Method, apparatus and computer program for framework for pru association for aiml positioning

Abstract

An apparatus comprising: means for receiving, from a network function, at least one condition for at least one positioning measurement from one or more candidate positioning reference units, PRUs, to be used for location refinement of the apparatus; means for receiving, from at least one of the network function and at least one candidate PRU of the one or more candidate PRUs, the at least one positioning measurement for each of the one or more candidate PRUs; means for determining at least one PRU of the one or more candidate PRUs for which the positioning measurement satisfies the at least one condition; means for determining location correction data from the positioning measurement of the at least one PRU; means for refining, using the location correction data, a location estimate of the apparatus.

Description

METHOD, APPARATUS AND COMPUTER PROGRAM FOR FRAMEWORK FOR PRU ASSOC IA TION FOR AIML POSITIONINGCROSS REFERENCE TO RELATED APPLICATION

[0001] This application claims the benefit of GB application No. 2311997.7 filed on 4 August 2023, which is incorporated herein by reference in its entirety.FIELD

[0002] The subject application relates to a method, apparatus, system and computer program.BACKGROUND

[0003] A communication system can be seen as a facility that enables communication sessions between two or more entities such as user terminals, base stations and / or other nodes by providing carriers between the various entities involved in the communications path. A communication system can be provided for example by means of a communication network and one or more compatible communication devices. The communication sessions may comprise, for example, communication of data for carrying communications such as voice, video, electronic mail (email), text message, multimedia and / or content data and so on. Nonlimiting examples of services provided comprise two-way or multi-way calls, data communication or multimedia services and access to a data network system, such as the Internet.

[0004] In a wireless communication system, at least a part of a communication session between at least two stations occurs over a wireless link. Examples of wireless systems comprise public land mobile networks (PLMN), satellite-based communication systems and different wireless local networks, for example wireless local area networks (WLAN). Some wireless systems can be divided into cells, and are therefore often referred to as cellular systems.

[0005] A user can access the communication system by means of an appropriate communication device or terminal. A communication device of a user may be referred to as user equipment (UE) or user device. A communication device is provided with an appropriate signal receiving and transmitting apparatus for enabling communications, for example enabling access to a communication network or communications directly with other users. The communication device may access a carrier provided by a station, for example a base station of a cell, and transmit and / or receive communications on the carrier.

[0006] The communication system and associated devices can operate in accordance with a given standard or specification which sets forth what the various entities associated with the system are permitted to do and how that should be achieved. Communication protocols and / or parameters which are to be used for the connection can also be defined.SUMMARY

[0007] According to a first example, there is provided an apparatus comprising means for receiving, from a network function, at least one condition for at least one positioning measurement from one or more candidate positioning reference units, PRUs, to be used for location refinement of the apparatus; means for receiving, from at least one of the network function and at least one candidate PRU of the one or more candidate PRUs, the at least one positioning measurement for each of the one or more candidate PRUs; means for determining at least one PRU of the one or more candidate PRUs for which the positioning measurement satisfies the at least one condition; means for determining location correction data from the positioning measurement of the at least one PRU; means for refining, using the location correction data, a location estimate of the apparatus.

[0008] According to some examples, the at least one condition is based on a similarity score between a positioning measurement of a candidate PRU of the one or more candidate PRUs and the positioning measurement of the apparatus.

[0009] According to some examples, at least one condition comprises a similarity score between a positioning measurement of a candidate PRU and the positioning measurement of the apparatus being above a threshold level.

[0010] According to some examples, the similarity score is determined using a first machine learning model, wherein the apparatus comprises: means for receiving, from the network entity, a configuration of the first machine learning model.

[0011] According to some examples, the apparatus comprises: means for receiving, from the network entity, a configuration of a model for determining the location correction data.

[0012] According to some examples, the model comprises a second machine learning model.

[0013] According to some examples, the apparatus comprises: means for receiving configuration of Siamese neural networks for determining the similarity score.

[0014] According to some examples, the apparatus comprises: means for determining the location estimate of the apparatus using radio access technology, RAT, positioning; means for sending the location estimate to the network function, wherein the network function uses the location estimate to determine that the one or more candidate PRUs are within a proximity threshold of the apparatus before sending the at least one positioning measurements of each of the one or more candidate PRUs to the apparatus.

[0015] According to some examples, the at least one positioning measurement from each of the one or more candidate PRUs is measured with respect to at least one reference signal transmitted by at least one transmission / reception point and the at least one positioning measurement of the apparatus is measured with respect to the at least one reference signal transmitted by the same at least one transmission / reception point.

[0016] According to some examples, the at least one positioning measurement from each of the one or more candidate PRUs is measured using a positioning method and the at least one positioning measurement of the apparatus is measured using the same positioning method.

[0017] According to some examples, the positioning measurements for each of one or more candidate PRUs are received from at least one of: the network function; at least one candidate PRU of the one or more candidate PRUs.

[0018] According to some examples, the at least one PRU comprises a qualified PRU.

[0019] According to a second example, there is provided a method comprising: receiving, from a network function, at least one condition for at least one positioning measurement from one or more candidate positioning reference units, PRUs, to be used for location refinement of a user equipment; receiving, from at least one of the network function and at least one candidate PRU of the one or more candidate PRUs, the at least one positioning measurement for each of the one or more candidate PRUs; determining at least one PRU of the one or more candidate PRUs for which the positioning measurement satisfies the at least one condition; determining location correction data from the positioning measurement of the at least one PRU; refining, using the location correction data, a location estimate of the user equipment.

[0020] According to some examples, the at least one condition is based on a similarity score between a positioning measurement of a candidate PRU of the one or more candidate PRUs and the positioning measurement of the apparatus.

[0021] According to some examples, at least one condition comprises a similarity score between a positioning measurement of a candidate PRU and the positioning measurement of the apparatus being above a threshold level.

[0022] According to some examples, the similarity score is determined using a first machine learning model, wherein the method comprises: receiving, from the network entity, a configuration of the first machine learning model.

