Device and method for reporting measurements

Abstract

The present disclosure relates to entities and methods for measuring and reporting reference signals. The disclosure proposes an entity for measuring reference signals, the entity being configured to: make one or more measurements of one or more reference signals on one or more receive branches or on one or more receive beams of the entity; and report a set of measurements among the one or more measurements, wherein each measurement in the set is associated with a first receive branch or a first receive beam. Further, the disclosure proposes a network entity being configured to receive the set of measurements from the entity.

Description

[0001] DEVICE AND METHOD FOR REPORTING MEASUREMENTS

[0002] TECHNICAL FIELD

[0003] The present disclosure relates to wireless positioning systems. More specifically, it is particularly relevant to new radio (NR) technologies and the use of reference signals for enhancing positioning performance, for example in scenarios involving non- line-of-sight (NLOS) conditions between transmission reception points (TRPs) and user equipment (UE).

[0004] BACKGROUND

[0005] In the context of wireless communication systems, accurate and reliable channel measurements are crucial for various applications, including positioning, signal processing, and network optimization. In NR systems, these measurements are often made using reference signals such as positioning reference signals (PRS) and sounding reference signals (SRS).

[0006] One of the challenges in wireless communication systems is the variability in channel measurements when different Rx beams are used. This variability can lead to inconsistencies between measurements collected at different times Such inconsistencies are particularly problematic for systems using artificial intelligence (Al) and machine learning (ML) models for tasks like positioning of a UE. The variability in channel measurements when different Rx beams are used can lead to inconsistencies the training and inference phases of systems using AI / ML techniques.

[0007] The development of NR systems within the framework of the 3rd Generation Partnership Project (3GPP) has explored the potential of AI / ML techniques to improve multiple facets of wireless communications and positioning One critical challenge in wireless positioning is the degradation of signal quality and reliability in NLOS scenarios, where obstacles obstruct the direct path between the TRPs and the UE.

[0008] AI / ML techniques offer promising solutions by leveraging channel measurements obtained at the receiver through reference signals transmitted by a transmitter. These measurements can be utilized in various use cases such as positioning. Ensuring consistency between training and inference is critical because any discrepancy can degrade the performance of the models and systems relying on these measurements.

[0009] For instance, if the channel measurements for a given UE position during training differ from those during inference due to different Rx beam configurations, the system may fail to accurately predict the UE position or position related parameters, leading to suboptimal performance. In scenarios with heavy NLOS conditions, conventional methods may struggle to achieve a required positioning accuracy. Based on channel measurements, AI / ML techniques can be used for positioning of a UE Channel measurements can be used to train an AI / ML model, to estimate or predict the UE’s position Channel measurements can also be used to train a model to estimate or predict channel parameters, e.g., time of arrival information, which can be used for determining the UE’s position In both cases, the model input is based on the channel measurements, while the model output can be the estimated position or the channel parameters In this way, the channel measurements associated with a given UE’s position can be viewed as a fingerprint for that position the channel measurements, collected in either the uplink (UL) or downlink (DL), provide detailed information about the signal environment, which can be used for positioning at the UE or at a network entity.

[0010] Different reference signals like PRS for DL and SRS for UL can be employed to facilitate these measurements. These signals help in capturing the necessary channel measurements to train AI / ML models that can predict the position of the UE or channel parameters. Several configurations have been studied based on where the AI / ML model is located (UE, next generation node B (gNB), or location management function (LMF)) and where the channel measurements are collected These configurations include signal processing and optimization with models at different nodes, and the necessary signaling to transfer measurements or predicted parameters for further processing

[0011] Legacy methods provide some information about receiving beam configurations, often based on transmission beams, which can lead to mismatches regarding the receiving beams. Ensuring consistent "fingerprints" of channel measurements is critical for maintaining model performance, especially when considering variations in transmission and reception beams.

[0012] Given these advancements and the detailed study outcomes, the integration of methods to ensure consistency in channel measurements represents a significant leap forward in improving the reliability and performance of NR systems under challenging conditions

[0013] SUMMARY

[0014] In view of the above, an objective of this disclosure is to accurately map and track Rx beam configurations, thereby ensuring the reliability of channel measurements across different phases of operation. Another objective is to enable consistency between training and inference considering that measurements can be made on different Rx beams / branches. Yet another objective is to reduce the overhead in the reporting of measurements.

