Device and method for determination of selection window in wireless communication networks

Abstract

The present disclosure relates to entities and methods for measuring and reporting reference signals. The disclosure proposes an entity for a wireless communication system, the entity being configured to: measure one or more reference signals, detect a first path of the one or more measured reference signals, determine a timing of the first detected path, determine one or more measurements of the one or more reference signals, wherein a timing of the one or more measurements is greater than or equal to the timing of the first detected path, adjusted by subtracting a first timing offset, and report the one or more measurements of the one or more reference signals to a network entity. The disclosure further proposes a network entity configured to obtain the one or more measurements of the one or more reference signals from the entity.

Description

[0001] DEVICE AND METHOD FOR DETERMINATION OF SELECTION WINDOW IN WIRELESS COMMUNICATION NETWORKS

[0002] TECHNICAL FIELD

[0003] The present disclosure relates to wireless communication systems, particularly in the context of new radio (NR) technologies. Specifically, it pertains to the application of artificial intelligence (Al) and machine learning (ML) techniques for enhancing the positioning accuracy of user equipment (UE), particularly in scenarios involving non-line-of-sight (NLOS) conditions between transmission and reception points (TRPs) and the UE.

[0004] BACKGROUND

[0005] Positioning of UE is crucial in modem wireless networks, particularly for applications that require high precision, such as navigation, autonomous systems, and location-based services. Traditional methods of positioning, such as time of arrival (TOA) and angle of arrival (AOA), rely on clear line-of-sight (LOS) between the UE and the TRPs. However, in complex urban environments, these signals often face obstructions, resulting in NLOS conditions that degrade positioning accuracy.

[0006] Under NLOS conditions, the performance of traditional positioning methods is significantly degraded due to signal reflections and diffractions, resulting in inaccurate position estimates. To overcome these limitations, the 3rd Generation Partnership Project (3GPP) has investigated the use of AI / ML techniques to enhance positioning in 5G NR systems. These methods use channel measurements derived from reference signals to either directly estimate a UE's position or assist in estimating channel parameters, such as time of arrival or power information, which can be used to improve positioning accuracy.

[0007] AI / ML-based positioning can be implemented through two main approaches: direct AI / ML positioning, where the AI / ML model predicts the UE's location, and AI / ML-assisted positioning, where the model estimates channel parameters that can be used by traditional positioning algorithms. Both approaches rely on large datasets of channel measurements to train models that recognize patterns associated with specific UE positions. During operation after training, these models use new channel measurements to infer the UE's position.

[0008] While AI / ML-based positioning provides significant advantages, it introduces a critical challenge: maintaining consistency between model training and inference. During model training, channel measurements are collected to create a "fingerprint" that associates a specific UE position (i.e., model output) with its corresponding signal characteristics (i.e., model input). Based on the channel measurements and labels for UE positions, a model can be trained to learn the model input / model output relationship. After the model has been trained, the model can be used during inference to predict a UE’s location based on channel measurements associated with the UE. However, inconsistencies between training data and measurements collected during inference can degrade the model’s accuracy.

[0009] For instance, if the channel measurements used during inference differ from those collected during training - due to variations in environmental conditions or changes in signal propagation paths - the model may fail to accurately predict the UE’s position. This is particularly problematic in NLOS scenarios, where the signal characteristics are highly sensitive to changes in the environment.

[0010] One of the key factors affecting consistency is the method used to select samples from the channel measurements, particularly to be used as model input in direct AI / ML positioning. The selection of samples (e.g., selected timing and power information of the channel measurements) is determined by defining a "selection window" during both training and inference phases. If the selection window is not consistently defined across both phases, the resulting model inputs may differ, leading to different fingerprints for the same UE position. As a result, the model may not be able to correctly associate a different fingerprint with the same position, reducing the accuracy of the positioning system.

[0011] Moreover, the inconsistency can be exacerbated when different timing granularity factors or sample selection rules are applied. For example, the model may select a different subset of samples from the channel response during inference than it did during training, leading to mismatched fingerprints. This issue becomes even more complex when channel measurements are collected from TRPs or base stations (gNBs) with variations in equipment and measurement methods that can further impact the consistency of the fingerprints.

[0012] Conventional methods have attempted to address this issue by defining the selection window based on specific timing references, such as the first detected signal path or the start of a time slot. However, these approaches still face limitations. If the selection window starts with the first detected path, stronger samples that appear earlier in the window may be missed. Conversely, if the window starts at the beginning of the slot, weaker samples may be included, requiring a larger selection window, which adds complexity and uncertainty to the measurement process.

[0013] In addition, the determination of the selection window length and the timing granularity for the samples remains a challenge. The location management function (LMF), which handles positioning computations, may not have sufficient knowledge of the best values for these parameters in advance, resulting in suboptimal configuration. If the LMF signals a window size that is too small, critical samples may be excluded. On the other hand, if the measurement entity (e.g., the UE or gNB) uses a larger selection window than indicated by the LMF, this discrepancy can lead to further inconsistencies when reporting measurements.

