Micro-doppler measurements reporting in mobile radio networks

Abstract

The present disclosure relates to measurements and sensing in radio access networks. The disclosure provides a measurement entity, configured to: obtain measurement information based on one or more sensing signals; and report the measurement information to a sensing entity, wherein the measurement information comprises one or more micro-Doppler signatures and / or one or more micro-Doppler signature related features. This disclosure further provides a sensing entity, configured to: obtain measurement information from at least one measurement entity, wherein the measurement information comprises one or more micro-Doppler signatures and / or one or more micro-Doppler signature related features; and extract sensing information related to one or more detected objects based on the obtained measurement information.

Description

[0001] MICRO-DOPPLER MEASUREMENTS REPORTING IN MOBILE RADIO NETWORKS

[0002] TECHNICAL FIELD

[0003] The present disclosure relates to the field of radio access networks, and their application in sensing and communication systems. It focuses on advancements in sensing and positioning technologies, essential for next-generation wireless networks, including but not limited to 5G-NR (New Radio), 5.5G, and future 6G systems. The disclosure provides entities and methods for measurement and sensing in radio access networks (RANs).

[0004] BACKGROUND

[0005] Traditionally, radio access networks have been primarily deployed for communication purposes, where radio signals facilitate the transfer of information between nodes. The inherent nature of radio signals to reflect and scatter when encountering objects in their environment presents an opportunity to extend their use beyond mere communication. In the proposed next generation of radio access networks, radio nodes (including base stations, user devices, and other radio signal-transmitting / receiving devices) function as dual-purpose entities, capable of both communication and environmental sensing.

[0006] The concept of a joint sensing and communication network is introduced, where radio nodes act as measurement units. These units analyze received radio signals to gather measurement information such as range, Doppler frequency shift, and angles. Sensing units then process this information to extract sensing data, revealing characteristics of surrounding objects. This process allows for the detection and classification of both static (e.g., buildings, vegetation) and dynamic objects (e.g., vehicles, pedestrians, animals), including the identification of specific entities or states (e.g., individual people, types of rooms).

[0007] This approach presents a significant shift from traditional radio network operations. In contrast to dedicated radar systems where the measurement and sensing units are typically collocated or belong to a proprietary system, these enhanced radio networks in the joint sensing and communication network face unique challenges as they are typically manufactured by different vendors with the communications signaling in between explicitly specified.

[0008] A dedicated radar system typically operates in the frequency band where a fine range resolution can be achieved. This results in dense scatter points sufficient to detect the contour or shape of an object. For a radio access network with sensing functionality integrated, the radio nodes operate in frequency bands with significantly lower bandwidth available compared to radar systems, only range observations with limited resolution can be obtained. This makes ranging-based object classification / recognition extremely difficult.

[0009] The integration of sensing functionalities into standard radio networks introduces complexities not present in traditional radar systems, as it requires careful consideration of network bandwidth, resolution, and the interactions between various network components, often from different manufacturers.

[0010] SUMMARY

[0011] In view of the above-mentioned difficulties, embodiments of the present disclosure aim to enable Micro-Doppler measurements reporting in a radio access network. One objective is, in particular, to integrate sensing functionalities into standard radio networks. Another objective is to exploit Micro-Doppler measurements for dynamic object classification and recognition. Another objective is to allow measurement configuration adjustment based on the characteristics of the object of interest, and / or the application scenarios.

[0012] This and other objectives are achieved by the embodiment provided in the enclosed independent claims. Advantageous implementations of the embodiments of the present disclosure are further defined in the dependent claims.

[0013] A first aspect of the disclosure provides a measurement entity, configured to: obtain measurement information based on one or more sensing signals; and report the measurement information to a sensing entity, wherein the measurement information comprises one or more micro-Doppler signatures and / or one or more micro-Doppler signature related features.

[0014] This disclosure proposes a measurement entity, which may be a measurement unit that resides in any node in RAN that is capable of transmitting and / or receiving radio signals. A significant aspect of this disclosure is the use of micro-Doppler signatures, which enables the enhancement of sensing performance and facilitates the detection of objects with micro-dynamics.

[0015] In an implementation form of the first aspect, the one or more micro-Doppler signatures and / or the one or more micro-Doppler signature related features are associated with one or more detected objects, wherein each of the one or more micro-Doppler signatures relates to dynamic characteristics of the one or more detected objects, and indicates a plurality of Doppler frequency variations.

[0016] Notably, micro-Doppler information describes a set of frequency variations on different propagation paths that correspond to different parts of an object. The micro-Doppler signature can be extracted based on such information. For sensing scenarios where multiple objects appear in the region of interest, the one or more micro-Doppler signatures may be associated with multiple objects.

[0017] In an implementation form of the first aspect, the measurement entity is configured to obtain configuration information from the sensing entity, wherein the configuration information comprises measurement configuration, which indicates to the measurement entity to obtain the measurement information based on the measurement configuration, wherein the measurement configuration comprises one or more of the following parameters: a type of a time-frequency analysis; a time and / or frequency resolution; a length of a sliding window; a window function; an overlapping factor; an observation time; and an indication of measurement information quality.

[0018] To enable the micro-Doppoer signature extraction, certain parameters are required.

[0019] In an implementation form of the first aspect, the measurement entity is configured to receive one or more sensing signals from environment; obtain measurements based on the one or more sensing signals; and extract the one or more micro-Doppler signatures from the measurements based on the measurement configuration

[0020] In an implementation form of the first aspect, the measurement entity is configured to extract a micro-Doppler signature based on a subset of the obtained measurements. In an implementation form of the first aspect, the subset of the obtained measurements is associated with one detected object.

[0021] Possibly, for each individual detected object, an individual micro-Doppler signature may be derived.

[0022] In an implementation form of the first aspect, the measurement entity is configured to transmit one or more signals, wherein the one or more received sensing signals are backscattered signals of the one or more transmitted signals.

[0023] In an implementation form of the first aspect, the configuration information further comprises reporting configuration, wherein the measurement entity is further configured to: periodically report the measurement information to the sensing entity based on the reporting configuration, wherein the reporting configuration comprises one or more of the following parameters: start time of reporting the measurement information, periodicity of the reporting, end time of the reporting, and a number of reporting; or report the measurement information to the sensing entity based on a trigger-event based on the reporting configuration, wherein the reporting configuration comprises one or more types of the trigger-event, wherein the type of the trigger-event comprises one or more of the following: a timer expires, an object appears, a state change, a dynamics change.

[0024] The reporting may be configured as periodic or event-triggered.

[0025] In an implementation form of the first aspect, the configuration information further comprises pre-processing configuration, wherein the pre-processing configuration comprises one or more of the following parameters: an association indication for indicating an association between the subset of the obtained measurements and the detected object; a number of objects to be detected; and thresholding information for selecting one or more objects based on predefined criteria.

