Proximity sensing analytics

Abstract

Various aspects of the present disclosure relate to an analytics function apparatus for proximity sensing in wireless communication. The apparatus comprises: at least one memory; and at least one processor coupled with the at least one memory and configured to cause the analytics function apparatus to: receive a request from a consumer for proximity sensing analytics, the request including: a list of target entities for which analytics information should be provided, the target entities including target UEs and target non-UE objects; filter information including conditions for which proximity information of each target entity should be generated; and a reporting destination for the generated proximity information; determine proximity information of the targeted UEs and / or target non-UE objects; and send the generated proximity information to the reporting destination.

Description

PROXIMITY SENSING ANALYTICS TECHNICAL FIELD

[0001] The present disclosure relates to wireless communications, and morespecifically to analytics used for wireless location and proximity sensing.BACKGROUND

[0002] A wireless communications system may include one or multiple networkcommunication 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 byutilizing 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 supportwireless communications across various radio access technologies including thirdgeneration (3G) radio access technology, fourth generation (4G) radio access technology, fifth generation (5G) radio access technology, among other suitable radio accesstechnologies beyond 5G (e.g., sixth generation (6G)).

[0003] Wireless sensing technologies aim at acquiring information about a remoteobject or environment and its characteristics without physically contacting it. Theperception data of the object and its surrounding can be utilized for analysis, so thatmeaningful information about the object or environment and its characteristics can be obtained. SUMMARY

[0004] An article “a” before an element is unrestricted and understood to refer to “atleast 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 prefacedAttorney Docket No. PC933899WO 14213070-1by 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 examplestep that is described as “based on condition A” may be based on both a condition A and a condition B without departing from the scope of the present disclosure. In other words, as used herein, the phrase “based on” shall be construed in the same manner as the phrase “based at least in part on. Further, as used herein, including in the claims, a “set” may include one or more elements.

[0005] Some implementations of the method and apparatuses described herein mayfurther include an analytics function apparatus for proximity sensing in wirelesscommunication, comprising: at least one memory; and at least one processor coupled withthe at least one memory and configured to cause the analytics function apparatus to: receivea request from a consumer for proximity sensing analytics, the request including: a list of target entities for which analytics information should be provided, the target entitiesincluding target UEs and target non-UE objects; filter information including conditions forwhich proximity information of each target entity should be generated; and a reportingdestination for the generated proximity information; determine proximity information of thetargeted UEs and / or target non-UE objects; and send the generated proximity informationto the reporting destination.

[0006] The proximity information may include predictions and / or statistics for one ormore of presence, relative distance and / or collision probability of a target UE or a target non-UE object relative to one or more of a target UE, a non-target UE, a non-target UE that is not on the list of target entities, and a non-target non-UE object that is not on the list of target entities.

[0007] The consumer may comprise one or more of a RAN node, SF, SesMF (residingin a core as a network function or in RAN), LMF, and a UE.

[0008] The reporting destination may be the consumer or a destination address orreporting identity included in the request.Attorney Docket No. PC933899WO 14213070-1

[0009] The analytics function apparatus may reside in a core network and comprise aNWDAF; in a RAN node; in an SF or SensMF (within RAN or within the core network); and in a UE.

[0010] The processor may be configured to cause the analytics function apparatus toreceive from one or more further entities one or more of the following pieces of information: UE speed; UE orientation; number of Target UEs; number of non-target UEs; number of target non-UE objects; number of non-target UE objects; list of objects including target and non-target UEs and non-UEs; attributes of UEs and non-UE objects including one or more of destination, route, average speed, time of arrival, size, dimension, physical type of non-UE object or object attached to or accompanied with or close by a target or non-target UE; and confidence in relative proximity data.

[0011] The processor may be configured to cause the analytics function apparatus toreceive from one or more further entities one or more of the following pieces of proximity related input data: Non-UE objects and / or IDs; Location information of UE or non-UEobject and / or presence information of the said UE or non-UE object (e.g., within an area ofinterest; Location accuracy of a UE or a non-UE object; Velocity estimate of a UE or anon-UE object; Collision risk distance for UE; Collision risk distance for non-UE object; Collision risk velocity for UE; Collision risk velocity for non-UE object; UE or non-UEobject heading; UE, non-UE object trajectory; UE location; Timestamp; UE and non-UEobject physical properties; and size, dimension, orientation, type, material of objects.

[0012] The processor may be configured to cause the analytics function apparatus toreceive from one or more further entities one or more of the following pieces of non-UE object-specific input data for each object: Speed; Orientation; Position; Physical properties; Object type; Expected mobility / route; Required proximity area; and Time stamp for the input information.

[0013] The processor may be configured to cause the analytics function apparatus toreceive from one or more further entities one or more of the following pieces of area specific input data: non-target object density; non-target mobility pattern / density; and collision probability per time-unit.Attorney Docket No. PC933899WO 14213070-1

[0014] The processor may be configured to cause the analytics function apparatus tosend to the one or more of the following pieces of TRP specific input data: speed; orientation; position; and physical properties.

[0015] The processor may be configured to cause the analytics function apparatus tosend to the reporting destination one or more of the following pieces of proximity sensing statistics: A UE group ID or set of UE IDs for which the statistics apply; A non-UE object group ID or set of non-UE object IDs for which the statistics apply; List of time slots during an analytics target period; Observed proximity sensing for a target UE and / or non-UE object; Observed proximity information; and Percentage of target and / or non-targetUEs / non-UE objects accounted based on proximity criteria.

[0016] The processor may be configured to cause the analytics function apparatus tosend to the reporting destination one or more of the following pieces of proximity sensing predictions: a UE group ID or set of UE IDs for which predictions apply; a Non-UE object group ID or set of non-UE object IDs for which the predictions apply; Time slot start and duration; UE proximity attributes, including relative proximity information, confidence and / or sampling ratio; Time to Collision or other time to proximity information; Type of collision; Risk of collision; Confidence; and Accuracy.

[0017] Some implementations of the method and apparatuses described herein mayfurther include a consumer function for proximity sensing in wireless communication, comprising: at least one memory; and at least one processor coupled with the at least one memory and configured to cause the consumer function to: send to an analytics function apparatus a request for proximity sensing analytics, the request including: a list of target entities for which analytics information should be provided, the target entities including target UEs and target non-UE objects; filter information including conditions for whichproximity information of each target entity should be generated; and a reporting destinationfor the generated proximity information; and receive from the analytics function apparatus proximity information of the targeted UEs and / or target non-UE objects.

[0018] The consumer function may reside in a UE, and / or may comprise one or more ofa RAN node, SF, SesMF, and LMF.Attorney Docket No. PC933899WO 14213070-1

[0019] Some implementations of the method and apparatuses described herein mayfurther include a processor for proximity sensing in wireless communication, comprising: atleast one controller coupled with at least one memory and configured to cause the processor to: obtain a request from a consumer for proximity sensing analytics, the request including:a list of target entities for which analytics information should be provided, the targetentities including target UEs and target non-UE objects; filter information including conditions for which proximity information of each target entity should be generated; and a reporting destination for the generated proximity information; determine proximity information of the targeted UEs and / or target non-UE objects; and output the generated proximity information to the reporting destination.

[0020] In some implementations of the method and apparatuses described herein, amethod performed by an analytics function apparatus for proximity sensing in wirelesscommunication may comprise: receiving a request from a consumer for proximity sensing analytics, the request including: a list of target entities for which analytics information should be provided, the target entities including target UEs and target non-UE objects; filter information including conditions for which proximity information of each target entity should be generated; and a reporting destination for the generated proximity information;

[0021] determining proximity information of the targeted UEs and / or target non-UEobjects; and sending the generated proximity information to the reporting destination. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 illustrates an example of a wireless communications system inaccordance with aspects of the present disclosure.

[0023] Figure 2 illustrates four proposals for enhancing the 5G core by introducing anoverview of NWDAF flavours including potential input data sources and output resultconsumers a Sensing Function (SF) as a dedicated or logical Network Function (NF).

[0024] Figure 3 illustrates an overview of NWDAF flavours including potential inputdata sources and output result consumers.Attorney Docket No. PC933899WO 14213070-1

[0025] Figure 4 illustrates an example of a process for deriving proximity analytics inaccordance with aspects of the present disclosure.

[0026] Figure 5 illustrates an example of a network equipment (NE) 500 in accordancewith aspects of the present disclosure.

[0027] Figure 6 illustrates an example of a processor 600 in accordance with aspects ofthe present disclosure.

[0028] Figure 7 illustrates an example of a user equipment (UE) 700 in accordance withaspects of the present disclosure.

[0029] Figure 8 illustrate a flowcharts of method performed by a NE in accordancewith aspects of the present disclosure.

