Method, apparatus and computer program

Abstract

There is provided a device comprising: means for receiving first measurement information from a first user equipment; means for receiving second measurement information from a second user equipment; and means for determining the location of the first user equipment on the basis of the first measurement information and the second measurement information; wherein the first user equipment and the second user equipment are co-located with each other; and wherein the type of the first measurement information is different from the type of the second measurement information.

Description

Technical Field

[0001] The present application relates to a method, an apparatus, and a computer program, and more particularly but not exclusively to user equipment positioning. Background Art

[0002] A communication system can be regarded as a facility that enables a communication session between two or more entities (such as user terminals, base stations, and / or other nodes) by providing a carrier wave between the various entities involved in the communication path. For example, a communication system can be provided through a communication network and one or more compatible communication devices. A communication session can include, for example, data communication for carrying communication such as voice, video, email, text messages, multimedia, and / or content data. Non-limiting examples of the services provided include two-way or multi-way calls, data communication, or multimedia services, as well as access to a data network system (such as the Internet). Summary of the Invention

[0003] According to one aspect, there is provided an apparatus, comprising: means for receiving first measurement information from a first user equipment; means for receiving second measurement information from a second user equipment; and means for determining the location of the first user equipment based on the first measurement information and the second measurement information; wherein the first user equipment and the second user equipment are co-located with each other; and wherein the type of the first measurement information is different from the type of the second measurement information.

[0004] According to some examples, the apparatus comprises means for receiving, from at least one of the first user equipment or the second user equipment, capability information indicating support for measurement aggregation.

[0005] According to some examples, the apparatus comprises means for indicating to at least one of the first user equipment or the second user equipment that the apparatus supports the measurement aggregation.

[0006] According to some examples, the first measurement information or the second measurement information comprises at least one of the following: frequency measurement information; cell measurement information; time of arrival measurement information; angle of arrival measurement information; or reference signal received power measurement information.

[0007] According to some examples, the first measurement information or the second measurement information has been output from a corresponding machine learning model at the first user equipment or the second user equipment.

[0008] According to some examples, the apparatus comprises one or more machine learning models, wherein the first measurement information and the second measurement information are input into the one or more machine learning models.

[0009] According to some examples, the device includes means for determining whether the first user equipment is collocated with the second user equipment based on a rough location estimate of the first user equipment and the second user equipment or using the received sidelink information of the first user equipment and the second user equipment.

[0010] According to some examples, the device includes means for sending the determined location information to at least one of the first user equipment or the second user equipment.

[0011] According to some examples, the device includes a location management function.

[0012] According to some examples, the device includes: 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 device to operate.

[0013] According to one aspect, there is provided an apparatus including: 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 to at least perform the following: receive first measurement information from a first user equipment; receive second measurement information from a second user equipment; and determine the location of the first user equipment based on the first measurement information and the second measurement information; wherein the first user equipment and the second user equipment are collocated with each other; and wherein the type of the first measurement information is different from the type of the second measurement information.

[0014] According to one aspect, there is provided a first user equipment including: means for sending first measurement information to a network entity; and means for receiving positioning information determined based on the first measurement information and second measurement information of a second user equipment; wherein the first user equipment is collocated with the second user equipment, and wherein the type of the first measurement information is different from the type of the second measurement information.

[0015] According to some examples, the device includes means for sending capability information indicating support for measurement aggregation to the network entity.

[0016] According to some examples, the device includes means for receiving an indication that the network entity supports the measurement aggregation.

[0017] According to some examples, the first measurement information or the second measurement information includes at least one of the following: frequency measurement information; cell measurement information; time of arrival measurement information; angle of arrival measurement information; reference signal received power measurement information.

[0018] According to some examples, the first measurement information or the second measurement information has been output from a corresponding machine learning model at the first user equipment or the second user equipment.

[0019] According to some examples, the device includes a user equipment.

[0020] According to some examples, a method is provided, including: receiving first measurement information from a first user equipment; receiving second measurement information from a second user equipment; determining a location of the first user equipment based on the first measurement information and the second measurement information; wherein the first user equipment and the second user equipment are co-located with each other; and wherein a type of the first measurement information is different from a type of the second measurement information.

[0021] According to some examples, the method includes receiving, from at least one of the first user equipment or the second user equipment, capability information indicating support for measurement aggregation.

[0022] According to some examples, the method includes indicating to at least one of the first user equipment or the second user equipment that the device supports measurement aggregation.

[0023] According to some examples, the first measurement information or the second measurement information includes at least one of the following: frequency measurement information; cell measurement information; time of arrival measurement information; angle of arrival measurement information; or reference signal received power measurement information.

[0024] According to some examples, the first measurement information or the second measurement information has been output from a corresponding machine learning model at the first user equipment or the second user equipment.

[0025] According to some examples, the apparatus includes one or more machine learning models, wherein the first measurement information and the second measurement information are input into the one or more machine learning models.

[0026] According to some examples, the method includes determining whether the first user equipment is co-located with the second user equipment based on a rough location estimate of the first user equipment and the second user equipment, or using received sidelink information of the first user equipment and the second user equipment.

[0027] According to some examples, the method includes sending the determined location information to at least one of the first user equipment or the second user equipment.

[0028] According to one aspect, a method is provided, including: sending first measurement information from a first user equipment to a network entity; and receiving positioning information determined based on the first measurement information and second measurement information of a second user equipment; wherein the first user equipment is co-located with the second user equipment, and wherein the type of the first measurement information is different from the type of the second measurement information.

[0029] According to some examples, the method includes sending capability information indicating support for measurement aggregation to the network entity.

[0030] According to some examples, the method includes receiving an indication that the network entity supports the measurement aggregation.

[0031] According to some examples, the first measurement information or the second measurement information includes at least one of the following: frequency measurement information; cell measurement information; time of arrival measurement information; angle of arrival measurement information; reference signal received power measurement information.

[0032] According to some examples, the first measurement information or the second measurement information has been output from a corresponding machine learning model at the first user equipment or the second user equipment.

[0033] According to one aspect, a computer program is provided, including instructions for causing a device to perform at least the following: receiving first measurement information from a first user equipment; receiving second measurement information from a second user equipment; determining the location of the first user equipment based on the first measurement information and the second measurement information; wherein the first user equipment and the second user equipment are co-located with each other; and wherein the type of the first measurement information is different from the type of the second measurement information.

[0034] According to one aspect, a non-transitory computer-readable medium including program instructions is provided, the program instructions for causing a device to perform at least the following: receiving first measurement information from a first user equipment; receiving second measurement information from a second user equipment; determining the location of the first user equipment based on the first measurement information and the second measurement information; wherein the first user equipment and the second user equipment are co-located with each other; and wherein the type of the first measurement information is different from the type of the second measurement information

[0035] According to one aspect, a computer program is provided, including instructions for causing a device to perform at least the following: sending first measurement information from a first user equipment to a network entity; and receiving positioning information determined based on the first measurement information and second measurement information of a second user equipment; wherein the first user equipment is co-located with the second user equipment, and wherein the type of the first measurement information is different from the type of the second measurement information.

[0036] According to one aspect, there is provided a non-transitory computer-readable medium including program instructions for causing a device to perform at least the following: sending first measurement information from a first user equipment to a network entity; and receiving positioning information determined based on the first measurement information and second measurement information of a second user equipment; wherein the first user equipment is co-located with the second user equipment, and wherein the type of the first measurement information is different from the type of the second measurement information.

[0037] According to one aspect, there is provided a device including: means for receiving capability information from one or more user equipments including a first user equipment; means for determining position information of the first user equipment based on measurement information of the first user equipment; and means for determining position information of a second user equipment by replicating the measurement information of the first user equipment; wherein the second user equipment is co-located with the first user equipment.

[0038] According to some examples, the capability information indicates support for measurement aggregation, and wherein at least the first user equipment supports measurement aggregation.

[0039] According to some examples, the device includes means for transmitting a positioning measurement request message based on the capability information; and

[0040] means for receiving measurement information from the first user equipment.

[0041] According to some examples, the capability information further includes information related to one or more of the following: battery power of the first user equipment; processor capability; memory capability.

[0042] According to some examples, the device includes means for performing time difference of arrival determination on the received measurement information.

[0043] According to some examples, the means for determining the position information of the second user equipment is configured to do so without receiving any measurement information from the second user equipment.

[0044] According to some examples, the device includes means for determining whether the first user equipment is co-located with the second user equipment.

[0045] According to some examples, the device includes means for implementing a rule for determining that the first user equipment is co-located with the second user equipment.

[0046] According to some examples, the rule is based on a rough position estimate of the first user equipment and the second user equipment.

[0047] According to some examples, the device includes means for receiving information that the first user equipment and the second user equipment are co-located based on sidelink information communicated between the first user equipment and the second user equipment.

