time resources for use or later reconfiguration for either DL or UL communication. UEs may be configured with a slot format through a received slot format indicator (SFI) (dynamically through DL control information (DCI),D&S Ref. No.: QCM2405903WOQualcomm Ref. No.: 2405903 WO 24 or semi-statically / statically through radio resource control (RRC) signaling). In the depicted examples, a 10 ms frame is divided into 10 equally sized 1 ms subframes. Each subframe may include one or more time slots. In some examples, each slot may include 12 or 14 symbols, depending on the cyclic prefix (CP) type (e.g., 12 symbols per slot for an extended CP or 14 symbols per slot for a normal CP). Subframes may also include mini-slots, which generally have fewer symbols than an entire slot. Other wireless communications technologies may have a different frame structure and / or different channels.

[0092] In certain aspects, the number of slots within a subframe (e.g., a slot duration in a subframe) is based on a numerology. A numerology may define a frequency domain subcarrier spacing and symbol duration, and may be configured for a given bandwidth part, carrier, cell, or network entity. In certain aspects, given a numerology p, there are 2gslots per subframe. Thus, numerologies (p) 0 to 6 may allow for 1, 2, 4, 8, 16, 32, and 64 slots, respectively, per subframe. In some cases, an extended CP (e.g., 12 symbols per slot) may be used with a specific numerology, such as numerology p = 2 allowing for 4 slots per subframe. The subcarrier spacing and symbol length / duration are a function of the numerology. The subcarrier spacing may be equal to 211x 15 kHz. As an example, the numerology p = 0 corresponds to a subcarrier spacing of 15 kHz, and the numerology p = 6 corresponds to a subcarrier spacing of 960 kHz. The symbol length / duration is inversely related to the subcarrier spacing. FIGS. 4A, 4B, 4C, and 4D provide an example of a slot format having 14 symbols per slot (e.g., a normal CP) and a numerology p = 2 with 4 slots per subframe. In such a case, the slot duration is 0.25 ms, the subcarrier spacing is 60 kHz, and the symbol duration is approximately 16.67 ps.

[0093] As depicted in FIGS. 4A, 4B, 4C, and 4D, a resource grid may be used to represent the frame structure. Each time slot includes a resource block (RB) (also referred to as a physical RB (PRB)) that extends across, for example, 12 consecutive subcarriers. The resource grid is divided into multiple resource elements (REs). An RE may include a single subcarrier in the frequency domain and a single symbol in the time domain. The number of bits carried by each RE depends on the modulation scheme including, for example, quadrature phase shift keying (QPSK) or quadrature amplitude modulation (QAM).

[0094] As illustrated in FIG. 4A, some of the REs carry reference (pilot) signals (shown as “RS”) for a UE (e.g., UE 104 of FIGS. 1 and 3). The RS may include aD&S Ref. No.: QCM2405903WOQualcomm Ref. No.: 2405903 WO 25 demodulation RS (DMRS) and / or a channel state information reference signals (CSI-RS) for channel estimation at the UE. The RS may additionally or alternatively include a beam measurement RS (BRS), a beam refinement RS (BRRS), and / or a phase tracking RS (PT- RS).

[0095] FIG. 4B illustrates an example of various DL channels within a subframe of a frame. The physical downlink control channel (PDCCH) carries DCI within one or more control channel elements (CCEs), each CCE including, for example, nine RE groups (REGs), each REG including, for example, four consecutive REs in an OFDM symbol.

[0096] A primary synchronization signal (PSS) may be within symbol 2 of particular subframes of a frame. The PSS is used by a UE (e.g., 104 of FIGS. 1 and 3) to determine subframe / symbol timing and a physical layer identity.

[0097] A secondary synchronization signal (SSS) may be within symbol 4 of particular subframes of a frame. The SSS is used by a UE to determine a physical layer cell identity group number and radio frame timing.

[0098] Based on the physical layer identity and the physical layer cell identity group number, the UE can determine a physical cell identifier (PCI). Based on the PCI, the UE can determine the locations of the aforementioned DMRS. The physical broadcast channel (PBCH), which carries a master information block (MIB), may be logically grouped with the PSS and SSS to form a synchronization signal (SS) / PBCH block (SSB), and in some cases, referred to as a synchronization signal block (SSB). The MIB provides a number of RBs in the system bandwidth and a system frame number (SFN). The physical downlink shared channel (PDSCH) carries user data, broadcast system information not transmitted through the PBCH such as system information blocks (SIBs), and / or paging messages.

[0099] As illustrated in FIG. 4C, some of the REs carry DMRS (indicated as “R” for one particular configuration, but other DMRS configurations are possible) for channel estimation at the base station. The UE may transmit DMRS for the PUCCH and DMRS for the PUS CH. The PUS CH DMRS may be transmitted, for example, in the first one or two symbols of the PUSCH. The PUCCH DMRS may be transmitted in different configurations depending on whether short or long PUCCHs are transmitted and depending on the particular PUCCH format used. UE 104 may transmit sounding reference signals (SRS). The SRS may be transmitted, for example, in the last symbol ofD&S Ref. No.: QCM2405903WOQualcomm Ref. No.: 2405903 WO 26 a subframe. The SRS may have a comb structure, and a UE may transmit SRS on one of the combs. The SRS may be used by a base station for channel quality estimation to enable frequency-dependent scheduling on the UE.

[0100] FIG. 4D illustrates an example of various UL channels within a subframe of a frame. The PUCCH may be located as indicated in one configuration. The PUCCH carries uplink control information (UCI), such as scheduling requests, a channel quality indicator (CQI), a precoding matrix indicator (PMI), a rank indicator (RI), and HARQ ACK / NACK feedback. The PUSCH carries data, and may additionally be used to carry a buffer status report (BSR), a power headroom report (PHR), and / or UCI.Example Artificial Intelligence for Wireless Communications

[0101] Certain aspects described herein may be implemented, at least in part, using some form of artificial intelligence (Al), e.g., the process of using a machine learning (ME) model to infer or predict output data based on input data. An example ML model may include a mathematical representation of one or more relationships among various objects to provide an output representing one or more predictions or inferences. Once an ML model has been trained, the ML model may be deployed to process data that may be similar to, or associated with, all or part of the training data and provide an output representing one or more predictions or inferences based on the input data.

[0102] ML is often characterized in terms of types of learning that generate specific types of learned models that perform specific types of tasks. For example, different types of machine learning include supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning.

[0103] Supervised learning algorithms generally model relationships and dependencies between input features (e.g., a feature vector) and one or more target outputs. Supervised learning uses labeled training data, which are data including one or more inputs and a desired output. Supervised learning may be used to train models to perform tasks like classification, where the goal is to predict discrete values, or regression, where the goal is to predict continuous values. Some example supervised learning algorithms include nearest neighbor, naive Bayes, decision trees, linear regression, support vector machines (SVMs), and artificial neural networks (ANNs).

[0104] Unsupervised learning algorithms work on unlabeled input data and train models that take an input and transform it into an output to solve a practical problem.D&S Ref. No.: QCM2405903WOQualcomm Ref. No.: 2405903 WO 27Examples of unsupervised learning tasks are clustering, where the output of the model may be a cluster identification, dimensionality reduction, where the output of the model is an output feature vector that has fewer features than the input feature vector, and outlier detection, where the output of the model is a value indicating how the input is different from a typical example in the dataset. An example unsupervised learning algorithm is k- Means.

[0105] Semi-supervised learning algorithms work on datasets containing both labeled and unlabeled examples, where often the quantity of unlabeled examples is much higher than the number of labeled examples. However, the goal of a semi-supervised learning is that of supervised learning. Often, a semi-supervised model includes a model trained to produce pseudo-labels for unlabeled data that is then combined with the labeled data to train a second classifier that leverages the higher quantity of overall training data to improve task performance.

[0106] Reinforcement learning algorithms use observations gathered by an agent from an interaction with an environment to take actions that may maximize a reward or minimize a risk. Reinforcement learning is a continuous and iterative process in which the agent learns from its experiences with the environment until it explores, for example, a full range of possible states. An example type of reinforcement learning algorithm is an adversarial network. Reinforcement learning may be particularly beneficial when used to improve or attempt to optimize a behavior of a model deployed in a dynamically changing environment, such as a wireless communication network.

[0107] ML models may be deployed in one or more devices (e.g., network entities such as base station(s) and / or user equipment(s)) to support various wired and / or wireless communication aspects of a communication system. For example, an ML model may be trained to identify patterns and relationships in data corresponding to a network, a device, an air interface, or the like. An ML model may improve operations relating to one or more aspects, such as transceiver circuitry controls, frequency synchronization, timing synchronization, channel state estimation, channel equalization, channel state feedback, modulation, demodulation, device positioning, transceiver tuning, beamforming, signal coding / decoding, network routing, load balancing, and energy conservation (to name just a few) associated with communications devices, services, and / or networks. Al-enhanced transceiver circuitry controls may include, for example, fdter tuning, transmit powerD&S Ref. No.: QCM2405903WOQualcomm Ref. No.: 2405903 WO 28 controls, gain controls (including automatic gain controls), phase controls, power management, and the like.

[0108] Aspects described herein may describe the performance of certain tasks and the technical solution of various technical problems by application of a specific type of ML model, such as an ANN. It should be understood, however, that other type(s) of Al models may be used in addition to or instead of an ANN. An ML model may be an example of an Al model, and any suitable Al model may be used in addition to or instead of any of the ML models described herein. Hence, unless expressly recited, subject matter regarding an ML model is not necessarily intended to be limited to just an ANN solution or machine learning. Further, it should be understood that, unless otherwise specifically stated, terms such “Al model,” “ML model,” “AI / ML model,” “trained ML model,” and the like are intended to be interchangeable.

[0109] FIG. 5 is a diagram illustrating an example Al architecture 500 that may be used for Al-enhanced wireless communications. As illustrated, the architecture 500 includes multiple logical entities, such as a model training host 502, a model inference host 504, data source(s) 506, and an agent 508. The Al architecture may be used in any of various use cases for wireless communications, such as those listed above. In some aspects, the model training host 502 may be an example of a model training function.

[0110] The model inference host 504, in the architecture 500, is configured to run an ML model based on inference data 512 provided by data source(s) 506. The model inference host 504 may produce an output 514 (e.g., a prediction or inference, such as a discrete or continuous value) based on the inference data 512, that is then provided as input to the agent 508. In some aspects, the model inference host 504 may be an example of a model inference agent.

[0111] The agent 508 may be an element or an entity of a wireless communication system including, for example, a RAN, a wireless local area network, a D2D communications system, etc. In some examples, the agent 508 may be a decision agent. In some examples, the agent 508 may be a UE, a base station, or any disaggregated network entity thereof including a CU, a DU, and / or an RU, an access point, a wireless station, a RIC in a cloud -based RAN, among some examples. Additionally, the type of agent 508 may also depend on the type of tasks performed by the model inference hostD&S Ref. No.: QCM2405903WOQualcomm Ref. No.: 2405903 WO 29504, the type of inference data 512 provided to model inference host 504, and / or the type of output 514 produced by model inference host 504.

