A communication awareness method and apparatus

Abstract

The embodiment of the application provides a kind of communication awareness method and device, it is related to communication field, can promote the sensing reliability of ISAC system, reduce the false alarm rate of target detection.The method is: obtaining sensing data, sensing data includes the measurement set corresponding to multiple reflection points of at least one sensing target, the measurement set corresponding to each reflection point includes the geometric information and energy information corresponding to the reflection point;The sensing data is clustered, and multiple candidate target clusters are output, each candidate target cluster includes a group of geometric information similar reflection points;For each candidate target cluster, the statistical distribution of the energy information of all reflection points in it is calculated respectively, and the reflection point with energy information deviating from the first threshold is removed from the candidate target cluster.

Description

Technical Field

[0001] This application relates to the field of communications, and more particularly to a communication sensing method and apparatus. Background Technology

[0002] Integrated sensing and communication (ISAC) is a core 6G technology. By sharing hardware, spectrum, and waveforms, it enables the same wireless platform to simultaneously perform communication and sensing, improving the utilization of time and frequency resources while reducing system hardware costs. The sensing function of an ISAC system typically involves four main steps: target detection, parameter estimation, identification, and tracking. As the first step in the sensing function, target detection is crucial for ISAC systems. Target detection refers to the sensing receiver determining the presence of a target in the echo signal based on the received signal.

[0003] However, in multipath and complex environments (such as typical deployment scenarios like urban roads, airports, and factories), target detection results may exhibit a large number of ghost points (false alarms) caused by reflections from non-real targets or signal superposition. These ghost points are fundamentally different from real target points (reflection points). The echo signals from real target points (reflection points) are signals returned by the same object through different propagation paths (main path, ground reflection, building reflection, etc.); while ghost points originate from signals with "no physical target correspondence," such as noise, mirror images, strong interference superposition, or spurious peaks in the reflection path. Their energy statistical characteristics are abnormal, their correlation is poor, and their temporal sequence is unstable, leading to a high false alarm rate and unstable detection results in target detection. Summary of the Invention

[0004] This application provides a communication sensing method and apparatus that can improve the sensing reliability of the ISAC system and reduce the false alarm rate of target detection.

[0005] To achieve the above objectives, the embodiments of this application adopt the following technical solutions:

[0006] In a first aspect, a communication sensing method is provided, applied to a first communication device. The method includes: performing a sensing task to obtain sensing data, the sensing data including a set of measurements corresponding to multiple reflection points of at least one sensing target, the set of measurements corresponding to each reflection point including geometric information and energy information corresponding to that reflection point; clustering the sensing data to output multiple candidate target clusters, each candidate target cluster including a group of reflection points with similar geometric information; for each candidate target cluster, calculating the statistical distribution of energy information of all reflection points within it, and removing reflection points whose energy information deviates from a first threshold from the candidate target cluster.

[0007] The method provided in this application introduces energy measurement indicators (such as SINR and / or RSRP) on the basis of geometric clustering. Through point-level energy consistency judgment (point-level screening, i.e., removing reflection points (ghost points) within candidate target clusters whose energy information deviates from a first threshold), accurate identification and removal of ghost points can be achieved. In this way, the sensing reliability and false alarm suppression performance of the ISAC system can be improved.

[0008] In one possible implementation, the geometric information includes at least one of the propagation delay, angle, and Doppler shift of the echo signal reflected from the reflection point, where the angle includes the incident angle and / or the exit angle; the energy information includes the signal-to-noise ratio (SINR) and / or the reference-signal receiving power (RSRP) of the echo signal reflected from the reflection point. Thus, by introducing energy measurement metrics (e.g., SINR and / or RSRP), point-level energy consistency decisions can be made for candidate target clusters, enabling accurate identification and removal of ghost points.

[0009] In one possible implementation, for each candidate target cluster, the statistical distribution of energy information of all reflection points within it is calculated. Reflection points whose energy information deviates from a first threshold are removed from the candidate target cluster. This includes: for a first reflection point in a first candidate target cluster, calculating the average energy similarity between the first reflection point and other reflection points within the first candidate target cluster; wherein the first candidate target cluster is any one of multiple candidate target clusters, and the first reflection point is any one of the reflection points in the first candidate target cluster; calculating the energy consistency confidence of the first reflection point based on the average energy similarity between the first reflection point and other reflection points within the first candidate target cluster: if the energy consistency confidence of the first reflection point is greater than or equal to the first threshold, the first reflection point is retained; if the energy consistency confidence of the first reflection point is less than the first threshold, the first reflection point is removed from the first candidate target cluster. This allows for the accurate identification and removal of ghost points with low energy consistency confidence, thereby improving the sensing reliability and false alarm suppression performance of the ISAC system.

[0010] In one possible implementation, the average energy similarity between the first reflection point and other reflection points within the first candidate target cluster is... as follows:

[0011] ;

[0012] ;

[0013] in, Indicates the first candidate target cluster. This represents the number of all reflection points within the first candidate target cluster. This represents the energy similarity between the first reflection point and point q, where point q represents any reflection point within the first candidate target cluster that is different from the first reflection point. ( ) represents an exponential function. This represents the energy tolerance threshold constant. This represents the normalized SINR value at the first reflection point. This represents the normalized SINR value at point q. This represents the normalized value of RSRP at the first reflection point. This represents the normalized value of RSRP at point q. These are the weights of SINR and RSRP.

[0014] In one possible implementation, the energy consistency confidence level of the first reflection point as follows:

[0015] ;

[0016] in, It is a nonlinear amplification factor, and its value ranges from 1.5 to 2.0.

[0017] In one possible implementation,

[0018] ;

[0019] in, This represents the lower limit of the SINR of all reflection points within the first candidate target cluster. This represents the upper limit of the SINR of all reflection points within the first candidate target cluster, where:

[0020] ;

[0021] ;

[0022] in, The SINR of all reflection points within the first candidate target cluster is represented by the [missing information]. Quantiles The RSRP of all reflection points within the first candidate target cluster represents the first... Quantiles The SINR of all reflection points within the first candidate target cluster is represented by the [missing information]. Quantiles The RSRP of all reflection points within the first candidate target cluster represents the first... Quantiles, x is less than y;

[0023] or,

[0024] ;

[0025] ;

[0026] in, This represents the expected signal-to-noise ratio of the first candidate target cluster. This indicates the signal-to-noise ratio fluctuation range of the first candidate target cluster. This represents the expected received reference signal power of the first candidate target cluster. This indicates the range of received power fluctuations in the reference signal for the first candidate target cluster. This is an adjustment coefficient, and its value ranges from 1.5 to 2.0.

[0027] In one possible implementation, for each candidate target cluster, the statistical distribution of energy information of all reflection points within it is calculated. Reflection points whose energy information deviates from a first threshold are removed from the candidate target cluster. The method further includes reporting the processed sensing data to a second communication device. The processed sensing data does not include the geometric and energy information of the removed reflection points. That is, after the first communication device (e.g., a terminal device) obtains sensing data by performing a sensing task, it can perform point-level cleaning / screening (i.e., removing "ghost points" within each candidate target cluster) and send the screening results (the results after point-level cleaning / screening) to the second communication device (e.g., a network device). This allows the network device to further perform cluster-level cleaning / screening (removing "ghost clusters"). In this way, the processing power of the first communication device can be fully utilized, reducing the processing complexity of the sensing data for the second communication device (e.g., the network device).

[0028] In one possible implementation, for each candidate target cluster, the statistical distribution of energy information of all reflection points within it is calculated. Reflection points whose energy information deviates from a first threshold are removed from the candidate target cluster. The method further includes: calculating the energy consistency confidence score of a first candidate target cluster, which is any one of multiple candidate target clusters; if the energy consistency confidence score of the first candidate target cluster is greater than or equal to a second threshold, the first candidate target cluster is retained; if the energy consistency confidence score of the first candidate target cluster is less than the second threshold, the first candidate target cluster is removed; and the processed sensing data is reported to a second communication device, where the processed sensing data does not include the removed candidate target clusters. That is, after the first communication device (e.g., a terminal device) obtains sensing data by performing a sensing task, it can perform point-level cleaning / screening (i.e., removing "ghost points" within each candidate target cluster) and cluster-level cleaning / screening (removing "ghost clusters"), and then send the screening results (the results after point-level and cluster-level screening) to the second communication device (e.g., a network device). In this way, the processing power of the first communication device can be fully utilized, reducing the processing complexity of the sensing data by the second communication device (e.g., network device).

[0029] In one possible implementation, the energy consistency confidence of the first candidate target cluster is as follows:

[0030] ;

[0031] in, This represents the energy consistency confidence level of the first candidate target cluster. This represents the confidence level of energy consistency of the i-th reflection point within the first candidate target cluster.

[0032] Secondly, a communication sensing method is provided, applied to a second communication device. The method includes: receiving sensing data from a first communication device, the sensing data including a set of measurements corresponding to multiple reflection points of at least one sensing target, the set of measurements corresponding to each reflection point including geometric information and energy information corresponding to that reflection point; clustering the sensing data to output multiple candidate target clusters, each candidate target cluster including a group of reflection points with similar geometric information; for each candidate target cluster, calculating the statistical distribution of energy information of all reflection points within it, and removing reflection points whose energy information deviates from a first threshold from the candidate target cluster.