[0048] According to some examples, the capability information of the first user equipment is included in a positioning request transmitted from the first user equipment.

[0049] According to some examples, the device includes means for sending the determined location information to at least one of the first user equipment or the second user equipment.

[0050] According to some examples, the device includes a location management function.

[0051] According to some examples, the device includes 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 device to operate.

[0052] According to one aspect, there is provided an apparatus including 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 to at least perform the following: receive capability information from one or more user equipments including a first user equipment; determine location information of the first user equipment based on measurement information of the first user equipment; determine location information of a second user equipment by replicating the measurement information of the first user equipment; wherein the second user equipment is co-located with the first user equipment.

[0053] According to one aspect, there is provided a device including: means for sending capability information to a network entity; means for receiving location information of the device from the network entity; wherein the device is co-located with a user equipment, and

[0054] wherein the location information of the device is determined by replicating measurement information of the user equipment.

[0055] According to some examples, the capability information indicates support for measurement aggregation, and wherein the device supports measurement aggregation.

[0056] According to some examples, the device is configured to receive the location information without having to send measurement information to the network entity.

[0057] According to some examples, the capability information further includes information related to one or more of the following: battery power of the device; processor capability; memory capability.

[0058] According to some examples, the device includes means for determining whether the device is co-located with the user equipment based on sidelink communication between the device and the user equipment.

[0059] According to some examples, the device includes a user equipment.

[0060] According to one aspect, a method is provided, including: receiving capability information from one or more user equipments including a first user equipment; determining location information of the first user equipment based on measurement information of the first user equipment; and determining location information of a second user equipment by replicating the location information of the first user equipment; wherein the second user equipment is co-located with the first user equipment.

[0061] According to some examples, the capability information indicates support for measurement aggregation, and at least the first user equipment supports measurement aggregation.

[0062] According to some examples, the method includes transmitting a positioning measurement request message based on the capability information; and receiving measurement information from the first user equipment.

[0063] According to some examples, the capability information further includes information related to one or more of the following: battery power of the first user equipment; processor capability; memory capability.

[0064] According to some examples, the method includes performing a time difference of arrival determination on the received measurement information.

[0065] According to some examples, determining the location information of the second user equipment is done without receiving any measurement information from the second user equipment.

[0066] According to some examples, the method includes determining whether the first user equipment is co-located with the second user equipment.

[0067] According to some examples, the method includes implementing rules for determining that the first user equipment is co-located with the second user equipment.

[0068] According to some examples, the rules are based on a rough location estimate of the first user equipment and the second user equipment.

[0069] According to some examples, the method includes receiving information that the first user equipment is co-located with the second user equipment based on sidelink information communicated between the first user equipment and the second user equipment.

[0070] According to some examples, the capability information of the first user equipment is included in a positioning request transmitted from the first user equipment.

[0071] According to some examples, the method includes sending the determined location information to at least one of the first user equipment or the second user equipment.

[0072] According to one aspect, there is provided a method including: sending capability information from a device to a network entity; receiving location information of the device from the network entity; wherein the device is co-located with a user equipment, and wherein the location information of the device is determined by replicating measurement information of the user equipment.

[0073] According to some examples, the capability information indicates support for measurement aggregation, and wherein the device supports measurement aggregation.

[0074] According to some examples, the method includes receiving location information without having to send measurement information to the network entity.

[0075] According to some examples, the capability information further includes information related to one or more of the following: battery power of the device; processor capability; memory capability.

[0076] According to some examples, the method includes determining whether the device is co-located with the user equipment based on sidelink communication between the device and the user equipment.

[0077] According to some examples, there is provided a computer program including instructions for causing a device to perform at least the following: receiving capability information from one or more user equipments including a first user equipment; determining location information of the first user equipment based on measurement information of the first user equipment; and determining location information of a second user equipment by replicating the measurement information of the first user equipment; wherein the second user equipment is co-located with the first user equipment.

[0078] According to some examples, there is provided a non-transitory computer-readable medium including program instructions for causing a device to perform at least the following: receiving capability information from one or more user equipments including a first user equipment; determining location information of the first user equipment based on measurement information of the first user equipment; and determining location information of a second user equipment by replicating the measurement information of the first user equipment; wherein the second user equipment is co-located with the first user equipment.

[0079] According to some examples, a computer program is provided that includes instructions for causing a device to perform at least the following: sending capability information to a network entity; receiving location information of the device from the network entity; wherein the device is co-located with a user equipment, and wherein the location information of the device is determined by replicating measurement information of the user equipment.

[0080] According to some examples, a non-transitory computer-readable medium is provided that includes program instructions for causing a device to perform at least the following: sending capability information to a network entity; receiving location information of the device from the network entity; wherein the device is co-located with a user equipment, and wherein the location information of the device is determined by replicating measurement information of the user equipment.

[0081] According to one aspect, a first user equipment is provided that includes: means for measuring first measurement information related to the first user equipment using a first measurement type; means for receiving second measurement information measured based on a second measurement type from a second user equipment co-located with the first user equipment; and means for performing measurement aggregation based on an aggregation of the first measurement information and the second measurement information.

[0082] According to some examples, the request includes a trigger condition, and wherein the first user equipment includes means for performing location determination when the trigger condition is met.

[0083] According to some examples, the trigger condition includes an indication that the first user equipment is co-located with the second user equipment

[0084] According to some examples, the trigger condition includes a threshold reference signal received power.

[0085] According to some examples, the second measurement information is output from a machine learning model of the second user equipment.

[0086] According to some examples, the device includes a machine learning model that applies the first measurement information.

[0087] According to some examples, the first measurement type or the second measurement type includes at least one of the following: frequency measurement information; cell measurement information; time of arrival measurement information; angle of arrival measurement information; reference signal received power measurement information.

[0088] According to some examples, the device includes means for exchanging capability information with the second user equipment, wherein the exchanging capability information includes an indication indicating that the second user equipment supports measurement aggregation, and receiving from the second user equipment an indication that the second user equipment supports the measurement aggregation.

[0089] According to some examples, the handover capability information includes configuring the second user equipment not to report its location to the network.

[0090] According to some examples, the device includes means for sending the location information of the first user equipment that has been determined by the measurement aggregation.

[0091] According to some examples, the device includes: at least one processor; and at least one memory including computer program code, the at least one memory and the computer program code being configured to, together with the at least one processor, cause the device to operate.

[0092] According to one aspect, there is provided a first user equipment, including: at least one processor; and at least one memory including computer program code; the at least one memory and the computer program code being configured to, together with the at least one processor, cause the first user equipment to at least perform: measuring first measurement information related to the first user equipment using a first measurement type; receiving second measurement information measured based on a second measurement type from a second user equipment co-located with the first user equipment; and performing measurement aggregation based on the aggregation of the first measurement information and the second measurement information.

[0093] According to one aspect, there is provided a method, including: measuring, by a first user equipment, first measurement information related to the first user equipment using a first measurement type; receiving second measurement information measured based on a second measurement type from a second user equipment co-located with the first user equipment; and performing measurement aggregation based on the aggregation of the first measurement information and the second measurement information.

[0094] According to some examples, the request includes a trigger condition, and the method includes performing location determination when the trigger condition is satisfied.

[0095] According to some examples, the trigger condition includes an indication of co-location of the first user equipment and the second user equipment.

[0096] According to some examples, the trigger condition includes a threshold reference signal received power.

[0097] According to some examples, the second measurement information is output from a machine learning model of the second user equipment.

[0098] According to some examples, the method includes applying the first measurement information to a machine learning model.

[0099] According to some examples, the first measurement type or the second measurement type includes at least one of the following: frequency measurement information; cell measurement information; time of arrival measurement information; angle of arrival measurement information; reference signal received power measurement information.

[0100] According to some examples, the method includes exchanging capability information with the second user equipment, wherein the exchanging capability information includes indicating support for measurement aggregation to the second user equipment and receiving an indication from the second user equipment that the second user equipment supports the measurement aggregation.

[0101] According to some examples, the exchanging capability information includes configuring the second user equipment not to report its location to the network.

[0102] According to some examples, the method includes sending location information of the first user equipment that has been determined by the measurement aggregation.

[0103] According to some examples, the method is performed by a user equipment.

[0104] According to one aspect, there is provided a computer program including instructions for causing a first user equipment to at least perform the following: measuring, by the first user equipment, first measurement information related to the first user equipment using a first measurement type; receiving, from a second user equipment co-located with the first user equipment, second measurement information measured based on a second measurement type; and performing measurement aggregation based on an aggregation of the first measurement information and the second measurement information.

[0105] According to one aspect, there is provided a non-transitory computer-readable medium including program instructions for causing a first user equipment to at least perform the following: measuring, by the first user equipment, first measurement information related to the first user equipment using a first measurement type; receiving, from a second user equipment co-located with the first user equipment, second measurement information measured based on a second measurement type; and performing measurement aggregation based on an aggregation of the first measurement information and the second measurement information.