[0014] These issues highlight the challenges associated with ensuring reliable and accurate UE positioning in NLOS environments using AI / ML techniques. Maintaining consistency between model training and inference, as well as harmonizing measurements across different network nodes, is critical for realizing the full potential of AI / ML-based positioning methods in complex wireless environments.

[0015] SUMMARY

[0016] In view of the above challenges, an objective of this disclosure is to ensure the consistency and reliability of channel measurements used for AI / ML-based positioning, specifically by enabling an efficient determination of the selection window for sample-based measurements. This is critical to avoid ambiguities in the channel measurements associated with the same E position, particularly when different selection windows are used across different nodes or instances.

[0017] Another objective is to ensure that the sample-based measurements reported to the LMF are consistent and reliable, regardless of variations in selection windows, thus improving the accuracy of direct Al-based positioning and minimizing performance degradation caused by inconsistent measurement inputs.

[0018] 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.

[0019] A first aspect of the disclosure provides an entity for a wireless communication system, the entity being configured to: measure one or more reference signals, detect a first path of the one or more measured reference signals, determine a timing of the first detected path, determine one or more measurements of the one or more reference signals, wherein a timing of the one or more measurements is greater than or equal to the timing of the first detected path, adjusted by subtracting a first timing offset, and report the one or more measurements of the one or more reference signals to a network entity. This disclosure accordingly proposes an entity or device that reports measurements that are made within a particular timing. Notably, the particular timing may be considered a selection window. This approach ensures that the selection window is adjusted to start before the first detected path, in order to allow strong signal samples before the first path to be considered, thereby improving measurement accuracy. Additionally, reporting these measurements provides the network entity with better data for signal analysis and system optimization.

[0020] In an implementation form of the first aspect, the entity is further configured to detect one or more additional paths of the one or more measured reference signals, and determine a timing of the additional detected path with the largest delay among the plurality of the one or more additional paths, wherein the timing of the one more measurements is also less than or equal to the timing of the detected path with the largest delay, adjusted by adding a second timing offset.

[0021] Optionally, this disclosure further proposes that the entity identifies the path with the largest delay, and adjusts the timing of the measurements using a second timing offset. This allows the entity to capture strong signal samples after the path with the largest delay, increasing the reliability and completeness of the measurements. Notably, by accounting for the path with the largest delay, the system can capture delayed signals more effectively, preventing missed measurements and enhancing the robustness of signal detection in multipath environments.

[0022] In an implementation form of the first aspect, the one or more measurements are selected from a set of measurements of the one or more reference signals, wherein the measurements of the set are taken at regular intervals on a time grid.

[0023] This ensures consistency and uniformity in the timing of measurements, facilitating the detection of subtle changes in signal strength over time. Regular intervals reduce the risk of gaps in data collection and enhance the reliability of the measurements.

[0024] In an implementation form of the first aspect, the start of the time grid is the timing of the first detected path minus the first timing offset. With other words, the start of the time grid is the timing of the first detected path adjusted by subtracting the first timing offset.

[0025] This configuration aligns the starting point of the selection window with a precise timing relative to the first detected path, enabling the capture of early strong samples that may otherwise be missed.

[0026] In an implementation form of the first aspect, the regular intervals on the time grid are determined based on a timing granularity factor.

[0027] For instance, this allows the system to adjust the time grid resolution according to channel conditions, improving the precision of sample selection in environments with different signal characteristics.

[0028] In an implementation form of the first aspect, the entity is further configured to receive the timing granularity factor and a number of intervals on the time grid, and determine a length of the time grid based on the timing granularity factor and the number of intervals on the time grid, wherein the end of the time grid is the timing of the first detected path adjusted by subtracting (minus) the first timing offset and by adding (plus) the length of the time grid.

[0029] This provides control over the length of the time grid, ensuring that it is optimally sized for the environment, enhancing the efficiency of data collection without unnecessary overhead.

[0030] In an implementation form of the first aspect, the end of the time grid is the timing of the detected path with the largest delay plus the second timing offset. With other words, the end of the time grid is the timing of the detected path with the largest delay adjusted by adding the second timing offset. This enables the time grid to fully cover all possible signal paths, ensuring that even the longest delayed signals are captured for measurement, improving performance in multipath environments.

[0031] In an implementation form of the first aspect, the entity is further configured to determine the number of intervals on the time grid based on the timing of the first detected path minus (adjusted by subtracting) the first timing offset and the timing of the detected path with the largest delay plus (adjusted by adding) the second timing offset.

[0032] Possibly, the length of the selection window can be determined based on the first timing offset and the second timing offset. This optimizes the number of measurement intervals by aligning the grid with the first detected path and the path with the largest delay, ensuring accurate signal capture while maintaining efficient resource use.

[0033] In an implementation form of the first aspect, the entity is further configured to indicate the determined number of intervals on the time grid to the network entity.

[0034] By sharing this information, the network can synchronize its processing with the entity’s measurements, improving overall system performance and coordination.

[0035] In an implementation form of the first aspect, the entity is further configured to determine the first timing offset based on the timing of a measurement of the one or more reference signals which has a timing less than or equal to the timing of the first detected path and a power within a first threshold from the power of the first detected path.

