Device and method for reporting reflection paths in wireless communication networks

Abstract

The present disclosure relates to entities and methods to improve the overall accuracy of sensing systems. The disclosure proposes an entity for a wireless communication system, the entity being configured to: receive a reference signal, determine a reflection likelihood for one or more detected paths related to the reference signal, and provide information of the one or more detected paths to a network entity, based on the reflection likelihood of one or more detected paths. The disclosure further proposes a network entity configured to obtain the information of the one or more detected paths from the entity.

Description

[0001] DEVICE AND METHOD FOR REPORTING REFLECTION PATHS IN WIRELESS COMMUNICATION

[0002] NETWORKS

[0003] TECHNICAL FIELD

[0004] The present disclosure relates to wireless communication systems, particularly to the integration of sensing and communication functionalities. More specifically, the present disclosure pertains to systems and methods for sensing of an object or of the environment in a wireless communication network, such as a 5G or future-generation communication network. The sensing of objects can involve the detection, tracking, and identification of device or object properties using radio signals and measurements of line-of-sight (LOS) and non-line-of-sight (NLOS) paths.

[0005] BACKGROUND

[0006] Wireless communication systems, such as 5G networks, have evolved to support a variety of applications beyond conventional data transmission. One such application is device positioning, where the position of a user equipment (UE) or other devices is determined using radio signals exchanged with transmit-receive points (TRPs) in the network. Positioning can be achieved using measurements of reference signals transmitted in the uplink, e.g., Sounding Reference Signals (SRS), or downlink, e.g., Positioning Reference Signals (PRS).

[0007] Positioning typically relies on the detection and measurement of line-of-sight (LOS) paths between the UE and one or more TRPs. However, positioning may also utilize non-line-of-sight (NLOS) paths. These NLOS paths are caused by interactions with environmental objects, such as reflections, scatterings, or refractions. Measurements related to NLOS paths may include parameters such as time delay, angle of arrival, and signal strength, which can be used for positioning.

[0008] While earlier wireless communication systems primarily focused on the positioning of active devices, i.e., devices that can transmit or receive signals, radio signals can also be considered for sensing of objects and of the environment. Sensing involves the detection, tracking, and identification of devices or objects that may not be actively connected to the network or capable of transmitting signals. This can be achieved by analyzing the interaction of transmitted radio signals with the environment or objects. The concept of Integrated Sensing and Communication (ISAC) has been proposed as a means to merge communication and sensing functionalities into a unified framework.

[0009] Current systems utilize NLOS paths to enhance positioning accuracy, but the characterization of these paths is often limited. While systems may distinguish between a LOS path and the NLOS paths, further classification of NLOS paths (e.g., reflection, scattering, or refraction) is generally not available. Existing methods may also report a LOS / NLOS indicator, but they do not provide specific information regarding the nature of the interaction that led to the NLOS path. Such limitations may reduce the efficiency of network-based sensing and positioning processes, especially in cases where detailed information about the interaction points would be beneficial.

[0010] SUMMARY

[0011] Given the above limitations, this disclosure aims to enhance the system's ability to sense passive objects and improve the accuracy and efficiency of sensing functions, particularly in the context of 5G NR and beyond. One objective of this disclosure is to better utilize NLOS paths and optimize the collection and utilization of path measurements, leading to improved sensing performance. Another objective is to reduce reporting overhead and ensure that only the most relevant path measurements are transmitted. These and other objectives are achieved by the solution of the present disclosure as provided in the independent claims. Advantageous implementations are further defined in the dependent claims.

[0012] A first aspect of the disclosure provides an entity for a wireless communication system. The entity is configured to receive a reference signal, determine a reflection likelihood for one or more detected paths related to the reference signal, and provide information of the one or more detected paths to a network entity based on the reflection likelihood of one or more detected paths.

[0013] This disclosure proposes an entity that can efficiently detect and report information on paths that are associated with a possible reflection. By basing the provision of path information on the reflection likelihood, the network entity is able to identify and process paths that have a higher probability of being caused by reflection points. This allows for more accurate collection of path-related information and improved processing for sensing and reflection point identification. The reflection likelihood related information allows the network entity to make more informed decisions on location and sensing calculations, thereby supporting more accurate sensing functionalities.

[0014] In an implementation form of the first aspect, the information provided to the network entity comprises the reflection likelihood of the one or more detected paths.

[0015] This implementation enables the network to be explicitly aware of the reflection likelihood of the reported paths. By receiving the reflection likelihood as part of the path information, the network entity can make more informed decisions about how to use the path information for sensing and reflection point identification.

[0016] In an implementation form of the first aspect, the information provided to the network entity comprises at least one of the following: timing information, power information, and angle information of the one or more detected paths.

[0017] This implementation enhances the richness of the path information provided to the network entity. By including timing, power, and angle information, the network can use these parameters for various signal processing techniques, considering measurements based on Time of Arrival (TOA), reference signal receive power per path (RSRPP), and Angle of Arrival (AOA). This leads to more precise localization and better determination of reflection points.

[0018] In an implementation form of the first aspect, the entity is further configured to receive configuration information from the network entity, wherein the configuration information configures the entity to provide the information of the one or more detected paths based on the reflection likelihood of the one or more detected paths.

[0019] This feature allows the network to dynamically control how the entity reports path information. By configuring the entity through network instructions, the system can adapt to changing network requirements, such as reducing signaling overhead or focusing on paths of specific interest. This provides flexibility and adaptability for network operation, improving overall system performance.