[0026] Optionally, the measurement entity may pre-process the measurements before extracting the micor-Doppler signature.

[0027] In an implementation form of the first aspect, the configuration information further indicates one or more of the following processes to be performed based on the one or more extracted micro-Doppler signatures: denoising, clutter suppression; interference mitigation; object detection; extracting the one or more micro-Doppler signature related features from the one or more extracted micro- Doppler signatures.

[0028] After the micro-Doppler signature is extracted, it may be further processed before reporting to the sensing entity, for instance, to remove the background noise, suppress the clutter, and / or cancel the interferences.

[0029] In an implementation form of the first aspect, the configuration information further comprises processing configuration, which comprises one or more of the following parameters: thresholding information; an inference model; one or more corresponding hyperparameters of the inference model; a number of objects to be detected; thresholding information for selecting one or more objects based on predefined criteria; indication information for indicating the one or more micro-Doppler signature related features to be extracted from the one or more extracted micro-Doppler signatures.

[0030] In an implementation form of the first aspect, the measurement entity is implemented at an access network device, or a user device.

[0031] A second aspect of the present disclosure provides a sensing entity, configured to: obtain measurement information from at least one measurement entity, wherein the measurement information comprises one or more micro-Doppler signatures and / or one or more micro-Doppler signature related features; and extract sensing information related to one or more detected objects based on the obtained measurement information.

[0032] Embodiments of the disclosure further propose a sensing entity, which may be a sensing unit implemented as one or multiple functional entities to compute sensing information based on the measurement reports.

[0033] In an implementation form of the second aspect, the one or more micro-Doppler signatures and / or the one or more micro- Doppler signature related features are associated with the one or more detected objects, wherein each of the one or more micro- Doppler signatures relates to dynamic characteristics of the one or more detected objects, and indicates a plurality of Doppler frequency variations.

[0034] In an implementation form of the second aspect, the sensing entity is further configured to provide configuration information to the at least one measurement entity, wherein the configuration information comprises measurement configuration, which indicates to the at least one measurement entity to obtain the measurement information, based on the measurement configuration, wherein the measurement configuration comprises one or more of the following parameters: a type of time-frequency analysis; a time and / or frequency resolution a length of a sliding window; a window function; an overlapping factor; an observation time; an indication of the measurement information quality.

[0035] In an implementation form of the second aspect, the configuration information further comprises reporting configuration, wherein the reporting configuration indicates to the at least one measurement entity to periodically report the measurement information to the sensing entity, wherein the reporting configuration comprises one or more of the following parameters: start time of reporting the measurement information, periodicity of the reporting, end time of the reporting, and a number of reporting; or the reporting configuration indicates to the at least one measurement entity to report the measurement information to the sensing entity based on a trigger-event, wherein the reporting configuration comprises one or more types of the triggerevent, wherein the type of the trigger-event comprises one or more of the following: a timer expires, an object appears, a state change, a dynamics change.

[0036] In an implementation form of the second aspect, the configuration information further comprises pre-processing configuration, wherein the pre-processing configuration comprises one or more of the following parameters: an association indication for indicating an association between a subset of the obtained measurements and a detected object; a number of objects to be detected; and thresholding information for selecting one or more objects based on predefined criteria.

[0037] In an implementation form of the second aspect, the configuration information further indicates one or more of the following processes to be performed based on the one or more extracted micro-Doppler signatures: denoising, clutter suppression; interference mitigation; object detection; extracting the one or more micro-Doppler signature related features from the one or more extracted microDoppler signatures.

[0038] In an implementation form of the second aspect, the configuration information further comprises post-processing configuration, which comprises one or more of the following parameters: thresholding information; an inference model; one or more corresponding hyperparameters of the inference model; a number of objects to be detected; thresholding information for selecting one or more objects based on predefined criteria; indication information for indicating the one or more micro-Doppler signature related features to be extracted from the one or more extracted micro-Doppler signatures.

[0039] In an implementation form of the second aspect, the sensing entity is further configured to determine to provide updated configuration information based on the extracted sensing information.

[0040] In an implementation form of the second aspect, the sensing entity is further configured to provide the updated configuration information to the at least one measurement entity, wherein the updated configuration information comprises updated measurement configuration, wherein the updated measurement configuration indicates to the at least one measurement entity to obtain the measurement information based on the updated measurement configuration.

[0041] In an implementation form of the second aspect, the sensing entity is implemented as a network function in a core network or an access network, or as a service in a computing platform.

[0042] In an implementation form of the second aspect, the sensing information related to the one or more detected objects comprises one or more of the following information: a position of the detected object, a velocity of the detected object, a classification of the detected object, a state of the detected object.

[0043] A third aspect of the present disclosure provides a method performed by a measurement entity, wherein the method comprises: obtaining measurement information based on one or more sensing signals; and reporting the measurement information to a sensing entity, wherein the measurement information comprises one or more micro-Doppler signatures and / or one or more micro-Doppler signature related features.

[0044] The method of the third aspect may have implementation forms that correspond to the implementation forms of the measurement entity of the first aspect. The method of the third aspect and its implementation forms provide the same advantages and effects as described above for the measurement entity of the first aspect and its respective implementation forms. A fourth aspect of the present disclosure provides a method performed by a sensing entity, wherein the method comprises: obtaining measurement information from at least one measurement entity, wherein the measurement information comprises one or more micro-Doppler signatures and / or one or more micro-Doppler signature related features; and extracting sensing information related to one or more detected objects based on the obtained measurement information.

[0045] The method of the fourth aspect may have implementation forms that correspond to the implementation forms of the sensing entity of the second aspect. The method of the fourth aspect and its implementation forms provide the same advantages and effects as described above for the sensing entity of the second aspect and its respective implementation forms.

[0046] A fifth aspect of the disclosure provides computer readable code instructions which, when run in a computer will cause the computer to perform 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.

[0047] A sixth aspect of the present disclosure provides computer program code instructions, being executable by a computer, for performing 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.

[0048] It has to be noted that all entities, elements, units, and means described in the present application could be implemented in the 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.

[0049] BRIEF DESCRIPTION OF DRAWINGS

[0050] 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:

[0051] FIG. 1 shows a measurement entity according to an embodiment of the disclosure.

[0052] FIG. 2 shows sensing information extraction based on radio signals.

[0053] FIG. 3 shows an application scenario of an integrated sensing and communication network.

[0054] FIG. 4 shows a signaling procedure according to an embodiment of the disclosure.

[0055] FIG. 5 shows configuration of micro-Doppler measurement and reporting according to an embodiment of the present disclosure.