[0023] According to some examples, the method comprises: receiving, from the network entity, a configuration of a model for determining the location correction data.

[0024] According to some examples, the model comprises a second machine learning model.

[0025] According to some examples, the method comprises: receiving configuration of Siamese neural networks for determining the similarity score.

[0026] According to some examples, the method comprises: determining the location estimate of the apparatus using radio access technology, RAT, positioning; sending the location estimate to the network function, wherein the network function uses the location estimate to determine that the one or more candidate PRUs are within a proximity threshold of the apparatus before sending the at least one positioning measurements.

[0027] According to a third example, there is provided an apparatus comprising at least one processor and at least one memory storing instructions that, when executed by the at least one processor, cause the apparatus at least to perform: receiving, from a network function, at least one condition for at least one positioning measurement from one or more candidate positioning reference units, PRUs, to be used for location refinement of a user equipment; receiving, from at least one of the network function and at least one candidate PRU of the one or more candidate PRUs, the at least one positioning measurement for each of the one or more candidate PRUs; determining at least one PRU of the one or more candidate PRUs for which the positioning measurement satisfies the at least one condition; determining location correction data from the positioning measurement of the at least one PRU; refining, using the location correction data, a location estimate of the user equipment.

[0028] According to a fourth example there is provided an apparatus comprising: circuitry for: receiving, from a network function, at least one condition for at least one positioning measurement from one or more candidate positioning reference units, PRUs, to be used for location refinement of a user equipment; receiving, from at least one of the network function and at least one candidate PRU of the one or more candidate PRUs, the at least one positioning measurement for each of the one or more candidate PRUs; determining at least one PRU of the one or more candidate PRUs for which the positioning measurement satisfies the at least one condition; determining location correction data from the positioning measurement of the at least one PRU; refining, using the location correction data, a location estimate of the user equipment.

[0029] According to a fifth example there is provided a computer program comprising instructions for causing an apparatus to perform at least the following: receiving, from a network function, at least one condition for at least one positioning measurement from one or more candidate positioning reference units, PRUs, to be used for location refinement of an apparatus; receiving, from at least one of the network function and at least one candidate PRU of the one or more candidate PRUs, the at least one positioning measurement for each of the one or more candidate PRUs; determining at least one PRU of the one or more candidate PRUs for which the positioning measurement satisfies the at least one condition; determining location correction data from the positioning measurement of the at least one PRU; refining, using the location correction data, a location estimate of the apparatus.

[0030] According to a sixth example there is provided a computer program comprising instructions stored thereon for performing at least the following: receiving, from a network function, at least one condition for at least one positioning measurement from one or more candidate positioning reference units, PRUs, to be used for location refinement of a user equipment; receiving, from at least one of the network function and at least one candidate PRU of the one or more candidate PRUs, the at least one positioning measurement for each of the one or more candidate PRUs; determining at least one PRU of the one or more candidate PRUs for which the positioning measurement satisfies the at least one condition; determining location correction data from the positioning measurement of the at least one PRU; refining, using the location correction data, a location estimate of the user equipment.

[0031] According to a seventh example there is provided a non-transitory computer readable medium comprising program instructions for causing an apparatus to perform at least the following: receiving, from a network function, at least one condition for at least one positioning measurement from one or more candidate positioning reference units, PRUs, to be used for location refinement of an apparatus; receiving, from at least one of the network function and at least one candidate PRU of the one or more candidate PRUs, the at least one positioning measurement for each of the one or more candidate PRUs; determining at least one PRU of the one or more candidate PRUs for which the positioning measurement satisfies the at least one condition; determining location correction data from the positioning measurement of the at least one PRU; refining, using the location correction data, a location estimate of the apparatus.

[0032] According to an eighth example there is provided a non-transitory computer readable medium comprising program instructions stored thereon for performing at least the following: receiving, from a network function, at least one condition for at least one positioning measurement from one or more candidate positioning reference units, PRUs, to be used for location refinement of a user equipment; receiving, from at least one of the network function and at least one candidate PRU of the one or more candidate PRUs, the at least one positioning measurement for each of the one or more candidate PRUs; determining at least one PRU of the one or more candidate PRUs for which the positioning measurement satisfies the at least one condition; determining location correction data from the positioning measurement of the at least one PRU; refining, using the location correction data, a location estimate of the user equipment.

[0033] According to a ninth example there is provided an apparatus comprising: means for determining one or more candidate positioning reference units, PRUs, for refining a location estimate of a user equipment; means for sending, to the user equipment, at least one condition for at least one positioning measurement from the one or more candidate PRUs to be used for location refinement of the user equipment; means for sending, to the user equipment, the at least one positioning measurements for each of the one or more candidate PRUs.

[0034] According to some examples, the at least one condition is based on a similarity score between a positioning measurement of a candidate PRU of the one or more candidate PRUs and the positioning measurement of the user equipment

[0035] According to some examples, the at least one condition comprises a similarity score between a positioning measurement of a candidate PRU and the positioning measurement of the user equipment being above a threshold level.

[0036] According to some examples, comprises: means for sending, to the user equipment, a configuration of a first machine learning model for determining the similarity score.

[0037] According to some examples, the apparatus comprises: means for sending, to the user equipment, a configuration of a model for determining the location correction data.

[0038] According to some examples, the model comprises a second machine learning model.

[0039] According to some examples, the apparatus comprises: means for configuring Siamese neural networks at the user equipment for determining the similarity score.

[0040] According to some examples, the apparatus comprises: means for receiving, from the user equipment, the location estimate of the user equipment; means for receiving location information for a set of PRUs; wherein determining the one or more candidate PRUs comprises determining, from the set of PRUs and based on the location estimate of the user equipment and the location information for the set of PRUs, one or more candidate PRUs that are within an estimated proximity threshold of the user equipment.

[0041] According to some examples, determining the one or more candidate PRUs comprises determining, from the set of PRUs, which PRUs have positioning measurements received withrespect to at least one reference signal transmitted by at least one same transmission / reception point as used for positioning measurements of the user equipment.

