Resource configuration method, communication device, system and storage medium

Abstract

The application provides a resource configuration method, a communication device, a system and a storage medium, which can be applied to the technical field of wireless communication. In the scheme, the network device can dynamically adjust the confidence level based on the historical error detection rate reported by the sensing node, and dynamically allocate the reference signal resources required for the current sensing task according to the adjusted confidence level. For example, in a complex scene with strong interference, the confidence level is improved to a higher level to ensure the reliability of the sensing result; in an ideal environment, the confidence level is appropriately reduced to save resources. In the ISAC scene, the sensing task faces complex and variable environmental interference. By configuring the sensing resources, the accuracy of the sensing system and the dynamic adjustment of the resource occupation are realized from a macro perspective, so that the reference signal resources adapt to the dynamic environment, and the robustness and reliability in different scenes are enhanced.

Description

Technical Field

[0001] This application relates to the field of wireless communication, and more particularly to a resource allocation method, communication device, system, and storage medium. Background Technology

[0002] To address the problems of resource waste, equipment redundancy, and poor coordination caused by the separation of traditional communication and sensing, the industry has proposed integrated sensing and communications (ISAC) technology.

[0003] Confidence level is a quantitative assessment of the reliability of sensing / communication results, and its value is strongly correlated with the intensity of environmental interference, equipment performance, target characteristics, and resource allocation status. In ISAC systems, sensing tasks face complex and variable environmental interference, such as multipath effects, noise interference, or target occlusion, making it difficult to maintain a stable confidence level for sensing results. In traditional solutions, the confidence level is set to a fixed value. While this design simplifies the system logic, in scenarios with strong interference and high dynamics, the mismatch between the fixed confidence level and the dynamic system environment can lead to problems such as decreased sensing performance and wasted resources. Summary of the Invention

[0004] This application provides a resource allocation method, communication device, system, and storage medium to solve problems such as decreased perception performance and wasted resources caused by the mismatch between confidence level and dynamic system environment.

[0005] To achieve the above objectives, this application adopts the following technical solution:

[0006] Firstly, a resource allocation method is provided. This method can be applied to network devices, such as base stations in an ISAC system. The method can be executed by the network device itself, or by components configured within the network device (such as circuits, chips, or chip systems), or by logic modules or software capable of implementing all or part of the network device's functions.

[0007] The method may include: determining a confidence level based on the historical error detection rate, where the historical error detection rate is the error detection rate reported by the sensing node before it performs the current sensing task, and the error detection rate refers to the probability of an erroneous detection event out of the total number of detection events; and sending indication information to the sensing node, where the indication information is used to indicate the reference signal resources required to perform the current sensing task, and the reference signal resources are configured according to the confidence level.

[0008] For example, the historical error detection rate includes the error detection rate of the previous J sensing tasks performed before the sensing node performs the current sensing task. The error detection rate includes at least one of the false alarm rate and the missed detection rate. J is an integer greater than or equal to 2.

[0009] In the above scheme, network devices can dynamically adjust the confidence level based on the historical error detection rate reported by the sensing nodes, and dynamically allocate the reference signal resources required for the current sensing task according to the adjusted confidence level. For example, in complex scenarios with strong interference, the confidence level can be increased to a higher level to ensure the reliability of the sensing results; in ideal environments, the confidence level can be appropriately reduced to save resources. In scenarios such as intelligent transportation, smart cities, industrial internet, emergency rescue, border security, drone control, marine and satellite communication sensing, or other possible scenarios based on ISAC, sensing tasks face complex and ever-changing environmental interference. Network devices quantify the reference signal resources required to execute the current sensing task based on the confidence level, configure the sensing resources, and dynamically adjust the accuracy and resource usage of the sensing system from a macroscopic perspective. This allows the reference signal resources to adapt to the dynamic environment, enhancing the robustness and reliability in different scenarios.

[0010] In one possible implementation, determining the confidence level based on the historical error detection rate may include: determining a lower limit for the confidence level based on the historical error detection rate; and setting the confidence level based on the lower limit for the confidence level.

[0011] In the above scheme, the confidence level, also known as the confidence threshold, is a pre-defined probability guarantee of the confidence interval of the population parameters, denoted by 1-α, where α is the significance level. The lower limit of the confidence level is the minimum threshold set for the confidence level to meet the reliability requirements of the actual scenario. Based on this, after determining the lower limit of the confidence level, network devices can also set the confidence level according to the lower limit.

[0012] In one possible implementation, determining the lower limit of the confidence level based on the historical error detection rate can include: determining the interval to which the historical error detection rate belongs based on the numerical relationship between the historical error detection rate and M thresholds; and determining the preset value corresponding to the interval to which the historical error detection rate belongs as the lower limit of the confidence level. Here, different intervals correspond to different preset values, and M is a positive integer.

[0013] For example, historical weighted false detection rate With confidence level lower limit The relationship is as follows:

[0014]

[0015] In the above scheme, three thresholds for error detection probability are pre-set. , After obtaining the historical weighted false detection rate Subsequently, network devices can base their decisions on historical weighted error detection rates. The lower limit of the confidence level is dynamically adjusted. If This indicates that the sensing performance is poor at this point, and system stability should be prioritized. To achieve higher accuracy, a higher confidence level lower limit should be adopted. To improve the system's perception performance, perception indicators (such as positioning accuracy and velocity accuracy) must fall within stricter error limits before the target is confirmed. By raising the decision threshold, false alarms, missed detections, and errors in positioning and velocity accuracy caused by noise fluctuations can be effectively suppressed, thus enhancing system stability. Then we can use The lower limit of the confidence level is determined by appropriately reducing system overhead while ensuring good perception performance, thereby improving response speed. If... Then we can use The lower bound of the confidence level indicates that the probability of historical false detections is relatively low, and the perception overhead of the system can be reduced by appropriately sacrificing accuracy. If If the performance is good, it means there's no need to maintain a high false detection probability, as this would increase resource overhead. Therefore, a lower confidence level can be used. This relaxes the statistical constraints on perception metrics, enabling the system to respond more quickly to real-world environmental changes and improve decision-making agility and response speed.

[0016] In one possible implementation, setting the confidence level based on the lower confidence level limit may include: setting the confidence level based on the lower confidence level limit when preset conditions are met, with the confidence level being higher than the lower confidence level limit; and setting the confidence level to the lower confidence level limit when preset conditions are not met.

[0017] The preset conditions may include at least one of the following: the weighted Doppler spectrum consistency is less than or equal to a first threshold, where the weighted Doppler spectrum consistency is used to reflect the matching degree between the Doppler frequency shift distribution of the target reflected signal and the predicted radial velocity of the target's movement when the sensing node performs the first J sensing tasks, where J is an integer greater than or equal to 2; the weighted polarization scattering entropy is greater than or equal to a second threshold, where the weighted polarization scattering entropy is used to reflect the distribution of the target scattering matrix eigenvalues ​​measured by the dual-polarized antenna when the sensing node performs the first J sensing tasks.

[0018] In the above scheme, the weighted Doppler spectrum consistency is calculated. and polarization scattering entropy Then, network devices can determine the consistency of the weighted Doppler spectrum. Is it less than or equal to the first threshold? And to determine the weighted polarization scattering entropy Is it greater than or equal to the second threshold? .if < ,and This indicates that the perceived speed may be inaccurate and the target is not a rigid target. Network devices can improve the confidence level based on the lower limit of the confidence level.

[0019] In one possible implementation, Doppler spectral consistency is determined based on the Doppler frequency shift value observed by the sensing node, the Doppler frequency shift value predicted based on the target trajectory, and the maximum Doppler frequency shift value.

[0020] For example, Doppler spectral consistency can be calculated using the following relationship:

[0021] .

