WO2025175815A1

WO2025175815A1 - Data collection for supported functionalities

Info

Publication number: WO2025175815A1
Application number: PCT/CN2024/127711
Authority: WO
Inventors: Jianfeng Wang; Seyedomid TAGHIZADEH MOTLAGH; Robin Rajan THOMAS
Original assignee: Lenovo Beijing Ltd
Current assignee: Lenovo Beijing Ltd
Priority date: 2024-10-28
Filing date: 2024-10-28
Publication date: 2025-08-28
Anticipated expiration: 2027-04-28

Abstract

Various aspects of the present disclosure relate to data collection for supported functionalities. In an aspect, a UE transmits, to a network entity, a data collection request including an identity (ID) which the network entity or a core network function has associated with at least one network parameter for use in a supported functionality. The UE receives, from the network entity, a reference signal or a dataset in response to the data collection request being transmitted to the network entity, wherein the reference signal or the dataset is for use in the supported functionality and associated with the ID that is comprised in the data collection request.

Description

DATA COLLECTION FOR SUPPORTED FUNCTIONALITIES

TECHNICAL FIELD

The present disclosure relates to wireless communications, and more specifically to a user equipment (UE) , a base station, a core network entity, processors, and methods for data collection for supported functionalities.

BACKGROUND

A wireless communications system may include one or multiple network communication devices, such as base stations, which may be otherwise known as an eNodeB (eNB) , a next-generation NodeB (gNB) , or other suitable terminology. Each network communication devices, such as a base station may support wireless communications for one or multiple user communication devices, which may be otherwise known as user equipment (UE) , or other suitable terminology. The wireless communications system may support wireless communications with one or multiple user communication devices by utilizing resources of the wireless communication system (e.g., time resources (e.g., symbols, slots, subframes, frames, or the like) or frequency resources (e.g., subcarriers, carriers) . Additionally, the wireless communications system may support wireless communications across various radio access technologies including third generation (3G) radio access technology, fourth generation (4G) radio access technology, fifth generation (5G) radio access technology, among other suitable radio access technologies beyond 5G (e.g., sixth generation (6G) ) .

Artificial Intelligence (AI) and Machine Learning (ML) leverage neural networks trained on extensive datasets to perform specific tasks, with successful applications in computer vision (CV) and natural language processing (NLP) . Numerous academic studies and field tests indicate that AI/ML-based methods can outperform traditional techniques when properly trained. These methods have been explored within a general framework, including evaluation methodologies and results on selected use cases. Further studies of AI/ML functionality are needed to enhance the air interface in targeted applications, such as beam management (e.g., prediction in spatial and temporal domains) and position accuracy improvement.

SUMMARY

The present disclosure relates to methods, apparatuses, and systems that support data collection for supported functionalities. By associating an identity (ID) with at least one network parameter for use in a supported functionality, the UE may trigger a data collection by transmitting the ID to the network without knowing the additional conditions at the network side for the supported functionality. In this way, the data collection for supported functionality may be achieved while avoiding disclosing the proprietary additional conditions of a deployed model/supported functionality of the AI/ML-enabled features/feature groups (FGs) . In addition, deployed models/supported functionalities with flexible conditions/configurations may be applied with low resource overhead.

In a first aspect of the solution, a UE transmits, to a network entity, a data collection request including an identity (ID) which the network entity or a core network function has associated with at least one network parameter for use in a supported functionality. The UE receives, from the network entity, a reference signal or a dataset in response to the data collection request being transmitted to the network entity, wherein the reference signal or the dataset is for use in the supported functionality and associated with the ID that is comprised in the data collection request. In this way, the data collection for the supported functionality may be achieved while avoiding disclosing the proprietary additional conditions of a deployed model/supported functionality.

In some implementations of the method and apparatuses described herein, the at least one network parameter includes at least one of the following: at least one network configuration for applying the supported functionality; or at least one condition for applying the supported functionality.

Some implementations of the method and apparatuses described herein may further include: transmitting, to the network entity or to a core network function, a capability report of the UE, wherein the capability report includes capability information on the supported functionality; and receiving, from the network entity or from the core network function, at least one ID associated with the supported functionality.

In some implementations of the method and apparatuses described herein, the capability report of the UE further includes an indication of requirements on network parameters for use in the supported functionality.

Some implementations of the method and apparatuses described herein may further include: receiving, from the network entity or from the core network function, a capability request on supported functionalities. The capability request on supported functionalities is carried in one of the following: a signaling enquiring capability information of the UE; a signaling enquiring capability information of the UE on sensing; or a signaling enquiring capability information of the UE on artificial intelligence/machine learning (AI/ML) features.

In some implementations of the method and apparatuses described herein, the core network function is a sensing function or a function to support sensing. In some implementations of the method and apparatuses described herein, the reference signal is a sensing signal or a signal for the supported functionality to assist data collection.

Some implementations of the method and apparatuses described herein may further include: performing a life-cycle management on the supported functionality based on collected data from the received reference signal or the dataset associated with the ID.

In a second aspect of the solution, a network entity receives, from a user equipment (UE) , a data collection request including an identity (ID) which the network entity or a core network function has associated with at least one network parameter for use in a supported functionality. The network entity transmits, to the UE, a reference signal or a dataset in response to the data collection request being received from the UE, wherein the reference signal or the dataset is for use in the supported functionality and associated with the ID that is comprised in the data collection request. In this way, the data collection for supported functionality may be achieved while avoiding disclosing the proprietary additional conditions of a deployed model/supported functionality.

Some implementations of the method and apparatuses described herein may further include: receiving, from the UE, a capability report of the UE, wherein the capability report includes capability information on the supported functionality and an indication of requirements on network parameters for use in the supported functionality; and transmitting, to a core network function, the capability information on the supported functionality and the network parameters for use in the supported functionality.

Some implementations of the method and apparatuses described herein may further include: transmitting, to the UE, a capability request on supported functionalities. The capability request on supported functionalities is carried in one of the following: a signaling enquiring capability information of the UE; a signaling enquiring capability information of the UE on sensing; or a signaling enquiring capability information of the UE on artificial intelligence/machine learning (AI/ML) features.

Some implementations of the method and apparatuses described herein may further include: receiving, from a core network function, an indication of requirements on network parameters for use in the supported functionality; and transmitting, to the core network function, the network parameters for use in the supported functionality.

Some implementations of the method and apparatuses described herein may further include: receiving, from the core network function, at least one ID associated with the supported functionality. Some implementations of the method and apparatuses described herein may further include: transmitting, to the UE, the at least one ID associated with the supported functionality.

Some implementations of the method and apparatuses described herein may further include: receiving, from the core network function, an indication of association between the at least one network parameter and the ID.

Some implementations of the method and apparatuses described herein may further include: determining an association between the at least one network parameter and the ID.

In some implementations of the method and apparatuses described herein, the reference signal is transmitted based on the at least one network parameter. In some implementations of the method and apparatuses described herein, the reference signal is a sensing signal or a signal for the supported functionality to assist data collection.

Some implementations of the method and apparatuses described herein may further include: constructing the dataset based on the at least one network parameter. Some implementations of the method and apparatuses described herein may further include: transmitting, a mono-static sensing signal based on the at least one network parameter; receiving, the mono-static sensing signal; and constructing the dataset based on the received mono-static sensing signal.

In some implementations of the method and apparatuses described herein, the core network function is a sensing function or a function to support sensing.

In a third aspect of the solution, a core network function determines an association between at least one network parameter for use in a supported functionality and an identity (ID) . The core network function transmits, to a user equipment (UE) , the ID associated with the supported functionality.

Some implementations of the method and apparatuses described herein may further include: receiving, from a network entity, capability information on the supported functionality and network parameters for use in the supported functionality.

Some implementations of the method and apparatuses described herein may further include: receiving, from the UE, a capability report of the UE, wherein the capability report includes capability information on the supported functionality and an indication of requirements on network parameters for use in the supported functionality; transmitting, to a network entity, the indication of the requirements on the network parameters for use in the supported functionality; and receiving, from the network entity, the network parameters for use in the supported functionality.

Some implementations of the method and apparatuses described herein may further include: transmitting, to the UE, a capability request on supported functionalities. The capability request on supported functionalities is carried in one of the following: a signaling enquiring capability information of the UE on sensing; or a signaling enquiring capability information of the UE on artificial intelligence/machine learning (AI/ML) features.

Some implementations of the method and apparatuses described herein may further include: associating the network parameters and at least one ID; and transmitting, to the UE, the at least one ID associated with the supported functionality.

Some implementations of the method and apparatuses described herein may further include: transmitting, to the network entity, the at least one ID associated with the supported functionality. Some implementations of the method and apparatuses described herein may further include: transmitting, to the network entity, an indication of association between the at least one network parameter and the ID.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A illustrates an example of a wireless communications system that supports data collection for supported functionalities in accordance with aspects of the present disclosure.

FIG. 1B illustrates an example of an architecture of introducing a sensing function in a radio access network associated with aspects of the present disclosure.

FIG. 1C illustrates an example of an architecture of introducing a sensing function in a core network associated with aspects of the present disclosure.

FIG. 2A illustrates an example of a signalling procedure of data collection for supported functionalities in accordance with aspects of the present disclosure.

FIG. 2B illustrates an example of a signalling procedure of identifying supported functionalities in accordance with aspects of the present disclosure.

FIG. 3 illustrates an example diagram for association and interactions among components in data collection for AI/ML functionalities/models in accordance with aspects of the present disclosure.

FIG. 4A illustrates an example procedure of identifying AI/ML functionalities/models in a sensing function via a radio access network node in accordance with aspects of the present disclosure.

FIG. 4B illustrates an example procedure of identifying AI/ML functionalities/models in a sensing function directly in accordance with aspects of the present disclosure.

FIG. 5A illustrates an example procedure of signal transmission for data collection for supported functionalities in accordance with aspects of the present disclosure.

FIG. 5B illustrates an example procedure of dataset construction for data collection for supported functionalities in accordance with aspects of the present disclosure.

FIG. 6 illustrates an example of a device that supports data collection for supported functionalities in accordance with aspects of the present disclosure.

FIG. 7 illustrates an example of a processor that supports data collection for supported functionalities in accordance with aspects of the present disclosure.

FIGS. 8 through 10 illustrate flowcharts of methods that support data collection for supported functionalities in accordance with aspects of the present disclosure.

DETAILED DESCRIPTION

Principles of the present disclosure will now be described with reference to some embodiments. It is to be understood that these embodiments are described only for the purpose of illustration and help those skilled in the art to understand and implement the present disclosure, without suggesting any limitation as to the scope of the disclosure. The disclosure described herein may be implemented in various manners other than the ones described below.

In the following description and claims, unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skills in the art to which this disclosure belongs.