[0042] According to some examples, determining the one or more candidate PRUs comprises determining, from the set of PRUs, which PRUs use a same positioning method as used by the user equipment.

[0043] According to some examples, the positioning measurements for each of one or more candidate PRUs are transmitted from at least one of the network function; at least one candidate PRU of the one or more candidate PRUs.

[0044] According to some examples, the at least one PRU comprises a qualified PRU.

[0045] According to a tenth example there is provided a method comprising: determining one or more candidate positioning reference units, PRUs, for refining a location estimate of a user equipment; sending, to the user equipment, at least one condition for at least one positioning measurement from the one or more candidate PRUs to be used for location refinement of the user equipment; sending, to the user equipment, the at least one positioning measurements for each of the one or more candidate PRUs.

[0046] According to some examples, the at least one condition is based on a similarity score between a positioning measurement of a candidate PRU of the one or more candidate PRUs and the positioning measurement of the user equipment.

[0047] According to some examples, the at least one condition comprises a similarity score between a positioning measurement of a candidate PRU and the positioning measurement of the user equipment being above a threshold level.

[0048] According to some examples, the method comprises: sending, to the user equipment, a configuration of a first machine learning model for determining the similarity score.

[0049] According to some examples, the method comprises: sending, to the user equipment, a configuration of a model for determining the location correction data.

[0050] According to some examples, the model comprises a second machine learning model.

[0051] According to some examples, the apparatus comprises: means for configuring Siamese neural networks at the user equipment for determining the similarity score.

[0052] According to some examples, the method comprises: receiving, from the user equipment, the location estimate of the user equipment; receiving location information for a set of PRUs; wherein determining the one or more candidate PRUs comprises determining, from the set of PRUs and based on the location estimate of the user equipment and the location information for the set of PRUs, one or more candidate PRUs that are within an estimated proximity threshold of the user equipment.

[0053] According to some examples, determining the one or more candidate PRUs comprises determining, from the set of PRUs, which PRUs have positioning measurements received with respect to at least one reference signal transmitted by at least one same transmission / reception point as used for positioning measurements of the user equipment.

[0054] According to some examples, determining the one or more candidate PRUs comprises determining, from the set of PRUs, which PRUs use a same positioning method as used by the user equipment.

[0055] According to some examples, the positioning measurements for each of one or more candidate PRUs are transmitted from at least one of the network function; at least one candidate PRU of the one or more candidate PRUs.

[0056] According to some examples, the at least one PRU comprises a qualified PRU.

[0057] According to an eleventh example there is provided an apparatus comprising: at least one processor; and at least one memory including computer program code; the at least one memory and the computer program code configured to, with the at least one processor, cause the apparatus at least to perform : determining one or more candidate positioning reference units, PRUs, for refining a location estimate of a user equipment; sending, to the user equipment, at least one condition for at least one positioning measurement from the one or more candidate PRUs to be used for location refinement of the user equipment; sending, to the user equipment, the at least one positioning measurements for each of the one or more candidate PRUs.

[0058] According to a twelfth example there is provided an apparatus comprising circuitry for determining one or more candidate positioning reference units, PRUs, for refining a location estimate of a user equipment; sending, to the user equipment, at least one condition for at least one positioning measurement from the one or more candidate PRUs to be used for location refinement of the user equipment; sending, to the user equipment, the at least one positioning measurements for each of the one or more candidate PRUs.

[0059] According to a thirteenth example there is provided a computer program comprising instructions for causing an apparatus to perform at least the following: determining one or more candidate positioning reference units, PRUs, for refining a location estimate of a user equipment; sending, to the user equipment, at least one condition for at least one positioning measurement from the one or more candidate PRUs to be used for location refinement of the user equipment; sending, to the user equipment, the at least one positioning measurements for each of the one or more candidate PRUs.

[0060] According to a fourteenth example there is provided a computer program comprising instructions stored thereon for performing at least the following: determining one or more candidate positioning reference units, PRUs, for refining a location estimate of a user equipment; sending, to the user equipment, at least one condition for at least one positioning measurement from the one or more candidate PRUs to be used for location refinement of the user equipment; sending, to the user equipment, the at least one positioning measurements for each of the one or more candidate PRUs.

[0061] According to a fifteenth example there is provided a non-transitory computer readable medium comprising program instructions for causing an apparatus to perform at least the following: determining one or more candidate positioning reference units, PRUs, for refining a location estimate of a user equipment; sending, to the user equipment, at least one condition for at least one positioning measurement from the one or more candidate PRUs to be used for location refinement of the user equipment; sending, to the user equipment, the at least one positioning measurements for each of the one or more candidate PRUs.

[0062] According to a sixteenth example there is provided a non-transitory computer readable medium comprising program instructions stored thereon for performing at least the following: determining one or more candidate positioning reference units, PRUs, for refining a location estimate of a user equipment; sending, to the user equipment, at least one condition for at least one positioning measurement from the one or more candidate PRUs to be used for location refinement of the user equipment; sending, to the user equipment, the at least one positioning measurements for each of the one or more candidate PRUs.

[0063] According to an example, there is provided a non-transitory computer readable medium comprising program instructions that, when executed by an apparatus, cause the apparatus to perform at least the method according to any of the preceding examples.

[0064] In the above, many different examples have been described. It should be appreciated that further examples may be provided by the combination of any two or more of the examples described above.DESCRIPTION OF FIGURES

[0065] Some examples will now be described, by way of illustration only, with reference to the accompanying Figures in which:

[0066] FIG. 1 shows an example system;

[0067] FIG. 2 shows an example message flow;

[0068] FIG. 3 shows an example message flow;

[0069] FIG. 4 shows a method flow diagram according to some examples;

[0070] FIG. 5 shows a method flow diagram according to some examples;

[0071] FIG. 6 shows an example apparatus;

[0072] FIG. 7 shows an example apparatus; and

[0073] FIG. 8 shows a schematic representation of a non-volatile memory medium storing instructions which when executed by a processor allow a processor to perform one or more of the steps of the methods disclosed herein.DETAILED DESCRIPTION

[0074] In the following certain examples are explained with reference to mobile communication devices configured to communicate via a wireless cellular system and mobile communication systems serving such mobile communication devices.