[0033] In one possible implementation, the geometric information includes at least one of the propagation delay, angle, and Doppler shift of the echo signal reflected from the reflection point, where the angle includes the incident angle and / or the exit angle; the energy information includes the signal-to-noise ratio (SINR) and / or the received power (RSRP) of the echo signal reflected from the reflection point.

[0034] In one possible implementation, for each candidate target cluster, the statistical distribution of energy information of all reflection points within it is calculated, and reflection points whose energy information deviates from a first threshold are removed from the candidate target cluster. This includes: for a first reflection point in a first candidate target cluster, calculating the average energy similarity between the first reflection point and other reflection points within the first candidate target cluster; wherein, the first candidate target cluster is any one of multiple candidate target clusters, and the first reflection point is any one of the reflection points in the first candidate target cluster; calculating the energy consistency confidence of the first reflection point based on the average energy similarity between the first reflection point and other reflection points within the first candidate target cluster: if the energy consistency confidence of the first reflection point is greater than or equal to the first threshold, the first reflection point is retained; if the energy consistency confidence of the first reflection point is less than the first threshold, the first reflection point is removed from the first candidate target cluster.

[0035] In one possible implementation, the average energy similarity between the first reflection point and other reflection points within the first candidate target cluster is... as follows:

[0036] ;

[0037] ;

[0038] in, Indicates the first candidate target cluster. This represents the number of all reflection points within the first candidate target cluster. This represents the energy similarity between the first reflection point and point q, where point q represents any reflection point within the first candidate target cluster that is different from the first reflection point. ( ) represents an exponential function. This represents the energy tolerance threshold constant. This represents the normalized SINR value at the first reflection point. This represents the normalized SINR value at point q. This represents the normalized value of RSRP at the first reflection point. This represents the normalized value of RSRP at point q. These are the weights of SINR and RSRP.

[0039] In one possible implementation, the energy consistency confidence level of the first reflection point as follows:

[0040] ;

[0041] in, It is a nonlinear amplification factor, and its value ranges from 1.5 to 2.0.

[0042] In one possible implementation, ;

[0043] in, This represents the lower limit of the SINR of all reflection points within the first candidate target cluster. This represents the upper limit of the SINR of all reflection points within the first candidate target cluster, where:

[0044] ;

[0045] ;

[0046] in, The SINR of all reflection points within the first candidate target cluster is represented by the [missing information]. Quantiles The RSRP of all reflection points within the first candidate target cluster represents the first... Quantiles The SINR of all reflection points within the first candidate target cluster is represented by the [missing information]. Quantiles The RSRP of all reflection points within the first candidate target cluster represents the first... Quantiles, x is less than y;

[0047] or,

[0048] ;

[0049] ;

[0050] in, This represents the expected signal-to-noise ratio of the first candidate target cluster. This indicates the signal-to-noise ratio fluctuation range of the first candidate target cluster. This represents the expected received reference signal power of the first candidate target cluster. This indicates the range of received power fluctuations in the reference signal for the first candidate target cluster. This is an adjustment coefficient, and its value ranges from 1.5 to 2.0.

[0051] In one possible implementation, the method further includes: calculating the energy consistency confidence of a first candidate target cluster, wherein the first candidate target cluster is any one of a plurality of candidate target clusters; if the energy consistency confidence of the first candidate target cluster is greater than or equal to a second threshold, the first candidate target cluster is retained; if the energy consistency confidence of the first candidate target cluster is less than the second threshold, the first candidate target cluster is removed.

[0052] In one possible implementation, the energy consistency confidence of the first candidate target cluster is as follows:

[0053] ;

[0054] in, This represents the energy consistency confidence level of the first candidate target cluster. This represents the confidence level of energy consistency of the i-th reflection point within the first candidate target cluster.

[0055] Thirdly, a communication sensing method is provided, applied to a second communication device. The method includes: receiving sensing data from a first communication device, the sensing data including multiple candidate target clusters, each candidate target cluster including a set of reflection points with similar geometric information, excluding reflection points whose energy information deviates from a first threshold, each reflection point corresponding to a set of measurements, the set of measurements corresponding to each reflection point including the geometric information and energy information corresponding to that reflection point; calculating the energy consistency confidence of a first candidate target cluster, the first candidate target cluster being any one of the multiple candidate target clusters; if the energy consistency confidence of the first candidate target cluster is greater than or equal to a second threshold, retaining the first candidate target cluster; if the energy consistency confidence of the first candidate target cluster is less than the second threshold, discarding the first candidate target cluster.

[0056] In one possible implementation, the energy consistency confidence of the first candidate target cluster is as follows:

[0057] ;

[0058] in, This represents the energy consistency confidence level of the first candidate target cluster. This represents the confidence level of energy consistency of the i-th reflection point within the first candidate target cluster.

[0059] The second and third aspects are the implementations of the opposite side to the first aspect. The explanations, supplements, and descriptions of the beneficial effects of the first aspect also apply to the second aspect, and will not be repeated here.

[0060] Fourthly, a communication device is provided, comprising a processing module and a transceiver module. The transceiver module is used to perform a sensing task to obtain sensing data, the sensing data including a set of measurements corresponding to multiple reflection points of at least one sensing target, each set of measurements corresponding to a reflection point including geometric information and energy information corresponding to that reflection point; the processing module is used to cluster the sensing data, outputting multiple candidate target clusters, each candidate target cluster including a group of reflection points with similar geometric information; for each candidate target cluster, calculating the statistical distribution of energy information of all reflection points within it, and removing reflection points whose energy information deviates from a first threshold from the candidate target cluster.

[0061] Fifthly, a communication device is provided, comprising a processing module and a transceiver module. The transceiver module is configured to receive sensing data, the sensing data including a set of measurements corresponding to multiple reflection points of at least one sensing target, each set of measurements corresponding to a reflection point including geometric information and energy information corresponding to that reflection point; the processing module is configured to cluster the sensing data, outputting multiple candidate target clusters, each candidate target cluster including a group of reflection points with similar geometric information; for each candidate target cluster, calculating the statistical distribution of energy information of all reflection points within it, and removing reflection points whose energy information deviates from a first threshold from the candidate target cluster.

[0062] In a sixth aspect, a communication device is provided, including a processor. The processor is coupled to a memory and can be used to execute instructions or data in the memory to implement the method in any possible implementation of the first aspect described above. Optionally, the communication device further includes a memory. Optionally, the communication device further includes a communication interface, and the processor is coupled to the communication interface.

[0063] In one implementation, the communication interface may be a transceiver, or an input / output interface.

[0064] In another implementation, the communication device is a chip configured in a terminal device. When the communication device is a chip configured in a terminal device, the communication interface can be an input / output interface.

[0065] A seventh aspect provides a communication device including a processor. The processor is coupled to a memory and can be used to execute instructions or data in the memory to implement the methods in any possible implementation of the second or third aspect described above. Optionally, the communication device further includes a memory. Optionally, the communication device further includes a communication interface, and the processor is coupled to the communication interface.

[0066] In one implementation, the communication interface may be a transceiver, or an input / output interface.

[0067] Eighthly, a processor is provided, comprising: an input circuit, an output circuit, and a processing circuit. The processing circuit is configured to receive signals through the input circuit and transmit signals through the output circuit, causing the processor to execute a method in any possible implementation of any aspect.

[0068] In specific implementation, the processor can be one or more chips, the input circuit can be input pins, the output circuit can be output pins, and the processing circuit can be transistors, gate circuits, flip-flops, and various logic circuits. The input signal received by the input circuit can be received and input by, for example, but not limited to, a receiver, and the signal output by the output circuit can be, for example, but not limited to, output to and transmitted by a transmitter. Furthermore, the input circuit and the output circuit can be the same circuit, which is used as both the input circuit and the output circuit at different times. This application does not limit the specific implementation of the processor and various circuits.

[0069] A ninth aspect provides a communication device including a processor and a memory. The processor is configured to read instructions stored in the memory, receive signals via a receiver, and transmit signals via a transmitter to execute the method in any possible implementation of any of the preceding aspects.

[0070] Optionally, the processor may be one or more, and the memory may be one or more.

[0071] In a tenth aspect, a computer program product is provided, the computer program product comprising: a computer program (also referred to as code or instructions) that, when the computer program is run, causes a computer to perform a method in any possible implementation of any of the above aspects.

[0072] Eleventhly, a computer-readable storage medium is provided that stores a computer program (also referred to as code or instructions) that, when run on a computer, causes the computer to perform the method in any possible implementation of any of the preceding aspects.

[0073] In a twelfth aspect, embodiments of this application provide a chip system including one or more processors for calling and executing instructions stored in memory, causing the methods in any of the above aspects or possible implementations to be performed. The chip system may be composed of chips or may include chips and other discrete devices.

[0074] The chip system may include input circuits or interfaces for transmitting information or data, and output circuits or interfaces for receiving information or data.