[0112] For example, if output 514 from the model inference host 504 is associated with beam management, the agent 508 may be or include a UE, a DU, or an RU. As another example, if output 514 from model inference host 504 is associated with transmission and / or reception scheduling, the agent 508 may be a CU or a DU.

[0113] After the agent 508 receives output 514 from the model inference host 504, the agent 508 may determine whether to act based on the output, and the agent 508 may notify a subject 510 of the action (illustrated as “subject of action 510”) or whether to perform an action. In certain cases, the agent 508 and the subject 510 may be the same entity. If the agent 508 is a UE and the output from model inference host 504 is associated with sidelink channel access (as further described herein), the agent 508 may determine whether to adjust a value of a parameter associated with sidelink channel access based on the output 514. If the agent 508 determines to act based on the output 514, the agent 508 may indicate the action to at least one subject 510, which may be the same entity as the agent 508. For example, if the agent 508 determines to adjust the value for the parameter associated with sidelink channel access, the agent 508 may notify the subject 510 (e.g., the UE), which may adjust the value.

[0114] In certain cases, the agent 508 and the subject 510 may be different entities. As an example, the agent 508 may be a UE, and the subject 510 may be a network entity. The model inference host 504 may determine whether to adjust a value for a parameter associated with sidelink channel access. Based on the prediction, the agent 508 (such as the UE) may send, to the subject 510 (such as the network entity) a request to adjust the value for the parameter.

[0115] The data sources 506 may be configured for collecting data that is used as training data 516 for training an ME model, or as inference data 512 for feeding an ML model inference operation. In particular, the data sources 506 may collect data from any of various entities (e.g., the UE and / or the BS), which may include the subject 510 of action, and provide the collected data to a model training host 502 for ML model training. For example, after a subject 510 of action (e.g., a UE) receives an indication to adjust a value for a parameter from agent 508, the subject 510 of action may provide performance feedback associated with application of the value to the data sources 506, where theD&S Ref. No.: QCM2405903WOQualcomm Ref. No.: 2405903 WO 30 performance feedback may be used by the model training host 502 for monitoring and / or evaluating the ML model performance, such as whether the output 514, provided to agent 508, is accurate. In some examples, if the output 514 provided to agent 508 is inaccurate (or the accuracy is below an accuracy threshold), the model training host 502 may determine to modify or retrain the ML model used by model inference host 504, such as via an ML model deploy ment / update.

[0116] In certain aspects, the model training host 502 may be deployed at or with the same or a different entity than that in which the model inference host 504 is deployed. For example, in order to offload model training processing, which can impact the performance of the model inference host 504, the model training host 502 may be deployed at a model server as further described herein. Further, in some cases, training and / or inference may be distributed amongst devices in a decentralized or federated fashion.

[0117] In certain aspects, an ML model is deployed at or on a UE for ALenhanced wireless communications. More specifically, a model inference host, such as model inference host 504 in FIG. 5, may be deployed at or on the UE for dynamic sidelink channel access, for example, as further described herein with respect to FIGS. 8-15.Example Artificial Intelligence Model

[0118] FIG. 6 is an illustrative block diagram of an example artificial neural network (ANN) 600.

[0119] ANN 600 may receive input data 606 which may include one or more bits of data 602, pre-processed data output from pre-processor 604 (optional), or some combination thereof. Here, data 602 may include training data, verification data, application-related data, or the like, e.g., depending on the stage of development and / or deployment of ANN 600. Pre-processor 604 may be included within ANN 600 in some other implementations. Pre-processor 604 may, for example, process all or a portion of data 602 which may result in some of data 602 being changed, replaced, deleted, etc. In some implementations, pre-processor 604 may add additional data to data 602.

[0120] ANN 600 includes at least one first layer 608 of artificial neurons 610 (e.g., perceptrons) to process input data 606 and provide resulting first layer output data via edges 612 to at least a portion of at least one second layer 614. Second layer 614 processes data received via edges 612 and provides second layer output data via edges 616 to atD&S Ref. No.: QCM2405903WOQualcomm Ref. No.: 2405903 WO 31 least a portion of at least one third layer 618. Third layer 618 processes data received via edges 616 and provides third layer output data via edges 620 to at least a portion of a final layer 622 including one or more neurons to provide output data 624. All or part of output data 624 may be further processed in some manner by (optional) post-processor 626. Thus, in certain examples, ANN 600 may provide output data 628 that is based on output data 624, post-processed data output from post-processor 626, or some combination thereof. Post-processor 626 may be included within ANN 600 in some other implementations. Post-processor 626 may, for example, process all or a portion of output data 624 which may result in output data 628 being different, at least in part, to output data 624, e.g., as result of data being changed, replaced, deleted, etc. In some implementations, post-processor 626 may be configured to add additional data to output data 624. In this example, second layer 614 and third layer 618 represent intermediate or hidden layers that may be arranged in a hierarchical or other like structure. Although not explicitly shown, there may be one or more further intermediate layers between the second layer 614 and the third layer 618.

[0121] The structure and training of artificial neurons 610 in the various layers may be tailored to specific requirements of an application. Within a given layer of an ANN, some or all of the neurons may be configured to process information provided to the layer and output corresponding transformed information from the layer. For example, transformed information from a layer may represent a weighted sum of the input information associated with or otherwise based on a non-linear activation function or other activation function used to “activate” artificial neurons of a next layer. Artificial neurons in such a layer may be activated by or be responsive to weights and biases that may be adjusted during a training process. Weights of the various artificial neurons may act as parameters to control a strength of connections between layers or artificial neurons, while biases may act as parameters to control a direction of connections between the layers or artificial neurons. An activation function may select or determine whether an artificial neuron transmits its output to the next layer or not in response to its received data. Different activation functions may be used to model different types of non-linear relationships. By introducing non-linearity into an MF model, an activation function allows the ML model to “learn” complex patterns and relationships in the input data (e.g., 506 in FIG. 5). Some non-exhaustive example activation functions include a linearD&S Ref. No.: QCM2405903WOQualcomm Ref. No.: 2405903 WO 32 function, binary step function, sigmoid, hyperbolic tangent (tanh), a rectified linear unit (ReLU) and variants, exponential linear unit (ELU), Swish, Softmax, and others.

[0122] Design tools (such as computer applications, programs, etc.) may be used to select appropriate structures for ANN 600 and a number of layers and a number of artificial neurons in each layer, as well as selecting activation functions, a loss function, training processes, etc. Once an initial model has been designed, training of the model may be conducted using training data. Training data may include one or more datasets within which ANN 600 may detect, determine, identify or ascertain patterns. Training data may represent various types of information, including written, visual, audio, environmental context, operational properties, etc. During training, parameters of artificial neurons 610 may be changed, such as to minimize or otherwise reduce a loss function or a cost function. A training process may be repeated multiple times to finetune ANN 600 with each iteration.

[0123] Various ANN model structures are available for consideration. For example, in a feedforward ANN structure each artificial neuron 610 in a layer receives information from the previous layer and likewise produces information for the next layer. In a convolutional ANN structure, some layers may be organized into filters that extract features from data (e.g., training data and / or input data). In a recurrent ANN structure, some layers may have connections that allow for processing of data across time, such as for processing information having a temporal structure, such as time series data forecasting.

[0124] In an autoencoder ANN structure, compact representations of data may be processed and the model trained to predict or potentially reconstruct original data from a reduced set of features. An autoencoder ANN structure may be useful for tasks related to dimensionality reduction and data compression.

[0125] A generative adversarial ANN structure may include a generator ANN and a discriminator ANN that are trained to compete with each other. Generative-adversarial networks (GANs) are ANN structures that may be useful for tasks relating to generating synthetic data or improving the performance of other models.

[0126] A transformer ANN structure makes use of attention mechanisms that may enable the model to process input sequences in a parallel and efficient manner. An attention mechanism allows the model to focus on different parts of the input sequence atD&S Ref. No.: QCM2405903WOQualcomm Ref. No.: 2405903 WO 33 different times. Attention mechanisms may be implemented using a series of layers known as attention layers to compute, calculate, determine or select weighted sums of input features based on a similarity between different elements of the input sequence. A transformer ANN structure may include a series of feedforward ANN layers that may learn non-linear relationships between the input and output sequences. The output of a transformer ANN structure may be obtained by applying a linear transformation to the output of a final attention layer. A transformer ANN structure may be of particular use for tasks that involve sequence modeling, or other like processing.

[0127] Another example type of ANN structure, is a model with one or more invertible layers. Models of this type may be inverted or “unwrapped” to reveal the input data that was used to generate the output of a layer.

[0128] Other example types of ANN model structures include fully connected neural networks (FCNNs) and long short-term memory (LSTM) networks.

[0129] ANN 600 or other ML models may be implemented in various types of processing circuits along with memory and applicable instructions therein, for example, as described herein with respect to FIGS. 5 and 6. For example, general-purpose hardware circuits, such as, such as one or more central processing units (CPUs) and one or more graphics processing units (GPUs) may be employed to implement a model. One or more ML accelerators, such as tensor processing units (TPUs), embedded neural processing units (eNPUs), or other special-purpose processors, and / or field- programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), or the like also may be employed. Various programming tools are available for developing ANN models.Aspects of Artificial Intelligence Model Training

[0130] There are a variety of model training techniques and processes that may be used prior to, or at some point following, deployment of an ML model, such as ANN 600 of FIG. 6.

[0131] As part of a model development process, information in the form of applicable training data may be gathered or otherwise created for use in training an ML model accordingly. For example, training data may be gathered or otherwise created regarding information associated with received / transmitted signal strengths, interference, and resource usage data, as well as any other relevant data that might be useful for training aD&S Ref. No.: QCM2405903WOQualcomm Ref. No.: 2405903 WO 34 model to address one or more problems or issues in a communication system. In certain instances, all or part of the training data may originate in one or more user equipments (UEs), one or more network entities, or one or more other devices in a wireless communication system. In some cases, all or part of the training data may be aggregated from multiple sources (e.g., one or more UEs, one or more network entities, the Internet, etc.). For example, wireless network architectures, such as self-organizing networks (SONs) or mobile drive test (MDT) networks, may be adapted to support collection of data for ML model applications. In another example, training data may be generated or collected online, offline, or both online and offline by a UE, network entity, or other device(s), and all or part of such training data may be transferred or shared (in real or near-real time), such as through store and forward functions or the like. Offline training may refer to creating and using a static training dataset, e.g., in a batched manner, whereas online training may refer to a real-time or near-real-time collection and use of training data. For example, an ML model at a network device (e.g., a UE) may be trained and / or fine-tuned using online or offline training. For offline training, data collection and training can occur in an offline manner at the network side (e.g., at a base station or other network entity) or at the UE side. For online training, the training of a UE-side ML model may be performed locally at the UE or by a server device (e.g., a server hosted by a UE vendor) in a real-time or near-real-time manner based on data provided to the server device from the UE.