[0106] According to one aspect, there is provided an apparatus including: means for receiving capability information from two or more user equipments; and means for using the capability information to determine whether to select any one of the following: (i) a positioning method for the two or more user equipments that prioritizes the accuracy of the positioning result; or (ii) a positioning method for the two or more user equipments that prioritizes minimizing measurement overhead.

[0107] According to one aspect, 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 are configured to, together with the at least one processor, cause the apparatus to at least perform the following: receive capability information from two or more user devices; use the capability information to determine whether to select any of the following: (i) a positioning method for the two or more user devices that prioritizes the accuracy of the positioning result; or (ii) a positioning method for the two or more user devices that prioritizes minimizing the measurement overhead.

[0108] According to one aspect, there is provided a method, comprising: receiving capability information from two or more user devices; and using the capability information to determine whether to select any of the following: (i) a positioning method for the two or more user devices that prioritizes the accuracy of the positioning result; or (ii) a positioning method for the two or more user devices that prioritizes minimizing the measurement overhead.

[0109] According to one aspect, there is provided a computer program, comprising instructions for causing a first user device to at least perform the following: receive capability information from two or more user devices; and use the capability information to determine whether to select any of the following: (i) a positioning method for the two or more user devices that prioritizes the accuracy of the positioning result; or (ii) a positioning method for the two or more user devices that prioritizes minimizing the measurement overhead.

[0110] According to one aspect, there is provided a non-transitory computer-readable medium including program instructions for causing an apparatus to at least perform the following: receive capability information from two or more user devices; and use the capability information to determine whether to select any of the following: (i) a positioning method for the two or more user devices that prioritizes the accuracy of the positioning result; or (ii) a positioning method for the two or more user devices that prioritizes minimizing the measurement overhead. BRIEF DESCRIPTION OF THE DRAWINGS

[0111] Embodiments will now be described by way of example only with reference to the drawings, in which:

[0112] Figure 1 A one-step positioning method is schematically shown;

[0113] Figure 2 A two-step positioning method is schematically shown;

[0114] Figure 3 A positioning scenario involving multiple user devices is schematically shown;

[0115] Figure 4It is a flowchart of a method according to an example;

[0116] Figure 5 Schematically shows a UE-assisted positioning scheme and a direct positioning scheme;

[0117] Figure 6 It is a signaling diagram according to an example;

[0118] Figure 7 It is a signaling diagram according to an example;

[0119] Figure 8 Schematically shows a formula for measurement aggregation;

[0120] Figure 9 Schematically shows a formula for measurement aggregation;

[0121] Figure 10 It is a signaling diagram according to an example;

[0122] Figure 11 It is a signaling diagram according to an example;

[0123] Figure 12 It is a signaling diagram according to an example;

[0124] Figure 13 It is a signaling diagram according to an example;

[0125] Figure 14 Schematically shows a user equipment according to an example;

[0126] Figure 15 Schematically shows a control device according to an example;

[0127] Figures 16 to 21 It is a flowchart according to some examples;

[0128] Figure 22 Schematically shows a schematic diagram of a non-volatile storage medium. Detailed implementation

[0129] Hereinafter, certain embodiments are explained with reference to a mobile communication device or user equipment (UE) capable of communicating via a wireless cellular system and a mobile communication system providing services for such a mobile communication device. The backbone structure of the communication system, such as the UE, radio access network (RAN), and core network (CN), is known and will not be discussed in detail for the sake of brevity.

[0130] In Rel-17, 3GPP initiated the New Radio (NR) positioning enhancement work [RP-210897], which is based on the Rel-16 solution and focuses on increasing accuracy, reducing latency, and improving efficiency (low complexity; low power consumption; low overhead). Reduced-capability (RedCap) devices are being designed and standardized in Rel-17 [RP-211574]. Compared with other IoT devices, RedCap NR devices are designed to have a relatively long battery life.

[0131] 3GPP RAN1 has initiated an AI / ML (Artificial Intelligence / Machine Learning) research project for the radio interface. One of the use cases under consideration is to utilize AI / ML to improve positioning accuracy.

[0132] Figure 1 A one-step positioning method based on an AI / ML solution proposed in Rel-18 is schematically shown. In Figure 1 the example, the model input includes possible measurements or channel observations from the UE, which are fed into the AI / ML model 102. The output of the model 102 is the UE location or information related to the UE location.

[0133] The one-step positioning method can have the AI / ML model hosted / deployed at the UE or the network / LMF (Location Management Function). The input to the model can include various channel observations such as RSRP (Reference Signal Received Power) measurements, CIR (Channel Impulse Response), cell ID, beam ID, angle of arrival / departure, etc. These values can be provided as input to the AI / ML model (e.g., Figure 1 the model - 0102 in

[0134] and the AI / ML model provides the UE location as the output. This method has the potential to have high accuracy even under severe NLOS (Non-Line-of-Sight) conditions and is relatively simple in terms of training and deployment - only a single node is used in this case. The potential drawback may be its sensitivity to changes in the propagation environment due to the frequency-selective fading channel, which may result in poor generalization ability of the trained model and higher computational complexity to achieve sufficiently high positioning accuracy and model usage scenario dependence.

[0135] Figure 2 A two-step positioning method for AI / ML positioning is schematically shown.

[0136] The two-step positioning method can have two main options:

[0137] · Option 1: Separate AI / ML models - "Model 1" 204 hosted at the UE and "Model 2" 206 hosted at the network / LMF, with intermediate features that may be exchanged between the UE and the network, or

[0138] · Option 2: An AI / ML model (model 1204) hosted at the UE or the network, with possible intermediate features and improved channel observations sent to a classical method (e.g., the non-AI / ML method schematically shown at 208) hosted at the network. The network then determines the location of the UE.

[0139] It should be noted that for the one-step and two-step methods, each step can be performed in a separate entity or a single entity, and the content of this application is not limited thereto. Additionally, for example, instead of the UE and the network, sidelink scenarios (where UEs are able to communicate with each other without relaying their data via the network) can also be considered. For example, model 1204 can be used at the first UE (UE-1), and model 2206 can be used at the second UE (UE-2).

[0140] A potential advantage of the two-step method is that it can have the option of distributing the computational complexity and storage requirements between the UE and the network. The two-step method can also use AI / ML to enhance existing methods, e.g., to improve accuracy.

[0141] For example, in the two-step "Option 1" discussed above, a lightweight AI / ML model (model 1) can be deployed at the UE, which provides intermediate features that can only be understood by a more complex AI / ML model (model 2) hosted at the network. Or the UE can provide measurements such as RSRP measurements, ToA (Time of Arrival) estimates, AoA / AoD (Angle of Arrival / Angle of Departure). Or the UE can provide intermediate results obtained from the model, such as LOS / NOS classification. Then, model 2 at the network can use the information from the UE to deduce the UE location.

[0142] In the two-step "Option 2", a similar lightweight AI / ML model at the UE can provide "classical" or traditional measurements with improved accuracy to the network, and then the network can use known methods to deduce the UE location. Since the output of AI / ML model 1 in Option 2 has higher accuracy than traditional methods (providing information such as LOS / NLOS classification, RSRP measurements, ToA, AoA / AoD, etc.), in the example, this method can provide improved positioning performance with minimal complexity and with little impact on the traditional implementation on the network side.

[0143] In some examples, the one-step method is referred to as direct AI / ML positioning. In some examples, the two-step method is referred to as indirect AI / ML positioning.

[0144] Thus, while the examples of this application can enable the UE location to be determined with high accuracy, it should be noted that AI / ML can help achieve this in various ways.

[0145] Note also that UE positioning can be the result or output of either a one-step or a two-step method, and either method can be used to achieve it.

[0146] Taking the position of a user equipment (UE) as a potential output, a one-step method can use model inputs including one or more of the following: UE cell ID; beam ID; and / or other cell-specific parameters that enable the model to identify the UE position. This may mean either using a large amount of data representing the entire network area to train the model, or retraining / updating / fine-tuning the model every time the model moves from one geographical area to another. Therefore, this method may require relatively large amounts of data computation.

[0147] In the case of a two-step method (e.g., two-step option 2), for example, the output of an AI / ML model can be one or more of LOS / NLOS classification and / or other angular or time-based features. In this case, the training data may not have any cell-specific dependencies, so the model may not need to be retrained, updated, or fine-tuned every time the UE moves from one geographical area to another.

[0148] Example embodiments of the present invention particularly contemplate scenarios of two-step and / or AI / ML-assisted positioning. This scenario can involve the Uu (UE-gNB interface) and / or the sidelink (UE-UE interface). This scenario can use an ML model involving cooperation between a selected group of UEs.

