Sensing in a wireless communication network

Abstract

Various aspects of the present disclosure relate to a network entity for wireless communication, comprising: at least one memory; and at least one processor coupled with the at least one memory and configured to cause the network entity to: receive a sensing request related to a sensing task; transmit a request message to an analytics network entity, the request message comprising an identifier indicating a request to provide analytics related to one or more performance parameters of one or more sensing nodes to perform the sensing task to sense a target in an environment of the one or more sensing nodes; and receive a response message from the analytics network entity, the response message comprising statistics and / or predictions related to the one or more performance parameters of the one or more sensing nodes.

Description

SENSING IN A WIRELESS COMMUNICATION NETWORKTECHNICAL FIELD

[0001] The present disclosure relates to wireless communications, and more specifically to sensing in a wireless communication network.BACKGROUND

[0002] A wireless communications system may include one or multiple network communication devices, such as base stations, which 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, or the like). 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)).

[0003] Wireless sensing technologies aim at acquiring information about a remote object or its environment and its characteristics without physically contacting it. This can be achieved by using a camera or radar. There are also investigations and solutions how communication technologies (e.g. 3GPP specified LTE or NR, but also WLAN) can be utilized for sensing.

[0004] There are also initiatives to enhance the cellular wireless communication systems, e.g. 5GS as specified by 3GPP, to also incorporate the wireless sensing. In other words, beside the traditional communication services, the wireless system can also perform a sensing task and report the result to an application, customer or vertical that is interested in the sensing result. The sensing can be also used internally in the wireless communication system to improve the network performance.SUMMARY

[0005] 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 constmed 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.

[0006] Some implementations of the method and apparatuses described herein may further include a network entity for wireless communication, comprising: at least one memory; and at least one processor coupled with the at least one memory and configured to cause the network entity to: receive a sensing request related to a sensing task; transmit a request message to an analytics network entity, the request message comprising an identifier indicating a request to provide analytics related to one or more performance parameters of one or more sensing nodes to perform the sensing task to sense a target in an environment of the one or more sensing nodes; and receive a response message from the analytics network entity, the response message comprising statistics and / or predictions related to the one or more performance parameters of the one or more sensing nodes.

[0007] The request message may comprise a description of the sensing task.

[0008] The request message may comprise an indication of one or more preferred sensing equipment types of node to perform the sensing task.

[0009] The request message may comprise at least one of: a target sensing area in which the sensing task is to be performed; and a geographical area in which the target is expected to travel.

[0010] The request message may comprise a time period during which the sensing task is to be performed.

[0011] The request message may comprise a target route of ordered locations.

[0012] The request message may comprise an identifier of one or more candidate user equipments operable to function as a sensing node to perform the sensing task.

[0013] In some implementations, the at least one processor is configured to cause the network entity, based on the received statistics and / or predictions, to: control at least one sensing node to perform the sensing task; or transmit a command to a sensing controller entity, the command instructing the sensing controller entity to control at least one sensing node to perform the sensing task.

[0014] Some implementations of the method and apparatuses described herein may further include a processor for wireless communication, comprising: at least one controller coupled with at least one memory and configured to cause the processor to: obtain a sensing request related to a sensing task; output a request message for transmission to an analytics network entity, the request message comprising an identifier indicating a request to provide analytics related to one or more performance parameters of one or more sensing nodes to perform the sensing task to sense a target in an environment of the one or more sensing nodes; and obtain a response message transmitted from the analytics network entity, the response message comprising statistics and / or predictions related to the one or more performance parameters of the one or more sensing nodes.

[0015] Some implementations of the methods and apparatuses described herein may further include a method performed by a network entity, the method comprising: receiving a sensing request related to a sensing task; transmitting a request message to an analytics network entity, the request message comprising an identifier indicating a request to provide analytics related to one or more performance parameters of one or more sensing nodes to perform the sensing task to sense a target in an environment of the one or more sensing nodes;and receiving a response message from the analytics network entity, the response message comprising statistics and / or predictions related to the one or more performance parameters of the one or more sensing nodes.

[0016] Some implementations of the method and apparatuses described herein may further include a network entity for wireless communication, comprising: at least one memory; and at least one processor coupled with the at least one memory and configured to cause the network entity to: receive a request message from a requesting network entity, the request message comprising an identifier indicating a request to provide analytics related to one or more performance parameters of one or more sensing nodes to perform the sensing task to sense a target in an environment of the one or more sensing nodes; obtain input data from one or more network nodes; determine statistics and / or predictions related to the one or more performance parameters of the one or more sensing nodes based on the request message and the input data; and transmit a response message to the requesting network entity, the response message comprising the statistics and / or predictions.

[0017] The input data may comprise one or more indications of: radio performance and / or radio utilization of sensing nodes; radio configuration information of sensing nodes; and / or line of sight between the network nodes in the environment.

[0018] The input data may comprise one or more indications of node positioning in the environment.

[0019] The input data may comprise one or more indications of: throughput of user equipments in the environment; mobility profile of user equipments in the environment; and / or hardware and software capabilities of user equipments in the environment.

[0020] The statistics and / or predictions may include sensing measurements.

[0021] The statistics and / or predictions may include non-3GPP measurements related to an application function.

[0022] The statistics and / or predictions may include sensing measurements related to non-3GPP sensing nodes.

[0023] The statistics and / or predictions may include, for radio access nodes among the one or more sensing nodes, indications of one or more of: positioning, mobility and radio performance; user equipment capabilities; and user equipment support related to non-3GPP sensing measurements.

[0024] The statistics and / or predictions may include, for user equipments among the one or more sensing nodes, indications of one or more of: positioning, mobility and radio performance; user equipment capabilities; and user equipment support related to non-3GPP sensing measurements.

[0025] The input data may be obtained from one or more of: a radio access node in a radio access network; a sensing function network entity; and an application function network entity, wherein the input data comprises non-3GPP sensing performance data.

[0026] Some implementations of the method and apparatuses described herein may further include a method performed by a network entity, the method comprising: receiving a request message from a requesting network entity, the request message comprising an identifier indicating a request to provide analytics related to one or more performance parameters of one or more sensing nodes to perform the sensing task to sense a target in an environment of the one or more sensing nodes; obtaining input data from one or more network nodes; determining statistics and / or predictions related to the one or more performance parameters of the one or more sensing nodes based on the request message and the input data; and transmitting a response message to the requesting network entity, the response message comprising the statistics and / or predictions.

[0027] Some implementations of the method and apparatuses described herein may further include a processor for wireless communication, comprising: at least one controller coupled with at least one memory and configured to cause the processor to: obtain a request message from a requesting network entity, the request message comprising an identifier indicating a request to provide analytics related to one or more performance parameters of one or more sensing nodes to perform the sensing task to sense a target in an environment of the one or more sensing nodes; obtain input data from one or more network nodes; determining statistics and / or predictions related to the one or more performance parameters of the one or more sensing nodes based on the request message and the input data; and outputa response message to the requesting network entity, the response message comprising the statistics and / or predictions.BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 illustrates an example of a wireless communications system in accordance with aspects of the present disclosure.

[0029] Figure 2 illustrates an example of an Analytics Consumer Network Entity 200 in accordance with aspects of the present disclosure.

[0030] Figure 3 illustrates an example of a processor 300 in accordance with aspects of the present disclosure.

[0031] Figure 4 illustrates an example of an Analytics Network Entity 400 in accordance with aspects of the present disclosure.

[0032] Figures 5A. 5B, 5C, 5D and 5E illustrate network architectures comprising a core network with a sensing function;

[0033] Figure 6 illustrates the input data source from which analytics network entity 400 may receive input data from, and recipients of data output by, the analytics network entity 400, when the analytics network entity 400 is a network entity configured to perform a Network Data Analytics Function (NWDAF).

[0034] Figure 7 illustrates a signalling flowchart of a sensing preparation process.

[0035] Figure 8 illustrates a signalling flowchart of a sensing preparation process involving RAN sensing nodes.

[0036] Figure 9 illustrates a signalling flowchart of a sensing preparation process focusing on non-3GPP nodes.

[0037] Figure 10 illustrates a flowchart of method performed an Analytics Consumer Network Entity in accordance with aspects of the present disclosure.

[0038] Figure 11 illustrates a flowchart of method performed by an Analytics Network Entity in accordance with aspects of the present disclosure.DETAILED DESCRIPTION

[0039] Integrated sensing and communication (ISAC), enables a new feature for mobile systems that allows the inclusion of sensing capabilities, i.e., radar alike sensing, in a communication network. Such sensing capabilities can be used to obtain information related to the shape, size, orientation, speed, location, distances, or relative motion between objects using New Radio (NR) Radio Frequency (RF) signals and, in some cases, previously defined information available in Evolved Packet Core (EPC) and / or Evolved Universal Terrestrial Radio Access (E-UTRA) as described in TR 22.837 vl9.0.0.