[0132] In certain instances, all or part of the training data may be shared within a wireless communication system, or even shared (or obtained from) outside of the wireless communication system.

[0133] Once an ML model has been trained with training data, its performance may be evaluated. In some scenarios, evaluation / verification tests may use a validation dataset, which may include data not in the training data, to compare the model’s performance to baseline or other benchmark information. If model performance is deemed unsatisfactory, it may be beneficial to fine-tune the model, e.g., by changing its architecture, re-training it on the data, or using different optimization techniques, etc. Once a model’ s performance is deemed satisfactory, the model may be deployed accordingly. In certain instances, a model may be updated in some manner, e.g., all or part of the model may be changed or replaced, or undergo further training, just to name a few examples.D&S Ref. No.: QCM2405903WOQualcomm Ref. No.: 2405903 WO 35

[0134] As part of a training process for an ANN, such as ANN 600 of FIG. 6, parameters affecting the functioning of the artificial neurons and layers may be adjusted. For example, backpropagation techniques may be used to train the ANN by iteratively adjusting weights and / or biases of certain artificial neurons associated with errors between a predicted output of the model and a desired output that may be known or otherwise deemed acceptable. Backpropagation may include a forward pass, a loss function, a backward pass, and a parameter update that may be performed in training iteration. The process may be repeated for a certain number of iterations for each set of training data until the weights of the artificial neurons / layers are adequately tuned.

[0135] Backpropagation techniques associated with a loss function may measure how well a model is able to predict a desired output for a given input. An optimization algorithm may be used during a training process to adjust weights and / or biases to reduce or minimize the loss function which should improve the performance of the model. There are a variety of optimization algorithms that may be used along with backpropagation techniques or other training techniques. Some initial examples include a gradient descent based optimization algorithm and a stochastic gradient descent based optimization algorithm. A stochastic gradient descent (or ascent) technique may be used to adjust weights / biases in order to minimize or otherwise reduce a loss function. A mini-batch gradient descent technique, which is a variant of gradient descent, may involve updating weights / biases using a small batch of training data rather than the entire dataset. A momentum technique may accelerate an optimization process by adding a momentum term to update or otherwise affect certain weights / biases.

[0136] An adaptive learning rate technique may adjust a learning rate of an optimization algorithm associated with one or more characteristics of the training data. A batch normalization technique may be used to normalize inputs to a model in order to stabilize a training process and potentially improve the performance of the model.

[0137] A “dropout” technique may be used to randomly drop out some of the artificial neurons from a model during a training process, e.g., in order to reduce overfitting and potentially improve the generalization of the model.

[0138] An “early stopping” technique may be used to stop an on-going training process early, such as when a performance of the model using a validation dataset starts to degrade.D&S Ref. No.: QCM2405903WOQualcomm Ref. No.: 2405903 WO 36

[0139] Another example technique includes data augmentation to generate additional training data by applying transformations to all or part of the training information.

[0140] A transfer learning technique may be used which involves using a pre-trained model as a starting point for training a new model, which may be useful when training data is limited or when there are multiple tasks that are related to each other.

[0141] A multi-task learning technique may be used which involves training a model to perform multiple tasks simultaneously to potentially improve the performance of the model on one or more of the tasks. Hyperparameters or the like may be input and applied during a training process in certain instances.

[0142] Another example technique that may be useful with regard to an ML model is some form of a “pruning” technique. A pruning technique, which may be performed during a training process or after a model has been trained, involves the removal of unnecessary (e.g., because they have no impact on the output) or less necessary (e.g., because they have negligible impact on the output), or possibly redundant features from a model. In certain instances, a pruning technique may reduce the complexity of a model or improve efficiency of a model without undermining the intended performance of the model.

[0143] Pruning techniques may be particularly useful in the context of wireless communication, where the available resources (such as power and bandwidth) may be limited. Some example pruning techniques include a weight pruning technique, a neuron pruning technique, a layer pruning technique, a structural pruning technique, and a dynamic pruning technique. Pruning techniques may, for example, reduce the amount of data corresponding to a model that may need to be transmitted or stored.

[0144] Weight pruning techniques may involve removing some of the weights from a model. Neuron pruning techniques may involve removing some neurons from a model. Layer pruning techniques may involve removing some layers from a model. Structural pruning techniques may involve removing some connections between neurons in a model. Dynamic pruning techniques may involve adapting a pruning strategy of a model associated with one or more characteristics of the data or the environment. For example, in certain wireless communication devices, a dynamic pruning technique may more aggressively prune a model for use in a low-power or low-bandwidth environment, and less aggressively prune the model for use in a high-power or high-bandwidthD&S Ref. No.: QCM2405903WOQualcomm Ref. No.: 2405903 WO 37 environment. In certain aspects, pruning techniques also may be applied to training data, e.g., to remove outliers, etc. In some implementations, pre-processing techniques directed to all or part of a training dataset may improve model performance or promote faster convergence of a model. For example, training data may be pre-processed to change or remove unnecessary data, extraneous data, incorrect data, or otherwise identifiable data. Such pre-processed training data may, for example, lead to a reduction in potential overfitting, or otherwise improve the performance of the trained model.

[0145] One or more of the example training techniques presented above may be employed as part of a training process. As above, some example training processes that may be used to train an ML model include supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning technique.

[0146] Decentralized, distributed, or shared learning, such as federated learning, may enable training on data distributed across multiple devices or organizations, without the need to centralize data or the training. Federated learning may be particularly useful in scenarios where data is sensitive or subject to privacy constraints, or where it is impractical, inefficient, or expensive to centralize data. In the context of wireless communication, for example, federated learning may be used to improve performance by allowing an ML model to be trained on data collected from a wide range of devices and environments. For example, an ML model may be trained on data collected from a large number of wireless devices in a network, such as distributed wireless communication nodes, smartphones, or internet-of-things (loT) devices, to improve the network's performance and efficiency. With federated learning, a user equipment (UE) or other device may receive a copy of all or part of a model and perform local training on such copy of all or part of the model using locally available training data. Such a device may provide update information (e.g., trainable parameter gradients) regarding the locally trained model to one or more other devices (such as a network entity or a server) where the updates from other-like devices (such as other UEs) may be aggregated and used to provide an update to a shared model or the like. A federated learning process may be repeated iteratively until all or part of a model obtains a satisfactory level of performance. Federated learning may enable devices to protect the privacy and security of local data, while supporting collaboration regarding training and updating of all or part of a shared model.D&S Ref. No.: QCM2405903WOQualcomm Ref. No.: 2405903 WO 38

[0147] In some implementations, one or more devices or services may support processes relating to a ML model’s usage, maintenance, activation, reporting, or the like. In certain instances, all or part of a dataset or model may be shared across multiple devices, e.g., to provide or otherwise augment or improve processing. In some examples, signaling mechanisms may be utilized at various nodes of wireless network to signal the capabilities for performing specific functions related to ML model, support for specific ML models, capabilities for gathering, creating, transmitting training data, or other ML related capabilities. ML models in wireless communication systems may, for example, be employed to support decisions relating to wireless resource allocation or selection, wireless channel condition estimation, interference mitigation, beam management, positioning accuracy, energy savings, or modulation or coding schemes, etc. In some implementations, model deployment may occur jointly or separately at various network levels, such as, a central unit (CU), a distributed unit (DU), a radio unit (RU), or the like.Example V2X Communications

[0148] FIG. 7 depicts an example V2X system 700. In this example, the V2X system includes multiple UEs 704a-d (collectively UEs 704) in communication with each other via UE-to-UE direct communications often called sidelink communications. In certain cases, the wireless communication channel for UE-to-UE direct communications is referred to as a PC5 interface. The V2X system 700 may further include a network entity 702, such as the BS 102 and / or any disaggregated network entity thereof, for example, as described herein with respect to FIGS. 1-3.

[0149] As an example, the first UE 704a and the second UE 704b are vehicles in communication with each other, where such communications are referred to as V2V communications. The third UE 704c may be a portable communication device including, for example, a cellular phone, laptop, or wearable device. The sidelink communications between the first UE 704a and the third UE 704c may be representative of vehicle-to- pedestrian (V2P) communications. The fourth UE 704d may be an RSU including, for example, a traffic sign, traffic light, lamp post, or any other wireless communication device deployed along or in proximity to a road or highway. The sidelink communications between the first UE 704a and the fourth UE 704d may be representative of vehicle-to- infrastructure (V2I) communications. As an RSU, the fourth UE 704d may collect and analyze traffic data generated by vehicles (e.g., the first UE 704a and the second UE 704b). RSUs may serve as a gateway to other communication networks, for example, viaD&S Ref. No.: QCM2405903WOQualcomm Ref. No.: 2405903 WO 39 a communication channel between the RSU and the network entity 702. Note that V2X communications may include other types of vehicle communications, such as vehicle -to- network (V2N), vehicle-to-grid (V2G), vehicle-to-device (V2D), vehicle-to-cloud (V2C), or the like. In some cases, the UEs 704 may communicate with each other through the network entity 702, for example, via a Uu interface (e.g., a wireless communication channel between a radio access network (RAN) and a UE). Such communications may be referred to as various forms of everything-to -network (X2N) communications, for example, V2N, pedestrian-to-network (P2N), infrastructure -to-network (I2N), etc.

[0150] Communication resource (e.g., time-frequency resource) allocations for sidelink communications may be implemented in various modes. In Mode 1, the network entity 702 may assign communication resources for sidelink communications, for example, via control signaling. As an example, the network entity 702 may assign communication resources for the first UE 704a to communicate with any of the other UEs 704b-d. In Mode 2, the UEs 704 autonomously select resources for sidelink communications, for example, in resource pool(s). The UEs 704 may communicate with each other autonomously without scheduling assistance from the network entity 702. An autonomous V2X system 700 may enable improved spectral efficiency, reduced cost, and increased reliability as network service interruptions do not occur during handover operations for moving vehicles.

[0151] In certain cases, the UEs 704 may communicate with each other via a licensed spectrum and / or an unlicensed or shared spectrum. As previously discussed, the unlicensed or shared spectrum may allow for increased wireless channel capacity for the V2X system, especially to accommodate the rapid increase in vehicles supporting sidelink communications.

[0152] Note the V2X system 700 depicted in FIG. 7 is an example to facilitate an understanding of sidelink communication. Aspects of the present disclosure may be applied to other types of environments that employ sidelink communications.Aspects Related to Dynamic Sidelink Channel Access

[0153] Aspects of the present disclosure provide techniques to perform dynamic sidelink channel access, for example, where a UE may determine parameter value(s) associated with sidelink channel access. Such local determination of parameter value(s) may enable reduced latencies, improved sidelink channel usage, and / or the like.D&S Ref. No.: QCM2405903WOQualcomm Ref. No.: 2405903 WO 40

[0154] FIG. 8 depicts an example scheme 800 for dynamic sidelink channel access. In this example, a first UE 804a may be in communication with a second UE 804b via a sidelink channel. In some examples, the sidelink channel may use a sidelink communication protocol, such as proximity services (ProSe) sidelink. In some examples, the sidelink channel may use another form of D2D communication protocol. The first UE 804a may transmit signaling to the second UE 804b based on a sidelink channel access procedure (e.g., an LBT procedure), for example, depending on whether the sidelink channel is busy or idle as described herein. The first UE 804a may perform dynamic sidelink access, for example, by determining value(s) for certain parameter(s) associated with the sidelink channel access.