[0015] 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.

[0016] A first aspect of the disclosure provides an entity for measuring reference signals, the entity being configured to: make one or more measurements of one or more reference signals on one or more receive branches or on one or more receive beams of the entity; and report a set of measurements among the one or more measurements, wherein each measurement in the set is associated with a first receive branch or a first receive beam

[0017] This disclosure accordingly proposes an entity or device that reports measurements that are made with the same Rx beam or same Rx branch of the device Notably, an Rx beam refers to the directional reception of wireless signals using beamforming techniques or a spatial filter, while an Rx branch represents an individual signal reception pathway within a multi-antenna system.

[0018] It can be understood that this disclosure proposes data categorization of measurements based on Rx beam / branch information Each reported measurement is associated with a specific Rx beam / branch, more specifically, each measurement has been made with the specific Rx beam / branch. This allows to ensure the reliability of channel measurements across different phases of operation This further allows to train the model with measurements collected with one Rx beam / branch which supports the consistency between model training and model inference. In addition, this can also allow to reduce the overhead in the reporting of measurements as only measurements made with the same Rx beam / branch are reported.

[0019] In an implementation form of the first aspect, the entity is further configured to report an identifier of the first receive branch, or an identifier of the first receive beam.

[0020] Optionally, this disclosure further proposes that the entity indicates an identifier for the Rx beam / branch associated with the set of measurements. This allows an Rx beam and Rx branch of the device to be identified without ambiguity, i.e , to be identified across different time instances and among measurements of different sets of reference signals of one or more transmitting devices.

[0021] In an implementation form of the first aspect, the entity is further configured to report the set of measurements and / or the identifier to a network entity.

[0022] Notably, the set of measurements and / or the identifier of the Rx beam / branch is reported to a network entity. The network entity may be a core network entity, e.g., the LMF. The LMF is responsible for managing and determining the location of UE within the network.

[0023] In an implementation form of the first aspect, the entity is further configured to receive configuration information, wherein the configuration information indicates the entity to make measurements on the first receive branch or the first receive beam, and / or to report measurements from the first receive branch or the first receive beam.

[0024] Possibly, the entity is configured to collect / report measurements related to a specific Rx beam / branch For instance, the entity may be configured by the LMF.

[0025] In an implementation form of the first aspect, the configuration information comprises an identifier of the first receive branch, or an identifier of the first receive beam.

[0026] For instance, this allows the collection of measurements during inference in a manner that is consistent with the model training, i.e., with the same Rx beam / branch used for collecting measurements during training.

[0027] In an implementation form of the first aspect, the configuration information indicates the entity to perform monitoring with measurements from the first receive branch and / or from the first receive beam.

[0028] It may be understood that performing monitoring with measurements refers to a process of continuously observing, collecting, and analyzing data, such as data on key performance indicators (KPIs) or metrics, to ensure that a system, device, or process operates within expected parameters. The measurement data may be collected and evaluated Possibly, the monitoring mentioned here may particularly relate to model monitoring It involves tracking the performance of machine learning models (e g , models for estimating orpredicting the UE’s position)to ensure they continue to provide accurate and reliable predictions This approach allows to ensure systems operate correctly.

[0029] In an implementation form of the first aspect, the entity is further configured to receive a threshold and perform monitoring using the threshold.

[0030] Possibly, the monitoring may comprise evaluating the metrics to detect any degradation in model performance, such as increased error in position estimates or longer latency times. Threshold-based monitoring is a method within model monitoring that involves setting specific limits or benchmarks for performance metrics For example, when these thresholds are crossed, it triggers an alert or indicates a need for further investigation. Threshold-based monitoring enhances this process by setting specific benchmarks for these metrics, allowing for timely detection and correction of performance issues This systematic approach ensures models and systems remain reliable and effective over time

[0031] In an implementation form of the first aspect, the entity is further configured to send an outcome of the monitoring to the network entity.

[0032] Possibly, the outcome may be sent in an extra message other than a measurement report but it may only need to indicate the outcome of the monitoring 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.