References in the present disclosure to “one embodiment, ” “an example embodiment, ” “an embodiment, ” “some embodiments, ” and the like indicate that the embodiment (s) described may include a particular feature, structure, or characteristic, but it is not necessary that every embodiment includes the particular feature, structure, or characteristic. Moreover, such phrases do not necessarily refer to the same embodiment (s) . Further, when a particular feature, structure, or characteristic is described in connection with an embodiment, it is submitted that it is within the knowledge of one skilled in the art to affect such feature, structure, or characteristic in connection with other embodiments whether or not explicitly described.

It shall be understood that although the terms “first” and “second” or the like may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another element. For example, a first element could also be termed as a second element, and similarly, a second element could also be termed as a first element, without departing from the scope of embodiments. As used herein, the term “and/or” includes any and all combinations of one or more of the listed terms.

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments. As used herein, the singular forms “a” , “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises” , “comprising” , “has” , “having” , “includes” and/or “including” , when used herein, specify the presence of stated features, elements, and/or components etc., but do not preclude the presence or addition of one or more other features, elements, components and/or combinations thereof.

As used herein, the term “communication network” refers to a network following any suitable communication standards, such as, 5G new radio (NR) , long term evolution (LTE) , LTE-advanced (LTE-A) , wideband code division multiple access (WCDMA) , high-speed packet access (HSPA) , narrow band internet of things (NB-IoT) , and so on. Further, the communications between a terminal device and a network device in the communication network may be performed according to any suitable generation communication protocols, including but not limited to, the first generation (1G) , the second generation (2G) , 2.5G, 2.75G, the third generation (3G) , the fourth generation (4G) , 4.5G, the fifth generation (5G) communication protocols, and/or any other protocols either currently known or to be developed in the future. Embodiments of the present disclosure may be applied in various communication systems. Given the rapid development in communications, there will also be future type communication technologies and systems in which the present disclosure may be embodied. It should not be seen as limiting the scope of the present disclosure to only the aforementioned systems.

As used herein, the term “network device” generally refers to a node in a communication network via which a terminal device can access the communication network and receive services therefrom. The network device may refer to a base station (BS) or an access point (AP) , for example, a node B (NodeB or NB) , a radio access network (RAN) node, an evolved NodeB (eNodeB or eNB) , a NR NB (also referred to as a gNB) , a remote radio unit (RRU) , a radio header (RH) , an infrastructure device for a V2X (vehicle-to-everything) communication, a transmission and reception point (TRP) , a reception point (RP) , a remote radio head (RRH) , a relay, an integrated access and backhaul (IAB) node, a low power node such as a femto BS, a pico BS, and so forth, depending on the applied terminology and technology.

As used herein, the term “terminal device” generally refers to any end device that may be capable of wireless communications. By way of example rather than a limitation, a terminal device may also be referred to as a communication device, a user equipment (UE) , an end user device, a subscriber station (SS) , an unmanned aerial vehicle (UAV) , a portable subscriber station, a mobile station (MS) , or an access terminal (AT) . The terminal device may include, but is not limited to, a mobile phone, a cellular phone, a smart phone, a voice over IP (VoIP) phone, a wireless local loop phone, a tablet, a wearable terminal device, a personal digital assistant (PDA) , a portable computer, a desktop computer, an image capture terminal device such as a digital camera, a gaming terminal device, a music storage and playback appliance, a vehicle-mounted wireless terminal device, a wireless endpoint, a mobile station, laptop-embedded equipment (LEE) , laptop-mounted equipment (LME) , a USB dongle, a smart device, wireless customer-premises equipment (CPE) , an internet of things (loT) device, a watch or other wearable, a head-mounted display (HMD) , a vehicle, a drone, a medical device (for example, a remote surgery device) , an industrial device (for example, a robot and/or other wireless devices operating in an industrial and/or an automated processing chain contexts) , a consumer electronics device, a device operating on commercial and/or industrial wireless networks, and the like. In the following description, the terms: “terminal device, ” “communication device, ” “terminal, ” “user equipment” and “UE, ” may be used interchangeably.

The life cycle management (LCM) of AI/ML functionality and models encompasses essential components such as data collection, model training, model delivery/transfer, model inference operations, model updates, as well as functionality/model identification, selection, activation, deactivation, switching, monitoring, fallback operations, and user equipment (UE) capability.

A critical aspect of LCM is determining whether the AI/ML functionality or model can be effectively deployed in a given scenario to achieve performance improvements over legacy systems. This assessment can be facilitated through a shared understanding of certain information between the NW and the UE, which is defined as functionality identification and model identification.

Functionality Identification is a process/method of identifying an AI/ML functionality to achieve a common understanding between the NW and the UE. It is noteworthy that information regarding the AI/ML functionality may be exchanged during the functionality identification process. The location of the AI/ML functionality is contingent upon specific use cases and sub-use cases. As used herein, an AI/ML functionality may refer to a functionality of AI/ML-enabled features/FGs.

Model Identification is a process/method of identifying an AI/ML model to achieve a common understanding between the NW and the UE. It is important to note that the model identification process may not be universally applicable. Additionally, information regarding the AI/ML model may be exchanged during the model identification process. As used herein, an AI/ML model may refer to a model of AI/ML-enabled features/FGs.

For inference for UE-sided models, to ensure consistency between training and inference regarding network (NW) -side additional conditions (if identified) , the following options can be taken as potential approaches (when feasible and necessary) : model identification to achieve alignment on the NW-side additional condition between NW-side and UE-side; model training at NW and transfer to UE, where the model has been trained under the additional condition; information and/or indication on NW-side additional conditions is provided to UE; and consistency assisted by monitoring (by UE and/or NW, the performance of UE-sided candidate models/functionalities to select a model/functionality) . Other approaches are not precluded. It is noteworthy that the possibility that different approaches can achieve the same function is not denied. These approaches not only apply for model training, but also other operations in LCM, e.g., monitoring and updating.

Regarding the model identification procedures, an option is proposed for UE-sided models: model identification with data collection related configuration (s) and/or indication (s) ) of model identification type B. further studies on the following aspects are needed: relationship between model ID and data collection related configuration (s) and/or indication (s) ; information transmitted from NW to UE (if any) ; information transmitted from UE to NW (if any) ; the associated procedure; and usage/applicable use case (s) . In particular, the information to be transmitted between NW and UE during the identification procedure needs to be further studied. For UE-sided model (s) developed (e.g., trained, updated) at UE side, the following procedures are proposed: for data collection, the NW signals the data collection related configuration (s) and it/their associated ID (s) , wherein the associated IDs for each sub use case is in relation with NW-sided additional conditions; the UE (s) collects the data corresponding to the associated ID (s) ; the AI/ML models are developed (e.g., trained, updated) at UE side based on the collected data corresponding to the associated ID (s) ; and the UE reports information of its AI/ML models corresponding to associated IDs to the NW.

Regarding the model identification procedures, another option is proposed for the UE part of two-sided model: model identification with dataset transfer. A dataset is transferred from the NW/NW-side to UE/UE-side via standardized signaling. The UE part of two-sided model (s) is (are) developed based on at least the dataset. The UE reports information of its UE part of two-sided model (s) corresponding to the above dataset to the NW. The example procedure is based on the assumption of NW-first training. Other procedures may apply to UE-first training.

As used herein, supported functionalities refer to functionalities that UE can indicate by using UE capability information (via RRC/LPP signalling) . Applicable functionalities refer to functionalities that the UE is ready to apply for inference. Activated functionalities refer to functionalities already enabled for performing inference.

The following procedures on functionalities are proposed. The network sends a UECapabilityEnquiry message to initiate the procedure to a UE reporting its supported AI/ML functionalities. The UE sends a UECapabilityInformation message to the network, containing supported functionalities at the UE side. Following configurations may be provided from the network to the UE: a configuration allowing the UE to do UAI reporting, NW-side additional condition, and configuration (e.g. inference configuration) of supported functionalities. The UE decides the applicable functionalities based on NW-side additional conditions (if provided) , UE-side additional conditions (internally known by UE) and model availability in device. The UE reports applicable functionality in the following scenarios: upon being configured to provide applicable functionality and upon change of applicable functionality via UAI; or as response to NW-side additional condition requesting applicable functionality reporting. The network configures inference configuration to UE after applicable functionality reporting, if inference configuration based on supported functionality is not previously provided. If inference configuration based on supported functionality is previously provided, it is up to network implementation whether to provide an updated configuration or not. It is noteworthy that, the NW-side additional conditions play an important role to activate the applicable functionality, which, however, needs for further study the necessity and how to indicate to UEs.

Regarding the data collection, it is proposed that the UE receives the measurement configuration for AI/ML-enabled features or feature groups (FGs) for data collection and logging of measurements. The network can explicitly configure the UE whether the corresponding data collection and logging (if supported) should be immediately started. The UE stores the logged training data at AS layer with a minimum AS layer memory size supported by the UE. FFS on the memory size. When the UE reaches its buffer limitation the UE stops measurement for data collection purposes and logging. Measurements for data collection purposes and logging based can be controlled based on power state of the UE. It is up to UE implementation how the UE determines power state. The data collection procedure is discussed for the measurements on UE based on the capability, e.g., memory size and power states.

In view of the above, the NW-side additional conditions with associated ID for data collection configuration and also dataset transfer need for further discussion, especially for the UE-sided and UE part of a two-sided models, which is also important for the future use cases to be enhanced by AI/ML in Rel-20 and beyond.

Regarding what kind of information need to be shared for either functionality identification or model identification, two kinds of conditions for the AI/ML-enabled Feature/FG are proposed: specific configurations/conditions and additional conditions. Specific configurations/conditions are applicability-related information that can be associated with UE capability of an AI/ML-enabled Feature/FG. Additional conditions (e.g., scenarios, sites, and datasets) refer to any aspects that are assumed for the training of the model but are not a part of UE capability for the AI/ML-enabled feature/FG, which can be further divided into two categories: NW-side additional conditions and UE-side additional conditions.

For example, for the AI/ML-based beam management use case, the NW-side additional condition could include the downlink spatial domain transmission filters (i.e., beamforming algorithms) corresponding to the beams in Set A (i.e., prediction beam set) and Set B (i.e., measurement beam set) and the relationship of Set A and Set B.

However, for a UE-side model or UE part of a two-sided model within an AI/ML functionality, the NW-side additional conditions are not always available for UEs. This may be derived from the following aspects.

In a first aspect, the additional conditions are very flexible for the AI/ML functionality/model. The models deployed in the system are not only use-case specific, but also scenario specific. In other words, there are a lot of factors to impact the data to be collected for model training, e.g., outdoor/indoor, velocity and propagation environment, which are impossible to be completely well defined within standards.

In a second aspect, the additional conditions to generate the data could be proprietary. In some use cases, the algorithms, e.g., spatial filter/beamformer for the AI/ML-based beam management, are always deployed by the network vendors in a proprietary way to be competitive with better performance in the network. For the operators, the selection on the vendors in the network is also proprietary for the UEs. Thus, they are all impossible to be disclosed to the UEs and other vendors, even in the same operator’s network.