[0056] FIG. 6 shows multipath component extraction according to an embodiment of the present disclosure. FIG. 7 shows operations to extract a micro-Doppler signature according to an embodiment of the present disclosure.

[0057] FIG. 8 shows periodic and event-based measurement information reporting according to an embodiment of the present disclosure.

[0058] FIG. 9 shows a sensing entity according to an embodiment of the present disclosure.

[0059] FIG. 10 shows exemplary system architectures according to an embodiment of the present disclosure.

[0060] FIG. 11 shows exemplary system architectures exemplary system architectures according to an embodiment of the present disclosure.

[0061] FIG. 12 shows exemplary system architectures according to an embodiment of the present disclosure.

[0062] FIG. 13 shows an intelligent transportation system with RAN-based object detection according to an embodiment of the present disclosure.

[0063] FIG. 14 shows a signaling procedure for micro-Doppler measurement and reporting according to an embodiment of the present disclosure.

[0064] FIG. 15 shows an exemplary clustering-based multipath association in a two-object scenario according to an embodiment of the present disclosure.

[0065] FIG. 16 shows micro-Doppler measurement based on sparse channel measurements according to an embodiment of the present disclosure.

[0066] FIG. 17 shows a method according to an embodiment of the present disclosure.

[0067] FIG. 18 shows a method according to an embodiment of the present disclosure.

[0068] DETAILED DESCRIPTION OF EMBODIMENTS

[0069] Illustrative embodiments of entities, methods, and program products relevant to micro-Doppler measurement and report 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.

[0070] Moreover, an embodiment / example may refer to other embodiments / examples. For example, any description including but not limited to terminology, element, process, explanation, and / or technical advantage mentioned in one embodiment / example is applicative to the other embodiments / examples.

[0071] FIG. 1 shows a measurement entity 100 according to an embodiment of the disclosure. The measurement entity 100 may comprise processing circuitry (not shown) configured to perform, conduct, or initiate the various operations of the measurement entity 100 described herein. The processing circuitry may comprise hardware and software. The hardware may comprise analog circuitry or 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.

[0072] 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 cany executable program code which, when executed by the one or more processors, causes the measurement entity 100 to perform, conduct, or initiate the operations or methods described herein.

[0073] In particular, the measurement entity 100 is configured to obtain measurement information 101 based on one or more sensing signals. The measurement entity 100 is further configured to report the measurement information 101 to a sensing entity 200, wherein the measurement information 101 comprises one or more micro-Doppler signatures and / or one or more micro-Doppler signature related features.

[0074] For instance, the measurement entity 100 may comprise a processor, a memory and a transceiver. The memory may be configured to store executable program codes executed by the processor. The transceiver may be configured to communicate with other devices in the same network. For instance, the transceiver may be configured to report the measurement information to a sensing entity 200. The processor may be configured to execute the operations or method steps described in the following.

[0075] As previously discussed, in a joint sensing and communication network, a radio node may be considered a measurement unit that measures the received radio signals and obtains measurement information such as range, Doppler frequency shift, angles, etc. Based on the measurement information collected from one or multiple measurement units, sensing information may be extracted at a sensing unit as illustrated in FIG. 2. The sensing information reflects the characteristics of objects in the surroundings. For instance, an object may be detected and classified as a vehicle, a human, or an animal. Based on the measurement information, the sensing unit may also identify a specific person from another or identify a room from another. The objects may be either static or dynamic. Exemplary static objects include buildings, walls, or furniture which comprise reflecting surfaces, vegetation, trees, etc. that may contribute to the background clutter. Dynamic objects refer to those that are moving or comprise moving components, such as vehicles, pedestrians, cyclists, animals, drones, or human hands.

[0076] One exemplary application scenario is depicted in FIG. 3. FIG. 3 provides an application scenario that enables object classification based on radio sensing in a Vehicle-to-Everything (V2X) scenario. The RAN nodes deployed on the street sides measure the backscattered radio signals from the environment and obtain measurement information in terms of range, angle Doppler frequency, etc. Based on the measurement information, objects of interest may be detected, localized, and further classified orrecognized as vehicles, pedestrians cyclists, etc. This information may be provided to the vehicles, as a complement to the sensing information obtained using local sensors mounted onboard. Benefitting from the measurement information from multiple RAN nodes with different perspectives, the vehicles extend their environmental awareness beyond their local field of view.

[0077] A significant aspect of this technology is the use of micro-Doppler signatures, a sensing metric prevalent in radar systems for target detection and classification. Given the mechanical vibration or rotation of any structure on the target, the backscattering signal comprises a superposition of multiple components, each of which has a different Doppler frequency and phase shift varying over time. Each component corresponds to a scattering point on the target. The superposition of the micro-Doppler frequency variations is the so-called Micro-Doppler signature. For example, a propeller with multiple blades rotates while moving away from the transmitter. The extracted micro-Doppler signature shall comprise multiple sinusoidal-like Doppler frequency components. The frequency of the sinusoidal variation corresponds to the radial velocity of the ventilator’s rotation. Another example is human gait analysis. By analyzing the micro-Doppler signatures of human targets, human activities may be classified, e.g. walking, falling, sitting, etc.

[0078] This is a novel approach, particularly as these networks typically operate at frequency bands with lower bandwidth and resolution compared to dedicated radar systems. This integration addresses the challenges of ranging-based object classification in these networks, potentially enhancing overall sensing performance.

[0079] Embodiments of the disclosure propose the integration of micro-Doppler signatures into radio access networks for sensing. In particular, this disclosure proposes to use the micro-Doppler signature to enhance sensing performance, facilitating the detection of objects with micro-dynamics. The measurement entity 100 proposed in this disclosure may reside in any node in RAN that is capable of transmitting and / or receiving radio signals.

[0080] Optionally, the one or more micro-Doppler signatures and / or the one or more micro-Doppler signature related features, which are comprised in the to-be-reported measurement information, are associated with one or more detected objects. It may be understood that each of the one or more micro-Doppler signatures relates to dynamic characteristics of the one or more detected objects, and indicates a plurality of Doppler frequency variations.

[0081] A signaling procedure of this measurement information reporting mechanism is illustrated in FIG. 4. The one or multiple measurement information 101 is obtained by the measurement entity 100 based on the received radio signals and a certain configuration. This configuration may be obtained by the radio node as a default set of parameters. Alternatively, a new or updated configuration may be provided to the radio node by the sensing unit. The measurement entity 100 may reside in a radio node in a radio access network, functioning as a measurement unit, provides to a sensing unit, i.e., the sensing entity 200, one or multiple measurement information 101 which represents a set of frequency variations over time.

[0082] According to an embodiment of this disclosure, the measurement entity 100 may be further configured to obtain configuration information 102 from the sensing entity 200, wherein the configuration information 102 comprises measurement configuration, which indicates to the measurement entity 100 to obtain the measurement information 101 based on the measurement configuration.