[0075] Some examples relate to locating a position of a User Equipment (UE). In some examples, a UE location estimate is refined using information from at least one Positioning Reference Unit (PRU).

[0076] In some examples, a position of a PRU can be associated with a UE. The position of the PRU can be used to infer and / or refine the location of the associated UE.

[0077] Associating a PRU with a UE for refining a location estimate of the UE is complicated by the use of Radio Access Technology (RAT). In RAT-dependent positioning, unlike in Global Navigation Satellite System (GNSS) positioning, a UE may experience different measurement characteristics than the PRU, even if the UE is close to the PRU.

[0078] Different measurement characteristics are experienced due to different propagation environments experienced by the target UE 102 and a PRU. With reference to FIG. 1, although UE 102 is in the close vicinity of PRU l 104a, UE 102 does not measure reference signal from neighbouring gNB_2 (ngNB_2) 108b in the same way as PRU_1 104a due to the existence of a blocking object 106 between UE 102 and ngNB_2 108b. This renders the UE 102 to ngNB_2 108b link a Non Line of Sight (NLOS) link, as an example, whereas the PRU l 104a to ngNB_2 108b link is Line of Sight (LOS).

[0079] As well as different propagation conditions e.g., LOS / NLOS profiles, a PRU (e.g., PRU l l-4a and / or PRU 2 l-4b) and a target UE 102 may have different measurement capabilities which render the PRU measurements improper to be used for refining the location estimate of target UE 102. As another example, PRU 104a / b and target UE 102 may experience dissimilar timing related errors, such as Timing Error Group (TEG) profiles for example.

[0080] It follows from the above that the relative location of a PRU with respect to a UE 102 may not solely provide enough information alone in order to determine if the PRU is suitable for determining / refming a UE location estimate. In other words, UE location estimate refinement can be improved by not relying on proximity alone for assigning PRUs. Even if the network is aware of the available PRUs in an area, neither the network nor the target UE 102 is aware about which PRU (if any) is best to use for refining the UE’s location estimate.

[0081] Examples describe a method for providing a UE with information to assess whether a PRU experiences similar measurement conditions as a target UE. This can help to designate an appropriate PRU for location refinement in order to provide more accurate UE location refinement.

[0082] Examples describe a method for PRU selection that can be used to enhanced UE positioning. A network entity (Location Management Function (LMF)) identifies a set of candidate PRUs that have the potential to be associated to the target LIE. This is the set of PRUs that satisfy proximity criteria to the coarse location of the target LIE. In some examples, the set of PRUs may also satisfy other criteria that relate to similar measurement capabilities of the candidate PRUs with respect to the measurement capabilities of the target UE. In other words, the set of PRUs comprise “shortlisted” candidate PRUs that can be examined on whether they are proper PRUs for the target UE.

[0083] The LMF then configures the target UE with a model for associating a PRU for location refinement of the target UE. The ML model may be trained at the LMF side (or even in UE side in another example) to extract what similarity conditions on the pair {PRU measurements, target UE measurements} are needed to deem one PRU associated to the respective target UE (such that it is suitable to use for location refinement of the UE. The trained model can then be used by the target UE to conduct the PRU association process. The model may comprise a trained Machine Learning (ML) model. In some examples, the configuration comprises similarity conditions that need to be satisfied for a candidate PRU to represent an associated PRU to the target UE. The LMF configures the target UE on what conditions and / or trained ML model the target UE should use to infer whether a candidate PRU can be used for location refinement of the target UE.

[0084] According to some examples, the target UE then requests and obtains from the LMF a detailed list of PRU measurements per candidate (shortlisted) PRU, as well as per neighboring gNB. Each PRU may measure multiple neighboring gNBs, hence the measurements pertain to each combination of PRU and set of (neighboring) gNBs. The target UE utilizes the configuration from the LMF (as example, the trained ML model and configured similarity conditions), along with the provided PRU measurements per neighboring gNB to infer whether each of the candidate PRUs takes measurements per gNB which are suitably similar in comparison with the equivalent target UE measurements.

[0085] The target UE can then extract PRU correction data from the qualified PRUs as per LMF configuration. The target UE can then refine is location estimate based on the PRU correction data.

[0086] Figure 2 shows a method for location refinement of a UE 202.

[0087] At 201, a Location Services (LCS) client 212 sends an LCS request to a network function 210 for UE 202. In the examples described herein, the network function is referred to as LMF 210, however any suitable network function may be used. Alternatively or additionally to 201, the LCS request may be sent from UE 202 to LMF 210. At 205, LMF 210 may decide to use a UE-based positioning method.

[0088] At 207, PRUs that are available in the area of interest (i.e., in the approximate area of UE 202) may register themselves in the core network. This may include registering with LMF 210. Although only one PRU 204 is shown in FIG. 2, in other examples other PRUs may also register themselves at this stage. The registration may comprise an indication of the availability for the PRUs to provide, upon request, measurements for correction data. As part of the registration process, the PRUs (e.g., PRU 204) may provide ground truth location information as well as measurement capabilities to LMF 210.

[0089] At 209, UE 202 provides to LMF 210 location estimated by UE 202 using RAT- dependent positioning. RAT -dependent positioning includes, for example, Downlink Time Difference of Arrival positioning, Angle of Departure positioning, Angle of Arrival positioning, etc. RAT-dependent positioning includes technology that uses cellular network to position a device. At this stage, this estimate may be relatively coarse location information i.e., still unrefined. The information many be provided in LTE Positioning Protocol (LPP) information.

[0090] At 211, UE 202 requests PRU correction data from LMF 210.