[0030] Figure 9 illustrate a flowcharts of method performed by a UE in accordancewith aspects of the present disclosure. DETAILED DESCRIPTION

[0031] Many use-cases of wireless sensing, include aspects of collision avoidance, forexample of an Unmanned Aerial Vehicle (UAV), vehicles on the road, or AutonomousMobile Robots and / or Automated Guided Vehicles (AMR / AGVs) in factories. An analyticsframework (“Relative proximity analytics”) is presented in 3GPP 23.288 for proximitymonitoring and collision avoidance among User Equipments (UEs), and it is of further interest to investigate proximity monitoring and collision avoidance with respect to non-UE objects, i.e., those without a sim card. Examples include other vehicles / UAVs etc. which are not connected UE nodes as exemplified in 22.837 use-cases. However, the Relative proximity analytics framework does not integrate the task of UE group proximity monitoring with the tracking / proximity monitoring with non-UE objects.

[0032] In particular, the collision avoidance is currently considered in the context ofanalytics in TS 23.288 as an analytics service with an Analytics ID = "Relative Proximity". The idea behind this analytics service is to derive statistics or predictions related to a given UE group assuming that all UE members: (i) reside and are served by a single PLMN and / or (ii) employ the same application. These assumptions are restrictive since UEAttorney Docket No. PC933899WO 14213070-1information can be provided by the network, i.e., by a specific PLMN, and / or from a specific application only. However, considering the generic problem of collision, i.e., in the situation where a UE shall avoid other UE or non-UE objects, it is broader in the sense thatthere shall be no limitation for considering UE related to a certain PLMN only and / orapplication only or be restricted to other UEs, i.e., consider only objects that hold a sim card. Collision avoidance shall be performed considering any kind of other UEs or non-UEs to be effective.

[0033] It is therefore necessary to consider integrating sensing and communicationsdata input that can assist in recognising objects irrespective of the PLMN or application thatthey belong to, and also irrespective of whether they are a UE or non-UE. This can be doneby the introduction of a new analytics service with a new analytics ID which can build on existing analytics IDs such as the Relative Proximity ID described in TS 23.388. For thesake of the present discussion this analytic service may be referred to as “ProximitySensing Analytics”.

[0034] Additional features included in the Proximity Sensing Analytics may includeprovisioning, as input information to the analytics, target UE IDs or target group UE IDsprovided by a consumer for which proximity sensing information is generated. This may also be applicable to device-based sensing targets, in which the UE is carried or embeddedwithin a target object (e.g. vehicle, UAV, etc.). Further input information may includeinformation, measurements and / or descriptions related to the non-UE objects, or to UEs that are not introduced by the analytics services via the consumer or not known by theanalytics service (e.g. belonging to different PLMN or different applications). Furtherinput information provisioned may include the event or conditions under which proximitysensing information (e.g. collision prediction information) should be considered and / orreported. Such collision prediction information may include: relative distance betweenobjects; relative distance or velocity (or a combination thereof) between a target UE ortarget object and another UE or object (at any direction or potentially at a given / specified set of directions by the consumer) or relative velocity; and / or estimation or prediction of a target / non-target UE or non-UE object that is at an area of interest (e.g., at a given / expected time in the future).Attorney Docket No. PC933899WO 14213070-1

[0035] In addition, output information from the Proximity Sensing Analytics mayinclude tracking proximity conditions of a group consisting of target UE and / or non-UE target objects with respect to both target UEs and target non-UE objects, as well as non- target UEs and non-UE objects; predictions and / or estimations regarding the collision risk and type; and report of unknown objects providing an object description for the situation where an object is observed but could not be identified since it does not match descriptions related to the non-UE objects.

[0036] Further additional features may include provisioning, as input information to theProximity Sensing Analytics, a flag to indicate whether non-UEs should be considered for proximity sensing information. The same flag may be used to indicate if the output relates to UEs without a sim via the means of sensing or UEs with a sim.

[0037] In some examples, this information may be communication information fromand towards the consumer and analytics service irrespective of where this analytics service is deployed. In other words, the analytics service can be an analytics function, or an analytics service collocated with another function in the core network or RAN or management or application. The analytics service may also obtain information retrieved from data sources including processed sensing related information and sensing object descriptions.

[0038] Aspects of the present disclosure are described in the context of a wirelesscommunications system.

[0039] Figure 1 illustrates an example of a wireless communications system 100 inaccordance with aspects of the present disclosure. The wireless communications system100 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 otherimplementations, the wireless communications system 100 may be a NR network, such as a5G network, a 5G-Advanced (5G-A) network, or a 5G ultrawideband (5G-UWB) network.In other implementations, the wireless communications system 100 may be a combination of a 4G network and a 5G network, or other suitable radio access technology includingAttorney Docket No. PC933899WO 14213070-1Institute of Electrical and Electronics Engineers (IEEE) 802.11 (Wi-Fi), IEEE 802.16(WiMAX), IEEE 802.20. The wireless communications system 100 may support radioaccess technologies beyond 5G, for example, 6G. Additionally, the wirelesscommunications system 100 may support technologies, such as time division multiple access (TDMA), frequency division multiple access (FDMA), or code division multiple access (CDMA), etc.

[0040] The one or more NE 102 may be dispersed throughout a geographic region toform the wireless communications system 100. One or more of the NE 102 describedherein may be or include or may be referred to as a network node, a base station, a networkelement, a network function, a network entity, a radio access network (RAN), a NodeB, aneNodeB (eNB), a next-generation NodeB (gNB), or other suitable terminology. An NE 102and a UE 104 may communicate via a communication link, which may be a wireless orwired connection. For example, an NE 102 and a UE 104 may perform wireless communication (e.g., receive signaling, transmit signaling) over a Uu interface.

[0041] An NE 102 may provide a geographic coverage area for which the NE 102 maysupport services for one or more UEs 104 within the geographic coverage area. Forexample, an NE 102 and a UE 104 may support wireless communication of signals relatedto services (e.g., voice, video, packet data, messaging, broadcast, etc.) according to one ormultiple radio access technologies. In some implementations, an NE 102 may be moveable,for example, a satellite associated with a non-terrestrial network (NTN). In someimplementations, different geographic coverage areas 112 associated with the same or different radio access technologies may overlap, but the different geographic coverageareas may be associated with different NE 102.

[0042] The one or more UE 104 may be dispersed throughout a geographic region ofthe wireless communications system 100. A UE 104 may include or may be referred to as aremote unit, a mobile device, a wireless device, a remote device, a subscriber device, atransmitter device, a receiver device, or some other suitable terminology. In someimplementations, 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 anAttorney Docket No. PC933899WO 14213070-1Internet-of-Things (IoT) device, an Internet-of-Everything (IoE) device, or machine-type communication (MTC) device, among other examples.

[0043] A UE 104 may be able to support wireless communication directly with otherUEs 104 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.

[0044] An NE 102 may support communications with the CN 106, or with another NE102, or both. For example, an NE 102 may interface with other NE 102 or the CN 106through one or more backhaul links (e.g., S1, N2, N2, or network interface). In someimplementations, the NE 102 may communicate with each other directly. In some otherimplementations, the NE 102 may communicate with each other or indirectly (e.g., via theCN 106. In some implementations, one or more NE 102 may include subcomponents, suchas 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).

[0045] The CN 106 may support user authentication, access authorization, tracking,connectivity, and other access, routing, or mobility functions. The CN 106 may be anevolved 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, thecontrol 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 oneor more UEs 104 served by the one or more NE 102 associated with the CN 106.Attorney Docket No. PC933899WO 14213070-1

[0046] The CN 106 may communicate with a packet data network over one or morebackhaul links (e.g., via an S1, 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., aprotocol data unit (PDU) session, or the like) with the CN 106 via an NE 102. The CN 106may 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). ThePDU session may be an example of a logical connection between the UE 104 and the CN106 (e.g., one or more network functions of the CN 106).

[0047] In the wireless communications system 100, the NEs 102 and the UEs 104 mayuse 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 someimplementations, 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 someimplementations, such as in 4G, the NEs 102 and the UEs 104 may support a single framestructure. In some other implementations, such as in 5G and among other suitable radioaccess 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 framestructures based on one or more numerologies.

[0048] One or more numerologies may be supported in the wireless communicationssystem 100, and a numerology may include a subcarrier spacing and a cyclic prefix. A firstnumerology (e.g., ^=0) may be associated with a first subcarrier spacing (e.g., 15 kHz) anda normal cyclic prefix. In some implementations, the first numerology (e.g., ^=0)associated with the first subcarrier spacing (e.g., 15 kHz) may utilize one slot per subframe. A second numerology (e.g., ^=1) may be associated with a second subcarrier spacing (e.g.,30 kHz) and a normal cyclic prefix. A third numerology (e.g., ^=2) may be associated witha third subcarrier spacing (e.g., 60 kHz) and a normal cyclic prefix or an extended cyclic prefix. A fourth numerology (e.g., ^=3) may be associated with a fourth subcarrier spacing Attorney Docket No. PC933899WO 14213070-1(e.g., 120 kHz) and a normal cyclic prefix. A fifth numerology (e.g., ^=4) may be associated with a fifth subcarrier spacing (e.g., 240 kHz) and a normal cyclic prefix.