[0020] In an implementation form of the first aspect, the entity is further configured to determines a subset of the one or more detected paths based on the configuration information, and the information provided to the network entity includes the reflection likelihood and / or at least one of the following: timing, power, and angle information of each detected path in the subset.

[0021] This implementation allows the network to reduce the amount of path information transmitted by focusing on a subset of paths as specified by the configuration information. By providing reflection likelihood and other key path parameters for the subset, the system reduces signaling overhead and enables more efficient data processing at the network entity. In an implementation form of the first aspect, the configuration information indicates the entity to provide the information of the detected paths in one of the following ways: provide the information of the one or more detected path with a reflection likelihood above a first threshold, provide the information of the one or more detected path with a power value above a second threshold, or provide the information of the one or more detected path with a largest reflection likelihood among the one or more detected paths.

[0022] This configuration provides a clear and efficient mechanism for selecting which paths should be reported to the network. By using configurable thresholds for reflection likelihood and power, or by focusing on the path with the largest reflection likelihood, the system ensures that only relevant and significant paths are reported. This improves system efficiency, reduces signaling overhead, and provides reflection-related data to the network entity for sensing and positioning purposes.

[0023] In an implementation form of the first aspect, the entity is further configured to determine the power value associated with each detected path.

[0024] This implementation provides the network with information about the strength of the detected paths. The power information can be used by the network for purposes such as filtering, prioritization, and improved path analysis. Additionally, the power information supports techniques such as power-based path selection.

[0025] In an implementation form of the first aspect, the configuration information comprises the first threshold and / or the second threshold.

[0026] This implementation allows the network to control the reflection likelihood and power thresholds used by the entity to determine which paths to report. By adjusting the thresholds, the network can balance between reporting overhead and the granularity of path information. This enables dynamic adaptation to network requirements and ensures optimal performance under varying conditions.

[0027] In an implementation form of the first aspect, the entity is further configured to detect the one or more paths related to the reference signal, for each of the one or more detected paths, assume that the detected path is a result of a reflection and compute information related to a reflection plane associated with the detected path, and determine the reflection likelihood of each detected path by comparing the computed information related to the reflection plane for the detected path with a corresponding information related to a reflection plane computed for a path based on a measurement collected at a different location, time or frequency.

[0028] This implementation allows the system to identify reflection paths with higher accuracy by incorporating reflection plane information from different times, locations, or frequencies. By comparing reflection planes from multiple measurements, the entity can better distinguish reflection paths from other types of paths, thereby improving the accuracy of path classification and supporting more precise determination of reflection points.

[0029] In an implementation form of the first aspect, the reference signal comprises an SRS or a PRS.

[0030] This implementation enables the use of existing reference signals (SRS or PRS) to detect and analyze paths. The compatibility with 5G NR standards allows for easy integration with existing network infrastructure, ensuring support for advanced sensing use cases. In an implementation form of the first aspect, the entity is one of the following: a base station, a TRP, a gNB, a PRU, or a UE.

[0031] This implementation provides flexibility for system deployment, allowing the entity to be implemented on a variety of network nodes, such as gNB, TRP, PRU, or UE, which have the capability of receiving reference signals as part of their implementation of the relevant specification. This enables a wide range of use cases, considering collection of measurements in the uplink and downlink.

[0032] A second aspect of the disclosure provides a network entity in a wireless communication system, configured to obtain information of one or more detected paths related to a reference signal, from an entity, wherein the information is based on a reflection likelihood of the one or more detected paths.

[0033] This disclosure proposes a network entity that coordinates the collection of measurements from the entity. This approach enables the network to receive path information that is related to reflection likelihood, which provides a probabilistic indication of whether a path is associated with a reflection. This allows the network to make better decisions for sensing and reflection point analysis, leading to improved system performance and more accurate path classification.

[0034] In an implementation form of the second aspect, the information comprises the reflection likelihood of the one or more detected paths.

[0035] This implementation enables the network to be explicitly aware of the reflection likelihood of the reported paths. By receiving the reflection likelihood as part of the path information, the network entity can make more informed decisions about how to use the path information for sensing and reflection point identification.

[0036] In an implementation form of the second aspect, the information comprises at least one of the following: timing information, power information and angle information of the one or more detected paths.

[0037] This implementation enhances the richness of the path information provided to the network entity. By including timing, power, and angle information, the network can use these parameters for various signal processing techniques, considering measurements based on TOA, RSRPP, and AOA. This leads to more precise localization and better determination of reflection points.

[0038] In an implementation form of the second aspect, the network entity is further configured to provide configuration information to the entity, wherein the configuration information configures the entity to provide the information of the one or more detected paths to the network entity based on the reflection likelihood of the one or more detected paths.

[0039] This enables the network to control the reporting strategy of the entity dynamically.

[0040] In an implementation form of the second aspect, the configuration information indicates the entity to provide the information of the detected paths in one of the following ways: provide the information of the one or more detected path with a reflection likelihood above a first threshold, provide the information of the one or more detected path with a power value above a second threshold, or provide the information of the one or more detected path with a largest reflection likelihood among the one or more detected paths. By configuring the entity to report paths using certain reflection likelihood thresholds, the network can achieve better control of the reporting overhead, ensuring that only the most important information is collected. This improves system efficiency, reduces signaling overhead, and provides reflection-related data to the network entity for sensing and positioning purposes.

[0041] In an implementation form of the second aspect, the configuration information comprises the first threshold and / or the second threshold.

[0042] This implementation allows the network to control the reflection likelihood and power thresholds used by the entity to determine which paths to report. By adjusting the thresholds, the network can balance between reporting overhead and the granularity of path information. This enables dynamic adaptation to network requirements and ensures optimal performance under varying conditions.