[0155] To perform the sidelink channel access procedure (for example, as described herein), the first UE 804a may obtain, from a network entity 802, a configuration (hereinafter “the sidelink configuration”) that (explicitly) indicates a function associated with determination of one or more values for a set of parameters associated with sidelink channel access. As used herein, the term “parameter value” with respect to dynamic sidelink channel access may refer to a value for a parameter associated with sidelink channel access. In certain aspects, the information conveyed or indicated via the sidelink configuration may be conveyed via one or more configurations. In certain aspects, the function may be configured to determine the parameter value(s) associated with sidelink channel access based on certain input, such as channel properties or conditions, as further described herein. In certain cases, the function may be or include an algorithm, which does not rely on Al and / or ML, such as a proportional-integral-derivative (PID) controller.

[0156] In certain aspects, the function may be or include one or more ML models or functionalities (hereinafter “the ML model 806”). The indication of the function may include an ML model identifier and / or an ML function name or identifier. The ML function name or identifier may be associated with sidelink channel access, such as dynamic sidelink channel access. In certain aspects, the indication of the function may include an indication that ML is allowed to be used for determination of the parameter value(s).

[0157] The ML model 806 may be deployed at or on the first UE 804a to enable dynamic sidelink channel access based on input data fed to the ML model 806, as further described herein. As an example, the ML model 806 may be stored in memory (such asD&S Ref. No.: QCM2405903WOQualcomm Ref. No.: 2405903 WO 41 the one or more memories 320 of FIG. 3) and accessible to one or more processors (such as the one or more processors 318 of FIG. 3).

[0158] In certain aspects, the ML model 806 may be trained or configured to provide output data 810 that includes, for example, an indication of a value for a parameter associated with sidelink channel access based on the input data 808 fed to the ML model 806, as further described herein. As an example, the ML model 806 may be trained to predict a parameter value based on one or more channel properties, such as received signal strength and / or received signal quality. In certain cases, the ML model 806 may be or include an ANN (for example, as described herein with respect to FIG. 6), regression analysis, decision tree learning, SVM, generative model, a deep learning reinforcement model, and / or the like.

[0159] In certain aspects, the first UE 804a may have access to multiple ML models 830, and the first UE 804a may select the ML model 806 among the ML models 830. In certain cases, the first UE 804a may select the ML model 806 based on one or more characteristics of the ML model 806, such as a model identifier, a model function name or identifier, model parameter(s), layers, structure, neuron connects, and / or the like. The model parameter(s) may include, for example, the type of input data 808 fed to the ML model 806 and / or output data 810 obtained from the ML model 806. In certain cases, the first UE 804a may select the ML model 806 based on an indication obtained from the network entity 802. For example, the sidelink configuration may indicate the ML model 806 to use for dynamic sidelink channel access.

[0160] In certain cases, the ML models 830 may have different characteristics, and the selection of the ML model 806 may depend on the characteristics of the ML model 806. The ML models 830 may predict the parameter value(s) with different levels of accuracy (e.g., accuracies of 80%, 95%, or 99%), different latencies (e.g., the processing time to predict the parameter value(s) associated with sidelink channel access), and / or different throughputs (e.g., the capacity to predict a certain number of parameter values concurrently). In certain cases, the ML models 830 may have different inference data and output pairings (e.g., different types of inference data may produce different types of output). In certain cases, the ML models 830 may have different ML model sizes, which may correspond to the file size of the respective ML model. As an example, the first UE may select the ML model 806 that is capable of predicting the parameters value(s) in accordance with certain performance specification(s), such as latency, accuracy, and / orD&S Ref. No.: QCM2405903WOQualcomm Ref. No.: 2405903 WO 42 the like. The performance specifications for selection of the ML model may depend on a quality of service (QoS) associated with a service and / or traffic being communicated between the first UE 804a and the second UE 804b, such as URLLC, V2X communications, and / or loT communications.

[0161] In certain aspects, the first UE 804a may provide the input data 808 to the ML model 806. The input data 808 may be or include certain characteristic(s) of the sidelink communication environment, for example, between the first UE 804a and the second UE 804b. As the first UE 804a may be located in an area where sidelink communications are occurring or expected to occur (as opposed to the network entity 802, which may be located outside of the area), the first UE 804a may collect the input data 808 that is fed to the ML model 806. For example, the first UE 804a may obtain one or more measurement(s) and / or information indicative of the sidelink communication environment between the first UE 804a and the second UE 804b. Thus, the measurement(s) and / or information used as (or included in) the input data 808 for dynamic sidelink channel access may not be directly accessible to the network entity 802. The UE determination of parameter value(s) associated with sidelink channel access may enable reliable sidelink channel access that takes into account the current and / or past sidelink communication environment encountered by the first UE 804a. Accordingly, the dynamic sidelink channel access may enable reduced latencies, improved sidelink channel usage, and / or the like.

[0162] As an example, the input data 808 may include one or more sidelink channel properties 812, a HARQ feedback rate (or ratio) 814, a sidelink channel access failure rate (or ratio) 816, a device position and / or orientation 818, a time 820, and / or the like. The sidelink channel properties 812 may include received signal strength on the sidelink channel, a received signal quality on the sidelink channel, a data error rate (e.g., a block error rate (BLER)), and / or the like. The received signal strength on the sidelink channel may include a reference signal received power (RSRP) and / or a received signal strength indicator (RS SI). The received signal quality on the sidelink channel may include a signal-to-noise ratio (SNR), a signal-to-interference plus noise ratio (SINR), a signal-to- noise-plus-distortion ratio (SNDR), and / or a reference signal received quality (RSRQ)), and / or the like.

[0163] The HARQ feedback rate 814 may be or include the number of HARQ ACKs or NACKs associated with sidelink transmissions received at the first UE 804a per a unitD&S Ref. No.: QCM2405903WOQualcomm Ref. No.: 2405903 WO 43 of time (e.g., second, minute, or the like) or in a given time window. A HARQ feedback ratio may be or include the ratio of the total number HARQ ACKs or NACKs received to the total number of transmissions. The sidelink channel access failure rate 816 may be the number of sidelink channel access attempts that resulted in a failure at the first UE 804a per a unit of time or in a given time window. A sidelink channel access failure ratio may be the ratio of the total number of access attempt failures to the total number of access attempts. The device position may be or include the position or location or speed of the first UE 804a (e.g., the sidelink transmitter) and / or the second UE 804b (e.g., the sidelink receiver). The device orientation may be or include an azimuth and / or elevation used for sidelink communications at the first UE 804a and / or the second UE 804b. The time 820 may be the time at which the input data 808 was measured or obtained.

[0164] In certain aspects, the input data 808 may include certain specification(s) associated with the sidelink communications between the first UE 804a and the second UE 804b. The input data 808 may include QoS specifications associated with the sidelink communications. For example, the input data 808 may include an expected latency for the sidelink communications, an expected level of throughput for the sidelink communications, an expected data error rate (e.g., BEER) for the sidelink communications, a specific type of traffic, and / or the like. The type of traffic may include V2X communications, loT communications, extended reality traffic, wearable traffic, or the like. Accordingly, the ME model 806 may be configured or trained to provide output data 810 that satisfies the specification(s) associated with the sidelink communications.

[0165] The first UE 804a may obtain the output data 810 from the ML model 806. The output data 810 may be or include an indication of a set of values for one or more parameters associated with sidelink channel access. As an example, the parameter(s) may be or include certain threshold(s) that determine whether a sidelink channel is treated as being busy or idle associated with sidelink channel access, for example, as described herein. The parameter(s) may include an EDT 822, a maximum value 824 for the EDT (labeled as “Max EDT”), an offset 826 associated with the maximum value (labeled as “EDT Offset”), and / or a ratio threshold 828 for contention window adjustment. Note that these parameter(s) are examples to facilitate an understanding of dynamic sidelink channel access. Aspects of the present disclosure may be applied to predict or determine parameter value(s) for additional or alternative parameter(s) associated with sidelink channel access.D&S Ref. No.: QCM2405903WOQualcomm Ref. No.: 2405903 WO 44

[0166] The EDT 822 may be the threshold that the first UE 804a uses to initiate sidelink channel access (e.g., to determine whether a sidelink channel is treated as being busy or idle). In certain cases, the EDT 822 may be the threshold to initiate a channel occupancy to be shared with other UE(s), such as the second UE 804b. As an example with respect to 5G NR systems, the EDT 822 may correspond to the field ue-ToUE-COT- SharingED-Threshold. The maximum value 824 for the EDT may set a ceiling for the EDT. The maximum value 824 may have a value greater than or equal to the EDT 822. As an example, the maximum value may correspond to the field sl- MaxEnergyDetectionThreshold. The offset 826 associated with the maximum value for the EDT may be used to adjust the maximum value, for example, to comply with certain regulator specifications for wireless communications in shared or unlicensed bands. As an example, the offset 826 may correspond to the field sl- EnergyDetectionThresholdOffset. The ratio threshold 828 for contention window adjustment may be the ratio threshold associated with groupcast communications as described herein. As an example, the ratio threshold 828 may correspond to the field harq- ACK-FeedbackRatioforCW-AdjustmentGC-Option2.

[0167] In certain aspects, the application of the parameter value(s) determined at the first UE 804a may be performed in various manners. For example, in certain cases, the first UE 804a may apply the parameter value(s) directly without notifying the network entity 802 (and / or the second UE 804b). For example, the first UE 804a may communicate with the second UE 804b via the sidelink channel without sending, to the network entity 802, an explicit indication of the parameter value(s) determined at the first UE 804a.

[0168] In certain cases, the first UE 804a may notify the network entity 802 (and / or the second UE 804b) of the parameter value(s) and apply the parameter value(s) for sidelink channel access. The parameter value(s) may be communicated via RRC signaling, MAC signaling, UCI, capability information (or a capability report), UE assistance information, and / or the like.

[0169] In certain cases, the first UE 804a may notify the network entity 802 (and / or the second UE 802b) of the parameter values, and then the network entity 802 may determine whether to apply the parameter value(s), adjust the parameter value(s), or maintain default parameter value(s). The network entity 802 may send, to the first UE 804a and / or the second UE 804b, an indication of a set of parameter values associated with sidelink channel access after reception of the notification of the parameter value(s)D&S Ref. No.: QCM2405903WOQualcomm Ref. No.: 2405903 WO 45 from the first UE 804a. The set of parameter values may be the same as or different from the parameter value(s) sent by the first UE 804a. In certain cases, the network entity 802 may obtain the parameter value(s) from multiple UEs, and the network entity may determine the set of parameter values based on the feedback from the UEs. The notification of the parameter value(s) may serve as recommended, preferred, or requested value(s) associated with sidelink channel access. The parameter value(s) may be communicated via RRC signaling, MAC signaling, UCI, capability information (or a capability report), UE assistance information, and / or the like.