[0091] At 213, LMF 210 determines a group (e.g., a shortlist) of candidate PRUs to use as location refinement for UE 202. In some examples, only one candidate PRU may be determined. The determination may be based on the unrefined (coarse) location information that UE 202 provided at 209 and the ground truth location information of the PRUs (as collected during their registration at 207), to shortlist those PRUs that have sufficient proximity to the target UE 202. In this example, PRU 204 is included in the group of PRUs.

[0092] In some examples, LMF 210 may use statistical information on past PRU assignments to target UEs in the area of interest to infer the shortlisted PRUs that can be candidate PRUs for location refinement at UE 202. In some examples, in addition to proximity, LMF 210 may consider the capability of PRUs as provided in the registration process (at 207) to shortlist candidate PRUs.

[0093] It should be noted that after 213, although LMF 210 has shortlisted candidate PRUs as being the most likely to be suitable for location refinement of UE 202, it is still not clear which PRU can be used due to potentially dissimilar propagation characteristics within a network. Such measurements are specific to the positioning method used as well as the list of detected base station nodes (e.g., gNBs) and / or Transmission Reception Points (TRPs). Both such parameters (positioning method of target UE and detected gNBs / TRPs) are known to LMF 210 for the target UE 202. A TRP may comprise any network node that transmits positioning reference signals. A TRP may comprise a base station.

[0094] At 215, LMF 210 configures the target UE 202 with at least one condition that must be satisfied for a candidate PRU to be used for location refinement for UE 202 (i.e., to be a “qualified” PRU). LMF 210 may also configure the UE 202 with a trained ML model to extract correction data from qualified PRUs.

[0095] In some examples, the at least one condition to be satisfied corresponds to a similarity score between measurements from target UE 202 and the PRU measurements, such that is the similarity score is above a configured threshold then the PRU qualifies for location refinement. In some examples, LMF 210 configures the target UE 202 on how to extract the similarityscore. In some examples, LMF 210 may provide assistance information for UE 202 to determine the similarity score, such as a (trained) ML model, e.g., Siamese (or twin) neural network that takes two inputs: i) UE positioning measurements; and ii) PRU’s positioning measurements, from the same TRP(s), and utilize the output, e.g., with a mapping function provided from LMF 210, to derive the (dis)similarity between the two inputs. Siamese (twin) neural networks comprise two or more identical subnetworks (same trained parameters and weights), each taking comparable inputs (e.g., the measurements we want to compare their similarity). The output of these subnetworks are then combined to compute a distance metric between.

[0096] At 217, LMF 210 requests, from the PRUs shortlisted at 213, positioning measurements. According to some examples, only positioning measurements that have a positioning method that matches the positioning method of UE 202 are requested. For example, if the target UE uses Down Link Time Difference Of Arrival (DL-TDOA), LMF 210 requests from PRU Reference Signal Time Different (RSTD) measurements. DL-TDOA uses measurements on the time of arrival difference of multiple Positioning Reference Signals (PRSs). Other positioning methods, such as Angle of Departure use power measurements (RSRP) instead. Carrier-phase-based positioning methods may also be used.

[0097] Further, at 217, LMF 210 may only request positioning measurements that are detected at UE 202. For example, if the target UE 202 reports RSTD for gNB / TRP IDs #1, #2, and #3, then LMF 210 requests to the shortlisted PRUs RSTD measurements for those specific gNB / TRP IDs.

[0098] At 219, the requested measurements are provided from PRU 204 to LMF 210. In some examples, other candidate PRUs may provide the requested measurements to LMF 210. The requested measurements may be provided per each specified base station and / or TRP.

[0099] At 221, LMF 210 provides to target UE 202 the obtained PRU measurements, which are specific per candidate PRU (i.e., it can correspond to multiple candidate PRUs) and perneighbor gNB / TRP. That is, LMF 210 designates the ID of the PRU the measurements are conducted, and the ID of the gNB / TRP that the measurements refer to.

[0100] At 223, target UE 202 extracts the similarity score as configured by LMF 210 at 215.

[0101] At 225, target UE 202 identifies PRUs that are, according to the configuration by LMF 210 at 215, suitable for refining location refinement of UE 202. These PRUs may be referred to as “qualified PRUs.” In some examples, there may be: a single qualified PRU; more than one qualified PRU; or no qualified PRU.

[0102] At 227, target UE 202 extracts the correction data from the qualified PRUs based on the (trained) ML model configured to UE 202 by LMF 210. In this example, there is only a single qualified PRU 204.

[0103] At 229, UE 202 refines its own location estimate (this could be the location estimate used 209, or another location estimate) based on legacy methods and updates LMF 210 at 231 about the refined location based on legacy LPP procedures.

[0104] Figure 3 shows a method for location refinement of a UE 302. FIG. 3 uses a “sidelink” interface between PRU 304 and UE 302.

[0105] 307 to 313 may be similar to 207 to 213 of FIG. 2. 215 may be similar to 315, but LMF 310 additionally configure UE 302 with IDs of candidate PRUs (shortlisted PRUs).

[0106] At 317 target UE 302 requests, via sidelink to the candidate PRUs indicated in 315, the positioning measurements that match the positioning method of the target UE 302 (e.g., if the target UE uses DL-TDOA, LMF requests from PRU RSTD measurements), as well as the list of detected gNBs / TRPs at the target UE 302. For example, if the target UE is using RSTD forgNB / TRP IDs #1, #2, and #3, then it requests to the candidate PRUs RSTD measurements for those specific gNB / TRP IDs.

[0107] At 319 the candidate PRUs provide to the target UE 302 via sidelink the requested measurements per specified base station / TRP.

[0108] 323 to 331 are then similar to 223 to 231 of FIG. 2. It should be noted that by using the sidelink interface in FIG. 3, no equivalent message to the message sent at 221 is required in the method of FIG. 3.