[0022] DSC stands for Doppler spectral consistency; This represents the observed Doppler frequency shift value; This represents the Doppler frequency shift value predicted based on the target trajectory; This represents the maximum Doppler frequency shift value.

[0023] In one possible implementation, the method may further include: configuring reference signal resources required for the current sensing task based on a confidence level. For example, the network device may determine a resource allocation factor based on the confidence level; and multiply the resource allocation factor by a pre-configured base resource value to obtain the reference signal resources configured for the current sensing task.

[0024] In the above scheme, when the required confidence level is low, the value of the resource allocation multiple ρ can be appropriately reduced, for example... This saves system resources. When a higher confidence level is required, the value of the resource allocation multiplier ρ can be appropriately increased, for example... This improves the accuracy of perception.

[0025] In one possible implementation, determining the resource allocation multiple based on the confidence level may include weighting the confidence level with the first coherent processing interval CPI to obtain the resource allocation multiple.

[0026] In the above scheme, CPI can be used to represent the continuous time window of coherent signal accumulation. The "correlation" in the coherent processing interval refers to the fact that the phase relationship of the signals is known and maintained during the accumulation process. However, it's not simply adding the signal amplitudes together; instead, the complex values ​​of the signals (including amplitude and phase, etc.) are vector-superimposed according to the correct phase relationship. Ultimately, the signal energy of multiple repeating pulses or sampling points within a CPI is vector-superimposed. This is done after calculating the historical false detection probability of the target. Then, the terminal device can utilize historical error detection probabilities. The CPI value is dynamically adjusted. By dynamically adjusting the CPI, the coherence interval of signal processing can be adjusted, achieving a balance between sensing accuracy and resource overhead. When the system's sensing performance is poor, the CPI value can be increased to improve sensing accuracy. When the system's sensing performance is good, the CPI value can be decreased to save system resources.

[0027] In one possible implementation, the method may further include: determining the interval to which the historical error detection rate belongs, where N is a positive integer, based on the numerical relationship between the historical error detection rate and N thresholds; and determining a first CPI based on the interval to which the historical error detection rate belongs, combined with a base CPI value, where the base CPI value is determined based on the pulse repetition interval and the number of pulses.

[0028] For example, if the historical error detection rate is greater than a first threshold, the base CPI value is added to an offset value to obtain the first CPI; if the historical error detection rate is less than a second threshold, the base CPI value is subtracted from the offset value to obtain the first CPI; if the historical error detection rate is less than or equal to the first threshold and greater than or equal to the second threshold, the base CPI value is used as the first CPI. The second threshold is less than the first threshold.

[0029] In the above scheme, if the historical error detection rate is greater than the first threshold, the detection accuracy of the system can be improved by increasing the CPI value, thereby improving system performance under high error detection probability conditions. If the historical error detection rate is less than the second threshold, it indicates that the system performance is stable, and the system resources can be saved by reducing the CPI value, at the expense of some perception performance.

[0030] Secondly, a resource allocation method is provided. This method can be applied to sensing nodes, such as TRPs or UEs in an ISAC system. This method can be executed by the sensing node, or by components (such as circuits, chips, or chip systems) configured within the sensing node, or by logic modules or software capable of implementing all or part of the sensing node's functions.

[0031] The method may include: receiving indication information from a network device, the indication information being used to indicate reference signal resources required to perform the current sensing task, the reference signal resources being configured according to a confidence level, the confidence level being determined based on a historical error detection rate; performing the current sensing task based on the reference signal resources; and reporting sensing performance evaluation parameters for performing the current sensing task to the network device, the sensing performance evaluation parameters including the error detection rate, the error detection rate being the probability of an erroneous detection event out of the total number of detection events.

[0032] In the above scheme, during each perception task, the perception node can quantitatively analyze the target's error detection rate and report it to the network device. This allows the network device to dynamically adjust the confidence level based on the historical error detection rate reported by the perception node before the perception task begins, and dynamically allocate the reference signal resources required for this perception task according to the adjusted confidence level. In scenarios such as intelligent transportation, smart cities, industrial internet, emergency rescue, border security, drone control, marine and satellite communication perception, or other possible scenarios based on ISAC, perception tasks face complex and ever-changing environmental interference. By configuring perception resources, the accuracy and resource utilization of the perception system can be dynamically adjusted from a macroscopic perspective, enabling the reference signal resources to adapt to the dynamic environment and enhancing robustness and reliability in different scenarios.

[0033] In one possible implementation, the false detection rate includes one or more of the false alarm rate and the false negative rate.

[0034] In one possible implementation, the sensing performance evaluation parameters also include at least one of Doppler spectral consistency and polarization scattering entropy. Doppler spectral consistency is used to reflect the degree of matching between the Doppler frequency shift distribution of the target reflected signal and the predicted radial velocity of the target's movement when the sensing node performs this sensing task. Polarization scattering entropy is used to reflect the distribution of the target scattering matrix eigenvalues ​​measured by the dual-polarized antenna when the sensing node performs this sensing task.

[0035] In the above scheme, after each sensing task, the sensing node can evaluate the sensing performance to obtain Doppler spectral consistency and polarization scattering entropy, and report the Doppler spectral consistency and polarization scattering entropy to the network device through a sensing report. By reporting Doppler spectral consistency and / or polarization scattering entropy, the sensing node can assist the network device in determining whether to improve the confidence level.

[0036] Thirdly, a communication device is provided, comprising a processing module and a communication module. The processing module is used to determine a confidence level based on a historical error detection rate, where the historical error detection rate is the error detection rate reported by the sensing node before it performs the current sensing task, and the error detection rate refers to the probability of an erroneous detection event relative to the total number of detection events. The communication module is used to send indication information to the sensing node, where the indication information indicates the reference signal resources required to perform the current sensing task, and the reference signal resources are configured according to the confidence level.

[0037] Fourthly, a communication device is provided, comprising a processing module and a communication module. The communication module receives indication information from a network device, the indication information indicating reference signal resources required to perform the current sensing task, the reference signal resources being configured according to a confidence level determined based on a historical error detection rate; the processing module performs the current sensing task based on the reference signal resources; the communication module also reports sensing performance evaluation parameters for performing the current sensing task to the network device, the sensing performance evaluation parameters including the error detection rate, which refers to the probability of erroneously detected events out of the total number of detected events.

[0038] Fifthly, 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 or the method in any possible implementation of the second aspect. 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.

[0039] In one implementation, the communication interface can be a transceiver, or an input / output interface.

[0040] 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.

[0041] In a sixth aspect, a processor is provided, comprising: an input circuit, an output circuit, and a processing circuit. The processing circuit is used 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.

[0042] 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.

[0043] In a seventh aspect, a communication device is provided, including a processor and a memory. The processor is used to read instructions stored in the memory and to 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.

[0044] Optionally, there may be one or more processors and one or more memories.

[0045] Eighthly, a computer program product is provided, comprising: a computer program (also referred to as code or instructions) that, when executed, causes a computer to perform a method in any possible implementation of any of the above aspects.

[0046] In a ninth aspect, a computer-readable storage medium is provided that stores a computer program (also referred to as code or instructions) that, when executed on a computer, causes the computer to perform the methods in any possible implementation of any of the above aspects.

[0047] In a tenth 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 executed. The chip system may be composed of chips or may include chips and other discrete devices.

[0048] The chip system may include input circuits or interfaces for transmitting information or data, and output circuits or interfaces for receiving information or data.

[0049] Eleventhly, a communication system is provided, including the aforementioned network device and sensing node. This communication system may be an ISAC system. The communication system may also include other devices that communicate with the network device and / or sensing node.