In a third aspect, the procedure to generate the data for collection needs further study. For model training, the data to be collected is use case specific. In this procedure, normal L1 measurement steps are needed with some configurations and reports in default, which are designed for communication, e.g., scheduling, link adaptation or radio link monitoring. The requirements on the data for training could be different.

Therefore, it is necessary to find some solutions to collect the expected data for training or other LCM operations in the context of enabling sensing operations and functionality and avoid the proprietary disclosed for the AI/ML functionality/model. Some embodiments of the present disclosure propose a solution directed towards abstraction of information, which may be exchanged between entities and nodes in manner that does not disclose any implementation-specific details of a deployed AI/ML model/functionality.

Aspects of the present disclosure are described in the context of a wireless communications system. FIG. 1A illustrates an example of a wireless communications system 100 that supports CSI compression accordance with aspects of the present disclosure. The wireless communications system 100 may include one or more network entities 102 (also referred to as network equipment (NE) ) , one or more UEs 104, a core network 106, and a packet data network 108. The wireless communications system 100 may support various radio access technologies. In some implementations, the wireless communications system 100 may be a 4G network, such as an LTE network or an LTE-advanced (LTE-A) network. In some other implementations, the wireless communications system 100 may be a 5G network, such as an NR network. In other implementations, the wireless communications system 100 may be a combination of a 4G network and a 5G network, or other suitable radio access technology including institute of electrical and electronics engineers (IEEE) 802.11 (Wi-Fi) , IEEE 802.16 (WiMAX) , IEEE 802.20. The wireless communications system 100 may support radio access technologies beyond 5G. Additionally, the wireless communications system 100 may support technologies, such as time division multiple access (TDMA) , frequency division multiple access (FDMA) , or code division multiple access (CDMA) , etc.

The one or more network entities 102 may be dispersed throughout a geographic region to form the wireless communications system 100. One or more of the network entities 102 described herein may be or include or may be referred to as a network node, a base station, a network element, a radio access network (RAN) , a base transceiver station, an access point, a NodeB, an eNodeB (eNB) , a next-generation NodeB (gNB) , or other suitable terminology. A network entity 102 and a UE 104 may communicate via a communication link 110, which may be a wireless or wired connection. For example, a network entity 102 and a UE 104 may perform wireless communication (e.g., receive signaling, transmit signaling) over a Uu interface.

A network entity 102 may provide a geographic coverage area 112 for which the network entity 102 may support services (e.g., voice, video, packet data, messaging, broadcast, etc. ) for one or more UEs 104 within the geographic coverage area 112. For example, a network entity 102 and a UE 104 may support wireless communication of signals related to services (e.g., voice, video, packet data, messaging, broadcast, etc. ) according to one or multiple radio access technologies. In some implementations, a network entity 102 may be moveable, for example, a satellite associated with a non-terrestrial network. In some implementations, different geographic coverage areas 112 associated with the same or different radio access technologies may overlap, but the different geographic coverage areas 112 may be associated with different network entities 102. Information and signals described herein may be represented using any of a variety of different technologies and techniques. For example, data, instructions, commands, information, signals, bits, symbols, and chips that may be referenced throughout the description may be represented by voltages, currents, electromagnetic waves, magnetic fields or particles, optical fields or particles, or any combination thereof.

The one or more UEs 104 may be dispersed throughout a geographic region of the wireless communications system 100. A UE 104 may include or may be referred to as a mobile device, a wireless device, a remote device, a remote unit, a handheld device, or a subscriber device, or some other suitable terminology. In some implementations, the UE 104 may be referred to as a unit, a station, a terminal, or a client, among other examples. Additionally, or alternatively, the UE 104 may be referred to as an internet-of-things (IoT) device, an internet-of-everything (IoE) device, or machine-type communication (MTC) device, among other examples. In some implementations, a UE 104 may be stationary in the wireless communications system 100. In some other implementations, a UE 104 may be mobile in the wireless communications system 100.

The one or more UEs 104 may be devices in different forms or having different capabilities. Some examples of UEs 104 are illustrated in FIG. 1A. A UE 104 may be capable of communicating with various types of devices, such as the network entities 102, other UEs 104, or network equipment (e.g., the core network 106, the packet data network 108, a relay device, an integrated access and backhaul (IAB) node, or another network equipment) , as shown in FIG. 1A. Additionally, or alternatively, a UE 104 may support communication with other network entities 102 or UEs 104, which may act as relays in the wireless communications system 100.

A UE 104 may also be able to support wireless communication directly with other UEs 104 over a communication link 114. For example, a UE 104 may support wireless communication directly with another UE 104 over a device-to-device (D2D) communication link. In some implementations, such as vehicle-to-vehicle (V2V) deployments, vehicle-to-everything (V2X) deployments, or cellular-V2X deployments, the communication link 114 may be referred to as a sidelink. For example, a UE 104 may support wireless communication directly with another UE 104 over a PC5 interface.

A network entity 102 may support communications with the core network 106, or with another network entity 102, or both. For example, a network entity 102 may interface with the core network 106 through one or more backhaul links 116 (e.g., via an S1, N2, N2, or another network interface) . The network entities 102 may communicate with each other over the backhaul links 116 (e.g., via an X2, Xn, or another network interface) . In some implementations, the network entities 102 may communicate with each other directly (e.g., between the network entities 102) . In some other implementations, the network entities 102 may communicate with each other or indirectly (e.g., via the core network 106) . In some implementations, one or more network entities 102 may include subcomponents, such as an access network entity, which may be an example of an access node controller (ANC) . An ANC may communicate with the one or more UEs 104 through one or more other access network transmission entities, which may be referred to as a radio heads, smart radio heads, or transmission-reception points (TRPs) .

In some implementations, a network entity 102 may be configured in a disaggregated architecture, which may be configured to utilize a protocol stack physically or logically distributed among two or more network entities 102, such as an integrated access backhaul (IAB) network, an open radio access network (O-RAN) (e.g., a network configuration sponsored by the O-RAN Alliance) , or a virtualized RAN (vRAN) (e.g., a cloud RAN (C-RAN) ) . For example, a network entity 102 may include one or more of a CU, a DU, a radio unit (RU) , a RAN intelligent controller (RIC) (e.g., a near-real time RIC (Near-RT RIC) , a non-real time RIC (Non-RT RIC) ) , a service management and orchestration (SMO) system, or any combination thereof.

An RU may also be referred to as a radio head, a smart radio head, a remote radio head (RRH) , a remote radio unit (RRU) , or a transmission reception point (TRP) . One or more components of the network entities 102 in a disaggregated RAN architecture may be co-located, or one or more components of the network entities 102 may be located in distributed locations (e.g., separate physical locations) . In some implementations, one or more network entities 102 of a disaggregated RAN architecture may be implemented as virtual units (e.g., a virtual CU (VCU) , a virtual DU (VDU) , a virtual RU (VRU) ) .

Split of functionality between a CU, a DU, and an RU may be flexible and may support different functionalities depending upon which functions (e.g., network layer functions, protocol layer functions, baseband functions, radio frequency functions, and any combinations thereof) are performed at a CU, a DU, or an RU. For example, a functional split of a protocol stack may be employed between a CU and a DU such that the CU may support one or more layers of the protocol stack and the DU may support one or more different layers of the protocol stack. In some implementations, the CU may host upper protocol layer (e.g., a layer 3 (L3) , a layer 2 (L2) ) functionality and signaling (e.g., radio resource control (RRC) , service data adaption protocol (SDAP) , packet data convergence protocol (PDCP) ) . The CU may be connected to one or more DUs or RUs, and the one or more DUs or RUs may host lower protocol layers, such as a layer 1 (L1) (e.g., physical (PHY) layer) or an L2 (e.g., radio link control (RLC) layer, medium access control (MAC) layer) functionality and signaling, and may each be at least partially controlled by the CU 160.

Additionally, or alternatively, a functional split of the protocol stack may be employed between a DU and an RU such that the DU may support one or more layers of the protocol stack and the RU may support one or more different layers of the protocol stack. The DU may support one or multiple different cells (e.g., via one or more RUs) . In some implementations, a functional split between a CU and a DU, or between a DU and an RU may be within a protocol layer (e.g., some functions for a protocol layer may be performed by one of a CU, a DU, or an RU, while other functions of the protocol layer are performed by a different one of the CU, the DU, or the RU) .

A CU may be functionally split further into CU control plane (CU-CP) and CU user plane (CU-UP) functions. A CU may be connected to one or more DUs via a midhaul communication link (e.g., F1, F1-c, F1-u) , and a DU may be connected to one or more RUs via a fronthaul communication link (e.g., open fronthaul (FH) interface) . In some implementations, a midhaul communication link or a fronthaul communication link may be implemented in accordance with an interface (e.g., a channel) between layers of a protocol stack supported by respective network entities 102 that are in communication via such communication links.

The core network 106 may support user authentication, access authorization, tracking, connectivity, and other access, routing, or mobility functions. The core network 106 may be an evolved packet core (EPC) , or a 5G core (5GC) , which may include a control plane entity that manages access and mobility (e.g., a mobility management entity (MME) , an access and mobility management functions (AMF) ) and a user plane entity that routes packets or interconnects to external networks (e.g., a serving gateway (S-GW) , a packet data network (PDN) gateway (P-GW) , or a user plane function (UPF) ) . In some implementations, the control plane entity may manage non-access stratum (NAS) functions, such as mobility, authentication, and bearer management (e.g., data bearers, signal bearers, etc. ) for the one or more UEs 104 served by the one or more network entities 102 associated with the core network 106.

The core network 106 may communicate with the packet data network 108 over one or more backhaul links 116 (e.g., via an S1, N2, N2, or another network interface) . The packet data network 108 may include an application server 118. In some implementations, one or more UEs 104 may communicate with the application server 118. A UE 104 may establish a session (e.g., a protocol data unit (PDU) session, or the like) with the core network 106 via a network entity 102. The core network 106 may route traffic (e.g., control information, data, and the like) between the UE 104 and the application server 118 using the established session (e.g., the established PDU session) . The PDU session may be an example of a logical connection between the UE 104 and the core network 106 (e.g., one or more network functions of the core network 106) .

In the wireless communications system 100, the network entities 102 and the UEs 104 may use resources of the wireless communications system 100 (e.g., time resources (e.g., symbols, slots, subframes, frames, or the like) or frequency resources (e.g., subcarriers, carriers) ) to perform various operations (e.g., wireless communications) . In some implementations, the network entities 102 and the UEs 104 may support different resource structures. For example, the network entities 102 and the UEs 104 may support different frame structures. In some implementations, such as in 4G, the network entities 102 and the UEs 104 may support a single frame structure. In some other implementations, such as in 5G and among other suitable radio access technologies, the network entities 102 and the UEs 104 may support various frame structures (i.e., multiple frame structures) . The network entities 102 and the UEs 104 may support various frame structures based on one or more numerologies.

