In-vehicle machine learning service discovery in perceptive wireless communications

A first UE and a second UE may exchange information relating to one or more machine learning data services. The one or more machine learning data services may be provided by the second UE. The first UE and the second UE may pair with each other for the one or more machine learning data services. The second UE may perform machine learning inference using at least one machine learning inference model, and may transmit the inference result (e.g., a link quality prediction) associated with a link between a network node and the first UE to the network node. The network node may switch (the direction of) the network node beam if the inference result predicts that the degree of link quality degradation associated with a current network node beam is going to be greater than a prespecified threshold.

TECHNICAL FIELD

The present disclosure relates generally to communication systems, and more particularly, to wireless communication involving machine learning.

INTRODUCTION

BRIEF SUMMARY

In an aspect of the disclosure, a method, a computer-readable medium, and an apparatus are provided. The apparatus may be a first user equipment (UE). The apparatus may exchange information relating to one or more machine learning data services with a second UE. The one or more machine learning data services may be provided by the second UE. The apparatus may pair with the second UE for the one or more machine learning data services.

In an aspect of the disclosure, a method, a computer-readable medium, and an apparatus are provided. The apparatus may be a second UE. The apparatus may exchange information relating to one or more machine learning data services with a first UE. The one or more machine learning data services may be provided by the second UE. The apparatus may pair with the first UE for the one or more machine learning data services.

DETAILED DESCRIPTION

By way of example, an element, or any portion of an element, or any combination of elements may be implemented as a “processing system” that includes one or more processors. Examples of processors include microprocessors, microcontrollers, graphics processing units (GPUs), central processing units (CPUs), application processors, digital signal processors (DSPs), reduced instruction set computing (RISC) processors, systems on a chip (SoC), baseband processors, field programmable gate arrays (FPGAs), programmable logic devices (PLDs), state machines, gated logic, discrete hardware circuits, and other suitable hardware configured to perform the various functionality described throughout this disclosure.

Referring again toFIG.1, in certain aspects, the UE104may include a machine learning data service component198that may be configured to exchange information relating to one or more machine learning data services with a second UE. The one or more machine learning data services may be provided by the second UE. The machine learning data service component198may be configured to discover and pair with the second UE for the one or more machine learning data services. In certain aspects, the UE104′ may include a machine learning data service component198′ that may be configured to exchange information relating to one or more machine learning data services with a first UE. The one or more machine learning data services may be provided by the second UE. The machine learning data service component198′ may be configured to discover and pair with the first UE for the one or more machine learning data services. Although the following description may be focused on 5G NR, the concepts described herein may be applicable to other similar areas, such as LTE, LTE-A, CDMA, GSM, and other wireless technologies.

Wireless networks may operate at higher frequency bands, such as within millimeter wave (mmW) bands (e.g., FR2 above 28 GHz, FR4 above 60 GHz, or THz band above 100 GHz, etc.), to offer potentially data rates of several tens or hundreds of Gbit/s. For example, wireless devices, such as a network node or UEs, may communicate with each other using beamforming techniques to increase communication speed and reliability. The beamforming techniques may enable a wireless device to transmit a signal towards a particular direction instead of transmitting an omnidirectional signal in all directions. For example, the width of a beam may be inversely correlated to the frequency, where the beam width may decrease as the transmitting frequency increases because more radiating elements may be placed per given area at a transmitter due to the smaller wavelength. In some examples, the transmission path of a narrower beam may be more likely to be tailored to a receiver, such that the transmission may be more likely to meet a line-of-sight (LOS) condition.

Herein a network node can be implemented as an aggregated base station, as a disaggregated base station, an integrated access and backhaul (IAB) node, a relay node, a sidelink node, etc. A network entity can be implemented in an aggregated or monolithic base station architecture, or alternatively, in a disaggregated base station architecture, and may include one or more of a central unit (CU), a distributed unit (DU), a radio unit (RU), a Near-Real Time (Near-RT) RAN Intelligent Controller (RIC), or a Non-Real Time (Non-RT) RIC.

While higher frequency bands may provide narrower beam structures and higher transmission rates, higher frequency bands may also encounter higher attenuation and diffraction losses, where a blockage of an LOS path may degrade a wireless link quality, sometimes significantly. For example, when two wireless devices are communicating with each other based on an LOS path at a higher frequency band and the LOS path is blocked by an obstacle, such as pedestrians, buildings, vehicles, etc., the received power may drop significantly. As such, wireless communications based on higher frequency bands may be more susceptible to environmental changes compared to lower frequency bands.

Aspects presented herein may improve the performance and reliability of wireless communications operating at higher frequency bands, such as mmW bands. Aspects presented herein may enable wireless communications to be adaptive to a dynamic environment, where wireless devices may manage wireless communications, such as performing beam managements, based at least in part on environmental conditions. For example, in one aspect of the present disclosure, to overcome rapid variations of the link quality of wireless communication systems operating at higher frequency bands caused by an LOS path blockage, sensing information provided by a vehicle's onboard sensor(s) (e.g., camera(s), radar(s), and/or light detection and ranging (lidar) sensors) may be leveraged to provide information associated with communication environments as well as obstacles (e.g., moving objects) that may potentially block the LOS path and degrade the communication quality for a wireless device, such as a UE. The vehicle may include motor vehicles (e.g., motorcycles, cars, trucks, and buses), railed vehicles (trains and trams, etc.), watercrafts (ships and boats), and/or aircraft (e.g., airplanes, helicopters, and aerostat), etc. In addition, in another aspect of the present disclosure, a vehicle may be configured to employ onboard computation resources and machine learning (ML) models to pre-process collected sensor data and feed inference data to an inference host collocated/associated with a network node (e.g., a base station) for predicting/estimating possible blockages (e.g., LOS path blockages) or best beam pairs so that the network node may proactively initiate beam management or hand-off procedures for a UE. For purposes of the present disclosure, an “inference” or an “ML inference” may refer to a process of running data points into an ML inference model (e.g., an inference host) to calculate an output such as one or more numerical scores, e.g., to use a trained ML algorithm to make one or more predictions. An “inference host” or an “ML inference host” may refer to a network function which hosts the ML inference model during an inference mode.

FIG.4is a diagram400illustrating an example of an open radio access network (O-RAN) architecture implemented with a RIC which may be used as an inference host in accordance with various aspects of the present disclosure. The O-RAN shown in the diagram400may also be disaggregated into multiple components/elements. For example, the radio side of an O-RAN architecture (e.g., the components for performing wireless communication) may include a near-RT RIC402, an O-RAN-CU-control plane (O-CU-CP)404, an O-RAN-CU-user plane (O-CU-UP)406, an O-RAN or open DU (O-DU)408, and an O-RAN or open RU (0-RU)410. The management side of the O-RAN architecture (e.g., the components for performing communication managements) may include a service management and orchestration framework412that contains a non-RT-RIC414function.

In some examples, the near-RT RIC402may be a logical function that enables near-RT control and optimization of O-RAN elements and resources via fine-grained data collection and actions over an E2 interface. The near-RT RIC402may be collocated with the RAN/network node (e.g., the O-CU-CP404) to provide the RT processing. The non-RT RIC414may be a logical function that enables non-RT control and optimization of RAN elements and resources, artificial intelligence (AI)/ML workflow including model training and updates, and policy-based guidance of applications/features in near-RT RIC. The non-RT RIC414may be located further from the RAN/network node, such as on a cloud-based server or on an edge server. The O-CU-CP404and the O-CU-UP406(collectively as “O-CU”) may be a logical node hosting RRC, SDAP and PDCP protocols. For example, the O-CU-CP404may be a logical node hosting the RRC and the CP part of the PDCP protocol, whereas the O-CU-UP406may be a logical node hosting the UP part of the PDCP protocol and the SDAP protocol. The O-DU408may be a logical node hosting RLC, MAC, and/or high-physical (PHY) layers based on a lower layer functional split. The O-RU410may be a logical node hosting low-PHY layer and RF processing based on a lower layer functional split. The O-RAN may include an O1 interface420between management entities in the service management and orchestration framework412and O-RAN managed elements, for operation and management, by which fault, configuration, accounting, performance and security (FCAPS) management, software management, file management are to be achieved. The O-RAN may also include an O1*interface416between the service management and orchestration framework412and an infrastructure management framework418supporting O-RAN virtual network functions.

In one aspect, the O-RAN may define an overall RIC architecture that consists of four functional software elements: a DU software function, a multi-radio access technology (RAT) CU protocol stack, the near-RT RIC402itself, and the orchestration/network management system (NMS) layer with the non-RT RIC414. The functional software elements may interact with RU hardware to make it run more efficiently and to be optimized real-time as a part of the RAN cluster to deliver a better network experience to end users.

In some examples, the functionality of the non-RT RIC414may include configuration management, device management, fault management, performance management, and lifecycle management for all network elements in the network. The non-RT RIC414may use data analytics and AI/ML training/inference to determine the RAN optimization actions for which it may leverage services of the service management and orchestration framework412such as data collection and provisioning services of the O-RAN nodes. On the other hand, the near-RT RIC402may use embedded intelligence for per-UE controlled load-balancing, RB management, interference detection, and mitigation, etc. This may also provide QoS management, connectivity management, and/or seamless handover control, etc. The near-RT RIC402may leverage the near RT state of the underlying network and feed RAN data to train the AI/ML inference models, which may then feed to the near-RT RIC402to facilitate radio resource management for subscribers.

FIG.5is a diagram500illustrating an example architecture of a functional framework for RAN intelligence enabled by data collection in accordance with various aspects of the present disclosure. In some scenarios, the functional framework for RAN intelligence may be enabled by further enhancement of data collection through use cases and/or examples. For example, principles/algorithms for RAN intelligence enabled by AI and the associated functional framework (e.g., the AI functionality and/or the input/output of the component for AI enabled optimization) have been utilized or studied to identify the benefits of AI enabled RAN (e.g., network node, NG-RAN, etc.) through possible use cases, e.g., energy saving, load balancing, mobility management, coverage optimization, etc. In one example, as shown by the diagram500, a functional framework for RAN intelligence may include multiple logical entities, such as a model training host502, a model inference host504, data sources506, and/or an actor508, etc.

The model inference host504may be configured to run an ML/AI model based on inference data provided by the data sources506, and the model inference host504may produce an output (e.g., a prediction) for the inference data to the actor508. The actor508may be an element or an entity of a core network or a RAN. For example, the actor508may be a network node (e.g., a base station, a gNB, etc.), a CU/DU/RU of a network node, etc. In addition, the actor508may also depend on the type of tasks performed by the model inference host504, type of inference data provided to the model inference host504, and/or type of output produced by the model inference host504, etc. For example, if the output from the model inference host504is associated with beam management, the actor508may be a DU/RU of a network node; whereas if the output from the model inference host504is associated with TX/RX scheduling, the actor508may be a CU/DU of a network node, etc.