[0049] A time interval of a resource (e.g., a communication resource) may be organizedaccording to frames (also referred to as radio frames). Each frame may have a duration, forexample, a 10 millisecond (ms) duration. In some implementations, each frame mayinclude multiple subframes. For example, each frame may include 10 subframes, and eachsubframe 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.

[0050] Additionally or alternatively, a time interval of a resource (e.g., acommunication 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 communicationssystem 100. For instance, the first, second, third, fourth, and fifth numerologies (i.e., ^=0,^=1, ^=2, ^=3, ^=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 someimplementations, the number (e.g., quantity) of slots for a subframe may depend on anumerology. 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 12symbols. The relationship between the number of symbols per slot, the number of slots persubframe, and the number of slots per frame for a normal cyclic prefix and an extendedcyclic prefix may depend on a numerology. It should be understood that reference to a firstnumerology (e.g., ^=0) associated with a first subcarrier spacing (e.g., 15 kHz) may be used interchangeably between subframes and slots.

[0051] In the wireless communications system 100, an electromagnetic (EM) spectrummay be split, based on frequency or wavelength, into various classes, frequency bands,frequency channels, etc. By way of example, the wireless communications system 100 maysupport one or multiple operating frequency bands, such as frequency range designations Attorney Docket No. PC933899WO 14213070-1FR1 (410 MHz – 7.125 GHz), FR2 (24.25 GHz – 52.6 GHz), FR3 (7.125 GHz – 24.25GHz), 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 mayperform 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.

[0052] FR1 may be associated with one or multiple numerologies (e.g., at least threenumerologies). For example, FR1 may be associated with a first numerology (e.g., ^=0), which includes 15 kHz subcarrier spacing; a second numerology (e.g., ^=1), which includes 30 kHz subcarrier spacing; and a third numerology (e.g., ^=2), which includes 60 kHz subcarrier spacing. FR2 may be associated with one or multiple numerologies (e.g., atleast 2 numerologies). For example, FR2 may be associated with a third numerology (e.g.,^=2), which includes 60 kHz subcarrier spacing; and a fourth numerology (e.g., ^=3),which includes 120 kHz subcarrier spacing.

[0053] Various scenarios of network-based and UE-based radio sensing operations arealready known. Alternatives include scenarios of radio sensing where the networkconfigures the participating sensing entities, i.e., network and UE nodes acting as sensingTx nodes, network and UE nodes acting as sensing Rx nodes, as well as the configuration of sensing signal and necessary measurements and reporting procedures from the nodes. In this regard, the functional split between the network and the UE nodes for a specific sensing task (e.g., task of detecting presence of a pedestrian in a road) may take various forms, depending on the availability of sensing-capable devices and the requirements of the specific sensing task. I. Sensing Tx as a network node and Sensing Rx as a separate network node:in this case, the sensing signal (sensing RS or another RS used for sensing, or the data / control channels known to the involved network / TRP nodes) is transmitted and received by network entities. The involvement of UE nodes is limited to the aspects Attorney Docket No. PC933899WO 14213070-1of interference management, when necessary. The network does not utilize UEs for sensing assistance in this scenario. II. Sensing Tx as a network node and Sensing Rx as the same network node:the sensing signal (e.g., sensing RS or another RS used for sensing, or the data / control channels known to the network / TRP nodes) is transmitted and received by the same network entity. The involvement of UE nodes is limited to the aspects of interference management, when necessary. The network does not utilize UEs for sensing assistance in this scenario. III.Sensing Tx as network node and Sensing Rx as a UE node: in this case, thesensing signal (e.g., sensing RS or other RS used for sensing or a data / control channel) is transmitted by a network entity and received by one or multiple UE nodes. The network configures the UEs to act as a sensing Rx node, according to the UE nodes capabilities for sensing, as well as the requirements of the desired sensing task. IV.Sensing Tx as a UE node and Sensing Rx as a network node: in this case, thesensing signal (e.g., sensing RS or other RS used for sensing or a data / control channel transmitted by the UE) is received by one or multiple network entities and transmitted by a UE node. The network configures the UE to act as a sensing Tx node, according to the UE nodes capabilities for sensing, as well as the nature of the desired sensing task. V. Sensing Tx as a UE node and Sensing Rx as a separate UE node: in thiscase, the sensing signal (e.g., sensing RS or other RS used for sensing of a data / control channel) is received by one or multiple UE nodes and transmitted by a UE node. In this case, the network, or a UE node may potentially decide on configuration of the sensing scenario. In one instance, the network configures the UEs to act as a sensing Tx and / or sensing Rx nodes, according to the UE nodes capabilities for sensing, as well as the nature of the desired sensing task. VI.Sensing Tx as a UE node and Sensing Rx as the same UE node: in this case,the sensing signal (e.g., a sensing-dedicated RS or another RS used for sensing, or the data / control channels known to the UE) is transmitted by a UE node and received by the same UE node. In this case, the UE or the network configures the Attorney Docket No. PC933899WO 14213070-1sensing scenario, according to the UE nodes capabilities for sensing, as well as the nature of the desired sensing task.

[0054] These above scenarios are not intended to be restricted to a specific UE type andmay include any UE category and / or functionality (e.g., a UE RSU). In any of the abovescenarios, and of the roles depicted for gNB and / or UE may be replaced (with equal validity as an example of a radio sensing scenario) with a smart repeater node, and IAB node, an RSU.

[0055] Figure 2 illustrates four proposals for enhancing the 5G core by introducing aSensing Function (SF) as a dedicated or logical Network Function (NF) considered in IMT- 2020. These include the following architecture flavours: ^Tight coupling ISAC network architecture where the SF appears as a dedicated NFhandling 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. ^Tight coupling ISAC network architecture with CP / UP split where the SF has twodedicated NF counter parts: (i) SF-C that handles the control plane aspects asdescribed above and (ii) SF-U 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., only singling, in the control plane. ^SF collocated with the LMF appears as a logical NF embedded in the LMF toperform sensing taking advantage of the knowledge of a UE location. ^Loose coupling ISAC network architecture where the SF is independent of the 5Gcore, i.e., typically used for local field scenarios or private networks, and the Attorney Docket No. PC933899WO 14213070-1interaction with the 5G core is minimal. The main idea is to use SF close to theRAN, 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).

[0056] In another description the sensing functionality may also play the role ofcontrolling a sensing operation, in some example implementations, this sensing functionality referred to as a sensing controller entity / function (SensMF), comprises one or multiple functionalities related to a UE, a RAN node, a gNB / gNB-CU, a gNB / DU an LMF, an SF, or a combination thereof, wherein the SensMF performs one or multiple of the following steps including: a) Receiving request for sensing information from a service consumer (e.g., arequesting third party application) b) Determining selection and / or configuration of a sensing operation, includingconfiguration of one or more of a sensing Tx node, sensing Rx node, c) Selecting and / or configuring the involved nodes for sensing transmission andsensing reception and sensing measurement and reporting of the conducted measurements, d) collecting the sensing measurements,e) performing or configuring or requesting computation of the sensingmeasurements and thereby determining the required sensing information based onthe obtained sensing measurements, f) reporting / exposing an obtained sensing information to the entity requesting thesensing information.

[0057] In some examples wherein the SensMF is comprised of multiple nodes / entities,one part of the steps above may be implemented by the first part of the SensMF and the second part of the above steps may be implemented by the second part of the SensMF, e.g., implemented in the SF and gNB. In some examples wherein the SensMF is comprised of multiple nodes / entities, the communication among the SensMF entities is transparent to the outside entities, nevertheless, the communication among the SensMF entities is assumed to be implicit to the overall procedure. In some examples, wherein a SensMF is comprised of Attorney Docket No. PC933899WO 14213070-1an SF and a gNB (e.g., serving / head gNB of a related UE to the sensing task or a selectedserving gNB for a sensing task), the SF performs the steps a, f, e, d whereas the steps b, care performed by the selected gNB node. In some other embodiments, the steps b, d arejointly performed by the SF and the selected gNB, wherein a first part of the configuration / configuration determination are performed by the SF and a second part of the configuration / configuration determination is performed by the selected gNB (e.g., SF supports the serving gNB of a sensing task with some recommendations and the serving gNB will decide and configure the involved nodes, or the serving gNB of a sensing task supports the SF with some recommendations and the SF will decide / choose among the possible configurations). In some other variations, The SensMF may be a RAN node (e.g., a selected gNB node acting as serving gNB of a sensing task), maybe a sensing function(SF) residing in core network as described in Figure 2.2.1, may be a sensing managementcomponent as a logical entity residing in RAN or a UE, may be a UE, or a combination thereof.