[0075] In a thirteenth aspect, a communication system is provided, including the aforementioned first communication device (e.g., a terminal device) and second communication device (e.g., a network device). Optionally, the communication system may further include other devices that communicate with the first communication device and / or the second communication device. Attached Figure Description

[0076] Figure 1 A schematic diagram of a dual-base sensing scenario provided in an embodiment of this application;

[0077] Figure 2 A schematic diagram of a single-base sensing scenario provided in an embodiment of this application;

[0078] Figure 3 A flowchart illustrating a communication sensing method provided in an embodiment of this application;

[0079] Figure 4 A flowchart illustrating yet another communication sensing method provided in an embodiment of this application;

[0080] Figure 5 A flowchart illustrating yet another communication sensing method provided in an embodiment of this application;

[0081] Figure 6 A flowchart illustrating yet another communication sensing method provided in an embodiment of this application;

[0082] Figure 7 A flowchart illustrating yet another communication sensing method provided in an embodiment of this application;

[0083] Figure 8 A schematic block diagram of a communication device provided in an embodiment of this application;

[0084] Figure 9 This is a schematic block diagram of another communication device provided in the embodiments of this application. Detailed Implementation

[0085] To ensure clarity and conciseness in the description of the following embodiments, a brief introduction to the relevant concepts or technologies is given first:

[0086] Currently, in multipath and complex environments (such as typical deployment scenarios like urban roads, airports, and factories), due to the presence of a large amount of reflection, scattering, and noise interference in the environment, the target detection results of the ISAC system may show a large number of ghost points (false alarm points) caused by reflections of non-real targets or signal superposition, resulting in a high false alarm rate and unstable detection results.

[0087] For example, in connected vehicle scenarios: vehicle signals are reflected from buildings, the ground, or other vehicle surfaces, forming geometrically "reasonable" false targets. Even if no real object exists, the system may still detect multiple "target points" (ghost points / false alarm points). In drone detection scenarios: multipath echoes from the drone body, the ground, or obstacles overlap, generating clusters of false targets (ghost clusters / false alarm clusters), affecting flight path planning and target recognition. In industrial sensing scenarios: mirrored signals reflected from the metal surfaces of equipment may be incorrectly identified as independent targets.

[0088] Currently, in the ISAC system, geometric clustering can be used to initially distinguish different target points. The core idea of ​​geometric clustering algorithms is to discover naturally formed, density-connected sets of points in the data space. Clusters can be dynamically formed based on the distribution density and mutual distance of data points in multidimensional space, without pre-specifying how many points each cluster should contain. The output of geometric clustering algorithms is entirely determined by the actual distribution of the input data, and it cannot guarantee that all target points within a cluster originate from the same real reflector.

[0089] The inventors of this application have discovered that while geometric features can describe the spatial distribution of signals, they cannot reflect the energy consistency of the signals. Multipath echoes from real targets typically exhibit high correlation and stability in the energy dimension, while the energy distribution of false targets is often discrete and inconsistent. Sensing decision mechanisms relying solely on geometric features lack stability and reliability in complex multipath environments.

[0090] For example, in complex multipath or mirror environments, as shown in Table 1, geometric clustering methods typically exhibit the following two common errors:

[0091] Table 1

[0092]

[0093] If only geometric clustering is used, the first case will pollute the real clusters, and the second case will retain the entire false alarm cluster, thus generating false targets.

[0094] Based on the above findings, this application provides a communication sensing method that introduces energy features / energy measurement indicators (such as SINR and RSRP) as accompanying metrics on the basis of traditional geometric clustering, thereby achieving accurate identification of false sensing targets. This can significantly reduce the false alarm rate (improve the false alarm suppression capability of the ISAC system), enhance the reliability of target identification, and improve adaptability to complex environments.

[0095] The embodiments of this application can be widely applied to wireless communication scenarios that require environmental perception and target recognition, such as:

[0096] Intelligent transportation / vehicle to everything (V2X): Terminal devices (such as vehicles or roadside units (RSUs)) use wireless signals to detect surrounding vehicles, pedestrians, and obstacles, enabling blind spot detection, assisted driving, and collision warning.

[0097] Smart Industry / Internet of Things (IoT): In environments such as factories (industrial parks), airports, and warehouses, robots, unmanned aerial vehicles (UAVs), equipment, and personnel are located, tracked, and their status monitored via wireless signals.

[0098] Smart cities and security: Intrusion detection, crowd monitoring, and abnormal behavior identification in specific areas.

[0099] The communication sensing method provided in this application can be applied to ISAC scenarios in 5G-Advanced (5G-A) and future 6G networks. ISAC scenarios can include dual-base sensing scenarios or single-base sensing scenarios. A dual-base sensing scenario refers to a sensing scenario where the transmitter of the sensing signal and the receiver of the echo signal are not the same communication device. A single-base sensing scenario refers to a sensing scenario where the transmitter of the sensing signal and the receiver of the echo signal belong to the same communication device. A single-base sensing scenario can also be called a self-sensing scenario.

[0100] For example, such as Figure 1 The diagram illustrates a dual-base sensing scenario. This scenario can include vehicle 1, vehicle 2, obstacles, pedestrians, a remote sensing unit (RSU) (e.g., a traffic light), and a base station. The RSU (e.g., a traffic light) can act as a transmitter, vehicle 1 as a receiver, and the sensing targets can include other vehicles (e.g., vehicle 2), obstacles, pedestrians, etc. The base station can act as a data collection point (data collection node), receiving sensing data from the receiver (e.g., vehicle 1).

[0101] For example, the transmitting end (e.g., RSU) can transmit a sensing signal SS1, and the echo signal ES1 generated by SS1 passing through vehicle 2 (e.g., the surface of vehicle 2) is received by the receiving end (vehicle 1). The RSU can transmit a sensing signal SS2, and the echo signal ES2 generated by SS2 passing through an obstacle is received by vehicle 1. The RSU can transmit a sensing signal SS3, and the echo signal ES3 generated by SS3 passing through a pedestrian is received by vehicle 1.

[0102] It should be understood that the sensing signals transmitted by the transmitter can generate echo signals not only on different sensing targets, but also at different locations (different reflection points / scattering points) of the same sensing target. Among them, the reflection point (scattering point) can be a physical location in the sensing target that can reflect the signal, such as the surface of a vehicle body, the surface of an obstacle, the surface of a pedestrian's body (or clothing), etc.

[0103] The signal receiving end (e.g., vehicle 1) can sample, quantize, and process the echo signals from each reflection point of the perceived target using algorithms to extract perception parameters such as distance, speed, and angle of the reflection point of the perceived target.

[0104] It should be noted that the sensing signals SS1, SS2, and SS3 can be sensing signals emitted from the same transmission beam or from different transmission beams. Different sensing signals encountering sensing targets at different locations (e.g., vehicle 2, obstacles, pedestrians) can generate echo signals in different directions, such as echo signals ES1, ES2, and ES3.

[0105] In a dual-base sensing scenario, echo signals generated by sensing signals transmitted from the same transmitter can be received by different receivers. Echo signals generated by sensing signals transmitted from different transmitters can also be received by the same receiver. Of course, echo signals generated by sensing signals transmitted from the same transmitter can also be received by only one receiver. This application does not limit the correspondence between transmitters and receivers; it is related to the number of transmitters or receivers within a certain area. In either case, the receiver can determine the sensing data based on its received echo signals. Alternatively, the receiver can transmit relevant data from the received echo signals to other communication devices for them to determine the sensing data.

[0106] For example, such as Figure 2 The image shown is a schematic diagram of a single-base sensing scenario. Figure 2 As shown, this single-base sensing scenario can include a measurement node (e.g., vehicle 1), vehicle 2, obstacles, pedestrians, and a base station. Vehicle 1 can act as both a transmitter and receiver; that is, vehicle 1 can both transmit sensing signals and receive echo signals. Sensing targets can include other vehicles (e.g., vehicle 2), obstacles, pedestrians, etc. The base station can receive sensing data from the receiver.

[0107] When a measurement node (e.g., vehicle 1) senses a target in the measurement environment, it can transmit one or more beams. The sensing signals SS on the one or more beams can detect different positions of the target. The measurement node then receives the corresponding echo signals ES, and can determine the sensing data based on the ES. Of course, the measurement node can also send relevant data from the received echo signals to other communication devices, which can then determine the sensing data.

[0108] For example, vehicle 1 can transmit sensing signals SS1, SS2, and SS3. The echo signal ES1 generated by SS1 passing through vehicle 2 (e.g., the surface of vehicle 2's body) is received by the signal receiver (vehicle 1). The echo signal ES2 generated by SS2 passing through an obstacle is received by vehicle 1. The echo signal ES3 generated by SS3 passing through a pedestrian is received by vehicle 1. Vehicle 1 can determine sensing data based on the echo signals ES1, ES2, and ES3.

[0109] Additionally, it should be noted that the above... Figure 1 and Figure 2 In the scenario described, there is one receiver, transmitter, or measurement node. In reality, there can be multiple receivers, transmitters, or measurement nodes. This application does not limit the number of receivers, transmitters, or measurement nodes; there can be one or more.

[0110] The above Figure 1 and Figure 2 In the described scenario, the receiving end, transmitting end, or measuring node can be referred to as a sensing node. The receiving end, transmitting end, and measuring node can be terminal equipment or network equipment (e.g., access network equipment). This application does not limit itself to the above. Figure 1 and Figure 2 The specific forms of the receiver, transmitter, and measurement node are shown.