One or more numerologies may be supported in the wireless communications system 100, and a numerology may include a subcarrier spacing and a cyclic prefix. A first numerology (e.g., μ=0) may be associated with a first subcarrier spacing (e.g., 15 kHz) and a normal cyclic prefix. In some implementations, the first numerology (e.g., μ=0) associated with the first subcarrier spacing (e.g., 15 kHz) may utilize one slot per subframe. A second numerology (e.g., μ=1) may be associated with a second subcarrier spacing (e.g., 30 kHz) and a normal cyclic prefix. A third numerology (e.g., μ=2) may be associated with a third subcarrier spacing (e.g., 60 kHz) and a normal cyclic prefix or an extended cyclic prefix. A fourth numerology (e.g., μ=3) may be associated with a fourth subcarrier spacing (e.g., 120 kHz) and a normal cyclic prefix. A fifth numerology (e.g., μ=4) may be associated with a fifth subcarrier spacing (e.g., 240 kHz) and a normal cyclic prefix.

A time interval of a resource (e.g., a communication resource) may be organized according to frames (also referred to as radio frames) . Each frame may have a duration, for example, a 10 millisecond (ms) duration. In some implementations, each frame may include multiple subframes. For example, each frame may include 10 subframes, and each subframe may have a duration, for example, a 1 ms duration. In some implementations, each frame may have the same duration. In some implementations, each subframe of a frame may have the same duration.

Additionally or alternatively, a time interval of a resource (e.g., a communication resource) may be organized according to slots. For example, a subframe may include a number (e.g., quantity) of slots. The number of slots in each subframe may also depend on the one or more numerologies supported in the wireless communications system 100. For instance, the first, second, third, fourth, and fifth numerologies (i.e., μ=0, μ=1, μ=2, μ=3, μ=4) associated with respective subcarrier spacings of 15 kHz, 30 kHz, 60 kHz, 120 kHz, and 240 kHz may utilize a single slot per subframe, two slots per subframe, four slots per subframe, eight slots per subframe, and 16 slots per subframe, respectively. Each slot may include a number (e.g., quantity) of symbols (e.g., OFDM symbols) . In some implementations, the number (e.g., quantity) of slots for a subframe may depend on a numerology. For a normal cyclic prefix, a slot may include 14 symbols. For an extended cyclic prefix (e.g., applicable for 60 kHz subcarrier spacing) , a slot may include 12 symbols. The relationship between the number of symbols per slot, the number of slots per subframe, and the number of slots per frame for a normal cyclic prefix and an extended cyclic prefix may depend on a numerology. It should be understood that reference to a first numerology (e.g., μ=0) associated with a first subcarrier spacing (e.g., 15 kHz) may be used interchangeably between subframes and slots.

In the wireless communications system 100, an electromagnetic (EM) spectrum may be split, based on frequency or wavelength, into various classes, frequency bands, frequency channels, etc. By way of example, the wireless communications system 100 may support one or multiple operating frequency bands, such as frequency range designations FR1 (410 MHz –7.125 GHz) , FR2 (24.25 GHz –52.6 GHz) , FR3 (7.125 GHz –24.25 GHz) , FR4 (52.6 GHz –114.25 GHz) , FR4a or FR4-1 (52.6 GHz –71 GHz) , and FR5 (114.25 GHz –300 GHz) . In some implementations, the network entities 102 and the UEs 104 may perform wireless communications over one or more of the operating frequency bands. In some implementations, FR1 may be used by the network entities 102 and the UEs 104, among other equipment or devices for cellular communications traffic (e.g., control information, data) . In some implementations, FR2 may be used by the network entities 102 and the UEs 104, among other equipment or devices for short-range, high data rate capabilities.

FR1 may be associated with one or multiple numerologies (e.g., at least three numerologies) . For example, FR1 may be associated with a first numerology (e.g., μ=0) , which includes 15 kHz subcarrier spacing; a second numerology (e.g., μ=1) , which includes 30 kHz subcarrier spacing; and a third numerology (e.g., μ=2) , which includes 60 kHz subcarrier spacing. FR2 may be associated with one or multiple numerologies (e.g., at least 2 numerologies) . For example, FR2 may be associated with a third numerology (e.g., μ=2) , which includes 60 kHz subcarrier spacing; and a fourth numerology (e.g., μ=3) , which includes 120 kHz subcarrier spacing.

The wireless communications system 100 may support radio access technologies beyond 5G, e.g., 6G. The UEs 102, the network entities 104 and the core network 106 may support 6G communication and may collaborate to deliver advanced services, incorporating technologies such as sensing, artificial intelligence/machine learning, etc. To support the sensing services in the communication system, it is proposed to introduce a logical function, a sensing function (SF) , for all sensing-related operations, e.g., quality of services indications, configurations, etc., which can be located in RAN (as illustrated in FIG. 1B) and/or core network (CN) (as illustrated in FIG. 1C) .

FIG. 2A illustrates an example of signalling procedure 200A for data collection for supported functionalities in accordance with aspects of the present disclosure. For the purpose of discussion, the procedure 200A will be described with reference to FIG. 1A, and the procedure 200A may involve a UE 104 and a network entity 102 as shown in FIG. 1A. The network entity 102 may be implemented as a base station. It is to be understood that the steps and the order of the steps in FIG. 2A are merely for illustration, and not for limitation. It is to be understood that procedure 200A may further include additional blocks not shown and/or omit some shown blocks, and the scope of the present disclosure is not limited in this regard.

As shown in FIG. 2A, the UE 104 transmits (202) a data collection request 204 to the base station 102. The data collection request 204 includes an identity (ID) which the base station 102 or a core network function has associated with at least one network parameter for use in a supported functionality. The base station 102 receives (206) the data collection request 204 and transmits (208) a reference signal or a dataset 210 to the UE 104 in response to receiving the data collection request 204. The reference signal or the dataset 210 is for use in the supported functionality and associated with the ID that is included in the data collection request 204. In other words, the base station 102 transmits the reference signal or constructs the dataset based on the at least one network parameter associated with the ID. Accordingly, the UE 104 receives (212) the reference signal or the dataset 210 from the base station 102. In this way, the data collection for supported functionality may be achieved while avoiding disclosing the proprietary additional conditions of a deployed model/functionality. In addition, deployed models/functionalities with flexible conditions/configurations may be applied with low resource overhead.

In some embodiments, the at least one network parameter may include at least one network configuration for applying the supported functionality. Additionally or alternatively, the at least one network parameter may include at least one condition for applying the supported functionality. For example, the at least one network parameter may include additional conditions and/or configurations, etc. Based on the ID comprised in the data collection request 204, the base station 102 may be aware of the at least one network parameter associated with the ID.

In some implementations, the base station 102 may transmit the reference signal based on the at least one network parameter. In some embodiments, the reference signal may be a sensing signal or a signal for the supported functionality to assist data collection. The UE 104 may perform a life-cycle management on the supported functionality based on collected data from the received reference signal associated with the ID.

In some implementations, the base station 102 may transmit construct the dataset based on the at least one network parameter. The base station 102 may construct the dataset in various manners. As an example, the base station 102 may transmit a mono-static sensing signal based on the at least one network parameter and receive the mono-static sensing signal. The base station may construct the dataset based on the received mono-static sensing signal. The UE 104 may perform a life-cycle management on the supported functionality based on collected data from the dataset associated with the ID.

In some embodiments, the UE 104 may transmit a capability report of the UE 104 to the base station 102. The capability report may include capability information on the functionality. In some embodiments, the capability report may further include an indication of requirements on network parameters for use in the supported functionality. Based on the indication of requirements, the base station 102 is aware of what network parameters are required for the supported functionality. For example, the UE 104 may receive a capability request on supported functionalities from the base station 102 and transmit the capability report to the base station 102. In an example implementation, the capability request on supported functionalities may be carried in a signaling enquiring capability information of the UE 104, e.g., UECapabilityEnquiry. In another example implementation, the capability request on supported functionalities may be carried in a signaling enquiring capability information of the UE 104 on AI/ML features, e.g., UEAICapabilityEnquiry. In a further example implementation, a sensing function or a function to support sensing may be located in the base station 102, and the capability request on supported functionalities may be carried in a signaling enquiring capability information of the UE 104 on sensing, e.g., SensingCapabilityEnquiry.

In some embodiments, the base station 102 may determine an association between at least one ID and network parameters for use in the supported functionality. After receiving the capability report of the UE 104, the base station 102 may transmit the at least one ID to the UE 104. For example, the association determination may be performed by the sensing function or the logical function to support sensing located in the base station 102. The association may be performed prior to the base station 102 receiving the capability report or after the base station 102 receiving the capability report. Based on the capability report, the base station 102 is aware that the UE 104 supports the functionality and thus may transmit the at least one ID to the UE 104.

Alternatively, the base station 102 may transmit the capability information on the supported functionality and the network parameters for use in the supported functionality to a network function (e.g., a sensing function or a function to support sensing located in the core network) . The network function may determine an association between at least one ID and network parameters for use in the supported functionality. The network function may then transmit the at least one ID to the base station 102. The base station 102 may transmit the at least one ID to the UE 104. In some embodiments, the base station 102 may receive, from the network function, an indication of the association between the at least one network parameter and the ID. In some embodiments, the base station 102 may receive, from the network function, an indication of the association between at least one ID and the network parameters for use in the supported functionality.

In some embodiments, the UE 104 may transmit a capability report of the UE 104 to a network function (e.g., a sensing function or a function to support sensing located in the core network) . For example, the UE 104 may transmit the capability report to the core network function via a direct interface (e.g., a non-access stratum (NAS) message) . The capability report may include capability information on the supported functionality and an indication of requirements on network parameters for use in the supported functionality. For example, the UE 104 may receive a capability request on supported functionalities from the network function and transmit the capability report to the network function. In an example implementation, the capability request on supported functionalities may be carried in a signaling enquiring capability information of the UE 104 on sensing, e.g., SensingCapabilityEnquiry. In another example implementation, the capability request on supported functionalities may be carried in a signaling enquiring capability information of the UE 104 on AI/ML features, e.g., UEAICapabilityEnquiry. Based on the capability report, the network function is aware that that the UE 104 supports the functionality and is aware of what network parameters are required for the supported functionality. In some embodiments, the network function may transmit the indication of requirements on network parameters for use in the supported functionality to the base station 102. The base station 102 may transmit the network parameters for use in the supported functionality to the network function. The network function may determine an association between at least one ID and network parameters for use in the supported functionality. The network function may request network parameters for use in the supported functionality from the base station 102 and associate at least one ID with these network parameters prior to receiving the capability report or after receiving the capability report. The network function may then transmit the at least one ID to the base station 102 and to the UE 104 directly or via the base station 102. In some embodiments, the base station 102 may receive, from the network function, an indication of the association between the at least one network parameter and the ID. In some embodiments, the base station 102 may receive, from the network function, an indication of the association between at least one ID and the network parameters for use in the supported functionality.