[0033] 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 with beamforming as part of their implementation of the relevant specification

[0034] In an implementation form of the first aspect, the entity is further configured to make the one or more measurements while maintaining a first orientation.

[0035] This allows to train the model with measurements collected with the same orientation, e.g. of a PRU or UE.

[0036] In an implementation form of the first aspect, the entity is further configured to indicate the first orientation when reporting the set of measurements.

[0037] The entity may indicate the orientation of the entity associated with the set of measurements. This can also be used to support consistency between training and inference.

[0038] In an implementation form of the first aspect, the configuration information further indicates the first orientation.

[0039] Optionally, the entity may be configured to collect measurements with an indicated orientation

[0040] A second aspect of the disclosure provides a network entity, configured to obtain a set of measurements of one or more reference signals from an entity, wherein each measurement in the set is associated with a first receive branch or a first receive beam of the entity

[0041] This disclosure further proposes a network entity receiving measurements after the data categorization With the reported measurements, the network entity can perform Al-based positioning with the provided measurements, or train a model for AI- based positioning. Possibly, the network entity may be the LMF.

[0042] In an implementation form of the second aspect, the network entity is further configured to receive an identifier of the first receive branch or an identifier of the first receive beam from the entity.

[0043] The entity may indicate the network entity the identifier for the Rx beam / branch associated with the set of measurements. This allows an Rx beam and Rx branch of the device to be identified without ambiguity, i.e., to be identified across different time instances and among measurements of different sets of reference signals of one or more transmitting devices.

[0044] 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 indicates the entity to make measurements on the first receive branch or the first receive beam, and / or to report measurements from the first receive branch or the first receive beam

[0045] Possibly, the network entity may first configure the entity to collect / report measurements related to a specific Rx beam / branch

[0046] In an implementation form of the second aspect, the configuration information comprises the identifier of the first receive branch, or the identifier of the first receive beam.

[0047] The network entity may indicate the entity of the specific Rx beam / branch to be measured This allows the collection of measurements during inference in a manner that is consistent with the model training, i.e., with the same Rx beam / branch used for collecting measurements during training In an implementation form of the second aspect, the configuration information indicates the entity to perform monitoring with measurements from the first receive branch and / or from the first receive beam.

[0048] For instance, the network entity may define key performance metrics that need to be monitored. Possibly, the monitoring mentioned here may particularly relate to model monitoring

[0049] In an implementation form of the second aspect, the network entity is further configured to send a threshold to the entity, and receive an outcome of the monitoring from the entity.

[0050] The network entity may set the threshold for the monitoring. For example, when the threshold is crossed, it triggers an alert or indicates a need for further investigation. Possibly, after receiving the monitoring outcome, the network entity may further request new measurements made with another Rx beam / branch for example.

[0051] In an implementation form of the second aspect, the configuration information further indicates a first orientation, indicating the entity to make the one or more measurements while maintaining the first orientation

[0052] This allows to train the model with measurements collected with the same orientation, e.g. of a PRU or UE.

[0053] A third aspect of the disclosure provides a method performed by an entity for measuring reference signals, wherein the method comprises: making one or more measurements of one or more reference signals, on one or more receive branches or receive beams of the entity; and reporting a set of measurements among the one or more measurements, wherein each measurement in the set is associated with a first receive branch or a first receive beam

[0054] 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.

[0055] A fourth aspect of the disclosure provides a method performed by a network entity, wherein the method comprises obtaining a set of measurements of one or more reference signals from an entity, wherein each measurement in the set is associated with a first receive branch or a first receive beam of the entity.

[0056] 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

[0057] 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.

[0058] 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 BRIEF DESCRIPTION OF DRAWINGS

[0059] 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:

[0060] FIG 1 shows an entity according to an embodiment of the disclosure;

[0061] FIG 2 shows a network entity according to an embodiment of the disclosure;

[0062] FIG 3 shows signaling exchanges among entities according to an embodiment of the disclosure;

[0063] FIG 4 shows signaling exchanges among entities according to an embodiment of the disclosure;

[0064] FIG 5 shows signaling exchanges among entities according to an embodiment of the disclosure;

[0065] FIG 6 shows signaling exchanges among entities according to an embodiment of the disclosure;

[0066] FIG 7 shows signaling exchanges among entities according to an embodiment of the disclosure;

[0067] FIG 8 shows a method according to an embodiment of the disclosure; and

[0068] FIG 9 shows a method according to an embodiment of the disclosure.