[0040] ISAC is expected to impact future cellular wireless networks, both as a mechanism to improve the network performance as well as an enabler to serve vertical usecases, wherein radio / RF signals are utilized to obtain information of the surrounding environment. Integrated sensing and communication may enhance 5G core architecture by introducing a new Sensing Function (SF) or a new logical SF, i.e., co-located with another Network Function (NF). The main benefit of IS AC in 5G is the fact that its operation is based on the existing wireless infrastructure, which provides coverage to leverage the benefits of radio signal sensing as well as on the use of 5G core that can assist in collecting further information related to the UEs, policies, analytics and can facilitate sensing exposure towards external network consumers, e.g., Application Functions (AFs). Currently, TR 22.837 V19.0.0 and 5G advanced IMT-2020, introduce some preliminary requirements related to requesting sensing services and reporting sensing results, i.e., processed sensing output information. The assumption is that a sensing consumer shall issue a sensing request towards the SF indicating the sensing service type, the sensing area or sensing target also providing the details of the sensing object (e.g., dimension details), the desired format of the sensing result and the expected sensing confidence, as well as how to transport the sensing results, i.e., which protocol to use and via which node, the sensing results shall be exposed to the sensing consumer. The sensing consumer may be any application that benefits from sensing results. As mere examples, the sensing consumer may be a vehicular application (e.g. for enhanced driving), an application related to drones (to ensure the drone navigation correctness), a smart city application related to safety (for elder people assistance), etc.

[0041] When a sensing subscription or request is issued towards a SF there is no knowledge of (i) which base stations or UEs or non-3GPP sensing nodes have sufficient resources and can be involved and have the highest possibility or potential to support the sensing task as a transmission sensing node (referred to herein as a sensing Tx node) or as a reception sensing node (referred to herein as a sensing Rx node) and for how long; or (ii) if sensing can be supported for a moving sensing object / situation or UE that carries the sensing target with a certain speed and direction, i.e., no knowledge of which base stations or UEs or non-3GPP sensing nodes can support the sensing task at the time when the sensing object / situation or UE will transverse specific locations and with which KPIs.

[0042] Since sensing relates to an activity or event of an object or UE for a specific future time window, and further in view of the time and space dynamics that may impact the sensing service availability and / or the supported network KPIs, the current performance measurements (PMs) that reflect the state of resource availability at the time instance of checking may not be sufficient to properly determine the supported sensing service / task.

[0043] Embodiments of the invention relate to the transmission of a request message from a requesting network entity(e.g. an analytics consumer network entity) to an analytics network entity, the request message comprising an identifier indicating a request to provide analytics related to one or more performance parameters of one or more sensing nodes to perform a sensing task to sense a target in an environment of the one or more sensing nodes. The analytics network entity provides a response comprising statistics and / or predictions related to the one or more performance parameters of the one or more sensing nodes. This advantageously provides the requesting network entity with statistics and / or predictions that can be used to ensure that, when the sensing task is performed in the future, it is performed in an optimal way. The statistics and / or predictions can cover a target sensing area in which the sensing task is to be performed and / or a geographical area in which the target is expected to travel.

[0044] In the following text, reference is made to sensing nodes, which may be transmission sensing nodes, referred to as a sensing Tx node, or a reception sensing node, referred to a sensing Rx node. A sensing node may be both a Tx node and an Rx node at the same time. When reference is made to a sensing node, it may be a Tx node, an Rx node or anode performing both transmission and reception. Sensing nodes may be any network node, including base stations (for example a gNB), user equipments and non-3GPP sensing devices.

[0045] The sensing nodes transmit and receive sensing signals, which are defined as transmissions on the 3 GPP radio interface that can be used for sensing purposes in order to sense a sensing target. Data, referred to as 3 GPP sensing data, is collected from the received sensing signals. This data is derived from 3GPP radio signals impacted (e.g., reflected, refracted, diffracted) by an object or environment of interest for sensing purposes, and may be processed within the 5G system. The process of collecting sensing data is referred to as a sensing measurement process. The outcome, in terms of processed 3 GPP sensing data, requested by the customer, is referred to as a sensing result.

[0046] A sensing target may be an object or a target area. When the sensing target is an object, the object may be a passive object e.g. an object which is not registered with the mobile network or cannot report sensing measurements to the network (a non-SIM device). For example, the passive object may be a person or a vehicle. In these examples, a UE may be attached to the object or may be inside the object. When the sensing target is an object, the object may be an active object e.g. an object which is registered with the mobile network and can report sensing measurements to the network. When the sensing target is a target area, in one example the target area may be a room of a house for intruder detection e.g., a target area is sensed to detect if and when an external object “appears”.

[0047] When performing a sensing task, radio / RF signals are utilized to obtain information of the surrounding environment via (i) transmission of a sensing signal, e.g., a sensing reference signal (RS), from a radio or UE entity, referred to herein as a sensing Tx node, (ii) reception of the reflections / echoes of the transmitted sensing excitation signal from the environment by a radio or UE entity, referred to herein as a sensing Rx node. A sensing Rx node may be a non-3GPP sensor (e.g. a radar or a camera) with capability of providing non-3GPP sensing data. A sensing Rx node may be a 3 GPP node (e.g., a UE or a RAN node) connected to a non-3GPP sensor which can obtain, process, and transfer the non-3GPP sensing data obtained by the non-3GPP sensor to other 3GPP nodes / entities, and (iii) processing of the received reflections and inferring relevant information from the environment.

[0048] The following terms are used herein:3GPP sensing data: Data derived from 3GPP radio signals impacted (e.g., reflected, refracted, diffracted) by an object or environment of interest for sensing purposes, and optionally processed within the 5G system.Sensing result: processed 3GPP sensing data requested by a sensing consumer.Sensing service area: a service area where sensing services would solely rely on infrastructures and sensing technologies that can be assumed to be present anywhere where 5G is present. This includes both indoor and outdoor environments.Sensing target area: an area that needs to be sensed by deriving the dynamic characteristics of the area from any moving obstacles (e.g., cars, human, animals) from the impacted (e.g., reflected, refracted, diffracted) wireless signals. There may be two kinds of target area: (a) Static sensing target area: a pre-defined area that does not move from the sensing transmitter’s perspective; or (b) Moving sensing target area: a trusted zone with a target that moves from the sensing transmitter’s perspective. non-3GPP sensor: a sensor which performs sensing using a technology not specified by the 3 GPP standard organization. non-3GPP sensing data: data obtained by a non-3GPP sensor.Sensing group: a set of sensing nodes (e.g. comprising at least one sensing Tx node and at least one sensing Rx node) e.g. in a particular location for which sensing measurement data can be collected synchronously.

[0049] Aspects of the present disclosure are described in the context of a wireless communications system.

[0050] Figure 1 illustrates an example of a wireless communications system 100 in accordance with aspects of the present disclosure. The wireless communications system 100 may include one or more NE 102, one or more UE 104, and a core network (CN) 106. 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 NR network, such as a 5G network, a 5G- Advanced (5G-A) network, or a 5G ultrawideband (5G-UWB) network. In otherimplementations, 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, for example, 6G. 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.

[0051] The one or more NE 102 may be dispersed throughout a geographic region to form the wireless communications system 100. One or more of the NE 102 described herein may be or include or may be referred to as a network node, a base station, a network element, a network function, a network entity, a radio access network (RAN), a NodeB, an eNodeB (eNB), a next-generation NodeB (gNB), or other suitable terminology. An NE 102 and a UE 104 may communicate via a communication link, which may be a wireless or wired connection. For example, an NE 102 and a UE 104 may perform wireless communication (e.g., receive signaling, transmit signaling) over a Uu interface.

[0052] An NE 102 may provide a geographic coverage area for which the NE 102 may support services for one or more UEs 104 within the geographic coverage area. For example, an NE 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, an NE 102 may be moveable, for example, a satellite associated with a non-terrestrial network (NTN). 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 may be associated with different NE 102.

[0053] The one or more UE 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 remote unit, a mobile device, a wireless device, a remote device, a subscriber device, a transmitter device, a receiver 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 anInternet-of- Things (loT) device, an Internet-of-Everything (loE) device, or machine-type communication (MTC) device, among other examples.

[0054] A UE 104 may be able to support wireless communication directly with other UEs104 over a communication link. 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.

[0055] An NE 102 may support communications with the CN 106, or with another NE 102, or both. For example, an NE 102 may interface with other NE 102 or the CN 106 through one or more backhaul links (e.g., SI, N2, N2, or network interface). In some implementations, the NE 102 may communicate with each other directly. In some other implementations, the NE 102 may communicate with each other or indirectly (e.g., via the CN 106. In some implementations, one or more NE 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).

[0056] The CN 106 may support user authentication, access authorization, tracking, connectivity, and other access, routing, or mobility functions. The CN 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 NE 102 associated with the CN 106.

[0057] The CN 106 may communicate with a packet data network over one or more backhaul links (e.g., via an SI, N2, N2, or another network interface). The packet data network may include an application server. In some implementations, one or more UEs 104 may communicate with the application server. A UE 104 may establish a session (e.g., a protocol data unit (PDU) session, or the like) with the CN 106 via an NE 102. The CN 106 may route traffic (e.g., control information, data, and the like) between the UE 104 and the application server 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 CN 106 (e.g., one or more network functions of the CN 106).