[0083] FIG. 5 shows the configuration of micro-Doppler measurement and reporting according to an embodiment of this disclosure. The configuration of measurement information reporting may comprise three aspects, namely measurement configuration, processing configuration, and reporting configuration, corresponding to the three major operations required at the radio node. These three aspects are illustrated in FIG. 5 and elaborated in detail in the following sections.

[0084] 1. Measurement configuration

[0085] The measurement configuration is a set of parameters that are crucial for obtaining micro-Doppler signatures. Optionally, the measurement configuration comprises one or more of the following parameters: a type of a time-frequency analysis; a time and / or frequency resolution; a length of a sliding window; a window function; an overlapping factor; an observation time; and an indication of measurement information quality . Accordingly, the measurement entity 100 is further configured to receive one or more sensing signals from the environment; obtain measurements based on the one or more sensing signals; and extract the one or more micro-Doppler signatures from the measurements based on the measurement configuration.

[0086] Possibly, in an application scenario, the measurement entity 100 resides in a RAN node, where the RAN node transmits one or more signals, and the one or more received sensing signals are backscattered signals of the one or more transmitted signals. Optionally, the measurement entity 100 may be configured to transmit the one or more signals.

[0087] This section describes how a micro-Doppler signature is obtained based on the parameters to be provided as the measurement configuration.

[0088] In most communication systems, channel impulse response estimation is used to obtain information about the environmental reflections of the signal such as the angle of arrivals and delays, so that the data transmission parameters can be properly configured. The channel impulse response contains information of each reflector, such as its distance, angular position with respect to the device, as well as its moving velocity and micro-Doppler. Take an OFDM system for example, the channel impulse response at OFDM symbol k can be represented as which consists of a superposition of the complex gain of L paths. The complex gain of path 1 at OFDM symbol k is denoted by aiQi). The corresponding phase is denoted by <pL( <) which may be decomposed to e~2nf°T‘ and e47T^°kTc~. The first term is induced by the delay of the 1-th path due to the distance from the reflector to the measuring device. The second term is dependent on the radial velocity of the reflector with respect to the measuring device. Here, f0denotes the carrier frequency, Tcthe symbol duration in the time domain and c the speed of light.

[0089] Micro-Doppler information describes a set of frequency variations on different propagation paths that correspond to different parts of an object. It can be extracted from subsequent estimates of the channel impulse response h(fc) over a period of time k using time-frequency analysis. Since not all L paths in the estimated channel impulse response contain information about the sensing objects, the time -frequency analysis may be applied to the significant paths that typically correspond to a detectable reflecting object based on thresholding. Alternatively, localization and tracking procedures may be performed to extract paths corresponding to the spatial locations where objects were detected.

[0090] In a particular embodiment, the obtained measurements are associated with multiple detected objects. The measurement entity 100 may be further configured to extract a micro-Doppler signature based on a subset of the obtained measurements. Possibly, the subset of the obtained measurements is associated with one detected object.

[0091] FIG. 6 shows multipath component extraction from the channel impulse response according to an embodiment of this disclosure. In particular, this figure illustrates an extracted channel impulse response of the path 1 over the slow time k. The time-frequency analysis is to be applied to ht(fc) over the slow time dimension k as illustrated in FIG. 7, where a sliding window mechanism is used.

[0092] FIG. 7 shows operations to extract a micro-Doppler signature according to an embodiment of this disclosure. The following parameters are to be obtained by the measuring unit in order to enable the operations in FIG. 7, including:

[0093] • Type of time-frequency analysis representation: in order to capture the time-varying frequency characteristics of the micro-Doppler, time-frequency analysis is utilized. The outcome of time -frequency analysis is a spectrogram, which visualizes the Doppler frequency variations of the multiple moving components of the object overtime. This is typically obtained using a short-time Fourier transform (STFT), which is applied to a time-limited windowed input signal. A default type of time -frequency analysis including the related parameters may be specified, such as STFT. Another type may be provided to the measurement unit as a configuration parameter if needed. Alternatively, the measurement unit may be given the desired time -frequency analysis representation type without implementation details defined.

[0094] • Length of the sliding window: The length of the sliding window defines the length of the input signal which is fed for time-frequency analysis at each time instance m. This parameter may be provided as either a time duration or as a number of sampling time instances. The window length determines the trade-off between the time resolution and the frequency resolution of the resulting spectrogram, namely, the micro-Doppler signature. A larger window has a higher frequency resolution but a poorer time resolution, and vice versa.

[0095] • Windowing function: for each segment of the time-limited input signal, a window function is applied. No windowing function is equivalent to the rectangular window. A default windowing function may be specified. Other windowing functions may be provided as a configuration parameter if needed. The advantage of using different windowing functions is to control the sidelobe levels, which influences the visibility of minor microDoppler frequency components in the spectrogram.

[0096] • Overlapping facto r / shifting factor: the overlapping factor indicates the length of the overlapped part between two consecutive time-frequency processing segments. Alternatively, a shifting factor may be defined instead. These may be given as a percentage of the length of the sliding window. Alternatively, it may be given as a time duration or a number of sampling time instances. The relationship between the two options exists as the number of overlapping samples + the number of shifting samples = the total number of samples in a sliding window. A higher percentage of overlapping factors results in a smoother spectrogram through higher processing complexity. This parameter may be chosen based on the characteristics of the object’s dynamics.

[0097] • Observation time : the observation time corresponds to the total time duration based on which a micro-Doppler signature measurement is obtained. It may be defined as a time duration or a number of sliding window operations. The length of observation time is dependent on the characteristics of the motion dynamics of the object under observation. For instance, the measurements of a rotating blade should capture a number of rotation periods; while the measurements for human gaits analysis should capture at least a couple of step cycles.

[0098] Alternatively, a desired time and / or frequency resolution or any indication which indicates the measurement quality of the micro-Doppler signature may be provided to the measurement unit. The measurement unit obtains the above listed parameters based on the given time and / or frequency resolution or measurement information quality indication. 2. Processing configuration

[0099] In order to facilitate object classification and / or recognition based on the micro-Doppler signatures, several pre-processing and / or post-processing may be carried out by the measurement unit. This processing may be configured by the sensing unit according to the application scenarios or specific sensing tasks. This section provides examples of potential pre-processing and post-processing which may be applied to micro-Doppler signature extraction. a) Multipath Association

[0100] The micro-Doppler signature is usually extracted for a single object when it is adopted for object classification / recognition or motion dynamics analysis. This implies that from the estimated channel impulse responses shown in FIG. 6, paths associated with a single object are to be extracted prior to the time-frequency analysis. Especially for sensing scenarios where multiple objects appear in the region of interest, the measurement unit may be configured to obtain the channel impulse responses for each individual detected object or only for the object of interest, so that individual micro-Doppler signatures can be further derived. The measurement unit may derive range, Doppler, and / or angular information based on the received signal and carry out operations such as denoising, clutter suppression, clustering, trajectory tracking, etc. to facilitate multipath association with respect to each individual object detection.