[0109] According to further examples, an LMF may configure an appropriate PRU based on UE capability and reported measurements. This may be performed as follows:• LMF may group PRU(s) similar to the UE capability within a given region of interest. The grouping may be performed based on number of TRPs, area, scenario, types of measurements performed, etc. As such, the grouped PRUs should be able to conduct the same (or at least approximately simialr) measurements with the target UE. This contrasts with associating a "powerful PRU" (which can conduct precise measurements) with a "weak UE" (which cannot conduct precise measurements), which would not be as useful.• The LMF may use an AI / ML model to blend information from multiple PRUs and the target UE(s) to output a similarity score. For example, the input to the AIML model can be LI -measurements (CIR, PDP) from PRU and the UE(s).• Based on the output of the AI / ML model, the LMF may select one or multiple PRU(s) to use for location refinement of the UE• For assisted AI / ML positioning, LMF may configure PRU(s) for the required measurements for the given AIML functionality.• PRU report back the requested measurement to LMF for correction and final positioning estimation of the UE. This can be used to correct UE location measurements received from the UE.

[0110] Figure 4 shows an example method flow. The method may be performed by a UE such as UE 102, UE 202, UE 302, etc.[oni] At 400, the method comprises receiving, from a network function, at least one condition for at least one positioning measurement from one or more candidate positioning reference units, PRUs, to be used for location refinement of a user equipment.

[0112] At 402, the method comprises receiving, from at least one of the network function and at least one candidate PRU of the one or more candidate PRUs, the at least one positioning measurement for each of the one or more candidate PRUs.

[0113] At 404, the method comprises determining at least one PRU of the one or more candidate PRUs for which the positioning measurement satisfies the at least one condition.

[0114] At 406, the method comprises determining location correction data from the positioning measurement of the at least one PRU.

[0115] At 408, the method comprises refining, using the location correction data, a location estimate of the user equipment.

[0116] Figure 5 shows an example method flow. The method may be performed, for example, by network function such as LMF 210, 310, for example.

[0117] At 500, the method comprises determining one or more candidate positioning reference units, PRUs, for refining a location estimate of a user equipment.

[0118] At 502, the method comprises sending, to the user equipment, at least one condition for at least one positioning measurement from the one or more candidate PRUs to be used for location refinement of the user equipment.

[0119] At 504, the method comprises sending, to the user equipment, the at least one positioning measurements for each of the one or more candidate PRUs.

[0120] FIG. 6 illustrates an example of a control apparatus 660 for controlling a network. The control apparatus may comprise at least one random access memory (RAM) 611a, at least on read only memory (ROM) 61 lb, at least one processor 612, 613 and an input / output interface614. The at least one processor 612, 613 may be coupled to the RAM 61 la and the ROM 61 lb. The at least one processor 612, 613 may be configured to execute an appropriate software code615. The software code 615 may, for example, allow the at least one processor 612, 613 to perform one or more steps of any method flow described herein. The software code 615 may be stored in the ROM 611b. The control apparatus 600 may be interconnected with another control apparatus 600 controlling another function of the RAN or the core network.

[0121] FIG. 7 illustrates an example of a terminal 700, such as a UE. The terminal 700 may be provided by any device capable of sending and receiving radio signals. In some examples, the terminal may comprise a user equipment, a mobile station (MS) or mobile device, such as a mobile phone or what is known as a ’smart phone’, a computer provided with a wireless interface card or other wireless interface facility (e.g., USB dongle), a personal data assistant (PDA) or a tablet provided with wireless communication capabilities, a machine-type communications (MTC) device, an Internet of things (loT) type communication device or any combinations of these or the like. The terminal 700 may provide, for example, communication of data for carrying communications. The communications may be one or more of voice, electronic mail (email), text message, multimedia, data, machine data and so on.

[0122] The terminal 700 may be configured to receive signals over an air or radio interface 707 via appropriate apparatus for receiving and may transmit signals via appropriate apparatus for transmitting radio signals. In FIG. 7, transceiver apparatus is designated schematically by block706. The transceiver apparatus 706 may be provided, for example, by means of a radio part and associated antenna arrangement. The antenna arrangement may be arranged internally or externally to the mobile device.

[0123] The terminal 700 may be provided with at least one processor 701, at least one memory ROM 702a, at least one RAM 702b and other possible components 703 and 704 for use in software and hardware aided execution of tasks it is designed to perform, including control of access to and communications with access systems and other communication devices. The at least one processor 701 is coupled to the RAM 702b and the ROM 702a. The at least one processor 701 may be configured to execute an appropriate software code 708. The software code 708 may for example allow to perform one or more of steps of any method flow described herein. The software code 708 may be stored in the ROM 702a.

[0124] The processor, storage and other relevant control apparatus can be provided on an appropriate circuit board and / or in chipsets. This example is denoted by reference 702. The device may optionally have a user interface, such as key pad 705, touch sensitive screen or pad, combinations thereof or the like. Optionally, one or more of a display, a speaker and a microphone may be provided depending on the type of the device.

[0125] FIG. 8 shows a schematic representation of non-volatile memory media 800a (e.g. computer disc (CD) or digital versatile disc (DVD)) and 800b (e.g. universal serial bus (USB) memory stick) storing instructions and / or parameters 802 which when executed by a processor allow the processor to perform one or more of the steps of any method flow described herein.

[0126] It should be understood that the apparatuses may comprise or be coupled to other units or modules etc., such as radio parts or radio heads, used in or for transmission and / or reception. Although the apparatuses have been described, in some examples as one entity, different modules and memory may be implemented in one or more physical or logical entities.

[0127] It is noted that whilst some examples have been described in relation to 5G networks, similar techniques and / or mechanisms can be applied in relation to other networks and communication systems (e.g., 6G and beyond). Therefore, although certain examples were described above, by way of illustration with reference to certain example architectures for wireless networks, technologies and standards, other examples may be applied to any other suitable forms of communication systems than those illustrated and described herein.

[0128] It is also noted herein that while the above details various examples, there are several variations and modifications which may be made to any of the aforementioned example solutions without departing from the scope of the examples described herein.

[0129] As used herein, “at least one of the following: ” and “at least one of ” and similar wording, where the list of two or more elements are joined by “and” or “or”, mean at least any one of the elements, or at least any two or more of the elements, or at least all the elements.