[0050] It is understood that the beneficial effects of the third to eleventh aspects mentioned above can be found in the relevant descriptions in the first or second aspects mentioned above, and will not be repeated here. Attached Figure Description

[0051] Figure 1 A schematic diagram of a communication system provided in an embodiment of this application;

[0052] Figure 2 A schematic diagram of another communication system provided in the embodiments of this application;

[0053] Figure 3 This is a schematic diagram illustrating resource configuration in an ISAC-based intelligent transportation scenario, provided as an embodiment of this application.

[0054] Figure 4 This is a schematic diagram illustrating another method for configuring resources in an ISAC-based intelligent transportation scenario, provided as an embodiment of this application.

[0055] Figure 5A schematic diagram of another communication system provided in the embodiments of this application;

[0056] Figure 6 A schematic diagram illustrating the configuration of resources in an ISAC sensing scenario in a smart factory, as provided in an embodiment of this application.

[0057] Figure 7 A flowchart illustrating a resource allocation method provided in an embodiment of this application;

[0058] Figure 8 A flowchart illustrating a method for determining a historical weighted error detection rate based on historical error detection rate, provided in an embodiment of this application.

[0059] Figure 9 A schematic diagram illustrating the relationship between historical weighted error detection rate and lower confidence level provided in this application embodiment;

[0060] Figure 10 Another schematic diagram illustrating the relationship between historical weighted error detection rate and lower confidence level provided for an embodiment of this application;

[0061] Figure 11 Another schematic diagram illustrating the relationship between historical weighted error detection rate and lower confidence level provided for an embodiment of this application;

[0062] Figure 12 A flowchart illustrating a method for determining a confidence level based on a lower confidence level limit, historical Doppler spectral consistency, and polarization scattering entropy, provided for embodiments of this application.

[0063] Figure 13 A flowchart illustrating another resource allocation method provided in an embodiment of this application;

[0064] Figure 14 A schematic diagram illustrating the dynamic adjustment of the CPI length based on historical error detection probability, provided for an embodiment of this application;

[0065] Figure 15 This is a schematic block diagram of a communication device provided in an embodiment of this application. Detailed Implementation

[0066] The terms "first" and "second," etc., used in the specification and drawings of this application are used to distinguish different objects or different treatments of the same object, rather than to describe a specific order of objects. Furthermore, the terms "comprising" and "having," and any variations thereof, mentioned in the description of this application are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units is not limited to the listed steps or units, but may optionally include other steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or devices. In the embodiments of this application, "multiple" includes two or more. In the embodiments of this application, words such as "exemplary" or "for example" are used to indicate examples, illustrations, or descriptions. Additionally, the network architecture and business scenarios described in the embodiments of this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. Those skilled in the art will understand that with the evolution of network architecture and the emergence of new business scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

[0067] The technical solutions provided in this application can be applied to various communication systems, such as: Global System for Mobile Communications (GSM), General Packet Radio Service (GPRS), Wireless Local Area Network (WLAN), Long Term Evolution (LTE), LTE Frequency Division Duplex (FDD), LTE Time Division Duplex (TDD), sidelink communication systems, Universal Mobile Telecommunication System (UMTS), Worldwide Interoperability for Microwave Access (WiMAX), non-terrestrial network (NTN), 5th generation (5G) mobile communication systems, or new radio (NR) systems, etc. Among them, 5G mobile communication systems can include non-standalone (NSA) and / or standalone (SA) networks. The technical solutions provided in this application can also be applied to future communication systems, and this application does not limit their application.

[0068] For example, Figure 1 This is a schematic diagram of a communication system 100 provided in an embodiment of this application.

[0069] Communication system 100 may include network devices, such as Figure 1 The network device 110 is shown. The communication system 100 may also include terminal devices, such as... Figure 1 The terminal device 120 shown. The network device 110 and the terminal device 120 can communicate via a wireless link.

[0070] Figure 1 An exemplary network device 110 and a terminal device 120 are shown. In one possible implementation, the communication system 100 may also include multiple network devices and / or multiple terminal devices.

[0071] The network device 110 in this embodiment can be a network-side device such as an access network device or a core network device. Access network devices are sometimes also called access nodes. Access network devices have wireless transceiver capabilities and are used to communicate with terminal devices. Access network devices include, but are not limited to, base stations, evolved NodeBs (eNodeBs), transmit / receive points (TRPs) in the aforementioned communication systems, NR nodes (gNBs) in 5G mobile communication systems, next-generation eNodeBs (ng-eNBs) in 5G mobile communication systems, access network devices or modules of access network devices in open RAN (ORAN) systems, satellites in non-terrestrial network (NTN) communication systems, base stations in future mobile communication systems, or access nodes in wireless fidelity (Wi-Fi) systems. Access network devices can also be modules or units capable of implementing some of the functions of a base station. Access network equipment can be macro base stations, micro base stations, indoor stations, relay nodes, donor nodes, or wireless controllers in cloud radio access network (CRAN) scenarios. Access network equipment can also be servers, wearable devices, or vehicle-mounted devices. Multiple access network devices in a communication system can be base stations of the same type or different types. Base stations can communicate directly with terminal devices or through relay stations. Terminal devices can communicate with multiple base stations using different access technologies. This application does not limit the specific technologies or device forms used in the access network equipment.

[0072] In this embodiment of the application, the device used to implement the function of the network device can be the network device 110, or it can be a device that enables the network device 110 to implement the function, such as a processor, circuit, chip or chip system, etc. The device can be installed in the network device 110 or connected to the network device 110 for use.

[0073] The terminal device 120 in this embodiment can be a wireless terminal device capable of receiving network device scheduling and instruction information. The wireless terminal device can be a device providing voice and / or data connectivity to a user, a handheld device with wireless connectivity, or other processing devices connected to a wireless modem. For example, the terminal device can communicate with one or more core networks or the Internet through a radio access network (RAN). The terminal device can also be referred to as a terminal, user equipment (UE), mobile station, mobile terminal, etc. Terminal devices can be widely used in various scenarios, such as device-to-device (D2D), vehicle-to-everything (V2X) communication, machine-type communication (MTC), Internet of Things (IoT), ultra-reliable low-latency communication (URLLC), virtual reality, augmented reality, industrial control, autonomous driving, telemedicine, smart grids, smart furniture, smart offices, smart wearables, smart transportation, smart cities, or satellite communication, etc. The terminal device can be a mobile phone, tablet computer, computer with wireless transceiver function, wearable device, vehicle, aircraft (such as drone, helicopter, airplane), hot air balloon, ship, robot, robotic arm, or smart home device, etc. The embodiments of this application do not limit the form of the terminal device.

[0074] In this embodiment of the application, the device used to implement the function of the terminal device can be the terminal device 120, or it can be a device that can support the terminal device 120 to implement the function, such as a processor, circuit, chip or chip system, etc. The device can be installed in the terminal device 120 or connected to the terminal device 120 for use.

[0075] Access network equipment and / or terminal equipment can be fixed or mobile. They can be deployed on land, including indoors or outdoors, handheld or vehicle-mounted; on water; or in the air on aircraft, balloons, and satellites. This application does not limit the application scenarios of the access network equipment and terminal equipment. They can be deployed in the same or different scenarios; for example, both can be deployed on land simultaneously; or the access network equipment can be deployed on land while the terminal equipment is deployed on water, etc., and so on.

[0076] To facilitate understanding of the embodiments of this application, the terminology used in these embodiments is briefly explained below. For explanations of some terms, please refer to the interpretations in the 3rd Generation Partnership Project (3GPP) standard protocol. It should be understood that the technical terminology in the embodiments of this application is merely illustrative and not limiting. For example, as technology evolves, technical terminology may change; however, other technical terms with the same technical meaning should also apply to this application.