[0058] In the wireless communications system 100, the NEs 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 NEs 102 and the UEs 104 may support different resource structures. For example, the NEs 102 and the UEs 104 may support different frame structures. In some implementations, such as in 4G, the NEs 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 NEs 102 and the UEs 104 may support various frame structures (i.e., multiple frame structures). The NEs 102 and the UEs 104 may support various frame structures based on one or more numerologies.

[0059] 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., / r=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., / r=0) associated with the first subcarrier spacing (e.g., 15 kHz) may utilize one slot per subframe. A second numerology (e.g., / r=l) may be associated with a second subcarrier spacing (e.g., 30 kHz) and a normal cyclic prefix. A third numerology (e.g., / r=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., / r=3) may be associated with a fourth subcarrier spacing (e.g., 120kHz) and a normal cyclic prefix. A fifth numerology (e.g., / r=4) may be associated with a fifth subcarrier spacing (e.g., 240 kHz) and a normal cyclic prefix.

[0060] 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.

[0061] 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., / r=0, jU=l, / r=2, jU=3, / r=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., / i =0) associated with a first subcarrier spacing (e.g., 15 kHz) may be used interchangeably between subframes and slots.

[0062] 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 designationsFR1 (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 NEs 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 NEs 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 NEs 102 and the UEs 104, among other equipment or devices for short-range, high data rate capabilities.

[0063] 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., / r=0), which includes 15 kHz subcarrier spacing; a second numerology (e.g., / r=l), which includes 30 kHz subcarrier spacing; and a third numerology (e.g., / r=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., / r=2), which includes 60 kHz subcarrier spacing; and a fourth numerology (e.g., / r=3), which includes 120 kHz subcarrier spacing.

[0064] Figure 2 illustrates an example of an analytics consumer network entity 200 in accordance with aspects of the present disclosure. The analytics consumer network entity 200 may include a processor 202, a memory 204, a controller 206, and a transceiver 208. The processor 202, the memory 204, the controller 206, or the transceiver 208, or various combinations thereof or various components thereof may be examples of means for performing various aspects of the present disclosure as described herein. These components may be coupled (e.g., operatively, communicatively, functionally, electronically, electrically) via one or more interfaces.

[0065] The processor 202, the memory 204, the controller 206, or the transceiver 208, or various combinations or components thereof may be implemented in hardware (e.g., circuitry). The hardware may include a processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), or other programmable logic device, or any combination thereof configured as or otherwise supporting a means for performing the functions described in the present disclosure.

[0066] The processor 202 may include an intelligent hardware device (e.g., a general- purpose processor, a DSP, a CPU, an ASIC, an FPGA, or any combination thereof). In some implementations, the processor 202 may be configured to operate the memory 204. In some other implementations, the memory 204 may be integrated into the processor 202. The processor 202 may be configured to execute computer-readable instructions stored in the memory 204 to cause the analytics consumer network entity 200 to perform various functions of the present disclosure.

[0067] The memory 204 may include volatile or non-volatile memory. The memory 204 may store computer-readable, computer-executable code including instructions when executed by the processor 202 cause the analytics consumer network entity 200 to perform various functions described herein. The code may be stored in a n on-transitory computer- readable medium such the memory 204 or another type of memory. 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.

[0068] In some implementations, the processor 202 and the memory 204 coupled with the processor 202 may be configured to cause the analytics consumer network entity 200 to perform one or more of the functions described herein (e.g., executing, by the processor 202, instructions stored in the memory 204). For example, the processor 202 may support wireless communication at the analytics consumer network entity 200 in accordance with examples as disclosed herein. The analytics consumer network entity 200 may be configured to support a means for receiving a sensing request related to a sensing task; transmitting a request message to an analytics network entity, the request message comprising an identifier indicating a request to provide analytics related to one or more performance parameters of one or more sensing nodes to perform the sensing task to sense a target in an environment of the one or more sensing nodes; and receiving a response message from the analytics network entity, the response message comprising statistics and / or predictions related to the one or more performance parameters of the one or more sensing nodes.

[0069] The controller 206 may manage input and output signals for the analytics consumer network entity 200. The controller 206 may also manage peripherals not integrated into the analytics consumer network entity 200. In some implementations, the controller 206 may utilize an operating system such as iOS®, ANDROID®, WINDOWS®, or other operating systems. In some implementations, the controller 206 may be implemented as part of the processor 202.

[0070] In some implementations, the analytics consumer network entity 200 may include at least one transceiver 208. In some other implementations, the analytics consumer network entity 200 may have more than one transceiver 208. The transceiver 208 may represent a wireless transceiver. The transceiver 208 may include one or more receiver chains 210, one or more transmitter chains 212, or a combination thereof.

[0071] A receiver chain 210 may be configured to receive signals (e.g., control information, data, packets) over a wireless medium. For example, the receiver chain 210 may include one or more antennas for receive the signal over the air or wireless medium. The receiver chain 210 may include at least one amplifier (e.g., a low-noise amplifier (LNA)) configured to amplify the received signal. The receiver chain 210 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 receiver chain 210 may include at least one decoder for decoding the processing the demodulated signal to receive the transmitted data.

[0072] A transmitter chain 212 may be configured to generate and transmit signals (e.g., control information, data, packets). The transmitter chain 212 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 transmitter chain 212 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 transmitter chain 212 may also include one or more antennas for transmitting the amplified signal into the air or wireless medium.

[0073] The analytics consumer network entity 200 may be a single network node / device or a plurality of network nodes / devices.

[0074] The analytics consumer network entity 200 may be a network entity in the CN 106. For example, the analytics consumer network entity 200 may be a network entity configured to implement a core network (CN) Network Function (NF) or a RAN related function or an Application Function (AF) or a Management Function (MF). The analytics consumer network entity 200 may be a network entity configured to implement a sensing function (SF), which may or not be in the CN 106. The analytics consumer network entity 200 may be network entity configured to implement Sensing Function Control-plane (SF-C) functionality (described below with reference to Figures 5A and 5B) or be a logical SF, i.e., co-located with other CN NFs, e.g., LMF (as in Figure 5C).

[0075] The analytics consumer network entity 200 may be a node (e.g. an aggregator node or base station) in a radio access network coupled to the CN 106.

[0076] In other implementations, the analytics consumer network entity 200 may be a sensing controller entity responsible for management of a sensing task (i.e. to handle the process of selecting and deselecting sensing nodes). The sensing controller entity may be a logical network function located at a network entity, wherein the network entity may be a single node, such as a base station, a core network node or a user equipment, or the logical network function may be distributed over a number of network nodes. The sensing controller entity may therefore be any of a RAN node, a CU-gNB, a DU-gNB, a gNB, a network entity configured to implement a sensing function (SF), a network entity configured to implement a management task e.g. a Location Management Function (LMF), and a UE, or distributed over any combination of these devices. In some embodiments, the logical function of the sensing controller entity spans across a 5G core network function and a gNB.

[0077] Figure 3 illustrates an example of a processor 300 in accordance with aspects of the present disclosure. The processor 300 may be an example of a processor configured to perform various operations in accordance with examples as described herein. The processor 300 may include a controller 302 configured to perform various operations in accordance with examples as described herein. The processor 300 may optionally include at least one memory 304, which may be, for example, an L1 / L2 / L3 cache. Additionally, or alternatively,the processor 300 may optionally include one or more arithmetic-logic units (ALUs) 306. 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).

[0078] The processor 300 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 300) 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).

[0079] The controller 302 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 300 to cause the processor 300 to support various operations in accordance with examples as described herein. For example, the controller 302 may operate as a control unit of the processor 300, generating control signals that manage the operation of various components of the processor 300. These control signals include enabling or disabling functional units, selecting data paths, initiating memory access, and coordinating timing of operations.

[0080] The controller 302 may be configured to fetch (e.g., obtain, retrieve, receive) instructions from the memory 304 and determine subsequent instruction(s) to be executed to cause the processor 300 to support various operations in accordance with examples as described herein. The controller 302 may be configured to track memory address of instructions associated with the memory 304. The controller 302 may be configured to decode instructions to determine the operation to be performed and the operands involved. For example, the controller 302 may be configured to interpret the instruction and determine control signals to be output to other components of the processor 300 to cause the processor300 to support various operations in accordance with examples as described herein. Additionally, or alternatively, the controller 302 may be configured to manage flow of data within the processor 300. The controller 302 may be configured to control transfer of data between registers, arithmetic logic units (ALUs), and other functional units of the processor 300.

[0081] The memory 304 may include one or more caches (e.g., memory local to or included in the processor 300 or other memory, such RAM, ROM, DRAM, SDRAM, SRAM, MRAM, flash memory, etc. In some implementations, the memory 304 may reside within or on a processor chipset (e.g., local to the processor 300). In some other implementations, the memory 304 may reside external to the processor chipset (e.g., remote to the processor 300).

[0082] The memory 304 may store computer-readable, computer-executable code including instructions that, when executed by the processor 300, cause the processor 300 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 302 and / or the processor 300 may be configured to execute computer-readable instructions stored in the memory 304 to cause the processor 300 to perform various functions. For example, the processor 300 and / or the controller 302 may be coupled with or to the memory 304, the processor 300, the controller 302, and the memory 304 may be configured to perform various functions described herein. In some examples, the processor 300 may include multiple processors and the memory 304 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.