[0069] DETAILED DESCRIPTION OF EMBODIMENTS

[0070] 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

[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 adapted for measuring reference signals according to an embodiment of the disclosure.

[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 orthe 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 The entity 100 is configured to make one or more measurements of one or more reference signals on one or more receive branches or on one or more receive beams of the entity 100. An Rx beam is a directional reception of wireless signals using (analog or digital) beamforming techniques. The Rx beam may also refer to spatial filter used to receive a signal. This involves an array of antennas working together to receive a signal from a specific direction. An Rx branch represents an individual signal reception pathway within a multi-antenna system. Each Rx branch includes its own components, such as an antenna, low-noise amplifier (LNA), filters, and an analog-to-digital converter (ADC).

[0074] The entity 100 is further configured to report a set of measurements 101 among the one or more measurements, wherein each measurement 101 in the set is associated with a first receive branch or a first receive beam. As shown in FIG. 1, the blocks with diagonal lines filling represent the measurements associated with the first receive branch or the first receive beam

[0075] This disclosure proposes data categorization of measurements based on Rx beam / branch information Each reported measurement is associated with a specific Rx beam / branch, more specifically, each measurement has been made with the specific Rx beam / branch. In particular, this disclosure proposes a device that reports measurements that are made with the same Rx beam / branch of the device. This allows to train the model with measurements collected with one Rx beam / branch, which supports the consistency between model training and model inference. In addition, this can also allow to reduce the overhead in the reporting of measurements as only measurements made with the same Rx beam / branch are reported

[0076] This disclosure is applicable to various use cases within wireless communication, including but not limited to AI / ML-based positioning, signal enhancement, and interference management By ensuring the consistency of channel measurements, the disclosure enhances the overall robustness and performance of NR systems in complex environments, particularly under NLOS conditions.

[0077] 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 application-specific integrated circuits (ASICs), field-programmable arrays (FPGAs), digital signal processors (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.

[0078] The network entity 200 is configured to obtain a set of measurements 101 of one or more reference signals from an entity 100, wherein each measurement 101 in the set is associated with a first receive branch or a first receive beam of the entity 100 Possibly, the entity 100 may be the entity 100 shown in FIG 1

[0079] This disclosure further proposes a device receiving measurements after the data categorization With the reported measurements, the network entity 200 can perform Al-based positioning with the provided measurements, or tram a model for Al-based positioning. Possibly, the network entity 200 may be the LMF

[0080] FIG 3 shows an embodiment of the proposed idea with measurements made in the uplink. In this embodiment, a gNB makes measurements of the channel based on one or more SRS sent by a PRU or UE. The gNB here may be the entity 100 shown in FIG 1 or FIG. 2. The gNB makes measurements of the SRS with multiple Rx beams / branches. The multiple Rx beams / branches can be of a TRP that belongs to a gNB. Different Rx beams / branches can be used to collect information from different spatial directions or for different SRS. The gNB then groups measurements were collected with the same Rx beam / branch.

[0081] The gNB then reports the measurements 101 that have been made with the same Rx beam / branch. The Rx beam / branch can correspond to the Rx beam / branch with which the most measurements were collected from the one or more SRS sent by the PRU or UE. The gNB then reports the measurements 101 collected with the same Rx beam / branch to the LMF. Notably, the LMF may be the network entity 200 as shown in FIG 2.

[0082] Optionally, the entity 100 may further indicate an identifier for the Rx beam / branch associated with a set of measurements to the LMF.

[0083] With the reported measurements 101, the LMF can train a model for Al-based positioning or perform model monitoring Furthermore, the gNB can also send an identifier of the Rx beam / branch associated with the reported measurements. A gNB can report sets of measurements for one or more TRPs that are associated with the gNB, i e., each set of measurements has been made with one Rx beam / branch of each TRP.