[0058] Figure 3 illustrates an overview of NWDAF flavours including potential inputdata sources and output result consumers. NWDAF considers the support of various analytics types, e.g., UE Mobility, User Data Congestion, NF load, and others as elaborated in TS 23.288 which are distinct and can be selected by a consumer using the Analytics ID.Each NWDAF may support one or more Analytics IDs and may have the role of inferencecalled NWDAF containing AnLF (or simply AnLF), or training called NWDAF containing MTLF (or simply MTLF) or both. AnLF that support a specific Analytics ID inference subscribes to a corresponding MTLF that is responsible for training. The various NWDAF flavours and their respective input data and output result consumers may include 5G core NFs, AFs, 5G core data repositories, e.g., ADRF, and the OAM (MnS Consumer or MF). Optionally, DCCF and MFAF may be involved to distribute and collect repeated data towards or from various data sources. An analytics capability with an Analytics ID = “Relative proximity” is presented in 3GPP 23.288, with the goal of localizing cluster or UEs and / or predicting proximity or collision behaviours therein. However, the shortcomings of this analytics service concentrate on its limitations to be applied for UEs that: (i) reside on the PLMN where the NWDAF that offer this analytics service reside, and / or (ii) employ the same application, e.g., a vehicular application for avoiding collision. Attorney Docket No. PC933899WO 14213070-1

[0059] The Relative Proximity Analytics among UEs provided by NWDAF can be usedto assist a consumer NF to more accurately localize a cluster (or a set) of UEs via provisioning statistics or prediction information related to their relative proximity. This analytics type may help the consumer improve the location estimation accuracy of a UE by using proximity information from nearby UEs, or it may help the consumer identify UEs in the vicinity of another UE. Relative proximity information can also be leveraged by NWDAF to provide location information with finer granularity than TA / cell. This is described in more detail in 3GPP TS 23.288.

[0060] In accordance with some examples of the present disclosure, proximity sensinganalytics among target / non-target UEs and / or target / non-target non-UE objects provided by analytics functions such as NWDAF may be used to assist a consumer NF to more accurately localize a cluster (or a set) of UEs and / or non-UE objects via provisioning statistics or prediction information related to their position, mobility and relative proximity / distance / velocity with other target or non / target UE / non-UE objects. Thisanalytics type may, for example, help the consumer improve the location estimationaccuracy of UE and / or non-UE objects of interest by using proximity information fromnearby UEs and / or non-UE objects, or it may help the consumer identify UEs and / or non-UE objects in the vicinity of another UE / non-UE object. The generated (output) relative proximity information can also be leveraged by NWDAF.

[0061] In some examples, as previously mentioned, these analytics may be anenhancement or variation of the “relative proximity analytics” of the NWDAF described in3GPP TS 23.288 by including examples of inputting and / or outputting information on non- UE related objects as described above. In some other examples, the Proximity Sensing Analytics is separately defined / functioning analytics supported by the NWDAF. In someother examples, the Proximity Sensing Analytics may be an analytics / computational blockat least defined based on the input and output information description as exemplified in thetables below and may reside, operate or be implemented as part of a core network functionof a function or entity such as for example NWDAF, AMF, LMF, SF, AF, as part of theRAN, e.g., within a gNB or be SMC / sensing controller entity residing in RAN, or in a UE.Attorney Docket No. PC933899WO 14213070-1

[0062] The Proximity Sensing Analytics may provide output information to one ormore of another analytics function (analytics defined within the NWDAF), to a NF of the core network, e.g., AMF, LMF, SF, AF, to RAN, a gNB or a SMC / sensing controller entityresiding in RAN, to a UE, to an AF or a third party application interacting with the networkvia NEF / AF, or a combination thereof. Any of these functions may be the consumer,

[0063] In some embodiments, the input to the Proximity Sensing Analytics may includeUE IDs (as target and / or non-target UE; and / or target and / or non-target non-UE object)belonging to another PLMN. In some examples, when a UE ID (of the same or different PLMN) is associated / accompanied with a physical description / property of an object (e.g.,size or RCS-related information, an object type) then it is interpreted as the objectattached / close to the said UE.

[0064] In some embodiments, the consumer of these analytics resides in NWDAF andmay indicate in the request or subscription, one or more of the following information fields: -Analytics ID = "Proximity Sensing"- Target of Analytics Reporting: indicates the UE / non-UE object(s) for whichAnalytics information is requested, entities such as specific list of UEs, i.e. a list of SUPIs, group of UEs, i.e. a list of Internal-Group-IDs, or any UE (i.e. allUEs), list of non-UE target objects. -Non-UE target / non-target objects – object dimensions, proximity distancethreshold, position / heading / velocity at a time instance (a timestamp information), etc. -Analytics Filter Information, optionally, S-NSSAI and / or DNN- An optional area of Interest; - specified area where these analytics will takeplace -Relative Event data that impacts proximity, i.e., the distance, which may changedepending on these values (e.g., distance defined with dependency with direction, velocity, specific target or non-target UE or non-UE object, etc. e.g., for an indicated UE and a specific direction, consider an area with radius of at least d1 for relative velocity v1 and at least d2 for relative velocity v2) -Relative distance to a specific UEAttorney Docket No. PC933899WO 14213070-1- Relative velocity,- Relative direction- An optional individual or set of direction(s) of interest (the directions accordingto the GCS or LCS of a target UE or object where the proximity sensing information shall be generated, e.g., for a target vehicle UE / non-UE, the direction of the road can be considered)- Optionally, one or several attributes to be accounted for relative proximity (i.e.additional information that can be provided in addition to distance between two UEs and / or non-UE objects): relative or absolute velocity, average speed, orientation, mobility trajectory; or a target UE / non-UE object or between a target UE / non-UE object and a target / non-target UE or non-UE object -optionally, one or several attributes to be accounted for relative proximity ofnon-UE objects, i.e, physical size, shape, type / properties, material characteristics etc. -Optionally, the list of analytics subsets that are requested among those specifiedin the “output data” section -Optionally, preferred level of accuracy of the analytics.- Optionally, preferred level of accuracy per analytics subset (see clause 3GPP TS23.288, Section 6.19.3); -An Analytics target period indicating the time period over which the statistics orpredictions are requested

[0065] Tables 1 to 6 provide tables of information which may be provided as input datato the Proximity Sensing Analytics.

[0066] In some examples, some UEs (described as “target UEs”) and / or non-UE objects(described as “target objects”) are expected to be part of the group for which the sensingproximity information is expected to be generated (e.g., a notification / warning when a (target / non-target) object or UE reaches a defined proximity area of the said target UE / object). Some UEs (described as “non-target UEs”) and / or some non-UE objects (described as “non-target objects”) may be included as part of the analytics input information (e.g., their presence / location can be used to derive proximity information of the Attorney Docket No. PC933899WO 14213070-1target UE / non-UE objects, e.g., for collision), however, the analytic is notexpected / required to compute their proximity information as output information.