After the actor508receives an output from the model inference host504, the actor508may determine whether to act based on the output. For example, if the actor508is a network node and the output from the model inference host504is associated with beam management, the actor508(e.g., the network node) may determine whether to change/modify a TX/RX beam based on the output. If the actor508determines to act based on the output, the actor508may transmit the action to at least one subject of action510. For example, if the actor508(e.g., the network node) determines to change/modify a TX/RX beam for a communication between the actor508and the subject of action510(e.g., a UE), the actor508may transmit a beam configuration to the subject of action510. Then, the actor508may modify its TX/RX beam based on the beam configuration, such as switching to a new TX/RX beam or applying different parameters for a TX/RX beam, etc.

The data sources506may also be configured for collecting data that is used for training an ML inference model. For example, the data sources506may collect data from one or more core network and/or RAN entities, which may include the subject of action510, and provide the collected data to the model training host502for ML inference model training. For example, after a subject of action510(e.g., a UE) receives a beam configuration from the actor508(e.g., a network node), the subject of action510may provide performance feedback associated with the beam configuration to the data sources506, where the performance feedback may be used by the model training host502for monitoring or evaluating the ML inference model performance, e.g., whether the output (e.g., prediction) provided by the actor508is accurate. In some examples, if the output provided by the actor508is inaccurate (or the accuracy is below an accuracy threshold), the model training host502may determine to modify or retrain the ML inference model used by the model inference host, such as via an ML inference model deployment/update.

FIG.6is a diagram600illustrating an example of leveraging data from a sensor of a vehicle to improve wireless communication in accordance with various aspects of the present disclosure. A vehicle602equipped with a sensor device604(which may be referred to as a “sensor data collector”) may enter into a coverage area of a network node606, where the coverage area of the network node606may include static objects (e.g., buildings) as well as non-static (e.g., moving) objects (e.g., cars, trucks, buses, and pedestrians). The sensor device604may be associated with or include one or more sensors, such as cameras, radars, ultra-sound sensors, and/or lidars, etc., which may collectively be referred to as “vehicle-sensors.” In addition, the sensor device604may be a UE or include UE functions (e.g., a vehicle UE), such that the sensor device604may communicate with a network node or a network entity, such as via an RRC connection.

In some scenarios, the radio link quality between the network node606and a UE may be impacted by both the static and the non-static objects. For example, a UE608in the vehicle602(e.g., a wireless device used by a user in the vehicle602, which may be referred to as an “in-vehicle UE” or “passenger UE”) may be communicating with the network node606based on an LOS path/link. As shown at610, at times, the communication (e.g., the LOS path/link) between the UE608and the network node606may be impacted by both moving and/or stationary objects. For example, a moving truck612may block the LOS path/link between the UE608and the network node606, which may degrade the wireless link quality between the UE608and the network node606.

In one aspect of the present disclosure, with sensing information provided by the sensor device604(e.g., the sensor data collector) of the vehicle602, on-board computational resources of the vehicle602, and/or an ML model used by the vehicle602for extracting features, the vehicle602may be configured to provide inference data and information of the UE608(e.g., location, speed, etc.) to an ML inference host614that may be collocated with the on-board computational resources or the feature extraction ML model of the vehicle602for enhancing the communication between the UE608and the network node606, such as improving the beam management for the UE608. For example, as shown at616, the sensor device604may use its sensors to capture images of the views surrounding the vehicle602, and the captured images may be processed by an ML model that is associated with the sensor device604for feature extraction (e.g., for object detection) which may be sent to one of one or more ML inference models associated with the sensor device604for ML inference, where the ML inference results (e.g., beam predictions for UE608) may be sent to the network node606.

FIG.7is a diagram700illustrating an example of leveraging data from one or more sensors of a vehicle to improve wireless communication in accordance with various aspects of the present disclosure. A vehicle702(e.g., UE2) equipped with suitable sensors may enter into a coverage area of a network node706, where the coverage area of the network node706may include static objects as well as moving objects. The sensors equipped at the vehicle UE702may include one or more such sensors as cameras, radars, ultra-sound sensors, and/or lidars, etc., which may collectively be referred to as “vehicle-sensors.” The vehicle702including the vehicle-sensors may be a UE or may include UE functions, such that the vehicle702may communicate with a network node706using the beam714paired with one or more network node beams716at a lower frequency band (e.g., with a wider beam). Accordingly, the vehicle702may be referred to as a vehicle UE702.

In some scenarios, the radio link quality between the network node706and a UE may be impacted by both the static and the moving objects. For example, a passenger UE708(e.g., UE1) in (e.g., collocated with) the vehicle UE702(e.g., UE2) may be communicating with the network node706at a high frequency band (e.g., with a narrow beam) based on an LOS path/link710using the beam712paired with one of high frequency band network node beams718(e.g., with a respective network node beam index of 2). In some examples, the passenger UE708may communicate with the network node706using a band (e.g., FR2) higher than the band (e.g., FR1) used for the communication between the vehicle UE702and the network node706. Accordingly, in some examples, the beam712associated with the passenger UE708may be a narrower beam than the beam714associated with the vehicle UE702. At times, the communication (e.g., the LOS path/link) between the passenger UE708and the network node706may be impacted by moving and/or stationary objects.

In one aspect of the present disclosure, with sensing information provided by the vehicle-sensors of the vehicle UE702and on-board computational resources of the vehicle UE702, an ML model may be used by the vehicle UE702for extracting features. The vehicle UE702may be configured to provide the inference data and information of the passenger UE708(e.g., location, speed, etc.) to an ML inference host that may be collocated or associated with the vehicle UE702for enhancing the communication between the passenger UE708and the network node706, such as improving the beam management for the passenger UE708. To utilize the vehicle UE702to enhance the communication between the passenger UE708and the network node706, a discovery and pairing process may be performed between the vehicle UE702and the passenger UE708, where once the two UEs702and708are paired based on the pairing process, the vehicle UE702may operate as a machine learning service provider, and the passenger UE708may operate as a machine learning service end user. The pairing process between the vehicle UE702and the passenger UE708may be described in further detail below in relation toFIGS.12A,12B,13A, and13B.

FIGS.8A and8Bare diagrams800A and800B illustrating additional examples of leveraging data from one or more sensors of a vehicle to improve wireless communication in accordance with various aspects of the present disclosure. In particular, the diagram800B may correspond to a time instant that is a short period after the time instant of the diagram800A. As shown in diagrams800A and800B, a vehicle UE802may be equipped with sensors, such as cameras, radars and/or lidars. Further, the vehicle UE802may be within the coverage area of a network node806. In the coverage area of the network node806, there may be static objects such as a building822as well as moving objects (e.g., trucks and buses). At the time instant of the diagram800A, the network node806and a passenger UE808collocated with the vehicle UE802may communicate with each other using an LOS path810based on a network node beam818aand a first UE beam812a.

Leveraging its UE capability and the readings of the vehicle-sensors, the vehicle UE802may be aware of the location of the network node806and information about the oncoming vehicle820. Further, the vehicle UE802may be aware of its own position and velocity, which, in some examples, may be provided by the on-board inertial measurement unit (IMU) and/or the on-board global positioning system (GPS) module.

In one or more aspects, based on the above information (e.g., the location of the network node806, the information about the oncoming vehicle820, the position of the vehicle UE802, and/or the velocity of the vehicle UE802), at the time instant of the diagram800A, the machine learning inference model deployed at the vehicle UE802may provide an inference result that may predict that the LOS path810between the network node806and the passenger UE808may be blocked in the near future by the oncoming vehicle820. As a result, if the same beams (e.g., beams812aand818a) are used without change, the communication between the network node806and the passenger UE808may be disrupted in the near future (e.g., the beam pair link quality between the network node806and the passenger UE808may degrade). Accordingly, the vehicle UE802may transmit the inference result to the network node806(e.g., via a lower band link, such as an FR1 link).

At the time instant of the diagram800B, the LOS path810between the network node806and the passenger UE808may indeed be blocked by the vehicle820, as predicted by the inference result. However, based on the inference result received from the vehicle UE802, the network node806may switch the active network node beam from the beam818ato the beam818b(which may correspond to, e.g., changing a steering direction of the network node beam, changing an angle-of-departure (AoD) of the network node beam, or switch to a network node beam with a different beam index, etc.) before the LOS path810becomes blocked, so that the network node806may have continued unblocked communication with the passenger UE808. Further, also based on the inference result received from the vehicle UE802, the passenger UE808may switch the active passenger UE beam from the beam812ato the beam812b(which may correspond to, e.g., switching a direction of the active passenger UE beam), such that subsequent to the switches at both the network node806and the passenger UE808, the network node806and the passenger UE808may continue to communicate with each other with an undegraded beam pair link quality using a reflected path814including a reflection point at the building822based on the new network node beam818band the new UE beam812b.

Aspects of the disclosure may relate to approaches, signaling procedures, and/or parameters used for device and/or service discovery to enable pairing of the first UE (the passenger UE) and the second UE (the vehicle UE). Additional aspects may relate to approaches, signaling procedures, and/or parameters used for configuring the machine learning inference service at the second UE (the vehicle UE) by the network node.

In some aspects, an approach of direct UE-to-UE device and/or service discovery may be utilized. In particular, in one configuration, a UE (a passenger UE or a vehicle UE) may transmit (e.g., broadcast) one or more probing requests to neighboring devices in order to initiate the pairing process. In another configuration, a UE (a passenger UE or a vehicle UE) may transmit (e.g., broadcast) a message to announce its presence to neighboring UEs in order to initiate the pairing process.

In some aspects, device and/or service discovery for the machine learning data services may be performed with the assistance and/or management of a network node. In one configuration, the network node may configure the machine learning inference at the second UE (the vehicle UE).

In one or more aspects, the machine learning inference model hosted at the second UE (the vehicle UE) may include two (sub-)models that may be used together: A first model (Model 1) may be common to all network node beam indices, whereas each model component of the second model (Model 2) may be associated with a respective beam index (e.g., a respective transmission beam, a respective beam steering direction, or a respective beam AoD). Herein in some examples, the terms model or sub-model may be used interchangeably.

In one or more aspects, the network node may select the network node beams whose associated beam pair link quality is to be monitored. For each of the network node beams whose associated beam pair link quality is to be monitored, the network node may configure a respective model component of Model 2 at the second UE (the vehicle UE).