[0101] The configuration parameters for object detection provided by the sensing unit may include:

[0102] • An indication that multipath association is to be performed, e.g. an association indication for indicating an association between a subset of the obtained measurements and an object of interest.

[0103] • Number of objects: corresponding to the number of micro-Doppler signatures to be obtained by the measurement unit.

[0104] • Thresholding information: The measurement unit may be configured to obtain micro-Doppler signatures for a selection of objects based on the thresholding information. For instance, a threshold may lead to micro-Doppler signature extraction for a number of significant or insignificant scatters. Thresholds in distance, angle, and / or velocity, namely Doppler frequency shift, may be applied in order to select the objects within a region of interest and / or moving with a certain speed.

[0105] According to an embodiment of this disclosure, the configuration information 102 further comprises pre-processing configuration, wherein the pre-processing configuration comprises one or more of the following parameters: an association indication for indicating an association between the subset of the obtained measurements and the detected object; a number of objects to be detected; and thresholding information for selecting one or more objects based on predefined criteria. b) Post-processing

[0106] The measurement unit may be configured to perform basic operations on the obtained micro-Doppler signature to remove the background noise, suppress the clutter, and / or cancel the interferences. Relevant parameters such as thresholding information, inference model, and corresponding hyperparameters may be provided by the sensing unit.

[0107] Additionally, the measurement unit may be configured to compensate for the motion of the detection object by removing the macro-Doppler frequency shift. This gives the benefit that the resulting micro-Doppler signatures of different objects can be easily aligned since they contain only features generated by the micro-motion.

[0108] The sensing unit may also indicate the measurement unit to extract features from the obtained micro-Doppler signature. It allows the measurement unit to report only essential information contained in the micro-Doppler signature. This essential information may be used to reconstruct the micro-Doppler signature by the sensing unit for further classification. Examples may include extracting the bases of the micro-Doppler signature and reporting and having the significant component reported to the sensing unit.

[0109] For example, in order to analyze the cyclic nature and inherent periodicities of human gait motions, a Cadence-Velocity Diagram (CVD) may be obtained by taking the Fourier transform of the spectrogram along each Doppler frequency bin. The CVD depicts how often certain Doppler components appear throughout a gait, independent of the components’ velocity. Depending on the sensing task and the characteristics of the targets, a measurement unit may be indicated to provide the features extracted based on the micro-Doppler signature, e.g., CVD, mean cadence spectrum, etc.

[0110] According to an embodiment of this disclosure, the configuration information 102 further indicates one or more of the following processes to be performed based on the one or more extracted micro-Doppler signatures: denoising, clutter suppression; interference mitigation; object detection; extracting the one or more micro-Doppler signature related features from the one or more extracted micro- Doppler signatures.

[0111] According to an embodiment of this disclosure, the configuration information 102 further comprises processing configuration, which comprises one or more of the following parameters: thresholding information; an inference model; one or more corresponding hyperparameters of the inference model; a number of objects to be detected; thresholding information for selecting one or more objects based on predefined criteria; indication information for indicating the one or more micro-Doppler signature related features to be extracted from the one or more extracted micro-Doppler signatures.

[0112] 3. Reporting configuration

[0113] The measurement unit provides measurement reports containing the micro-Doppler signature to the sensing unit based on the reporting configuration obtained. The reporting may be configured as periodic or event-triggered. FIG. 8 shows the two types of measurement information reporting.

[0114] For periodic reporting, the parameters to be configured by the sensing unit may include start time, periodicity, number of reporting, or their equivalents which leads to the reporting pattern illustrated in FIG. 8(a).

[0115] For event-triggered reporting, a measurement unit may be configured to monitor a given type of event. Measurement reports are only provided to the sensing unit when the configured event occurs, as shown in FIG. 8(b). The types of events may include but are not limit to timer expires, object appearance, state change, dynamics change, etc. Parameters such as thresholding information which characterizes the occurrence of the events shall be provided to the measurement units.

[0116] According to an embodiment of this disclosure, the configuration information 102 further comprises reporting configuration. Optionally, the measurement entity 100 is configured to periodically report the measurement information 101 to the sensing entity 200 based on the reporting configuration, wherein the reporting configuration comprises one or more of the following parameters: start time of reporting the measurement information 101, periodicity of the reporting, end time of the reporting, and a number of reporting.

[0117] Alternatively, the measurement entity 100 is further configured to report the measurement information 101 to the sensing entity

[0118] 200 based on a trigger-event based on the reporting configuration, wherein the reporting configuration comprises one or more types of the trigger-event, wherein the type of the trigger-event comprises one or more of the following: a timer expires, an object appears, a state change, a dynamics change.

[0119] The proposed micro-Doppler measurement configuration and reporting mechanism may be applied to various sensing scenarios where the measurement unit and the sensing unit are implemented in different network nodes. The network nodes under consideration are radio nodes which are able to transmit and / or receive radio signals as well as any node where a sensing functionality may be implemented to compute sensing information based on the measurement information. For instance, a sensing function may be implemented as a network function in the core network or as a service in the cloud, on the edge, etc.

[0120] FIG. 9 shows a sensing entity 200 according to an embodiment of the disclosure. The sensing entity 200 may comprise processing circuitry (not shown) configured to perform, conduct, or initiate the various operations of the sensing 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.

[0121] 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 sensing entity 200 to perform, conduct, or initiate the operations or methods described herein.

[0122] In particular, the sensing entity 200 is configured to obtain measurement information 101 from at least one measurement entity 100, wherein the measurement information 101 comprises one or more micro-Doppler signatures and / or one or more microDoppler signature related features. The sensing entity 200 sensing entity 200 is further configured to extract sensing information

[0123] 201 related to one or more detected obj ects based on the obtained measurement information 101.

[0124] For instance, the sensing entity 200 may comprise a processor, a memory, and a transceiver. The memory may be configured to store executable program codes executed by the processor. The transceiver may be configured to communicate with other devices in the same network. For instance, the transceiver may be configured to receive the measurement information 101 from the at least one measurement entity 100. The processor may be configured to execute the operations or method steps described in the following.

[0125] Embodiments of the disclosure further propose a sensing entity 200, which may be a sensing unit implemented as one or multiple functional entities to compute sensing information based on the measurement reports.