[0067] In some examples, information of a target UE and / or non-UE object may beprovided by an application server or AF or NEF, as part of the request for obtaining related proximity sensing information. Information Source DescriptionPer (target / non-target) UE information OAM >Speed UE Speed> Orientation UE OrientationTable 1: Orchestration and Management (OAM) input data for relative proximity analytics.Attorney Docket No. PC933899WO 14213070-1Information Source DescriptionProximity Attribute DCAF / Characterise a set of UEs in relative NEF proximity. >Number of objects Total number of target UEs and / or non-target UEs and / or target non-UE objects and / or non-target non-UE objects in proximity of target UE and / or in proximity of a non-UE object >> Number of Target Total number of target UEs UEs >>Number of non- Total number of non-target UEs target UEs >>Number of target Total number of target non-UE objects non-UE objects >> Number of non- Total number of non-target UE objects target UE objects >Timestamp A time stamp of time that the proximityattribute derived. >Application ID(s) Identifying the applications(s) providingthis information. > List of target and / or UE / non-UE object IDs in proximity of non-target UE IDs target UE or target non-UE object, if and / or target / non-target available. non-UE objects >Other attribute(s) Other attributes for the set of UEs / non-UE objects i.e. destination, route, average speed, time of arrival, size / dimension / RCS, physical type of a (target or non-target) non-UE object or an object attached to / accompanied with / close by a (target or non-target) UE >Confidence Confidence on relative proximity data.Table 2: Proximity related input data collected via DCAF / NEF Attorney Docket No. PC933899WO 14213070-1Information Source DescriptionUE ID(s) AMF SUPI(s) of individual UE(s).Non-UE IDs SF, AF,E.g., sensing function reports a detected RAN object within an area of interest, AF informs of the available map information Location information AMF, Absolute location of a UE. of UE or non-UE GMLC, SF, object AF, RAN / SMC location accuracy of a GMLC, SF, Accuracy of the location estimation. UE or a non-UE object AF, RAN / SMC Velocity estimate of a LCS, SF, UE velocity. UE or a non-UE AF, object, e.g., absolute RAN / SMC or relative velocity Collision risk distanceAF Derived from the UE connected physicalfor UE object dimensions e.g. length, width, height. Collision risk distanceSF, AF Derived from the physical object dimensionsfor non-UE object of the object and / or mobility pattern / velocity Collision risk velocityAF Derived from the rate of change of distancefor UE of a UE connected physical object dimensions e.g. length, width, height. Collision risk velocitySF,AF Derived from the rate of change of distancefor non-UE object of a from the physical object dimensions of the object and / or mobility pattern / velocity. UE or non-UE obj. LCS, SF, UE moving direction. heading AF, RAN / SMC >Absolute heading Heading of the UE movement with respect tothe true north. >Relative heading Heading of the UE movement with respect toanother UE. UE, non-UE object LCS, SF, Timestamped UE positions. trajectory AF, RAN / SMC >UE location Geographical area where the UE is located.>Timestamp Time stamp for the UE location. UE, non-UE object LCS, SF, physical properties AF, RAN / SMC Attorney Docket No. PC933899WO 14213070-1Size, dimension, Size, dimension, orientation, type, material of orientation, type, the non-UE object or of a one or more material of objects physical object attached to / close by / accompanied by the UETable 3: Example Proximity related input data from 5GC / AFInformation Source DescriptionPer target / non- 5GC, AF, target object SF / SensMF, information SMC >Speed UE Speed> Orientation UE Orientation> Position > Physical Shape / size / type / material, RCS information properties of a non-UE object >Object type Type of object that this information relatesto, e.g., car, drone, etc. > Expected mobility / route (serv. / ext.) > Required Area (e.g., radius) of proximity of the proximity area (for served UE for which the proximity served objects) information needs to be generated > the time stamp The time for which an input information for the input (position, orientation) holds. information Table 4: Example Non-UE (object)-specific analytics input data Attorney Docket No. PC933899WO 14213070-1Information Source DescriptionPer Area information OAM, SF, AF > (non-target) As assisting information, the object density statistics / predictions related to an area [within an area] (and potentially related to the trajectory > (non-target) of the movement of a target UE / object) mobility can be provided to the analytics, pattern / density including, e.g., the density of the objects, [within an area] potential collusion risk within a time- Collision unit, mobility statistics of the objects within the area, etc. This may include probability per time-unit indication of, e.g., how crowded a street is or how probable it is for a collision to take place for a UAV traveling at a certain area.Table 5: Example Area-specific analytics input data - expected environment informationInformation Source DescriptionPer TRP OAM, RAN, NF information (e.g., SF, LMF) >Speed TRP (a mobile IAB / VMR or a UAVcarrying a TRP or a satellite TRP) Speed >Orientation TRP Orientation> Position > Physical Physical properties properties (size / shape / type / material characteristics) of the objects attached to, accompanied by or close to a TRP Table 6: Example TRP-specific analytics input data

[0068] The Proximity Sensing Analytics may provide at least all or subset of outputinformation as exemplified in tables 7 and 8 below.Attorney Docket No. PC933899WO 14213070-1Information DescriptionUE group ID or set of UE IDs Identifies the UE(s) for which the statistics applyby a list of SUPIs or GPSIs, or a group of UEs by a list of Internal-Group-Ids. Non-UE object group ID or set Identifies the objects for which the statistics of non-UE object IDsapplied by a list of object IDs or group IDs. Agroup ID may represent a set of combination of UE IDs and non-UE object IDs. Time slot entry (1..max) List of time slots during the Analytics targetperiod. >Time slot start Time slot start within the Analytics target period.> Duration Duration of the time slot.> Proximity sensing Observed proximity sensing for a target UE / non- information UE object >> proximity information Observed proximity information.>> Sampling Ratio Percentage of (target and / or non-target) UEs / non-UE objects accounted based on proximity criteria.Table 7: Proximity sensing output – statistics

[0069] In the examples of table 7, the proximity sensing information of a target UEand / or non-UE object may include at least one or more of presence of a target / non-target UE and / or a target / non-target non-UE object (e.g., which is within a collision distance of a target UE or a target non-UE object), and distance / relative location / relative velocity or a combination thereof with respect to a target / non-target UE and / or a target / non-target non- UE object (which may be determined to be within a collision distance of a target UE or a target non-UE object).

[0070] In some of the examples above, the collision distance may be given explicitlyfor a target UE / non-UE object (e.g., given by the consumer / AF / NEF) or determined (e.g., by the network via an analytics within the NWDAF, another NF residing in the core).

[0071] In some examples of the output analytics information, a separate information isreported for proximity information between two UEs, a UE and a non-UE object, and two non-UE object. Attorney Docket No. PC933899WO 14213070-1Information DescriptionUE group ID or set of UE IDs Identifies the UE(s) for which the statistics applyby a list of SUPIs or GPSIs, or a group of UEs by a list of Internal-Group-Ids. Non-UE object group ID or set Identifies the objects for which the statistics apply of non-UE object IDs by a list of object IDs or group IDs. A group ID may represent a set of combination of UE IDs and non-UE object IDs. Time slot entry (1..max) List of predicted time slots (e.g., the time forwhich a prediction is intended) >Time slot start Time slot start time within the Analytics targetperiod. >Duration Duration of the time slot.> UE proximity attributes Predicted proximity data. This includes distancebetween UEs, between a UE and a non-UE object, between two non-UE objects where at least one of the said non-UE object is a served / of-interest object, in the group / list (of UEs, non-UE objects, or a combination) and other proximity data. This may also include predicted relative velocity between UEs between two non-UE objects where at least one of the said non-UE object is a target object, in the group / list (of UEs, non-UE objects, or a combination) and other proximity data. >> relative proximity Predicted proximity information TBA information >> Confidence Confidence of this prediction.>> Sampling Ratio Percentage of UEs accounted based on proximitycriteria. Time To Collision (TTC) or other time to proximity information (NOTE 1)> Time To Collision Time until a collision with another UE and / or anon-UE object happens. >Type To Collision With which other object or UE a collision mayhappen >Risk To Collision What impact the collision may have depending onthe object size and velocity - direction> Confidence Confidence of the prediction.Attorney Docket No. PC933899WO 14213070-1> Accuracy Accuracy of TTC (dependent on both the UElocation accuracy and confidence of the prediction). NOTE 1: Analytics subset that can be used in "list of analytics subsets that arerequested" and "Preferred level of accuracy per analytics subset".Table 8: Proximity Sensing output - predictions

[0072] In some embodiments, the analytics filter information (given by the consumer)may include time for generating predictions, e.g., the time into the future for which ananalytics prediction is needed. E.g., prediction information for at least the time window Tinto the future if a UE or a non-UE object may be present within a distance d of a target UEor a non-UE object.

[0073] In some embodiments, the presented proximity sensing analytics of thisdisclosure is not limited to the stated target UE and target non-UE objects and may include a reference point / area treated as a target (a target UE with an assumed fixed position and a relative distance / direction defining an area of interest). One such example is that the proximity information is generated relative to a reference point or a reference area ofinterest. In some such examples, prediction or statistics of presence of target (or non-target)UE / non-UE objects is generated for a fixed area or direction of interest for sensing. As such, the presented analytics can be used as a general technique to produce prediction and statistics for proximity information within an area of interest (which can be defined as an absolute area or a relative area to one or more target UE / non-UE objects).

[0074] In some embodiments, the proximity information may further include anypresence information of the target / non-target objects within the neighbourhood of a target UE / non-UE object, and not necessarily tied to determination of a collision condition / statistics. In some such examples, the proximity information event is defined for a set of target UE / non-target UEs remain in close proximity (to remain in a group), and the proximity information includes an event when one or more of the target UE / objects leave the neighbourhood of the group. In some other examples, the proximity informationincludes the statistics / prediction that a group of target nodes (UE / non-UE object) remainconnected (e.g., remain within a given proximity distance with respect to at least one, at least a given number of the target nodes or with respect to all of the target nodes) Attorney Docket No. PC933899WO 14213070-1

[0075] Figure 4 illustrates an example of a process for deriving proximity analytics inaccordance with aspects of the present disclosure.

[0076] The entities involved include an application function (AF) 401, Gateway MobileLocation Centre (GMLC) 402, Access & Mobility Management Function (AMF) 403,Consumer function 404, Network Data Analytics Function (NWDAF) 405, Minimization ofDrive Test (MDT) / Operations and Maintenance (OAM) function 406, sensing controller entity / function (SensMF) 407, Radio Access Network (RAN) 408, one or more UEs 409,and Data Collection Application Function (DCAF) 410.