In one or more aspects, the second UE (the vehicle UE) may perform the machine learning inference, and may transmit the inference result to the network node. In one configuration, based on the inference result, the network node may implement event triggered reporting. For example, if the output from the machine learning inference model (e.g., the inference result) is greater than a prespecified threshold, the network node may generate a report. In another configuration, the network node may switch (the direction of) the network node beam if the inference result predicts that the degree of beam pair link quality degradation associated with a current network node beam is going to be greater than a prespecified threshold.

FIG.9is a diagram900illustrating an example of network node assisted or managed device and/or service discovery according to one or more aspects. In some examples, the first UE908(the passenger UE) may be connected to the network node904with a higher band link (e.g., an FR2 link), which may be associated with a higher bandwidth. Further, the second UE902(the vehicle UE) may be connected to the network node904with a lower band link (e.g. an FR1 link), which may be associated with a lower bandwidth. In one aspect, the first UE908(the passenger UE) and the second UE902(the vehicle UE) may complete the pairing process with the assistance from the network node904. In one configuration, the first UE908(the passenger UE) and/or the second UE902(the vehicle UE) may inform the network node904of the pairing between the first UE908(the passenger UE) and the second UE902(the vehicle UE).

In one or more configurations, the network node904may be aware of the sensing and machine learning inference capabilities of the second UE902(the vehicle UE) associated with predicting the beam pair link quality of the link between the first UE908(the passenger UE) and the network node904(which, in some examples, may be a higher band link).

In one or more configurations, the second UE902(the vehicle UE) may feed, as the input, the sensing information into the machine learning inference model. Further, the second UE902(the vehicle UE) may transmit the machine learning inference result to the network node904via the link between the network node904and the second UE902(the vehicle UE) (which, in some examples, may be a lower band link).

In one or more configurations, the machine learning inference model may be used to predict the beam pair link quality of the link between the network node904and the first UE908(the collocated/passenger UE) (which, in some examples, may be a higher band link). In some examples, the beam pair link quality of the link between the network node904and the first UE908(the collocated/passenger UE) may correspond to the probability of a beam on the link between the network node904and the first UE908(the collocated/passenger UE) being blocked.

FIGS.10A and10Bare diagrams1000A and1000B illustrating an example of a machine learning model hosted at a vehicle UE according to one or more aspects. As shown in the diagram1000A, the machine learning inference model1002hosted at the second UE (the vehicle UE) may take, as the input, the sensing information as well as the location of the second UE (the vehicle UE) (e.g., in particular, the receiver location of the second UE), and may output a prediction of the beam pair link quality between the first UE (the passenger UE) and the network node (which, in some examples, may be a higher band link).

In one or more aspects, the machine learning inference model1002hosted at the second UE (the vehicle UE) may include two (sub-)models that may be used together: A first model1004(Model 1) may be common to all network node beam indices, whereas each model component of the second model1006(Model 2) may be associated with a respective beam index (e.g., a respective transmission beam, a respective beam steering direction, or a respective beam AoD). For example, a first model component1006aof the second model1006(Model 2-1) may be associated with the network node beam with the beam index 1. For another example, a second model component1006bof the second model1006(Model 2-2) may be associated with the network node beam with the beam index 2, and so on.

FIG.11is a diagram1100illustrating an example of generating a prediction associated with a network node beam based on a machine learning inference model hosted at the vehicle UE according to one or more aspects. To generate the inference result (e.g., the predicted link quality) associated with the network node beam with the beam index i, the second UE1102(the vehicle UE) may use the first model1110(Model 1) in combination with a respective model component1112of the second model (Model 2-i) associated with the beam index i.

In one or more aspects, the network node1106may select the network node beams whose associated beam pair link quality is to be monitored. For each of the network node beams whose associated beam pair link quality is to be monitored, the network node may configure a respective model component1112of the second model (Model 2) at the second UE1102(the vehicle UE).

In one or more aspects, the second UE1102(the vehicle UE) may perform the machine learning inference using the first model1110(Model 1) in combination with the respective model component1112of the second model (Model 2-i), and may transmit the inference result (e.g., the beam pair link quality prediction) to the network node1106. In one configuration, based on the inference result, the network node1106may implement event triggered reporting. For example, if the output from the machine learning inference model (e.g., the inference result) is greater than a prespecified threshold, the network node1106may generate a report. In another configuration, the network node1106may switch (the direction of) the network node beam if the inference result predicts that the degree of beam pair link quality degradation associated with a current network node beam (e.g., the network node beam with the beam index i) is going to be greater than a prespecified threshold.

FIGS.12A and12Bare diagrams of communication flows of methods of wireless communication.FIG.12Ais a diagram of a communication flow1200A of a method of wireless communication where the first UE1202(e.g., the passenger UE) initiates the pairing process with the second UE1204(e.g., the vehicle UE) by transmitting a service discovery request message. At1208, the first UE1202may transmit, to the second UE1204, and the second UE1204may receive, from the first UE1202, a service discovery request message indicating that the first UE1202(the passenger UE) is searching for a machine learning service provider in the vicinity (e.g., in the coverage area of the first UE1202).

At1210, the second UE1204may transmit, to the first UE1202, and the first UE1202may receive, from the second UE1204, a service discovery response message based on the service discovery request message1208. The service discovery response message1210may indicate at least one of a machine learning service provider status associated with the second UE1204(e.g., that the second UE1204is a machine learning service provider), a machine learning service provider identifier (ID) associated with the second UE1204, or information about one or more machine learning services provided by the second UE1204. The information about the one or more machine learning services may include one or more of a machine learning application ID, a machine learning service ID, or an indication of one or more machine learning service types (e.g., sensor data collection, feature extraction, training, inference, etc.) or one or more machine learning service parameters (e.g., a data input format, a machine learning feature extraction model supported by the second UE1204, or a machine learning inference model supported by the second UE1204, etc.). In some examples, an application (which may be identified based on an application ID) may support more than one service (each of which may be identified based on a service ID). For example, a vehicle-to-everything (V2X) application may support basic safety service and sensor data sharing service, etc. As another example, a social media application may support chat service and video service, etc. Different services may have different QoS specifications. Herein a data input format may refer to the structure and/or encoding of the input data that may be used by the machine learning feature extraction and inference models. For example, the data input format may indicate one or more formats for different data types (e.g., text (character or string), number (Boolean, integer, fixed point, float point, real, complex, etc.), or meta (machine type, non-descriptive, etc.), and so on).

At1212, the first UE1202may transmit, to the second UE1204, and the second UE1204may receive, from the first UE1202, a pairing request message based on the received service discovery response message1210or a capability of the first UE1202(e.g., the capability to update the UE information (which may include such first UE information as beam information, speed, direction, location, etc.), and/or the capability to provide the update to the second UE via the sidelink). The pairing request message1212may include at least one of a machine learning application ID, a machine learning service ID, the machine learning service provider ID associated with the second UE1204, an indication of a machine learning service type, an indication of a machine learning feature extraction model supported by the second UE1204, an indication of a machine learning inference model supported by the second UE1204, or a machine learning service user ID associated with the first UE1202.

At1214, the second UE1204may transmit, to the first UE1202, and the first UE1202may receive, from the second UE1204, a pairing acknowledgement message based on the pairing request message1212.

At1218, the second UE1204may transmit, to a network node1206, an inference result indicative of an upcoming blockage associated with a beam or a beam steering direction used for communication between the network node1206and the first UE1202. The inference result may be generated based on one or more machine learning inference models. At least one machine learning inference model component in the one or more machine learning inference models may be associated with the beam or the beam steering direction.

At1216, the first UE1202may receive, from a network node1206, an indication to switch to a different beam for communicating with the network node1206based at least in part on one or more machine learning inference models.

FIG.12Bis a diagram of a communication flow1200B of a method of wireless communication where the second UE1254(e.g., the vehicle UE) initiates the pairing process with the first UE1252(e.g., the passenger UE) by transmitting a service discovery request message. At1258, the second UE1254may transmit, to the first UE1252, and the first UE1252may receive, from the second UE1254, a service discovery request message indicating that the second UE1254(e.g., the vehicle UE) is searching for a machine learning service user in the vicinity (e.g., a coverage area of the second UE1254).

At1260, the first UE1252may transmit, to the second UE1254, and the second UE1254may receive, from the first UE1252, a service discovery response message based on the service discovery request message1258. The service discovery response message1260may indicate at least one of a machine learning service user status associated with the first UE1252(e.g., that the first UE1252is a machine learning service user), a machine learning service user ID associated with the first UE1252, or information about one or more machine learning services searched for by the first UE1252. The information about the one or more machine learning services searched for by the first UE1252may include one or more of a machine learning application ID, a machine learning service ID, or an indication of one or more machine learning service types (e.g., sensor data collection, feature extraction, training, inference, etc.) or one or more machine learning service parameters (e.g., a machine learning feature extraction model, or a machine learning inference model, etc.).

At1262, the second UE1254may transmit, to the first UE1252, and the first UE1252may receive, from the second UE1254, a pairing request message based on the service discovery response message1260or a capability of the second UE1254(e.g., computation or storage capability for supporting sensor data collection, feature extraction, or machine learning inference, etc.). The pairing request message1262may include at least one of a machine learning application ID, a machine learning service ID, a machine learning service user ID associated with the first UE1252, an indication of a machine learning service type, an indication of a machine learning feature extraction model supported by the second UE1254, an indication of a machine learning inference model supported by the second UE1254, or a machine learning service provider ID associated with the second UE1254.

At1264, the first UE1252may transmit, to the second UE1254, and the second UE1254may receive, from the first UE1252, a pairing acknowledgement message based on the pairing request message1262.

At1268, the second UE1254may transmit, to a network node1256, an inference result indicative of an upcoming blockage associated with a beam or a beam steering direction used for communication between the network node1256and the first UE1252. The inference result may be generated based on one or more machine learning inference models. At least one machine learning inference model component in the one or more machine learning inference models may be associated with the beam or the beam steering direction.

At1266, the first UE1252may receive, from a network node1256, an indication to switch to a different beam for communicating with the network node1256based at least in part on one or more machine learning inference models.