[0170] In certain aspects, the first UE 804a may notify the network entity 802 of the capability of the first UE 804a to perform dynamic sidelink channel access. The first UE 804a may send, to the network entity 802, capability information associated with dynamic sidelink channel access. The capability information may include an indication that the first UE 804a supports usage of the function for determination of the parameter value(s). For example, the indication that the first UE 804a supports usage of the function may include the ML model identifier(s) and / or ML function name(s) or identifier(s) (among a set of identifiers or names known to the network entity) supported by the first UE 804a to perform dynamic sidelink channel access. The ML model identifier and / or the ML function name or identifier may be associated with the ML model 806.

[0171] In certain cases, the capability information may include an indication of a set of metrics for initiation (e.g., activation) of the function for determination of the parameter value(s). The capability information may include one or more performance metrics used to activate and / or deactivate the dynamic sidelink channel access (e.g., parameter value selection via the ML model). In certain cases, the capability information may include an indication of a set of metrics for performance evaluation of the parameter value(s) selected via dynamic sidelink channel access. The performance metric(s) for activation, deactivation, and / or evaluation of the dynamic sidelink channel access may include one or more thresholds associated with the sidelink channel properties, the HARQ feedback rate associated with sidelink communications, the sidelink channel access failure rate, latency, throughput, and / or the like.

[0172] In certain aspects, the sidelink configuration may indicate certain specification(s) associated with dynamic sidelink channel access. The specification(s) may be or include a set of values in which a parameter value associated with sidelink channel access may be selected or determined. A set of selectable values may be the setD&S Ref. No.: QCM2405903WOQualcomm Ref. No.: 2405903 WO 46 of values in which a parameter value may be selected or determined. In certain cases, the specification(s) may be or include a range of values in which a value may be selected for a parameter, such as a floor (e.g., a lower bound) and / or a ceiling (e.g., an upper bound) for a parameter.

[0173] In certain cases, the specification(s) may specify at which time a parameter value may be updated based on dynamic sidelink channel access. Such a specification may prevent ping-ponging between values and / or enable stable, reliable sidelink channel access. As an example, the specification(s) may specify a minimum amount of time (or a minimum number of instances of channel access attempts) for which a value may be used for sidelink channel access. The minimum amount of time may be indicated as a duration of a timer and / or a number of time-domain resource units (e.g., symbol(s), slots(s), frame(s), or the like). For example, the first UE 804a may start a timer after application of a parameter value, and the first UE 804a may use the parameter value until expiration of the timer. The minimum amount of time may be indicated in terms of the system frame number (SFN) and / or a direct frame number (DFN) associated with sidelink communications.

[0174] In certain cases, the specification(s) may specify that a parameter value may be updated if one or more performance metric(s) are satisfied (or not satisfied). The specification(s) may indicate the performance metric(s) and / or the corresponding threshold(s) or target(s). The specification(s) may indicate the duration of the performance history window to evaluate the performance metric(s). If the performance metric(s) are not satisfied, the first UE 804a may fall back to default parameter value(s).

[0175] In certain cases, application of certain specification(s) may depend on certain criteria being satisfied. For example, if a first condition is satisfied, a first set of specifications (e.g., a first set of bound values) may be used; otherwise (if the first condition is not satisfied), a second set of specifications (e.g., a second set of bound values) may be used. In certain cases, the input data 808 fed to the ML model 806 may include the specification(s) for the dynamic sidelink channel access.

[0176] FIG. 9 depicts an example configuration 900 associated with dynamic sidelink channel access. The configuration 900 may include one or more fields that set certain specification(s) on dynamic sidelink channel access. The configuration 900 may be an example of the sidelink configuration as described herein with respect to FIG. 8.D&S Ref. No.: QCM2405903WOQualcomm Ref. No.: 2405903 WO 47Additional or alternative field(s) may be included in the sidelink configuration with respect to FIG. 8. As an example, the configuration 900 may indicate a set of selectable values for a parameter and / or whether AI / ML may be used for determination of the respective parameter value. The set of selectable values may be indicated as a range of values, such as a ceiling (e.g., an upper bound) and / or a floor (e.g., a lower bound) for the values. In certain aspects, the set of selectable values may be set with either a ceiling (e.g., an upper bound) or a floor (e.g., a lower bound) for the values.

[0177] The configuration 900 may be or include a set of configurations including a sidelink energy detection configuration 902, an EDT configuration 904, a ratio threshold configuration 906, a maximum value configuration 908, and / or an offset configuration 910. The sidelink energy detection configuration 902 may be linked to the maximum value configuration 908 and / or the offset configuration 910. Each of the configurations 904, 906, 908, 910 may be associated with a particular parameter of the output data 810 of FIG. 8.

[0178] As an example, the EDT configuration 904 may indicate certain specification(s) associated with the EDT for channel occupancy sharing between UEs, such as the first UE 804a and the second UE 804b of FIG. 8. The EDT configuration 904 may include an indication of a lower bound (e.g., a minimum value), an indication of an upper bound (e.g., a maximum value), and / or an indication of whether Al and / or ME may be used for determination of the EDT. Any of the other configurations (e.g., the ratio threshold configuration 906, the maximum value configuration 908, and / or the offset configuration 910) may include any of the corresponding fields of the EDT configuration 904, such as a lower bound, an upper bound, and / or an indication of whether Al and / or ME may be used for determination of the respective parameter value.Aspects of Training a Machine Learning Model for Dynamic Sidelink Channel Access

[0179] FIG. 10 illustrates an example architecture 1000 for training an ML model for dynamic sidelink channel access. The architecture 1000 may be implemented by a model training host (e.g., the model training host 502 of FIG. 5). In certain cases, the model training host may be or include the first UE 804a and / or the network entity 802 of FIG. 8. In certain cases, the model training host may be or include a processing system (e.g., the processing system 306 of FIG. 3) configured to perform certain life cycle management task(s) associated with ML model(s) deployed at a UE. For example, the model trainingD&S Ref. No.: QCM2405903WOQualcomm Ref. No.: 2405903 WO 48 host may be or include a model server, which may collect training data from one or more wireless communications devices (e.g., the first UE 804a and / or the second UE 804b) and perform the ML model training as further described herein.

[0180] The model training host may obtain training data 1002 including training input data 1004 and / or corresponding labels 1006 for the training input data 1004. The training input data 1004 may include samples of received signal measurement(s) (e.g., received signal quality and / or received signal strength), HARQ feedback rates, channel access failure rates, latencies, throughputs, service types, and / or the like. The training input data 1004 may be simulated (e.g., computer generated) and / or collected from performance characterizations (e.g., measurements) of a UE, for example, under various operating conditions, as further discussed herein.

[0181] The model training host may use the labels 1006 to evaluate the performance of an ML model 1008 and adjust the ML model 1008 (e.g., weights of the ANN 600) as described herein. Each of the labels 1006 may be associated with a sample of the training input data 1004. In certain cases, each of the labels 1006 may include an expected parameter value for the respective sample. In some cases, each of the labels 1006 may include a performance metric for the expected parameter value.

[0182] The model training host provides the training input data 1004 to the ML model 1008. In certain aspects, the ML model 1008 may include a neural network. The ML model 1008 may be an example of the ML model(s) described herein with respect to FIGS. 5, 6, and 8. The ML model 1008 provides output data 1010, which may include an indication of parameter value(s) associated with sidelink channel access, for example, as described herein with respect to FIG. 8.

[0183] The model training host may evaluate the performance of the ML model 1008 and determine whether to update the ML model 1008, for example, based on the labels 1006 and / or the performance achieved with the predicted parameter value(s). The model training host may evaluate the quality and / or accuracy of the output data 1010. In some cases, the model training host may determine whether the output data 1010 matches the corresponding label 1006 of the training input data 1004. For example, the model training host may determine whether the predicted parameter value(s) output by the ML model 1008 matches the expected parameter value(s) of the corresponding label 1006. In some cases, the performance evaluation may determine whether the predicted parameterD&S Ref. No.: QCM2405903WOQualcomm Ref. No.: 2405903 WO 49 value(s) achieves the performance metric associated with the expected parameter value(s) of the corresponding label 1006.

[0184] In certain aspects, the model training host may evaluate the performance of the ML model 1008 using a cost or loss function 1012 (hereinafter “the loss function 1012”). The loss function 1012 may be or include a comparison between the expected parameter value(s) corresponding to the label 1006 and the predicted parameter value(s) output by the ML model 1008. In some cases, the loss function 1012 may be or include a comparison between the expected performance corresponding to the parameter value(s) of the label 1006 and the actual performance of a UE configured with the predicted parameter value(s) of the output data 1010. In certain aspects, the loss function 1012 may be or include a difference between the actual performance of the UE configured with the predicted parameter value(s) and the expected performance corresponding to the label 1006, for example, as a mean squared error between the respective performance metrics. The loss function 1012 may provide a loss value or score 1014 based on the comparison of the output data 1010 and the label 1006.

[0185] The model training host may provide the loss score 1014 to an optimizer 1016, which may determine one or more updated weights 1018 for the ML model 1008. The optimizer 1016 may adjust the ML model 1008 (e.g., any of the weights in a layer of a neural network) to reduce the loss score 1014 associated with the ML model 1008. In certain aspects, the optimizer 1016 may perform backpropagation to determine the updated weights 1018. The model training host may continue to provide the training input data 1004 to the ML model 1008 and adjust the ML model 1008 using the weights 1018 until the loss score 1014 of the ML model 1008 satisfies a threshold and / or reaches a specific value (e.g., a minimum value) or after a certain number of training iterations. The model training host may perform online training of the ML model 1008 or train the ML model 1008 using one or more batches of training data 1002. In certain aspects, the optimizer 1016 may be or include a root mean square propagation (RMSprop) optimizer, a descent gradient optimizer (e.g., a stochastic descent gradient (SGD)), a momentum optimizer, an Adam optimizer, or like to minimize the loss score 1014 associated with the ML-based sidelink channel access.

[0186] In certain aspects, the model training host may train multiple ML models to perform dynamic sidelink channel access. The ML models may be trained or configured with different model performance characteristics, different operating environments (e.g.,D&S Ref. No.: QCM2405903WOQualcomm Ref. No.: 2405903 WO 50 sidelink channel conditions), and / or different input-output schemes (e.g., different input data and different output data). For example, the ML models may be trained to predict the parameter value(s) with different levels of accuracy (e.g., accuracies of 80%, 95%, or 99%) of meeting a target performance metric and / or different latencies (e.g., the processing time to predict the parameter value(s)). Thus, a UE may select the ML model that is capable of predicting parameter value(s) associated with sidelink channel access in accordance with certain performance characteristic(s), operating environments, and / or input-output schemes as described herein.