[0036] The threshold for determining the first timing offset can either be specified internally by the entity or indicated by the LMF. This flexibility allows for adaptable control over the threshold, depending on the network's configuration or requirements. Additionally, when the entity, such as the UE or gNB, determines the threshold autonomously, it can report or indicate the threshold to the LMF. This capability ensures that the LMF is informed of the specific criteria used for adjusting the timing offset, allowing for better alignment between the training and the inference phases.

[0037] By dynamically adjusting the first timing offset based on signal strength and a threshold, the entity enhances the accuracy of the selection window's start time, ensuring that relevant signal samples are captured while maintaining reliable and precise measurements. The threshold can be provided by the LMF to the entity or it can be specified.

[0038] In an implementation form of the first aspect, the entity is further configured to indicate the first timing offset to the network entity.

[0039] This provides the network with information about the offset, improving coordination and enabling the network to better interpret the measurements.

[0040] In an implementation form of the first aspect, the entity is further configured to determine the second timing offset based on the timing of a measurement of the one or more reference signals which has a timing larger than or equal to the timing of the detected path with the largest delay and a power within a second threshold from the power of said detected path.

[0041] This approach dynamically adjusts the second timing offset, ensuring the selection window captures delayed signal paths with high accuracy, further improving measurement reliability. The threshold can be provided by the LMF to the entity or it can be specified.

[0042] In an implementation form of the first aspect, the entity is further configured to indicate the second timing offset to the network entity. By communicating the second timing offset to the network, the network is aware how the end of the timing window is determined.

[0043] In an implementation form of the first aspect, the entity is further configured to: obtain a number of Nt’ measurements to be reported, wherein the number Nt’ is determined or received by the entity, and determine the one or more measurements to be sent to the network entity based on the Nt’ measurements on the time grid which have the highest power among all the measurements on the time grid.

[0044] Possibly, the entity selects and reports the highest power measurements from a set of samples taken at intervals on the time grid, based on a pre-determined or configured number of measurements (Nt’). Focusing on the strongest signals ensures that only the most relevant measurements are reported, improving data quality while reducing overhead in reporting.

[0045] In an implementation form of the first aspect, the entity is further configured to receive the first timing offset from the network entity.

[0046] This allows the network to dynamically control and configure the start of the measurement window, ensuring consistency across different scenarios and improving system flexibility.

[0047] In an implementation form of the first aspect, the first timing offset is larger than or equal to 0.

[0048] This allows to have the start of the selection window equal to the timing of the first detected path.

[0049] In an implementation form of the first aspect, the entity is further configured to receive the second timing offset from the network entity.

[0050] This allows the network to configure the end of the selection window, ensuring that measurements capture relevant signals across the full range of possible delays.

[0051] In an implementation form of the first aspect, the second timing offset is larger than or equal to 0.

[0052] This allows to have the end of the selection window equal to the timing of the detected path with the largest delay.

[0053] In an implementation form of the first aspect, the one or more measurements comprise timing and / or power information.

[0054] Specifically, the reported measurements may include both timing and power information of samples of the channel response. The timing information can represent various metrics such as the delay of a measurement, uplink Reference Time of Arrival (UL RTOA), gNB receive-transmit (Rx-Tx) time difference, UE receive-transmit (Rx-Tx) time difference, or Reference Signal Time Difference (RSTD). The power information can be reported as the Reference Signal Received Power per Path (RSRPP). By providing both timing and power data, the network entity gains a comprehensive set of information that can be used to optimize system performance and enhance the accuracy of positioning. This dual-level reporting enables precise signal characterization, improving the network’s ability to adapt to dynamic channel conditions and maximize overall efficiency.

[0055] In an implementation form of the first aspect, the entity is one of the following: a base station, a position reference unit, or a UE.

[0056] Possibly, the proposed entity may be implemented in a gNB, a TRP, a positioning reference unit (PRU), or a UE, which have the capability of receiving reference signals as part of their implementation of the relevant specification. Notably, this disclosure is applicable to various types of network devices, increasing its flexibility and utility in different wireless communication environments.

[0057] A second aspect of the disclosure provides a network entity, configured to obtain one or more measurements of one or more reference signals from an entity, wherein a timing of the one or more measurements is greater than or equal to a timing of a first path detected by the entity, adjusted by subtracting a first timing offset.

[0058] This disclosure further proposes a network entity receiving measurements from the entity, with timing adjusted based on the first detected path and a timing offset. With the reported measurements, the network entity may perform Al-based positioning with the provided measurements, or train a model for Al-based positioning. Possibly, the network entity may be the LMF.

[0059] In an implementation form of the second aspect, the timing of the one or more measurements is also less than or equal to a timing of a path detected by the entity with the largest delay among all detected paths, adjusted by adding a second timing offset.

[0060] The network entity also receives measurements adjusted based on the path with the largest delay and a second timing offset. This ensures that the network can account for delayed signals, improving the system’s ability to handle multipath environments.

[0061] In an implementation form of the second aspect, the one or more measurements are selected by the entity from a set of measurements of the one or more reference signals, wherein the measurements in the set are taken at regular intervals on a time grid.