[0130] In general, the various examples may be implemented in hardware or special purpose circuitry, software, logic or any combination thereof. Some examples detailed in the subject disclosure may be implemented in hardware, while other aspects may be implemented in firmware or software which may be executed by a controller, microprocessor or other computing device, although the subject disclosure is not limited thereto. While various aspects of the subject disclosure may be illustrated and described as block diagrams, flow charts, or using some other pictorial representation, it is well understood that these blocks, apparatus, systems, techniques or methods described herein may be implemented in, as non-limiting examples, hardware, software, firmware, special purpose circuits or logic, general purpose hardware or controller or other computing devices, or some combination thereof.

[0131] As used herein, the term “circuitry” may refer to one or more or all of the following examples:(a) hardware-only circuit implementations (such as, implementations in only analog and / or digital circuitry) and(b) combinations of hardware circuits and software, such as (as applicable):(i) a combination of analog and / or digital hardware circuit(s) with software / firmware and(ii) any portions of hardware processor(s) with software (including digital signal processor(s)), software, and memory(ies) that work together to cause an apparatus, such as a mobile phone or server, to perform various functions) and(c) hardware circuit(s) and or processor(s), such as a microprocessor s) or a portion of a microprocessor(s), that requires software (e.g., firmware) for operation, but the software may not be present when it is not needed for operation.

[0132] This definition of circuitry applies to all uses of this term herein, including in any claims. As a further example, as used herein, the term circuitry also covers an implementation of merely a hardware circuit or processor (or multiple processors) or portion of a hardware circuit or processor and its (or their) accompanying software and / or firmware. The term circuitry also covers, for example and if applicable to the particular claim element, a baseband integrated circuit or processor integrated circuit for a mobile device or a similar integrated circuit in server, a cellular network device, or other computing or network device.

[0133] The various examples detailed in the subject disclosure may be implemented by computer software executable by a data processor of the mobile device, such as in the processor entity, or by hardware, or by a combination of software and hardware. Computer software or program, also called program product, including software routines, applets and / or macros, may be stored in any apparatus-readable data storage medium and they comprise program instructions to perform particular tasks. A computer program product may comprise one or more computer-executable components which, when the program is run, are configured to carry out one or more steps of any method flow described herein. The one or more computerexecutable components may be at least one software code or portions of it.

[0134] Further in this regard it should be noted that any blocks of the logic flow as in the Figures may represent program steps, or interconnected logic circuits, blocks and functions, or a combination of program steps and logic circuits, blocks and functions. The software may be stored on such physical media as memory chips, or memory blocks implemented within the processor, magnetic media such as hard disk or floppy disks, and optical media such as DVDand the data variants thereof, CD. The physical media may be implemented as a non-transitory media.

[0135] The term “non-transitory,” as used herein, is a limitation of the medium itself (e.g., tangible, not a signal) as opposed to a limitation on data storage persistency (e.g., RAM vs. ROM).

[0136] The memory may be of any type suitable to the local technical environment and may be implemented using any suitable data storage technology, such as semiconductor based memory devices, magnetic memory devices and systems, optical memory devices and systems, fixed memory and removable memory. The data processors may be of any type suitable to the local technical environment, and may comprise one or more of general purpose computers, special purpose computers, microprocessors, digital signal processors (DSPs), application specific integrated circuits (ASIC), FPGA, gate level circuits and processors based on multi core processor architecture, as non-limiting examples.

[0137] Examples of the subject disclosure may be practiced in various components, such as integrated circuit modules. The design of integrated circuits is by and large a highly automated process. Complex and powerful software tools are available for converting a logic level design into a semiconductor circuit design ready to be etched and formed on a semiconductor substrate.

[0138] The scope of protection sought for the various examples described herein is set out by the independent claims. The examples, if any, described herein that do not fall under the scope of the independent claims are to be interpreted as examples useful for understanding various facets of the subject disclosure.

[0139] The foregoing description has provided by way of non-limiting examples to provide a full and informative description the subject disclosure. However, various modifications and adaptations may become apparent to those skilled in the relevant arts in view of the foregoing description, when read in conjunction with the accompanying drawings and the claims.However, all such and similar modifications of the teachings of this disclosure will still fall within the scope of the examples described herein. Indeed, there is a further example comprising a combination of one or more examples with any of the other example as previously described herein.

Claims

CLAIMS1. An apparatus, comprising: means for receiving, from a network function, at least one condition for at least one positioning measurement from one or more candidate positioning reference units, PRUs, to be used for location refinement of the apparatus; means for receiving, from at least one of the network function and at least one candidate PRU of the one or more candidate PRUs, the at least one positioning measurement for each of the one or more candidate PRUs; means for determining at least one PRU of the one or more candidate PRUs for which the positioning measurement satisfies the at least one condition; means for determining location correction data from the positioning measurement of the at least one PRU; means for refining, using the location correction data, a location estimate of the apparatus.

2. An apparatus according to claim 1, wherein the at least one condition is based on a similarity score between a positioning measurement of a candidate PRU of the one or more candidate PRUs and the positioning measurement of the apparatus.

3. An apparatus according to claim 2, wherein the similarity score is determined using a first machine learning model, wherein the apparatus comprises: means for receiving, from the network entity, a configuration of the first machine learning model.

4. An apparatus according to any preceding claim, the apparatus comprising: means for receiving, from the network entity, a configuration of a model for determining the location correction data.

5. An apparatus according to any preceding claim, the apparatus comprising: means for determining the location estimate of the apparatus using radio access technology, RAT, positioning;means for sending the location estimate to the network function, wherein the network function uses the location estimate to determine that the one or more candidate PRUs are within a proximity threshold of the apparatus before sending the at least one positioning measurements of each of the one or more candidate PRUs to the apparatus.

6. An apparatus according to any preceding claim, wherein the at least one positioning measurement from each of the one or more candidate PRUs is measured with respect to at least one reference signal transmitted by at least one transmission / reception point and the at least one positioning measurement of the apparatus is measured with respect to the at least one reference signal transmitted by the same at least one transmission / reception point.