With the procedure 200A, the supported functionality management may be achieved without disclosing the network-side additional conditions and/or configurations to the UE. The data collection procedure may be triggered based on associated ID (s) of the supported functionality.

FIG. 2B illustrates an example of a signalling procedure 200B of identifying supported functionalities in accordance with aspects of the present disclosure. For the purpose of discussion, the procedure 200B will be described with reference to FIG. 1A, and the procedure 200B may involve a UE 104 and a network function 222 as shown in FIG. 1A. The network function 222 may be implemented as a sensing function or a function to support sensing located in a radio access network (e.g., the sensing function 122 in FIG. 1B) or in the core network (e.g., the sensing function 122 in FIG. 1C) . In some embodiments, the procedure 200B may also involve a base station (not shown) . It is to be understood that the steps and the order of the steps in FIG. 2B are merely for illustration, and not for limitation. It is to be understood that procedure 200B may further include additional blocks not shown and/or omit some shown blocks, and the scope of the present disclosure is not limited in this regard. The procedure 200B may be implemented as a part of the procedure 200A or may be implemented independent of the procedure 200A.

As shown in FIG. 2B, the network function 222 determines (214) an association between the at least one network parameter for use in a supported functionality and an ID 218. The network function 222 transmits (216) the ID 218 associated with the supported functionality to the UE 104. Accordingly, the UE 104 receives (220) the ID 218 associated with the supported functionality. In this way, the network function may manage the supported functionalities to assist data collection with the un-disclosed NW-side additional conditions and/or configurations.

In some embodiments, the at least one network parameter may include at least one of the following: at least one network configuration for applying the supported functionality; or at least one condition for applying the supported functionality. The network function 222 may be a sensing function or a function to support sensing.

In some embodiments, the network function 222 may be located in the base station and may receive a capability report of the UE 104 from the UE 104. The capability report may include capability information on the supported functionality and an indication of requirements on network parameters for use in the supported functionality. For example, the UE 104 may receive a capability request on supported functionalities from the network function and transmit the capability report to the network function. In an example implementation, the capability request on supported functionalities may be carried in a signaling enquiring capability information of the UE 104, e.g., UECapabilityEnquiry. In another example implementation, the capability request on supported functionalities may be carried in a signaling enquiring capability information of the UE 104 on AI/ML features, e.g., UEAICapabilityEnquiry. In a further example implementation, a sensing function or a function to support sensing may be located in the base station 102, and the capability request on supported functionalities may be carried in a signaling enquiring capability information of the UE 104 on sensing, e.g., SensingCapabilityEnquiry. The network function 222 may associate the network parameters for use in the supported functionality with at least one ID and transmit the at least one ID associated with the supported functionality to the UE 104.

Alternatively, the network function 222 may be located in the core network and may receive, from a base station, capability information on the supported functionality and network parameters for use in the supported functionality. The network function 222 may associate the network parameters for use in the supported functionality with at least one ID.

Alternatively, the network function 222 may be located in the core network and may receive a capability report of the UE 104 from the UE 104. The capability report may include capability information on the supported functionality and an indication of requirements on network parameters for use in the supported functionality. For example, the UE 104 may receive a capability request on supported functionalities from the network function and transmit the capability report to the network function. In an example implementation, the capability request on supported functionalities may be carried in a signaling enquiring capability information of the UE 104 on sensing, e.g., SensingCapabilityEnquiry. In another example implementation, the capability request on supported functionalities may be carried in a signaling enquiring capability information of the UE 104 on AI/ML features, e.g., UEAICapabilityEnquiry. The network function 222 may transmit, to a base station, the indication of the requirements on the network parameters for use in the supported functionality; and receive, from the base station, the network parameters for use in the supported functionality. The network function 222 may associate the network parameters for use in the supported functionality with at least one ID.

In some embodiments, the network function 222 may associate the network parameters and at least one ID and transmit the at least one ID associated with the supported functionality to the UE 104, either directly or via a base station.

In some embodiments, the network function 222 may transmit the at least one ID associated with the supported functionality to the base station. Additionally or alternatively, the network function 222 may transmit an indication of association between the at least one network parameter and the ID to the base station. Additionally or alternatively, the network function 222 may transmit an indication of association between the network parameters and the at least one ID to the base station.

FIG. 3 illustrates an example diagram for association and interactions among components in data collection for AI/ML functionalities/models in accordance with aspects of the present disclosure. These components may be implemented in at least one of UEs 104, network entities 102 or the core network 106 as shown in FIG. 1A. Other procedures for data collection for the supported functionality are also possible.

As shown in FIG. 3, at 301, the sensing function (SF) may identify the AI/ML functionalities/models. The related information of the AI/ML functionalities/models, including the NW-side additional conditions, is registered in the sensing function. This may be exemplified with a transmitted request and received response including relevant information related to AI/ML functionalities/models identification. In some examples, the AI/ML functionality for which the additional conditions are provisioned may reside in a UE or UE side, in the SF, or may reside in another network function communicating, via a direct or an indirect interface, with the SF.

At 302, the data collection procedure with some configurations may be triggered, according to the AI/ML functionality/model. The trigger of the data collection procedure may be determined by the SF and/or the entity where the AI/ML functionality is residing, upon one or more of the following: performance model training or updating with the collected data; performance monitoring of a model (e.g., when a measure of error on a model output is above a threshold) ; a determined change in the conditions associated with an AI/ML model (change of scenario, statistics, site associated with a sensing operation) ; based on the desired sensing QoS/sensing KPI, where the non-AI/ML sensing methods are not deemed sufficient to achieve the sensing QoS/sensing KPIs; upon reception of a sensing request or initiation of a sensing operation for which the related data may be collected/used by the AI/ML model for inference or training; or based on availability of the data associated to a sensing operation and/or an AI/ML functionality/model.

At 303, the network entity (e.g., SF, LMF) may provide the NW-side additional conditions to assist data collection procedure. According to the registered information on the AI/ML functionalities/models, the NW-side additional conditions are provided to assist data collection. The provided NW-side additional conditions may be provided to another NW entity/UE upon request (based on a solicitation message) or without a request (unsolicited message) .

At 304, the dataset for training may be generated from the collected data. If the data is collected for model training, a dataset is constructed, associated with the data collection configurations. At 305, the dataset may be provided for training after construction. Once ready, the dataset may be provided for the model training with the indications on the associated data collection configurations, i.e., additional conditions. At 306, the dataset may be optionally provided to a network entity handling sensing-related tasks (e.g., SF, LMF) for sensing service. For some sensing services, the dataset may be provided to derive the sensing results. This is especially beneficial if the AI/ML model to improve sensing measurements or results is deployed at this sensing related network entity, e.g., for training or inference purposes.

In the initialization steps, it is proposed for one or more RAN entities to identify the AI/ML functionalities/models in the sensing function. Example procedures for identifying AI/ML functionalities/models in a sensing function as illustrated in FIGS. 4A and 4B. Other procedures are also possible.

FIG. 4A illustrates an example procedure 400A of identifying AI/ML functionalities/models in a sensing function via a radio access network node in accordance with aspects of the present disclosure. For the purpose of discussion, the procedure 400A will be described with reference to FIG. 1A, and the procedure 400A may involve a UE 104 and a network entity 102 as shown in FIG. 1A and a SF 122 as shown in FIG. 1C. The network entity 102 may be implemented as a RAN node. It is to be understood that the steps and the order of the steps in FIG. 4A are merely for illustration, and not for limitation. It is to be understood that procedure 400A may further include additional blocks not shown and/or omit some shown blocks, and the scope of the present disclosure is not limited in this regard. The procedure 400A may be regarded as a specific example of the process 200B in FIG. 2B.

At 402, the RAN node 102 may enquire the UE capability on the support of AI/ML features. The RAN node 102 may transmit the enquiry information to the UE 104 for the capability on the support of AI/ML features via a the legacy signaling on the UE capability enquiry information (i.e., UECapabilityEnquiry) or via a RRC signaling to enquire the capability on the AI/ML features (e.g., UEAICapabilityEnquiry)

At 404, the UE 104 may report the capability with relevant information/requests of the supported AI/ML functionalities/models. Once receiving the enquiry information, the UE 104 may report the UE capability information on the supported AI/ML functionalities/models via an enhanced legacy signaling of UE capability information (i.e., UECapabilityInformation) to include the AI/ML-related information or via a RRC signaling (e.g., UEAICapabilityInformation) to report the capability to support AI/ML features with the AI/ML-related information. The AI/ML-related information on the supported AI/ML functionalities/models may include the requests on the configurations and additional conditions.

At 406, the RAN node 102 may provide the AI/ML-related information to the SF 122. The RAN node 102 may provide the AI/ML-related information about the reported AI/ML functionalities/models to the SF 122, at least including following contents: the candidate values of the requested configurations to apply/activate the AI/ML functionalities/models, e.g., the number of antenna ports, CSI-RS set and report configurations; and the candidate values of the additional conditions to apply/activate the AI/ML functionalities/models, e.g., the beamforming algorithm descriptions, the statistical values of propagation channel (SINR, delay spread, Doppler shift/spread, etc. ) .

At 408, the SF 122 may associate the AI/ML-related information with a set of IDs. Once receiving the contents of the configurations and additional conditions of each supported AI/ML functionality/model, the SF 122 may associate the values with a set of IDs for management, via, e.g., one-to-one or one-to-multiple mapping. For example, an ID for an AI/ML model means that the model may be activated when the number of antenna port is eight with a dedicated CSI-RS set and report configuration, and the dedicated beamforming algorithm for beam sets in a SINR range.

At 410, the SF 122 may provide the set of associated IDs to the RAN node 102 and the UE 104. The set of associated IDs may be provided to RAN nodes and UEs via a signaling (e.g., dedicated RRC signals) to map the reported AI/ML functionalities/models to the IDs associated with the potential configurations and additional conditions.

With the procedure 400A, the AI/ML functionalities/models at the UE side are registered in the SF via the RAN node 102. The candidates of potential configurations and additional conditions of the RAN nodes from different vendors may be well managed by SF for the supported AI/ML functionalities/models.

FIG. 4B illustrates an example procedure of identifying AI/ML functionalities/models in a sensing function via a radio access network node in accordance with aspects of the present disclosure. For the purpose of discussion, the procedure 400B will be described with reference to FIG. 1A, and the procedure 400B may involve a UE 104 and a network entity 102 as shown in FIG. 1A and a SF 122 as shown in FIG. 1C. The network entity 102 may be implemented as a RAN node. It is to be understood that the steps and the order of the steps in FIG. 4B are merely for illustration, and not for limitation. It is to be understood that procedure 400B may further include additional blocks not shown and/or omit some shown blocks, and the scope of the present disclosure is not limited in this regard. The procedure 400B may be regarded as a specific example of the process 200B in FIG. 2B. The same reference numerals are used to denote the elements or components described in FIG. 4B having the same operations as the elements or components described in FIG. 4A, and detailed description thereof will be omitted.