[0083] The one or more ALUs 306 may be configured to support various operations in accordance with examples as described herein. In some implementations, the one or more ALUs 306 may reside within or on a processor chipset (e.g., the processor 300). In some other implementations, the one or more ALUs 306 may reside external to the processor chipset (e.g., the processor 300). One or more ALUs 306 may perform one or more computations such as addition, subtraction, multiplication, and division on data. For example, one or more ALUs 306 may receive input operands and an operation code, which determinesan operation to be executed. One or more ALUs 306 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 306 may support logical operations such as AND, OR, exclusive-OR (XOR), not-OR (NOR), and not-AND (NAND), enabling the one or more ALUs 306 to handle conditional operations, comparisons, and bitwise operations.

[0084] The processor 300 may support wireless communication in accordance with examples as disclosed herein. The analytics consumer network entity 200 may comprise the processor 300. That is, the processor 300 may be configured to or operable to support a means for obtaining a sensing request related to a sensing task; outputting a request message for transmission to an analytics network entity, the request message comprising an identifier indicating a request to provide analytics related to one or more performance parameters of one or more sensing nodes to perform the sensing task to sense a target in an environment of the one or more sensing nodes; and obtaining a response message from the analytics network entity, the response message comprising statistics and / or predictions related to the one or more performance parameters of the one or more sensing nodes.

[0085] Figure 4 illustrates an example of an analytics network entity 400 in accordance with aspects of the present disclosure. The analytics network entity 400 may include a processor 402, a memory 404, a controller 406, and a transceiver 408. The processor 402, the memory 404, the controller 406, or the transceiver 408, or various combinations thereof or various components thereof may be examples of means for performing various aspects of the present disclosure as described herein. These components may be coupled (e.g., operatively, communicatively, functionally, electronically, electrically) via one or more interfaces.

[0086] The processor 402, the memory 404, the controller 406, or the transceiver 408, or various combinations or components thereof may be implemented in hardware (e.g., circuitry). The hardware may include a processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), or other programmable logic device, or any combination thereof configured as or otherwise supporting a means for performing the functions described in the present disclosure.

[0087] The processor 402 may include an intelligent hardware device (e.g., a general- purpose processor, a DSP, a CPU, an ASIC, an FPGA, or any combination thereof). In some implementations, the processor 402 may be configured to operate the memory 404. In some other implementations, the memory 404 may be integrated into the processor 402. The processor 402 may be configured to execute computer-readable instructions stored in the memory 404 to cause the analytics network entity 400 to perform various functions of the present disclosure.

[0088] The memory 404 may include volatile or non-volatile memory. The memory 404 may store computer-readable, computer-executable code including instructions when executed by the processor 402 cause the analytics network entity 400 to perform various functions described herein. The code may be stored in a non-transitory computer-readable medium such the memory 404 or another type of memory. 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.

[0089] In some implementations, the processor 402 and the memory 404 coupled with the processor 402 may be configured to cause the analytics network entity 400 to perform one or more of the functions described herein (e.g., executing, by the processor 402, instructions stored in the memory 404). For example, the processor 402 may support wireless communication at the analytics network entity 400 in accordance with examples as disclosed herein. The analytics network entity 400 may be configured to support a means for receiving a request message from a requesting network entity, the request message comprising an identifier indicating a request to provide analytics related to one or more performance parameters of one or more sensing nodes to perform the sensing task to sense a target in an environment of the one or more sensing nodes; obtaining input data for sensing analytics from one or more network nodes; determining statistics and / or predictions related to the one or more performance parameters of the one or more sensing nodes based on the request message and the input data; and transmitting a response message to the requesting network entity, the response message comprising the statistics and / or predictions.

[0090] The controller 406 may manage input and output signals for the analytics network entity 400. The controller 406 may also manage peripherals not integrated into the analytics network entity 400. In some implementations, the controller 406 may utilize an operating system such as iOS®, ANDROID®, WINDOWS®, or other operating systems. In some implementations, the controller 406 may be implemented as part of the processor 402.

[0091] In some implementations, the analytics network entity 400 may include at least one transceiver 408. In some other implementations, the analytics network entity 400 may have more than one transceiver 408. The transceiver 408 may represent a wireless transceiver and / or a wired transceiver (e.g. for wireline communications). The transceiver 408 may include one or more receiver chains 410, one or more transmitter chains 412, or a combination thereof.

[0092] The analytics network entity 400 may comprise the processor 300. That is, the processor 300 may be configured to or operable to support a means for obtaining a request message from a requesting network entity, the request message comprising an identifier indicating a request to provide analytics related to one or more performance parameters of one or more sensing nodes to perform the sensing task to sense a target in an environment of the one or more sensing nodes; obtaining input data from one or more network nodes; determining statistics and / or predictions related to the one or more performance parameters of the one or more sensing nodes based on the request message and the input data; and outputting a response message to the requesting network entity, the response message comprising the statistics and / or predictions..

[0093] The analytics network entity 400 may be a single network node / device or a plurality of network nodes / devices.

[0094] The analytics network entity 400 may be a network entity in the CN 106. For example, the analytics network entity 400 may be a network entity configured to implement a Network Data Analytics Function (NWDAF). In other examples, the analytics network entity 400 may be a core network entity configured to implement an Application Function (AF) or a Management Function (MF). Alternatively, the analytics network entity 400 may be a node (e.g. an aggregator node or base station) in a radio access network coupled to the CN 106.

[0095] Whilst embodiments are described herein with reference to the analytics network entity 400 being a core network entity i.e. an NWDAF the analytics network entity 400 can also be realized in the 5G management plane, i.e., by defining a new Management Data Analytics (MDA) type, or in the application plane, i.e., by defining a new Application Data Analytics Enablement Service (AD AES).

[0096] The sensing described herein may relate to (i) a target UE, (ii) objects without network connectivity, i.e.., with no sim-card or (iii) for obtaining the environment characteristics of a target area, e.g., sensing weather conditions to realize if it is raining. The sensing described may use the radio signals from one or more base stations, i.e., sensing group whose location is known and whose sensing measurement data can be collected synchronously. The collected sensing data may then be processed (e.g., at the UE, at the RAN, at the core network or a combination thereof), which determines the sensing target and its corresponding characteristics. ISAC may enhance 5G core architecture by introducing a new Sensing Function (SF) or a new logical SF co-located with another CN NF, e.g., LMF.

[0097] Figure 5A shows a possible network architecture where a network entity implements a SF located in the Core network (CN) and can be connected to multiple other CN network functions.

[0098] In the network architecture shown in Figure 5 A, the SF appears as a dedicated network function (NF) handling both: (i) the sensing control plane aspects such as the interaction with the sensing consumer via Network Exposure Function (NEF) and information exchange with other NFs, for gathering UE information, (i.e., from the Access and Mobility Management Function (AMF), Unified Data Management (UDM), Location Management Function (LMF), UE related policies from the Policy Control Function (PCF), and analytics from the Network Data Analytics Function (NWDAF)) and (ii) the sensing radio signals for performing the analysis or prediction for determining the sensing target.

[0099] The network architecture comprises network interfaces for sensing NS1 to NS8 between the SF and other network function (NFs) in the Core network control plane. The architecture further comprises an Access and Mobility Management Function (AMF) connected to a UE and to the (R)AN via N1 and N2 respectively, a Unified Data Management (UDM) connected to the AMF via the N8 interface. The AMF is connected to the SF via NS 1. The SF is further connected to a Network Data Analytics Function (NWDAF), LocationManagement Function (LMF), Policy Control Function (PCF), Network Exposure Function (NEF) and a User Plane Function (UPF). The NEF is also connected to the PCF via the N5 interface and to an Application Function (AF) via N33. The AF may represent a sensing consumer and may send a sensing request to the SF via the NEF.

[0100] Other network architectures to enhance the 5G core by introducing a SF as a dedicated or logical Network Function (NF) are considered in IMT-2020.

[0101] For example, as illustrated in Figure 5B, the SF has two dedicated NF counter parts: (i) Sensing Function Control-plane (SF-C) functionality that handles the control plane aspects as described above and (ii) Sensing Function User-plane (SF-U) functionality that is responsible for collecting the sensing radio signals via the user plane, i.e., via the Radio Access Network (RAN) and User Plane Function (UPF). The idea of this architecture is to split and offload heavy data volumes associated with sensing radio signals to the user plane to ensure light traffic, i.e., singling, in the control plane.

[0102] Figure 5C illustrates another architecture in which the SF is collocated with the LMF (Fig.lc) appears as a logical NF embedded in the Location Management Function (LMF) to perform sensing taking advantage of the knowledge of a UE location.

[0103] Figure 5D illustrates a loose coupling ISAC network architecture, in which the SF is independent of the 5G core, i.e., typically used for local field scenarios or private networks, and the interaction with the 5G core is minimal. The main idea is to use SF close to the RAN, i.e., collect and process the sensing radio signals locally, and interact with 5G core for the purpose of exposure via NEF, for getting the UE location from the AMF and for analytics (NWDAF).

[0104] Figure 5E illustrates an arrangement similar to that of Figure 5 A, but instead of showing the interfaces explicitly, it shows a service based version.