[0149] The present application identifies potential problems in the case of multiple UEs being co-located. For example, multiple UEs can be co-located in a mall, railway station, indoor factory, V2X (vehicle-to-everything). The co-located UEs may need to be positioned. Traditionally, the network will handle the positioning process for each UE separately. The separate process for each UE can include a positioning measurement request, reporting positioning measurements, and processing the positioning measurements to estimate the UE position. Generally, in this case, the determination of the relative position is sufficient compared to the absolute position. In addition, in the case of positioning a RedCap UE, the performance KPIs (key performance indicators) may be limited by the UE capabilities (e.g., computing capabilities). Therefore, it may be difficult for the UE to calculate complex AI / ML models individually, especially when the UE is a RedCap UE.

[0150] Therefore, one of the aspects considered in the embodiments is how to optimize the positioning process between UEs such that the measurement overhead / processing for each UE is reduced while ensuring high-precision positioning. Accordingly, some examples of the present application solve the positioning optimization problem for co-located UEs and provide a procedure set-up and related required signaling enhancements.

[0151] A positioning method that combines (or fuses) data from multiple sources and / or multiple different types can be referred to as fusion-based positioning or positioning measurement aggregation.

[0152] In view of the above, this embodiment proposes a method and apparatus for optimizing co-located UE positioning. Co-located UEs can be located, for example, in stadiums, stations, vehicles in a convoy, or in co-located devices such as factories.

[0153] Figure 3 An example is shown where three UEs (i.e., UE1, UE2, and UE3) need to be positioned. Five transmit and receive points (TRPs) are shown in the figure, namely TRP1, TRP2, TRP3, TRP4, and TRP5. Traditionally, the positioning process is performed separately for each UE. In this example, UE1 and UE2 are close to each other and can thus be considered co-located. Therefore, position estimation optimization can be used to avoid unnecessary separate processing for each of UE1 and UE2.

[0154] According to some examples, the following steps are proposed as described below and depicted in Figure 4 to achieve positioning optimization for co-located UEs.

[0155] At S400, a positioning support request is sent. For example, this request can be sent by the UE to the LMF. The UE can proactively send this request so that data fusion techniques described in more detail below can be used. In some examples, the request can be sent based on one or more of the following: UE capabilities; positioning requirements. For example, the request can be initiated based on a battery power threshold or a computing resource threshold of the UE. In some examples, these thresholds can be pre-configured by the network. For example, if the UE determines that it requires precise positioning but does not have the threshold computing resources to perform measurements with the required accuracy, this can trigger a request from the UE to the LMF.

[0156] At S401, co-located UEs are identified. In this step, UEs co-located with the UE that sent the request are identified. In some examples, the identification of co-located UEs is performed by the LMF. For example, the LMF can initially use rough (non-precise) UE location data. Then, the LMF can utilize a location database to identify co-located UEs. In some examples, this can be achieved by using the rough location and historical information of the UE, such as using information from the corresponding serving gNB. In some examples, beam ID and / or cell ID information can be used to obtain rough location information. In some examples, the identification of co-located UEs can be performed by the UE that initiated the request. For example, the UE that initiated the request can use one or more sidelink channels with other UEs to determine whether they are co-located.

[0157] At S402, a strategy for optimizing positioning is selected. Once a set of co-located UEs is identified in S401 (e.g., by the LMF), one or more different strategies can be selected from them to perform the positioning process. It can be understood that there may be a trade-off between positioning accuracy and measurement overhead reduction. Therefore, in some examples, the UE capabilities or UE requirement information can be analyzed, for example, by the LMF, to determine which strategy to use.

[0158] In some examples, the capabilities and / or requirements of the requesting UE are analyzed and considered. Additionally or alternatively, the capabilities and / or requirements of the co-located UEs are analyzed and considered. For example, if one or more UEs require highly accurate positioning information, a strategy that prioritizes the accuracy of positioning information can be selected. On the other hand, if one or more UEs have reduced capabilities or low battery capacity, or depending on the channel state (or LoS / NLoS indication), a strategy that minimizes the measurement overhead can be selected.

[0159] Another scenario can be that the network has limited radio resources for configuring positioning measurement report feedback, in which case a measurement overhead strategy can be selected.

[0160] In some examples, for example, in the case where one or more UEs require precise positioning and one or more UEs have reduced capabilities, rules can be implemented and followed (e.g., in the UE or LMF). In some examples, the rules are selected by the LMF and applied at the UE. For example, the rule can be that the capabilities of the requesting UE are prioritized. In another example, the rule can be to prioritize the strategy that is beneficial to the majority of UEs.

[0161] Two different strategy selections, namely Option 1 and Option 2, are briefly introduced below.

[0162] Option 1: Improved positioning accuracy:

[0163] This option can improve positioning accuracy. In some examples, the same number of positioning measurements are requested from the requesting UE and from the co-located UEs: this can include requesting different measurements and / or different types of measurements from the co-located UEs. For example, different types of measurements can include one or more of the following: information from different cells; information about different frequencies; information about different types of measurements. For example, the type of measurement information can include at least one of the following: time of arrival (ToA) of a signal; angle of arrival (AoA) of a signal; reference signal received power (RSRP). Then, different types of measurement information can be aggregated to infer the accurate location. In some examples, this aggregation can also be referred to as fusion.

[0164] Option 2: Reduced measurement overhead:

[0165] This option can improve positioning measurement overhead. For example, this option can reduce signals associated with positioning. According to some examples, in this option, positioning measurements are requested only from a subset {A} of co-located UEs. The remaining co-located UEs {B} do not measure or report any positioning measurement values. The positions of UEs in set {B} can then be copied from the estimated positions of UEs in set {A}. The co-located UE subset may include at least one UE.

[0166] At S403, the UEs identified as co-located (including the requesting UE) are notified that they need to provide measurements. For example, indicating the type of measurement provided by the co-located UE may be based on the positioning optimization strategy selected in S402. For example, the LMF may request positioning measurements from the identified UEs. If the measurement overhead reduction strategy (option 2) is selected, the signals (e.g., positioning reference signals (PRS)) for the UEs in set {B} are muted / not transmitted.

[0167] At S404, the UE position is estimated based on the selected strategy and the positioning measurements received from the UE. In various examples, the positioning measurements received from the co-located UE will be processed in a manner that depends on the selected positioning optimization strategy.

[0168] For example, if the accuracy improvement strategy (option 1) is selected, an aggregation or fusion based solution can be used. This aggregates the positioning measurements from the co-located UEs. In some examples, the aggregation is performed on the LMF side. In some examples, two positioning use cases are proposed, namely UE assisted positioning and direct positioning (see Figure 5 ):

[0169] ο UE-assisted positioning : Each of the co-located UEs performs different types of measurements. An ML model is run at the UE level to process these measurements. The output of the ML model at the UE is sent to the LMF to perform data fusion and infer the position.

[0170] ο Direct positioning : Each UE performs different types of measurements. The set of these measurements is reported directly to the LMF. Then, on the LMF side, one or more ML models are run on the received measurements to process these positioning measurements received from the UEs. After that, an aggregation or fusion step can be performed to estimate the position of these co-located user equipments (UEs).

[0171] For the measurement overhead reduction strategy (option 2), in some examples, the UE positions of the UEs in set {A} can be estimated using conventional methods so that the selected UEs report their positioning measurements. For other UEs (i.e., UEs in set {B}), their positions can be copied from those in set {A}.

[0172] Figure 5Schematically shows the difference between UE-assisted positioning method and direct positioning method.

[0173] Figure 5 The left side schematically shows the UE-assisted positioning method. UE1 520 and UE2 522 communicate with the LMF 524. As shown, UE1 520 executes Measurement Set 1 run by the ML application 526. UE2 executes Measurement Set 2 run by the ML application 528. The outputs of the ML application 526 and the ML application 528 are sent to the LMF 524. At the LMF 524, data fusion and position extraction are performed, as schematically shown at 530.

[0174] Figure 5 The right side schematically shows the direct positioning method. UE1 520 and UE2 522 communicate with the LMF 524. As shown, UE1 520 executes Measurement Set 1. UE2 executes Measurement Set 2. In this example, Measurement Set 1 is sent to the ML application 532 in the LMF 524. Measurement Set 2 is sent to the ML application 534 in the LMF 524. In some examples, the ML applications 532 and 534 can be separate applications. In some examples, the ML applications 532 and 534 can be the same ML application. Then data fusion and position extraction are performed at the LMF 524, as schematically shown at 536.

[0175] In an example, each of the ML applications can employ a neural network (NN).

[0176] Combined Figure 6 and Figure 7 the signaling enhancement method discussed.

[0177] Before discussing in detail Figure 6 and Figure 7 it can first refer to Figure 12 which schematically shows the exchange of information (such as capability information) between the UE and network entities (such as gNB and / or LMF).

[0178] UE capability exchange

[0179] According to some examples, UE capability exchange can be used to assist network-based fusion and UE-based fusion.