[0084] FIG 4 shows an embodiment where the gNB is configured to make measurements with an indicated Rx beam / branch Similar to the embodiment of FIG 3, the gNB here may be the entity 100 shown in FIG 1 or FIG 2

[0085] Optionally, the entity 100 may receive configuration information 102 from the network entity 200. The configuration information 102 indicates the entity 100 to make measurements on the first receive branch or the first receive beam, and / or to report measurements from the first receive branch or the first receive beam.

[0086] In this embodiment, the LMF, i e , the network entity 200 as shown in FIG 2, may indicate an Rx beam / branch identifier to the gNB. The Rx beam / branch can correspond to an Rx beam / branch of a TRP that is associated with the gNB. The gNB then makes measurements of one or more SRS with the indicated Rx beam / branch The one or more SRS are sent by a PRU or UE The gNB then reports the measurements 101 made with the indicated Rx beam / branch to the LMF.

[0087] This allows an Rx beam and Rx branch of the entity 100 to be identified without ambiguity, i.e , to be identified across different time instances and among measurements of different sets of reference signals of one or more transmitting devices.

[0088] The LMF can then perform Al-based positioning with the provided measurements or the LMF can train the model for Al-based positioning. The PRU or UE can also be configured to transmit SRS with a Tx beam that is not aligned with the spatial direction of the Rx beam at the gNB

[0089] Optionally, this disclosure proposes that the entity 100 is configured to collect measurements with an indicated identifier of an Rx beam / branch This allows the collection of measurements during inference in a manner that is consistent with the model training, i.e., with the same Rx beam and Rx branch used for collecting measurements during training.

[0090] FIG 5 shows an embodiment of the proposed idea with model monitoring at the gNB Similar to the embodiments of FIG 3 and FIG. 4, the gNB here may be the entity 100 shown in FIG. 1 or FIG. 2, and the LMF may be the network entity 200 as shown in FIG. 2.

[0091] The LMF configures the gNB to perform model monitoring with an indicated Rx beam / branch. For instance, the configuration information 102 may further indicate the entity 100 to perform monitoring with measurements from the first receive branch and / or from the first receive beam. The LMF can also send a threshold 103 to the gNB to be used for the model monitoring The indicated Rx beam / branch and threshold 103 can be determined based on prior measurements and indication from the gNB about the Rx beam / branch ID, e.g., as described in the embodiment of FIG 4.

[0092] For the model monitoring, a PRU or UE sends one or more SRS. The gNB makes measurements of the transmitted SRS with the indicated Rx beam / branch. The gNB can then perform model monitoring based on the measurements and the threshold 103 sent by the LMF, e.g., it can check if the amplitude or power of the measurements is above the threshold. If it is below the threshold 103, this can be an indication that the model needs to be updated or that the gNB needs to employ another Rx beam / branch for collecting the measurements. The gNB can provide feedback, i.e., an outcome 104, about the model monitoring to the LMF, i.e., it can indicate whether the amplitude or power of the measurements is above the threshold 103.

[0093] In a further embodiment, the gNB can make measurements with multiple Rx beams / branches and check if the strongest measurements are collected with the indicated Rx beam / branch. If this is not the case, the gNB indicates this to the LMF and could also indicate an identifier of the Rx beam / branch with which the strongest measurements were collected

[0094] Notably, this disclosure also proposes model monitoring at the device based on the Rx beam. This allows to exploit the spatial information for model monitoring. For this purpose, the device can be configured to perform model monitoring with an indicated Rx beam / branch and threshold. The device can then make measurements with the indicated Rx beam / branch Based on the measurements and indicated threshold, the device can perform the monitoring. The device can also perform monitoring by checking whether the strongest measurements are collected with the indicated Rx beam. In case the strongest measurements are made with another Rx beam, this can be an indication that the model needs to be updated Furthermore, this disclosure proposes that the device indicates the outcome 104 of the monitoring, i.e., it indicates that the strongest measurements are made with another Rx beam.

[0095] FIG 6 shows an embodiment of the proposed idea with measurements done in the downlink. A PRU or UE makes measurements of the channel based on one or more PRS sent by a gNB, i.e. a TRP. The PRU or UE here may be the entity 100 shown in FIG. 1 or FIG 2. The PRU or UE makes measurements of the PRS with multiple Rx beams / branches. The PRU or UE then reports the measurements 101 that have been made with the same Rx beam / branch The Rx beam / branch can correspond to the Rx beam / branch with which the most measurements were collected from the one or more PRS sent by the gNB. The PRU or UE then reports the measurements collected with the same Rx beam / branch to the LMF Notably, the LMF may be the network entity 200 as shown in FIG. 2.