[0077] Integrated sensing and communications (ISAC) refers to a new generation of wireless technology that deeply integrates wireless communication and environmental sensing functions within a unified hardware platform, signal system, and resource scheduling framework. This enables efficient reuse of spectrum, time slots, antennas, and computing resources, allowing for the acquisition of information about the surrounding physical environment while simultaneously transmitting data. ISAC aims to address the resource waste, equipment redundancy, and poor coordination caused by the separation of traditional communication and sensing, providing low-cost, highly reliable integrated solutions for scenarios such as intelligent transportation, smart cities, the industrial internet, and emergency rescue.

[0078] Confidence level is a quantitative assessment of the reliability of perception / communication results. It is typically expressed as 0-1 or as a percentage. The role of confidence level is to quantify uncertainty, support cross-functional decision-making, optimize resource scheduling, and improve system robustness; it serves as a "reliability anchor" for sensor fusion. One example is its use in target detection and parameter estimation. For instance, by labeling the distance, velocity, and angle spectra of echo signals with confidence levels, low-confidence clutter can be filtered out, while high-confidence target points are retained. Another example is its application in safety decision-making and risk control. It provides decision thresholds for safety-critical scenarios such as vehicle-to-everything (V2X) and autonomous driving, such as triggering obstacle avoidance for high-confidence targets and triggering secondary detection or manual verification for low-confidence targets.

[0079] Confidence level is essentially a quantitative assessment of the reliability of sensing / communication results, and its value is strongly correlated with the intensity of environmental interference, equipment performance, target characteristics, and resource allocation status. In ISAC systems, sensing tasks face complex and variable environmental interference, such as multipath effects, noise interference, or target occlusion, making it difficult to maintain a stable confidence level for sensing results.

[0080] In traditional solutions, the confidence level is set to a fixed value, such as 90% or 95%, and is not dynamically adjusted according to environmental conditions, equipment status, and business needs. While this design simplifies system logic, in scenarios with strong interference and high dynamism, the mismatch between the fixed confidence level and the dynamic system environment can lead to problems such as decreased perception performance, increased false negative rate, and increased signaling overhead.

[0081] In view of the above problems, this application provides a resource allocation method based on error detection rate. Before the task begins, the network device can dynamically adjust the confidence level based on the historical error detection rate reported by the sensing nodes, and then dynamically allocate the reference signal resources required for this sensing task according to the adjusted confidence level. After the sensing task begins, the sensing nodes perform the sensing task based on the reference signal resources configured by the network device, and report the error detection rate to the network device for the next round of optimization.

[0082] The aforementioned error detection rate refers to the probability of erroneously detected events out of the total number of detected events, representing the overall error level of the sensing node's perception decision. For example, the error detection rate includes one or more of the false alarm rate and the missed detection rate. The false alarm rate is the probability that the system incorrectly determines "a target exists" when no real target exists; that is, the ratio of the number of false alarm events to the total number of detections without a target. The missed detection rate is the probability that the system correctly determines "a target exists" when a real target exists. The error detection rate is related to the intensity of environmental interference and channel quality. For example, strong interference can suppress the echo signal of a real target, reducing the signal-to-noise ratio (SNR) of that echo signal, leading to an increase in the error detection rate. Conversely, for high-SNR, low-fading, high-quality channels, the echo signal characteristics of the target are clear, the probability of false alarms and missed detections is low, and the error detection rate is low.

[0083] In the above scheme, network devices can adjust the confidence level based on the error detection rate. For example, in complex scenarios with strong interference, network devices can increase the confidence level to a higher level based on historical error detection rates to ensure the reliability of the sensing results; in ideal environments, network devices can appropriately decrease the confidence level based on historical error detection rates to save resources. This adaptive adjustment method enhances the robustness and reliability of ISAC in different scenarios.

[0084] It should be noted that the adjusted confidence level can be used not only for dynamic scheduling of sensing resources, such as dynamically allocating the reference signal resources required for the current sensing task to the sensing node according to the adjusted confidence level, but also for safety decision-making and risk prevention and control, algorithm calibration and system self-learning, or sensing data quality control. The application scenarios of the adjusted confidence level are limited in this application embodiment.

[0085] The aforementioned sensing nodes, also known as wireless communication nodes or wireless transceiver endpoints, refer to sensing functional nodes that possess the ability to transmit and receive wireless signals and participate in wireless communication. Sensing nodes can be transceiver entities / access terminals on the user side with sensing capabilities, such as... Figure 1 The terminal device 120 shown is an example. Sensing nodes can also be network-side devices, such as base stations (e.g., gNBs), roadside units (RSUs), and controllers of distributed antenna systems (DAS). Sensing nodes can also be dedicated sensing nodes.

[0086] The resource allocation method provided in this application can be applied to ISAC-based intelligent transportation, smart cities, industrial internet, emergency rescue, border security, drone management, marine and satellite communication sensing, or other possible scenarios. In these scenarios, sensing tasks face complex and variable environmental interference, such as multipath effects, noise interference, or target occlusion. In this method, the confidence level is adjusted based on the error detection rate, thereby adjusting the reference signal resources required for the sensing task, so that the reference signal resources adapt to the dynamic environment.

[0087] To facilitate understanding, the application scenarios of the resource configuration method provided in this application are illustrated below.

[0088] For example, Figure 2 This is a schematic diagram of a communication system 200 provided in an embodiment of this application. The communication system 200 may include a 5G NR network, a V2X wireless network, and / or any other suitable network, such as an LTE network.

[0089] The communication system 200 may include a heterogeneous network architecture, such as a core network 210, multiple base stations 220, a cloud server 230, and various mobile devices. Base stations 220 can communicate with the core network 210 via wired / wireless communication links. The core network 210 can communicate with the cloud server 230 via wired / wireless communication links. Mobile devices may include various mobile terminal devices, such as… Figure 2 The mobile phone 251, mobile phone 252, vehicle 261, and vehicle 262 are shown. The communication system 200 may also include an RSU 240 that supports V2X communication with the OBUs of vehicles 261 and 262 via a V2X wireless communication link.

[0090] Core network 210 can be any type of core network, such as an LTE core network (e.g., an evolved packet core (EPC) network), a 5G core network, or a non-aggregated network.

[0091] Base station 220 is a network element that communicates with wireless devices (such as mobile phone 251 and RSU 240) via a wireless communication link. For example, base station 220 can be a Node B, an LTE evolved Node B (eNode B or eNB), an access point (AP), a radio head, a TRP, a New Radio Base Station (NR BS), a 5G Node B (NB), or a Next Generation Node B (gNode B or gNB). Base station 220 can provide communication coverage for a specific geographic area or cell. In 3GPP, a "cell" refers to the coverage area of ​​a base station, the base station subsystem serving that coverage area, or a combination thereof.

[0092] RSU 240 can communicate with core network 210 via wired or wireless communication links. RSU 240 can communicate with vehicles 261 and 262 via V2X wireless communication links. RSU 240 is deployed at intersections and highway sides, using massive MIMO antennas to achieve beam scanning and wider coverage. RSU 240 integrates edge computing capabilities, enabling fusion perception of multiple vehicle targets locally, reducing the transmission pressure on the cloud.

[0093] Vehicles 261 and 262 may be equipped with on-board units (OBUs). The OBU's radio frequency unit uses an integrated antenna, which has both communication signal transmission and reception capabilities and sensing echo reception capabilities. The OBU's baseband unit integrates a dedicated sensing chip, which can simultaneously process communication data and sensing signals, providing environmental data for the on-board autonomous driving system.