[0180] Figure 12 Shows an example of capability exchange, which shows the communication between UE 1202 and network 1204.

[0181] At S1201, network 1208 (e.g., gNB and / or LMF) may send UE capability query information elements to UE 1202. The query information element may include one or more of the following: indication of support for fusion (e.g., measurement value aggregation) at the network; query for one or more of UE processing capabilities, memory, UE type, battery level, and whether fusion should be applied at the network; and sidelink capabilities for fusion (e.g., as part of SIB12 or a new AI / ML positioning SIB).

[0182] At S1202, in the case where the UE supports fusion-based positioning, UE 1202 may send UE capability information to network 1208. In some examples, this may initiate a fusion request at the network. In some examples, in response to the S1201 query, the UE capability information may be included in a new information element (IE) signaled from the UE to the network. In some examples, this new IE may be included in the SidelinkUEInformationNR signaling.

[0183] Thus, for network-based fusion, for "Option 1: Increased accuracy" and "Option 2: Reduced measurement overhead" (shown below Figure 6 and Figure 7 respectively), the network may signal support for fusion as part of the UECapabilityEnquiry signaling (e.g., Figure 12 S1201). Then, the UE will learn of the network-based fusion capability based on the receipt of the relevant information element (IE) within the capability query message. In response to this message (e.g., as shown in S1202), the UE may indicate its processing capabilities / memory, UE type (RedCap, etc.), and request activation of the fusion option while initiating a search for co-located UEs to achieve this optimization. The request to activate the fusion option at the network may require a new IE to be included as part of the UECapabilitylnformation response from the UE to the network.

[0184] Signaling enhancement

[0185] Now returning to Figure 6 and Figure 7 , these figures depict signaling messages between co-located UEs 602 and 604, gNB 606, and LMF 608. Although the example is described for two co-located UEs, it can be understood that it can be extended to multiple co-located devices.

[0186] Consider a scenario where an area is identified as likely to include co-located UEs, such as a subway station, a stadium, or a factory, as non-limiting examples. In some examples, an additional condition can be that the UEs have a low probability of high speed, such that the UEs can remain co-located for a long period of time. In some examples, the area can correspond to a set of cell IDs.

[0187] In some examples, a fusion-based scheme (or measurement aggregation) is initiated upon request by UE 602 at S601 (e.g., this step can be performed as shown in S1202 of Figure 12 ). Based on its capabilities (e.g., battery level, memory), the UE or REDCAP UE can request additional support from the LMF 608 for positioning. This can include activation of fusion options and search for co-located UEs to achieve this optimization.

[0188] At S602, the LMF 608 identifies co-located UEs based on predefined rules or conditions. In some examples, the rules can be based on the estimated rough locations of UEs 602 and 604. The LMF 608 can utilize a local database to identify co-located UEs.

[0189] In some examples, the local database includes one or more of the following:

[0190] - Distance between UEs, e.g., a location matrix for a pair of UEs (UE i and UE j )

[0191] - Adjacency indication of UEs, e.g., indicating whether a pair of UEs (UE i and UE j ) are within a maximum coverage distance d Max of each other such that:

[0192]

[0193] - Sidelink (SL) resource allocation mode Mode index of UEs, e.g., mode 1 or mode 2

[0194] - SL resource pool allocated to each UE

[0195] - SL resource RBs allocated to each UE ij

[0196] - SL channel conditions between each pair of UEs, e.g., LOS / NLOS, path loss PL ij , RSRP, CSI, etc.

[0197] - Associated timestamps (e.g., time f) or validity of the above information

[0198] According to some examples, the LMF 608 selects a strategy to optimize the positioning of co-located UEs. This is schematically shown at S603.

[0199] The first strategy option (Option 1) may focus on improving accuracy, as described above. The second strategy option (Option 2) may focus on reducing measurement overhead, as described above. Option 1 is discussed with reference to Figure 6 and Option 2 is discussed with reference to Figure 7 .

[0200] In the first option, as Figure 6 shown, the LMF 608 decides to improve the positioning accuracy through the cooperation between UE1 602 and UE2 604. This may include requesting different measurements from each device (e.g., different frequencies, cells, measurement types such as RSRP, AoA). As shown in S604, a request for positioning measurement M 1 is sent from the LMF 608 to UE1 602.

[0201] As shown in S605, a request for positioning measurement M 2 is sent from the LMF 608 to UE2 604.

[0202] At S606, UE1 602 sends the first measurement information (e.g., positioning measurement M1) to the LMF 608.

[0203] At S607, UE2 604 sends the second measurement information (e.g., positioning measurement M2) to the LMF 608.

[0204] Thereafter, the LMF 608 performs fusion-based positioning based on the first and second measurement information. For example, the LMF 608 performs measurement aggregation based on the obtained first and second measurement values {M 1 + M 2} as shown in S608 to estimate the accurate location:

[0205] Location(UE1) = f(M 1 , M 2 )

[0206] Location(UE2) = f(M 1 , M 2 )

[0207] where f is a fusion function for aggregating the received measurement values (example fusion functions will be described in more detail below).

[0208] Note that in some examples, UE2 604 can be a PRU (Positioning Reference Unit).

[0209] As shown in S609, the estimated location can be associated with UE1 602 and UE2 604.

[0210] In some examples, the positioning information calculated / measured by the LMF can be sent only to UE1 602 (i.e., the initially requesting UE), as shown at S610. In some examples, the positioning information is also sent to UE2 604, as shown in S611, because this can exempt UE2 604 from future positioning requests, thereby further reducing signaling overhead.

[0211] In the second option, as described with reference to Figure 7 S701 to S703 are similar to Figure 6 S601 to S603 in Figure 7 At the same time, alternatively or additionally, at S701, the LMF 608 receives UE capability information elements from multiple UEs, which indicate whether the UEs support measurement aggregation (or fusion). At S702, the LMF verifies whether the multiple UEs are co-located. If at least two of the multiple UEs are co-located, the LMF 608 selects a positioning optimization strategy for these co-located UEs. In the example of

[0212] In Figure 7 the example of 1 the LMF 608 makes a decision to reduce measurement overhead. Therefore, the LMF 608 can transmit a positioning measurement request to at least two UEs (e.g., UE1 602 and UE2 604), as shown in S704. The selection of which UE 602 performs the measurement among the co-located UEs 602, 604 can be performed based on several criteria. For example, these criteria can include battery power and / or the priority of ongoing services.

[0213] Using the positioning measurement value M1, the LMF 608 can apply a positioning method to the measurement value M1 and determine the location of UE1 602, as shown in S706. For example, applying the positioning method can include applying TDOA to the ToA measurement value (M1) reported by UE1 602.

[0214] Thereafter, the estimated location of UE1 602 is copied to UE2 604, as shown in S707.

[0215] Location(UE2) = Location(UE1)

[0216] In some examples, the positioning information is sent only to UE1 602 (i.e., the initially requested UE), as shown in S708. In some examples, the positioning information is also sent to UE2 604, as shown in S709. The location information sent to UE2 604 can be used to enhance the location services of UE2 604. Additionally, by sending the location information without a request from UE2, UE2 604 can further reduce the signaling overhead for positioning requests.

[0217] Figure 6 and Figure 7 A signaling flow for implementing a fusion-based scheme is provided. In some examples, Figure 6 the process corresponds to the case of direct positioning for Option 1. However, it should be understood that the disclosed method can also be applied to the UE-assisted positioning case, where instead of directly sending the measurement sets M1 and M2, the ML function is run at the level of each UE 602, 604, and the ML function output is sent to the LMF 608 for data fusion.

[0218] Additionally or alternatively, UE2 604 can transmit UE capability information to network nodes (e.g., gNB 606 and / or LMF 608). If the UE capability information indicates that UE2 604 supports measurement aggregation (e.g., fusion-based measurement), the network nodes can send the positioning information of UE2 to UE2 604 periodically or in an event-triggered manner. At this time, the positioning information measurement of UE2 is based on the embodiments (or examples) described above and below. Additionally, in some examples, the network nodes can transmit this positioning information without a request from UE2 604.

[0219] Fusion-based method

[0220] The fusion (or aggregation)-based method will be described in more detail below, and the fusion-based method can be applied to some or all of the embodiments. In some examples, the fusion or combination of data and / or data sources can effectively improve the positioning performance by combining the complementarities between various systems, information, and technologies, thereby achieving accuracy and robustness / stability. Figure 8 An example of a fusion formula is shown. In some examples, fusion can also be referred to as aggregation.

[0221] According to Figure 8 the formula, the weight w is used to efficiently combine the positioning results to produce a better estimate. In some examples, the gradient method or the w* can be determined by an ML method. For example, the ML method can be performed via offline training of supervised learning or via unsupervised learning in an online phase.