At 412, the SF 122 may enquire the UE capability on the support of AI/ML features. The SF 122 may transmit enquiry information to the UE 104for the capability on the support of AI/ML features via a sensing related protocol with a new RRC signaling, e.g., SensingCapabilityEnquiry.

At 414, the UE 104 may report the UE capability with relevant information/requests of the supported AI/ML functionalities/models. Once receiving the enquiry information, the UE 104 may report the UE capability information on the supported AI/ML functionalities/models via sensing related protocol with a RRC signaling (e.g., SensingAICapabilityInformation) . The AI/ML-related information on the supported AI/ML functionalities/models may include the requests on the configurations and additional conditions.

At 416, the SF 122 may enquire the configurations and additional conditions of the reported AI/ML functionalities/models. According to the received information, the SF 122 may request the RAN node 102 on the necessary configurations and conditions.

In some examples, the SF enquiry and the response of the RAN node for the NW-related configurations and conditions may be transmitted subsequent to the UE capability report.

In some other examples, said SF enquiry may be transmitted prior to the specific UE report and stored/maintained at the SF 122, based on which the SF 122 generates and transmits the associated IDs at step 410. This scenario may include when the AI/ML models or functionalities are not UE specific.

In some examples, said enquiry/response for the AI/ML models/functionalities may be done towards an entity, other than the RAN node 102, where such AI/ML models/functionalities may be available. In some examples, said entity may include a logical entity within the RAN (e.g., communicated via a dedicated interface between the SF 122 and said logical entity) , a core network function/entity different than the SF 122 (e.g., LMF, NWDAF, etc. ) , or a third-party application where such AI/ML model/functionality information is available.

At 418, the RAN node 102 may provide the AI/ML-related information to the SF 122. Once receiving the enquiry information, the RAN node 102 may report the configurations and conditions to the SF 122. The RAN node 102 may further provide the AI/ML-related information about the reported AI/ML functionalities/models to the SF 122, at least including following contents: the candidate values of the requested configurations to apply/activate the AI/ML functionalities/models, e.g., the number of antenna ports, CSI-RS set and report configurations; and the candidate values of the additional conditions to apply/activate the AI/ML functionalities/models, e.g., the beamforming algorithm descriptions, the statistical values of propagation channel (SINR, delay spread, Doppler shift/spread, etc. ) .

At 410, the SF 122 may provide the set of associated IDs to the RAN node 102 and the UE 104. With the configurations and conditions, the SF 122 may associate them with a set of IDs and provide them to the RAN node 102 and UE to assist following data collection procedure for the supported AI/ML functionalities/models.

In some embodiments, the communication of the UE 104 and the SF 122 in Step 412, Step 414 and Step 410 may be performed, at least in part, via a direct interface between the UE 104 and the SF 122 (e.g., a NAS message indicating the AI/ML functionalities to the SF and/or receiving additional conditions and information from the SF 122) . The communication of the RAN node 102 and the SF 122 in Step 416, Step 418 and Step 410 may be performed, at least in part, via the interface between the RAN node 102 and the SF 122. With the procedure 400B, the AI/ML functionalities/models at the UE side are registered in the SF directly via a sensing related protocol.

After this initialization procedure, the SF may manage the AI/ML functionalities/models to assist data collection with the un-disclosed NW-side additional conditions. If a UE wants to collect data for LCM on an AI/ML functionality/model, e.g., model training, monitoring or other LCM operations, it is necessary to trigger the procedure with sensing assisted data collection. Example procedures with the interaction between the UE and the RAN node based on the associated IDs for data collection for an AI/ML functionality/model as illustrated in FIGS. 5A and 5B. Other procedures are also possible.

FIG. 5A illustrates an example procedure of signal transmission for data collection for supported functionalities in accordance with aspects of the present disclosure. For the purpose of discussion, the procedure 500A will be described with reference to FIG. 1A, and the procedure 500A may involve a UE 104 and a network entity 102 as shown in FIG. 1A. The network entity 102 may be implemented as a RAN node. It is to be understood that the steps and the order of the steps in FIG. 5A are merely for illustration, and not for limitation. It is to be understood that procedure 500A may further include additional blocks not shown and/or omit some shown blocks, and the scope of the present disclosure is not limited in this regard. The procedure 500A may be regarded as a specific example of the process 200A in FIG. 2A.

At 502, the UE 104 may trigger the data collection procedure with a request information to the RAN node 102. The request information for data collection may include the indication on the AI/ML functionality/model, which requests for the data collection, and the associated ID on the configurations and conditions, which is selected from the assigned set ID in the initialization procedure (e.g., the procedure 400A in FIG. 4A or the procedure 400B in FIG. 4B) .

At 504, the RAN node 102 may apply the configurations and conditions associated with the ID. Once receiving the request with the associated ID, the RAN node 102 may apply the configuration and conditions on the following reference signal transmission, such as CSI-RS, DL-PRS or else new reference signals.

At 506, the RAN node 102 may transmit the reference signal with the configurations and additional conditions to the UE 104 along with the associated ID. The configurations and NW-side additional conditions are derived at the RAN node 102 according to the associated ID to configure the reference signals (e.g., CSI-RS or some other RS, e.g., DL-PRS, sensing reference signals) and the proper transmission schemes according to the NW-side additional conditions. In some cases, if the sensing signal for bi-static sensing mode is used, the configurations on this signal may also be determined by the SF and/or the RAN node 102.

At 508, the UE 104 may receive and process the reference signal (s) to collect the needed data. After receiving the reference signals according to the configurations, the data (e.g., CSI or L1-RSRP) may be generated for the LCM operations after processing. In some variation examples, the received bi-static measurement of the UE 104 may be utilized as a model input during the training or quality monitoring, whereas the transmitted data from the RAN node 102, generated from the RAN-based (monostatic or bistatic) measurements, may be indicated to the UE 104 to act as a label.

The procedure 500A involves transmitting the configured reference signal with a proper transmission schemes according to the additional conditions, together with the associated ID. With the procedure 500A, the data is collected as measurement results from the configured reference signal as the model input for an AI/ML functionality/model for the LCM operations, e.g., monitoring, model updating or tuning, where the amount is not large.

FIG. 5B illustrates an example procedure of dataset construction for data collection for supported functionalities in accordance with aspects of the present disclosure. For the purpose of discussion, the procedure 500B will be described with reference to FIG. 1A, and the procedure 500B may involve a UE 104 and a network entity 102 as shown in FIG. 1A. The network entity 102 may be implemented as a RAN node. It is to be understood that the steps and the order of the steps in FIG. 5B are merely for illustration, and not for limitation. It is to be understood that procedure 500B may further include additional blocks not shown and/or omit some shown blocks, and the scope of the present disclosure is not limited in this regard. The procedure 500B may be regarded as a specific example of the process 200A in FIG. 2A. The same reference numerals are used to denote the elements or components described in FIG. 5B having the same operations as the elements or components described in FIG. 5A, and detailed description thereof will be omitted.

In addition, to potentially request the dataset from sensing in Step 512, it is also necessary to provide the expected requirements on the data, e.g., type, duration, quality and amount. An example of such requirements may include sensing QoS/requirements/KPIs that may need to be fulfilled from application/service perspective or in other implementations, may include sensing RS configurations or sensing mode by other entities/nodes. In some examples, the requested data type includes description of one or more target object and/or the associated mobility pattern and/or area of interest for which said data is requested.

Beside of the transmission with UE as mentioned in Step 506 in the FIG. 5A, it is also possible for the RAN node 102 to do mono-static sensing with dedicate sensing signal to collect the data.

At 512, the RAN node 102 may transmit the mono-static sensing signals utilizing the configurations and proper transmission scheme according to the NW-additional conditions. The configurations and proper transmission scheme for transmitting the mono-static sensing signals may be determined according to the NW-additional conditions based on the associated ID.

At 514, the RAN node 102 may receive and process the echo sensing signals to construct the dataset. The RAN node 102 may process the echo sensing signals to construct the dataset according to the requirements.

At 516, the RAN node 102 may transmit to the UE 103 the constructed dataset constructed by the data collection from the mono-static sensing with the ID. Then, the UE 104 may use the dataset to train a model. Alternatively, the UE 104 may use the dataset as the assistance information for an AI/ML functionality/model to do inference, e.g., reconstructed environment.

In some variation examples, the RAN transmission of the sensing signal may be received by the same RAN node 102 (monostatic sensing) and/or the other RAN nodes (bi-static sensing measurements) , or a combination thereof. In some such examples, the plurality of the obtained measurements by the multiple RAN nodes are collected (by said RAN node 102 or at the SF) and based on which the sensing data/result is derived. Said sensing result/data is then transmitted to the UE 104.

In some variation examples, said reference signal transmission by the RAN node 102 as part of the monostatic sensing and bi-static sensing are the same, i.e., the same RS transmission is used for UE reception and measurement (bi-static reception by the UE 104) as well as the RAN node (s) reception (monostatic RAN reception and/or bi-static RAN reception) .

With the procedures 500A and 500B, the configurations and conditions of the supported AI/ML functionalities/models are registered into the sensing function. The sensing function manages the configurations and conditions with a set of associated IDs, followed by assignment to the RAN node and UEs. The data collection procedure for an AI/ML functionality/model is performed with sensing assistance. The reference signals with proper transmission schemes for data collection are jointly configured by the SF and RAN node based on the assigned associated ID. The data collected from the reference signal or the dataset for the UE may be transmitted with an ID associated with the NW-side additional conditions assigned by SF. Thus, a method is proposed to collect the data for the AI/ML functionalities/models with sensing assistance to avoid disclosing NW-side additional conditions.

FIG. 6 illustrates an example of a device 600 that supports data collection for supported functionalities in accordance with aspects of the present disclosure. The device 600 may be an example of a UE 104 or a base station 102 or a network function 222 as described herein. The device 600 may support wireless communication with one or more network entities 102, UEs 104, or any combination thereof. The device 600 may include components for bi-directional communications including components for transmitting and receiving communications, such as a processor 602, a memory 604, a transceiver 606, and, optionally, an I/O controller 608. These components may be in electronic communication or otherwise coupled (e.g., operatively, communicatively, functionally, electronically, electrically) via one or more interfaces (e.g., buses) .

The processor 602, the memory 604, the transceiver 606, or various combinations thereof or various components thereof may be examples of means for performing various aspects of the present disclosure as described herein. For example, the processor 602, the memory 604, the transceiver 606, or various combinations or components thereof may support a method for performing one or more of the operations described herein.