[0096] With the reported measurements 101, the LMF can train a model for Al-based positioning or perform model monitoring Furthermore, the PRU or UE may additionally also send an identifier of the Rx beam / branch associated with the reported measurements The PRU or UE can additionally also report the orientation of the PRU or UE with which the measurements were made.

[0097] It should be understood that this disclosure also proposes that the device reports measurements that are made with the same orientation of the device. This allows to train the model with measurements collected with the same orientation, e.g. of a PRU or UE. This disclosure also proposes that the device indicates the orientation of the device associated with a set of measurements. This can also be used to support consistency between training and inference.

[0098] This disclosure also proposes that the device is configured to collect measurements with an indicated orientation of the device This allows the collection of measurements during inference consistently with the model training, i.e , with the same orientation

[0099] FIG 7 shows an embodiment of the proposed idea with model monitoring at the PRU or UE Similar to the embodiment of FIG 6, the PRU or UE here may be the entity 100 shown in FIG. 1 or FIG. 2. In this embodiment, the LMF, i.e., the network entity 200 as shown in FIG. 2, configures the PRU or UE to perform model monitoring with an indicated Rx beam / branch The LMF can also send a threshold 103 to the PRU or UE to be used for the model monitoring. The indicated Rx beam / branch and threshold 103 can be determined based on prior measurements and indication from the PRU or UE about the Rx beam / branch ID, e g., as described in the embodiment of FIG. 5.

[0100] For the model monitoring, a gNB can send one or more PRS. The PRU or UE makes measurements 101 of the transmitted PRS with the indicated Rx beam. The PRU or UE can then perform model monitoring based on the measurements 101 and the threshold 103 sent by the LMF, e g., it can check if the amplitude or power of the measurements is above the threshold. If it is below the threshold, this can be an indication that the model needs to be updated or that the PRU or UE needs to employ another Rx beam / branch for collecting the measurements The PRU or UE can provide feedback, i e , an outcome 104, about the model monitoring to the LMF, i.e., it can indicate whether the amplitude or power of the measurements is above the threshold.

[0101] In a further embodiment, the PRU or UE can make measurements with multiple Rx beams / branches and check if the strongest measurements are collected with the indicated Rx beam / branch. If this is not the case, the PRU or UE indicates this to the LMF and could also indicate an identifier of the Rx beam / branch with which the strongest measurements were collected.

[0102] FIG 8 shows a method 800 according to an embodiment of the disclosure, particularly for groupcast transmission. In a particular embodiment, the method 800 is performed by an entity 100 shown in FIG. 1 or FIG. 2 The method 800 comprises a step 801 of making one or more measurements of one or more reference signals, on one or more receive branches or receive beams of the entity The method 800 further comprises a step 802 of reporting a set of measurements 101 among the one or more measurements, wherein each measurement 101 in the set is associated with a first receive branch or a first receive beam Possibly, the set of measurements is reported to a network entity 200 shown in FIG. 2.

[0103] FIG 9 shows a method 900 according to an embodiment of the disclosure, particularly for groupcast transmission. In a particular embodiment, the method 900 is performed by a network entity 200 shown in FIG. 2. The method 900 comprises a step 901 of obtaining a set of measurements 101 of one or more reference signals from an entity 100, wherein each measurement 101 in the set is associated with a first receive branch or a first receive beam of the entity Possibly, the entity 100 may be the entity 100 shown in FIG. 1 or FIG. 2.

[0104] To summarize, embodiments of this disclosure propose to configure a device to send measurements that are made with the same Rx beam or Rx branch, thereby enabling the model training with measurements made with the same Rx beam or Rx branch. One embodiment enables the device to indicate an identifier of the Rx beam or Rx branch associated with the measurements, in order to support consistency between model training and inference Further, the device is configured to make measurements with the same orientation of the device, thereby enabling the model training with measurements made with the same orientation of the device. One embodiment enables the device to indicate the orientation of the device associated with the measurements, in order to support consistency between model training and inference.