[0222] For algorithms in the algorithm space, the following (non-limiting examples) can be used:

[0223] Type 1 : Maximum Likelihood (ML), Least Squares (LS), Maximum A Posteriori (MAP), Minimum Mean Square Error (MMSE), Hidden Markov Model (HMM), Kalman filter, etc.

[0224] Type 2 : ML methods, Neural Networks (NN), Support Vector Machines (SVM), k-Nearest Neighbors, Random Forests (RF), etc. can be used to integrate location information and improve location accuracy.

[0225] Consider an example where two UEs are co-located. Based on the request of the UEs, a fusion-based scheme is initiated to improve location accuracy. Collaboration between UE1 and UE2 is thus initiated. Measurement values M1 (e.g., RSRP, AoA...) are obtained from UE1, and measurement value M2 is obtained from UE2. f is used to represent the fusion function for aggregating the received measurement values to improve location accuracy.

[0226] To optimally fuse M1 and M2 to determine the locations of UE1 and UE2, optimal weights are selected to minimize the location error.

[0227] Location(UE1) = f(w11*M1, w12*M2)

[0228] Location(UE2) = f(w21*M1, w22*M2),

[0229] where wij corresponds to the fusion weight for calculating the UE location by using the measurement value Mj. wij is selected to minimize the location error (other network performance metrics can also be considered in other examples).

[0230] In one example, as Figure 9 shown in the learning process is used to determine w11 and w12 of UE1 (as an example), which corresponds to the linear aggregation of the received measurement values:

[0231] Location(UE i) = f(∑j wij*Mj)

[0232] Some examples can alternatively use the gradient descent method to determine the optimal weights. For example, the gradient of f(w11*M1, w12*M2) is given by

[0233]

[0234] And similarly, we can write the gradient of f(w21*M1, w22*M2) below.

[0235]

[0236] For a given M1 and M2, the directions of the fastest decrease in positioning error for UE1 can be selected to determine w11 and w12 (for example) by following the gradient of “f” that points in the direction of the largest change in the performance metric.

[0237] So far, the communication between the gNB and the UE has been discussed for UE positioning. This application also considers using sidelink (UE-UE) communication to perform accurate UE positioning estimation. The above fusion techniques can be used anywhere fusion occurs, such as in the network (LMF) or at the UE.

[0238] Sidelink measurement example

[0239] Some examples of optimizing positioning by leveraging sidelink communication (e.g., UE-UE) will now be described. In some sidelink examples, fusion can occur on the LMF side and / or the UE side.

[0240] New Radio (NR) sidelink transmissions have the following two resource allocation modes:

[0241] ο Mode 1: Sidelink resources are scheduled by the gNB.

[0242] ο Mode 2: The UE autonomously selects sidelink resources from a (pre-configured) sidelink resource pool based on a channel sensing mechanism

[0243] In Mode 2, the UE performs resource sensing. When traffic arrives at the UE, the transmitting UE sets the moment as the trigger for resource (re)selection, which can be denoted as n. The moment n can also be considered as the moment when the UE needs to transmit data to its neighboring UE via the sidelink. Two windows called the “sensing window” and the “selection window” are set before and after n, respectively.

[0244] During the sensing window (from moment T0 to n), the transmitting UE measures the reference signal received power (RSRP) of all considered subchannels. In some examples, according to the configuration, the RSRP can be regarded as the power level of the DMRS (demodulation reference signal) on the PSSCH (physical sidelink shared channel) or the DMRS on the PSCCH (physical sidelink control channel). To measure the RSRP, the transmitting UE should know the PSSCH, or the resources of the PSSCH initiated by other UEs. For this purpose, the transmitting UE can detect the PSCCH transmitted by other UEs (and thus receive sidelink control information (SCI)) to find out which subchannels have been occupied by other sidelink transmitters.

[0245] The described sidelink measurements can be applied to embodiments related to the sidelink operations of each UE.

[0246] LMF-based fusion for sidelink measurement

[0247] In the case of network - or LMF - based fusion, sidelink information can be used to identify co - located UEs. In some examples, corresponding to Figure 4 S401 in, if the measured RSRP_sidelink is lower than a predefined threshold, the UE is identified as a co - located UE. For an area (such as a mall) where co - located UE cases may exist, the LMF can send a request to the UE to use sidelink measurements and notify the LMF whether the co - location condition is verified.

[0248] In some examples, the LMF establishes rules or conditions to identify co - located UEs. In one example, the rule can take the form of comparing RSRP_sidelink with a predefined threshold. If RSRP sidelink >th, the UE indicates to the LMF the UEID (and possibly sidelink information) that verifies this condition. In some examples, to assist, the network can indicate to the UE the IDs of UEs that may be close to the UE, and the UE can perform sidelink measurements with those identified UEs accordingly. Additionally or alternatively, in some examples, the UE can perform a "sidelink discovery" procedure to check for the presence of adjacent / nearby UEs.

[0249] In some examples, the sidelink threshold parameter th is selected based on the required positioning accuracy. In some examples, this means that if high - precision location information is required, a relatively low sidelink threshold is needed to correspond to very close UEs. However, in cases where only low precision is required, the sidelink threshold condition th can be relaxed.

[0250] Corresponding to Figure 4 S404, then the LMF can aggregate the obtained measurements {M 1 +M 2} to estimate the accurate UE location. In some examples, the LMF can use the sidelink information {sL 1,2} to account for the small distance between UE1 and UE2 and can adjust the measurements accordingly when estimating the location

[0251] Location(UE1)=f(M 1 ,M 2 ,SL 1,2 )

[0252] Location(UE2)=f(M 1 ,M 2 ,SL 2,1 )

[0253] where f is a fusion function for aggregating the received measurements.

[0254] Figure 10 Shows an example signaling flow for LMF-based fusion, where sidelink information is used.

[0255] Before discussing in detail Figure 10 it may be helpful to refer to Figure 13 which shows the UE capability exchange on the sidelink.

[0256] UE-based fusion for sidelink

[0257] Currently, the sidelink-related communication configuration is sent by the network to the UE using SIB12 signaling. Therefore, the network can query UE-based fusion using the sidelink by adding new information elements within this SIB (or optionally using a new AI / ML-based positioning SIB) or a separate RRC message or RRC IE. After receiving this IE, the UE responds to the network using the new IE within SidelinkUEInformationNR, providing additional information related to the positioning fusion capability. In some examples, the network can also determine which UE should be the entity for fusion-based positioning based on this signaling exchange.

[0258] An example of the RRC signaling related to the UE capability exchange for UE-based fusion is Figure 13 shown, which shows the communication between UE1 1302 and UE2 1304. Figure 11 Step 1103 between UE1 and UE2 in Figure 13 corresponds to the signaling shown, clarifying the RRC signaling that a fusion request (e.g., measurement aggregation request) between UEs may involve.

[0259] As shown in S1301, UE1 1302 indicates to UE2 1304 whether UE1 supports fusion-based positioning. In some examples, UE1 1302 can also configure UE2 not to report its location separately to the network. This information can be included in the RRCReconfigurationSidelink (RRC reconfiguration sidelink) message. At this time, UE2 sends positioning measurement information to UE1.

[0260] As shown in S1302, UE2 indicates acceptance of the fusion-based positioning method at S1304. UE2 1304 can also send its positioning measurement value (e.g., M2) to UE1 1304. In some examples, this information is included in the RRCReconfigurationCompleteSidelink (RRC reconfiguration complete sidelink) message.

[0261] Now review Figure 10 .

[0262] At S1001, UE1 1002 sends a request to LMF 1008. For example, this can be a positioning request that includes UE capability information. The request can also indicate the seeking of a fusion technology.

[0263] At S1002 and S1003, LMF 1008 indicates co-location trigger conditions (e.g., the threshold th described above) to UE1 1002 and UE2 1004 respectively.

[0264] At S1004, UE1 1002 and / or UE2 1004 verify whether they are co-located. In practice, this step can occur at one or both of UE1 1002 and UE2 1004.

[0265] In this example, at S1005, UE1 1002 indicates to LMF 1008 that it is co-located with UE2 1004. UE1 1002 can also provide sidelink information in this step. For example, the sidelink information can include the RSRP measured by UE1 on the channel between UE1 and UE2.

[0266] In this example, at S1006, UE2 1004 indicates to LMF 1008 that it is co-located with UE1 1002. UE2 1004 can also provide sidelink information in this step. S1006 can be optionally executed.

[0267] In S1007, based on the received information, LMF 1008 then selects a positioning optimization strategy for the co-located UEs.

[0268] Thereafter, the method described in Figure 6 or Figure 7 can be followed depending on the selected strategy.

[0269] UE-based fusion for sidelink measurement

[0270] This application also considers the case where the UE position is estimated on the UE side. Then, the UE can use a fusion scheme, leveraging its own measurements and the measurements (or ML outputs) from adjacent co-located UEs via the sidelink.