[0126] Notably, the terminology employed throughout the various embodiments described in this application maintains consistent definitions. Particularly, it should be noted that terms associated with the same reference signs in the various embodiments of this application are used consistently and carry identical meanings throughout. To ensure brevity and avoid redundancy, detailed explanations of each term are not repeated in each instance.

[0127] Possibly, the sensing information 201 related to the one or more detected objects may comprise one or more of the following information: a position of the detected object, a velocity of the detected object, a classification of the detected object, such as vehicle, human, animal, hand, etc, a state of the detected object, such as rolling, walking, flying, running, sitting, waving, turning, lying around, and etc.

[0128] Optionally, the sensing entity 200 may be configured to provide configuration information 102 to the at least one measurement entity 100.

[0129] According to an embodiment of this disclosure, the sensing entity may be configured to determine to provide updated configuration information 102 based on the extracted sensing information 201.

[0130] According to an embodiment of this disclosure, the sensing entity may be configured to provide the updated configuration information 102 to the at least one measurement entity 100, wherein the updated configuration information 102 comprises updated measurement configuration, wherein the updated measurement configuration indicates to the at least one measurement entity 100 to obtain the measurement information 101 based on the updated measurement configuration.

[0131] Optionally, the sensing entity 200 is implemented as a network function in a core network or an access network, or as a service in a computing platform (e.g., cloud).

[0132] Several exemplary system architectures are provided in the following.

[0133] 1. Monostatic RAN node based sensing

[0134] As illustrated in FIG. 10(a), the RAN node may be configured to transmit a sensing signal and receive the backscattered signal from the environment. The transmitted sensing signal may be either dedicated to sensing environmental information or a communication signal for conveying information to another device. The RAN node is configured to obtain micro-Doppler signatures of the objects of interest based on the received backscattered signal. This measurement is provided to a network function for sensing information extraction, namely to a sensing function, i.e., the sensing entity 200. The sensing function may also provide measurement configuration to the RAN node to adjust the measurement setting according to the scenario and the properties of the detected objects.

[0135] 2. Bistatic sensing with different RAN nodes acting as the transmitter and receiver

[0136] FIG. 10(b) presents a scenario where bistatic sensing is implemented between two RAN nodes. A RAN node is configured to receive the radio signal transmitted by another RAN node, where the backscattered paths potentially cany physical information about the environment and objects. The radio signal may be scheduled exclusively for sensing purposes. Alternatively, the receiving RAN node may be configured to pick up any radio signal transmitted by another RAN node regardless of the purpose of transmission. Based on the measurement configuration obtained, the receiving RAN node performs micro-Doppler measurement and provides the report to the sensing function.

[0137] 3. Bistatic sensing with a user device acting as the transmitter and a RAN node acting as the receiver

[0138] As shown in FIG. 11(a), bistatic sensing may also be implemented using the signal transmitted by a user device. A RAN node is configured to obtain measurements based on an uplink signal transmitted by a user device, where the non-Line-of-Sight paths may contain physical information about the environment and objects of interest. This uplink signal may be scheduled exclusively to sense an object of interest. Alternatively, the RAN node may be configured to obtain measurements based on any opportunistic signal transmitted by a user device. One or multiple measurement reports containing micro-Doppler signatures are then provided to the sensing function. 4. Bistatic sensing with a RAN node acting as the transmitter and a user device acting as the receiver

[0139] As shown in FIG. 11(b), bistatic sensing may be implemented using the downlink signal transmitted by a RAN node. The sensing function may configure a user device to obtain micro-Doppler measurements based on any radio signal transmitted in the downlink by a RAN node. The obtained measurement reports are to be provided to the sensing function.

[0140] 5. Bistatic sensing with different user devices acting as the transmitter and receiver

[0141] FIG. 12(a), presents a bistatic sensing scenario implemented using the sensing signal transmitted between two user devices. The sensing function may select the user devices to be involved and configure the receiving user device to obtain micro-Doppler measurements based on the sensing signal transmitted by the other user device. The sensing signal may be either dedicated to the sensing task or any radio signal transmitted over the sidelink. The measurement reports are to be provided by the receiving user device to the sensing function.

[0142] 6. Monostatic user device based sensing (i.e. a single user device acting as the transmitter and receiver)

[0143] As illustrated in FIG. 12(b), a user device may be configured to transmit a sensing signal and receive the backscattered signal from the environment. The transmitted sensing signal may be either dedicated to sensing environmental information or a communication signal for conveying information to another device. The user device is configured to obtain micro-Doppler signatures of the objects of interest based on the received backscattered signal. This measurement is provided to the sensing function. The sensing function may also provide measurement configuration to the user device to adjust the measurement setting according to the scenario and the properties of the detected objects.

[0144] Based on the above-discussed embodiment, it should be understood that the present disclosure applies to the network entities where a measurement entity and / or a sensing entity may be implemented.

[0145] A measurement entity unit may reside in any node in a radio access network that is capable of receiving radio signals. This includes but is not limited to:

[0146] • Network nodes, e.g. base stations, next-generation Node B (gNB), gNB-Distributed Unit (gNB-DU), gNB- Radio Unit (gNB-RU), Remote-Radio-Head (RRH), and the equivalent variants such as the radio units in the Open-RAN architecture.

[0147] • Any device with a role of User Equipment (UE), e.g., UE, Road Side Unit (RSU), Position Reference Unit (PRU), or any vehicle with a UE module on board.

[0148] A sensing entity may be implemented as one or multiple functional entities to compute sensing information based on the measurement reports. The functional entity may be implemented as a network function in the core network, in the RAN, or as a service provided by a computing platform such as cloud, edge, etc. Alternatively, the functional entity may be implemented in a distributed manner in a device with a UE role, such as UE, RSU, PRU, etc. Notably, the interactions between the measurement unit and the sensing unit must be specified so that devices manufactured by different vendors may function jointly.

[0149] One of the advantages of integrating sensing functionality in a communication network is to exploit the existing deployment of RAN nodes for radio sensing. The mobile cellular network offers intrinsic coverage of the environment and is capable of collecting measurement information from different perspectives. With such diversity, the micro-motion of objects may be potentially better captured, leading to improved detection performance. For example, for the scenario depicted in FIG. 13, RAN node A may fail to capture the cyclist’s micro-Doppler information since its Doppler frequency components relative to RAN node A are close to zero. Instead, the sensing function may configure RAN node B to obtain the micro-Doppler signature of the cyclist. FIG. 14 shows a signaling procedure for micro-Doppler information reporting according to an embodiment of this disclosure.

[0150] 1. Sensing function, which may be the sensing entity 200 shown in FIG. 1 or FIG. 9, in a next-generation core network, provides an initial configuration of the micro-Doppler information reporting to the RAN nodes A and B. RAN node A or B may be the measurement entity 100 shown in FIG. 1 or FIG. 9.