[0111] The communication system applicable to this application includes a first communication device and a second communication device. The first communication device can be a sensing node, such as a receiver of echo signals and / or a transmitter of sensing signals. This first communication device can be a network device (access network device), a terminal device, or a chip within a network device or a chip within a terminal device. The second communication device can be a data collection node, which is used to collect sensing data. This data collection node can be a network device (access network device), a terminal device, or a chip within a network device or a chip within a terminal device; the data collection node can also be a server.

[0112] In this application, network equipment (e.g., access network equipment) can be a base station, which can be an evolved NodeB (eNB or eNodeB) in LTE, a base station in NR, a relay station or access point, or a base station in a future network, etc. This application does not limit the scope of the application. In NR, a base station can also be called a transmission reception point (TRP) or gNB. In this application, network equipment can be independently sold network equipment, such as a base station, or it can be a chip within a network equipment that implements corresponding functions (e.g., a media access control (MAC) layer scheduler and a physical layer (PHY) signaling generation module). In this application, the chip system can be composed of chips or can include chips and other discrete components. In the technical solutions provided in this application, the example of a network equipment being used to implement the functions of a network equipment is used to describe the technical solutions provided in this application.

[0113] In this context, a terminal device, also known as a terminal, is a device with wireless transceiver capabilities. A terminal device can be a user equipment (UE) supporting ISAC functionality. For example, a UE can be a vehicle-mounted communication unit, a mobile phone, a tablet computer, a computer with wireless transceiver capabilities, an industrial terminal, etc. Terminal devices can also be virtual reality (VR) terminal devices, augmented reality (AR) terminal devices, wireless terminals in industrial control, wireless terminals in autonomous driving, wireless terminals in telemedicine, wireless terminals in smart grids, wireless terminals in smart cities, wireless terminals in smart homes, and so on. Terminals can be deployed on land, including indoors or outdoors, handheld or vehicle-mounted; they can also be deployed on water (such as ships); and they can be deployed in the air (e.g., on airplanes, balloons, and satellites). In this application embodiment, the terminal device can be a separately sold terminal or a chip within a terminal. The technical solutions provided in this application embodiment use a terminal device as an example to describe the technical solutions provided in this application embodiment.

[0114] The network device or terminal device in the embodiments of this application can be implemented by a single device or a functional module within a single device; this application does not specifically limit this. It is understood that the aforementioned function can be a network element in a hardware device, a software function running on dedicated hardware, a virtualization function instantiated on a platform (e.g., a cloud platform), or a chip system. In the embodiments of this application, the chip system can be composed of chips or can include chips and other discrete devices.

[0115] The technical solutions of the embodiments of this application will now be described with reference to the accompanying drawings. In the description of this application, unless otherwise stated, "at least one" refers to one or more, and "multiple" refers to two or more. Furthermore, to facilitate a clear description of the technical solutions of the embodiments of this application, the terms "first," "second," etc., are used in the embodiments of this application to distinguish identical or similar items with substantially the same function and effect. Those skilled in the art will understand that the terms "first," "second," etc., do not limit the quantity or execution order, and that "first," "second," etc., do not necessarily imply differences.

[0116] In the embodiments of this application, the name of the message or information is only an example and does not constitute a limitation on the function or carrying form of the message or information. Any message or information with equivalent function is within the protection scope of this application.

[0117] For ease of understanding, the communication sensing method provided in the embodiments of this application will be described in detail below with reference to the accompanying drawings.

[0118] like Figure 3 As shown, this application provides a communication sensing method, taking a first communication device as a terminal device and a second communication device as a network device as an example, including:

[0119] 301. Network devices configure sensing tasks to terminal devices.

[0120] Optionally, after a terminal device (e.g., a UE) accesses the network, a network device (e.g., a gNB) can configure a sensing task (also known as a sensing measurement task) for it through radio resource control (RRC) messages (signaling) and / or downlink control information (DCI), specifying a set of measurement resources based on a synchronization signal block (SSB), a channel state information reference signal (CSI-RS), or a sounding reference signal (SRS).

[0121] 302. The terminal device performs a sensing task to obtain sensing data. The sensing data includes a set of measurements corresponding to multiple reflection points of at least one sensing target. The set of measurements corresponding to each reflection point includes geometric information and energy information.

[0122] The terminal device can perform sensing tasks (e.g., transmitting sensing signals and / or receiving echo signals) according to its configuration to obtain sensing data. The sensing data includes a set of measurements corresponding to multiple reflection points of at least one sensing target. Each sensing target can correspond to one or more reflection points (target point / environment scattering point). The transmission point (also called the detection point, scattering point, target point, etc.) corresponding to the sensing target refers to the location (or position point) of the sensing target reflecting the sensing signal (or transmitting the echo signal). Each reflection point can correspond to a set of measurements, which includes a geometric feature dimension and a communication energy measurement dimension.

[0123] For example, the set of measurements can be: Among them, Delay, Angle, and Doppler represent the propagation delay, incident angle / outgoing angle, and Doppler frequency shift of the echo signal reflected from the reflection point, respectively, and are geometric characteristic dimensions. SINR and RSRP reflect the quality and power of the echo signal, respectively, and are communication energy measurement dimensions.

[0124] It should be understood that the sets of measurements corresponding to multiple reflection points can constitute a multidimensional feature space containing geometric and energy information, which can provide basic data for subsequent steps (such as energy consistency determination).

[0125] 303. The terminal device sends sensing data to the network device, and the network device receives the sensing data from the terminal device accordingly.

[0126] That is, the terminal device can report to the network device the set of measurements corresponding to multiple reflection points it has acquired.

[0127] 304. The network device performs geometric clustering on the sensed data to obtain multiple candidate target clusters.

[0128] Geometric clustering is a clustering method based on the geometric information of sample points (reflection points) in a multidimensional feature space. Its core principle is to group sample points (reflection points) that are close in distance and have a tight geometric distribution into the same cluster. Sample points (reflection points) within a cluster have high geometric similarity, while sample points (reflection points) between clusters have low geometric similarity. In other words, each candidate target cluster includes a group of geometrically similar reflection points.

[0129] For example, network devices can employ standard density clustering algorithms (such as DBSCAN) to perform preliminary clustering of reflection points in a three-dimensional space (e.g., spaces corresponding to the three dimensions of Delay, Angle, and Doppler), outputting multiple candidate target clusters. Each candidate target cluster represents a group of reflection points with similar geometric features (e.g., Delay, Angle, and Doppler). Geometric clustering enables coarse screening at the spatial level and can provide input data for subsequent steps (e.g., energy consistency determination).

[0130] 305. For each candidate target cluster, the network device removes ghost points within the cluster.

[0131] For each candidate target cluster, the network device calculates the statistical distribution of energy characteristics (e.g., SINR and RSRP) of all reflection points within it. Based on a threshold configured in the network, reflection points whose energy characteristics significantly deviate from the first threshold are marked as ghost points / false alarm points (e.g., multipath interference points and / or noise points) and removed from the cluster, retaining the real target points (real reflection points). This ensures the internal consistency of each candidate target cluster in both geometric and energy dimensions, suppressing ghost points (multipath interference points and / or noise points) that infiltrate real candidate target clusters.

[0132] In some embodiments, the steps for removing ghost nodes within a cluster are as follows:

[0133] (1) For the first reflection point (e.g., point p) in the first candidate target cluster (e.g., cluster k), calculate the average energy similarity between the reflection point and other reflection points in cluster k. Wherein, the first candidate target cluster is any one of the multiple candidate target clusters, and the first reflection point is any (arbitrary) reflection point in the first candidate target cluster.

[0134] First, for the first reflection point (e.g., point p), calculate the normalized SINR and normalized RSRP values ​​for that reflection point, as follows:

[0135] ;

[0136] in, This represents the normalized SINR value at point p. Indicates the SINR at point p. This represents the lower bound of the SINR of all reflection points within a candidate target cluster (e.g., cluster k). This represents the upper limit of the SINR of all reflection points within a candidate target cluster (e.g., cluster k). This represents the normalized value of RSRP at point p. Represents the RSRP of point p. This represents the lower bound of the RSRP of all reflection points within a candidate target cluster (e.g., cluster k). This represents the upper limit of the RSRP of all reflection points within a candidate target cluster (e.g., cluster k).

[0137] Then, the similarity of the energy characteristics of two reflection points is quantified by calculating the "energy inconsistency" between them.

[0138] For example, the energy similarity between a reflection point (e.g., point p) and another reflection point (e.g., point q) in a candidate target cluster (e.g., cluster k). as follows:

[0139] ;

[0140] in, This represents the integral phase / interference phase function in wireless communication. The closer the value is to 1, the higher the similarity between the two reflection points (i.e., the lower the "energy inconsistency"), and the closer it is to 0, the lower the similarity between the two reflection points (i.e., the higher the "energy inconsistency").

[0141] exp() represents the exponential function. It is the energy tolerance threshold constant (unit: dB). These are the weights (coefficients) of SINR and RSRP. The weights can be adjusted according to the scenario. In high interference scenarios, the SINR weight can be increased, and in scenarios with stable echo intensity, the RSRP weight can be increased.