[0105] As explained above, the analytics network entity 400 may correspond to the NWDAF. The NWDAF may consider the support of various analytics types, e.g., UE Mobility, User Data Congestion, NF load, and others as elaborated in TS 23.288 v.18.2.0 which are distinct and can be selected by an analytics consumer network entity 200 using the Analytics ID. Each NWDAF may support one or more Analytics IDs and may have the roleof (i) inference called NWDAF containing AnLF (or simply AnLF), or (ii) training called NWDAF containing MTLF (or simply MTLF) or (iii) both AnLF and MTLF. An AnLF that supports a specific Analytics ID inference subscribes to a corresponding MTLF that is responsible for training.

[0106] Figure 6 illustrates the various variants of how the NWDAF may be configured. Figure 6 further illustrates the sources of input data which the NWDAF may receive. In particular, as shown in Figure 6 the NWDAF may receive input data from one or more of: 5G core NFs, AFs, 5G core data repositories (e.g., an Analytics Data Repository Function (ADRF)), and the Operations, Administration and Maintenance (0AM) e.g. Management Service (MnS) consumer or Management Function (MF).

[0107] Figure 6 further illustrates the recipients of the analytics data output by the NWDAF. In particular, as shown in Figure 6 the NWDAF may output analytics data to one or more of: 5G core NFs, AFs, 5G core data repositories, e.g., ADRF, and the 0AM (MnS Consumer or MF). Optionally, a Data Collection AF (DCAF), or a Data Collection Coordination Function (DCCF) and a Messaging Framework Adaptor Function (MFAF), may be involved to distribute and collect data towards or from various data sources.

[0108] Embodiments of the invention relate to requesting and providing analytics statistics and / or predictions for the purpose of a sensing task. The Analytics Network Entity 400 provides statistics and / or predictions based on an identifier (e.g. an Analytics ID) which indicates a request to provide analytics related to one or more performance parameters of one or more sensing nodes to perform the sensing task to sense a target in an environment (i.e. area) of the one or more sensing nodes. The Analytics Network Entity 400 can help to predict the expected base stations, TRPs, and / or UEs and / or non-3GPP sensing nodes that should be involved in sensing in the sensing task e.g. for a specific future time schedule and / or for a given geographical area of interest.

[0109] Reference is now made to Figure 7 which illustrates a signalling flowchart of a sensing preparation process which involves gathering input data and analysing this data to obtain statistics and / or predictions.

[0110] The sensing preparation process commences with the analytics consumer network entity 200 receiving a sensing consumer request (otherwise referred to herein as a sensing request) from a sensing consumer. The reception of the sensing consumer request by the analytics consumer network entity 200 is not shown in Figure 7.

[0111] Before processing the received sensing request the analytics consumer network entity 200 in turn requests or subscribes to the analytics network entity 400 at step S702 in order to receive statistics and / or predictions associated with sensing nodes for enabling a sensing task e.g. statistics and / or predictions related to the sensing performance of base stations, and / or TRPs, and / or other RAN nodes and / or UEs, and / or non-3GPP sensing nodes. Such statistics and / or predictions can assist the analytics consumer network entity 200 to determine the most appropriate sensing process.

[0112] At step S702, the analytics consumer network entity 200 transmits a request message to the analytics network entity 400. The request message transmitted at step S702 comprises an identifier (e.g. an Analytics ID) indicating a request to provide analytics related to one or more performance parameters of one or more sensing nodes to perform the sensing task to sense a target in an environment of the one or more sensing nodes with respect to a given sensing identifier (Sensing ID) which identifies a specific sensing task. The analytics consumer network entity 200 obtains the sensing identifier (e.g. a Sensing ID) from the sensing consumer request. The analytics consumer network entity 200 obtains the identifier (e.g. an Analytics ID) and the details of the analytics entity that supports this identifier from a network repository that holds network function information, e.g., Network Repository Function (NRF), by issuing a discovery request as specified in TS 23.502.

[0113] As a mere example, the Analytics ID may be “Sensing Preparation”. It will be appreciated that this only forms an example, and any other identifier name can be used to describe a request related to analytics associated with a sensing task, e.g. to describe a request related to the analysis of statistics and / or predictions of the performance and capabilities of base stations and / or UEs and / or non-3GPP sensing nodes to be involved in a sensing task. Other examples of the Analytics ID include, but are not limited to, , “sensing node selection”, “sensing feasibility check”, “sensing applicability”, etc.

[0114] The request message transmitted at step S702 may be a subscription request in order for the analytics consumer network entity 200 to subscribe to analytics for a specified time duration. That is the request message may be an “Nnwdaf_AnalyticsSubscription_Subscribe” message comprising the Analytics ID.

[0115] Alternatively, the request message transmitted at step S702 may be an on-demand request in order for the analytics consumer network entity 200 to receive a single set of statistics and / or predictions. That is the request message may be an “Nnwdaf_AnalyticsInfo_Request” message comprising the Analytics ID.

[0116] The request message transmitted at step S702 may comprise a description of the sensing task. The description of the sensing task included in the request message transmitted at step S702 may be included in the sensing consumer request received from the sensing consumer. Alternatively, the description of the sensing task may be derived by the analytics consumer network entity 200 from information in the sensing consumer request received from the sensing consumer. The description of the sensing task may comprise a sensing identifier (Sensing ID) which identifies a specific sensing task.

[0117] Alternatively or additionally, the request message transmitted at step S702 may comprise an indication of one or more preferred sensing equipment types of sensing nodes to perform the sensing task (i.e. the target of Analytics Reporting). The preferred sensing equipment type of sensing nodes to perform the sensing task may identify one or any combination of: a single UE e.g. by way of a Subscription Permanent Identifier (SUPI) or multiple UEs. This may include a list of UEs (e.g. an Internal Group ID); a single RAN node (e.g., gNB, TRPs, WiFi) or a list of gNBs. The list of gNBs may exclude gNBs not eligible for a specific sensing task due to legal issues related to their location, e.g., a gNB close to a military camp may be used for sensing weather conditions, e.g., rain, but not for sensing vehicles or flying objects; a single non-3GPP sensor (e.g., CCTV, or another sensor) or a list of non-3GPP sensors indicated using the corresponding address of the related AF, or the UE(SUPI), or group of UEs (an Internal Group ID), in case UE(s) are equipped with the non-3GPP sensor and the user give consent to use non-3GPP sensing capabilities.

[0118] Alternatively or additionally, the request message transmitted at step S702 may comprise Analytics Filter Information which may comprise one or any combination of: a target sensing area in which the sensing task is to be performed i.e. an area of interest. The area of interested may be defined by one or more tracking areas (TA), cells, or geographical coordinates which restricts the area in focus. This may include an optional indication of the Radio Access Technology (RAT) that shall be used in case of multiple options. an identifier of one or more candidate user equipments operable to function as a sensing node to perform the sensing task. That is, the Analytics Filter Information may specify UE(s) of interest. If this parameter is specified, sensing is applied only in relation with certain UE(s) or on certain UE(s), i.e., wherein sensing target that is co-located or contained, e.g., perform sensing for an object inside a lorry track.A geographical area in which the target is expected to travel (i.e. a target route of ordered locations). For example, a specified location or path where one or more sensing Tx / Rx nodes may have coverage and are involved in a sensing task within an Area of Interest.

[0119] Alternatively or additionally, the request message transmitted at step S702 may comprise a time period (i.e. in the future) during which the sensing task is to be performed. The time period may be specified by a start and end time. The time period may be included in, or be derivable from information in, the sensing consumer request.

[0120] The request message transmitted at step S702 may comprise a request for one or more specific performance parameters associated with the analytics data of the statistics and / or predictions associated with the sensing task. This may include a:Preferred level of accuracy of the analytics (e.g. at a per positioning or velocity level) defined for example as “Low", "Medium", "High" or "Highest”. The accuracy of the analytics may be an accuracy of a positioning estimate which describes the closeness of the measured sensing result (i.e., position) of the target object to its true position value. It can befurther derived into a horizontal sensing accuracy - referring to the sensing result error in a 2D reference or horizontal plane, and into a vertical sensing accuracy - referring to the sensing result error on the vertical axis or altitude. The accuracy of the recommendation may be an accuracy of a velocity estimate which describes the closeness of the measured sensing result (i.e., velocity) of the target object’s velocity to its true velocity.Preferred sensing resolution per sensing task, considering a sensing target object or situation. The sensing resolution describes the minimum difference in the measured magnitude of target objects (e.g., range, velocity) to be allowed to detect objects in different magnitude.Preferred refreshing rate at which sensing results are generated. The refreshing rate is the inverse of the time elapsed between two successive sensing results.A confidence level which describes the percentage of all the possible measured sensing results that can be expected to include the true sensing result considering the accuracy.Preferred amount of resources consumed for sensing.Preferred false alarm ratio / false alarm probability in detecting an erroneous event or target object or situation per sensing task. The false alarm probability denotes the ratio of detecting an event that does not represent the characteristics of a target object or environment over all events during any predetermined period when the 5G system attempts to acquire a sensing result. It applies only to binary sensing results.Preferred missed-detection ratio / missed detection probability of missing an event, target object or situation per sensing task. The missed detection probability denotes the ratio of missing an event to acquire a sensing result over all events during any predetermined period when the 5G system attempts to acquire a sensing result. It applies only to binary sensing results.Preferred sensing reliability related to sensing nodes.Preferred sensing service latency for reporting sensing results. The sensing service latency refers to the time elapsed between the event triggering the determination of the sensing result and the availability of the sensing result to a sensing consumer.Time until when analytics information is needed, i.e., it indicates to the analytics network entity 400 the latest time the analytics consumer network entity 200 expects to receive statistics and / or predictions provided by the analytics network entity 400.Time periodicity the analytics consumer network entity 200 expects to receive statistics and / or predictions provided by the analytics network entity 400 (applicable only for the case of a subscription to analytics).Context of use of the analytics, i.e., sensing use case or purpose of sensing (e.g., to identify a car, or the route taken by a drone, etc.), to select the most relevant AI / ML model.Notification target address to send the analytics results in case an analytics consumer network entity 200 is issuing the request message at step S702 on behalf of another node.Preferred order of results of a list of sensing KPIs, e.g., in ascending, or descending order.Maximum number of obj ects, requested by the analytics consumer network entity 200 to limit the number of objects in a list of the analytics response (per response).