[0151] 2. RAN nodes transmit sensing signals to the area of interest. Based on the backscattered signal, measurement information including micro-Doppler signatures is obtained based on the given configuration

[0152] 3. RAN nodes report the measurement information including the micro-Doppler signature to the sensing function.

[0153] 4. The sensing function provides an updated configuration of the micro-Doppler measurement with a different timefrequency resolution to RAN node B, based on the target of interest, e.g. pedestrian, truck, cyclist, etc.

[0154] 5. RAN node B obtains micro-Doppler information based on the new configuration.

[0155] 6. RAN node B provides a micro-Doppler signature report to the sensing function based on the updated configuration.

[0156] 7. The sensing function extracts sensing information based on at least the micro-Doppler signature of the detected objects. The sensing information may include position, velocity, type of object, etc.

[0157] The method provided in this embodiment allows the sensing function to configure micro-Doppler measurement and reporting flexibly when it is needed, leading to efficient usage of network resources while exploiting sensing spatial diversity.

[0158] FIG. 15 illustrates an exemplary clustering-based multipath association in a two-object scenario according to an embodiment of this disclosure.

[0159] Due to the lack of range information, the separation of different objects from the micro-Doppler signature is difficult. This makes multipath association a critical pre-processing step where backscattering paths associated with one object should be used for micro-Doppler signature extraction. Depending on the number of objects detected within the field of view, multiple micro- Doppler signatures may be reported.

[0160] The multipath component association problem may be solved based on the Range-Angle-Doppler (RAD) measurements obtained from the received radio signal, namely the three-dimensional radar data cube. The selected scattering points may be partitioned into multiple clusters corresponding to multiple potential objects based on e.g. distance, amplitude, density, etc. (see FIG. 15). This clustering and assignment may be updated over time using subsequent RAD measurements. Tracking methods such as Kalman filtering may be applied to estimate the objects’ trajectories so that a superposition of object clusters can be better identified. Based on the sensing information previously obtained, the sensing unit may provide an association indication to the measurement unit indicating an association between a subset of the obtained measurements and a detected object of interest.Eventually, the multipath components associated with a single object are used to obtain a micro-Doppler signature for this object.

[0161] FIG. 16 illustrates micro-Doppler measurements based on sparse channel measurements according to an embodiment of this disclosure.

[0162] Unlike in a radar system where signals are transmitted continuously to probe the environment, in an integrated sensing and communication system, only limited radio resources can be exploited for sensing purposes. Especially for scenarios illustrated in FIG. 16 where the sensing object cannot be covered by a communication link, a dedicated radio resource needs to be allocated for sensing, causing significant system overhead. Instead, sparse recovery techniques may be employed to obtain micro- Doppler signatures based on the channel measurements on the existing signals transmitted as synchronization signal blocks, on the physical broadcast channel, channel state information reference signal, etc. Given the channel impulse response of path 1 over slow time k, within the processing window of length W, only K« W measurements can be obtained. The multiple micro-Doppler components may be recovered by solving a sparse recovery problem. The sensing unit informs the measurement unit a desired quality of the micro-Doppler signature to be obtained, e.g. a time and / or frequency resolution, or any indication of measurement quality. The measurement unit may determine which part of the transmitted signal is to be utilized for micro-Doppler signature extraction, such as synchronization signal, reference signal for channel estimation purposes, or dedicated reference signal. The measurement unit may apply sparse recovery techniques in case the configured signals are sparse.

[0163] FIG. 17 shows a method 1700 according to an embodiment of the present disclosure. In particular, the method 1700 is performed by a measurement entity 100 as shown in FIG. 1. The method 1700 comprises a step 1701 of obtaining measurement information 101 based on one or more sensing signals. The method 1700 also comprises a step 1702 of reporting the measurement information 101 to a sensing entity 200, wherein the measurement information 101 comprises one or more micro- Doppler signatures and / or one or more micro-Doppler signature related features. Possibly, the sensing entity 200 is the sensing entity 200 shown in FIG. 9.

[0164] FIG. 18 shows a method 1800 according to an embodiment of the present disclosure. In particular, the method 1800 is performed by a sensing entity 200 as shown in FIG. 9. The method 1800 comprises a step 1801 of obtaining measurement information 101 from at least one measurement entity 100, wherein the measurement information 101 comprises one or more micro-Doppler signatures and / or one or more micro-Doppler signature related features. The method 1800 also comprises a step 1802 of extracting sensing information 201 related to one or more detected objects based on the obtained measurement information 101. Possibly, the measurement entity 100 is the measurement entity 100 shown inFIG. 1.

[0165] To summarize, this disclosure introduces additional measurement information representing the dynamics of multiple objects, which can be exploited especially for dynamic object classification and recognition. Embodiments of this disclosure provide a measurement unit that may obtain the micro-Doppler measurement information using the desired configuration required by a sensing unit. For instance, time-frequency resolution may be adjusted in order that micro motions of the objects of interest are well-represented. The sensing unit may adjust measurement configuration based on the characteristics of the object of interest, and / or the application scenarios.

[0166] 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 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 mutual different dependent claims does not indicate that a combination of these measures cannot be used in an advantageous implementation.

Claims

CLAIMS1. A measurement entity (100), configured to: obtain measurement information (101) based on one or more sensing signals; and report the measurement information (101) to a sensing entity (200), wherein the measurement information (101) comprises one or more micro-Doppler signatures and / or one or more micro-Doppler signature related features.

2. The measurement entity (100) according to claim 1, wherein the one or more micro-Doppler signatures and / or the one or more micro-Doppler signature related features are associated with one or more detected objects, wherein each of the one or more micro-Doppler signatures relates to dynamic characteristics of the one or more detected objects, and indicates a plurality of Doppler frequency variations.

3. The measurement entity (100) according to claim 1 or 2, configured to: obtain configuration information (102) from the sensing entity (200), wherein the configuration information (102) comprises measurement configuration, which indicates to the measurement entity (100) to obtain the measurement information (101) based on the measurement configuration, wherein the measurement configuration comprises one or more of the following parameters: a type of a time-frequency analysis; a time and / or frequency resolution; a length of a sliding window; a window function; an overlapping factor; an observation time; and an indication of measurement information (101) quality.

4. The measurement entity (100) according to claim 3, configured to: receive one or more sensing signals from environment; obtain measurements based on the one or more sensing signals; and extract the one or more micro-Doppler signatures from the measurements based on the measurement configuration.

5. The measurement entity (100) according to claim 4, configured to: extract a micro-Doppler signature based on a subset of the obtained measurements.

6. The measurement entity (100) according to claim 5, wherein the subset of the obtained measurements is associated with one detected object.