[0043] In an implementation form of the second aspect, the reference signal comprises an SRS or a PRS.

[0044] This feature supports the use of common reference signals in positioning, enabling the system to flexibly adapt to different signaling types and links.

[0045] In an implementation form of the second aspect, the network entity is one of the following: an LMF, a sensing management function (SMF), or a sensing function node (SFN).

[0046] This implementation defines the possible network elements that can serve as the network entity, allowing flexibility in system deployment. By supporting the LMF, SMF, or SFN as the network entity, this aspect enables various network components to receive reflection likelihood-based path information. Each of these entities can use the provided information for improved sensing and reflection point detection, thereby enhancing network performance and supporting a wide range of 5G and beyond use cases.

[0047] A third aspect of the disclosure provides a method performed by an entity for a wireless communication system, wherein the method comprises: receiving a reference signal, determining reflection likelihood for one or more detected paths related to the reference signal, and providing information of the one or more detected paths to a network entity, based on the reflection likelihood of one or more detected paths.

[0048] This method allows the entity to determine the reflection likelihood for detected paths and report relevant information to the network. By embedding this logic in the method, the system enables efficient and context-aware reporting of path information, supporting network-side sensing, and reflection point analysis.

[0049] Implementation forms of the method of the third aspect may correspond to the implementation forms of the entity of the first aspect described above. The method of the third aspect and its implementation forms achieve the same advantages and effects as described above for the entity of the first aspect and its implementation forms.

[0050] A fourth aspect of the disclosure provides a method performed by a network entity, wherein the method comprises obtaining information of one or more detected paths related to a reference signal from an entity, wherein the information is based on a reflection likelihood of the one or more detected paths.

[0051] This method enables the network to receive only the most relevant path information, including reflection likelihood, from the entity. This approach reduces signaling overhead while ensuring that the network receives the most critical path data, supporting efficient reflection point identification and enhanced positioning accuracy. Implementation forms of the method of the fourth aspect may correspond to the implementation forms of the network entity of the second aspect described above. The method of the fourth aspect and its implementation forms achieve the same advantages and effects as described above for the network entity of the second aspect and its implementation forms.

[0052] A fifth aspect of the disclosure provides a computer program or computer program product comprising a program code for carrying out, when implemented on a processor, the method according to the third aspect and any implementation forms of the third aspect, or the fourth aspect and any implementation forms of the fourth aspect.

[0053] It has to be noted that all devices, elements, units and means described in the present application could be implemented in software or hardware elements or any kind of combination thereof. All steps that are performed by the various entities described in the present application as well as the functionalities described to be performed by the various entities are intended to mean that the respective entity is adapted to or configured to perform the respective steps and functionalities. Even if, in the following description of specific embodiments, a specific functionality or step to be performed by external entities is not reflected in the description of a specific detailed element of that entity that performs that specific step or functionality, it should be clear for a skilled person that these methods and functionalities can be implemented in respective software or hardware elements or any kind of combination thereof.

[0054] BRIEF DESCRIPTION OF DRAWINGS

[0055] The above-described aspects and implementation forms of the present disclosure will be explained in the following description of specific embodiments in relation to the enclosed drawings, in which:

[0056] FIG. 1 shows an entity according to an embodiment of the disclosure;

[0057] FIG. 2 shows a network entity according to an embodiment of the disclosure;

[0058] FIG. 3 shows an entity according to an embodiment of the disclosure;

[0059] FIG. 4 shows a path determination procedure according to an embodiment of the disclosure;

[0060] FIG. 5 shows a reflection likelihood determination procedure according to an embodiment of the disclosure;

[0061] FIG. 6 shows signaling exchanges among entities according to an embodiment of the disclosure;

[0062] FIG. 7 shows signaling exchanges among entities according to an embodiment of the disclosure;

[0063] FIG. 8 shows signaling exchanges among entities according to an embodiment of the disclosure;

[0064] FIG. 9 shows signaling exchanges among entities according to an embodiment of the disclosure;

[0065] FIG. 10 shows signaling exchanges among entities according to an embodiment of the disclosure;

[0066] FIG. 11 shows a method according to an embodiment of the disclosure;

[0067] FIG. 12 shows a method according to an embodiment of the disclosure; and

[0068] FIG. 13 shows exemplary measurement information elements (IES) according to an embodiment of the disclosure. DETAILED DESCRIPTION OF EMBODIMENTS

[0069] The present disclosure describes an entity and a network entity in a wireless communication system, as well as various methods and embodiments related to determining and reporting information related to detected paths. The embodiments introduce improved mechanisms for efficient and accurate positioning and sensing of the UE, thereby supporting advanced use cases for location-based services in future wireless communication networks.

[0070] Illustrative embodiments of the entity, the network entity, and corresponding methods, are described with reference to the figures. Although this description provides a detailed example of possible implementations, it should be noted that the details are intended to be exemplary and in no way limit the scope of the application.

[0071] Moreover, an embodiment or example may refer to other embodiments or examples. For example, any description including but not limited to terminology, element, process, explanation, and / or technical advantage mentioned in one embodiment / example is applicable to the other embodiments or examples.

[0072] FIG. 1 shows an entity 100 in a wireless communication system according to an embodiment of this disclosure. The entity 100 is configured to perform various functions related to the detection, classification, and reporting of path information associated with a reference signal. The entity 100 may be implemented as a UE, a base station (gNB), a PRU, a TRP, or any other device capable of receiving reference signals and processing path information.