[0121] In embodiments in which the request message transmitted at step S702 is a subscription request in order for the analytics consumer network entity 200 to subscribe to analytics results for a specified time duration, the request message may additionally or alternatively include at least one or more of the following parameters:A Notification Correlation ID, used for identifying and verifying analytics transactions that belong to the same subscription.Analytics target period which indicates the time-period (e.g., start and end time), either in the past or future over which the statistics and / or predictions are requested.Reporting thresholds (e.g., distance) or events (e.g., when sensing target get out of the Area of Interest or when sensing target starts moving), that indicate conditions on the level to be reached for respective analytics information, e.g., per sensing KPI, to be notified by the analytics network entity 400.Output strategy which indicates the relevant factors for determining when the analytics are reported e.g. a (i) binary strategy that reports analytics results only when a preferred level of accuracy is reached or when threshold conditions are met, or (ii) gradient strategy that reports analytics results according with the periodicity defined in the analytics reporting irrespective if the preferred level of accuracy has been reached.The parameters described above may be included in, or be derivable from information in, the sensing consumer request.

[0122] The analytics network entity 400 obtains input data from one or more network nodes. The input data may relate to the performance of one or more sensing nodes, which may act as a sensing Tx node and / or a sensing Rx node to perform the sensing task. As explained in more detail below, the input data is used by the analytics network entity 400 to determine statistics and / or predictions related to the performance parameters of the one or more sensing nodes. The analytics network entity 400 may regularly obtain the input data. Additionally or alternatively, the analytics network entity 400 may obtain the input data upon request e.g. in response to receiving the request message from the analytics consumer network entity 200.

[0123] The analytics network entity 400 may obtain input data comprising base station (e.g. gNB) input data (i.e. from base stations that may act as a sensing Tx node and / or a sensing Rx node to perform the sensing task). The base station input data may comprise one or more of: base station status information, base station resource information, base station sensing capabilities, and measurements that the base station can perform. The analytics network entity 400 may obtain the base station input data from the sources listed in Table 1. In embodiments whereby the request message transmitted at step S702 specifies an Area of Interest, the base station sensing data relates to base stations in the Area of Interest.Table 1 Base station data (e.g. RAN status, resource utilization and performance information collected by an NWDAF)

[0124] Alternatively or additionally, the analytics network entity 400 may obtain input data comprising UE input data (i.e. from UEs that may act as a sensing Tx node and / or a sensing Rx node to perform the sensing task). The UE input data may comprise one or more of: UE status information, UE resource information, UE sensing capabilities, and measurements that the UE can perform. The analytics network entity 400 may obtain the UE input data from the sources listed in Table 2. In embodiments whereby the request message transmitted at step S702 specifies an Area of Interest, the UE input data relates to UEs in the Area of Interest.Table 2 UE data (e.g. UE status, resource utilization and performance information collected by an NWDAF)

[0125] Alternatively or additionally, the analytics network entity 400 may obtain input data comprising non-3GPP input data (i.e. from non-3GPP sensors that may act as a sensing Tx node and / or a sensing Rx node to perform the sensing task). The non-3GPP input data may comprise one or more of: non-3GPP sensor status information, non-3GPP sensor resource information, non-3GPP sensor sensing capabilities, and measurements that the non- 3GPP sensor can perform. The analytics network entity 400 may obtain the non-3GPP sensor input data from the sources listed in Table 3. In embodiments whereby the request message transmitted at step S702 specifies an Area of Interest, the non-3GPP sensor input data relates to non-3GPP sensors in the Area of Interest.Table 3 Non-3GPP sensor data (e.g. Non-3GPP sensor status and performance information collected by an NWDAF)

[0126] It will be appreciated that a combination of all three types of input data can be used to enable fix sensing scenarios, in which radio nodes and UE may act as sensing Tx and sensing Rx nodes consuming also non-3GPP sensing data (e.g., CCTV).

[0127] In any of the above examples the measurement reporting may be done per measurement instance (i.e., comprising a single measurement) or as an average of group of measurement instances, or as per UE or per group of UEs or per TRP wherein the reported measurements may be accompanied with a UE ID or TRP ID or UE group ID associated with the reported measurements.

[0128] The measurement mentioned above shall also be considered as measurement that can be performed between TRPs, since measurement that reflect the relation of TRPs matter to assist sensing services.

[0129] Reported measurements from UEs are beneficial if collected from static UEs since these UEs can report more accurately LON / NLOS, because they are not moving and having other more dynamic nature causes of LOS disruption.

[0130] Some further or future measurements or even some the already mentioned measurements can also be performed between TRPs.

[0131] It shall be noted that for the non-3GPP measurement provision, an AF can reside on a cloud platform together with a RAN or an AF can reside in another location that covers the same area with one or more RAN nodes. Alternatively, a the non-3GPP measurement can be provided by a UE (e.g., using it camera or another sensor), which can feed an associated AF. An AF can be in the mobile operator premises or outside the mobile operator premises and belong to a 3rdparty (i.e., untrusted).

[0132] The AF location (i.e., contact address or identifier) is assumed to be known to the NWDAF (i.e., there is no expectation that the NWDAF would discover the AF) by preconfiguring specific NWDAFs with the access information to contact the specific AFs from which non-3GPP data would be required.

[0133] In embodiments whereby the request message transmitted at step S702 specifies an Area of Interest, at step S704 the analytics network entity 400 may transmit a request message to a network entity configured to perform a Network Repository Function (NRF) 704. That is, the analytics network entity 400 may transmit a Nnrf_NFDisovery_Request (LMF / SF Region) message to the NRF 704. The request message transmitted at step S704 requests information on the network entity configured to perform a Location Management Function (LMF) 706 and the network entity configured to perform a Sensing Function (SF) 708 belonging to the geographical region(s) that include the Area of Interest. At step S706 the analytics network entity 400 receives a response message from the NRF 704. That is, the analytics network entity 400 may receive a Nnrf_NFDisovery_Request Response message from the NRF 704 from which it discovers from the NRF 704, the LMF 706 and SF 708 belonging to the geographical region(s) that include the Area of Interest.

[0134] Once the analytics network entity 400 has discovered the LMF 706, at step S708 the analytics network entity 400 may transmit a request message to the LMF 706. That is, the analytics network entity 400 may transmit a Nlmf LocationDetermineLocation Request message to the LMF 706. The request message transmitted at step S708 may requestinformation on the location of “representative” UEs (i.e., that may be selected randomly among UEs that provide consent to collect measurements) in the Area of Interest that may provide LOS / NLOS measurements and / or have a perception for indoor / outdoor measurements. Alternatively or in addition, the request message at step S708 may include a request to determine the location of UEs included in the request from the analytics consumer. The request message transmitted at step S708 may be a subscription request in order for the analytics consumer network entity 400 to subscribe to the LMF, or the request message transmitted at step S708 may be an on-demand request. At step S710, the LMF 706 determines the UE identifiers (e.g. GPSI or SUPI) of the UEs in the Area of Interest that may assist the sensing task. The LMF 706 may determine the UE identifiers from a User Data manager (UDM) as per TS 23.273. At step S712 the analytics network entity 400 receives a response message from the LMF 706. That is, the analytics network entity 400 may receive a Nlmf LocationDetermineLocation Response message from the LMF 706. The response message received at step S712 includes the location of the UEs. The response message received at step S712 may additionally include the respective LOS / NLOS and indoor / outdoor measurements.

[0135] At step S714 the analytics network entity 400 may transmit a request message to the SF 708. The SF 708 may have been discovered based on the response message received at step S706. The request message transmitted at step S714 requests sensing measurements (e.g., related to reflection paths) and sensing service performance (e.g., false alarm ratio and missed-detection ratio) indications. The request message transmitted at step S714 may be a subscription request in order for the analytics consumer network entity 400 to subscribe to the SF 708. For example, the request message transmitted at step S714 may be a Nsf_EventExposure_Subscribe message. Alternatively, the request message transmitted at step S714 may be an on-demand request. For example, the request message transmitted at step S714 may be a Nsf_SensingPerfromance_Request message.