7. An apparatus according to any preceding claim, wherein the at least one positioning measurement from each of the one or more candidate PRUs is measured using a positioning method and the at least one positioning measurement of the apparatus is measured using the same positioning method.

8. A method comprising: receiving, from a network function, at least one condition for at least one positioning measurement from one or more candidate positioning reference units, PRUs, to be used for location refinement of a user equipment; receiving, from at least one of the network function and at least one candidate PRU of the one or more candidate PRUs, the at least one positioning measurement for each of the one or more candidate PRUs; determining at least one PRU of the one or more candidate PRUs for which the positioning measurement satisfies the at least one condition; determining location correction data from the positioning measurement of the at least one PRU; refining, using the location correction data, a location estimate of the user equipment.

9. A method according to claim 8, wherein the at least one condition is based on a similarity score between a positioning measurement of a candidate PRU of the one or more candidate PRUs and the positioning measurement of the apparatus.

10. A method according to claim 8 or claim 9, wherein the similarity score is determined using a first machine learning model, and wherein the method comprises: receiving, from the network entity, a configuration of the first machine learning model.

11. A method according to any of claims 8 to 10, wherein the method comprises: receiving, from the network entity, a configuration of a model for determining the location correction data.

12. A computer program comprising instructions stored thereon for performing at least the following: receiving, from a network function, at least one condition for at least one positioning measurement from one or more candidate positioning reference units, PRUs, to be used for location refinement of a user equipment; receiving, from at least one of the network function and at least one candidate PRU of the one or more candidate PRUs, the at least one positioning measurement for each of the one or more candidate PRUs; determining at least one PRU of the one or more candidate PRUs for which the positioning measurement satisfies the at least one condition; determining location correction data from the positioning measurement of the at least one PRU; refining, using the location correction data, a location estimate of the user equipment.

13. An apparatus comprising: means for determining one or more candidate positioning reference units, PRUs, for refining a location estimate of a user equipment;means for sending, to the user equipment, at least one condition for at least one positioning measurement from the one or more candidate PRUs to be used for location refinement of the user equipment; means for sending, to the user equipment, the at least one positioning measurements for each of the one or more candidate PRUs.

14. An apparatus according to claim 13, wherein the at least one condition is based on a similarity score between a positioning measurement of a candidate PRU of the one or more candidate PRUs and the positioning measurement of the user equipment.

15. An apparatus according to claim 14, the apparatus comprising: means for sending, to the user equipment, a configuration of a first machine learning model for determining the similarity score.

16. An apparatus according to any of claims 13 to 15, the apparatus comprising: means for sending, to the user equipment, a configuration of a model for determining the location correction data.

17. An apparatus according to any of claims 13 to 16, the apparatus comprising: means for receiving, from the user equipment, the location estimate of the user equipment; means for receiving location information for a set of PRUs; wherein determining the one or more candidate PRUs comprises determining, from the set of PRUs and based on the location estimate of the user equipment and the location information for the set of PRUs, one or more candidate PRUs that are within an estimated proximity threshold of the user equipment.

18. An apparatus according to claim 17, wherein determining the one or more candidate PRUs comprises determining, from the set of PRUs, which PRUs have positioning measurements received with respect to at least one reference signal transmitted by at least one same transmission / reception point as used for positioning measurements of the user equipment.

19. An apparatus according to claim 17 or claim 18, wherein determining the one or more candidate PRUs comprises determining, from the set of PRUs, which PRUs use a same positioning method as used by the user equipment.

20. A method comprising: determining one or more candidate positioning reference units, PRUs, for refining a location estimate of a user equipment; sending, to the user equipment, at least one condition for at least one positioning measurement from the one or more candidate PRUs to be used for location refinement of the user equipment; sending, to the user equipment, the at least one positioning measurements for each of the one or more candidate PRUs.

21. A method according to claim 20, wherein the at least one condition is based on a similarity score between a positioning measurement of a candidate PRU of the one or more candidate PRUs and the positioning measurement of the user equipment22. A method according to claim 21, the method comprising: sending, to the user equipment, a configuration of a first machine learning model for determining the similarity score.

23. A method according to any of claims 20 to 22, the method comprising: sending, to the user equipment, a configuration of a model for determining the location correction data.

24. A method according to any of claims 20 to 23, the method comprising: receiving, from the user equipment, the location estimate of the user equipment; receiving location information for a set of PRUs; wherein determining the one or more candidate PRUs comprises determining, from the set of PRUs and based on the location estimate of the user equipment and the location information for the set of PRUs, one or more candidate PRUs that are within an estimated proximity threshold of the user equipment.

25. A computer program comprising instructions stored thereon for performing at least the following: determining one or more candidate positioning reference units, PRUs, for refining a location estimate of a user equipment; sending, to the user equipment, at least one condition for at least one positioning measurement from a candidate PRU to be used for location refinement of the user equipment; sending, to the user equipment, the at least one positioning measurements for each of the one or more candidate PRUs.

26. An apparatus, comprising: at least one processor; and at least one memory storing instructions that, when executed by the at least one processor, cause the apparatus at least to: receive, from a network function, at least one condition for at least one positioning measurement from one or more candidate positioning reference units, PRUs, to be used for location refinement of a user equipment; receive, from at least one of the network function and at least one candidate PRU of the one or more candidate PRUs, the at least one positioning measurement for each of the one or more candidate PRUs; determine at least one PRU of the one or more candidate PRUs for which the positioning measurement satisfies the at least one condition; determine location correction data from the positioning measurement of the at least one PRU; refine, using the location correction data, a location estimate of the user equipment.

27. An apparatus, comprising: at least one processor; and at least one memory storing instructions that, when executed by the at least one processor, cause the apparatus at least to: determine one or more candidate positioning reference units, PRUs, for refining a location estimate of a user equipment;send, to the user equipment, at least one condition for at least one positioning measurement from the one or more candidate PRUs to be used for location refinement of the user equipment; send, to the user equipment, the at least one positioning measurements for each of the one or more candidate PRUs.

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