[0073] The entity 100 may comprise processing circuitry (not shown) configured to perform, conduct, or initiate the various operations of the entity 100 described herein. The processing circuitry may comprise hardware and software. The hardware may comprise analog circuitry digital circuitry, or both analog and digital circuitry. The digital circuitry may comprise components such as application-specific integrated circuits (ASICs), field-programmable arrays (FPGAs), digital signal processors (DSPs), or multi-purpose processors. The entity 100 may further comprise memory circuitry, which stores one or more instruction(s) that can be executed by the processor or by the processing circuitry, in particular under the control of the software. For instance, the memory circuitry may comprise a non-transitory storage medium storing executable software code which, when executed by the processor or the processing circuitry, causes the various operations of the entity 100 to be performed. In one embodiment, the processing circuitry comprises one or more processors and a non-transitory memory connected to the one or more processors. The non-transitory memory may carry executable program code which, when executed by the one or more processors, causes entity 100 to perform, conduct or initiate the operations or methods described herein.

[0074] The entity 100 is configured to receive a reference signal 101. Possibly, the reference signal 101 is transmitted by one or more network nodes in the wireless communication system. For instance, the reference signal 101 may include an SRS sent by a PRU or UE. In another instance, the reference signal 101 may include a PRS sent by a gNB or TRP. This reference signal serves as the basis for detecting paths related to the signal's propagation.

[0075] Notably, the entity 100 may identify one or more paths associated with the received reference signal 101. A detected path may correspond to a LOS paths or a NLOS path, e.g., a path that result from reflection, scattering, or diffraction. For each detected path, relevant parameters such as timing, power, and angle are measured and stored for further processing.

[0076] The entity 100 is configured to determine a reflection likelihood 102 for one or more detected paths related to the reference signal 101.

[0077] Possibly, the entity 100 may calculate a reflection likelihood 102 for each of the detected paths. The reflection likelihood 102 quantifies the probability that a specific path is a result of a reflection. The reflection likelihood 102 may be represented as a value between 0 and 1, where a value closer to 1 indicates a high likelihood of reflection, and a value closer to 0 indicates a low likelihood. The reflection likelihood can be determined by comparing information related to a reflection plane associated with each detected path. This comparison may involve reflection plane information computed for measurements at different times, locations, or frequencies.

[0078] The entity 100 is further configured to provide information 103 of the one or more detected paths to a network entity 200, based on the reflection likelihood 102 of one or more detected paths.

[0079] Optionally, the transmitted information 103 may include one or more of the following for each selected path: reflection likelihood, timing information, power information, and angle information.

[0080] In one implementation, this information is transmitted according to configuration instructions provided by the network entity 200.

[0081] For example, entity 100 may receive configuration information from a network entity, such as thresholds or instructions on processing the reflection likelihood. For example, the network entity 200 may specify that only paths with a reflection likelihood above a certain threshold should be reported or that only paths with a power level above a certain threshold should be reported. The entity 100 updates the logic and conditions for path selection and reporting accordingly.

[0082] In one implementation, based on the reflection likelihood and other measured parameters (e.g., power, timing, or angle), the entity 100 selects a subset of detected paths for reporting. This selection is controlled by the configuration information received from the network entity. For example, the entity 100 may be instructed to report only the path with the largest reflection likelihood or only paths that exceed specific power or reflection likelihood thresholds.

[0083] This disclosure proposes an entity or device enabling efficient and selective reporting of path information while reducing signaling overhead and supporting better identification of reflection points.

[0084] Accordingly, FIG. 2 shows a network entity 200 according to an embodiment of the disclosure. The network entity 200 may comprise processing circuitry (not shown) configured to perform, conduct, or initiate the various operations of the network entity 200 described herein. The processing circuitry may comprise hardware and software. The hardware may comprise analog circuitry digital circuitry, or both analog and digital circuitry. The digital circuitry may comprise components such as ASICs, FPGAs, DSPs, or multi-purpose processors. The network entity 200 may further comprise memory circuitry, which stores one or more instruction(s) that can be executed by the processor or by the processing circuitry, in particular under the control of the software. For instance, the memory circuitry may comprise a non-transitory storage medium storing executable software code which, when executed by the processor or the processing circuitry, causes the various operations of the network entity 200 to be performed. In one embodiment, the processing circuitry comprises one or more processors and a non-transitory memory connected to the one or more processors. The non-transitory memory may carry executable program code which, when executed by the one or more processors, causes the network entity 200 to perform, conduct or initiate the operations or methods described herein.

[0085] The network entity 200 illustrated in FIG. 2 can be implemented as an LMF, an SMF, or an SFN. The network entity 200 is responsible for configuring the reporting strategy of the entity 100 and for receiving and processing path information related to a reference signal. The detailed description of its components and functionalities is provided below. The entity 100 may possibly be the entity 100 shown in FIG. 1.

[0086] The network entity 200 is configured to obtain information 103 of one or more detected paths related to a reference signal 101, from an entity 100. The information 103 is based on a reflection likelihood 102 of the one or more detected paths. Possibly, the received information may include one or more of the following for each detected path: reflection likelihood, timing information, power information, and angle information. This information is critical for the network to identify reflection points, improve positioning, and enable advanced sensing functionalities.

[0087] The network entity 200 may be further configured to provide configuration information to the entity 100. The configuration information may include parameters such as reflection likelihood thresholds, power thresholds, and instructions on which paths should be reported. For example, the configuration generator may instruct the entity 100 to report only paths with a reflection likelihood above a first threshold or paths with power above a second threshold. This enables adaptive reporting and ensures that only the most relevant paths are transmitted.