[0062] This regular sampling enables the network to track signal variations more effectively, improving measurement consistency and accuracy.

[0063] In an implementation form of the second aspect, the network entity is further configured to receive one or more of indications from the entity, wherein the indications correspond to one or more of: the first timing offset, the second timing offset, a number of intervals on the time grid, or a timing granularity factor.

[0064] This allows the network entity to be informed about parameters used by the entity to determine the selection window.

[0065] In an implementation form of the second aspect, the network entity is further configured to provide, to the entity, one or more of the following: an indication indicative of the first timing offset, an indication indicative of the second timing offset, an indication indicative of the number of intervals, an indication indicative of the number Nt’, or an indication indicative of the timing granularity factor.

[0066] This enables the network entity to control and optimize the measurement process, improving consistency and adaptability across different network conditions.

[0067] In an implementation form of the second aspect, the network entity is further configured to send a report request to the entity, wherein the report request indicates the entity to report the one or more measurements of the one or more reference signals.

[0068] This allows the network to control when and what data is reported, reducing unnecessary communication and optimizing system performance.

[0069] In an implementation form of the second aspect, the network entity is an LMF. This enhances the applicability of the system for positioning and location-based services, ensuring precise signal measurements for location determination in a wireless network.

[0070] A third aspect of the disclosure provides a method performed by an entity for a wireless communication system, wherein the method comprises: measuring one or more reference signals, detecting a first path of the one or more measured reference signals, determining a timing of the first detected path, determining one or more measurements of the one or more reference signals, wherein a timing of the one or more measurements is greater than or equal to the timing of the first detected path, adjusted by subtracting a first timing offset, and reporting one or more measurements of the one or more reference signals to a network entity.

[0071] 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.

[0072] A fourth aspect of the disclosure provides a method performed by a network entity, wherein the method comprises obtaining one or more measurements of one or more reference signals from an entity, wherein a timing of the one or more measurements is greater than or equal to a timing of a first path detected by the entity, adjusted by subtracting a first timing offset.

[0073] 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.

[0074] 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.

[0075] 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.

[0076] BRIEF DESCRIPTION OF DRAWINGS

[0077] 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:

[0078] FIG. 1 shows an entity according to an embodiment of the disclosure;

[0079] FIG. 2 shows an exemplary selection window according to an embodiment of the disclosure;

[0080] FIG. 3 shows a procedure performed by the entity according to an embodiment of the disclosure;

[0081] FIG. 4 shows a network entity according to an embodiment of the disclosure; FIG. 5 shows signaling exchanges among entities according to an embodiment of the disclosure;

[0082] FIG. 6 shows signaling exchanges among entities according to an embodiment of the disclosure;

[0083] FIG. 7 shows signaling exchanges among entities according to an embodiment of the disclosure;

[0084] FIG. 8 shows signaling exchanges among entities according to an embodiment of the disclosure;

[0085] FIG. 9 shows a method according to an embodiment of the disclosure; and

[0086] FIG. 10 shows a method according to an embodiment of the disclosure.

[0087] DETAILED DESCRIPTION OF EMBODIMENTS

[0088] Illustrative embodiments of an entity, a network entity, and corresponding methods for measurement reporting, 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.

[0089] 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.

[0090] FIG. 1 shows an entity 100 adapted for measuring reference signals according to an embodiment of the disclosure.

[0091] 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 instructions) 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.

[0092] The entity 100 is configured to measure one or more reference signals 101. Possibly, the one or more reference signals 101 are transmitted by one or more network nodes in the wireless communication system. For instance, the one or more reference signals 101 may include a sounding reference signal (SRS) sent by a PRU or UE. In another instance, the one or more reference signals 101 may include positioning reference signal (PRS) sent by a gNB or TRP.

[0093] The entity 100 is configured to detect a first path of the one or more measured reference signals 101, and determine a timing of the first detected path. Notably, a path of the reference signal may refer to a path through which the reference signals arrives at the entity. The entity 100 is further configured to determine one or more measurements 102 of the one or more reference signals 101, wherein a timing of the one or more measurements 102 is greater than or equal to the timing of the first detected path, adjusted by subtracting a first timing offset.

[0094] The entity 100 is further configured to report the one or more measurements 102 of the one or more reference signals 101 to a network entity 200.

[0095] This disclosure proposes an entity for efficient determination of a selection window for sample-based measurements in wireless communication systems. The disclosure is applicable to various network entities, such as base stations (e.g., gNB), UE, or PRU, and focuses on optimizing the measurement of reference signals, such as SRS or PRS, transmitted by a network node in the wireless communication system. The disclosure particularly addresses the need to define an optimal start and / or end of the selection window for such measurements, ensuring that the most relevant signal samples are captured while minimizing the overhead of signal processing.

[0096] In wireless communication systems, selecting the optimal start and end points for the measurement window is crucial for ensuring accurate signal measurement and efficient resource utilization. Traditionally, the starting time of the selection window may be set based on the first detected path or even the beginning of a time slot. However, these approaches come with significant limitations that can lead to either missing strong signal samples or requiring excessive measurement resources.