[0187] Note that the training architecture 1000 is an example of deep learning to facilitate an understanding of training an ML model for dynamic sidelink channel access. Any suitable training architecture may be used in addition to or instead of the training architecture 1000 to train the ML model(s) described herein.Example Signaling of Dynamic Sidelink Channel Access

[0188] FIG. 11 depicts a process flow 1100 for dynamic sidelink channel access in a system including a network entity 1102, a first UE 1104a, and a second UE 1104b. In some aspects, the network entity 1102 may be an example of the BS 102 depicted and described with respect to FIG. 1, the first network entity 300 or the second network entity 302 depicted and described with respect to FIG. 3, or a disaggregated base station depicted and described with respect to FIG. 2. Similarly, the UE 1104a, 1104b may be an example of UE 104 depicted and described with respect to FIG. 1 or the UE 304 depicted and described with respect to FIG. 3. However, in other aspects, UE 1104 may be another type of wireless communications device, and network entity 1102 may be another type of network entity or network node, such as those described herein. Note that any operations or signaling illustrated with dashed lines may indicate that that operation or signaling is an optional or alternative example.

[0189] At 1106, the first UE 1104a optionally sends, to the network entity 1102, an indication of capability information. The capability information may indicate any of the capability information described herein with respect to FIG. 8. The capability information may indicate that the UE supports determination of parameter value(s) associated with sidelink channel access. In certain cases, the capability information may indicate one or more ML model identifiers and / or one or more ML function names or identifiers associated with dynamic sidelink channel access. As an example, the capabilityD&S Ref. No.: QCM2405903WOQualcomm Ref. No.: 2405903 WO 51 information may include an indication of an ML model identifier associated with an ML model trained or configured to determine or predict parameter value(s) associated with sidelink channel access as described herein with respect to FIG. 8.

[0190] At 1108, the first UE 1104a obtains, from the network entity 1102, one or more configurations associated with dynamic sidelink channel access. As an example, the configuration(s) may include an (explicit) indication of a function associated with determination of one or more values for a set of parameters associated with sidelink channel access, for example, as described herein with respect to FIG. 8 and 9. The configuration(s) may include any of the information used to configure the first UE 1104a to perform dynamic sidelink channel access, for example, as described herein with respect to FIGS. 8 and 9. In certain cases, the network entity 1102 may select the configuration(s) for the first UE 1104a based on the capability information obtained at 1106. As an example, the configuration(s) may indicate an ML model identifier to use for determination of parameter value(s) associated with sidelink channel access, where the ML model identifier may be indicated in the capability information communicated at 1106. The configuration(s) may be communicated via system information, RRC signaling, MAC signaling, DCI, and / or the like.

[0191] At 1110, the first UE 1104a determines a set of values for one or more parameters associated with sidelink channel access, for example, as described herein with respect to FIG. 8. As an example, the first UE 1104a may provide input data to an ML model (e.g., the ML model 806 of FIG. 8), and the first UE 1104a may obtain output data from the ML model. The output data may include an indication of one or more parameter values associated with sidelink channel access. As the first UE 1104a may collect measurement(s) and / or information associated with the sidelink communications environment between the first UE 1104a and the second UE 1104b, the first UE 1104a may determine the set of values that take into account the current (and / or past) sidelink communications environment. In certain cases, the measurement(s) and / or information may not be directly accessible to the network entity 1102. As an example, if the measurement(s) indicate that the contention level is high for sidelink communications with the second UE 1104b, the ML model may be configured or trained to adjust the EDT to enable a reliable sidelink access attempt with the second UE 1104b. Accordingly, the determination of the parameter value(s) at the first UE 1104a may enable reduced latencies, improved sidelink channel usage, and / or the like, for sidelink channel access.D&S Ref. No.: QCM2405903WOQualcomm Ref. No.: 2405903 WO 52

[0192] At 1112, the first UE 1104a optionally sends, to the network entity 1102, an indication of the parameter value(s) determined at 1110. In certain cases, the first UE 1104a may apply the parameter value(s) for sidelink communications without any reply from the network entity 1102. The indication of the parameter value(s) may be communicated via RRC signaling, MAC signaling, UCI, capability information, UE assistance information, and / or the like.

[0193] At 1114, the first UE 1104a optionally obtains, from the network entity 1102, a reply to the indication of the parameter value(s). In certain cases, the reply may include an acknowledgement that the first UE 1104a is expected to apply the parameter value(s). In certain cases, the reply may indicate a duration (or a number of access attempts) for which to apply the parameter value(s). In certain cases, the reply may include a configuration that indicates a set of parameter value(s) (which may be different from the parameter value(s)) based on the parameter value(s) communicated at 1112. As an example, the network entity 1102 may obtain requested parameter value(s) from multiple UEs, such as the first UE 1104a and the second UE 1104b, and the network entity 1102 may take into account the requested parameter value(s) to determine the configuration communicated at 1114. The reply may be communicated via system information, RRC signaling, MAC signaling, DCI, and / or the like.

[0194] At 1116, the first UE 1104a communicates with the second UE 1104b via a sidelink channel, such as an unlicensed and / or shared spectrum. The UE 1104a may send, to the second UE 1104b, signaling based at least in part on the set of values determined at 1110. As an example, the set of values may include an EDT for determination of whether the sidelink channel is treated as being busy or idle. When the first UE 1104a determines that the sidelink channel is idle based on the value for the EDT, the first UE 1104a may send the signaling to the second UE 1104b. In certain cases, the first UE 1104a may apply the set of values for sidelink channel access for a specified duration 1118, which may be set according to a timer (or a time window) indicated in the configuration(s) communicated at 1108, for example, as described herein with respect to FIG. 8.

[0195] Note that the process flow illustrated in FIG. 11 is described herein to facilitate an understanding of dynamic sidelink channel access, and aspects of the present disclosure may be performed in various manners via alternative or additional signaling and / or operations. In certain aspects, the operations and / or signaling of FIG. 11 mayD&S Ref. No.: QCM2405903WOQualcomm Ref. No.: 2405903 WO 53 occur in an order different from that described or depicted, and various actions, operations, and / or signaling may be added, omitted, or combined.Example Operations of Dynamic Sidelink Channel Access

[0196] FIG. 12 shows a method 1200 for wireless communications by an apparatus, such as UE 104 of FIG. 1 or UE 304 of FIG. 3.

[0197] Method 1200 begins at block 1205 with obtaining a configuration that indicates a function associated with determination of one or more values for a set of parameters associated with sidelink channel access, for example, as described herein with respect to FIGS. 8, 9, and 11.

[0198] Method 1200 then proceeds to block 1210 with communicating via a sidelink channel based at least in part on a first set of values for the set of parameters, wherein the first set of values are according to the function, for example, as described herein with respect to FIGS. 8 and 11.

[0199] In certain aspects, the configuration further indicates that the function includes a machine learning model. In certain aspects, the machine learning model may be used, trained, and / or configured as described herein with respect to FIGS. 5, 6, 8, and 10.

[0200] In certain aspects, method 1200 further includes determining the first set of values for the set of parameters according to the function.

[0201] In certain aspects, the set of parameters comprises one or more of: an energy detection threshold; a maximum value for the energy detection threshold; an offset associated with the maximum value; or a ratio for contention window adjustment.

[0202] In certain aspects, method 1200 further includes providing, to a machine learning model, input data. In certain aspects, method 1200 further includes obtaining, from the machine learning model, output data comprising an indication of the first set of values for the set of parameters.

[0203] In certain aspects, the input data comprises one or more of: a received signal strength on the sidelink channel; a received signal quality on the sidelink channel; a HARQ feedback rate; or a sidelink channel access failure rate.

[0204] In certain aspects, method 1200 further includes sending capability information that includes an indication that the UE supports usage of the function for determination of the one or more values. In certain aspects, the indication that the UED&S Ref. No.: QCM2405903WOQualcomm Ref. No.: 2405903 WO 54 supports usage of the function includes an indication of one or more machine learning models configured to determine the one or more values. In certain aspects, the capability information further comprises one or more of: an indication of a first set of metrics for initiation of the function for determination of the one or more values; or an indication of a second set of metrics for performance evaluation of the one or more values.

[0205] In certain aspects, block 1210 includes communicating via the sidelink channel without sending, to a network node, an explicit indication of the first set of values.

[0206] In certain aspects, method 1200 further includes sending, to a network node, an indication of the first set of values.

[0207] In certain aspects, method 1200 further includes obtaining, from the network node, an indication to apply the first set of values for sidelink channel access.

[0208] In certain aspects, the configuration further indicates a set of selectable values for at least one parameter of the set of parameters. In certain aspects, the configuration further indicates the set of selectable values as a range of values.

[0209] In certain aspects, the configuration further indicates a minimum duration of time that the one or more values are allowed to be used for sidelink channel access.

[0210] In certain aspects, the configuration further indicates a set of metrics for performance evaluation of the one or more values.

[0211] In certain aspects, method 1200, or any aspect related to it, may be performed by an apparatus, such as communications device 1400 of FIG. 14, which includes various components operable, configured, or adapted to perform the method 1200. Communications device 1400 is described below in further detail.

[0212] Note that FIG. 12 is just one example of a method, and other methods including fewer, additional, or alternative operations are possible consistent with this disclosure.

[0213] FIG. 13 shows a method 1300 for wireless communications by an apparatus, such as BS 102 of FIG. 1, a first network entity 300 or second network entity 302 of FIG. 3, or a disaggregated base station as discussed with respect to FIG. 2.

[0214] Method 1300 begins at block 1305 with sending, to a UE, a configuration that indicates a function for determination of one or more values for a set of parametersD&S Ref. No.: QCM2405903WOQualcomm Ref. No.: 2405903 WO 55 associated with sidelink channel access, for example, as described herein with respect toFIGS. 8, 9, and 11.

[0215] Method 1300 then proceeds to block 1310 with obtaining, from the UE, an indication of a first set of values for the set of parameters, for example, as described herein with respect to FIGS. 8 and 11.

[0216] In certain aspects, the configuration further indicates that the function includes a machine learning model.

[0217] In certain aspects, the set of parameters comprises one or more of: an energy detection threshold; a maximum value for the energy detection threshold; an offset associated with the maximum value; or a ratio for contention window adjustment.

[0218] In certain aspects, method 1300 further includes obtaining capability information that includes an indication that the UE supports usage of the function for determination of the one or more values. In certain aspects, the indication that the network node supports usage of the function includes an indication of one or more machine learning models configured to determine the one or more values. In certain aspects, the capability information further comprises one or more of: an indication of a first set of metrics for initiation of the function for determination of the one or more values; or an indication of a second set of metrics for performance evaluation of the one or more values.

[0219] In certain aspects, method 1300 further includes sending, to the UE, an indication to apply the first set of values for sidelink channel access.

[0220] In certain aspects, the configuration further indicates a set of selectable values for at least one parameter of the set of parameters. In certain aspects, the configuration further indicates the set of selectable values as a range of values.

[0221] In certain aspects, the configuration further indicates a minimum duration of time that the one or more values are allowed to be used for sidelink channel access.