[0142] Average energy similarity between a reflection point (e.g., point p) and other reflection points (e.g., point q) within cluster k. as follows:

[0143] ;

[0144] in, Denotes cluster k, The number of all reflection points in cluster k is represented by q (point), and q (point) represents any other reflection point in cluster k that is different from point p.

[0145] It should be noted that, in order to adapt to the energy distribution characteristics under different scenarios, this application provides two optional methods for defining the upper and lower limits of intra-cluster SINR and RSRP normalization, which can be used independently or selected in combination.

[0146] Method 1: Define the normalized upper and lower limits of intra-cluster SINR and RSRP based on the statistically robust method of quantile pruning.

[0147] For example, the SINR and RSRP of all reflection points within a cluster can be statistically sorted, and the first quantile can be taken (e.g., ) is used as the lower bound threshold for SINR and RSRP normalization, and the second quantile is taken (e.g., y The following are the upper threshold values ​​for the normalization of SINR and RSRP:

[0148] ;

[0149] ;

[0150] in, The SINR of all reflection points within the first candidate target cluster is represented by the [missing information]. Quantiles The RSRP of all reflection points within the first candidate target cluster represents the first... Quantiles The SINR of all reflection points within the first candidate target cluster is represented by the [missing information]. Quantiles The RSRP of all reflection points within the first candidate target cluster represents the first... Quantiles, where x is less than y.

[0151] For example, x can be 5 and y can be 95, as follows:

[0152] ;

[0153] ;

[0154] Right now The SINR of all reflection points within a candidate target cluster (e.g., cluster k) can be taken as the first value. Quantiles The SINR of all reflection points within a candidate target cluster (e.g., cluster k) can be taken as the first value. Quantiles. The RSRP of all reflection points within a candidate target cluster (e.g., cluster k) can be taken as the first value. Quantiles The RSRP of all reflection points within a candidate target cluster (e.g., cluster k) can be taken as the first value. Quantiles.

[0155] This method is based on actual observation data within the cluster and can effectively suppress the impact of extreme outliers on the normalization results. It is statistically robust and simple to implement, and is suitable for scenarios with a large number of samples within the cluster and stable measurement distribution.

[0156] Method 2, based on a physical modeling method using geometric measurements, defines the normalized upper and lower limits of intra-cluster SINR and RSRP. Specifically, it includes the following steps:

[0157] First, for any candidate target cluster (e.g., cluster k), calculate the geometric measurements of the candidate target cluster as follows:

[0158] Intra-cluster relative delay difference: ;in, This represents the maximum time delay of all reflection points within cluster k (using i as the unified traversal index for all reflection points within the cluster). This represents the minimum time delay of all reflection points within cluster k. It should be understood that the larger the relative time delay difference within a cluster, the more severe the multipath effect and the greater the time difference in signal propagation.

[0159] Intra-cluster angular expansion: ;in, This represents the largest incident angle (the incident angle of the echo signal reaching the receiver) among all the angle values ​​of all reflection points within cluster k. This represents the smallest incident angle among all the angle values ​​of all reflection points within cluster k.

[0160] Cluster propagation (transmission) distance: (The median or mean of the propagation distances corresponding to all reflection points within the cluster can be taken.) The propagation distance corresponding to a reflection point can refer to the propagation path length of the echo signal corresponding to that reflection point from the transmitting end to the receiving end.

[0161] Then, the probability of line of sight (LOS) is calculated using a logistic function:

[0162] ;

[0163] in, It is the line-of-sight probability of the candidate target cluster (e.g., cluster k); , , , These are empirical parameters used to adjust the influence of geometric measurements on the LOS probability. When the intra-cluster relative delay difference, intra-cluster angular spread, and cluster propagation distance are small, Approaching 1 (high probability of LOS); conversely, It approaches 0 (i.e., the probability of non-line-of-sight (NLOS) is high).

[0164] Furthermore, weighted calculation of the expected power within the cluster With fluctuation range :

[0165] ;

[0166] ;

[0167] in, , These represent the average received power under LOS and NLOS conditions, respectively. , These represent the power standard deviation (reflecting signal fluctuation amplitude) under LOS and NLOS states, respectively.

[0168] Similarly, the expected signal-to-noise ratio within the cluster can be obtained. and fluctuation range :

[0169] ;

[0170] ;

[0171] In summary, the normalized upper thresholds for SINR and RSRP are as follows:

[0172] ;

[0173] ;

[0174] in, This is an adjustment coefficient (e.g., it can be 1.5 to 2.0) used to control the width of the threshold range.

[0175] This method relies on the spatial characteristics of the cluster (e.g., the relative delay difference of the cluster's multipath, angular spread, and propagation distance) to estimate the line-of-sight probability, and calculates the expected power and fluctuation range within the cluster by weighting the typical LOS / NLOS power models. It sets the normalized upper and lower limits of the intra-cluster SINR and RSRP based on the propagation mechanism, which can reflect the geometric environment characteristics of the cluster and the differences between LOS / NLOS. It is suitable for scenarios where the sample is sparse or the channel characteristics are significantly affected by geometry.

[0176] (2) Calculate the energy consistency confidence level of reflection point p based on the average similarity of reflection point p. :

[0177] ;

[0178] in, This is a nonlinear amplification factor, typically taken as 1.5 to 2.0, used to "amplify anomalies." This is achieved by introducing a nonlinear mapping based on exponential decay (e.g., ...). This can enhance the suppression and penalty of outliers (outliers, outlier samples) (the deviation of outliers is amplified sharply), that is, amplify the difference between outliers (such as multipath interference points, noise points) and normal reflection points, thereby improving the distinguishability of outliers and providing better feature data for subsequent processing.

[0179] (3) Passing through the first threshold (e.g., point-level threshold) Determine whether the reflection point (e.g., point p) is a false alarm point (ghost point):

[0180] like If p is a false alarm point, then it is determined to be a false alarm point and is removed.

[0181] like If so, then point p is determined to be the true target point and is retained.

[0182] For example, point-level thresholds can be determined according to statistical criteria. :

[0183] ;

[0184] That is, under the assumption that "the reflection point (e.g., point p) is a false target (ghost point)" ), its point-level confidence level (e.g., The probability of exceeding the threshold is less than or equal to the maximum allowable false alarm rate allocated by the system to point-level decisions. ).

[0185] The maximum false alarm rate issued by the system can be used as a performance constraint at the upper level. Based on the actual measured energy consistency confidence distribution, background samples are statistically analyzed within a small range or sliding window, and the corresponding empirical quantiles are calculated to obtain the threshold. Through threshold The point-level confidence level (energy consistency confidence level of the reflection point) is judged. Unqualified points are regarded as ghost points within the cluster and removed. Even if the point belongs to the cluster geometrically, it can be removed separately due to energy anomalies. In this way, the cleaned point set is obtained (e.g., This set of points exhibits high consistency in both geometric and energy dimensions, and can be considered as a set of candidate reflection points for the same target cluster. Its physical consistency can be further verified in subsequent cluster-level energy consistency determinations.

[0186] 306. The network device removes ghost clusters from multiple candidate target clusters from which ghost points have been eliminated.

[0187] For candidate target clusters that have undergone point-level cleaning / point-level screening (i.e., candidate target clusters that have eliminated ghost points), calculate their cluster-level energy consistency index. If the index is lower than the second threshold, the cluster is determined to be a ghost cluster (false alarm cluster) with abnormal energy and is eliminated as a whole. Clusters that pass the consistency test are retained as real target clusters.

[0188] In some implementations, the steps for eliminating phantom clusters are as follows:

[0189] (1) Perform an overall energy consistency assessment on each candidate target cluster (candidate target clusters after removing ghost points, such as cluster k). For example, the energy consistency confidence of cluster k is as follows:

[0190] ;

[0191] in, This represents the energy consistency confidence level of cluster k. This represents the confidence level of energy consistency at the i-th reflection point within cluster k.

[0192] It is used for quantizing cluster k ( The consistency index of the energy dimension among the reflection points of cluster k is a probabilistic index, and its value range can be [0,1] (or [0%,100%]). The closer its value is to 1, the stronger the consistency and the higher the reliability of the result, that is, the higher the overall "health" of cluster k; the closer its value is to 0, the weaker the consistency and the greater the possibility of anomalies or differences, that is, the lower the overall "health" of cluster k.

[0193] (2) Through a second threshold (e.g., cluster-level threshold) Determine whether the candidate target cluster is a false alarm cluster:

[0194] like Then determine These are false alarm clusters (ghost clusters), which are eliminated to prevent multiple false alarm points from "covering" each other and forming false targets.

[0195] like Then determine The actual target cluster is preserved.

[0196] For example, cluster-level thresholds can be determined according to statistical criteria. :

[0197] ;

[0198] That is, under the assumption that "this cluster is a false target (ghost cluster)" ( ), its cluster-level confidence (e.g., the confidence of cluster k) The probability of exceeding the threshold must be less than or equal to the maximum allowable false alarm rate allocated by the system to cluster-level decisions. ).

[0199] Thus, through dual decision-making (point-level screening and cluster-level screening), highly reliable perception results can be output (including highly reliable target clusters, excluding false alarm clusters and false alarm points), thereby reducing the false alarm rate of target detection and improving the perception reliability of the ISAC system.