[0097] FIG. 2 shows exemplary samples of detected paths according to an embodiment of the disclosure. It can be seen that if the selection window starts exactly at the time of the first detected path, some strong signal samples that occur before this point may be missed. These pre-path samples can provide valuable information, particularly in environments with multipath propagation, where signal reflections might contain relevant data even before the main signal peak. By excluding these earlier samples, the measurement entity risks losing important signal characteristics, which could degrade the accuracy of the measurement and the overall system performance.

[0098] On the other hand, starting the selection window at the beginning of the slot, as depicted in another scenario in FIG. 2, presents a different challenge. While this approach ensures that no early samples are missed, it often requires the configuration of a large value of Nt.

[0099] (the number of samples in the selection window) to ensure that the strongest signal samples, which may occur much later in the slot, are captured. Configuring such a large Ntleads to inefficient use of resources, as the measurement entity must process and report a larger set of samples, many of which may not contribute significantly to the measurement's quality. This introduces unnecessary overhead and can burden the system, particularly in scenarios where multiple measurements are required.

[0100] Moreover, determining the end of the selection window remains problematic in both cases. If the end of the window is not properly aligned with the relevant signal paths, critical samples with high signal strength could be excluded, again affecting the accuracy of the measurements.

[0101] A further complication arises when the LMF attempts to indicate the selection window parameters to the measurement entity. The parameters include such as the number of samples Ntin the selection window and the timing granularity factor k used to determine the timing granularity of the samples. The LMF may not have full awareness of the measured channel conditions, leading to the possibility of incorrectly specifying the selection window. For example, if the LMF indicates a value of Ntthat is too small, important signal samples might be missed. Conversely, the measurement entity might determine that a larger Ntis necessary, but the LMF would not be aware of this adjustment when the measurement results are sent back. This misalignment between the LMF and the measurement entity creates ambiguity in the measurements, especially when the same UE location is being measured multiple times with different window configurations. Inaccurate window alignment could result in degraded system performance, especially when using the measurements for direct Al-based positioning.

[0102] Thus, the technical problem addressed by the present application is how to efficiently determine the selection window, particularly the starting time and the optimal value of Nt, in a way that aligns the measurement entity's decision with the LMF’s expectations. This alignment is crucial to ensure that the sample-based measurements are reliable and consistent across different phases of operation, especially when they are used as input for Al models in positioning systems.

[0103] The present disclosure introduces a solution to this problem by proposing the use of a first timing offset for determining the starting time of the selection window. The entity 100 may be also referred to as a measurement entity in this application. As shown in FIG. 2, this approach allows the measurement entity to adjust the start of the selection window to a point before the first detected path, thereby capturing strong pre-path samples that would otherwise be missed. Additionally, an embodiment of this disclosure further proposes a second timing offset for determining the end of the selection window, ensuring that strong samples after the last detected path are also considered. These offsets enable the measurement entity to dynamically adjust the selection window based on the observed channel conditions, thus optimizing the number of samples Ntand the timing granularity k without requiring excessive overhead or risking the exclusion of important signal samples.

[0104] By using these optimized offsets, the disclosure ensures that the sample-based measurements remain consistent and reliable, avoiding the ambiguities of conventional methods and improving the overall performance of positioning systems that rely on these measurements.

[0105] FIG. 3 illustrates an embodiment where the entity 100, e.g., a gNB, receives a transmission of a reference signal, such as an SRS, from a transmitting device, e.g., a UE or PRU. Upon receiving the signal, the entity 100 detects the first path in the channel using conventional methods for path detection. The time and power of this first detected path are denoted as iistpath and Plstpath, respectively.

[0106] The entity 100 determines the first timing offset, denoted as Zl , . based on the timing of the first detected path. This offset is determined by analyzing the signal samples received before the first detected path. Specifically, the entity 100 selects samples with a power greater than a threshold relative to the power of the first detected path. The threshold could be defined as P| st path—Prei,i ■ where Prei, i is a relative power threshold which can be specified or indicated, i.e., by the LMF. The sample with the smallest timing among these strong samples is then chosen to define the start of the selection window, denoted as ts l. The first timing offset is then calculated as: Zl , = tlst path— ts l.

[0107] This method allows strong signal samples that occur before the first detected path to be included in the selection window, improving the overall accuracy of the measurement. Additionally, the first timing offset can be dynamically determined by the measurement entity, i.e., gNB or UE, based on the observed channel conditions during training. The entity 100 may then indicate this first offset to the LMF, ensuring that the LMF is aware of the exact offset used to determine the start of the selection window. This enables alignment between the start times of the selection window during both training and inference phases.

[0108] The first timing offset may also be determined based on the timing granularity factor fc, which governs the resolution or granularity of the time domain samples. Alternatively, it may be based on the bandwidth of the signal. The use of timing granularity allows the starting time of the selection window to align with a timing grid, where the granularity can be defined as = 2k• Tc, where Tcis a base timing resolution. This ensures precise synchronization of the first sample with the grid, enhancing measurement consistency.