[0222] In certain aspects, the configuration further indicates a set of metrics for performance evaluation of the one or more values.

[0223] In certain aspects, method 1300, or any aspect related to it, may be performed by an apparatus, such as communications device 1500 of FIG. 15, which includes various components operable, configured, or adapted to perform the method 1300. Communications device 1500 is described below in further detail.D&S Ref. No.: QCM2405903WOQualcomm Ref. No.: 2405903 WO 56

[0224] Note that FIG. 13 is just one example of a method, and other methods including fewer, additional, or alternative operations are possible consistent with this disclosure.Example Communications Devices

[0225] FIG. 14 depicts aspects of an example communications device 1400 configured for wireless communications. In some aspects, communications device 1400 is a user equipment, such as UE 104 described above with respect to FIG. 1 or UE 304 described with respect to FIG. 3.

[0226] The communications device 1400 includes a processing system 1405 coupled to a transceiver 1475 (e.g., a transmitter and / or a receiver). The transceiver 1475 is configured to transmit and receive signals for the communications device 1400 via an antenna 1480, such as the various signals as described herein. The processing system 1405 may be configured to perform processing functions for the communications device 1400, including processing signals received and / or to be transmitted by the communications device 1400.

[0227] The processing system 1405 includes one or more processors 1410 and a computer-readable medium / memory 1440. In various aspects, the one or more processors 1410 may be representative of the one or more processors 318 described with respect to FIG. 3. The one or more processors 1410 are coupled to a computer-readable medium / memory 1440 via a bus 1470. In some aspects, the computer-readable medium / memory 1440 may be representative of the one or more memories 320 described with respect to FIG. 3. The computer-readable medium / memory 1440 is a non-transitory computer-readable medium / memory. In certain aspects, the computer-readable medium / memory 1440 is configured to store instructions (e.g., computer-executable code), that when executed by the one or more processors 1410, cause the one or more processors 1410 to perform the method 1200 described with respect to FIG. 12, or any aspect related to it, including any operations described in relation to FIG. 12. Note that reference to a processor performing a function of communications device 1400 may include one or more processors performing that function of communications device 1400, such as in a distributed fashion.

[0228] In the depicted example, computer-readable medium / memory 1440 stores code (e.g., executable instructions), including code for obtaining 1445, code forD&S Ref. No.: QCM2405903WOQualcomm Ref. No.: 2405903 WO 57 communicating 1450, code for determining 1455, code for providing 1460, and code for sending 1465. Processing of the code 1445-1465 may enable and cause the communications device 1400 to perform the method 1200 described with respect to FIG. 12, or any aspect related to it.

[0229] The one or more processors 1410 include circuitry configured to implement (e.g., execute) the code stored in the computer-readable medium / memory 1440, including circuitry for obtaining 1415, circuitry for communicating 1420, circuitry for determining 1425, circuitry for providing 1430, and circuitry for sending 1435. Processing with circuitry 1415-1435 may enable and cause the communications device 1400 to perform the method 1200 described with respect to FIG. 12, or any aspect related to it.

[0230] More generally, means for communicating, transmitting, sending or outputting for transmission may include the one or more transceivers 324, one or more antenna 322 and / or processing system 316 of the UE 304 illustrated in FIG. 3, transceiver 1475 and / or antenna 1480 of the communications device 1400 in FIG. 14, and / or one or more processors 1410 of the communications device 1400 in FIG. 14. Means for communicating, receiving or obtaining may include the one or more transceivers 324, one or more antennas 322, and / or processing system 316 of the UE 304 illustrated in FIG. 3, transceiver 1475 and / or antenna 1480 of the communications device 1400 in FIG. 14, and / or one or more processors 1410 of the communications device 1400 in FIG. 14. For example, means for obtaining, means for determining, and / or means for providing of the method 1200 described with respect to FIG. 12, or any aspect related to it, may include processing system 316 of the UE 304 illustrated in FIG. 3, and / or one or more processors 1410 of the communications device 1400 in FIG. 14.

[0231] FIG. 15 depicts aspects of an example communications device configured for wireless communications. In some aspects, communications device 1500 is a network entity, such as BS 102 of FIG. 1, first network entity 300 or second network entity 302 of FIG. 3, or a disaggregated base station as discussed with respect to FIG. 2.

[0232] The communications device 1500 includes a processing system 1505 coupled to a transceiver 1545 (e.g., a transmitter and / or a receiver) and / or a network interface 1555. The transceiver 1545 is configured to transmit and receive signals for the communications device 1500 via an antenna 1550, such as the various signals as described herein. The network interface 1555 is configured to obtain and send signals forD&S Ref. No.: QCM2405903WOQualcomm Ref. No.: 2405903 WO 58 the communications device 1500 via communications link(s), such as a backhaul link, midhaul link, and / or fronthaul link as described herein, such as with respect to FIG. 2. The processing system 1505 may be configured to perform processing functions for the communications device 1500, including processing signals received and / or to be transmitted by the communications device 1500.

[0233] The processing system 1505 includes one or more processors 1510 and a computer-readable medium / memory 1525. In various aspects, one or more processors 1510 may be representative of the one or more processors 308, as described with respect to FIG. 3. The one or more processors 1510 are coupled to the computer-readable medium / memory 1525 via a bus 1540. In certain aspects, the computer-readable medium / memory 1525 is configured to store instructions (e.g., computer-executable code), including code 1530 and 1535, that when executed by the one or more processors 1510, cause the one or more processors 1510 to perform the method 1300 described with respect to FIG. 13, or any aspect related to it, including any operations described in relation to FIG. 13. The computer-readable medium / memory 1525 is a non-transitory computer-readable medium / memory. Note that reference to a processor of communications device 1500 performing a function may include one or more processors of communications device 1500 performing that function, such as in a distributed fashion.

[0234] In the depicted example, the computer-readable medium / memory 1525 stores code (e.g., executable instructions), including code for sending 1530 and code for obtaining 1535. Processing of the code 1530 and 1535 may enable and cause the communications device 1500 to perform the method 1300 described with respect to FIG. 13, or any aspect related to it.

[0235] The one or more processors 1510 include circuitry configured to implement (e.g., execute) the code stored in the computer-readable medium / memory 1525, including circuitry for sending 1515 and circuitry for obtaining 1520. Processing with circuitry 1515 and 1520 may enable and cause the communications device 1500 to perform the method 1300 described with respect to FIG. 13, or any aspect related to it.

[0236] Various components of the communications device 1500 may provide means for performing the method 1300 described with respect to FIG. 13, or any aspect related to it. Means for communicating, transmitting, sending or outputting for transmission may include the one or more transceivers 312, one or more antennas 314, and / or processingD&S Ref. No.: QCM2405903WOQualcomm Ref. No.: 2405903 WO 59 system 306 of the first network entity 300 or the second network entity 302 illustrated in FIG. 3, transceiver 1545, antenna 1550, and / or network interface 1555 of the communications device 1500 in FIG. 15, and / or one or more processors 1510 of the communications device 1500 in FIG. 15. Means for communicating, receiving or obtaining may include the one or more transceivers 312, one or more antennas 314, and / or processing system 306 of the first network entity 300 or the second network entity 302 illustrated in FIG. 3, transceiver 1545, antenna 1550, and / or network interface 1555 of the communications device 1500 in FIG. 15, and / or one or more processors 1510 of the communications device 1500 in FIG. 15.Example Clauses

[0237] Implementation examples are described in the following numbered clauses:

[0238] Clause 1 : A method for wireless communications by a UE comprising: obtaining a configuration that indicates a function associated with determination of one or more values for a set of parameters associated with sidelink channel access; and communicating via a sidelink channel based at least in part on a first set of values for the set of parameters, wherein the first set of values are according to the function.

[0239] Clause 2: The method of Clause 1, wherein the configuration further indicates that the function includes a machine learning model.

[0240] Clause 3: The method of any one of Clauses 1-2, further comprising determining the first set of values for the set of parameters according to the function.

[0241] Clause 4: The method of any one of Clauses 1-3, wherein the set of parameters comprises one or more of: an energy detection threshold; a maximum value for the energy detection threshold; an offset associated with the maximum value; or a ratio for contention window adjustment.

[0242] Clause 5: The method of any one of Clauses 1-4, further comprising: providing, to a machine learning model, input data; and obtaining, from the machine learning model, output data comprising an indication of the first set of values for the set of parameters.

[0243] Clause 6: The method of Clause 5, wherein the input data comprises one or more of: a received signal strength on the sidelink channel; a received signal quality on the sidelink channel; a HARQ feedback rate; or a sidelink channel access failure rate.D&S Ref. No.: QCM2405903WOQualcomm Ref. No.: 2405903 WO 60

[0244] Clause 7: The method of any one of Clauses 1-6, further comprising sending capability information that includes an indication that the UE supports usage of the function for determination of the one or more values.

[0245] Clause 8: The method of Clause 7, wherein the indication that the UE supports usage of the function includes an indication of one or more machine learning models configured to determine the one or more values.

[0246] Clause 9: The method of Clause 7 or 8, wherein the capability information further comprises one or more of: an indication of a first set of metrics for initiation of the function for determination of the one or more values; or an indication of a second set of metrics for performance evaluation of the one or more values.

[0247] Clause 10: The method of any one of Clauses 1-9, wherein communicating via the sidelink channel comprises communicating via the sidelink channel without sending, to a network node, an explicit indication of the first set of values.

[0248] Clause 11 : The method of any one of Clauses 1-10, further comprising sending, to a network node, an indication of the first set of values.

[0249] Clause 12: The method of Clause 11, further comprising obtaining, from the network node, an indication to apply the first set of values for sidelink channel access.

[0250] Clause 13: The method of any one of Clauses 1-12, wherein the configuration further indicates a set of selectable values for at least one parameter of the set of parameters.

[0251] Clause 14: The method of Clause 13, wherein the configuration further indicates the set of selectable values as a range of values.

[0252] Clause 15: The method of any one of Clauses 1-14, wherein the configuration further indicates a minimum duration of time that the one or more values are allowed to be used for sidelink channel access.

[0253] Clause 16: The method of any one of Clauses 1-15, wherein the configuration further indicates a set of metrics for performance evaluation of the one or more values.

[0254] Clause 17: A method for wireless communications by a network node comprising: sending, to a UE, a configuration that indicates a function for determinationD&S Ref. No.: QCM2405903WOQualcomm Ref. No.: 2405903 WO 61 of one or more values for a set of parameters associated with sidelink channel access; and obtaining, from the UE, an indication of a first set of values for the set of parameters.

[0255] Clause 18: The method of Clause 17, wherein the configuration further indicates that the function includes a machine learning model.

[0256] Clause 19: The method of any one of Clauses 17-18, wherein the set of parameters comprises one or more of: an energy detection threshold; a maximum value for the energy detection threshold; an offset associated with the maximum value; or a ratio for contention window adjustment.

[0257] Clause 20: The method of any one of Clauses 17-19, further comprising obtaining capability information that includes an indication that the UE supports usage of the function for determination of the one or more values.