[0200] In some embodiments, network devices (e.g., base stations) can optimize their own communication scheduling based on sensing results. For example, a base station can use the position / angle / movement trajectory of the sensed target (e.g., vehicle (onboard equipment), pedestrian, etc.) calculated from the sensing data to dynamically adjust the direction, width, and gain of the communication beam, avoid blind beam scanning, and improve the signal-to-noise ratio of the communication beam.

[0201] In other embodiments, network devices can report perception results to the application function (AF) / core network on demand to support the complex computational needs of upper-layer applications (e.g., vehicle applications) such as blind spot detection, target tracking, and dynamic obstacle recognition.

[0202] In some other embodiments, network devices (e.g., base stations) can share sensing results with neighboring base stations / edge nodes to achieve multi-base station collaborative sensing and make up for the coverage blind spots and accuracy shortcomings of single-base station sensing.

[0203] The following is based on Figure 4 Taking an example, the flow of the communication sensing method provided in this application embodiment will be described. First, sensing data is acquired. For example, a terminal device can perform a sensing task to acquire sensing data. Then, geometric clustering can be performed on the sensing data to obtain multiple candidate target clusters. Next, point-level energy consistency judgment can be performed, that is, for each candidate target cluster, it is determined whether any reflection point (e.g., point p) in the candidate target cluster meets / satisfies the point-level energy consistency judgment condition (e.g., ...). If the reflection point (e.g., point p) in the candidate target cluster meets the decision condition (e.g., if...), If a reflection point in the candidate target cluster (e.g., point p) does not meet the decision criteria (e.g., if...), then point p is determined to be the real target point and is retained; if the reflection point in the candidate target cluster (e.g., point p) does not meet the decision criteria (e.g., if...), then... If p is identified as a ghost point (false alarm), it is removed. Refer to step 305 for details. Further, a cluster-level energy consistency decision can be performed, determining whether any candidate target cluster (e.g., cluster k) meets / satisfies the cluster-level energy consistency decision conditions (e.g., ...). If the candidate target cluster (e.g., cluster k) does not meet the decision criteria (e.g., if...), If cluster k is a ghost cluster, it is determined to be a ghost cluster and is removed. If the candidate target cluster (e.g., cluster k) meets the decision criteria (e.g., if...), then cluster k is considered a ghost cluster and is removed. If cluster k is identified as a real target cluster, it is retained. Refer to the relevant explanation in step 306 for details. Finally, the perception results can be output, including high-reliability target clusters, excluding false alarm clusters and points.

[0204] The following example uses a multi-target detection scenario in vehicle-to-everything (V2X) communication. Figure 5 The provided communication sensing method is illustrated by example. It should be understood that in vehicle-to-everything (V2X) multi-target detection scenarios, network devices (e.g., roadside base stations gNB) and terminal devices (e.g., vehicle-mounted terminals) can operate collaboratively through the ISAC system. Since the sensing signals and communication signals of vehicle-mounted radar share time-frequency resources, in complex urban environments (e.g., high-rise building reflections, multipath propagation, obstructions, etc.), ghost points caused by multipath propagation often appear in the sensing results.

[0205] like Figure 5 As shown, taking a multi-target detection scenario in vehicle-to-everything (V2X) communication as an example, the communication sensing methods include:

[0206] S1: Perception task triggered.

[0207] For example, upper-layer applications (e.g., vehicle applications) can submit sensing requirements (such as blind spot detection, target tracking, or dynamic obstacle recognition) to the AF (Awareness Controller). The AF can then forward the sensing request (including sensing type, sensing area, accuracy requirements, refresh rate, etc.) to the core network (e.g., the network exposure function (NEF) of 5GC), which in turn forwards it to the base station (gNB). Upon receiving the sensing request, the base station can configure sensing tasks for the UE (e.g., vehicle terminal) and request the UE to perform data collection.

[0208] S2: The UE collects sensing data and transmits the sensing data back to the base station.

[0209] The sensing data includes a set of measurements corresponding to multiple reflection points of at least one sensing target. Each set of measurements corresponding to a reflection point includes geometric and energy information. Further details can be found in step 302 and will not be repeated here.

[0210] After receiving the sensing data, the base station can perform geometric clustering on the sensing data to obtain multiple candidate target clusters.

[0211] For example, by clustering the reflection points in three-dimensional space (e.g., delay-angle-Doppler space), three candidate target clusters can be generated, as shown in Table 2:

[0212] Table 2

[0213]

[0214] Each candidate target cluster can contain multiple reflection points. Taking cluster C1 as an example, cluster C1 can include 5 reflection points. The SINR and RSRP corresponding to these 5 reflection points are shown in Table 3.

[0215] Table 3

[0216]

[0217] S3: For each candidate target cluster, the network device eliminates "ghost points" within the cluster.

[0218] For example, assume an energy tolerance threshold. =3dB, nonlinear amplification factor =1.5, confidence threshold =0.5. As shown in Table 4, taking point P4 in cluster C1 as an example, the point pair energy similarity of point P4 relative to points P1, P2, P3 and P5 is calculated respectively. , , and Then, the average energy similarity of the above points is averaged to obtain the average similarity of point P4. This leads to the energy consistency confidence level L(P4), which is then compared with point-level thresholds. The relationship between the energy consistency confidence level L(P4) and the value of P4 is used to determine whether P4 needs to be removed.

[0219] Table 4

[0220]

[0221] As shown in Table 4, since L(P4) > ,determination These are genuine reflection points and do not need to be removed.

[0222] For other reflection points in cluster C1, and any reflection point in other candidate target clusters (such as clusters C2 and C3), calculations can be performed based on the above steps to determine whether to remove the corresponding reflection point.

[0223] S4: The network device removes ghost clusters from multiple candidate target clusters from which ghost points have been eliminated.

[0224] For related instructions, please refer to step 306.

[0225] Subsequently, the network equipment can send the perception results (including high-reliability target clusters, excluding false alarm clusters and points) after dual-decision (point-level filtering and cluster-level filtering) back to the AF that initiated the perception request. The AF can then perform algorithm calculations for blind spot detection, target tracking, and dynamic obstacle recognition based on the perception data sent back by the base station. Afterward, the AF can send the calculated perception results (such as "moving vehicle exists in blind spot XX" or "dynamic obstacle at location XX is XX meters away") to the vehicle terminal / roadside equipment / vehicle central control unit, and finally, the upper-layer vehicle application will execute the decision (such as active braking, lane change warning, and blind spot reminder).

[0226] In other embodiments, after the terminal device performs a sensing task and obtains sensing data, it can perform point-level cleaning / screening (i.e., removing "ghost points" within each candidate target cluster) and send the screening results (the results after point-level cleaning / screening) to the network device. Furthermore, the network device can perform cluster-level cleaning / screening (removing "ghost clusters").

[0227] like Figure 6 As shown, steps 303-306 can be replaced by steps 308-310.

[0228] 308. The terminal device performs geometric clustering on the sensed data to obtain multiple candidate target clusters.

[0229] The process of the terminal device performing geometric clustering on the sensed data can be referred to in step 303, which describes the process of the network device performing geometric clustering on the sensed data.

[0230] 309. The terminal device performs point-level cleaning / point-level filtering (i.e., removes ghost points within each candidate target cluster) on each candidate target cluster and sends the filtering results to the network device.

[0231] The process of point-level cleaning / point-level filtering for each candidate target cluster by the terminal device can refer to the process of point-level cleaning / point-level filtering for each candidate target cluster by the network device in step 306.

[0232] Optionally, when the terminal device performs point-level cleaning / screening of candidate target clusters, the network device (e.g., gNB) can configure the maximum allowable false alarm rate for point-level decision to the terminal device via RRC signaling. Energy tolerance threshold constant ( ), nonlinear amplification factor ( Parameters such as confidence threshold.

[0233] 310. Based on the screening results of the terminal devices, the network devices remove ghost clusters from multiple candidate target clusters that have already eliminated ghost points.

[0234] Step 310 can be referred to the relevant explanation of step 306, and will not be repeated here.

[0235] In some other embodiments, after the terminal device performs a sensing task and obtains sensing data, it can perform point-level cleaning / point-level filtering (i.e., removing "ghost points" in each candidate target cluster) and perform cluster-level cleaning / cluster-level filtering (removing "ghost clusters"), and then send the filtering results (the results after point-level filtering and cluster-level filtering) to the network device.

[0236] like Figure 7 As shown, steps 303-306 can be replaced by steps 311-314.

[0237] 311. The terminal device performs geometric clustering on the sensed data to obtain multiple candidate target clusters.

[0238] The process of the terminal device performing geometric clustering on the sensed data can be referred to in step 303, which describes the process of the network device performing geometric clustering on the sensed data.

[0239] 312. The terminal device performs point-level cleaning / point-level screening for each candidate target cluster (i.e., removes ghost points within each candidate target cluster).

[0240] The process of point-level cleaning / point-level filtering for each candidate target cluster by the terminal device can refer to the process of point-level cleaning / point-level filtering for each candidate target cluster by the network device in step 306.

[0241] It is understandable that, when the terminal device performs point-level cleaning / screening of candidate target clusters, the gNB can configure the maximum allowable false alarm rate for point-level decision to the terminal device via RRC signaling. Energy tolerance threshold constant ( ), nonlinear amplification factor ( Parameters such as confidence threshold.