FIGS.13A and13Bare diagrams of communication flows of methods of wireless communication.FIG.13Ais a diagram of a communication flow1300A of a method of wireless communication where the first UE1302(e.g., the passenger UE) initiates the pairing process with the second UE1304(e.g., the vehicle UE) by transmitting a service discovery announcement message. At1308, the first UE1302may transmit, to the second UE1304, and the second UE1304may receive, from the first UE1302, a service discovery announcement message indicating one or more machine learning services that the first UE1302(e.g., the passenger UE) is searching for in the vicinity (e.g., a coverage area of the first UE1302). The service discover announcement message1308may include at least one of a machine learning application ID, a machine learning service ID, a machine learning service user ID associated with the first UE1302, or an indication of a machine learning service type or parameters. The machine learning service type may correspond to at least one of sensor data collection, feature extraction, training, inference, etc., and the machine learning service parameters may correspond to at least one of a data input format, one or more machine learning extraction models or one or more machine learning inference models supported by the second UE1304.

At1310, the second UE1304may transmit, to the first UE1302, and the first UE1302may receive, from the second UE1304, a pairing request message based on at least one of the service discovery announcement message1308, a machine learning service provider ID associated with the second UE1304(which may be included in the service discovery announcement message1308), or a capability of the second UE1304(e.g., computation or storage capability for supporting sensor data collection, feature extraction, or machine learning inference, etc.). The pairing request message1310may include at least one of the machine learning application ID, the machine learning service ID, the machine learning service user ID associated with the first UE1302, the indication of the machine learning service type, an indication of one or more machine learning extraction models or an indication of the one or more machine learning inference models supported by the second UE1304.

At1312, the first UE1302may transmit, to the second UE1304, and the second UE1304may receive, from the first UE1302, a pairing acknowledgement message based on the pairing request message1310.

At1316, the second UE1304may transmit, to a network node1306, an inference result indicative of an upcoming blockage associated with a beam or a beam steering direction used for communication between the network node1306and the first UE1302. The inference result may be generated based on one or more machine learning inference models. At least one machine learning inference model component in the one or more machine learning inference models may be associated with the beam or the beam steering direction.

At1314, the first UE1302may receive, from a network node1306, an indication to switch to a different beam for communicating with the network node1306based at least in part on one or more machine learning inference models.

FIG.13Bis a diagram of a communication flow1300B of a method of wireless communication where the second UE1354(e.g., the vehicle UE) initiates the pairing process with the first UE1352(e.g., the passenger UE) by transmitting a service announcement message. At1358, the second UE1354may transmit, to the first UE1352, and the first UE1352may receive, from the second UE1354, a service announcement message indicating one or more machine learning services. The service announcement message may include at least one of a machine learning application ID, a machine learning service ID, a machine learning service provider ID associated with the second UE1354, or an indication of a machine learning service type. The machine learning service type may correspond to at least one of sensor data collection, feature extraction, a data input format, one or more machine learning feature extraction models supported by the second UE1354, or one or more machine learning inference models supported by the second UE1354.

At1360, the first UE1352may transmit, to the second UE1354, and the second UE1354may receive, from the first UE1352, a pairing request message based on at least one of the service announcement message1358, a machine learning service user ID associated with the first UE1352(which may be included in the service announcement message1358), or a capability of the first UE1352(e.g., the capability to update the UE information (which may include such first UE information as beam information, speed, direction, location, etc.), and/or the capability to provide the update to the second UE via the sidelink). The pairing request message1360may include at least one of the machine learning application ID, the machine learning service ID, the machine learning service provider ID associated with the second UE1354, the indication of the machine learning service type, or an indication of the one or more machine learning inference models supported by the second UE1354.

At1362, the second UE1354may transmit, to the first UE1352, and the first UE1352may receive, from the second UE1354, a pairing acknowledgement message based on the pairing request message1360.

At1366, the second UE1354may transmit, to a network node1356, an inference result indicative of an upcoming blockage associated with a beam or a beam steering direction used for communication between the network node1356and the first UE1352. The inference result may be generated based on one or more machine learning inference models. At least one machine learning inference model component in the one or more machine learning inference models may be associated with the beam or the beam steering direction.

At1364, the first UE1352may receive, from a network node1356, an indication to switch to a different beam for communicating with the network node1356based at least in part on one or more machine learning inference models.

FIG.14is a flowchart1400of a method of wireless communication. The method may be performed by a first UE (e.g., the UE104; the first UE1202/1252/1302/1352; the apparatus1802). At1402, the first UE may exchange information relating to one or more machine learning services with a second UE. The one or more machine learning services may be provided by the second UE. For example,1402may be performed by the machine learning service component1840inFIG.18. Referring toFIGS.12A,12B,13A, and13B, at1208,1210,1258,1260,1308, and1358the first UE1202/1252/1302/1352may exchange information relating to one or more machine learning services with a second UE1204/1254/1304/1354.

At1404, the first UE may pair with the second UE for the one or more machine learning services. For example,1404may be performed by the machine learning service component1840inFIG.18. Referring toFIGS.12A,12B,13A, and13B, at1212,1214,1262,1264,1310,1312,1360, and1362, the first UE1202/1252/1302/1352may pair with the second UE1204/1254/1304/1354for the one or more machine learning services.

FIG.15is a flowchart1500of a method of wireless communication. The method may be performed by a first UE (e.g., the UE104; the first UE1202/1252/1302/1352; the apparatus1802). At1502, the first UE may exchange information relating to one or more machine learning services with a second UE. The one or more machine learning services may be provided by the second UE. For example,1502may be performed by the machine learning service component1840inFIG.18. Referring toFIGS.12A,12B,13A, and13B, at1208,1210,1258,1260,1308, and1358the first UE1202/1252/1302/1352may exchange information relating to one or more machine learning services with a second UE1204/1254/1304/1354.

At1504, the first UE may pair with the second UE for the one or more machine learning services. For example,1504may be performed by the machine learning service component1840inFIG.18. Referring toFIGS.12A,12B,13A, and13B, at1212,1214,1262,1264,1310,1312,1360, and1362, the first UE1202/1252/1302/1352may pair with the second UE1204/1254/1304/1354for the one or more machine learning services.

In one configuration, to exchange the information relating to the one or more machine learning services with the second UE, at1502a, the first UE may transmit, to the second UE, a service discovery request message indicating that the first UE is searching for a machine learning service provider. For example,1502amay be performed by the machine learning service component1840inFIG.18. Referring toFIG.12A, at1208, the first UE1202may transmit, to the second UE1204, a service discovery request message indicating that the first UE is searching for a machine learning service provider.

At1502b, the first UE may receive, from the second UE, a service discovery response message based on the service discovery request message. For example,1502bmay be performed by the machine learning service component1840inFIG.18. Referring toFIG.12A, at1210, the first UE1202may receive, from the second UE1204, a service discovery response message based on the service discovery request message1208.

In one configuration, referring toFIG.12A, the service discovery request message1208may indicate at least one of a machine learning service user ID associated with the first UE1202or that the first UE1202is a user of the one or more machine learning services.

In one configuration, referring toFIG.12A, the service discovery response message1210may indicate at least one of a machine learning service provider status associated with the second UE, a machine learning service provider ID associated with the second UE, information about the one or more machine learning services provided by the second UE, a machine learning application ID, a machine learning service ID, an indication of a machine learning service type, or an indication of one or more machine learning service parameters. The machine learning service type may correspond to at least one of sensor data collection, feature extraction, or inference. The one or more machine learning service parameters may correspond to at least one of a data input format, a machine learning feature extraction model supported by the second UE, or a machine learning inference model supported by the second UE.

In one configuration, to pair with the second UE for the one or more machine learning services, at1504a, the first UE may transmit, to the second UE, a pairing request message based on the received service discovery response message or a capability of the first UE. The pairing request message may include at least one of a machine learning application ID, a machine learning service ID, a machine learning service provider ID associated with the second UE, an indication of a machine learning service type, an indication of a machine learning feature extraction model supported by the second UE, an indication of a machine learning inference model supported by the second UE, or a machine learning service user ID associated with the first UE. For example,1504amay be performed by the machine learning service component1840inFIG.18. Referring toFIG.12A, at1212, the first UE1202may transmit, to the second UE1204, a pairing request message based on the received service discovery response message1210or a capability of the first UE1202.

At1504b, the first UE may receive, from the second UE, a pairing acknowledgement message based on the pairing request message. For example,1504bmay be performed by the machine learning service component1840inFIG.18. Referring toFIG.12A, at1214, the first UE1202may receive, from the second UE1204, a pairing acknowledgement message based on the pairing request message1212.

In one configuration, to exchange the information relating to the one or more machine learning services with the second UE, at1502c, the first UE may receive, from the second UE, a service discovery request message indicating that the second UE is searching for a machine learning service user. For example,1502cmay be performed by the machine learning service component1840inFIG.18. Referring toFIG.12B, at1258, the first UE1252may receive, from the second UE1254, a service discovery request message indicating that the second UE is searching for a machine learning service user.

At1502d, the first UE may transmit, to the second UE, a service discovery response message based on the service discovery request message. For example,1502dmay be performed by the machine learning service component1840inFIG.18. Referring toFIG.12B, at1260, the first UE1252may transmit, to the second UE1254, a service discovery response message based on the service discovery request message1258.

In one configuration, referring toFIG.12B, the service discovery request message1258may indicate at least one of a machine learning service provider ID associated with the second UE1254or that the second UE1254is a provider of the one or more machine learning services.

In one configuration, referring toFIG.12B, the service discovery response message1260may indicate at least one of a machine learning service user status associated with the first UE, a machine learning service user ID associated with the first UE, a machine learning application identifier ID, a machine learning service ID, an indication of a machine learning service type, or an indication of one or more machine learning service parameters. The machine learning service type may correspond to at least one of sensor data collection, feature extraction, or inference. The one or more machine learning service parameters may correspond to at least one of a data input format, a machine learning feature extraction mode supported by the second UE, or a machine learning inference model supported by the second UE.

In one configuration, to pair with the second UE for the one or more machine learning services, at1504c, the first UE may receive, from the second UE, a pairing request message based on the transmitted service discovery response message or a capability of the second UE. The pairing request message may include at least one of a machine learning application ID, a machine learning service ID, a machine learning service user ID associated with the first UE, an indication of a machine learning service type, an indication of a machine learning feature extraction model supported by the second UE, an indication of a machine learning inference model supported by the second UE, or a machine learning service provider ID associated with the second UE. For example,1504cmay be performed by the machine learning service component1840inFIG.18. Referring toFIG.12B, at1262, the first UE1252may receive, from the second UE1254, a pairing request message based on the transmitted service discovery response message1260or a capability of the second UE1254.

At1504d, the first UE may transmit, to the second UE, a pairing acknowledgement message based on the pairing request message. For example,1504dmay be performed by the machine learning service component1840inFIG.18. Referring toFIG.12B, at1264, the first UE1252may transmit, to the second UE1254, a pairing acknowledgement message based on the pairing request message1262.