[0136] At step S716 the analytics network entity 400 receives a response message from the SF 708 in which the SF 708 provides the requested sensing measurements and sensing service performance indications. In embodiments whereby the request message transmitted at step S714 is a subscription request, the response message received at step S716 may be a Nsf_EventExposure_Notify message. In embodiments whereby the request messagetransmited at step S714 is an on-demand request, the response message received at step S716 may be a Nsf SensingPerfromance Request response message.

[0137] At step S718 the analytics network entity 400 may transmit a request message to a network entity configured to implement a Network Data Analytics Function (NWDAF) 710. The request message transmitted at step S716 requests mobility analytics (e.g. for a UE ID set provided by the analytics consumer network entity 200) and / or performance analytics of a WLAN that can be involved in the sensing task. As noted above, the analytics network entity 400 itself may be a NWDAF. In these embodiments, step S718 is optional assuming that the NWDAF 400 does not support at the same time the Analytics ID = UE Mobility Analytics or the Analytics ID = WLAN Performance Analytics in the same Area of Interest.

[0138] The request message transmited at step S718 may be a subscription request in order for the analytics consumer network entity 400 to subscribe to the NWDAF 710. For example, the request message transmited at step S718 may be a Nnwdaf_AnalyticsSubscription_Subscribe message. Alternatively, the request message transmited at step S718 may be an on-demand request. For example, the request message transmitted at step S718 may be an Nnwdaf_AnalyticsInfo_Request message.

[0139] At step S720 the analytics network entity 400 receives a response message from the NWDAF 710 in which the NWDAF 710 provides mobility predictions for each UE ID of the given set and / or providing predictions related to WLAN performance. In embodiments whereby the request message transmited at step S718 is a subscription request, the response message received at step S720 may be a Nnwdaf_AnalyticsSubscription_Notify message. In embodiments whereby the request message transmited at step S714 is an on-demand request, the response message received at step S716 may be an Nnwdaf_AnalyticsInfo_Request response message.

[0140] Based on these mobility predictions the analytics network entity 400 can assess the suitability of each UE to act as a sensing Tx node and / or a sensing Rx node with respect to the target or sensing situation and to determine the risk of disruption for an ongoing sensing task due to UE mobility. Similarly, the analytics network entity 400 can assess the suitability of specific WLAN access points to act as a sensing Tx node and / or sensing Rx node (if applicable).

[0141] At step S722, the analytics consumer network entity 400 may transmit a request message to RAN 702 (e.g. to one or more nodes in the RAN). The request message transmitted at step S722 may be transmitted to one or more base stations (e.g. gNBs) in the Area of Interest. The request message transmitted at step S722 may request radio specific measurements, UE based measurements, or NLOS / LOS indications that can be used for the purpose of sensing. At step S722 the analytics consumer network entity 400 may receive a response message from the RAN 702 comprising the requested information.

[0142] At step S724 the analytics network entity 400 may transmit a request message to a network entity configured to implement an Application Function (AF) 712. The request message transmitted at step S724 requests data associated with at least one non-3GPP sensor (referred to herein as non -3 GPP data) from the AF 712. This non -3 GPP data can be collected by the AF 712 from standalone sensing applications, e.g., CCTV, or it can be supplied to the AF 712 by UEs after collecting non -3 GPP data via the application layer (assuming user consent).

[0143] The request message transmitted at step S724 may be a subscription request in order for the analytics consumer network entity 400 to subscribe to the AF 712. For example, the request message transmitted at step S724 may be an Naf_EventExposure_Subscribe message. Alternatively, the request message transmitted at step S724 may be an on-demand request. For example, the request message transmitted at step S724 may be an Naf_EventExposure_Request message.

[0144] At step S726 the analytics network entity 400 receives a response message from the AF 712 in which the AF 712 provides the requested non-3GPP data. In embodiments whereby the request message transmitted at step S724 is a subscription request, the response message received at step S726 may be a Naf_EventExposure_Notify message. In embodiments whereby the request message transmitted at step S724 is an on-demand request, the response message received at step S726 may be an Naf_EventExposure_Request response message.

[0145] The analytics network entity 400 may be preconfigured with the details of the AF 712 (address and capabilities - e.g., coverage area). In case the AF 712 resides in an untrusted domain then the interaction with the analytics network entity 400 may be handled via NEF (Nnef_EventExposure) as per TS 23.502 clause 5.2.6.2.

[0146] At step S728 the analytics network entity 400 may transmit a request message to a network entity configured to implement the 0AM 714. The request message transmitted at step S728 may request or subscribe to 0AM services performance measurements (PMs) (e.g. as per TS 28.552), and / or KPIs (e.g. as per TS 28.554), and / or Management Data Analytics (MDA) output (e.g. as per TS 28.104) in order to receive radio specific measurements including radio status, information on radio quality, information on radio coverage, and / or information on radio resource utilization related to the Area of Interest. The 0 AM 704 may also provide WLAN PMs if the respective measurements are collected (e.g. as per clause 5.1.1.3.3 of TS 37.320). At step S728 the analytics consumer network entity 400 may receive a response message from the 0AM 714 comprising the requested information.

[0147] Above we have described how the analytics network entity 400 may obtain different types of input data. At step S730 the analytics network entity 400 uses the obtained input data and information in the request message received at step S702 in order to carry out an analysis of the input data and generate statistics and / or predictions based on this analysis.

[0148] At step S732 the analytics consumer network entity 200 receives a response message from the analytics network entity 400 in which the SF analytics network entity 400 provides the requested statistics and / or predictions for the area of interest. In embodiments whereby the request message transmitted at step S732 is a subscription request, the response message received at step S732 may be a Nnwdaf_AnalyticsSubscription_Notify message. In embodiments whereby the request message transmitted at step S714 is an on-demand request, the response message received at step S732 may be a Nnwdaf_AnalyticsInfo_Request response message.

[0149] If the analytics network entity 400 encounters a problem in determining and / or providing the requested statistics and / or predictions in connection with the sensing task, then the analytics network entity 400 may issue a sensing termination request to inform the analytics consumer network entity 200 that the subscription shall be cancelled or that the response can no longer be provided. For error response or error notification, a revised waiting time to indicate to the analytics consumer network entity 200 a revised waiting period can be issued by the analytics network entity 400. Each analytics network entity 400 may include this as part of error response or error notification to "Time when analytics information is needed" as described in TS 23.288 v.18.2.0 clause 6.2.5. Revised waiting time is theminimum time interval recommended by analytics network entity 400 to use as "Time when analytics information is needed" for similar future recommendation requests / subscriptions.

[0150] The statistics and predictions can be broken down by sensing node type. These are expressed in tables 4 to 9 below. The Sensing Preparation statistics for RAN nodes are shown in Table 4, while those related to UEs statistics are illustrated in Table 5. The Sensing Preparation statistics for non-3GPP sensing nodes wherein UEs are used to produce non- 3 GPP data are also included, while statistics for standalone non-3GPP sensing nodes are shown in Table 6. Similarly, the Sensing Preparation predictions for RAN nodes are shown in Table 7, for UE predictions are illustrated in Table 8 including also non-3GPP sensing where UEs are used and for standalone non-3GPP sensing nodes are illustrated in Table 9. It shall be noted that RAN, non-3GPP node and UE statistics and / or predictions can be combined.Table 4: Examples of Sensing KPIs statistics for sensing Tx / Rx RAN nodesTable 5: Examples of Sensing KPIs statistics for sensing Tx / Rx UEsTable 6: Examples of Sensing KPIs statistics for non-3GPP sensing nodesTable 7: examples of Sensing KPIs predictions for sensing Tx / Rx RAN nodes.Table 8: examples of sensing KPIs predictions for sensing Tx / Rx UEsTable 9: Examples of sensing KPIs statistics for non-3GPP sensing nodes

[0151] Thus, given the time schedule and area of interest, the results of the analysis can provide statistics and / or predictions of: i) expected KPIs for a sensing task / service and / or LOS / NLOS in an area of interest for sensing and / or indication of location (e.g., indoor / outdoor) of a sensing service / KPI availability, and / or other sensing specific radio PMs, e.g., RSRPP and RSRQP, at different times. ii) expected sensing service KPIs, in terms of, e.g., latency, availability, accuracy, false alarm ratio, missed-detection ratio, or other exemplified service KPIs. iii) expected resource availability of the sensing Tx, sensing Rx, base stations and / or other radio related nodes and / or serving area. iv) expected reliability, i.e., against faults or errors, of the sensing Tx, sensing Rx, base stations and / or other radio related nodes and / or serving area. v) suitability of UEs as sensing Tx and / or sensing Rx nodes considering radio conditions and / or throughput, UE mobility, and UE sensing capabilities in terms of the expected sensing area, sensing specific radio performance and / or sensing service measurements. vi) suitability of a non-3GPP sensing node considering the type of measurements, the measurement format, vendor interoperability and mobile operator accessibility, as well as the sensing performance in terms of latency, availability rate, sensing coverage, pause status, reliability. vii) expected radio resources consumed for a sensing task (e.g., how many sensing Tx / Rx nodes, how much of bandwidth, energy, processing power, time, etc. is needed)and / or with respect to a specific KPI (e.g., for a specific sensing task with a given KPI).