[0094] The aforementioned wireless communication links may include multiple carrier signals, frequencies, or frequency bands. Each carrier signal, frequency, or frequency band may include multiple logical channels. Wireless communication links may utilize one or more radio access technologies (RATs), examples of RATs used in wireless communication links include 3GPP LTE, 3G, 4G, 5G, GSM, code division multiple access (CDMA), wideband code division multiple access (WCDMA), world interoperability for microwave access (WiMAX), time division multiple access (TDMA), and other cellular RATs for mobile phone communication technologies. The RATs used in one or more of the various wireless communication links within the communication system 200 may include mid-range protocols such as WiFi, LTE-U, LTE-Direct, LAA, and MuLTEfire, as well as relatively short-range RATs such as ZigBee, Bluetooth, and Bluetooth Low Energy.

[0095] In a V2X integrated sensing system, the RSU 240 and OBU transmit directional beam reference signals, such as sounding reference signals (SRS) and positioning reference signals (PRS), and utilize the reflection and scattering characteristics of the signals to achieve real-time perception of the physical environment of the coverage area.

[0096] As an example, such as Figure 3 As shown, the network device is an RSU 240, and the sensing node is the vehicle's OBU.

[0097] 1. Before a perception task (such as intersection blind spot monitoring or highway collision avoidance warning) begins, the RSU 240 can dynamically adjust the confidence level based on the historical error detection rate of the perception task, and dynamically allocate the SRS resources required for this perception task to the OBU based on the adjusted confidence level. This historical error detection rate includes the error detection rate in multiple rounds of perception tasks.

[0098] 2. The RSU 240 sends SRS resources, such as transmit time slots and spectrum resources, to the OBU.

[0099] 3. The OBU, according to the instructions of RSU 240, transmits SRS signals in a directional manner on the configured SRS resources as a "cooperative detection source" to enhance the perception of blind spots around the vehicle (such as areas obscured by large vehicles).

[0100] 4. During propagation, the directional SRS signal will be reflected by physical targets (such as vehicles, pedestrians, guardrails, road signs or obstacles) within the coverage area, and scattered by rough surfaces (road surface, green belt).

[0101] 5. The OBU's radio frequency unit captures echo signals through its receiving antenna. Subsequently, the baseband unit's signal processing module executes a series of algorithms to extract effective sensing information. For example, it calculates the target's radial velocity based on the Doppler effect, the target's azimuth angle based on the beam's transmission direction, and the target's distance based on the signal round-trip time delay. Then, the OBU fuses the "point" information extracted from multiple beams and pulses to complete real-time modeling of the physical environment. For instance, by combining the amplitude and phase characteristics of the echo signal, artificial intelligence (AI) algorithms distinguish target types, such as motor vehicles, non-motor vehicles, pedestrians, and static obstacles. Another example is the correlation of sensing data from consecutive time slots to generate target trajectories and predict their next direction of movement, such as determining whether a pedestrian intends to cross the road. Yet another example is the integration of the location, type, and trajectory information of all targets to generate a real-time environmental perception map of the covered area, which is then synchronized to the V2X application layer, such as autonomous driving decision-making systems and traffic dispatch platforms.

[0102] In addition, the OBU can evaluate the perception performance during the current perception task, calculate perception performance evaluation parameters, and report these parameters to the RSU 240 for use in the next round of optimization. In the perception function of the ISAC system, perception performance evaluation parameters are core parameters that quantify capabilities such as target detection, parameter estimation, and trajectory tracking. For example, perception performance evaluation parameters may include, but are not limited to, one or more of the following: false alarm rate, false negative rate, Doppler shift, and the eigenvalue distribution of the target scattering matrix.

[0103] As another example, such as Figure 4 As shown, the network device is base station 220, and the sensing node is RSU 240.

[0104] 1. Before the start of a sensing task (such as traffic condition perception and monitoring, traffic safety incident perception and early warning, and environmental and facility condition perception), the base station 220 can dynamically adjust the confidence level based on the historical error detection rate of the sensing task, and dynamically allocate the SRS / PRS resources required for this sensing task to the RSU 240 based on the adjusted confidence level. This historical error detection rate includes the error detection rate in multiple rounds of sensing tasks.

[0105] 2. Base station 220 sends SRS / PRS resources, such as the number of time and frequency resource blocks and bandwidth, to RSU 240.

[0106] 3. The RSU 240's radio frequency unit uses a massive MIMO antenna and beamforming technology to focus the PRS signal into a narrow beam and transmit it in a preset direction. As an example, the RSU 240 can also assign SRS resources to the OBU, and the OBU can then transmit SRS signals directionally on the configured SRS resources.

[0107] 4. During propagation, directional PRS / SRS signals will be reflected by physical targets (such as vehicles, pedestrians, guardrails, road signs or obstacles) within the coverage area, and scattered by rough surfaces (road surface, green belt).

[0108] 5. The RSU 240's radio frequency unit captures echo signals via a receiving antenna. Subsequently, the baseband unit's signal processing module executes a series of algorithms to extract effective sensing information, such as radial velocity, target azimuth, and target distance. As an example, the RSU 240 can also receive echo signals from SRS signals transmitted by other devices (such as an OBU) after reflection / scattering. Then, the RSU 240 fuses the "trace" information extracted from multiple beams and pulses to complete real-time modeling of the physical environment.

[0109] In addition, RSU 240 can also evaluate the sensing performance during the current sensing task, calculate the sensing performance evaluation parameters, and report these parameters to base station 220 for use in the next round of optimization. For example, the sensing performance evaluation parameters may include, but are not limited to, one or more of the following: false alarm rate, missed detection rate, Doppler shift, and target scattering matrix eigenvalue distribution.

[0110] In the V2X scenario provided in the above embodiments, vehicle perception coordination is required, and the scene and target change significantly, with rapid channel changes. Since the intensity of environmental interference, channel quality, and error detection rate are related, network devices can adjust the confidence level in real time based on historical error detection rates and dynamically allocate the reference signal resources required for the current perception task based on the adjusted confidence level, thereby enhancing the robustness and reliability of the V2X ISAC system.

[0111] For example, Figure 5 This is a schematic diagram of another communication system 300 provided in an embodiment of this application.

[0112] The communication system 300 can be applied in a smart factory. The communication system 300 may include a base station 310, multiple robotic arms 320, and a server 330. The base station 310 can communicate with the server 330 via wired / wireless communication links. The base station 310 can also communicate with the robotic arms 320 via wireless communication links. The implementation of the wireless communication links can be referred to the description in the above embodiments, and will not be repeated here.

[0113] In the ISAC sensing scenario of a smart factory, server 330 is the core hub connecting terminal devices such as base station 310 and robotic arm 320 with the upper-level production system. It undertakes key functions such as sensing data aggregation and processing, algorithm decision optimization, and system collaborative management and control, serving as the core carrier for achieving high-precision collaboration and adaptive environmental sensing of robotic arm 320. Sensing data aggregation and processing includes receiving sensing data from the base station (such as robotic arm position, speed, and posture parameters), robotic arm status data (such as joint angles, running trajectory, and load status), and production line environmental data (such as metal reflection intensity, equipment layout changes, and interference power spectrum), and fusing and preprocessing this data. Algorithm decision optimization includes calculating and distributing adaptive confidence thresholds, online training and updating of the target detection model based on new environmental data after production line upgrades, and calculating the intersection of motion trajectories of each robotic arm based on real-time relative position data. System collaborative management and control includes uniformly managing the sensing parameters of the base station (such as signal transmission power, sensing frequency, and beam direction) and the operating parameters of the robotic arm (such as movement speed and operating range), achieving dynamic allocation of sensing resources.

[0114] As an example, the network device is server 330, and the sensing node is base station 310 and / or robotic arm 320.

[0115] As another example, the network device is base station 310, and the sensing node is robotic arm 320.

[0116] Taking base station 310 as the network device and robotic arm 320 as the sensing node as an example, Figure 6 This is a schematic diagram illustrating resource configuration in an ISAC sensing scenario of a smart factory, as provided in an embodiment of this application.