[0088] In one implementation, the network entity 200 may process the received path information to extract meaningful insights about the paths. It can use reflection likelihood, timing, power, and angle information to classify the paths as being reflection-based or not. The network entity 200 may use this information for sensing and reflection point identification, or radio environment mapping. It can also determine relationships between different paths, such as whether multiple paths are associated with the same reflection point or surface.

[0089] The network entity 200 possibly processes and aggregates information received from one or more entities 100. Combining data from multiple devices allows the network entity 200 to obtain a holistic view of the network environment.

[0090] In one implementation, the network entity 200 may be equipped with a sensing and positioning function. It can use the information after the path analysis to determine the position of a UE or of an object. It enhances location accuracy by using reflection likelihoods, timing, power, and angle information. This functionality supports positioning and sensing methods considering measurements such as TOA, RSRPP, and AOA, allowing the network to perform sensing based on reflection points.

[0091] The entity 100, as shown in FIG. 1 , and network entity 200, as shown in FIG. 2, work together to enable efficient and selective reporting of path information, thereby reducing signaling overhead while maintaining high-quality reflection point identification. The entity 100 computes the reflection likelihood for each detected path and reports information as the network entity 200 instructed. The network entity 200 processes the received path information, coordinates the reporting logic of the entity 100, and supports applications such as sensing. This system provides a dynamic and efficient mechanism for reflectionbased path processing, supporting advanced network use cases in 5G NR and beyond.

[0092] The following embodiments describe detailed scenarios where the entity 100 as shown in FIG. 1 interacts with a network entity 200 as shown in FIG. 2 in determining and reporting channel-related information for positioning purposes.

[0093] FIG. 3 illustrates an entity 100 that receives a reference signal 101 and detects one or more paths associated with the received signal, according to an embodiment of the disclosure. The entity 100 here may be the entity 100 shown in FIG. 1 or FIG. 2, which can be any node in the wireless communication system, such as a gNB, TRP, PRU, or UE. The reference signal 101 can be a signal such as a SRS or a PRS.

[0094] Upon receiving the reference signal 101 , the entity 100 analyzes the signal and identifies one or more propagation paths through which the signal arrived. Each path represents a specific route the signal takes from the transmitter (e.g., TRP, gNB) to the receiver, i.e., the entity 100. The paths can include LOS paths, which are direct paths without any reflections, andNLOS paths, which can be due to reflections, scattering, or diffractions.

[0095] To distinguish between these paths, the entity 100 may compute a reflection likelihood 102 for each detected path. This likelihood indicates the probability that the detected path (NLOS path) corresponds to a reflection. The likelihood value ranges from 0 to 1 , where 1 indicates a high probability of reflection, and 0 indicates a low probability of reflection, such as a direct LOS path. The entity 100 can further perform timing, power, and angle measurements for each detected path. The timing information can include UL RTOA, DL RSTD, or Rx-Tx time differences. Power information may be reported as Reference Signal Received Power per path (RSRPP), and angle information can correspond to Angle of Departure (AOD) or Angle of Arrival (AOA). The results of these measurements may be reported to a network entity 200, as depicted in FIG. 2.

[0096] Optionally, once the reflection likelihood has been calculated for each path, the entity 100 may identify a subset of the detected paths with the highest reflection likelihood. The selection of this subset can be performed using different approaches, for instance, including: Top-N selection: select the N paths with the highest reflection likelihood scores and threshold-based selection: select paths with reflection likelihoods greater than a certain threshold.

[0097] The threshold can be pre-configured, for example, hardcoded within entity 100, or dynamically provided, such as being sent to entity 100 from the network entity 200 as part of control signaling. In some cases, additional criteria may be used to refine the subset, such as requiring the power of the path to be above a certain power threshold. For example, paths with both a high reflection likelihood and a power level above a certain threshold may be selected.

[0098] Once the subset of reflection paths is determined, the entity 100 generates a path report and transmits it to the network entity 200. This report can contain the following information for each of the selected paths:

[0099] • Reflection Likelihood: The likelihood score for each path.

[0100] • Timing Information: Path delay or related timing information (UL RTOA, DL RSTD, etc.).

[0101] • Power Information: Signal power of the path (e.g., RSRPP).

[0102] • Angle Information: Angle of arrival or departure (e.g., AOA, AOD).

[0103] The information 103 can be transmitted periodically or at the request of the network entity 200. The network entity 200 can then use this information to infer the presence of reflective surfaces, improve positioning accuracy, or update the network’s knowledge of the surrounding environment or of an object.

[0104] An alternative approach to the embodiment depicted in FIG. 3 involves using both reflection likelihood and power information to select the subset of paths. In this embodiment, the entity 100 calculates the reflection likelihood for each path. Also, it analyzes the power information for each path and filters paths with power levels below a certain threshold. The final subset of paths to be reported to the network entity 200 includes paths that satisfy both criteria — i.e., paths with reflection likelihood greater than a reflection threshold and power level greater than a power threshold.

[0105] This dual-filtering process ensures that only the most relevant paths are reported, reducing unnecessary signaling and focusing on the paths most useful for sensing considering reflections.

[0106] FIG. 4 illustrates a procedure for determining if a path is related to a reflection based on measurements from multiple UE locations, according to an embodiment of this disclosure. In this embodiment, the entity 100 may be a UE.

[0107] As shown in FIG. 4(a), a UE at a first location receives multiple detected paths and assumes they are due to reflections. The UE computes a potential reflection plane for each path.