[0077] In step 1 the Consumer function 404 (which may be a Network Function ofApplication Function) or a consumer residing in RAN (e.g., a sensing managementcomponent residing in RAN) sends a request to the NWDAF 405 for analytics related to proximity, e.g., using either the Nnwdaf_AnalyticsInfo or Nnwdaf_AnalyticsSubscription service. Examples of consumer NF may include SF or LMF. The Analytics ID is set to"Proximity Sensing". Analytic filters may be provided as described above.

[0078] The Consumer NF / AF or a consumer residing in RAN may request statistics orpredictions or both for a given Analytics target period.

[0079] In step 2 data collection takes place. If the request is authorised, and in order toprovide the requested analytics, one or more of the following actions may take place: -The NWDAF 405 may subscribe to OAM services 406 to retrieve relevantinformation to proximity analytics. -The NWDAF may collect MDT input data per individual UE from OAM 406.- The NWDAF 405 may collect sensing information from SensMF 407 residing incore (e.g., SF) and / or SensMF residing in RAN (e.g., SMC) Such sensinginformation may include, for example, presence / position / velocity / shape of target / non-target UEs or non-UE objects -The NWDAF 405 may collect sensing information from RAN nodes 408 (e.g., theposition, velocity, physical characteristics of an object attached to a RAN node) -The NWDAF 405 may follow the UE Input Data Collection Procedure via theDCAF 410. The DCAF may collect proximity related input data directly from theAttorney Docket No. PC933899WO 14213070-1UE Application 409, for NWDAF 405 to determine a list of UEs fulfilling certainproximity criterion. In some examples, different Application IDs of the same DCAF or different DCAFs may be selected for different UEs, since each DCAF can onlycollect the proximity information for the UEs that have PDU session between UE and DCAF.

[0080] In the case of a trusted DCAF 410, the NWDAF 405 may provide the Area ofInterest, proximity range, predefined geographical area, or other criteria to the DCAF on the resolution of TAIs or any other finer resolution recognizable by the 5GC. In the case of an untrusted DCAF, NEF translates the requested criteria provided as event filter by the NWDAF into geographic zone identifier(s) or other geographic range identifier(s) or geographic direction identifier(s) that act as event filter(s) for the DCAF.

[0081] In step 3 the NWDAF 405 collects input data from the AMF 403. This may bedone for example via the AMF event exposure service. In some examples, AMF 403 reports on the data related to the target UEs. In some examples, the AMF feedback includes the detected (e.g., paged) set of target / non-target UEs within one or more of indicated area of interest, velocity / time-window of interest.

[0082] In step 4 the NWDAF 405 collects input data from GMLC 402. In someexamples, this includes the data (e.g., position) related to the target UEs. In some examples,the GMLC input includes the detected set of target / non-target UEs within one or more ofindicated area of interest, velocity / time-window of interest.

[0083] In step 5 the NWDAF 405 collects input data from AF 401. The informationprovided from AF 401 may include input information elements, e.g., information on the target / non-target UEs or objects, etc.

[0084] In step 6 the NWDAF 405 derives the requested analytics.

[0085] In step 7 the NWDAF 405 provides the derived analytics information to theconsumer function 404 as requested by the consumer, including one or more of the output prediction or statistics information. This includes, for example, proximity information of the target and / or non-target UEs or target and / or non-target objects. Attorney Docket No. PC933899WO 14213070-1

[0086] In some examples, the described analytics or computational model (e.g., anAI / ML model) comprises multiple analytics or computational models, wherein each of thesaid analytics / computational models may receive (as input information) one or more of any of the received input information described within this disclosure and generate any of the input and / or output information as described in the current disclosure. In some such examples, an output of the first analytics may be an input information to the second analytics.

[0087] In some embodiments, as input information to the analytics, the measurements(time, delay, angle, Doppler shift measurements of a reflective path associated to a sensingtarget object) related to a sensing operation, generated by a UE and / or a RAN node, are further given as input to one or more of the analytics or computational models. In some embodiments, the reported measurements (e.g., AoA / ZoA of a reflective path) by a UE or aRAN node are not initially indicated or determined to be associated with a target object. Assuch, the measurements are first inputted to analytics or a computational model which receives the path measurements (i.e., measurements including measurements of one or more paths potentially related to the sensing operation / target), and further, may receive information on the target object (object type, size, position / mobility pattern / velocity at a given time / snapshot / timestamp). As such, the analytics / computation model pairs / matches / filters the received path measurements to the related target information (e.g., dropping the path measurements which are not related to the physical target / non-target objects, matching the path measurements to the related physical object, e.g., path measurement #m is associated with object #n) and outputs the selected path measurements and / or the associated physical objects. The outputted set of the path measurements and / or the associated target or non-target object / UE ID are then inputted to the second analytics / computation model, in order to generate one or more of the defined predictions / statistics of the proximity data, as described within this disclosure as analytics output information. In some embodiments, the first analytics / computational model (which performs pairing of the path measurements to the physical objects) receives an indication of a path measurement associated with a physical object or receives description of a physical object to which the path is related to (as a known relation or as a label to be used for training and / or adjusting / improving the first analytics) Attorney Docket No. PC933899WO 14213070-1

[0088] Figure 5 illustrates an example of a NE 500 in accordance with aspects of thepresent disclosure. The NE 500 may include a processor 502, a memory 504, a controller 506, and a transceiver 508. The processor 502, the memory 504, the controller 506, or the transceiver 508, 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. The NE may be configured to operate as an analytics function apparatus.

[0089] The processor 502, the memory 504, the controller 506, or the transceiver 508,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.

[0090] The processor 502 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 502 may be configured to operate the memory 504. In some other implementations, the memory 504 may be integrated into the processor 502. The processor 502 may be configured to execute computer-readable instructions stored in the memory 504 to cause the NE 500 to perform various functions of the present disclosure.

[0091] The memory 504 may include volatile or non-volatile memory. The memory504 may store computer-readable, computer-executable code including instructions when executed by the processor 502 cause the NE 500 to perform various functions described herein. The code may be stored in a non-transitory computer-readable medium such the memory 504 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. Attorney Docket No. PC933899WO 14213070-1

[0092] In some implementations, the processor 502 and the memory 504 coupled withthe processor 502 may be configured to cause the NE 500 to perform one or more of thefunctions described herein (e.g., executing, by the processor 502, instructions stored in the memory 504). For example, the processor 502 may support wireless communication at the NE 500 in accordance with examples as disclosed herein. The NE 500 may be configured to support an analytics function apparatus for proximity sensing in wireless communication,comprising: at least one memory; and at least one processor coupled with the at least onememory and configured to cause the analytics function apparatus to: receive a request from a consumer for proximity sensing analytics, the request including: a list of target entities for which analytics information should be provided, the target entities including target UEs and target non-UE objects; filter information including conditions for which proximity information of each target entity should be generated; and a reporting destination for the generated proximity information; determine proximity information of the targeted UEs and / or target non-UE objects; and send the generated proximity information to the reporting destination.

[0093] In some examples, the non-UE objects may be defined via an ID referring to apreviously defined object or defined via indication of the object size / type and its position / velocity / orientation at a given timestamp, for example object defined as the object with ID X, or object (e.g. a vehicle) located at position X at the time instance Y, with dimension Z.

[0094] In some examples the reporting destination may be not explicitly indicated, forexample when the reporting destination is the same as the requesting entity. In some examples, a plurality of reporting destinations may be given for plurality of different proximity information which may be generated by the analytics. In some examples, the reporting entity is the same target UEs for which an analytics proximity information is generated. For example, when a collision risk is detected for any of the given target UEs, that target UE is notified with the derived / inferred collision condition. In some other examples, a reporting destination is associated to one or more target UE and / or target non- UE objects. As such, upon generation of a proximity information for the target UE or non- UE object, the generated proximity information is reported to the destination associated Attorney Docket No. PC933899WO 14213070-1with the target UE / non-UE object. In one such example, reporting destination associated with an indicated non-UE vehicle includes a UE ID associated with the vehicle (e.g., the UE associated in the network with the vehicle, either as the vehicle owner, or a UEaccompanying or attached to the vehicle).

[0095] The controller 506 may manage input and output signals for the NE 500. Thecontroller 506 may also manage peripherals not integrated into the NE 500. In some implementations, the controller 506 may utilize an operating system such as iOS®, ANDROID®, WINDOWS®, or other operating systems. In some implementations, the controller 506 may be implemented as part of the processor 502.

[0096] In some implementations, the NE 500 may include at least one transceiver 508.In some other implementations, the NE 500 may have more than one transceiver 508. The transceiver 508 may represent a wireless transceiver. The transceiver 508 may include one or more receiver chains 510, one or more transmitter chains 512, or a combination thereof.