[0117] 1. Before the sensing task begins, base station 310 can dynamically adjust the confidence level based on the historical error detection rate of the sensing task, and dynamically allocate the SRS resources required for this sensing task to robotic arm 320 based on the adjusted confidence level. This historical error detection rate includes the error detection rate in multiple rounds of sensing tasks.

[0118] 2. Base station 310 sends SRS resources, such as the number of time and frequency resource blocks and bandwidth, to robotic arm 320.

[0119] 3. The radio frequency unit of the robotic arm 320 transmits SRS signals directionally on the configured SRS resources.

[0120] 4. During the propagation of directional SRS signals, they will be reflected by physical targets (such as conveyor belts, tooling fixtures, material pallets, and other robotic arms) within the coverage area.

[0121] 5. The radio frequency unit of robotic arm 320 captures echo signals through a receiving antenna. Subsequently, the signal processing module of the baseband unit executes a series of algorithms to extract effective sensing information, such as radial velocity, target azimuth, and target distance. Robotic arm 320 fuses the "point trace" information extracted from multiple beams and pulses to complete real-time modeling of the physical environment. For example, by combining the amplitude and phase characteristics of the echo signals, AI algorithms can distinguish target types, such as conveyor belts, tooling fixtures, material pallets, and other robotic arms.

[0122] In addition, the robotic arm 320 can also evaluate the perception performance during the current perception task, calculate the perception performance evaluation parameters, and report these parameters to the base station 310 for use in the next round of optimization. For example, the perception performance evaluation parameters may include, but are not limited to, one or more of the following: false alarm rate, missed detection rate, Doppler frequency shift, and target scattering matrix eigenvalue distribution.

[0123] In the intelligent factory ISAC sensing scenario provided in the above embodiments, a fixed confidence threshold cannot adapt to the drastic environmental changes after a production line change. For example, when a production line switches product models, the electromagnetic environment, target characteristics, and multipath reflection conditions in the work area will change abruptly, directly disrupting the signal analysis foundation of the sensing system. Since the intensity of environmental interference, channel quality, and error detection rate are related, network devices can adjust the confidence level in real time based on historical error detection rates, and dynamically allocate the reference signal resources required for the current sensing task to the sensing nodes based on the adjusted confidence level, thereby enhancing the robustness and reliability of the intelligent factory ISAC system.

[0124] The following detailed description of the solutions provided in the embodiments of this application, in conjunction with the corresponding flowcharts, is provided. It is understood that the illustrative flowcharts provided in the embodiments of this application primarily use different devices (e.g., network devices and sensing nodes) as examples of the execution subjects of the interaction to illustrate the method, but this application does not limit the execution subjects of the interaction. For example, the devices (e.g., network devices and sensing nodes) in the illustrative flowcharts can also be chips, chip systems, or processors that support the implementation of the method by the device, or logic modules or software capable of implementing all or part of the functions of the device. For ease of description, the following embodiments use network devices and sensing nodes as examples; it is understood that other names can also be used for description.

[0125] For clarity, network devices can be network-side devices, such as base stations. Sensing nodes can refer to independent sensing nodes capable of transmitting and receiving wireless signals and participating in wireless communication, such as terminal devices, TRPs, or dedicated sensing nodes. Network devices and sensing nodes can communicate via wireless communication links. The specific implementation of these wireless communication links can be found in the descriptions of the above embodiments and will not be repeated here.

[0126] In the interaction process of this application embodiment, the message or signaling interaction involved can adopt standard messages or signaling, or it can be newly introduced messages or signaling. This application embodiment does not make specific limitations on this.

[0127] For example, Figure 7 This is a flowchart illustrating a resource allocation method provided in an embodiment of this application.

[0128] like Figure 7 As shown, the method may include the following S01 to S06.

[0129] S01, the network device sends sensing configuration parameters to the sensing node.

[0130] SensingConfig is a set of configuration parameters in the ISAC system that defines sensing task parameters, resource allocation, and signal rules. Its function is to transform upper-layer sensing requirements into signal and algorithm parameters that can be executed by the sensing nodes. For example, in the ISAC scenario of a smart factory, sensingConfig determines the base station's sensing accuracy, update rate, resistance to metallic clutter, and coordination strategy with communication resources for the robotic arm.

[0131] Network devices manage the sensing behavior of sensing nodes (including sensing-capable TRPs and UEs) through standard radio resource control (RRC) signaling. For example, network devices can use RRC Reconfiguration messages to carry sensing configuration parameters (SensingConfig) to initially configure sensing nodes and establish or modify sensing tasks.

[0132] For example, the perception configuration parameters may include at least one of the following:

[0133] The transmission configuration list includes transmission parameters of the physical sidelink shared channel (PSSCH) related to the absolute speed of the sensing node, such as modulation and coding scheme (MCS), number of physical resource blocks (PRB), number of retransmissions, etc.

[0134] Minimum number of candidate subframes, where the value of the minimum number of candidate subframes is an integer between 1 and 13;

[0135] Sensing gap candidates;

[0136] List of reference signal received power (RSRP) thresholds.

[0137] For example, the awareness configuration parameters can be carried by any of the following messages:

[0138] The Sensing-RadioBearerConfig includes time-frequency resources, quality of service (QoS) parameters, etc.

[0139] SensingReferenceSignalConfig includes SRS / PRS mode identifier (ID), port mapping, and beam direction, etc.

[0140] The SensingMeasurementConfig includes measurement type (such as distance / angle / velocity measurement), reporting cycle, and event triggering conditions (such as reporting sudden location changes).

[0141] S02, the network device determines the confidence level based on the historical error detection rate.

[0142] In integrated sensing and communication systems, sensing nodes (such as TRPs and UEs) can capture the reflection and scattering characteristics of the physical environment within their coverage area in real time by transmitting directional beam detection signals (such as SRS and PRS). Then, the sensing nodes can calculate the distance, velocity, and orientation information of targets using Doppler frequency shift analysis, time delay measurement, and angle estimation algorithms. Combined with deep learning-based multi-source data fusion technology, this enables accurate identification and 3D trajectory tracking of targets (such as moving vehicles, pedestrians, and dynamic obstacles). Regarding sensing performance evaluation, sensing nodes can quantitatively analyze the false detection rate of targets.

[0143] The false detection rate refers to the probability of erroneously detected events out of the total number of detected events when performing a perception task. It represents the overall error level of the perception decisions made by the perception node. For example, the false detection rate can include one or more of the false alarm rate and the false detection rate. The false alarm rate is the probability of incorrectly determining "the presence of a target" when no real target exists; that is, the ratio of the number of false alarm events to the total number of detections without a target. The false detection rate is the probability of correctly determining "the presence of a target" when a real target exists.

[0144] As an example, the false alarm rate can be calculated using the following formula:

[0145] .

[0146] in, Represents the false alarm rate; N represents the total number of untargeted drops. This represents the number of targets detected in the nth delivery but not associated with any real target; This represents the total number of targets detected in the nth delivery; This represents the number of times a false alarm target has been deployed.

[0147] As an example, the false negative rate can be calculated using the following formula:

[0148] , .

[0149] in, N represents the false negative rate; N represents the total number of items deployed. This represents the number of targets lost in the nth drop, i.e., the actual targets that are irrelevant to any estimated targets; This represents the number of actual targets in the nth delivery.

[0150] Historical error detection rate refers to the error detection rate reported by the sensing node to the network device before executing the current sensing task. For example, the historical error detection rate can include the error detection rates of the previous J sensing tasks executed by the sensing node before executing the current sensing task, where J is an integer greater than or equal to 2. The sensing node can report the error detection rate of each sensing task to the network device after each sensing task is executed.