In one configuration, to exchange the information relating to the one or more machine learning services with the second UE, at1502e, the first UE may transmit, to the second UE, a service discovery announcement message indicating that the first UE is searching for the one or more machine learning services. The service discovery announcement message may include at least one of a machine learning application ID, a machine learning service ID, a machine learning service user ID associated with the first UE, an indication of a machine learning service type, or an indication of one or more machine learning service parameters. The machine learning service type may correspond to at least one of sensor data collection, feature extraction, or inference. The one or more machine learning service parameters may correspond to at least one of a data input format, a machine learning feature extraction model supported by the second UE, or a machine learning inference model supported by the second UE. For example,1502emay be performed by the machine learning service component1840inFIG.18. Referring toFIG.13A, at1308, the first UE1302may transmit, to the second UE1304, a service discovery announcement message indicating the one or more machine learning services.

In one configuration, to pair with the second UE for the one or more machine learning services, at1504e, the first UE may receive, from the second UE, a pairing request message based on at least one of the transmitted service discovery announcement message, a machine learning service provider ID associated with the second UE, or a capability of the second UE. For example,1504emay be performed by the machine learning service component1840inFIG.18. Referring toFIG.13A, at1310, the first UE1302may receive, from the second UE1304, a pairing request message based on at least one of the transmitted service discovery announcement message1308, a machine learning service provider ID associated with the second UE1304, or a capability of the second UE1304.

In one configuration, at1504f, the first UE may transmit, to the second UE, a pairing acknowledgement message based on the pairing request message. For example,1504fmay be performed by the machine learning service component1840inFIG.18. Referring toFIG.13A, at1312, the first UE1302may transmit, to the second UE1304, a pairing acknowledgement message based on the pairing request message1310.

In one configuration, to exchange the information relating to the one or more machine learning services with the second UE, at1502f, the first UE may receive, from the second UE, a service announcement message indicating the one or more machine learning services. The service announcement message may include at least one of a machine learning application ID, a machine learning service ID, a machine learning service provider ID associated with the second UE, an indication of a machine learning service type, or an indication of one or more machine learning service parameters. The machine learning service type may correspond to at least one of sensor data collection, feature extraction, or inference. The one or more machine learning service parameters may correspond to at least one of a data input format, a machine learning feature extraction model supported by the second UE, or a machine learning inference model supported by the second UE. For example,1502fmay be performed by the machine learning service component1840inFIG.18. Referring toFIG.13B, at1358, the first UE1352may receive, from the second UE1354, a service announcement message indicating the one or more machine learning services.

In one configuration, to pair with the second UE for the one or more machine learning service, at1504g, the first UE may transmit, to the second UE, a pairing request message based on at least one of the received service announcement message, a machine learning service user ID associated with the first UE, or a capability of the first UE. For example,1504gmay be performed by the machine learning service component1840inFIG.18. Referring toFIG.13B, at1360, the first UE1352may transmit, to the second UE1354, a pairing request message based on at least one of the received service announcement message1358, a machine learning service user ID associated with the first UE1352, or a capability of the first UE1352.

At1504h, the first UE may receive, from the second UE, a pairing acknowledgement message based on the pairing request message. For example,1504hmay be performed by the machine learning service component1840inFIG.18. Referring toFIG.13B, at1362, the first UE1352may receive, from the second UE1354, a pairing acknowledgement message based on the pairing request message1360.

In one configuration, at1506, the first UE may receive, from a network node, an indication to switch to a different beam for communicating with the network node based at least in part on one or more machine learning inference models. For example,1506may be performed by the machine learning service component1840inFIG.18. Referring toFIGS.12A,12B,13A, and13B, at1216,1266,1314, and1364, the first UE1202/1252/1302/1352may receive, from a network node1206/1256/1306/1356, an indication to switch to a different beam for communicating with the network node1206/1256/1306/1356based at least in part on one or more machine learning inference models.

FIG.16is a flowchart1600of a method of wireless communication. The method may be performed by a second UE (e.g., the UE104′; the second UE1204/1254/1304/1354; the apparatus1902). At1602, the second UE may exchange information relating to one or more machine learning services with a first UE. The one or more machine learning services may be provided by the second UE. For example,1602may be performed by the machine learning service component1940inFIG.19. Referring toFIGS.12A,12B,13A, and13B, at1208,1210,1258,1260,1308, and1358the second UE1204/1254/1304/1354may exchange information relating to one or more machine learning services with a first UE1202/1252/1302/1352.

At1604, the second UE may pair with the first UE for the one or more machine learning services. For example,1604may be performed by the machine learning service component1940inFIG.19. Referring toFIGS.12A,12B,13A, and13B, at1212,1214,1262,1264,1310,1312,1360, and1362, the second UE1204/1254/1304/1354may pair with the first UE1202/1252/1302/1352for the one or more machine learning services.

FIG.17is a flowchart1700of a method of wireless communication. The method may be performed by a second UE (e.g., the UE104′; the second UE1204/1254/1304/1354; the apparatus1902). At1702, the second UE may exchange information relating to one or more machine learning services with a first UE. The one or more machine learning services may be provided by the second UE. For example,1702may be performed by the machine learning service component1940inFIG.19. Referring toFIGS.12A,12B,13A, and13B, at1208,1210,1258,1260,1308, and1358the second UE1204/1254/1304/1354may exchange information relating to one or more machine learning services with a first UE1202/1252/1302/1352.

At1704, the second UE may pair with the first UE for the one or more machine learning services. For example,1704may be performed by the machine learning service component1940inFIG.19. Referring toFIGS.12A,12B,13A, and13B, at1212,1214,1262,1264,1310,1312,1360, and1362, the second UE1204/1254/1304/1354may pair with the first UE1202/1252/1302/1352for the one or more machine learning services.

In one configuration, to exchange the information relating to the one or more machine learning services with the first UE, at1702a, the second UE may transmit, to the first UE, a service discovery request message indicating the one or more machine learning services. For example,1702amay be performed by the machine learning service component1940inFIG.19. Referring toFIG.12B, at1258, the second UE1254may transmit, to the first UE1252, a service discovery request message indicating the one or more machine learning services.

At1702b, the second UE may receive, from the first UE, a service discovery response message based on the service discovery request message. For example,1702bmay be performed by the machine learning service component1940inFIG.19. Referring toFIG.12B, at1260, the second UE1254may receive, from the first UE1252, a service discovery response message based on the service discovery request message1258.

In one configuration, referring toFIG.12B, the service discovery request message1258may indicate at least a machine learning service provider ID associated with the second UE1254or that the second UE1254is a provider of the one or more machine learning services.

In one configuration, referring toFIG.12B, the service discovery response message1260may include at least one of a machine learning application ID, a machine learning service ID, a machine learning service user ID associated with the first UE, an indication of a machine learning service type, or an indication of one or more machine learning service parameters. The machine learning service type may correspond to at least one of sensor data collection, feature extraction, or inference. The one or more machine learning service parameters may correspond to at least one of a data input format, a machine learning feature extraction model supported by the second UE, or a machine learning inference model supported by the second UE.

In one configuration, to pair with the first UE for the one or more machine learning services, at1704a, the second UE may transmit, to the first UE, a pairing request message based on the received service discovery response message or a capability of the second UE. The pairing request message may include at least one of a machine learning application ID, a machine learning service ID, a machine learning service user ID associated with the first UE, an indication of a machine learning service type, or an indication of one or more supported machine learning inference models. For example,1704amay be performed by the machine learning service component1940inFIG.19. Referring toFIG.12B, at1262, the second UE1254may transmit, to the first UE1252, a pairing request message based on the received service discovery response message1260or a capability of the second UE1254.

At1704b, the second UE may receive, from the first UE, a pairing acknowledgement message based on the pairing request message. For example,1704bmay be performed by the machine learning service component1940inFIG.19. Referring toFIG.12B, at1264, the second UE1254may receive, from the first UE1252, a pairing acknowledgement message based on the pairing request message1262.

In one configuration, to exchange the information relating to the one or more machine learning services with the first UE, at1702c, the second UE may receive, from the first UE, a service discovery request message indicating that the first UE is searching for a machine learning service provider. For example,1702cmay be performed by the machine learning service component1940inFIG.19. Referring toFIG.12A, at1208, the second UE1204may receive, from the first UE1202, a service discovery request message indicating that the first UE is searching for a machine learning service provider.

At1702d, the second UE may transmit, to the first UE, a service discovery response message based on the service discovery request message. For example,1702dmay be performed by the machine learning service component1940inFIG.19. Referring toFIG.12A, at1210, the second UE1204may transmit, to the first UE1202, a service discovery response message based on the service discovery request message1208.

In one configuration, referring toFIG.12A, the service discovery request message1208may indicate at least one of a machine learning service user ID associated with the first UE1202or that the first UE1202is a user of the one or more machine learning services.

In one configuration, referring toFIG.12A, the service discovery response message1210may include at least one of a machine learning application ID, a machine learning service ID, a machine learning service provider ID associated with the second UE, an indication of a machine learning service type, or an indication of one or more machine learning service parameters. The machine learning service type may correspond to at least one of sensor data collection, feature extraction, or inference. The one or more machine learning service parameters may correspond to at least one of a data input format, a machine learning feature extraction model supported by the second UE, or a machine learning inference model supported by the second UE.

In one configuration, to pair with the first UE for the one or more machine learning services, at1704c, the second UE may receive, from the first UE, a pairing request message based on the transmitted service discovery response message or a capability of the first UE. The pairing request message may include at least one of a machine learning application ID, a machine learning service ID, a machine learning service provider ID associated with the second UE, an indication of a machine learning service type, or an indication of one or more supported machine learning inference models. For example,1704cmay be performed by the machine learning service component1940inFIG.19. Referring toFIG.12A, at1212, the second UE1204may receive, from the first UE1202, a pairing request message based on the transmitted service discovery response message1210or a capability of the first UE1202.

At1704d, the second UE may transmit, to the first UE, a pairing acknowledgement message based on the pairing request message. For example,1704dmay be performed by the machine learning service component1940inFIG.19. Referring toFIG.12A, at1214, the second UE1204may transmit, to the first UE1202, a pairing acknowledgement message based on the pairing request message1212.

In one configuration, to exchange the information relating to the one or more machine learning services with the first UE, at1702e, the second UE may transmit, to the first UE, a service announcement message indicating the one or more machine learning services. The service announcement message may include at least one of a machine learning application ID, a machine learning service ID, a machine learning service provider ID associated with the second UE, or an indication of a machine learning service type, or an indication of one or more machine learning service parameters, wherein the machine learning service type corresponds to at least one of sensor data collection, feature extraction, or inference, and the one or more machine learning service parameters correspond to at least one of a data input format, a machine learning feature extraction model supported by the second UE, or a machine learning inference model supported by the second UE.