[0109] This approach ensures that strong signal samples occurring after the last detected path are included in the measurement, improving the comprehensiveness of the signal capture. As with the first offset, the second offset can be dynamically determined by the measurement entity during training, and the entity can indicate this second offset to the LMF. This allows the LMF to understand the exact offset used to define the end of the selection window and ensures consistency during both the training and inference phases.

[0110] The second timing offset can also be adjusted based on the timing granularity factor fc, or the bandwidth of the signal, ensuring that the end of the selection window accurately captures relevant signal paths, aligned with the timing grid.

[0111] Using the first and second timing offsets, the number of samples Ntwithin the selection window may be determined by:

[0112] This calculation accounts for the total duration of the selection window and the timing granularity T. By accurately determining Nt, the entity 100 ensures that the correct number of samples is captured, optimizing resource use and providing reliable, high- quality measurements for the network's positioning and signal analysis processes.

[0113] In this embodiment, the entity 100 can also indicate the value of Nt, the first and second timing offsets, and the timing granularity factor k to the LMF. This information ensures that the LMF is aware of the parameters used to determine the selection window and can use this data to perform model training and inference with consistent sample-based measurements. By indicating these values, the entity ensures proper alignment between the measurements in the training and inference.

[0114] FIG. 4 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 instructions) 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.

[0115] The network entity 200 is configured to obtain one or more measurements 102 of one or more reference signals 101 from an entity 100, wherein a timing of the one or more measurements 102 is greater than or equal to a timing of a first path detected by the entity 100, adjusted by subtracting a first timing offset. Possibly, the entity 100 may be the entity 100 shown in FIG. 1. This disclosure further proposes a device receiving measurements. With the reported measurements, the network entity 200 can perform Al-based positioning with the provided measurements, or train a model for Al-based positioning. Possibly, the network entity 200 may be the LMF.

[0116] FIG. 5 shows an embodiment of the proposed selection and reporting of signal samples. In this embodiment, a gNB receives an SRS transmission from a PRU or UE, detects the first path and the path with the largest delay, and determines the first timing offset and the second timing offset. The gNB here may be the entity 100 shown in FIG. 1 or FIG. 4. The start and end times of the selection window are then determined based on these offsets, and the gNB calculates the number of samples Ntin the selection window, as well as the timing granularity factor k. The gNB can determine the best value of the timing granularity factor k to better capture the profile of the channel response.

[0117] Within the selection window, the gNB selects A / samples that correspond to the strongest power measurements. The gNB reports the timing and power information of these A / selected samples to the network's LMF, along with the first and second timing offsets, the total number of samples Nt, and the timing granularity factor k. This information enables the LMF to better understand the parameters used to determine the selection window. The LMF here may be the network entity 200 shown in FIG. 1 or FIG. 4.

[0118] FIG. 6 demonstrates training and inference at the LMF according to an embodiment of this disclosure.

[0119] As shown in FIG. 6, this embodiment details the training and inference phase of the model at the LMF. During the training phase, a PRU transmits an SRS to the gNB, which follows the same steps as in the previous embodiments to determine the selection window, first and second timing offsets, Ntand k. The gNB then selects N samples from the selection window and sends these samples, along with the relevant offsets and timing information, to the LMF. The gNB here may be the entity 100 shown in FIG. 1 or FIG. 4. The LMF here may be the network entity 200 shown in FIG. 1 or FIG. 4.

[0120] During the inference phase, the LMF can request measurements from the gNB for an SRS transmission from another UE. The LMF provides the gNB with the first and second timing offsets, as well as the number of samples Ntand the timing granularity factor fc, enabling the gNB to determine the selection window in the same manner as during the training phase. The gNB then selects A / samples and reports them to the LMF for inference, ensuring that the same procedure is followed for consistent model performance.

[0121] FIG. 7 demonstrates the determination of the selection window according to an embodiment of this disclosure.

[0122] As shown in FIG. 7, the LMF can request measurements from a gNB for an SRS transmission from a PRU or UE. The LMF can requests the measurements for training or inference. The LMF provides the gNB with the first and second timing offsets, as well as the number of samples Ntand the timing granularity factor fc, enabling the gNB to determine the selection window based on the parameters provided by the LMF. The gNB then selects N samples and reports them to the LMF, ensuring that the same procedure is followed as configured by the LMF. Afterwards, the LMF performs training of the model or inference.

[0123] FIG. 8 demonstrates how the LMF determines sample selection according to an embodiment of this disclosure.

[0124] In FIG. 8, a variant of the previous embodiment is shown where the gNB transmits all Ntsamples within the selection window to the LMF, rather than pre-selecting N samples. The LMF, based on the received timing granularity factor fcand sample measurements, determines the first and second timing offsets, as well as the number of selected samples A / . This allows the LMF to take more control over the model training, ensuring that the selection of samples aligns with the characteristics of the channel and the training process. During the inference phase, the LMF provides the first and second timing offsets and instructs the gNB to select / samples from the selection window, ensuring consistency between training and inference.