7. The measurement entity (100) according to one of the claims 4 to 6, configured to: transmit one or more signals, wherein the one or more received sensing signals are backscattered signals of the one or more transmitted signals.

8. The measurement entity (100) according to one of the claims 3 to 7, wherein the configuration information (102) further comprises reporting configuration, wherein the measurement entity (100) is further configured to:periodically report the measurement information (101) to the sensing entity (200) based on the reporting configuration, wherein the reporting configuration comprises one or more of the following parameters: start time of reporting the measurement information (101), periodicity of the reporting, end time of the reporting, and a number of reporting, or report the measurement information ( 101 ) to the sensing entity (200) based on a trigger-event based on the reporting configuration, wherein the reporting configuration comprises one or more types of the trigger-event, wherein the type of the trigger-event comprises one or more of the following: a timer expires, an object appears, a state change, a dynamics change.

9. The measurement entity (100) according to one of the claims 3 to 8 when depending on claim 2, wherein the configuration information (102) further comprises pre-processing configuration, wherein the pre-processing configuration comprises one or more of the following parameters: an association indication for indicating an association between the subset of the obtained measurements and the detected object; a number of objects to be detected; and thresholding information for selecting one or more objects based on predefined criteria.

10. The measurement entity (100) according to one of the claims 4 to 9, wherein the configuration information (102) further indicates one or more of the following processes to be performed based on the one or more extracted micro-Doppler signatures: denoising, clutter suppression; interference mitigation; object detection; extracting the one or more micro-Doppler signature related features from the one or more extracted microDoppler signatures.

11. The measurement entity (100) according to claim 10, wherein the configuration information (102) further comprises processing configuration, which comprises one or more of the following parameters: thresholding information; an inference model; one or more corresponding hyperparameters of the inference model; a number of objects to be detected; thresholding information for selecting one or more objects based on predefined criteria; indication information for indicating the one or more micro-Doppler signature related features to be extracted from the one or more extracted micro-Doppler signatures.

12. The measurement entity (100) according to one of the claims 1 to 11, wherein the measurement entity (100) is implemented at an access network device, or a user device.

13. A sensing entity (200), configured to : obtain measurement information (101) from at least one measurement entity (100), wherein the measurement information (101) comprises one or more micro-Doppler signatures and / or one or more micro-Doppler signature related features; and extract sensing information (201) related to one or more detected objects based on the obtained measurement information (101).

14. The sensing entity (200) according to claim 13, wherein the one or more micro-Doppler signatures and / or the one or more micro-Doppler signature related features are associated with the one or more detected objects, wherein each of the one or more micro-Doppler signatures relates to dynamic characteristics of the one or more detected objects, and indicates a plurality of Doppler frequency variations.

15. The sensing entity (200) according to claim 13 or 14, configured to: provide configuration information (102) to the at least one measurement entity (100), wherein the configuration information (102) comprises measurement configuration, which indicates to the at least one measurement entity (100) to obtain the measurement information (101), based on the measurement configuration, wherein the measurement configuration comprises one or more of the following parameters: a type of time-frequency analysis; a time and / or frequency resolution; a length of a sliding window; a window function; an overlapping factor; an observation time; an indication of the measurement information (101) quality .

16. The sensing entity (200) according to claim 15, wherein the configuration information (102) further comprises reporting configuration, wherein the reporting configuration indicates to the at least one measurement entity (100) to periodically report the measurement information (101) to the sensing entity (200), wherein the reporting configuration comprises one or more of the following parameters: start time of reporting the measurement information (101), periodicity of the reporting, end time of the reporting, and a number of reporting, or the reporting configuration indicates to the at least one measurement entity (100) to report the measurement information (101) to the sensing entity (200) based on a trigger-event, wherein the reporting configuration comprises one or more types of the trigger-event, wherein the type of the trigger-event comprises one or more of the following: a timer expires, an object appears, a state change, a dynamics change.

17. The sensing entity (200) according to claim 15 or 16, wherein the configuration information (102) further comprises pre-processing configuration, wherein the pre-processing configuration comprises one or more of the following parameters: an association indication for indicating an association between a subset of the obtained measurements and a detected object; a number of objects to be detected; and thresholding information for selecting one or more objects based on predefined criteria.

18. The sensing entity (200) according to one of the claims 15 to 17, wherein the configuration information (102) further indicates one or more of the following processes to be performed based on the one or more extracted micro-Doppler signatures: denoising, clutter suppression; interference mitigation; object detection; extracting the one or more micro-Doppler signature related features from the one or more extracted micro- Doppler signatures.

19. The sensing entity (200) according to claim 18, wherein the configuration information (102) further comprises postprocessing configuration, which comprises one or more of the following parameters: thresholding information; an inference model; one or more corresponding hyperparameters of the inference model; a number of objects to be detected; thresholding information for selecting one or more objects based on predefined criteria; indication information for indicating the one or more micro-Doppler signature related features to be extracted from the one or more extracted micro-Doppler signatures.

20. The sensing entity (200) according to one of the claims 15 to 1 , configured to: determine to provide updated configuration information (102) based on the extracted sensing information (201).

21. The sensing entity (200) according to claim 20, configured to: provide the updated configuration information (102) to the at least one measurement entity (100), wherein the updated configuration information (102) comprises updated measurement configuration, wherein the updated measurement configuration indicates to the at least one measurement entity (100) to obtain the measurement information (101) based on the updated measurement configuration.

22. The sensing entity (200) according to one of the claims 15 to 21, wherein the sensing entity (200) is implemented as a network function in a core network or an access network, or as a service in a computing platform.

23. The sensing entity (200) according to one of the claims 15 to 22, wherein the sensing information (201) related to the one or more detected objects comprises one or more of the following information: a position of the detected object, a velocity of the detected object, a classification of the detected object, a state of the detected object.

24. A method (1700) performed by a measurement entity (100), comprises: obtaining (1701) measurement information (101) based on one or more sensing signals; and reporting (1702) the measurement information (101) to a sensing entity (200), wherein the measurement information (101) comprises one or more micro-Doppler signatures and / or one or more micro-Doppler signature related features.

25. A method (1800) performed by a sensing entity (200), comprising: obtaining (1801) measurement information (101) from at least one measurement entity (100), wherein the measurement information (101) comprises one or more micro-Doppler signatures and / or one or more micro-Doppler signature related features; and extracting (1802) sensing information (201) related to one or more detected objects based on the obtained measurement information (101).

26. A computer program product comprising computer readable code instructions which, when run in a computer will cause the computer to perform the method (1700, 1800) according to claim 24 or 25.

27. A computer readable storage medium comprising computer program code instructions, being executable by a computer, for performing a method (1700, 1800) according to claim 24 or 25 when the computer program code instructions runs on a computer.

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