In one configuration, to pair with the first UE for the one or more machine learning services, at1704e, the second UE may receive, from the first UE, a pairing request message based on at least one of the transmitted service announcement message, a machine learning service user ID associated with the first UE, or a capability of the first UE. For example,1704emay be performed by the machine learning service component1940inFIG.19. Referring toFIG.13B, at1360, the second UE1354may receive, from the first UE1352, a pairing request message based on at least one of the transmitted service announcement message1358, a machine learning service user ID associated with the first UE1352, or a capability of the first UE1352.

At1704f, the second UE may transmit, to the first UE, a pairing acknowledgement message based on the pairing request message. For example,1704fmay be performed by the machine learning service component1940inFIG.19. Referring toFIG.13B, at1362, the second UE1354may transmit, to the first UE1352, a pairing acknowledgement message based on the pairing request message1360.

In one configuration, to exchange the information relating to the one or more machine learning services with the first UE, at1702f, the second UE may receive, from the first UE, a service discovery announcement message indicating the one or more machine learning services. The service discovery announcement message may include at least one of a machine learning application ID, a machine learning service ID, a machine learning service user ID associated with the first UE, an indication of a machine learning service type, or an indication of one or more machine learning service parameters. The machine learning service type may correspond to at least one of sensor data collection, feature extraction, or inference. The one or more machine learning service parameters may correspond to at least one of a data input format, a machine learning feature extraction model supported by the second UE, or a machine learning inference model supported by the second UE. For example,1702fmay be performed by the machine learning service component1940inFIG.19. Referring toFIG.13A, at1308, the second UE1304may receive, from the first UE1302, a service discovery announcement message indicating the one or more machine learning services.

In one configuration, to pair with the first UE for the one or more machine learning services, at1704g, the second UE may transmit, to the first UE, a pairing request message based on at least one of the received service discovery announcement message, a machine learning service provider ID associated with the second UE, or a capability of the second UE. For example,1704gmay be performed by the machine learning service component1940inFIG.19. Referring toFIG.13A, at1310, the second UE1304may transmit, to the first UE1302, a pairing request message based on at least one of the received service discovery announcement message1308, a machine learning service provider ID associated with the second UE1304, or a capability of the second UE1304.

At1704h, the second UE may receive, from the first UE, a pairing acknowledgement message based on the pairing request message. For example,1704hmay be performed by the machine learning service component1940inFIG.19. Referring toFIG.13A, at1312, the second UE1304may receive, from the first UE1302, a pairing acknowledgement message based on the pairing request message1310.

In one configuration, at1706, the second UE may transmit, to a network node, an inference result indicative of an upcoming blockage associated with a beam or a beam steering direction used for communication between the network node and the first UE. The inference result may be generated based on one or more machine learning inference models. At least one machine learning inference model component in the one or more machine learning inference models may be associated with the beam or the beam steering direction. For example,1706may be performed by the machine learning service component1940inFIG.19. Referring toFIGS.12A,12B,13A,13B, at1218,1268,1316, and1366, the second UE1204/1254/1304/1354may transmit, to a network node1206/1256/1306/1356, an inference result indicative of an upcoming blockage associated with a beam or a beam steering direction used for communication between the network node1206/1256/1306/1356and the first UE1202/1252/1302/1352.

FIG.18is a diagram1800illustrating an example of a hardware implementation for an apparatus1802. The apparatus1802may be a first UE, a component of a first UE, or may implement first UE functionality. In some aspects, the apparatus1802may include a cellular baseband processor1804(also referred to as a modem) coupled to a cellular RF transceiver1822. In some aspects, the apparatus1802may further include one or more subscriber identity modules (SIM) cards1820, an application processor1806coupled to a secure digital (SD) card1808and a screen1810, a Bluetooth module1812, a wireless local area network (WLAN) module1814, a Global Positioning System (GPS) module1816, or a power supply1818. The cellular baseband processor1804communicates through the cellular RF transceiver1822with the UE104and/or BS102/180. The cellular baseband processor1804may include a computer-readable medium/memory. The computer-readable medium/memory may be non-transitory. The cellular baseband processor1804is responsible for general processing, including the execution of software stored on the computer-readable medium/memory. The software, when executed by the cellular baseband processor1804, causes the cellular baseband processor1804to perform the various functions described supra. The computer-readable medium/memory may also be used for storing data that is manipulated by the cellular baseband processor1804when executing software. The cellular baseband processor1804further includes a reception component1830, a communication manager1832, and a transmission component1834. The communication manager1832includes the one or more illustrated components. The components within the communication manager1832may be stored in the computer-readable medium/memory and/or configured as hardware within the cellular baseband processor1804. The cellular baseband processor1804may be a component of the UE350and may include the memory360and/or at least one of the TX processor368, the RX processor356, and the controller/processor359. In one configuration, the apparatus1802may be a modem chip and include just the baseband processor1804, and in another configuration, the apparatus1802may be the entire UE (e.g., see350ofFIG.3) and include the additional modules of the apparatus1802.

The communication manager1832includes a machine learning service component1840that may be configured to exchange information relating to one or more machine learning services with a second UE, e.g., as described in connection with1402inFIGS.14and1502inFIG.15. The machine learning service component1840may be configured to transmit, to the second UE, a service discovery request message indicating that the first UE is searching for a machine learning service provider, e.g., as described in connection with1502ainFIG.15. The machine learning service component1840may be configured to receive, from the second UE, a service discovery response message based on the service discovery request message, e.g., as described in connection with1502binFIG.15. The machine learning service component1840may be configured to receive, from the second UE, a service discovery request message indicating that the second UE is searching for a machine learning service user, e.g., as described in connection with1502cinFIG.15. The machine learning service component1840may be configured to transmit, to the second UE, a service discovery response message based on the service discovery request message, e.g., as described in connection with1502dinFIG.15. The machine learning service component1840may be configured to transmit, to the second UE, a service announcement message indicating the one or more machine learning services, e.g., as described in connection with1502einFIG.15. The machine learning service component1840may be configured to receive, from the second UE, a service announcement message indicating the one or more machine learning services, e.g., as described in connection with1502finFIG.15. The machine learning service component1840may be configured to pair with the second UE for the one or more machine learning services, e.g., as described in connection with1404inFIGS.14and1504inFIG.15. The machine learning service component1840may be configured to transmit, to the second UE, a pairing request message based on the received service discovery response message or a capability of the first UE, e.g., as described in connection with1504ainFIG.15. The machine learning service component1840may be configured to receive, from the second UE, a pairing acknowledgement message based on the pairing request message, e.g., as described in connection with1504binFIG.15. The machine learning service component1840may be configured to receive, from the second UE, a pairing request message based on the transmitted service discovery response message or a capability of the second UE, e.g., as described in connection with1504cinFIG.15. The machine learning service component1840may be configured to transmit, to the second UE, a pairing acknowledgement message based on the pairing request message, e.g., as described in connection with1504dinFIG.15. The machine learning service component1840may be configured to transmit, to the second UE, a pairing acknowledgement message based on the pairing request message, e.g., as described in connection with1504einFIG.15. The machine learning service component1840may be configured to transmit, to the second UE, a pairing acknowledgement message based on the pairing request message, e.g., as described in connection with1504finFIG.15. The machine learning service component1840may be configured to transmit, to the second UE, a pairing request message based on at least one of the received service announcement message, a machine learning service user ID associated with the first UE, or a capability of the first UE, e.g., as described in connection with1504ginFIG.15. The machine learning service component1840may be configured to receive, from the second UE, a pairing acknowledgement message based on the pairing request message, e.g., as described in connection with1504hinFIG.15. The machine learning service component1840may be configured to receive, from a network node, an indication to switch to a different beam for communicating with the network node based at least in part on one or more machine learning inference models, e.g., as described in connection with1506inFIG.15.

As shown, the apparatus1802may include a variety of components configured for various functions. In one configuration, the apparatus1802, and in particular the cellular baseband processor1804, includes means for exchanging information relating to one or more machine learning services with a second UE. The one or more machine learning services may be provided by the second UE. The apparatus1802, and in particular the cellular baseband processor1804, includes means for pairing with the second UE for the one or more machine learning services.

The means may be one or more of the components of the apparatus1802configured to perform the functions recited by the means. As described supra, the apparatus1802may include the TX Processor368, the RX Processor356, and the controller/processor359. As such, in one configuration, the means may be the TX Processor368, the RX Processor356, and the controller/processor359configured to perform the functions recited by the means.

FIG.19is a diagram1900illustrating an example of a hardware implementation for an apparatus1902. The apparatus1902may be a second UE, a component of a second UE, or may implement second UE functionality. In some aspects, the apparatus1902may include a cellular baseband processor1904(also referred to as a modem) coupled to a cellular RF transceiver1922. In some aspects, the apparatus1902may further include one or more subscriber identity modules (SIM) cards1920, an application processor1906coupled to a secure digital (SD) card1908and a screen1910, a Bluetooth module1912, a wireless local area network (WLAN) module1914, a Global Positioning System (GPS) module1916, or a power supply1918. The cellular baseband processor1904communicates through the cellular RF transceiver1922with the UE104and/or BS102/180. The cellular baseband processor1904may include a computer-readable medium/memory. The computer-readable medium/memory may be non-transitory. The cellular baseband processor1904is responsible for general processing, including the execution of software stored on the computer-readable medium/memory. The software, when executed by the cellular baseband processor1904, causes the cellular baseband processor1904to perform the various functions described supra. The computer-readable medium/memory may also be used for storing data that is manipulated by the cellular baseband processor1904when executing software. The cellular baseband processor1904further includes a reception component1930, a communication manager1932, and a transmission component1934. The communication manager1932includes the one or more illustrated components. The components within the communication manager1932may be stored in the computer-readable medium/memory and/or configured as hardware within the cellular baseband processor1904. The cellular baseband processor1904may be a component of the UE350and may include the memory360and/or at least one of the TX processor368, the RX processor356, and the controller/processor359. In one configuration, the apparatus1902may be a modem chip and include just the baseband processor1904, and in another configuration, the apparatus1902may be the entire UE (e.g., see350ofFIG.3) and include the additional modules of the apparatus1902.