[0271] Figure 11 An example signaling diagram depicting a UE-based fusion scenario shows the signaling between UE1 1102, UE2 1104, gNB 1106, and LMF 1108. Considering running an ML model on the UE side to estimate the UE's position, the LMF can assist in performing fusion-based positioning through an indication to the UE. Such a request is shown at S1101. In some examples, the request at S1101 can be executed after determining the rough position of the co-located UEs and identifying nearby devices.

[0272] When receiving an LMF request, UE1 1102 performs sidelink measurements and identifies UE2 1104 as being in close proximity (i.e., co-located) (e.g., sidelink RSRP < a predefined threshold). This is shown at S1102. In some examples, if the LMF 1108 has already performed an estimation on the co-located UE based on rough information, S1102 can be considered a verification step.

[0273] As shown at S1103, a request message is then sent to UE2 1104 to indicate which positioning measurement values are required (e.g., setting M2). UE1 1102 can also indicate that it is necessary to obtain the M2 measurement values directly from UE2 1104, or from the output of an ML model that uses these measurement values M2.

[0274] As shown at S1104, UE2 1104 performs positioning measurements based on the request sent at S1103. For example, the positioning measurements can be based on positioning reference signals (PRS).

[0275] At S1105, UE1 1102 performs its own M1 measurements, which are different from the M2 measurements. For example, the positioning measurements can be based on PRS.

[0276] At S1106, UE2 1104 sends the M2 measurement values (e.g., the measurement information of UE2) to UE1 1102. In some examples, this can occur before S1105.

[0277] Thereafter, at S1107, UE1 1102 performs fusion-based positioning based on both the M1 and M2 measurement values. In this case, at least one of the M1 or M2 measurement values is an ML output.

[0278] The estimated position of UE1 1102 is shared with UE2 1104 and the LMF 1108, as shown at S1108 and S1109 respectively. The estimated position is transmitted to the LMF 1108.

[0279] In some examples, step S1108 is optionally performed. When step 1108 is performed, future signaling for positioning can be reduced in case UE2 1104 subsequently requires accurate positioning information (in which case it does not need to perform an additional positioning process).

[0280] It can be understood that, in the case of referring to ML models, these models can be trained in a suitable manner. For example, in the case where an ML model is used to infer location information based on measurement values, historical or pre-configured data related to the association between a location (or relative location) and measurement values can be used to train the ML model. For example, the training data can include the correlation between RSRP and the proximity between UEs.

[0281] As discussed herein, UEs that are within a certain range of each other can be considered co-located. In the examples and as discussed herein, what constitutes co-location can vary depending on the situation. In some examples, whether a UE is considered co-located depends on one or more thresholds. For example, a threshold RSRP value can be used to determine whether UEs are co-located. In some examples, GPS coordinates or classical non-AI / ML-based mechanisms (such as AoA / TDoA / ToA-based location estimation) can be used to determine whether UEs are co-located.

[0282] It can be understood that, in many examples, two co-located UEs (e.g., UE1 and UE2) are depicted for illustrative purposes. However, it should be understood that this is merely an example, and in other examples, more than two UEs can be co-located.

[0283] Now reference will be made to Figure 14 describe possible wireless communication devices in more detail, Figure 14 A schematic partial cross-sectional view of a communication device 1400 is shown. Such a communication device is commonly referred to as a user equipment (UE), user device, or terminal, and these names can be used interchangeably. The wireless device 1400 can receive signals in the air or on a radio interface 1407 via a suitable device for receiving radio signals, and can transmit signals via a suitable device for transmitting radio signals. In Figure 14In [the figure], the transceiver device is schematically represented by block 1406. The transceiver device 1406 can be provided, for example, by means of radio components and associated antenna devices. The antenna devices can be arranged inside or outside the wireless device. The wireless device is typically equipped with at least one data processing entity 1401, at least one memory 1402, and other possible components 1403 for software and hardware assisted execution of the tasks it is designed to perform, including control of access to and communication with access systems and other communication devices. The data processing, storage, and other associated control means can be provided on appropriate circuit boards and / or in a chipset. This feature is represented by reference numeral 1404. The user can control the operation of the wireless device by means of an appropriate user interface (such as a keyboard 1405, voice commands, touch screen or touchpad, combinations thereof, etc.). A display 1408, speakers, and a microphone can also be provided. In addition, the wireless communication device can include appropriate connectors (wired or wireless) for connecting to other devices and / or for connecting external accessories thereto, such as a hands-free device. The UEs (such as UEs 520, 522, 602, 604, 1002, 1004, 1102, 1104, 1202, 1302, 1304) described herein can adopt Figure 14 the form of the communication device 1400 shown.

[0284] Figure 15 An example of a control device for a communication system is shown, for example, coupled to and / or for controlling a site of an access system, such as a RAN node (e.g., a base station, gNB, central unit of a cloud architecture) or a core network node such as an MME or S-GW; a scheduling entity such as a spectrum management entity; or a server or host; or a network function (e.g., LMF). The control device 1500 includes at least one memory 1501, at least one data processing unit 1502, 1503, and an input / output interface 1504. According to some examples, the gNBs 606, 1006, 1106 can adopt the form of the control device 1500. Similarly, the LMFs 524, 608, 1008, 1108, 1208 can adopt the form of the control device 1500.

[0285] Figures 16 to 21 is a flowchart according to some examples.

[0286] Figure 16 is a flowchart of a method according to an example as seen from the perspective of a device such as an LMF.

[0287] As shown in S1601, the method includes receiving first measurement information from a first user equipment.

[0288] At S1602, the method includes receiving second measurement information from a second user equipment.

[0289] In S1603, the method includes determining the location of a first user equipment based on first measurement information and second measurement information; wherein the first user equipment and the second user equipment are co-located with each other; and wherein the type of the first measurement information is different from the type of the second measurement information.

[0290] Figure 17 is a flowchart of a method according to an example as seen from the perspective of an apparatus such as a user equipment.

[0291] As shown in S1701, the method includes sending first measurement information from the first user equipment to a network entity.

[0292] As shown in S1702, the method includes receiving positioning information determined based on the first measurement information and second measurement information of a second user equipment; wherein the first user equipment is co-located with the second user equipment, and the type of the first measurement information is different from the type of the second measurement information.

[0293] Figure 18 is a flowchart of a method according to an example as seen from the perspective of an apparatus such as an LMF.

[0294] As shown in S1801, the method includes receiving capability information from one or more user equipments including the first user equipment.

[0295] In S1802, the method includes determining location information of the first user equipment based on measurement information of the first user equipment.

[0296] In S1803, the method includes determining location information of a second user equipment by copying the measurement information of the first user equipment; wherein the second user equipment is co-located with the first user equipment.

[0297] Figure 19 is a flowchart of a method according to an example as seen from the perspective of an apparatus such as a user equipment.

[0298] As shown in S1901, the method includes sending capability information from an apparatus to a network entity.

[0299] As shown in S1902, the method includes receiving location information of the apparatus from the network entity; wherein the apparatus is co-located with a user equipment, and wherein the location information of the apparatus is determined by copying measurement information of the user equipment.

[0300] Figure 20 is a flowchart of a method according to an example as seen from the perspective of an apparatus such as a user equipment.

[0301] As shown in S2001, the method includes the first user equipment measuring first measurement information related to the first user equipment using a first measurement type.

[0302] As shown in S2002, the method includes receiving second measurement information measured based on a second measurement type from a second user equipment co-located with a first user equipment.

[0303] As shown in S2003, the method includes performing measurement aggregation based on an aggregation of the first measurement information and the second measurement information

[0304] Figure 21 is a flowchart of a method according to an example as seen from the perspective of a device such as an LMF.

[0305] As shown in S2101, the method includes receiving capability information from two or more user equipments.

[0306] As shown in S2102, the method includes using the capability information to determine whether to select: (i) a positioning method for two or more user equipments prioritizing positioning result accuracy; or (ii) a positioning method for two or more user equipments prioritizing minimizing measurement overhead.

[0307] Figure 22 Shows a schematic diagram of non-volatile storage media 2200a (e.g., a computer optical disc (CD) or a digital versatile disc (DVD)) and 2200b (e.g., a universal serial bus (USB) storage stick) storing instructions and / or parameters 2202, which when executed by a processor, allow the processor to execute Figures 16 to 21 one or more steps of the method. Generally, various embodiments can be implemented using hardware or dedicated circuits, software, logic, or any combination thereof. Certain aspects of the present application can be implemented in hardware, while other aspects can be implemented using firmware or software executable by a controller, microprocessor, or other computing device, but the present invention is not limited thereto. Although various aspects of the present invention can be illustrated and described using block diagrams, flowcharts, or some other graphical representation, it is understood that the blocks, devices, systems, techniques, or methods described herein can be implemented as non-limiting examples, in hardware, software, firmware, dedicated circuits or logic, general hardware or a controller or other computing device, or some combination thereof.