In some implementations, the processor 602, the memory 604, the transceiver 606, or various combinations or components thereof may be implemented in hardware (e.g., in communications management circuitry) . The hardware may include a processor, a digital signal processor (DSP) , an application-specific integrated circuit (ASIC) , a field-programmable gate array (FPGA) or other programmable logic device, a discrete gate or transistor logic, discrete hardware components, or any combination thereof configured as or otherwise supporting a means for performing the functions described in the present disclosure. In some implementations, the processor 602 and the memory 604 coupled with the processor 602 may be configured to perform one or more of the functions described herein (e.g., executing, by the processor 602, instructions stored in the memory 604) .

For example, the processor 602 may support wireless communication at the device 600 in accordance with examples as disclosed herein. The processor 602 may be configured to operable to support a means for transmitting, to a network entity, a data collection request comprising an identity (ID) which the network entity or a core network function has associated with at least one network parameter for use in a supported functionality; and a means for receiving, from the network entity, a reference signal or a dataset in response to the data collection request being transmitted to the network entity, wherein the reference signal or the dataset is for use in the supported functionality and associated with the ID that is comprised in the data collection request.

In another example, the processor 602 may support wireless communication at the device 600 in accordance with examples as disclosed herein. The processor 602 may be configured to operable to support a means for receiving, from a user equipment (UE) , a data collection request comprising an identity (ID) which the network entity or a core network function has associated with at least one network parameter for use in a supported functionality; and a means for transmitting, to the UE, a reference signal or a dataset in response to the data collection request being received from the UE, wherein the reference signal or the dataset is for use in the supported functionality and associated with the ID that is comprised in the data collection request.

In a further example, the processor 602 may support wireless communication at the device 600 in accordance with examples as disclosed herein. The processor 602 may be configured to operable to support a means for determining an association between at least one network parameter for use in a supported functionality and an identity (ID) ; and a means for transmitting, to a user equipment (UE) , the ID associated with the supported functionality.

The processor 602 may include an intelligent hardware device (e.g., a general-purpose processor, a DSP, a CPU, a microcontroller, an ASIC, an FPGA, a programmable logic device, a discrete gate or transistor logic component, a discrete hardware component, or any combination thereof) . In some implementations, the processor 602 may be configured to operate a memory array using a memory controller. In some other implementations, a memory controller may be integrated into the processor 602. The processor 602 may be configured to execute computer-readable instructions stored in a memory (e.g., the memory 604) to cause the device 600 to perform various functions of the present disclosure.

The memory 604 may include random access memory (RAM) and read-only memory (ROM) . The memory 604 may store computer-readable, computer-executable code including instructions that, when executed by the processor 602 cause the device 600 to perform various functions described herein. The code may be stored in a non-transitory computer-readable medium such as system memory or another type of memory. In some implementations, the code may not be directly executable by the processor 602 but may cause a computer (e.g., when compiled and executed) to perform functions described herein. In some implementations, the memory 604 may include, among other things, a basic I/O system (BIOS) which may control basic hardware or software operation such as the interaction with peripheral components or devices.

The I/O controller 608 may manage input and output signals for the device 600. The I/O controller 608 may also manage peripherals not integrated into the device M02. In some implementations, the I/O controller 608 may represent a physical connection or port to an external peripheral. In some implementations, the I/O controller 608 may utilize an operating system such as or another known operating system. In some implementations, the I/O controller 608 may be implemented as part of a processor, such as the processor 606. In some implementations, a user may interact with the device 600 via the I/O controller 608 or via hardware components controlled by the I/O controller 608.

In some implementations, the device 600 may include a single antenna 610. However, in some other implementations, the device 600 may have more than one antenna 610 (i.e., multiple antennas) , including multiple antenna panels or antenna arrays, which may be capable of concurrently transmitting or receiving multiple wireless transmissions. The transceiver 606 may communicate bi-directionally, via the one or more antennas 610, wired, or wireless links as described herein. For example, the transceiver 606 may represent a wireless transceiver and may communicate bi-directionally with another wireless transceiver. The transceiver 606 may also include a modem to modulate the packets, to provide the modulated packets to one or more antennas 610 for transmission, and to demodulate packets received from the one or more antennas 610. The transceiver 606 may include one or more transmit chains, one or more receive chains, or a combination thereof.

A transmit chain may be configured to generate and transmit signals (e.g., control information, data, packets) . The transmit chain may include at least one modulator for modulating data onto a carrier signal, preparing the signal for transmission over a wireless medium. The at least one modulator may be configured to support one or more techniques such as amplitude modulation (AM) , frequency modulation (FM) , or digital modulation schemes like phase-shift keying (PSK) or quadrature amplitude modulation (QAM) . The transmit chain may also include at least one power amplifier configured to amplify the modulated signal to an appropriate power level suitable for transmission over the wireless medium. The transmit chain may also include one or more antennas 610 for transmitting the amplified signal into the air or wireless medium.

A receive chain may be configured to receive signals (e.g., control information, data, packets) over a wireless medium. For example, the receive chain may include one or more antennas 610 for receive the signal over the air or wireless medium. The receive chain may include at least one amplifier (e.g., a low-noise amplifier (LNA) ) configured to amplify the received signal. The receive chain may include at least one demodulator configured to demodulate the receive signal and obtain the transmitted data by reversing the modulation technique applied during transmission of the signal. The receive chain may include at least one decoder for decoding the processing the demodulated signal to receive the transmitted data.

FIG. 7 illustrates an example of a processor 700 that supports data collection for supported functionalities in accordance with aspects of the present disclosure. The processor 700 may be an example of a processor configured to perform various operations in accordance with examples as described herein. The processor 700 may include a controller 702 configured to perform various operations in accordance with examples as described herein. The processor 700 may optionally include at least one memory 704, such as L1/L2/L3 cache. Additionally, or alternatively, the processor 700 may optionally include one or more arithmetic-logic units (ALUs) 706. One or more of these components may be in electronic communication or otherwise coupled (e.g., operatively, communicatively, functionally, electronically, electrically) via one or more interfaces (e.g., buses) .

The processor 700 may be a processor chipset and include a protocol stack (e.g., a software stack) executed by the processor chipset to perform various operations (e.g., receiving, obtaining, retrieving, transmitting, outputting, forwarding, storing, determining, identifying, accessing, writing, reading) in accordance with examples as described herein. The processor chipset may include one or more cores, one or more caches (e.g., memory local to or included in the processor chipset (e.g., the processor 700) or other memory (e.g., random access memory (RAM) , read-only memory (ROM) , dynamic RAM (DRAM) , synchronous dynamic RAM (SDRAM) , static RAM (SRAM) , ferroelectric RAM (FeRAM) , magnetic RAM (MRAM) , resistive RAM (RRAM) , flash memory, phase change memory (PCM) , and others) .

The controller 702 may be configured to manage and coordinate various operations (e.g., signaling, receiving, obtaining, retrieving, transmitting, outputting, forwarding, storing, determining, identifying, accessing, writing, reading) of the processor 700 to cause the processor 700 to support various operations of a base station in accordance with examples as described herein. For example, the controller 702 may operate as a control unit of the processor 700, generating control signals that manage the operation of various components of the processor 700. These control signals include enabling or disabling functional units, selecting data paths, initiating memory access, and coordinating timing of operations.

The controller 702 may be configured to fetch (e.g., obtain, retrieve, receive) instructions from the memory 704 and determine subsequent instruction (s) to be executed to cause the processor 700 to support various operations in accordance with examples as described herein. The controller 702 may be configured to track memory address of instructions associated with the memory 704. The controller 702 may be configured to decode instructions to determine the operation to be performed and the operands involved. For example, the controller 702 may be configured to interpret the instruction and determine control signals to be output to other components of the processor 700 to cause the processor 700 to support various operations in accordance with examples as described herein. Additionally, or alternatively, the controller 702 may be configured to manage flow of data within the processor 700. The controller 702 may be configured to control transfer of data between registers, arithmetic logic units (ALUs) , and other functional units of the processor 700.

The memory 704 may include one or more caches (e.g., memory local to or included in the processor 700 or other memory, such RAM, ROM, DRAM, SDRAM, SRAM, MRAM, flash memory, etc. In some implementation, the memory 704 may reside within or on a processor chipset (e.g., local to the processor 700) . In some other implementations, the memory 704 may reside external to the processor chipset (e.g., remote to the processor 700) .

The memory 704 may store computer-readable, computer-executable code including instructions that, when executed by the processor 700, cause the processor 700 to perform various functions described herein. The code may be stored in a non-transitory computer-readable medium such as system memory or another type of memory. The controller 702 and/or the processor 700 may be configured to execute computer-readable instructions stored in the memory 704 to cause the processor 700 to perform various functions. For example, the processor 700 and/or the controller 702 may be coupled with or to the memory 704, and the processor 700, the controller 702, and the memory 704 may be configured to perform various functions described herein. In some examples, the processor 700 may include multiple processors and the memory 704 may include multiple memories. One or more of the multiple processors may be coupled with one or more of the multiple memories, which may, individually or collectively, be configured to perform various functions herein.

The one or more ALUs 706 may be configured to support various operations in accordance with examples as described herein. In some implementation, the one or more ALUs 706 may reside within or on a processor chipset (e.g., the processor 700) . In some other implementations, the one or more ALUs 706 may reside external to the processor chipset (e.g., the processor 700) . One or more ALUs 706 may perform one or more computations such as addition, subtraction, multiplication, and division on data. For example, one or more ALUs 706 may receive input operands and an operation code, which determines an operation to be executed. One or more ALUs 706 be configured with a variety of logical and arithmetic circuits, including adders, subtractors, shifters, and logic gates, to process and manipulate the data according to the operation. Additionally, or alternatively, the one or more ALUs 706 may support logical operations such as AND, OR, exclusive-OR (XOR) , not-OR (NOR) , and not-AND (NAND) , enabling the one or more ALUs 706 to handle conditional operations, comparisons, and bitwise operations.

For example, the processor 700 may support wireless communication in accordance with examples as disclosed herein. The processor 700 may be configured to or operable to support a means for transmitting, to a network entity, a data collection request comprising an identity (ID) which the network entity or a core network function has associated with at least one network parameter for use in a supported functionality; and a means for receiving, from the network entity, a reference signal or a dataset in response to the data collection request being transmitted to the network entity, wherein the reference signal or the dataset is for use in the supported functionality and associated with the ID that is comprised in the data collection request.

In another example, the processor 700 may support wireless communication in accordance with examples as disclosed herein. The processor 700 may be configured to or operable to support a means for receiving, from a user equipment (UE) , a data collection request comprising an identity (ID) which the network entity or a core network function has associated with at least one network parameter for use in a supported functionality; and a means for transmitting, to the UE, a reference signal or a dataset in response to the data collection request being received from the UE, wherein the reference signal or the dataset is for use in the supported functionality and associated with the ID that is comprised in the data collection request.