[0097] A receiver chain 510 may be configured to receive signals (e.g., controlinformation, data, packets) over a wireless medium. For example, the receiver chain 510 may include one or more antennas for receive the signal over the air or wireless medium. The receiver chain 510 may include at least one amplifier (e.g., a low-noise amplifier (LNA)) configured to amplify the received signal. The receiver chain 510 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 510 may include at least one decoder for decoding the processing the demodulated signal to receive the transmitted data.

[0098] A transmitter chain 512 may be configured to generate and transmit signals(e.g., control information, data, packets). The transmitter chain 512 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 512 may also include at least one power amplifier configured to amplify the modulated signal to an appropriate power level suitable Attorney Docket No. PC933899WO 14213070-1for transmission over the wireless medium. The transmitter chain 512 may also include one or more antennas for transmitting the amplified signal into the air or wireless medium.

[0099] Figure 6 illustrates an example of a processor 600 in accordance with aspects ofthe present disclosure. The processor 600 may be an example of a processor configured toperform various operations in accordance with examples as described herein and may besuitable for proximity sensing in wireless communication. The processor 600 may include acontroller 602 configured to perform various operations in accordance with examples asdescribed herein. The processor 600 may optionally include at least one memory 604,which may be, for example, an L1 / L2 / L3 cache. Additionally, or alternatively, theprocessor 600 may optionally include one or more arithmetic-logic units (ALUs) 606. Oneor 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).

[0100] The processor 600 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., memorylocal to or included in the processor chipset (e.g., the processor 600) 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).

[0101] The controller 602 may be configured to manage and coordinate variousoperations (e.g., signaling, receiving, obtaining, retrieving, transmitting, outputting,forwarding, storing, determining, identifying, accessing, writing, reading) of the processor600 to cause the processor 600 to support various operations in accordance with examplesas described herein. For example, the controller 602 may operate as a control unit of theprocessor 600, generating control signals that manage the operation of various components Attorney Docket No. PC933899WO 14213070-1of the processor 600. These control signals include enabling or disabling functional units, selecting data paths, initiating memory access, and coordinating timing of operations.

[0102] The controller 602 may be configured to fetch (e.g., obtain, retrieve, receive)instructions from the memory 604 and determine subsequent instruction(s) to be executed to cause the processor 600 to support various operations in accordance with examples as described herein. The controller 602 may be configured to track memory address ofinstructions associated with the memory 604. The controller 602 may be configured todecode instructions to determine the operation to be performed and the operands involved. For example, the controller 602 may be configured to interpret the instruction and determine control signals to be output to other components of the processor 600 to cause the processor 600 to support various operations in accordance with examples as described herein. Additionally, or alternatively, the controller 602 may be configured to manage flow of data within the processor 600. The controller 602 may be configured to control transfer of data between registers, arithmetic logic units (ALUs), and other functional units of the processor 600.

[0103] The memory 604 may include one or more caches (e.g., memory local to orincluded in the processor 600 or other memory, such RAM, ROM, DRAM, SDRAM, SRAM, MRAM, flash memory, etc. In some implementations, the memory 604 may reside within or on a processor chipset (e.g., local to the processor 600). In some other implementations, the memory 604 may reside external to the processor chipset (e.g., remote to the processor 600).

[0104] The memory 604 may store computer-readable, computer-executable codeincluding instructions that, when executed by the processor 600, cause the processor 600 to perform various functions described herein. The code may be stored in a non-transitory computer-readable medium such as system memory or another type of memory. The controller 602 and / or the processor 600 may be configured to execute computer-readable instructions stored in the memory 604 to cause the processor 600 to perform various functions. For example, the processor 600 and / or the controller 602 may be coupled with orto the memory 604, the processor 600, the controller 602, and the memory 604 may beconfigured to perform various functions described herein. In some examples, the processor Attorney Docket No. PC933899WO 14213070-1600 may include multiple processors and the memory 604 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.

[0105] The one or more ALUs 606 may be configured to support various operations inaccordance with examples as described herein. In some implementations, the one or moreALUs 606 may reside within or on a processor chipset (e.g., the processor 600). In someother implementations, the one or more ALUs 606 may reside external to the processorchipset (e.g., the processor 600). One or more ALUs 606 may perform one or morecomputations such as addition, subtraction, multiplication, and division on data. Forexample, one or more ALUs 606 may receive input operands and an operation code, whichdetermines an operation to be executed. One or more ALUs 606 be configured with avariety of logical and arithmetic circuits, including adders, subtractors, shifters, and logic gates, to process and manipulate the data according to the operation. Additionally, oralternatively, the one or more ALUs 606 may support logical operations such as AND, OR,exclusive-OR (XOR), not-OR (NOR), and not-AND (NAND), enabling the one or moreALUs 606 to handle conditional operations, comparisons, and bitwise operations.

[0106] The processor 600 may support wireless communication in accordance withexamples as disclosed herein. The processor 600 may be configured to or operable tosupport a means for proximity sensing in wireless communications. The controller may beconfigured to cause the processor to: obtain a request from a consumer for proximity sensing analytics, the request including: a list of target entities for which analytics information should be provided, the target entities including target UEs and target non-UE objects; filter information including conditions for which proximity information of each target entity should be generated; and a reporting destination for the generated proximity information; determine proximity information of the targeted UEs and / or target non-UE objects; and output the generated proximity information to the reporting destination.

[0107] Figure 7 illustrates an example of a UE 700 in accordance with aspects of thepresent disclosure. The UE may operate as a consumer function as described herein. The UE 700 may include a processor 702, a memory 704, a controller 706, and a transceiver Attorney Docket No. PC933899WO 14213070-1708. The processor 702, the memory 704, the controller 706, or the transceiver 708, 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.

[0108] The processor 702, the memory 704, the controller 706, or the transceiver 708,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.

[0109] The processor 702 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 702 may be configured to operate the memory 704. In some other implementations, the memory 704 may be integrated into the processor 702. The processor 702 may be configured to execute computer-readable instructions stored in the memory 704 to cause the UE 700 to perform various functions of the present disclosure.

[0110] The memory 704 may include volatile or non-volatile memory. The memory704 may store computer-readable, computer-executable code including instructions when executed by the processor 702 cause the UE 700 to perform various functions described herein. The code may be stored in a non-transitory computer-readable medium such the memory 704 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.

[0111] In some implementations, the processor 702 and the memory 704 coupled withthe processor 702 may be configured to cause the UE 700 to perform one or more of thefunctions described herein (e.g., executing, by the processor 702, instructions stored in the memory 704). For example, the processor 702 may support wireless communication at the Attorney Docket No. PC933899WO 14213070-1UE 700 in accordance with examples as disclosed herein. The UE 700 may be configured to support a means for a consumer function for proximity sensing in wireless communication, comprising: at least one memory; and at least one processor coupled with the at least one memory and configured to cause the consumer function to: send to an analytics function apparatus a request for proximity sensing analytics, the requestincluding: a list of target entities for which analytics information should be provided, thetarget entities including target UEs and target non-UE objects; filter information including conditions for which proximity information of each target entity should be generated; and a reporting destination for the generated proximity information; and receive from the analytics function apparatus proximity information of the targeted UEs and / or target non- UE objects.

[0112] The controller 706 may manage input and output signals for the UE 700. Thecontroller 706 may also manage peripherals not integrated into the UE 700. In some implementations, the controller 706 may utilize an operating system such as iOS®, ANDROID®, WINDOWS®, or other operating systems. In some implementations, thecontroller 706 may be implemented as part of the processor 702.

[0113] In some implementations, the UE 700 may include at least one transceiver 708.In some other implementations, the UE 700 may have more than one transceiver 708. The transceiver 708 may represent a wireless transceiver. The transceiver 708 may include one or more receiver chains 710, one or more transmitter chains 712, or a combination thereof.

[0114] A receiver chain 710 may be configured to receive signals (e.g., controlinformation, data, packets) over a wireless medium. For example, the receiver chain 710 may include one or more antennas for receive the signal over the air or wireless medium. The receiver chain 710 may include at least one amplifier (e.g., a low-noise amplifier (LNA)) configured to amplify the received signal. The receiver chain 710 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 710 may include at least one decoder for decoding the processing the demodulated signal to receive the transmitted data. Attorney Docket No. PC933899WO 14213070-1

[0115] A transmitter chain 712 may be configured to generate and transmit signals(e.g., control information, data, packets). The transmitter chain 712 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 712 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 712 may also include one or more antennas for transmitting the amplified signal into the air or wireless medium.

[0116] Figure 8 illustrates a flowchart of a method in accordance with aspects of thepresent disclosure. The operations of the method may be implemented by an analyticsfunction as described herein. In some implementations, the analytics function apparatusmay execute a set of instructions to control the function elements to perform the describedfunctions.