[0242] 313. The terminal device removes ghost clusters from multiple candidate target clusters from which ghost points have been eliminated.

[0243] Step 313 can be referred to the relevant description of the process of eliminating ghost clusters by network devices in step 306, and will not be repeated here.

[0244] Optionally, when the terminal device performs cluster-level cleaning / cluster-level filtering on candidate target clusters, the network device can configure the maximum allowable false alarm rate for cluster-level decision to the terminal device via RRC signaling. or cluster-level threshold Parameters such as these.

[0245] 314. The terminal device sends the filtering results (results after point-level filtering and cluster-level filtering) to the network device. These filtering results do not include ghost points and ghost clusters.

[0246] The method provided in this application fully utilizes energy measurement metrics (such as SINR and RSRP) in the communication system. Based on geometric clustering, it introduces an energy consistency criterion. Through point-level energy consistency decision (point-level screening, i.e., removing "ghost points" within candidate target clusters) and cluster-level energy consistency decision (cluster-level screening, i.e., removing ghost clusters) mechanisms, the system can identify abnormal points within clusters and also identify overall abnormal clusters at the cluster level. This enables accurate identification and removal of ghost points and ghost clusters. This mechanism is geared towards integrated communication and sensing systems for 5G-Advanced and future 6G. Without altering the waveform and physical layer signaling, it can suppress false targets in the sensing results, achieving the identification and removal of local ghost points and overall false clusters, significantly improving sensing reliability and false alarm suppression performance in complex multipath environments.

[0247] It should be understood that Figures 1 to 7 The illustrated diagrams are for illustrative purposes only and are not intended to limit the embodiments of this application to the examples shown. In fact, those skilled in the art can interpret the embodiments based on the examples depicted. Figures 1 to 7 The examples in the document can be transformed into equivalent ways to obtain more implementations.

[0248] The above text combined Figures 1 to 7 This document describes in detail the communication method provided in the embodiments of this application. The following will combine... Figures 8 to 9 The device embodiments of this application are described in detail below. It should be understood that the communication device of this application embodiment can execute the various communication methods of the foregoing embodiments of this application, that is, the specific working processes of the various products below can be referred to the corresponding processes in the foregoing method embodiments.

[0249] In the embodiments described above, the terminal device may execute some or all of the steps in each embodiment; the network device may execute some or all of the steps in each embodiment. These steps or operations are merely examples, and the embodiments of this application may also perform other operations or variations thereof. Furthermore, the steps may be executed in different orders as presented in the embodiments, and it is not necessary to execute all the operations in the embodiments of this application. Moreover, the sequence number of each step does not imply the order of execution; the execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0250] Figure 8 This is a schematic block diagram of a communication device provided in an embodiment of this application. Figure 8As shown, the communication device 800 may include a processing module 810 and a communication module 820. The processing module 810 can read instructions and / or data from the storage module to enable the communication device 800 to implement the aforementioned method embodiments. The communication module 820 can implement corresponding communication functions, which can be internal communication functions of the communication device 800 or communication functions between the communication device 800 and other devices. Optionally, the communication module 820 may also be referred to as a communication interface or transceiver module.

[0251] Optionally, the communication device 800 also includes a storage module that can be used to store instructions and / or data.

[0252] In one possible design, the communication device 800 may correspond to the terminal device (e.g., UE) in the above method embodiments, or a component (such as a circuit, chip, or chip system) configured in the terminal device. The communication device 800 can be used to perform the steps or processes performed by the terminal device in any of the above method embodiments.

[0253] For example, the communication module 820 is used to perform a sensing task to obtain sensing data, the sensing data including a set of measurements corresponding to multiple reflection points of at least one sensing target, the set of measurements corresponding to each reflection point including the geometric information and energy information corresponding to the reflection point; the processing module 810 is used to cluster the sensing data and output multiple candidate target clusters, each candidate target cluster including a group of reflection points with similar geometric information; for each candidate target cluster, the statistical distribution of energy information of all reflection points within it is calculated, and reflection points whose energy information deviates from a first threshold are removed from the candidate target cluster.

[0254] In another possible design, the communication device 800 may correspond to a network device (e.g., a base station) in the above method embodiments, or a component (such as a circuit, chip, or chip system) configured in a network device. The communication device 800 can be used to perform the steps or processes performed by the network device in any of the above method embodiments.

[0255] For example, the communication module 820 is used to receive sensing data, which includes a set of measurements corresponding to multiple reflection points of at least one sensing target, and the set of measurements corresponding to each reflection point includes geometric information and energy information corresponding to that reflection point; the processing module 810 is used to cluster the sensing data and output multiple candidate target clusters, each candidate target cluster including a group of reflection points with similar geometric information; for each candidate target cluster, the statistical distribution of energy information of all reflection points within it is calculated, and reflection points whose energy information deviates from a first threshold are removed from the candidate target cluster.

[0256] The above are merely examples; for detailed steps or procedures, please refer to the descriptions in the foregoing embodiments.

[0257] Figure 9 This is another schematic block diagram of the communication device 900 provided in the embodiments of this application. The communication device 900 may be a chip, chip system, or processor, etc., used in a terminal or network device to implement the above-described methods. The communication device 900 can be used to implement the methods described in the above-described method embodiments; for details, please refer to the descriptions in the above-described method embodiments.

[0258] like Figure 9 As shown, the communication device 900 may include one or more processors 910, which may also be referred to as processing units or processing modules, and can implement certain control functions. The processor 910 may be a general-purpose processor or a dedicated processor, such as a baseband processor or a central processing unit. The baseband processor can be used to process communication protocols and communication data, while the central processing unit can be used to control the communication device 900 (e.g., a base station, baseband chip, user, user chip), execute software programs, and process data from the software programs.

[0259] In an alternative design, the processor 910 may also store instructions and / or data that can be executed by the processor 910 to cause the communication device 900 to perform the methods described in the above method embodiments.

[0260] In another alternative design, the communication device 900 may include a communication interface 920 for implementing receiving and transmitting functions. For example, the communication interface 920 may be a transceiver circuit, interface, interface circuit, or transceiver. The transceiver circuit, interface, interface circuit, or transceiver for implementing receiving and transmitting functions may be separate or integrated. The aforementioned transceiver circuit, interface, interface circuit, or transceiver may be used for reading and writing code / data, or it may be used for transmitting or relaying signals.

[0261] Optionally, the communication device 900 may include one or more memories 930, which may store instructions that can be executed on the processor 910, causing the communication device 900 to perform the methods described in the above method embodiments. Optionally, the memories 930 may also store data. Optionally, the processor 910 may also store instructions and / or data. The processor 910 and the memories 930 may be provided separately or integrated together.

[0262] It should be understood that, in one possible design, the steps in the method embodiments provided in this application can be implemented by integrated logic circuits in the processor's hardware or by instructions in software form. The steps of the methods disclosed in the embodiments of this application can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules in the processor. The software modules can reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. This storage medium is located in memory, and the processor reads information from the memory and, in conjunction with its hardware, completes the steps of the above method. To avoid repetition, detailed descriptions are not provided here.

[0263] In one implementation, the communication device 900 may correspond to the terminal in the above method embodiments and may be used to execute the various steps and / or processes executed by the terminal in the above method embodiments. The processor 910 may be used to execute instructions stored in the memory 930, and when the processor 910 executes the instructions stored in the memory, the processor 910 is used to execute the various steps and / or processes of the above method embodiments corresponding to the terminal.

[0264] In another implementation, the communication device 900 may correspond to the network device in the above method embodiments and may be used to execute the various steps and / or processes executed by the network device in the above method embodiments. The processor 910 may be used to execute instructions stored in the memory 930, and when the processor 910 executes the instructions stored in the memory, the processor 910 is used to execute the various steps and / or processes of the above method embodiments corresponding to the network device.

[0265] It should be understood that the aforementioned processing device can be one or more chips. For example, the processing device can be a field-programmable gate array (FPGA), an application-specific integrated circuit (ASIC), a system-on-chip (SoC), a central processor unit (CPU), a network processor (NP), a digital signal processor (DSP), a microcontroller unit (MCU), a programmable logic device (PLD), or other integrated chips.

[0266] It is understood that the memory in the embodiments of this application can be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of RAM are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous linked dynamic random access memory (SLDRAM), and direct rambus RAM (DR RAM). It should be noted that the memory used in the systems and methods described herein is intended to include, but is not limited to, these and any other suitable types of memory.

[0267] According to the method provided in the embodiments of this application, this application also provides a chip system, which includes one or more processors for calling and executing instructions stored in memory, thereby causing the method described in the embodiments of this application to be executed. The chip system may be composed of chips or may include chips and other discrete devices.

[0268] The chip system may include input circuits or interfaces for transmitting information or data, and output circuits or interfaces for receiving information or data.

[0269] According to the method provided in the embodiments of this application, this application also provides a communication system, which includes the aforementioned network device and terminal device.

[0270] According to the method provided in the embodiments of this application, this application also provides a computer program product, which includes: computer program code, which, when run on a computer, causes the computer to execute the various steps or processes executed by the network device or terminal in any of the foregoing method embodiments.

[0271] According to the method provided in the embodiments of this application, this application also provides a computer-readable storage medium storing program code, which, when run on a computer, causes the computer to execute the various steps or processes executed by the network device or terminal in any of the foregoing method embodiments.