[0105] Notably, embodiments of this disclosure also propose to configure a device to make measurements with an indicated Rx beam or Rx branch or make measurements with an indicated orientation, thereby enabling the consistency between model training and inference. In addition, the device is configured to perform monitoring (e.g., model monitoring) based on measurements with an indicated Rx beam and a threshold, to support model monitoring with beamforming at the device Furthermore, the device can be a UE or gNB, therefore supporting both uplink and downlink positioning.

[0106] 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.

[0107] 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

[0108] 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

[0109] 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 measuring reference signals, the entity (100) being configured to: make one or more measurements of one or more reference signals on one or more receive branches or on one or more receive beams of the entity (100); and report a set of measurements (101) among the one or more measurements, wherein each measurement ( 101 ) in the set is associated with a first receive branch or a first receive beam.2 The entity (100) according to claim 1, further configured to: report an identifier of the first receive branch, or an identifier of the first receive beam.3 The entity (100) according to claim 1 or 2, further configured to: report the set of measurements (101) and / or the identifier to a network entity (200).4 The entity (100) according to claim 1 to 3, further configured to: receive configuration information (102), wherein the configuration information (102) indicates the entity (100) to make measurements on the first receive branch or the first receive beam, and / or to report measurements from the first receive branch or the first receive beam.5 The entity (100) according to claim 4, wherein the configuration information (102) comprises an identifier of the first receive branch, or an identifier of the first receive beam.6 The entity (100) according to claim 4 or 5, wherein the configuration information (102) indicates the entity (100) to perform monitoring with measurements from the first receive branch and / or from the first receive beam.7 The entity (100) according to claim 6, further configured to: receive a threshold (103) and perform monitoring using the threshold (103).8 The entity (100) according to claim 6, further configured to: send an outcome (104) of the monitoring to the network entity (200).9 The entity (100) according to one of the claims 1 to 7, wherein the entity (100) is one of the following: a base station, a position reference unit, or a user equipment, UE.

10. The entity (100) according to one of the claims 1 to 9, further configured to: make the one or more measurements while maintaining a first orientation.

11. The entity (100) according to claim 10, further configured to: indicate the first orientation when reporting the set of measurements12. The entity (100) according to claim 10 or 11, wherein the configuration information (102) further indicates the first orientation.

13. A network entity (200), configured to: obtain a set of measurements (101) of one or more reference signals from an entity (100), wherein each measurement (101) in the set is associated with a first receive branch or a first receive beam of the entity (WO).

14. The network entity (200) according to claim 13, further configured to: receive an identifier of the first receive branch or an identifier of the first receive beam from the entity (100)15. The network entity (200) according to claim 13 or 14, further configured to: provide configuration information (102) to the entity (100), wherein the configuration information (102) indicates the entity (100) to make measurements on the first receive branch or the first receive beam, and / or to report measurements from the first receive branch or the first receive beam16. The network entity (200) according to claim 15, wherein the configuration information (102) comprises the identifier of the first receive branch, or the identifier of the first receive beam.

17. The network entity (200) according claims 15 or 16, wherein the configuration information (102) indicates the entity (100) to perform monitoring with measurements from the first receive branch and / or from the first receive beam. f 8 The network entity (200) according to claim 17, further configured to: send a threshofd (103) to the entity (100), and receive an outcome (104) of the monitoring from the entity (100).

19. The network entity (200) according to one of the claims 13 to 18, wherein the configuration information (102) further indicates a first orientation, indicating the entity (100) to make the one or more measurements while maintaining the first orientation.

20. A method (800) performed by an entity (100) for measuring reference signals, the method comprising: making (801) one or more measurements of one or more reference signals, on one or more receive branches or receive beams of the entity (100); and reporting (802) a set of measurements (101) among the one or more measurements, wherein each measurement (101) in the set is associated with a first receive branch or a first receive beam.

21. A method (900) performed by a network entity (200), the method comprising: obtaining (901) a set of measurements (101) of one or more reference signals from an entity (100), wherein each measurement (101) in the set is associated with a first receive branch or a first receive beam of the entity (100).

22. 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 20 or 21.

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