[0108] At a second location, as shown in FIG. 4(b), the UE again assumes the paths are reflections and computes new reflection planes for each detected path. After collecting at least two measurements, the UE compares the measurements collected at different locations to determine which paths are due to reflections. In particular, the UE compares the reflection planes for corresponding paths detected at the two locations. If the planes for a particular path are consistent across the two locations, the path is confirmed to be a reflection. If the planes differ, the path is unlikely to be a reflection. For instance, Path 2 is confirmed to be a reflection as its reflection planes at both locations are consistent (as shown in FIG. 4(c)), while Path 1 is identified as not associated with a reflection.

[0109] Based on the measurements at the two locations, the UE can also determine a reflection likelihood for each path. For example, for Path 1, the UE can determine a reflection likelihood equal to 0, while for Path 2, the UE can determine a reflection likelihood equal to 1. The reflection likelihood could also be determined based on the inner product of the normal vectors of the corresponding planes determined at different UE locations. If the inner product is close to 1, it implies high similarity between the planes, indicating the path is likely a reflection.

[0110] FIG. 5 shows a process for computing the reflection likelihood of paths using multiple measurements. In this embodiment, the entity 100 may consider signals received from multiple UE locations, different polarizations, different frequencies, or measurements from different receiver positions. For each received signal, one or more paths are detected.

[0111] For each detected path, the entity 100 determines the position of the reflection interaction point. The position of the interaction point can be determined based on measurements of the path. For example, based on delay measurements of a path and with the known location of the transmitting device of a signal and the known location of the receiving device of the received signal, the interaction point lies on an ellipse. The position of the interaction point can be determined based on an angle measurement of the path. For example, with the angle of arrival of a path estimated by the receiving device, the position of the interaction point can be determined.

[0112] In addition, for each detected path, the normal of the reflection plane is determined, i.e., assuming the interaction point is a result of a reflection of a planar surface in the vicinity of the interaction point. For example, the normal of the plane corresponds to the bisector of the inner angle between the line segment Tx location - position of interaction point and the line segment position of interaction point - Rx location. Afterward, the reflection likelihood for one or more paths can be determined based on the determined interaction point, i.e., the location of the interaction points and the computed normal, assuming a path is due to a reflection. For example, two or more paths detected from the two or more received signals can be associated with a reflection. The reflection likelihood of the associated paths can be computed based on the normal of the associated paths, e.g., based on the inner product of the normal. That is, the inner product of these vectors is used to assess whether multiple paths are likely caused by a reflection.

[0113] This approach enables a robust calculation of reflection likelihood for paths detected from diverse measurement conditions.

[0114] FIG. 6 shows signaling exchanges between entities according to an embodiment of this disclosure. In the embodiment, the network entity 200, e.g., the SFN / LMF, configure the entity 100, i.e., a UE, to report paths that have the highest reflection likelihood. The SFN / LMF may provide the UE with thresholds for reflection likelihood, power, or other criteria.

[0115] In this embodiment, the gNB transmits a PRS, the reference signal 101, e.g., through a TRP belonging to the gNB. The UE makes path measurements and identifies the paths most likely to be caused by reflections, using reflection likelihood thresholds provided by the SFN / LMF. The UE reports the timing, power, and angle measurements of the paths that meet the reflection likelihood criteria to the SFN / LMF, in this example, the measurements of the paths with the largest likelihood of being due to a reflection.

[0116] The SFN / LMF can dynamically configure reflection likelihood and power thresholds, allowing flexible control of reporting behavior. This enables optimization for specific use cases. FIG. 7 shows signaling exchanges between entities according to an embodiment of this disclosure. In the embodiment, the network entity 200, e.g., the SFN / LMF, configure the entity 100, i.e., a UE, to measure paths at multiple UE locations. The UE takes measurements at different positions and identifies which paths are likely reflections.

[0117] In this embodiment, the gNB transmits two PRS signals via TRPs. The UE measures the paths at different positions, detects paths, and computes the reflection likelihood for each.

[0118] The UE identifies which paths are most likely associated with a reflection based on measurements of the PRS at the different UE locations. The UE then reports to the SFN / LMF the measurements of the paths with the largest likelihood of being due to a reflection. The measurements can comprise timing, power, and / or angle information associated with the path.

[0119] By collecting path information at multiple UE locations, the system can identify and track the position of interaction points on reflective surfaces. This approach enables the detection of common reflection surfaces across different locations.

[0120] Further, instead of reporting all detected paths, the system filters and reports only the most relevant reflection paths (those with the highest reflection likelihood). This significantly reduces reporting overhead, especially in dense multipath environments where numerous paths may be detected.

[0121] FIG. 8 shows signaling exchanges between entities according to an embodiment of this disclosure. In the embodiment, the network entity 200, e.g., the SFN / LMF, configure the entity 100, i.e., a UE, to report the reflection likelihood for each detected path.

[0122] The gNB transmits PRS through a TRP. The UE detects paths and computes the reflection likelihood for each detected path. The UE then reports the measurements of the paths to the SFN / LMF, including the reflection likelihood of each path. Further measurements can comprise timing, power, and / or angle information associated with each path.

[0123] By computing reflection likelihoods for each path, the system enables the SFN / LMF to distinguish between different types of NLOS paths. This provides the SFN / LMF with a better understanding of reflective surfaces and their locations in the environment.

[0124] FIG. 9 shows signaling exchanges between entities according to an embodiment of this disclosure. In the embodiment, the network entity 200, e.g., the SFN / LMF, configures the entity 100, in this case, a gNB, to report paths that have the highest reflection likelihood.

[0125] In addition, the SFN or LMF may provide the gNB with one or more thresholds to assist the gNB in the determination of the paths with the largest reflection likelihood. The one or more thresholds can be a threshold for the minimum reflection likelihood or a threshold for the minimum received power (or for the maximum received power).