[0272] The computer-readable storage medium may be the aforementioned volatile memory or non-volatile memory, or it may include both volatile memory and non-volatile memory.

[0273] In the embodiments of this application, the terms and English abbreviations are exemplary examples given for ease of description and should not be construed as limiting the application in any way. This application does not preclude the possibility of defining other terms that can achieve the same or similar functions in existing or future agreements.

[0274] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions. When these computer instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated.

[0275] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0276] It should be understood that in the various embodiments of this application, the sequence number of each process does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0277] In summary, the above description is merely a preferred embodiment of the technical solution of this application and is not intended to limit the scope of protection of this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A communication sensing method, characterized in that, Applied to a first communication device, the method includes: The perception task is performed to obtain perception data, which includes a set of measurements corresponding to multiple reflection points of at least one perception target. The set of measurements corresponding to each reflection point includes geometric information and energy information corresponding to that reflection point. The energy information includes the signal-to-noise ratio (SINR) and / or the reference signal received power (RSRP) of the echo signal reflected by the reflection point. The perceived data is clustered to output multiple candidate target clusters, each candidate target cluster including a group of reflection points with similar geometric information; For a first reflection point in a first candidate target cluster, calculate the average energy similarity between the first reflection point and other reflection points in the first candidate target cluster; wherein, the first candidate target cluster is any one of the plurality of candidate target clusters, and the first reflection point is any one of the reflection points in the first candidate target cluster; Calculate the energy consistency confidence level of the first reflection point based on the average energy similarity between the first reflection point and other reflection points within the first candidate target cluster: If the energy consistency confidence level of the first reflection point is greater than or equal to the first threshold, the first reflection point is retained. If the energy consistency confidence of the first reflection point is less than the first threshold, the first reflection point is removed from the first candidate target cluster. Calculate the energy consistency confidence of the first candidate target cluster; If the energy consistency confidence of the first candidate target cluster is greater than or equal to the second threshold, the first candidate target cluster is retained. If the energy consistency confidence of the first candidate target cluster is less than the second threshold, the first candidate target cluster is eliminated. The processed sensing data is reported to the second communication device, wherein the processed sensing data does not include the candidate target clusters that have been removed.

2. The method according to claim 1, characterized in that, The geometric information includes at least one of the propagation delay, angle, and Doppler shift of the echo signal reflected from the reflection point, wherein the angle includes the incident angle and / or the exit angle.

3. The method according to claim 1, characterized in that, The average energy similarity between the first reflection point and other reflection points within the first candidate target cluster as follows: ； ； in, Indicates the first candidate target cluster. This represents the number of all reflection points within the first candidate target cluster. This represents the energy similarity between the first reflection point and point q, where point q represents any reflection point within the first candidate target cluster that is different from the first reflection point. ( ) represents an exponential function. This represents the energy tolerance threshold constant. This represents the normalized SINR value of the first reflection point. This represents the normalized SINR value at point q. This represents the normalized value of RSRP at the first reflection point. This represents the normalized value of RSRP at point q. These are the weights of SINR and RSRP.

4. The method according to claim 3, characterized in that, Energy consistency confidence level of the first reflection point as follows: ； in, It is a nonlinear amplification factor, and its value ranges from 1.5 to 2.

0.

5. The method according to claim 3 or 4, characterized in that, ； in, This represents the lower limit of the SINR of all reflection points within the first candidate target cluster. This represents the upper limit of the SINR of all reflection points within the first candidate target cluster, where: ； ； in, The SINR of all reflection points within the first candidate target cluster is represented by the first... Quantiles The RSRP of all reflection points within the first candidate target cluster represents the first... Quantiles The SINR of all reflection points within the first candidate target cluster is represented by the first... Quantiles The RSRP of all reflection points within the first candidate target cluster represents the first... Quantiles, x is less than y; or, ； ； in, This represents the expected signal-to-noise ratio of the first candidate target cluster. This indicates the signal-to-noise ratio fluctuation range of the first candidate target cluster. This represents the expected reference signal received power of the first candidate target cluster. This indicates the range of reference signal received power fluctuation for the first candidate target cluster. This is an adjustment coefficient, and its value ranges from 1.5 to 2.

0.

6. The method according to claim 1 or 2, characterized in that, If the energy consistency confidence level of the first reflection point is less than the first threshold, the first reflection point is removed from the first candidate target cluster. The method further includes: The processed sensing data is reported to the second communication device. The processed sensing data does not include the geometric and energy information of the removed reflection points.

7. The method according to claim 1, characterized in that, The energy consistency confidence of the first candidate target cluster is as follows: ； in, This represents the energy consistency confidence level of the first candidate target cluster. This represents the confidence level of energy consistency of the i-th reflection point within the first candidate target cluster.

8. A communication sensing method, characterized in that, Applied to a second communication device, the method includes: Sensing data is received from a first communication device. The sensing data includes a set of measurements corresponding to multiple reflection points of at least one sensing target. The set of measurements corresponding to each reflection point includes geometric information and energy information corresponding to that reflection point. The energy information includes the signal-to-noise ratio (SINR) and / or the reference signal received power (RSRP) of the echo signal reflected by the reflection point. The perceived data is clustered to output multiple candidate target clusters, each candidate target cluster including a group of reflection points with similar geometric information; For a first reflection point in a first candidate target cluster, calculate the average energy similarity between the first reflection point and other reflection points in the first candidate target cluster; wherein, the first candidate target cluster is any one of the plurality of candidate target clusters, and the first reflection point is any one of the reflection points in the first candidate target cluster; Calculate the energy consistency confidence level of the first reflection point based on the average energy similarity between the first reflection point and other reflection points within the first candidate target cluster: If the energy consistency confidence level of the first reflection point is greater than or equal to the first threshold, the first reflection point is retained. If the energy consistency confidence of the first reflection point is less than the first threshold, the first reflection point is removed from the first candidate target cluster. Calculate the energy consistency confidence of the first candidate target cluster; If the energy consistency confidence of the first candidate target cluster is greater than or equal to the second threshold, the first candidate target cluster is retained. If the energy consistency confidence of the first candidate target cluster is less than the second threshold, the first candidate target cluster is eliminated. The processed sensing data is reported to the second communication device, wherein the processed sensing data does not include the candidate target clusters that have been removed.

9. The method according to claim 8, characterized in that, The geometric information includes at least one of the propagation delay, angle, and Doppler shift of the echo signal reflected from the reflection point, wherein the angle includes the incident angle and / or the exit angle.

10. The method according to claim 8, characterized in that, The average energy similarity between the first reflection point and other reflection points within the first candidate target cluster as follows: ； ； in, Indicates the first candidate target cluster. This represents the number of all reflection points within the first candidate target cluster. This represents the energy similarity between the first reflection point and point q, where point q represents any reflection point within the first candidate target cluster that is different from the first reflection point. ( ) represents an exponential function. This represents the energy tolerance threshold constant. This represents the normalized SINR value of the first reflection point. This represents the normalized SINR value at point q. This represents the normalized value of RSRP at the first reflection point. This represents the normalized value of RSRP at point q. These are the weights of SINR and RSRP.

11. The method according to claim 10, characterized in that, Energy consistency confidence level of the first reflection point as follows: ； in, It is a nonlinear amplification factor, and its value ranges from 1.5 to 2.

0.

12. The method according to claim 10 or 11, characterized in that, ； in, This represents the lower limit of the SINR of all reflection points within the first candidate target cluster. This represents the upper limit of the SINR of all reflection points within the first candidate target cluster, where: ； ； in, The SINR of all reflection points within the first candidate target cluster is represented by the [missing information]. Quantiles The RSRP of all reflection points within the first candidate target cluster represents the first... Quantiles The SINR of all reflection points within the first candidate target cluster is represented by the [missing information]. Quantiles The RSRP of all reflection points within the first candidate target cluster represents the first... Quantiles, x is less than y; or, ； ； in, This represents the expected signal-to-noise ratio of the first candidate target cluster. This indicates the signal-to-noise ratio fluctuation range of the first candidate target cluster. This represents the expected reference signal received power of the first candidate target cluster. This indicates the range of reference signal received power fluctuation for the first candidate target cluster. This is an adjustment coefficient, and its value ranges from 1.5 to 2.

0.

13. The method according to claim 8, characterized in that, The energy consistency confidence of the first candidate target cluster is as follows: ； in, This represents the energy consistency confidence level of the first candidate target cluster. This represents the confidence level of energy consistency of the i-th reflection point within the first candidate target cluster.

14. A communication device, characterized in that, The communication device is a first communication device or a second communication device, which is a terminal device or a network device. The communication device includes: a wireless communication module, a memory, and one or more processors; the wireless communication module, the memory, and the processor are coupled together. The memory is used to store computer program code, which includes computer instructions; when the computer instructions are executed by the processor, the communication device performs the method as described in any one of claims 1-7, or the method as described in any one of claims 8-13.

15. A computer-readable storage medium, characterized in that, Includes computer instructions; When the computer instructions are executed on a terminal device, the terminal device performs the method as described in any one of claims 1-7; or, when the computer instructions are executed on a network device, the network device performs the method as described in any one of claims 8-13.

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