The communication manager1932includes a machine learning service component1940that may be configured to exchange information relating to one or more machine learning services with a first UE, e.g., as described in connection with1602inFIGS.16and1702inFIG.17. The machine learning service component1940may be configured to transmit, to the first UE, a service discovery request message indicating that the second UE is searching for a machine learning service user, e.g., as described in connection with1702ainFIG.17. The machine learning service component1940may be configured to receive, from the first UE, a service discovery response message based on the service discovery request message, e.g., as described in connection with1702binFIG.17. The machine learning service component1940may be configured to receive, from the first UE, a service discovery request message indicating that the first UE is searching for a machine learning service provider, e.g., as described in connection with1702cinFIG.17. The machine learning service component1940may be configured to transmit, to the first UE, a service discovery response message based on the service discovery request message, e.g., as described in connection with1702dinFIG.17. The machine learning service component1940may be configured to transmit, to the first UE, a service announcement message indicating the one or more machine learning services, e.g., as described in connection with1702einFIG.17. The machine learning service component1940may be configured to receive, from the first UE, a service announcement message indicating the one or more machine learning services, e.g., as described in connection with1702finFIG.17. The machine learning service component1940may be configured to pair with the first UE for the one or more machine learning services, e.g., as described in connection with1604inFIGS.16and1704inFIG.17. The machine learning service component1940may be configured to transmit, to the first UE, a pairing request message based on the received service discovery response message or a capability of the second UE, e.g., as described in connection with1704ainFIG.17. The machine learning service component1940may be configured to receive, from the first UE, a pairing acknowledgement message based on the pairing request message, e.g., as described in connection with1704binFIG.17. The machine learning service component1940may be configured to receive, from the first UE, a pairing request message based on the transmitted service discovery response message or a capability of the first UE, e.g., as described in connection with1704cinFIG.17. The machine learning service component1940may be configured to transmit, to the first UE, a pairing acknowledgement message based on the pairing request message, e.g., as described in connection with1704dinFIG.17. The machine learning service component1940may be configured to receive, from the first UE, a pairing request message based on at least one of the transmitted service announcement message, a machine learning service user ID associated with the first UE, or a capability of the first UE, e.g., as described in connection with1704einFIG.17. The machine learning service component1940may be configured to transmit, to the first UE, a pairing acknowledgement message based on the pairing request message, e.g., as described in connection with1704finFIG.17. The machine learning service component1940may be configured to transmit, to the first UE, a pairing request message based on at least one of the received service announcement message, a machine learning service provider ID associated with the second UE, or a capability of the second UE, e.g., as described in connection with1704ginFIG.17. The machine learning service component1940may be configured to receive, from the first UE, a pairing acknowledgement message based on the pairing request message, e.g., as described in connection with1704hinFIG.17. The machine learning service component1940may be configured to transmit, to a network node, an inference result indicative of an upcoming blockage associated with a beam or a beam steering direction used for communication between the network node and the first UE, e.g., as described in connection with1706inFIG.17.

As shown, the apparatus1902may include a variety of components configured for various functions. In one configuration, the apparatus1902, and in particular the cellular baseband processor1904, includes means for exchanging information relating to one or more machine learning services with a first UE. The one or more machine learning services may be provided by the second UE. The apparatus1902, and in particular the cellular baseband processor1904, includes means for pairing with the first UE for the one or more machine learning services.

In one configuration, to exchange the information relating to the one or more machine learning services with the first UE, the apparatus1902, and in particular the cellular baseband processor1904, includes means for transmitting, to the first UE, a service discovery request message indicating the one or more machine learning services. The apparatus1902, and in particular the cellular baseband processor1904, includes means for receiving, from the first UE, a service discovery response message based on the service discovery request message. In one configuration, the service discovery request message may indicate at least a machine learning service provider ID associated with the second UE or that the second UE is a provider of the one or more machine learning services. In one configuration, the service discovery response message may include at least one of a machine learning application ID, a machine learning service ID, a machine learning service user ID associated with the first UE, an indication of a machine learning service type, or an indication of one or more machine learning service parameters. The machine learning service type may correspond to at least one of sensor data collection, feature extraction, or inference. The one or more machine learning service parameters may correspond to at least one of a data input format, a machine learning feature extraction model supported by the second UE, or a machine learning inference model supported by the second UE. In one configuration, to pair with the first UE for the one or more machine learning services, the apparatus1902, and in particular the cellular baseband processor1904, includes means for transmitting, to the first UE, a pairing request message based on the received service discovery response message or a capability of the second UE. The pairing request message may include at least one of a machine learning application ID, a machine learning service ID, a machine learning service user ID associated with the first UE, an indication of a machine learning service type, or an indication of one or more supported machine learning inference models. The apparatus1902, and in particular the cellular baseband processor1904, includes means for receiving, from the first UE, a pairing acknowledgement message based on the pairing request message. In one configuration, to exchange the information relating to the one or more machine learning services with the first UE, the apparatus1902, and in particular the cellular baseband processor1904, includes means for receiving, from the first UE, a service discovery request message indicating that the first UE is searching for a machine learning service provider. The apparatus1902, and in particular the cellular baseband processor1904, includes means for transmitting, to the first UE, a service discovery response message based on the service discovery request message. In one configuration, the service discovery request message may indicate at least one of a machine learning service user ID associated with the first UE or that the first UE is a user of the one or more machine learning services. In one configuration, the service discovery response message may include at least one of a machine learning application ID, a machine learning service ID, a machine learning service provider ID associated with the second UE, an indication of a machine learning service type, or an indication of one or more machine learning service parameters. The machine learning service type may correspond to at least one of sensor data collection, feature extraction, or inference. The one or more machine learning service parameters may correspond to at least one of a data input format, a machine learning feature extraction model supported by the second UE, or a machine learning inference model supported by the second UE. In one configuration, to pair with the first UE for the one or more machine learning services, the apparatus1902, and in particular the cellular baseband processor1904, includes means for receiving, from the first UE, a pairing request message based on the transmitted service discovery response message or a capability of the first UE. The pairing request message may include at least one of a machine learning application ID, a machine learning service ID, a machine learning service provider ID associated with the second UE, an indication of a machine learning service type, or an indication of one or more supported machine learning inference models. The apparatus1902, and in particular the cellular baseband processor1904, includes means for transmitting, to the first UE, a pairing acknowledgement message based on the pairing request message. In one configuration, to exchange the information relating to the one or more machine learning services with the first UE, the apparatus1902, and in particular the cellular baseband processor1904, includes means for transmitting, to the first UE, a service announcement message indicating the one or more machine learning services. The service announcement message may include at least one of a machine learning application ID, a machine learning service ID, a machine learning service provider ID associated with the second UE, or an indication of a machine learning service type, or an indication of one or more machine learning service parameters, wherein the machine learning service type corresponds to at least one of sensor data collection, feature extraction, or inference, and the one or more machine learning service parameters correspond to at least one of a data input format, a machine learning feature extraction model supported by the second UE, or a machine learning inference model supported by the second UE. In one configuration, to pair with the first UE for the one or more machine learning services, the apparatus1902, and in particular the cellular baseband processor1904, includes means for receiving, from the first UE, a pairing request message based on at least one of the transmitted service announcement message, a machine learning service user ID associated with the first UE, or a capability of the first UE. The apparatus1902, and in particular the cellular baseband processor1904, includes means for transmitting, to the first UE, a pairing acknowledgement message based on the pairing request message. In one configuration, to exchange the information relating to the one or more machine learning services with the first UE, the apparatus1902, and in particular the cellular baseband processor1904, includes means for receiving, from the first UE, a service discovery announcement message indicating the one or more machine learning services. The service discovery announcement message may include at least one of a machine learning application ID, a machine learning service ID, a machine learning service user ID associated with the first UE, an indication of a machine learning service type, or an indication of one or more machine learning service parameters. The machine learning service type may correspond to at least one of sensor data collection, feature extraction, or inference. The one or more machine learning service parameters may correspond to at least one of a data input format, a machine learning feature extraction model supported by the second UE, or a machine learning inference model supported by the second UE. In one configuration, to pair with the first UE for the one or more machine learning services, the apparatus1902, and in particular the cellular baseband processor1904, includes means for transmitting, to the first UE, a pairing request message based on at least one of the received service discovery announcement message, a machine learning service provider ID associated with the second UE, or a capability of the second UE. The apparatus1902, and in particular the cellular baseband processor1904, includes means for receiving, from the first UE, a pairing acknowledgement message based on the pairing request message. In one configuration, the apparatus1902, and in particular the cellular baseband processor1904, includes means for transmitting, to a network node, an inference result indicative of an upcoming blockage associated with a beam or a beam steering direction used for communication between the network node and the first UE. The inference result may be generated based on one or more machine learning inference models. At least one machine learning inference model component in the one or more machine learning inference models may be associated with the beam or the beam steering direction.

The means may be one or more of the components of the apparatus1902configured to perform the functions recited by the means. As described supra, the apparatus1902may include the TX Processor368, the RX Processor356, and the controller/processor359. As such, in one configuration, the means may be the TX Processor368, the RX Processor356, and the controller/processor359configured to perform the functions recited by the means.

Referring back toFIGS.4-19, a first UE and a second UE may exchange information relating to one or more machine learning services. The one or more machine learning services may be provided by the second UE. The first UE and the second UE may pair with each other for the one or more machine learning services. Accordingly, the second UE may perform machine learning inference using at least one machine learning inference model, and may transmit the inference result (e.g., the link quality prediction) associated with a link between a network node and the first UE to the network node. The network node may switch (the direction of) the network node beam if the inference result predicts that the degree of link quality degradation associated with a current network node beam is going to be greater than a prespecified threshold. Therefore, beam management for the communication between the network node and the first UE may be enhanced based on the machine learning techniques.

Aspect 1 is an apparatus for wireless communication at a first UE including at least one processor coupled to a memory and configured to exchange information relating to one or more machine learning services with a second UE, the one or more machine learning services being provided by the second UE; and pair with the second UE for the one or more machine learning services.

Aspect 2 is the apparatus of aspect 1, where to exchange the information relating to the one or more machine learning services with the second UE, the at least one processor is further configured to: transmit, to the second UE, a service discovery request message indicating that the first UE is searching for a machine learning service provider; and receive, from the second UE, a service discovery response message based on the service discovery request message.

Aspect 3 is the apparatus of aspect 2, where the service discovery request message indicates at least one of a machine learning service user ID associated with the first UE or that the first UE is a user of the one or more machine learning services.

Aspect 4 is the apparatus of any of aspects 2 and 3, where the service discovery response message indicates at least one of a machine learning service provider status associated with the second UE, a machine learning service provider ID associated with the second UE, or information about the one or more machine learning services provided by the second UE, a machine learning application ID, a machine learning service ID, an indication of a machine learning service type, or an indication of one or more machine learning service parameters, the machine learning service type corresponds to at least one of sensor data collection, feature extraction, or inference, and the one or more machine learning service parameters correspond to at least one of a data input format, a machine learning feature extraction model supported by the second UE, or a machine learning inference model supported by the second UE.