[0126] The PRU or UE transmits SRS and the gNB receives it, i.e., via a TRP belonging to the gNB. The gNB / TRP makes measurements of the SRS and performs path detection. The gNB identifies which paths are most likely associated with a reflection. For this, the gNB may use the thresholds provided by the SFN or LMF. The gNB then reports to the SFN / LMF the measurements of the paths with the largest likelihood of being due to a reflection. The measurements can comprise timing, power, and / or angle information associated with the path.

[0127] FIG. 10 shows signaling exchanges between entities according to an embodiment of this disclosure. In the embodiment, the network entity 200, e.g., the SFN / LMF, configure the entity 100, i.e., the gNB, to report measurements of paths, where the measurements include the reflection likelihood of each path. The PRU or UE transmits SRS and the gNB receives it, i.e., via a TRP belonging to the gNB. The gNB / TRP makes measurements of the SRS and performs path detection. The gNB determines the reflection likelihood for each detected path. The gNB then reports the measurements of the paths to the SFN or LMF, including the reflection likelihood of each path. Further measurements can comprise timing, power, and / or angle information associated with each path.

[0128] FIG. 11 shows a method 1100 according to an embodiment of the disclosure. In a particular embodiment, the method 1100 is performed by an entity 100, shown in one of FIG. 1 to FIG. 10. The method 1100 comprises a step 1101 of receiving a reference signal 101, a step 1102 of determining a reflection likelihood 102 for one or more detected paths related to the reference signal 101 , a step 903 of providing information 103 of the one or more detected paths to a network entity 200, based on the reflection likelihood 102 of one or more detected paths.

[0129] Possibly, the network entity 200 may be the network entity 200 shown in one of the FIG. 1 to FIG. 3, or FIG. 6 to FIG. 10.

[0130] FIG. 12 shows a method 1200 according to an embodiment of the disclosure. In a particular embodiment, the method 1200 is performed by a network entity 200, shown in one of the FIG. 1 to FIG. 3, or FIG. 6 to FIG. 10. The method 1200 comprises a step 1201 of obtaining information 103 of one or more detected paths related to a reference signal 101, from an entity 100, wherein the information 103 is based on a reflection likelihood 102 of the one or more detected paths. Possibly, the entity 100 may be the entity 100 shown in one of FIG. 1 to FIG. 10.

[0131] FIG. 13 shows an embodiment of a system where a new Information Element (IE) reflection indicator is introduced into the Additional Path List IE and Extended Additional Path List IE that is sent from a gNB to the LMF. The new IE reflection indicator could also be referred to as reflection likelihood or reflection information. The new IE reflection indicator provides the likelihood of a path being due to a reflection. The new IE is associated with each additional path. In addition, as the first detected path may also be a NLOS path, a new IE reflection indicator could also be included within the TRP Measurement Result IE which would then indicate the likelihood of the first path being due to a reflection. The reflection indicator for the first detected path only needs to be reported in cases the first detected path is not a LOS path.

[0132] The introduction of these IBs enables the LMF or the sensing function node to receive information about the reflection likelihood of the paths using the legacy IES for reporting information about paths.

[0133] To summarize, embodiments of this disclosure an efficient method for reflection likelihood computation and path reporting. By enabling a UE or gNB to compute and report reflection likelihoods for each detected path, the system provides the LMF or SFN with information that can be used for sensing. The LMF or SFN can use this information to identify reflection points, track changes in the radio environment, and build an accurate model of reflective surfaces.

[0134] The ability to filter paths based on reflection likelihood thresholds and power thresholds optimizes the use of network resources, reduces reporting overhead, and enables the LMF or SFN to make more informed decisions. Additionally, the system's support for multi-location path correlation and reflection surface identification further enhances the network's ability to map the radio environment.

[0135] The described methods and systems are applicable to various network nodes, including gNBs, TRPs, PRUs, and UEs, thereby providing a unified and adaptable approach to positioning in 5G and beyond wireless communication networks. The proposed system supports both uplink (SRS) and downlink (PRS) positioning and can be flexibly implemented in multiple deployment scenarios.

[0136] The present disclosure has been described in conjunction with various embodiments as examples as well as implementations. However, other variations can be understood and effected by those skilled in the art and practicing the claimed embodiments of the disclosure, from the studies of the drawings, this disclosure, and the independent claims. In the claims as well as in the description the word “comprising” does not exclude other elements or steps and the indefinite article “a” or “an” does not exclude a plurality. A single element or other unit may fulfill the functions of several entities or items recited in the claims. The mere fact that certain measures are recited in the mutually different dependent claims does not indicate that a combination of these measures cannot be used in an advantageous implementation.

[0137] Furthermore, any method according to embodiments of the disclosure may be implemented in a computer program, having code means, which when run by processing means causes the processing means to execute the steps of the method. The computer program is included in a computer-readable medium of a computer program product. The computer-readable medium may comprise essentially any memory, such as a ROM (Read-Only Memory), a PROM (Programmable Read-Only Memory), an EPROM (Erasable PROM), a Flash memory, an EEPROM (Electrically Erasable PROM), or a hard disk drive.

[0138] Moreover, it is realized by the skilled person that embodiments of the entity 100 or the network entity 200, comprise the necessary communication capabilities in the form of e.g., functions, means, units, elements, etc., for performing the solution. Examples of other such means, units, elements, and functions are processors, memory, buffers, control logic, encoders, decoders, rate matchers, de-rate matchers, mapping units, multipliers, decision units, selecting units, switches, interleavers, deinterleavers, modulators, demodulators, inputs, outputs, antennas, amplifiers, receiver units, transmitter units, DSPs, trelliscoded modulation (TCM) encoder, TCM decoder, power supply units, power feeders, communication interfaces, communication protocols, etc. which are suitably arranged together for performing the solution.