[0151] Figure 8 A flowchart illustrating a method for determining a historical weighted error detection rate based on historical error detection rate, provided in an embodiment of this application.

[0152] Taking the false detection rate, which includes both false alarm rate and false negative rate, as an example. Figure 8As shown, after each sensing task, the sensing node can evaluate its sensing performance to obtain the false alarm rate and false negative rate, and report these rates to the network device via a Sensing Report. Before the latest sensing task begins, the network device can calculate the historical weighted error detection rate based on the error detection rates (such as false alarm rate and false negative rate) from the previous J sensing tasks. The historical weighted error detection rate is a weighted average of the error detection rates from the previous J sensing tasks, representing the error level of the sensing decisions made by the sensing node during those previous J sensing tasks.

[0153] As an example, the error detection rate when performing a certain perception task can be calculated using the following formula:

[0154] .

[0155] in, This represents the error detection rate when performing a single perception task. This represents the false negative rate when performing a single perception task. This represents the false alarm rate when performing a single perception task.

[0156] The historical weighted error detection rate can be calculated using the following formula:

[0157] .

[0158] in, Represents the historical weighted error detection rate; Representing history The error detection rate, Representatives and History The weighted average of each error detection rate; J represents the number of historical error detection rates, which can be set to a fixed value. Error detection rate equal to the The false negative rate and the first The sum of false alarm rates.

[0159] It should be noted that the above embodiments are based on the error detection rate. Including false alarm rate and false negative rate This is used as an example for illustration and does not constitute a limitation of this application. In other embodiments, if the error detection rate... Only includes false alarm rate ,but In some other embodiments, if the false detection rate Only includes the false negative rate ,but .

[0160] In addition, the embodiments of this application provide for The value of is not limited. For example, in the first J perception tasks, the weighted weight corresponding to each perception task is equal. As another example, in the first J perception tasks, the weighted weight corresponding to the perception task with the earlier execution time is smaller, while the weighted weight corresponding to the perception task with the later execution time is larger.

[0161] In wireless communication environments, sensing nodes are prone to false alarms (FA) and missed detections (MD) due to noise and interference, resulting in significant uncertainty in their output sensing metrics (such as positioning accuracy and velocity accuracy). In this embodiment, instead of using a fixed confidence level, a trade-off between accuracy and efficiency is intelligently struck based on the reliability of real-time assessments (represented by historical error detection rates).

[0162] The historical weighted error detection rate of the previous J perception tasks was calculated using the above method. Subsequently, network devices can base their decisions on historical weighted error detection rates. Determine the confidence level. The confidence level, also known as the confidence level, is a pre-defined probability guarantee for the confidence interval of the population parameter, denoted by 1-α, where α is the significance level.

[0163] Figure 9 The flowchart illustrates the method for determining the lower limit of confidence level based on historical weighted error detection rate, as provided in this application embodiment.

[0164] like Figure 9 As shown, three thresholds for error detection probability are preset. , The historical weighted error detection rate of the previous J perception tasks was calculated using the above method. Subsequently, network devices can base their decisions on historical weighted error detection rates. The lower limit of the confidence level is dynamically adjusted. The lower limit of the confidence level is the minimum threshold set to meet the reliability requirements of real-world scenarios; that is, the confidence level cannot be lower than the lower limit. For example, in a high-precision collaborative scenario with robotic arms, if 90% is set as the lower limit of the confidence level, the actual confidence level used can be 90%, 95%, or 99%, but cannot be 80%, 85%, or lower values.

[0165] Network devices can compare historical weighted error detection rates. With threshold The size relationship, and based on the historical weighted error detection rate Determine the lower limit of the confidence level for the interval to which it belongs.

[0166] As an example, historical weighted false detection rate The relationship with the lower limit of the confidence level is as follows:

[0167]

[0168] in, This indicates the lower limit of the confidence level.

[0169] if This indicates that the sensing performance is poor at this point, and system stability should be prioritized. To achieve higher accuracy, a higher confidence level lower limit should be adopted. To improve the system's perception performance, perception indicators (such as positioning accuracy and velocity accuracy) must fall within stricter error limits before the target is confirmed. By raising the decision threshold, false alarms, missed detections, and errors in positioning and velocity accuracy caused by noise fluctuations can be effectively suppressed, thereby enhancing system stability.

[0170] if Then we can use The lower limit of the confidence level is determined, and the system overhead is appropriately reduced while ensuring good perception performance, thereby improving the response speed.

[0171] if Then we can use The lower limit of the confidence level is such that the probability of historical false detection is relatively low. By appropriately sacrificing accuracy, the perception overhead of the system can be reduced.

[0172] if If the performance is good, it means there's no need to maintain a high false detection probability, as this would increase resource overhead. Therefore, a lower confidence level can be used. This relaxes the statistical constraints on perception metrics, enabling the system to respond more quickly to real-world environmental changes and improve decision-making agility and response speed.

[0173] It should be noted that the above embodiment uses a pre-set threshold of three error detection probabilities. Furthermore, based on these three thresholds and the historical weighted false detection rate... The example using four confidence level lower limits illustrates the relationship between magnitudes, but this does not limit the scope of this application. In real-time implementation, the number of thresholds for error detection probability, the number of confidence level lower limits based on the thresholds, the values ​​of the thresholds, and the values ​​of the confidence level lower limits can all be adjusted according to usage requirements.

[0174] As another example, such as Figure 10 As shown, two thresholds for error detection probability are preset. , Historical weighted error detection rate With confidence level lower limit The relationship is as follows:

[0175]

[0176] As yet another example, such as Figure 11 As shown, four thresholds for error detection probability are preset. , Historical weighted false detection rate With confidence level lower limit The relationship is as follows:

[0177]

[0178] It should be noted that the above embodiments are illustrated by taking the network device as an example to obtain a historical weighted error detection rate by weighting the error detection rates of the previous J sensing tasks, and determining the lower limit of the confidence level based on the historical weighted error detection rate. This does not limit the scope of this application. In other embodiments, the network device may also remove the highest and lowest error detection rates from the error detection rates of the J sensing tasks, then calculate the average error detection rate of the remaining error detection rates, and determine the lower limit of the confidence level based on the average error detection rate.

[0179] The lower limit of confidence level is the minimum threshold set for the confidence level to meet the reliability requirements of real-world scenarios. Based on this, after determining the lower limit of confidence level, network devices can also set the confidence level according to the lower limit of confidence level.

[0180] In the first implementation, the network device directly sets the lower limit of the confidence level to the confidence level, that is, the confidence level is equal to the lower limit of the confidence level.

[0181] In the second implementation, the network device can set a certain value greater than the lower confidence level as the confidence level. This value can be a preset value greater than the lower confidence level; for example, if the lower confidence level is 50%, then the confidence level can be 75%, 90%, 95%, or 99%.

[0182] In the third implementation, the network device can determine the confidence level based on the lower limit of the confidence level, combined with at least one of historical Doppler spectral consistency and historical polarization scattering entropy.

[0183] The essence of the Doppler spectrum is the radial velocity distribution of a target or scatterer, including: the peak position of the spectrum corresponding to the radial velocity of the target, and the peak amplitude corresponding to the energy proportion of the velocity component. Doppler spectrum consistency represents the degree of matching or discrepancy between the Doppler frequency shift distribution of the target's reflected signal (i.e., the actual moving velocity of the target) and the predicted radial velocity of the target.

[0184] As an example, Doppler spectral consistency is determined based on the Doppler frequency shift value observed by the sensing node, the Doppler frequency shift value predicted based on the target trajectory, and the maximum Doppler frequency shift value.

[0185] For example, Doppler spectral consistency can be calculated using the following formula:

[0186] .