[0125] It may be understood that this disclosure also proposes further embodiments involve using a PRU or UE as the measurement entity, instead of the gNB. In this case, the PRU or UE follows similar procedures to determine the first and second timing offsets, as well as the number of samples Ntand the timing granularity factor k. The PRU / UE reports these measurements and associated timing information to the LMF, enabling location determination and model training / inference processes to function in a similar manner as when the gNB acts as the measurement entity.

[0126] FIG. 9 shows a method 900 according to an embodiment of the disclosure. In a particular embodiment, the method 900 is performed by an entity 100 shown in FIG. 1, or one of FIG. 4 to FIG. 8. The method 900 comprises a step 901 of measuring one or more reference signals 101. The method 900 begins with the entity 100 measuring one or more reference signals 101, such as Sounding Reference Signals (SRS), transmitted by a UE or PRU. These reference signals are essential for estimating the channel characteristics and serve as a basis for detecting signal paths and determining their timing and power.

[0127] The method 900 further comprises a step 902 of detecting a first path of the one or more measured reference signals, and a step 903 of determining a timing of the first detected path. Once the reference signals are measured, the entity 100 detects the first path in the channel, which corresponds to the earliest significant signal arriving at the entity. This detection step typically involves analyzing the received signal to identify the time and power of the first detected path. Conventional path detection methods may be used to identify the first path, which is a crucial point for starting the measurement process. After detecting the first path, the entity 100 determines the precise timing of the first detected path, denoted as t1 st path- This step is critical because the timing of this path forms the basis for adjusting the selection window, which determines when and how signal measurements will be captured.

[0128] The method 900 further comprises a step 904 of determining one or more measurements 102 of the one or more reference signals 101, wherein a timing of the one or more measurements 102 is greater than or equal to the timing of the first detected path, adjusted by subtracting a first timing offset. In this step, the entity 100 determines one or more measurements 102 of the reference signals 101, such as timing and power information. The timing of these measurements is adjusted based on the timing of the first detected path, specifically by subtracting a first timing offset This allows the selection window to begin at a point earlier than the first detected path, capturing important signal samples that might otherwise be missed.

[0129] Then, the method 900 further comprises a step 905 of reporting one or more measurements 102 of the one or more reference signals 101 to a network entity 200. Finally, the entity 100 reports the one or more measurements 102 to a network entity 200, such as the LMF or another network component. These reported measurements include the timing and power information, ensuring that the network has comprehensive data to optimize performance and signal processing. The network entity may use this information to further refine its models, adjust system parameters, or improve positioning accuracy. Possibly, the network entity 200 may be the network entity 200 shown in in FIG. 1, or one of FIG. 4 to FIG. 8.

[0130] FIG. 10 shows a method 1000 according to an embodiment of the disclosure. In a particular embodiment, the method 1000 is performed by a network entity 200 shown in FIG. 1, or one of FIG. 4 to FIG. 8. The method 1000 comprises a step 1001 of obtaining one or more measurements 102 of one or more reference signals 101 from an entity 100, wherein a timing of the one or more measurements 102 is greater than or equal to a timing of a first path detected by the entity 100, adjusted by subtracting a first timing offset. Possibly, the entity 100 may be the entity 100 shown in FIG. 1 or one of FIG. 4 to FIG. 8.

[0131] These measurements 102 include timing and power data for the reference signals, which were captured based on the timing of the first detected path and adjusted by subtracting a first timing offset This ensures that the reported measurements start before the first detected path, allowing the network to consider important signal samples that occurred earlier in the transmission.

[0132] By using this method, the network entity 200 can accurately synchronize its operations with the measurements obtained from the entity 100, allowing it to optimize signal processing, improve the accuracy of positioning and time alignment, and ensure consistency across different phases of network operations. This synchronization is essential when the network entity 200 uses the measurements for tasks such as direct Al-based positioning or optimizing network resource allocation.

[0133] To summarize, embodiments of this disclosure introduces an efficient method for defining a selection window for samplebased measurements in wireless communication systems. By leveraging dynamic offsets based on signal strength and timing, the application ensures that the most relevant signal samples are captured within the selection window, thereby improving the accuracy of signal measurements while reducing overhead. The described embodiments enable the system to function effectively during both training and inference phases, ensuring consistent performance and adaptability to different wireless environments.

[0134] 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 persons 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.

[0135] 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.

[0136] 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.

[0137] Especially, the processor(s) 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: measure one or more reference signals (101), detect a first path of the one or more measured reference signals (101), determine a timing of the first detected path, determine one or more measurements (102) of the one or more reference signals (101), wherein a timing of the one or more measurements (102) is greater than or equal to the timing of the first detected path, adjusted by subtracting a first timing offset, and report the one or more measurements (102) of the one or more reference signals (101) to a network entity (200).

2. The entity (100) according to claim 1, further configured to: detect one or more additional paths of the one or more measured reference signals (101), and determine a timing of the additional detected path with the largest delay among the plurality of the one or more additional paths, wherein the timing of the one or more measurements (102) is also less than or equal to the timing of the detected path with the largest delay, adjusted by adding a second timing offset.