Aspect 5 is the apparatus of any of aspects 2 to 4, where to pair with the second UE for the one or more machine learning services, the at least one processor is further configured to: transmit, to the second UE, a pairing request message based on the received service discovery response message or a capability of the first UE, the pairing request message including at least one of a machine learning application ID, a machine learning service ID, a machine learning service provider ID associated with the second UE, an indication of a machine learning service type, an indication of a machine learning feature extraction model supported by the second UE, an indication of a machine learning inference model supported by the second UE, or a machine learning service user ID associated with the first UE; and receive, from the second UE, a pairing acknowledgement message based on the pairing request message.

Aspect 6 is the apparatus of aspect 1, where to exchange the information relating to the one or more machine learning services with the second UE, the at least one processor is further configured to: receive, from the second UE, a service discovery request message indicating that the second UE is searching for a machine learning service user; and transmit, to the second UE, a service discovery response message based on the service discovery request message.

Aspect 7 is the apparatus of aspect 6, where the service discovery request message indicates at least one of a machine learning service provider ID associated with the second UE or that the second UE is a provider of the one or more machine learning services.

Aspect 8 is the apparatus of any of aspects 6 and 7, where the service discovery response message indicates at least one of a machine learning service user status associated with the first UE, a machine learning service user ID associated with the first UE, a machine learning application identifier ID, a machine learning service ID, an indication of a machine learning service type, or an indication of one or more machine learning service parameters, the machine learning service type corresponds to at least one of sensor data collection, feature extraction, or inference, and the one or more machine learning service parameters correspond to at least one of a data input format, a machine learning feature extraction mode supported by the second UE, or a machine learning inference model supported by the second UE.

Aspect 9 is the apparatus of any of aspects 6 to 8, where to pair with the second UE for the one or more machine learning services, the at least one processor is further configured to: receive, from the second UE, a pairing request message based on the transmitted service discovery response message or a capability of the second UE, the pairing request message including at least one of a machine learning application ID, a machine learning service ID, a machine learning service user ID associated with the first UE, an indication of a machine learning service type, an indication of a machine learning feature extraction model supported by the second UE, an indication of a machine learning inference model supported by the second UE, or a machine learning service provider ID associated with the second UE; and transmit, to the second UE, a pairing acknowledgement message based on the pairing request message.

Aspect 10 is the apparatus of aspect 1, where to exchange the information relating to the one or more machine learning services with the second UE, the at least one processor is further configured to: transmit, to the second UE, a service discovery announcement message indicating that the first UE is searching for the one or more machine learning services, the service discovery announcement message including at least one of a machine learning application ID, a machine learning service ID, a machine learning service user ID associated with the first UE, an indication of a machine learning service type, or an indication of one or more machine learning service parameters, where the machine learning service type corresponds to at least one of sensor data collection, feature extraction, or inference, and the one or more machine learning service parameters correspond to at least one of a data input format, a machine learning feature extraction model supported by the second UE, or a machine learning inference model supported by the second UE.

Aspect 11 is the apparatus of aspect 10, where to pair with the second UE for the one or more machine learning services, the at least one processor is further configured to: receive, from the second UE, a pairing request message based on at least one of the transmitted service announcement message, a machine learning service provider ID associated with the second UE, or a capability of the second UE; and transmit, to the second UE, a pairing acknowledgement message based on the pairing request message.

Aspect 12 is the apparatus of aspect 1, where to exchange the information relating to the one or more machine learning services with the second UE, the at least one processor is further configured to: receive, from the second UE, a service announcement message indicating the one or more machine learning services, the service announcement message including at least one of a machine learning application ID, a machine learning service ID, a machine learning service provider ID associated with the second UE, an indication of a machine learning service type, or an indication of one or more machine learning service parameters, wherein the machine learning service type corresponds to at least one of sensor data collection, feature extraction, or inference, and the one or more machine learning service parameters correspond to at least one of a data input format, a machine learning feature extraction model supported by the second UE, or a machine learning inference model supported by the second UE.

Aspect 13 is the apparatus of aspect 12, where to pair with the second UE for the one or more machine learning service, the at least one processor is further configured to: transmit, to the second UE, a pairing request message based on at least one of the received service announcement message, a machine learning service user ID associated with the first UE, or a capability of the first UE; and receive, from the second UE, a pairing acknowledgement message based on the pairing request message.

Aspect 14 is the apparatus of any of aspects 1 to 13, the at least one processor being further configured to: receive, from a network node, an indication to switch to a different beam for communicating with the network node based at least in part on one or more machine learning inference models.

Aspect 15 is the apparatus of any of aspects 1 to 14, further including a transceiver coupled to the at least one processor.

Aspect 16 is an apparatus for wireless communication at a second UE including at least one processor coupled to a memory and configured to exchange information relating to one or more machine learning services with a first UE, the one or more machine learning services being provided by the second UE; and pair with the first UE for the one or more machine learning services.

Aspect 17 is the apparatus of aspect 16, where to exchange the information relating to the one or more machine learning services with the first UE, the at least one processor is further configured to: transmit, to the first UE, a service discovery request message indicating that the second UE is searching for a machine learning service user; and receive, from the first UE, a service discovery response message based on the service discovery request message.

Aspect 18 is the apparatus of aspect 17, where the service discovery request message indicates at least a machine learning service provider ID associated with the second UE or that the second UE is a provider of the one or more machine learning services.

Aspect 19 is the apparatus of any of aspects 17 and 18, where the service discovery response message includes at least one of a machine learning application ID, a machine learning service ID, a machine learning service user ID associated with the first UE, an indication of a machine learning service type, or an indication of one or more machine learning service parameters, the machine learning service type corresponds to at least one of sensor data collection, feature extraction, or inference, and the one or more machine learning service parameters correspond to at least one of a data input format, a machine learning feature extraction model supported by the second UE, or a machine learning inference model supported by the second UE.

Aspect 20 is the apparatus of any of aspects 17 to 19, where to pair with the first UE for the one or more machine learning services, the at least one processor is further configured to: transmit, to the first UE, a pairing request message based on the received service discovery response message or a capability of the second UE, the pairing request message including at least one of a machine learning application ID, a machine learning service ID, a machine learning service user ID associated with the first UE, an indication of a machine learning service type, or an indication of one or more supported machine learning inference models; and receive, from the first UE, a pairing acknowledgement message based on the pairing request message.

Aspect 21 is the apparatus of aspect 16, where to exchange the information relating to the one or more machine learning services with the first UE, the at least one processor is further configured to: receive, from the first UE, a service discovery request message indicating that the first UE is searching for a machine learning service provider; and transmit, to the first UE, a service discovery response message based on the service discovery request message.

Aspect 22 is the apparatus of aspect 21, where the service discovery request message indicates at least one of a machine learning service user ID associated with the first UE or that the first UE is a user of the one or more machine learning services.

Aspect 23 is the apparatus of any of aspects 21 and 22, where the service discovery response message includes at least one of a machine learning application ID, a machine learning service ID, a machine learning service provider ID associated with the second UE, an indication of a machine learning service type, or an indication of one or more machine learning service parameters, the machine learning service type corresponds to at least one of sensor data collection, feature extraction, or inference, and the one or more machine learning service parameters correspond to at least one of a data input format, a machine learning feature extraction model supported by the second UE, or a machine learning inference model supported by the second UE.

Aspect 24 is the apparatus of any of aspects 21 to 23, where to pair with the first UE for the one or more machine learning services, the at least one processor is further configured to: receive, from the first UE, a pairing request message based on the transmitted service discovery response message or a capability of the first UE, the pairing request message including at least one of a machine learning application ID, a machine learning service ID, a machine learning service provider ID associated with the second UE, an indication of a machine learning service type, or an indication of one or more supported machine learning inference models; and transmit, to the first UE, a pairing acknowledgement message based on the pairing request message.

Aspect 25 is the apparatus of aspect 16, where to exchange the information relating to the one or more machine learning services with the first UE, the at least one processor is further configured to: transmit, to the first UE, a service announcement message indicating the one or more machine learning services, the service announcement message including at least one of a machine learning application ID, a machine learning service ID, a machine learning service provider ID associated with the second UE, an indication of a machine learning service type, or an indication of one or more machine learning service parameters, where the machine learning service type corresponds to at least one of sensor data collection, feature extraction, or inference, and the one or more machine learning service parameters correspond to at least one of a data input format, a machine learning feature extraction model supported by the second UE, or a machine learning inference model supported by the second UE.

Aspect 26 is the apparatus of aspect 25, where to pair with the first UE for the one or more machine learning services, the at least one processor is further configured to: receive, from the first UE, a pairing request message based on at least one of the transmitted service announcement message, a machine learning service user ID associated with the first UE, or a capability of the first UE; and transmit, to the first UE, a pairing acknowledgement message based on the pairing request message.

Aspect 27 is the apparatus of aspect 16, where to exchange the information relating to the one or more machine learning services with the first UE, the at least one processor is further configured to: receive, from the first UE, a service discovery announcement message indicating the one or more machine learning services, the service discovery announcement message including at least one of a machine learning application ID, a machine learning service ID, a machine learning service user ID associated with the first UE, an indication of a machine learning service type, or an indication of one or more machine learning service parameters, wherein the machine learning service type corresponds to at least one of sensor data collection, feature extraction, or inference, and the one or more machine learning service parameters correspond to at least one of a data input format, a machine learning feature extraction model supported by the second UE, or a machine learning inference model supported by the second UE.

Aspect 28 is the apparatus of aspect 27, where to pair with the first UE for the one or more machine learning services, the at least one processor is further configured to: transmit, to the first UE, a pairing request message based on at least one of the received service announcement message, a machine learning service provider ID associated with the second UE, or a capability of the second UE; and receive, from the first UE, a pairing acknowledgement message based on the pairing request message.

Aspect 29 is the apparatus of any of aspects 16 to 28, the at least one processor being further configured to: transmit, to a network node, an inference result indicative of an upcoming blockage associated with a beam or a beam steering direction used for communication between the network node and the first UE, where the inference result is generated based on one or more machine learning inference models, and at least one machine learning inference model component in the one or more machine learning inference models is associated with the beam or the beam steering direction.

Aspect 30 is the apparatus of any of aspects 16 to 29, further comprising a transceiver coupled to the at least one processor.

Aspect 31 is a method of wireless communication for implementing any of aspects 1 to 30.

Aspect 32 is an apparatus for wireless communication including means for implementing any of aspects 1 to 30.