[0152] As described above with reference to figure 7, the sensing preparation process has been described in the context of providing statistics and / or predictions from some or all of RAN Sensing Tx / Rx nodes, UEs as Sensing Tx / Rx nodes and as non-3GPP sensing nodes that provide data, (e.g., using the mobile device camera) to an AF, and standalone non-3GPP sensing nodes, i.e., as a separate entity that collects input data, e.g., CCTV. In some embodiments it may be that the analysis is only required for a subset of these. Figure 8 illustrates a signalling flowchart of a sensing preparation process which involves gathering input data and analysing this data to obtain statistics and / or predictions for RAN Sensing Tx / Rx nodes. Figure 9 illustrates a signalling flowchart of a sensing preparation process which involves gathering input data and analysing this data to obtain statistics and / or predictions for standalone non-3GPP sensing nodes. In both Figure 8 and Figure 9 the steps are a subset of those of Figure 7 and therefore labelled with the same reference numerals.

[0153] It should be noted that Figure 9 includes an additional step, S924, in which the analytics network entity 400 determines if non-3GPP input data is needed and from which AF it should be collected. This determination takes place before the request at step S724 is transmitted.

[0154] Figure 10 illustrates a flowchart of a method in accordance with aspects of the present disclosure. The operations of the method may be implemented by a analytics consumer network entity 200 as described herein. In some implementations, the analytics consumer network entity 200 may execute a set of instructions to control the function elements of the analytics consumer network entity 200 to perform the described functions.

[0155] At step SI 002, the method may include receiving a sensing request related to a sensing task. The operations of step SI 002 may be performed in accordance with examples as described herein. In some implementations, aspects of the operations of step SI 002 may be performed by an analytics consumer network entity 200 as described with reference to Figure 2.

[0156] At step SI 004, the method may include transmitting a request message to an analytics network entity, the request message comprising an identifier indicating a request toprovide analytics related to one or more performance parameters of one or more sensing nodes to perform the sensing task to sense a target in an environment of the one or more sensing nodes. The operations of step S804 may be performed in accordance with examples as described herein. In some implementations, aspects of the operations of step SI 004 may be performed by an analytics consumer network entity 200 as described with reference to Figure 2.

[0157] At step SI 006, the method may include receiving a response message from the analytics network entity, the response message comprising statistics and / or predictions related to the one or more performance parameters of the one or more sensing nodes. The operations of step SI 006 may be performed in accordance with examples as described herein. In some implementations, aspects of the operations of step SI 006 may be performed by an analytics consumer network entity 200 as described with reference to Figure 2.

[0158] It should be noted that the method described herein describes a possible implementation, and that the operations and the steps may be rearranged or otherwise modified and that other implementations are possible.

[0159] Figure 11 illustrates a flowchart of a method in accordance with aspects of the present disclosure. The operations of the method may be implemented by an analytics network entity 400 as described herein. In some implementations, the analytics network entity 400 may execute a set of instructions to control the function elements of the analytics network entity 400 to perform the described functions.

[0160] At step SI 102, the method may include receiving a request message from a requesting network entity, the request message comprising an identifier indicating a request to provides analytics related to one or more performance parameters of one or more sensing nodes to perform a sensing task to sense a target in an environment of the one or more sensing nodes. The operations of step SI 102 may be performed in accordance with examples as described herein. In some implementations, aspects of the operations of step SI 102 may be performed by an analytics network entity 400 as described with reference to Figure 4.

[0161] At step SI 104, the method may include obtaining input data from one or more network nodes. The operations of step SI 104 may be performed in accordance with examplesas described herein. In some implementations, aspects of the operations of step SI 104 may be performed by an analytics network entity 400 as described with reference to Figure 4.

[0162] At step SI 106, the method may include determining statistics and / or predictions related to the one or more performance parameters of the one or more sensing nodes based on the request message and the input data. The operations of step SI 106 may be performed in accordance with examples as described herein. In some implementations, aspects of the operations of step SI 106 may be performed by an analytics network entity 400 as described with reference to Figure 4.

[0163] At step SI 108, the method may include transmitting a response message to the requesting network entity, the response message comprising the statistics and / or predictions. The operations of step SI 108 may be performed in accordance with examples as described herein. In some implementations, aspects of the operations of step SI 108 may be performed by an analytics network entity 400 as described with reference to Figure 4.

[0164] It should be noted that the method described herein describes a possible implementation, and that the operations and the steps may be rearranged or otherwise modified and that other implementations are possible.

[0165] 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

What is claimed is:

1. A network entity for wireless communication, comprising: at least one memory; and at least one processor coupled with the at least one memory and configured to cause the network entity to: receive a sensing request related to a sensing task; transmit a request message to an analytics network entity, the request message comprising an identifier indicating a request to provide analytics related to one or more performance parameters of one or more sensing nodes to perform the sensing task to sense a target in an environment of the one or more sensing nodes; and receive a response message from the analytics network entity, the response message comprising statistics and / or predictions related to the one or more performance parameters of the one or more sensing nodes.

2. The network entity according to claim 1 , wherein the request message comprises a description of the sensing task.

3. The network entity according to claim 1 or 2, wherein the request message comprises an indication of one or more preferred sensing equipment types of node to perform the sensing task.

4. The network entity according to any preceding claim, wherein the request message comprises at least one of: a target sensing area in which the sensing task is to be performed; and a geographical area in which the target is expected to travel.

5. The network entity according to any preceding claim, wherein the request message comprises a time period during which the sensing task is to be performed.

6. The network entity according to any preceding claim, wherein the request message comprises a target route of ordered locations.

7. The network entity according to any preceding claim, wherein the request message comprises an identifier of one or more candidate user equipments operable to function as a sensing node to perform the sensing task.

8. The network entity according to any preceding claim, wherein the at least one processor is configured to cause the network entity, based on the received statistics and / or predictions, to: control at least one sensing node to perform the sensing task; or transmit a command to a sensing controller entity, the command instructing the sensing controller entity to control at least one sensing node to perform the sensing task.

9. A processor for wireless communication, comprising: at least one controller coupled with at least one memory and configured to cause the processor to: obtain a sensing request related to a sensing task; output a request message for transmission to an analytics network entity, the request message comprising an identifier indicating a request to provide analytics related to one or more performance parameters of one or more sensing nodes to perform the sensing task to sense a target in an environment of the one or more sensing nodes; and obtain a response message transmitted from the analytics network entity, the response message comprising statistics and / or predictions related to the one or more performance parameters of the one or more sensing nodes.

10. A method performed by a network entity, the method comprising: receiving a sensing request related to a sensing task; transmitting a request message to an analytics network entity, the request message comprising an identifier indicating a request to provide analytics related to one or moreperformance parameters of one or more sensing nodes to perform the sensing task to sense a target in an environment of the one or more sensing nodes; and receiving a response message from the analytics network entity, the response message comprising statistics and / or predictions related to the one or more performance parameters of the one or more sensing nodes.

11. A network entity for wireless communication, comprising: at least one memory; and at least one processor coupled with the at least one memory and configured to cause the network entity to: receive a request message from a requesting network entity, the request message comprising an identifier indicating a request to provide analytics related to one or more performance parameters of one or more sensing nodes to perform the sensing task to sense a target in an environment of the one or more sensing nodes; obtain input data from one or more network nodes; determine statistics and / or predictions related to the one or more performance parameters of the one or more sensing nodes based on the request message and the input data; and transmit a response message to the requesting network entity, the response message comprising the statistics and / or predictions.

12. The network entity according to claim 11, wherein the input data comprises one or more indications of: radio performance and / or radio utilization of sensing nodes; radio configuration information of sensing nodes; and / or line of sight between the network nodes in the environment.

13. The network entity according to claim 11 or 12, wherein the input data comprises one or more indications of node positioning in the environment.

14. The network entity according to any of claims 11 to 13, wherein the input data comprises one or more indications of: throughput of user equipments in the environment; mobility profile of user equipments in the environment; and / or hardware and software capabilities of user equipments in the environment.

15. The network entity according to any of claims 11 to 14, wherein the statistics and / or predictions include sensing measurements.

16. The network entity according to any of claims 11 to 15, wherein the statistics and / or predictions include non-3GPP measurements related to an application function.

17. The network entity according to any of claims 11 to 16, wherein the statistics and / or predictions include sensing measurements related to non-3GPP sensing nodes.

18. The network entity according to any of claims 11 to 17, wherein the statistics and / or predictions include, for radio access nodes among the one or more sensing nodes, indications of one or more of: radio suitability for sensing; radio resource utilization; and sensing measurements.

19. The network entity according to any of claims 11 to 18, wherein the statistics and / or predictions include, for user equipments among the one or more sensing nodes, indications of one or more of: positioning, mobility and radio performance; user equipment capabilities; and user equipment support related to non-3GPP sensing measurements.

20. The network entity according to any of claims 10 to 19, wherein the input data for sensing related analytics is obtained from one or more of: a radio access node in a radio access network; a sensing function network entity; and an application function network entity, wherein the input data comprises non- 3 GPP sensing performance data.

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