3. The entity (100) according to claim 1 or 2, wherein the one or more measurements (102) are selected from a set of measurements of the one or more reference signals (101), wherein the measurements of the set are taken at regular intervals on a time grid.

4. The entity (100) according to claim 3, wherein the start of the time grid is the timing of the first detected path, adjusted by subtracting the first timing offset.

5. The entity (100) according to claim 3 or 4, wherein the regular intervals on the time grid are determined based on a timing granularity factor.

6. The entity (100) according to claim 5, further configured to: receive the timing granularity factor and a number of intervals on the time grid, and determine a length of the time grid based on the timing granularity factor and the number of intervals on the time grid, wherein the end of the time grid is the timing of the first detected path, adjusted by subtracting the first timing offset, and adding the length of the time grid.

7. The entity (100) according to claim 2, and one of claims 3 to 6, wherein the end of the time grid is the timing of the detected path with the largest delay, adjusted by adding the second timing offset.

8. The entity (100) according to claim 7, further configured to: determine the number of intervals on the time grid based on the timing of the first detected path, adjusted by subtracting the first timing offset, and the timing of the detected path with the largest delay, adjusted by adding the second timing offset.

9. The entity (100) according to claim 8, further configured to: indicate the determined number of intervals on the time grid to the network entity (200).

10. The entity (100) according to any preceding claims, further configured to: determine the first timing offset based on the timing of a measurement of the one or more reference signals (101) which has a timing less than or equal to the timing of the first detected path and a power within a first threshold from the power of the first detected path.

11. The entity (100) according to claim 10, further configured to: indicate the first timing offset to the network entity (200).

12. The entity (100) according to any preceding claims when depending on claim 2, further configured to: determine the second timing offset based on the timing of a measurement of the one or more reference signals (101) which has a timing larger than or equal to the timing of the detected path with the largest delay and a power within a second threshold from the power of said detected path.

13. The entity (100) according to claim 12, further configured to: indicate the second timing offset to the network entity (200).

14. The entity (100) according to any preceding claims when depending on claim 3, further configured to: obtain a number of Nt’ measurements to be reported, wherein the number Nt’ is determined or received by the entity (100), and determine the one or more measurements (102) to be sent to the network entity (200) based on the Nt’ measurements on the time grid which have the highest power among all the measurements on the time grid.

15. The entity (100) according to any preceding claims, further configured to: receive the first timing offset from the network entity (200).

16. The entity (100) according to any preceding claims, wherein the first timing offset is larger than or equal to 0.

17. The entity (100) according to any preceding claims when depending on claim 2, further configured to: receive the second timing offset from the network entity (200).

18. The entity (100) according to any preceding claims when depending on claim 2, wherein the second timing offset is larger than or equal to 0.

19. The entity (100) according to any preceding claims, wherein the one or more measurements (102) comprise timing and / or power information.

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

21. A network entity (200), configured to: obtain one or more measurements (102) of one or more reference signals (101) from an entity, wherein a timing of the one or more measurements (102) is greater than or equal to a timing of a first path detected by the entity (100), adjusted by subtracting a first timing offset.

22. The network entity (200) according to claim 21, wherein the timing of the one or more measurements (102) is also less than or equal to a timing of a path detected by the entity (100) with the largest delay among all detected paths, adjusted by adding a second timing offset.

23. The network entity (200) according to claim 21 or 22, wherein the one or more measurements (102) are selected by the entity (100) from a set of measurements of the one or more reference signals (101), wherein the measurements in the set are taken at regular intervals on a time grid.

24. The network entity (200) according to claim 23, further configured to: receive one or more of indications from the entity (100), wherein the indications correspond to one or more of: the first timing offset, the second timing offset, a number of intervals on the time grid, or a timing granularity factor.

25. The network entity (200) according to claims 24, further configured to: provide, to the entity (100), one or more of the following: an indication indicative of the first offset, an indication indicative of the second offset, an indication indicative of the number of intervals, an indication indicative of the number Nt’, or an indication indicative of the timing granularity factor.

26. The network entity (200) according to any preceding claims, further configured to: send a report request to the entity (100), wherein the report request indicates the entity (100) to report the one or more measurements (102) of the one or more reference signals (101).

27. The network entity (200) according to any preceding claims, wherein the network entity (200) is a location management function.

28. A method (800) performed by an entity for a wireless communication system, the method comprising: measuring (801) one or more reference signals (101), detecting (802) a first path of the one or more measured reference signals, determining (803) a timing of the first detected path, determining (804) one or more measurements (102) of the one or more reference signals (101), wherein a timing of the one or more measurements (102) is greater than or equal to the timing of the first detected path, adjusted by subtracting a first timing offset, and reporting (805) one or more measurements (102) of the one or more reference signals (101) to a network entity (200).

29. A method (900) performed by a network entity (200), the method comprising: obtaining (901) one or more measurements (102) of one or more reference signals (101) from an entity, wherein a timing of the one or more measurements (102) is greater than or equal to a timing of a first path detected by the entity (100), adjusted by subtracting a first timing offset.

30. A computer program product comprising computer readable code instructions which, when run in a computer will cause the computer to perform the method (800, 900) according to claim 28 or 29.17

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