[0308] It should be understood that these devices can include or be coupled to other units or modules for transmitting and / or receiving, such as radio components or radio heads. Although the device has been described as one entity, different modules and memories can be implemented in one or more physical or logical entities.

[0309] It should be noted that although some embodiments regarding 5G networks have been described, similar principles can also be applied to other networks and communication systems. Thus, although certain embodiments have been described above by way of example with reference to certain exemplary architectures for wireless networks, technologies, and standards, the embodiments can be applied to any other suitable form of communication system other than the communication systems shown and described herein.

[0310] It should also be noted herein that although exemplary embodiments have been described above, several changes and modifications can be made to the disclosed solutions without departing from the scope of the present invention.

[0311] As used herein, "at least one of the following: <list of two or more elements>" and "at least one of <list of two or more elements>" and similar phrases, where the list of two or more elements is joined by "and" or "or", means at least any one element, or at least any two or more elements, or at least all elements, or any combination of the elements. Similarly, it should be understood that the phrase "and / or" between two features can mean either one of the two features alone ("or"), or both features together ("and").

[0312] Generally, the various embodiments can be implemented in hardware or a dedicated circuit, software, logic, or any combination thereof. Some aspects of the present application can be implemented in hardware, while other aspects can be implemented in firmware or software that can be executed by a controller, microprocessor, or other computing device, but the present application is not limited thereto. Although the various aspects of the present application can be shown and described using block diagrams, flowcharts, or some other graphical representation, it should be fully understood that the blocks, devices, systems, technologies, or methods described herein can be implemented as non-limiting examples in hardware, software, firmware, a dedicated circuit or logic, general hardware or a controller or other computing device, or some combination thereof.

[0313] In the present application, the term "circuit" can refer to one or more or all of the following:

[0314] a) A circuit implementation that is only hardware (such as an implementation that is only in analog and / or digital circuits) and

[0315] b) A combination of hardware circuits and software, for example (as applicable):

[0316] i) A combination of analog and / or digital hardware circuits and software / firmware; and

[0317] ii) A hardware processor and any part of the software (including a digital signal processor), the software, and one or more memories that work together to enable a device such as a mobile phone to perform various functions); and

[0318] c) Hardware circuits and / or processors that require software (e.g., firmware) to operate, such as a microprocessor or a part of a microprocessor, but the software may not be present when not required to operate.”

[0320] This definition of circuit applies to all uses of the term in this application (including in any claims). As a further example, as used in this application, the term “circuit” also encompasses implementations of only hardware circuits or processors (or multiple processors) or a part of a hardware circuit or processor and its (or their) accompanying software and / or firmware. For example, and in the case of being applicable to a particular claim element, the term “circuit” also encompasses a baseband integrated circuit or a processor integrated circuit for a mobile device, or a similar integrated circuit in a server, a cellular network device, or other computing or network devices.”

[0321] Embodiments of this application can be implemented, for example, by computer software executable by a data processor of a mobile device in a processor entity, or by hardware, or by a combination of software and hardware. The computer software or program, also referred to as a program product, includes software routines, applets, and / or macros, and can be stored in any device-readable data storage medium, and they include program instructions for performing specific tasks. The computer program product can include one or more computer-executable components, which are configured to perform the embodiments when the program runs. The one or more computer-executable components can be at least one software code or a part thereof.”

[0322] In addition, in this regard, it should be noted that any box in the logical flow of the drawings can represent a program step, or interconnected logical circuits, boxes, and functions, or a combination of program steps and logical circuits, boxes, and functions. The software can be stored on such physical media as: storage chips, or storage blocks implemented within a processor; magnetic media such as hard disks or floppy disks; and optical media such as DVDs and their data variants, CDs. The physical media is a non-transitory medium.”

[0323] The term “non-transitory” used herein is a limitation of the medium itself (i.e., tangible, rather than a signal), rather than a limitation of data storage persistence (e.g., RAM vs. ROM).”

[0324] The memory can be of any type suitable for the local technical environment and can 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 processor can be of any type suitable for the local technical environment and can include, by way of non-limiting example, one or more of a general-purpose computer, a special-purpose computer, a microprocessor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), an FPGA, a gate-level circuit, and a processor based on a multi-core processor architecture.

[0325] Embodiments of the present application can be practiced in various components such as integrated circuit modules. The design of an integrated circuit is generally a highly automated process. Sophisticated and powerful software tools can be used to transform a logic-level design into a semiconductor circuit design to be etched and formed on a semiconductor substrate.

[0326] The scope of protection sought for various embodiments of the present application is set forth by the independent claims. Embodiments and features (if any) described in this specification that do not fall within the scope of the independent claims should be construed as examples that help to understand the various embodiments of the present application.

[0327] The foregoing description has provided a complete and informative description of the exemplary embodiments of the present application by way of non-limiting examples. However, in light of the foregoing description, various modifications and adaptations will be apparent to those skilled in the relevant art when read in conjunction with the accompanying drawings and the appended claims. However, all such and similar modifications to the teachings of the present application will still fall within the scope of the present invention as defined by the appended claims. In fact, there are further embodiments that include combinations of one or more embodiments with any other previously discussed embodiments.

Claims

1. An apparatus, comprising: means for receiving first measurement information from a first user equipment; means for receiving second measurement information from a second user equipment; and means for determining a location of the first user equipment based on the first measurement information and the second measurement information; wherein the first user equipment and the second user equipment are co-located with each other; and wherein a type of the first measurement information is different from a type of the second measurement information.

2. The apparatus according to claim 1, comprising: means for receiving, from at least one of the first user equipment or the second user equipment, capability information indicating support for measurement aggregation.

3. The apparatus according to claim 2, comprising: means for indicating to at least one of the first user equipment or the second user equipment that the apparatus supports the measurement aggregation.

4. The apparatus according to any one of claims 1 to 3, wherein the first measurement information or the second measurement information comprises at least one of the following: frequency measurement information; cell measurement information; time of arrival measurement information; angle of arrival measurement information; or reference signal received power measurement information.

5. The apparatus according to any one of claims 1 to 4, wherein the first measurement information or the second measurement information has been output from a corresponding machine learning model at the first user equipment or the second user equipment.

6. The apparatus according to any one of claims 1 to 5, comprising: one or more machine learning models, wherein the first measurement information and the second measurement information are input into the one or more machine learning models.

7. The apparatus according to any one of claims 1 to 6, wherein the apparatus comprises means for determining whether the first user equipment is co-located with the second user equipment based on a rough location estimate of the first user equipment and the second user equipment or by using sidelink information of the received first user equipment and the second user equipment.

8. The apparatus according to any one of claims 1 to 7, comprising: means for sending the determined location information to at least one of the first user equipment or the second user equipment.

9. A first user equipment, comprising: means for sending first measurement information to a network entity; and means for receiving location information determined based on the first measurement information and second measurement information of a second user equipment; wherein the first user equipment is co-located with the second user equipment, and wherein a type of the first measurement information is different from a type of the second measurement information.

10. The apparatus according to claim 9, comprising: means for sending, to the network entity, capability information indicating support for measurement aggregation.

11. The apparatus according to claim 10, comprising: means for receiving an indication that the network entity supports the measurement aggregation.

12. The apparatus according to any one of claims 9 to 11, wherein The first measurement information or the second measurement information includes at least one of the following: frequency measurement information; cell measurement information; time of arrival measurement information; angle of arrival measurement information; reference signal received power measurement information.

13. The apparatus according to any one of claims 9 to 12, wherein, the first measurement information or the second measurement information has been output from a corresponding machine learning model at the first user equipment or the second user equipment.

14. A method, comprising: receiving first measurement information from a first user equipment; receiving second measurement information from a second user equipment; determining a location of the first user equipment based on the first measurement information and the second measurement information; wherein the first user equipment and the second user equipment are co-located with each other; and wherein a type of the first measurement information is different from a type of the second measurement information.

15. A method, comprising: sending first measurement information from a first user equipment to a network entity; and receiving location information determined based on the first measurement information and second measurement information of a second user equipment; wherein the first user equipment is co-located with the second user equipment, and wherein a type of the first measurement information is different from a type of the second measurement information.

16. A computer program comprising instructions for causing an apparatus to perform at least the following: receiving first measurement information from a first user equipment; receiving second measurement information from a second user equipment; determining a location of the first user equipment based on the first measurement information and the second measurement information; wherein, the first user equipment and the second user equipment are co-located with each other; and wherein a type of the first measurement information is different from a type of the second measurement information.

17. A computer program comprising instructions for causing an apparatus to perform at least the following: sending first measurement information from a first user equipment to a network entity; and receiving location information determined based on the first measurement information and second measurement information of a second user equipment; wherein, the first user equipment is co-located with the second user equipment, and wherein a type of the first measurement information is different from a type of the second measurement information.

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