[0117] At 802, the method may include receiving a request request from a consumer forproximity sensing analytics, the request including: a list of target entities for which analytics information should be provided, the target entities including target UEs and target non-UE objects; filter information including conditions for which proximity information ofeach target entity should be generated; and a reporting destination for the generatedproximity information The operations of 802 may be performed in accordance with examples as described herein. In some implementations, aspects of the operations of 802 may be performed by an NE and / or analytics function as described with reference to Figure 5.

[0118] At 804, the method may include determining proximity information of thetargeted UEs and / or target non-UE objects The operations of 804 may be performed inaccordance with examples as described herein. In some implementations, aspects of the operations of 802 may be performed by an NE and / or analytics function as described with reference to Figure 5. Attorney Docket No. PC933899WO 14213070-1

[0119] At 806, the method may include sending the generated proximity information tothe reporting destination. The operations of 806 may be performed in accordance with examples as described herein. In some implementations, aspects of the operations of 806 may be performed by an NE and / or analytics function as described with reference to Figure 5.

[0120] It should be noted that the method described herein describes a possibleimplementation, and that the operations and the steps may be rearranged or otherwise modified and that other implementations are possible.

[0121] Figure 9 illustrates a flowchart of a method in accordance with aspects of thepresent disclosure. The operations of the method may be implemented by a consumer function, optionally residing on a UE, as described herein. In some implementations, the UE may execute a set of instructions to control the function elements of the UE to perform the described functions.

[0122] At 902, the method may include sending to an analytics function apparatus arequest for proximity sensing analytics, the request including: a list of target entities for which analytics information should be provided, the target entities including target UEs and target non-UE objects; filter information including conditions for which proximityinformation of each target entity should be generated; and a reporting destination for thegenerated proximity information. The operations of 902 may be performed in accordance with examples as described herein. In some implementations, aspects of the operations of902 may be performed by a UE as described with reference to Figure 7.

[0123] At 904, the method may include receiving from the analytics function apparatusproximity information of the targeted UEs and / or target non-UE objects. The operations of904 may be performed in accordance with examples as described herein. In someimplementations, aspects of the operations of 904 may be performed by a UE as describedwith reference to Figure 7.

[0124] It should be noted that the method described herein describes a possibleimplementation, and that the operations and the steps may be rearranged or otherwise modified and that other implementations are possible. Attorney Docket No. PC933899WO 14213070-1

[0125] The description herein is provided to enable a person having ordinary skill in theart 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. Attorney Docket No. PC933899WO 14213070-1

Claims

What is claimed is:

1. An analytics function apparatus for proximity sensing in wirelesscommunication, comprising: at least one memory; and at least one processor coupled with the at least one memory and configured to cause the analytics function apparatus to: receive a request from a consumer for proximity sensing analytics, the request including: a list of target entities for which analytics information should be provided, the target entities including target UEs and target non-UE objects; filter information including conditions for which proximity information of each target entity should be generated; and a reporting destination for the generated proximity information; determine proximity information of the targeted UEs and / or target non-UE objects; and send the generated proximity information to the reporting destination.

2. The analytics function apparatus of claim 1, wherein the proximityinformation includes predictions and / or statistics for one or more of presence, relative distance and / or collision probability of a target UE or a target non-UE object relative to one or more of a target UE, a non-target UE, a non-target UE that is not on the list of target entities, and a non-target non-UE object that is not on the list of target entities.

3. The analytics function apparatus of claim 1 or 2, wherein theconsumer comprises one or more of a RAN node, SF, SesMF residing in a core as a network function or in RAN, LMF, and a UE. Attorney Docket No. PC933899WO 14213070-14. The analytics function apparatus of claim 1, 2 or 3, wherein thereporting destination is the consumer or a destination address or reporting identity included in the request.

5. The analytics function apparatus of claim 4, wherein the reportingidentity included in the request is a UE, RAN node or a sensing configuration entity.

6. The analytics function apparatus of any preceding claim, wherein theanalytics function apparatus resides in one or more of the following entities: a core network and comprise a NWDAF; a RAN node; an SF or SensMF within RAN or within the core network; and a UE.

7. The analytics function apparatus of any preceding claim, wherein theprocessor is configured to cause the analytics function apparatus to receive from one or more further entities one or more of the following pieces of information: UE speed; UE orientation; number of Target UEs; number of non-target UEs; number of target non-UE objects; number of non-target UE objects; list of objects including target and non-target UEs and non-UEs; attributes of UEs and non-UE objects including one or more of destination, route, average speed, time of arrival, size, dimension, physical type of non-UE object or object attached to or accompanied with or close by a target or non-target UE; and confidence in relative proximity data.

8. The analytics function apparatus of any preceding claim, wherein theprocessor is configured to cause the analytics function apparatus to receive from one ormore further entities one or more of the following pieces of proximity related input data:Non-UE objects and / or IDs;Attorney Docket No. PC933899WO 14213070-1Location information and / or presence information of UE or non-UE object; Location accuracy of a UE or a non-UE object; Velocity estimate of a UE or a non-UE object; Collision risk distance for UE; Collision risk distance for non-UE object; Collision risk velocity for UE; Collision risk velocity for non-UE object; UE or non-UE object heading;UE, non-UE object trajectory; UE location; Timestamp; UE and non-UE object physical properties; and Size, dimension, orientation, type, material of objects.

9. The analytics function apparatus of any preceding claim, wherein theprocessor is configured to cause the analytics function apparatus to receive from one or more further entities one or more of the following pieces of non-UE object-specific input data for each object: Speed; Orientation; Position; Physical properties; Object type; Expected mobility / route; Required proximity area; and Time stamp for the input information.

10. The analytics function apparatus of any preceding claim, wherein theprocessor is configured to cause the analytics function apparatus to receive from one or more further entities one or more of the following pieces of area specific input data: non-target object density; non-target mobility pattern / density; and collision probability per time-unit. Attorney Docket No. PC933899WO 14213070-111. The analytics function apparatus of any preceding claim, wherein theprocessor is configured to cause the analytics function apparatus to send to the one or more of the following pieces of TRP specific input data: speed; orientation; position; and physical properties.

12. The analytics function apparatus of any preceding claim, wherein theprocessor is configured to cause the analytics function apparatus to send to the reporting destination one or more of the following pieces of proximity sensing statistics: A UE group ID or set of UE IDs for which the statistics apply; A non-UE object group ID or set of non-UE object IDs for which the statistics apply; List of time slots during an analytics target period; Observed proximity sensing for a target UE and / or non-UE object; Observed proximity information; and Percentage of target and / or non-target UEs / non-UE objects accounted based on proximity criteria.

13. The analytics function apparatus of any preceding claim, wherein theprocessor is configured to cause the analytics function apparatus to send to the reporting destination one or more of the following pieces of proximity sensing predictions: a UE group ID or set of UE IDs for which predictions apply; a Non-UE object group ID or set of non-UE object IDs for which the predictions apply; Time slot start and duration; UE proximity attributes, including relative proximity information, confidence and / or sampling ratio; Time to Collision or other time to proximity information; Type of collision; Risk of collision; Confidence; andAccuracy. Attorney Docket No. PC933899WO 14213070-114. A consumer function for proximity sensing in wirelesscommunication, comprising: at least one memory; and at least one processor coupled with the at least one memory and configured to cause the consumer function to: send to an analytics function apparatus a request for proximity sensing analytics, the request including: a list of target entities for which analytics information should be provided, the target entities including target UEs and target non-UE objects; filter information including conditions for which proximity information of each target entity should be generated; and a reporting destination for the generated proximity information; and receive from the analytics function apparatus proximity information of the targeted UEs and / or target non-UE objects.

15. The consumer function of claim 14, which consumer function residesin a UE.

16. The consumer function of claim 14, which consumer functioncomprises one or more of a RAN node, SF, SesMF, and LMF.

17. A processor for proximity sensing in wireless communication,comprising: at least one controller coupled with at least one memory and configured to cause the processor to:obtain a request from a consumer for proximity sensing analytics, the request including: a list of target entities for which analytics information should be provided, the target entities including target UEs and target non-UE objects; Attorney Docket No. PC933899WO 14213070-1filter information including conditions for which proximity information of each target entity should be generated; and a reporting destination for the generated proximity information; determine proximity information of the targeted UEs and / or target non-UE objects; and output the generated proximity information to the reporting destination.

18. A method performed by an analytics function apparatus forproximity sensing in wireless communication, comprising: receiving a request from a consumer for proximity sensing analytics, the request including: a list of target entities for which analytics information should be provided, the target entities including target UEs and target non-UE objects; filter information including conditions for which proximity information of each target entity should be generated; and a reporting destination for the generated proximity information; determining proximity information of the targeted UEs and / or target non-UE objects; and sending the generated proximity information to the reporting destination. Attorney Docket No. PC933899WO 14213070-1

Images