[0139] Especially, the processors) of the entity 100 or the network entity 200 may comprise, e.g., one or more instances of a CPU, a processing unit, a processing circuit, a processor, an ASIC, a microprocessor, or other processing logic that may interpret and execute instructions. The expression “processor” may thus represent a processing circuitry comprising a plurality of processing circuits, such as, e.g., any, some, or all of the ones mentioned above. The processing circuitry may further perform data processing functions for inputting, outputting, and processing of data comprising data buffering and device control functions, such as call processing control, user interface control, or the like.

Claims

CLAIMS1. An entity (100) for a wireless communication system, the entity (100) being configured to: receive a reference signal (101), determine a reflection likelihood (102) for one or more detected paths related to the reference signal (101), and provide information (103) of the one or more detected paths to a network entity (200), based on the reflection likelihood (102) of one or more detected paths.

2. The entity (100) according to claim 1, wherein the information (103) provided to the network entity (200) comprises the reflection likelihood (102) of the one or more detected paths.

3. The entity (100) according to claim 1 or 2, wherein the information (103) provided to the network entity (200) comprises at least one of the following: timing information, power information and angle information of the one or more detected paths.

4. The entity (100) according to any preceding claims, configured to: receive configuration information (201) from the network entity (200), wherein the configuration information (201) configures the entity (100) to provide the information (103) of the one or more detected paths to the network entity (200) based on the reflection likelihood (102) of the one or more detected paths.

5. The entity (100) according to claim 4, configured to: determine a subset of the one or more detected paths based on the configuration information (201), wherein the information (103) provided to the network entity (200) comprises the reflection likelihood (102) of each detected path of the subset, and / or wherein the information (103) provided to the network entity (200) comprises at least one of the following: timing information, power information and angle information of each detected path of the subset.

6. The entity (100) according to claim 4 or 5, wherein the configuration information (201) indicates the entity (100) to provide the information (103) of the one or more detected path in at least one of the following manner: provide the information (103) of the one or more detected path with a reflection likelihood (102) above a first threshold, provide the information (103) of the one or more detected path with a power value above a second threshold, or provide the information (103) of the one or more detected path with a largest reflection likelihood among the one or more detected paths.

7. The entity (100) according to claim 6, configured to: determine the power value associated with each of the one or more detected paths.

8. The entity (100) according to claims 6 or 7, wherein the configuration information (201) comprises the first threshold and / or the second threshold.

9. The entity (100) according to any preceding claims, configured to: detect the one or more paths related to the reference signal (101), for each of the one or more detected paths, assume that the detected path is a result of a reflection and compute information related to a reflection plane associated with the detected path, anddetermine the reflection likelihood (102) of each detected path by comparing the computed information related to the reflection plane for the detected path with a corresponding information related to a reflection plane computed for a path based on a measurement collected at a different location, time or frequency.

10. The entity (100) according to any preceding claims, wherein the reference signal (101) comprises a sounding reference signal, SRS, or a positioning reference signal, PRS.

11. The entity (100) according to any preceding claims, wherein the entity (100) is one of the following: a base station, a transmit receive point, a gNB, a position reference unit, or a user equipment.

12. A network entity (200) in a wireless communication system, the network entity (200) being configured to: obtain information (103) of one or more detected paths related to a reference signal (101), from an entity (100), wherein the information (103) is based on a reflection likelihood (102) of the one or more detected paths.

13. The network entity (200) according to claim 12, wherein the information (103) comprises the reflection likelihood (102) of the one or more detected paths.

14. The network entity (200) according to claim 12 or 13, wherein the information (103) comprises at least one of the following: timing information, power information and angle information of the one or more detected paths.

15. The network entity (200) according to any of claims 12 to 14, configured to: provide configuration information (201) to the entity (100), wherein the configuration information (201) configures the entity (100) to provide the information (103) of the one or more detected paths to the network entity (200) based on the reflection likelihood (102) of the one or more detected paths.

16. The network entity (200) according to claim 15, wherein the configuration information (201) indicates the entity (100) to provide the information (103) of the one or more detected path in at least one of the following manner: provide the information (103) of the one or more detected path with a reflection likelihood (102) above a first threshold, provide the information (103) of the one or more detected path with a power value above a second threshold, or provide the information (103) of the one or more detected path with a largest reflection likelihood among the one or more detected paths.

17. The network entity (200) according to claim 16, wherein the configuration information (201) comprises the first threshold and / or the second threshold.

18. The network entity (200) according to any of claims 12 to 17, wherein the reference signal (101) comprises a sounding reference signal, SRS, or a positioning reference signal, PRS.

19. The network entity (200) according to any of claims 12 to 18, wherein the network entity (200) is one of the following: a location management function, a sensing management function, or a sensing function node.

20. A method performed by an entity (100) for a wireless communication system, the method comprising: receiving a reference signal (101), determining a reflection likelihood (102) for one or more detected paths related to the reference signal (101), andproviding information (103) of the one or more detected paths to a network entity (200), based on the reflection likelihood of one or more detected paths.

21. A method performed by a network entity (200), the method comprising: obtaining information (103) of one or more detected paths related to a reference signal (101), from an entity (100), wherein the information (103) is based on a reflection likelihood (102) of the one or more detected paths.

22. A computer program product comprising computer readable code instructions which, when run in a computer will cause the computer to perform the method according to claim 20 or 21.

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