In a further example, the processor 700 may support wireless communication in accordance with examples as disclosed herein. The processor 700 may be configured to or operable to support a means for determining an association between at least one network parameter for use in a supported functionality and an identity (ID) ; and a means for transmitting, to a user equipment (UE) , the ID associated with the supported functionality.

FIG. 8 illustrates a flowchart of a method 800 that supports data collection for supported functionalities in accordance with aspects of the present disclosure. The operations of the method 800 may be implemented by a device or its components as described herein. For example, the operations of the method 800 may be performed by the UE 104 as described herein. In some implementations, the device may execute a set of instructions to control the function elements of the device to perform the described functions. Additionally, or alternatively, the device may perform aspects of the described functions using special-purpose hardware.

At 805, the method may include transmitting, to a network entity, a data collection request comprising an identity (ID) which the network entity or a core network function has associated with at least one network parameter for use in a supported functionality. The operations of 805 may be performed in accordance with examples as described herein. In some implementations, aspects of the operations of 805 may be performed by a device as described with reference to FIG. 1A.

At 810, the method may include receiving, from the network entity, a reference signal or a dataset in response to the data collection request being transmitted to the network entity, wherein the reference signal or the dataset is for use in the supported functionality and associated with the ID that is comprised in the data collection request. The operations of 810 may be performed in accordance with examples as described herein. In some implementations, aspects of the operations of 810 may be performed by a device as described with reference to FIG. 1A.

FIG. 9 illustrates a flowchart of a method 900 that supports data collection for supported functionalities in accordance with aspects of the present disclosure. The operations of the method 900 may be implemented by a device or its components as described herein. For example, the operations of the method 900 may be performed by the base station 104 as described herein. In some implementations, the device may execute a set of instructions to control the function elements of the device to perform the described functions. Additionally, or alternatively, the device may perform aspects of the described functions using special-purpose hardware.

At 905, the method may include receiving, from a user equipment (UE) , a data collection request comprising an identity (ID) which the network entity or a core network function has associated with at least one network parameter for use in a supported functionality. The operations of 905 may be performed in accordance with examples as described herein. In some implementations, aspects of the operations of 905 may be performed by a device as described with reference to FIG. 1A.

At 910, the method may include transmitting, to the UE, a reference signal or a dataset in response to the data collection request being received from the UE, wherein the reference signal or the dataset is for use in the supported functionality and associated with the ID that is comprised in the data collection request. The operations of 910 may be performed in accordance with examples as described herein. In some implementations, aspects of the operations of 910 may be performed by a device as described with reference to FIG. 1A.

FIG. 10 illustrates a flowchart of a method 1000 that supports data collection for supported functionalities in accordance with aspects of the present disclosure. The operations of the method 1000 may be implemented by a device or its components as described herein. For example, the operations of the method 1000 may be performed by the network function 222 as described herein. In some implementations, the device may execute a set of instructions to control the function elements of the device to perform the described functions. Additionally, or alternatively, the device may perform aspects of the described functions using special-purpose hardware.

At 1005, the method may include determining an association between at least one network parameter for use in a supported functionality and an identity (ID) . The operations of 1005 may be performed in accordance with examples as described herein. In some implementations, aspects of the operations of 1005 may be performed by a device as described with reference to FIG. 1A.

At 1010, the method may include transmitting, to a user equipment (UE) , the ID associated with the supported functionality. The operations of 1010 may be performed in accordance with examples as described herein. In some implementations, aspects of the operations of 1010 may be performed by a device as described with reference to FIG. 1A.

It should be noted that the methods described herein describes possible implementations, and that the operations and the steps may be rearranged or otherwise modified and that other implementations are possible. Further, aspects from two or more of the methods may be combined.

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

The functions described herein may be implemented in hardware, software executed by a processor, firmware, or any combination thereof. If implemented in software executed by a processor, the functions may be stored on or transmitted over as one or more instructions or code on a computer-readable medium. Other examples and implementations are within the scope of the disclosure and appended claims. For example, due to the nature of software, functions described herein may be implemented using software executed by a processor, hardware, firmware, hardwiring, or combinations of any of these. Features implementing functions may also be physically located at various positions, including being distributed such that portions of functions are implemented at different physical locations.

Computer-readable media includes both non-transitory computer storage media and communication media including any medium that facilitates transfer of a computer program from one place to another. A non-transitory storage medium may be any available medium that may be accessed by a general-purpose or special-purpose computer. By way of example, non-transitory computer-readable media may include RAM, ROM, electrically erasable programmable ROM (EEPROM) , flash memory, compact disk (CD) ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other non-transitory medium that may be used to carry or store desired program code means in the form of instructions or data structures and that may be accessed by a general-purpose or special-purpose computer, or a general-purpose or special-purpose processor.

As used herein, including in the claims, an article “a” before an element is unrestricted and understood to refer to “at least one” of those elements or “one or more” of those elements. The terms “a, ” “at least one, ” “one or more, ” and “at least one of one or more” may be interchangeable. As used herein, including in the claims, “or” as used in a list of items (e.g., a list of items prefaced by a phrase such as “at least one of” or “one or more of” or “one or both of” ) indicates an inclusive list such that, for example, a list of at least one of A, B, or C means A or B or C or AB or AC or BC or ABC (i.e., A and B and C) . Also, as used herein, the phrase “based on” shall not be construed as a reference to a closed set of conditions. For example, an example step that is described as “based on condition A” may be based on both a condition A and a condition B without departing from the scope of the present disclosure. In other words, as used herein, the phrase “based on” shall be construed in the same manner as the phrase “based at least in part on. Further, as used herein, including in the claims, a “set” may include one or more elements.

The description herein is provided to enable a person having ordinary skill in the art to make or use the disclosure. Various modifications to the disclosure will be apparent to a person having ordinary skill in the art, and the generic principles defined herein may be applied to other variations without departing from the scope of the disclosure. Thus, the disclosure is not limited to the examples and designs described herein but is to be accorded the broadest scope consistent with the principles and novel features disclosed herein.

Claims

A user equipment (UE) , comprising:

a processor; and

a transceiver coupled to the processor,

wherein the processor is configured to:

transmit, via the transceiver to a network entity, a data collection request comprising an identity (ID) which the network entity or a core network function has associated with at least one network parameter for use in a supported functionality; and

receive, via the transceiver from the network entity, a reference signal or a dataset in response to the data collection request being transmitted to the network entity, wherein the reference signal or the dataset is for use in the supported functionality and associated with the ID that is comprised in the data collection request.
The UE of claim 1, wherein the processor is further configured to:

transmit, via the transceiver to the network entity or to the core network function, a capability report of the UE, wherein the capability report comprises capability information on the supported functionality; and

receive, via the transceiver from the network entity or from the core network function, at least one ID associated with the supported functionality.
The UE of claim 2, wherein the capability report of the UE further comprises an indication of requirements on network parameters for use in the supported functionality.
The UE of claim 2, wherein the processor is further configured to:

receive, via the transceiver from the network entity or from the core network function, a capability request on supported functionalities, wherein the capability request on supported functionalities is carried in one of the following:

a signaling enquiring capability information of the UE;

a signaling enquiring capability information of the UE on sensing; or

a signaling enquiring capability information of the UE on artificial intelligence/machine learning (AI/ML) features.
The UE of claim 1 or 2, wherein the core network function is a sensing function or a function to support sensing.
A network entity, comprising:

a processor; and

a transceiver coupled to the processor,

wherein the processor is configured to:

receive, via the transceiver from a user equipment (UE) , a data collection request comprising an identity (ID) which the network entity or a core network function has associated with at least one network parameter for use in a supported functionality; and

transmit, via the transceiver to the UE, a reference signal or a dataset for data collection in response to the data collection request being received from the UE, wherein the reference signal or the dataset is for use in the supported functionality and associated with the ID that is comprised in the data collection request.
The network entity of claim 6, wherein the at least one network parameter comprises at least one of the following:

at least one network configuration for applying the supported functionality; or

at least one condition for applying the supported functionality.
The network entity of claim 6, wherein the processor is further configured to:

receive, via the transceiver from the UE, a capability report of the UE, wherein the capability report comprises capability information on the supported functionality and an indication of requirements on network parameters for use in the supported functionality; and

transmit, to a core network function, the capability information on the supported functionality and the network parameters for use in the supported functionality.
The network entity of claim 6, wherein the processor is further configured to:

receive, from a core network function, an indication of requirements on network parameters for use in the supported functionality; and

transmit, to the core network function, the network parameters for use in the supported functionality.
The network entity of claim 8 or 9, wherein the processor is further configured to:

receive, from the core network function, an indication of association between the at least one network parameter and the ID.
The network entity of claim 6, wherein the processor is further configured to:

construct the dataset based on the at least one network parameter.
The network entity of claim 11, wherein the processor is further configured to:

transmit, via the transceiver, a mono-static sensing signal based on the at least one network parameter;

receive, via the transceiver, the mono-static sensing signal; and

construct the dataset based on the received mono-static sensing signal.
A core network function, comprising:

at least one memory; and

at least one processor coupled with the at least one memory and configured to cause the core network function to:

determine an association between at least one network parameter for use in a supported functionality and an identity (ID) ; and

transmit, to a user equipment (UE) , the ID associated with the supported functionality.
The core network function of claim 13, wherein the processor is further configured to cause the core network function to:

receive, from a network entity, capability information on the supported functionality and network parameters for use in the supported functionality.
The core network function of claim 13, wherein the processor is further configured to cause the core network function to:

receive, from the UE, a capability report of the UE, wherein the capability report comprises capability information on the supported functionality and an indication of requirements on network parameters for use in the supported functionality;

transmit, to a network entity, the indication of the requirements on the network parameters for use in the supported functionality; and

receive, from the network entity, the network parameters for use in the supported functionality.
The core network function of claim 15, wherein the processor is further configured to cause the core network function to:

transmit, to the UE, a capability request on supported functionalities, wherein the capability request on supported functionalities is carried in one of the following:

a signaling enquiring capability information of the UE on sensing; or

a signaling enquiring capability information of the UE on artificial intelligence/machine learning (AI/ML) features.
The core network function of claim 14 or 15, wherein the processor is further configured to cause the core network function to:

associate the network parameters and at least one ID; and

transmit, to the UE, the at least one ID associated with the supported functionality.
The core network function of claim 17, wherein the processor is further configured to:

transmit, to the network entity, an indication of association between the at least one network parameter and the ID.
The core network function of claim 13, wherein the core network function is a sensing function or a function to support sensing.
A processor for wireless communication, comprising:

at least one memory; and

a controller coupled with the at least one memory and configured to cause the processor to:

determine an association between at least one network parameter for use in a supported functionality and an identity (ID) ; and

transmit, to a user equipment (UE) , the ID associated with the supported functionality.