[0258] Clause 21 : The method of Clause 20, wherein the indication that the network node supports usage of the function includes an indication of one or more machine learning models configured to determine the one or more values.

[0259] Clause 22: The method of Clause 20 or 21, wherein the capability information further comprises one or more of: an indication of a first set of metrics for initiation of the function for determination of the one or more values; or an indication of a second set of metrics for performance evaluation of the one or more values.

[0260] Clause 23: The method of any one of Clauses 17-22, further comprising sending, to the UE, an indication to apply the first set of values for sidelink channel access.

[0261] Clause 24: The method of any one of Clauses 17-23, wherein the configuration further indicates a set of selectable values for at least one parameter of the set of parameters.

[0262] Clause 25: The method of Clause 24, wherein the configuration further indicates the set of selectable values as a range of values.

[0263] Clause 26: The method of any one of Clauses 17-25, wherein the configuration further indicates a minimum duration of time that the one or more values are allowed to be used for sidelink channel access.D&S Ref. No.: QCM2405903WOQualcomm Ref. No.: 2405903 WO 62

[0264] Clause 27 : The method of any one of Clauses 17-26, wherein the configuration further indicates a set of metrics for performance evaluation of the one or more values.

[0265] Clause 28: One or more apparatuses, comprising: one or more memories comprising executable instructions; and one or more processors configured to execute the executable instructions and cause the one or more apparatuses to perform a method in accordance with any one of Clauses 1-27.

[0266] Clause 29: One or more apparatuses configured for wireless communications, comprising: one or more memories; and one or more processors, coupled to the one or more memories, configured to cause the one or more apparatuses to perform a method in accordance with any one of Clauses 1-27.

[0267] Clause 30: One or more apparatuses configured for wireless communications, comprising: one or more memories; and one or more processors, coupled to the one or more memories, configured to perform a method in accordance with any one of Clauses 1-27.

[0268] Clause 31 : One or more apparatuses, comprising means for performing a method in accordance with any one of Clauses 1-27.

[0269] Clause 32: One or more non-transitory computer-readable media comprising executable instructions that, when executed by one or more processors of one or more apparatuses, cause the one or more apparatuses to perform a method in accordance with any one of Clauses 1-27.

[0270] Clause 33 : One or more computer program products embodied on one or more computer-readable storage media comprising code for performing a method in accordance with any one of Clauses 1-27.

[0271] Clause 34: One or more apparatuses configured for wireless communications, comprising: a processing system that includes one or more processors and one or more memories coupled with the one or more processors, the processing system configured to cause the one or more apparatuses to perform a method in accordance with any one of Clauses 1-27.D&S Ref. No.: QCM2405903WOQualcomm Ref. No.: 2405903 WO 63Additional Considerations

[0272] The preceding description is provided to enable any person skilled in the art to practice the various aspects described herein. The examples discussed herein are not limiting of the scope, applicability, or aspects set forth in the claims. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other aspects. For example, changes may be made in the function and arrangement of elements discussed without departing from the scope of the disclosure. Various examples may omit, substitute, or add various procedures or components as appropriate. For instance, the methods described may be performed in an order different from that described, and various actions may be added, omitted, or combined. Also, features described with respect to some examples may be combined in some other examples. For example, an apparatus may be implemented or a method may be practiced using any number of the aspects set forth herein. In addition, the scope of the disclosure is intended to cover such an apparatus or method that is practiced using other structure, functionality, or structure and functionality in addition to, or other than, the various aspects of the disclosure set forth herein. It should be understood that any aspect of the disclosure disclosed herein may be embodied by one or more elements of a claim.

[0273] The various illustrative logical blocks, modules and circuits described in connection with the present disclosure may be implemented or performed with a general purpose processor, an Al processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic device (PhD), 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 the alternative, the processor may be any commercially available processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing devices, e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, a SoC, a SiP, or any other such configuration.

[0274] As used herein, a phrase referring to “at least one of’ a list of items refers to any combination of those items, including single members. As an example, “at least one of: a, b, or c” is intended to cover a, b, c, a-b, a-c, b-c, and a-b-c, as well as anyD&S Ref. No.: QCM2405903WOQualcomm Ref. No.: 2405903 WO 64 combination with multiples of the same element (e.g., a-a, a-a-a, a-a-b, a-a-c, a-b-b, a-c-c, b-b, b-b-b, b-b-c, c-c, and c-c-c or any other ordering of a, b, and c).

[0275] As used herein, the term “determining” encompasses a wide variety of actions. For example, “determining” may include calculating, computing, processing, deriving, investigating, looking up (e.g., looking up in a table, a database or another data structure), ascertaining and the like. Also, “determining” may include receiving (e.g., receiving information), accessing (e.g., accessing data in a memory) and the like. Also, “determining” may include resolving, selecting, choosing, establishing and the like.

[0276] As used herein, “coupled to” and “coupled with” generally encompass direct coupling and indirect coupling (e.g., including intermediary coupled aspects) unless stated otherwise. For example, stating that a processor is coupled to a memory allows for a direct coupling or a coupling via an intermediary aspect, such as a bus.

[0277] The methods disclosed herein comprise one or more actions for achieving the methods. The method actions may be interchanged with one another without departing from the scope of the claims. In other words, unless a specific order of actions is specified, the order and / or use of specific actions may be modified without departing from the scope of the claims. Further, the various operations of methods described above may be performed by any suitable means capable of performing the corresponding functions. The means may include various hardware and / or software component(s) and / or module(s), including, but not limited to a circuit, an ASIC, or processor.

[0278] The following claims are not intended to be limited to the aspects shown herein, but are to be accorded the full scope consistent with the language of the claims. Reference to an element in the singular is not intended to mean only one unless specifically so stated, but rather “one or more.” The subsequent use of a definite article (e.g., “the” or “said”) with an element (e.g., “the processor”) is not intended to invoke a singular meaning (e.g., “only one”) on the element unless otherwise specifically stated. For example, reference to an element (e.g., “a processor,” “the processor,” etc.), unless otherwise specifically stated, should be understood to refer to one or more elements (e.g., “one or more processors,” or the like). The terms “set” and “group” are intended to include one or more elements, and may be used interchangeably with “one or more.” Where reference is made to one or more elements performing functions (e.g., steps of a method), one element may perform all functions, or more than one element mayD&S Ref. No.: QCM2405903WOQualcomm Ref. No.: 2405903 WO 65 collectively perform the functions. When more than one element collectively performs the functions, each function need not be performed by each of those elements (e.g., different functions may be performed by different elements) and / or each function need not be performed in whole by only one element (e.g., different elements may perform different sub-functions of a function). Similarly, where reference is made to one or more elements configured to cause another element (e.g., an apparatus) to perform functions, one element may be configured to cause the other element to perform all functions, or more than one element may collectively be configured to cause the other element to perform the functions. Unless specifically stated otherwise, the term “some” refers to one or more. All structural and functional equivalents to the elements of the various aspects described throughout this disclosure that are known or later come to be known to those of ordinary skill in the art are intended to be encompassed by the claims. Moreover, nothing disclosed herein is intended to be dedicated to the public regardless of whether such disclosure is explicitly recited in the claims.D&S Ref. No.: QCM2405903WO

Claims

Qualcomm Ref. No.: 2405903 WO 66CLAIMS1. An apparatus for wireless communications, comprising a processing system that includes one or more processors and one or more memories coupled with the one or more processors, the processing system configured to cause a user equipment (UE) to: obtain a configuration that indicates a function associated with determination of one or more values for a set of parameters associated with sidelink channel access; and communicate via a sidelink channel based at least in part on a first set of values for the set of parameters, wherein the first set of values are according to the function.

2. The apparatus of claim 1, wherein the configuration further indicates that the function includes a machine learning model.

3. The apparatus of claim 1, wherein the processing system is configured to cause the UE to determine the first set of values for the set of parameters according to the function.

4. The apparatus of claim 1, wherein the set of parameters comprises one or more of: an energy detection threshold; a maximum value for the energy detection threshold; an offset associated with the maximum value; or a ratio for contention window adjustment.

5. The apparatus of claim 1, wherein the processing system is configured to cause the UE to: provide, to a machine learning model, input data; and obtain, from the machine learning model, output data comprising an indication of the first set of values for the set of parameters.

6. The apparatus of claim 5, wherein the input data comprises one or more of: a received signal strength on the sidelink channel; a received signal quality on the sidelink channel; a hybrid automatic repeat request (HARQ) feedback rate; orD&S Ref. No.: QCM2405903WOQualcomm Ref. No.: 2405903 WO 67 a sidelink channel access failure rate.

7. The apparatus of claim 1, wherein the processing system is configured to cause the UE to send capability information that includes an indication that the UE supports usage of the function for determination of the one or more values.

8. The apparatus of claim 7, wherein the indication that the UE supports usage of the function includes an indication of one or more machine learning models configured to determine the one or more values.

9. The apparatus of claim 7, wherein the capability information further comprises one or more of: an indication of a first set of metrics for initiation of the function for determination of the one or more values; or an indication of a second set of metrics for performance evaluation of the one or more values.

10. The apparatus of claim 1, wherein to cause the UE to communicate via the sidelink channel, the processing system is configured to cause the UE to communicate via the sidelink channel without sending, to a network node, an explicit indication of the first set of values.

11. The apparatus of claim 1, wherein the processing system is configured to cause the UE to send, to a network node, an indication of the first set of values.

12. The apparatus of claim 11, wherein the processing system is configured to cause the UE to obtain, from the network node, an indication to apply the first set of values for sidelink channel access.

13. The apparatus of claim 1, wherein the configuration further indicates a set of selectable values for at least one parameter of the set of parameters.

14. The apparatus of claim 13, wherein the configuration further indicates the set of selectable values as a range of values.D&S Ref. No.: QCM2405903WOQualcomm Ref. No.: 2405903 WO 6815. The apparatus of claim 1, wherein the configuration further indicates a minimum duration of time that the one or more values are allowed to be used for sidelink channel access.

16. The apparatus of claim 1, wherein the configuration further indicates a set of metrics for performance evaluation of the one or more values.

17. An apparatus for wireless communications, comprising a processing system that includes one or more processors and one or more memories coupled with the one or more processors, the processing system configured to cause a network node to: send, to a user equipment (UE), a configuration that indicates a function for determination of one or more values for a set of parameters associated with sidelink channel access; and obtain, from the UE, an indication of a first set of values for the set of parameters.

18. The apparatus of claim 17, wherein the configuration further indicates that the function includes a machine learning model.

19. The apparatus of claim 17, wherein the set of parameters comprises one or more of: an energy detection threshold; a maximum value for the energy detection threshold; an offset associated with the maximum value; or a ratio for contention window adjustment.

20. A method for wireless communications by a user equipment (UE), comprising: obtaining a configuration that indicates a function associated with determination of one or more values for a set of parameters associated with sidelink channel access; and communicating via a sidelink channel based at least in part on a first set of values for the set of parameters, wherein the first set of values are according to the function.D&S Ref. No.: QCM2405903WO

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