[0187] DSC stands for Doppler spectral consistency; This represents the observed Doppler frequency shift value; This represents the Doppler frequency shift value predicted based on the target trajectory; This represents the maximum Doppler frequency shift value, which can be determined based on historical Doppler frequency shift values.

[0188] It should be noted that the above formula for calculating Doppler spectral consistency is merely an illustrative example and does not limit the scope of this application. In actual implementation, other parameters or other formulas can also be used to calculate Doppler spectral consistency.

[0189] Polarimetric scattering entropy is a metric used to quantify the complexity of a target's scattering mechanisms. It was proposed using the Cloude-Pottier decomposition model and typically has a value range of 0 ≤ H ≤ 1. The polarimetric scattering characteristics of a target can be described by the polarimetric coherence matrix or the polarimetric covariance matrix. The core idea of ​​polarimetric scattering entropy is to decompose the total scattering power of a target into the contributions of multiple independent scattering mechanisms, and to measure the randomness and complexity of scattering behavior through the power distribution of each scattering mechanism.

[0190] As an example, the polarization scattering entropy can be calculated using the following relationship:

[0191] .

[0192] Where H represents the polarization scattering entropy.

[0193] , These represent the ratios of the characteristic values ​​of the two polarized antennas to the total energy, and can be preset values.

[0194] To facilitate understanding, the following will be combined with... Figure 12 This paper introduces a method for determining the confidence level based on the lower limit of the confidence level, the consistency of the historical Doppler spectrum, and the historical polarization scattering entropy.

[0195] After each sensing task, the sensing node can evaluate its sensing performance to obtain Doppler spectral consistency and polarization scattering entropy, and report these parameters to the network device via a SensingReport. As an example, the SensingReport can simultaneously carry Doppler spectral consistency, polarization scattering entropy, false alarm rate, and false negative rate.

[0196] Before the latest sensing task begins, the network device can calculate the weighted Doppler spectral consistency and polarization scattering entropy based on the Doppler spectral consistency (referred to as historical Doppler spectral consistency) and polarization scattering entropy (referred to as historical polarization scattering entropy) from the previous J sensing tasks. Here, J is an integer greater than or equal to 2.

[0197] The weighted Doppler spectrum consistency is obtained by weighting the Doppler spectrum consistency of the sensing node after performing the previous J sensing tasks. It reflects the matching degree between the Doppler frequency shift distribution of the target reflected signal and the predicted radial velocity of the target when the sensing node performs the previous J sensing tasks.

[0198] As an example, the weighted Doppler spectral consistency can be calculated using the following formula:

[0199] .

[0200] in, This represents the consistency of the weighted Doppler spectrum; Represents the uniformity of the j-th Doppler spectrum; The weighting weights corresponding to the consistency with the j-th Doppler spectrum are the same as those used in the above embodiment to calculate the historical weighted error detection rate. To maintain consistency; J represents the number of Doppler spectral consistency values.

[0201] The weighted polarization scattering entropy is obtained by weighting the polarization scattering entropy of the sensing node after performing the first J sensing tasks. It reflects the distribution of the target scattering matrix eigenvalues ​​measured by the dual-polarized antenna when the sensing node performs the first J sensing tasks.

[0202] As an example, the weighted polarization scattering entropy can be calculated using the following relationship:

[0203] .

[0204] in, Represents the weighted polarization scattering entropy; Represents the entropy of the j-th polarization scattering; The weighting weights are those corresponding to the j-th polarization scattering entropy, and are used in the above embodiments to calculate the historical weighted error detection rate. To maintain consistency; J represents the quantity of polarization scattering entropy.

[0205] Calculate the consistency of the weighted Doppler spectrum and polarization scattering entropy Then, network devices can determine the consistency of the weighted Doppler spectrum. Is it less than or equal to the first threshold? And to determine the weighted polarization scattering entropy Is it greater than or equal to the second threshold? .

[0206] if < ,and This indicates that the sensing speed may be inaccurate and the target is not a rigid target. Network devices can increase the confidence level based on the lower confidence level limit. That is, the confidence level is greater than the lower confidence level limit.

[0207] For example, the confidence level can be calculated using the following formula:

[0208] .

[0209] Where L represents the confidence level; This represents the lower limit of the confidence level; The value between (0,1) represents the increase in confidence level. For a preset value, or, Based on the consistency of the weighted Doppler spectrum Weighted polarization scattering entropy Calculated.

[0210] if ,or Therefore, the confidence level L can be set as the lower limit of the confidence level. :

[0211] .

[0212] It should be noted that the above embodiments are illustrated by taking the determination of the confidence level of the network device based on the lower limit of the confidence level, combined with the historical Doppler spectrum consistency and historical polarization scattering entropy, and do not constitute a limitation on this application.

[0213] In other embodiments, the network device determines the confidence level based solely on historical Doppler spectral consistency, in addition to the lower confidence level limit. For example, the network device can determine the weighted Doppler spectral consistency. Is it less than or equal to the first threshold? ,if < That would increase the confidence level. If Therefore, the confidence level L can be set as the lower limit of the confidence level. .

[0214] In other embodiments, the network device determines the confidence level based solely on the historical polarization scattering entropy, in addition to the lower confidence level limit. For example, the network device can determine the weighted polarization scattering entropy. Is it greater than or equal to the second threshold? ,if That would increase the confidence level. If Therefore, the confidence level L can be set as the lower limit of the confidence level. .

[0215] It is understandable that, in addition to historical Doppler spectral consistency and historical polarization scattering entropy, network devices can determine the confidence level based on the lower limit of the confidence level, combined with other possible parameters.

[0216] S03, the network device configures the reference signal resources required for this sensing task based on the confidence level obtained through S02.

[0217] After obtaining the confidence level through the above S02 calculation, the network device can quantify the reference signal resources required to perform this sensing task based on the confidence level, configure the sensing resources, and dynamically adjust the accuracy and resource usage of the sensing system from a macroscopic perspective. Here, "reference signal" may include, but is not limited to, SRS and / or PRS.

[0218] In some embodiments, the reference signal resources described above may include, but are not limited to, at least one of the following:

[0219] Time-frequency resources, such as the number of resource blocks, the number and location of coincidences within a time slot, and the subcarrier spacing;

[0220] The sampling period is the period during which the reference signal is transmitted.

[0221] The triggering method of the reference signal is as follows: periodic triggering (normal sensing) and non-periodic triggering (event-driven sensing).

[0222] Antenna and port for transmitting the test signal;

[0223] Sequences and mappings.

[0224] As an example, network devices can calculate resource allocation multiples using the following formula:

[0225] .

[0226] Where ρ represents the resource allocation multiple; L represents the confidence level calculated using the above SO2; For the preset weights, Greater than 0.

[0227] Then, the network device can calculate the reference signal resources required for this sensing task using the following formula:

[0228] .

[0229] Where S represents the reference signal resources configured for this sensing task; ρ represents the resource allocation factor. This represents a pre-configured base resource value for performing a perception task. Different types of perception tasks may be pre-configured with the same base resource value, or different types of perception tasks may be pre-configured with different base resource values.

[0230] It's understandable that when the required confidence level is low, the value of ρ can be appropriately reduced, for example... This saves system resources. When a higher confidence level is required, the value of ρ can be appropriately increased, for example... This improves the accuracy of perception.

[0231] S04, the network device sends instruction information to the sensing node.

[0232] Accordingly, the sensing node receives the instruction information.

[0233] The above indication information is used to indicate reference signal resources.

[0234] As an example, network devices can carry configured reference signal resources via medium access control (MAC) control element (CE) signaling to send adjusted reference signals (such as SRS resources) to sensing nodes. That is, network devices can execute S01, S02, S03, and S04 sequentially.

[0235] As another example, network devices can also carry sensing configuration parameters and reference signal resources in the same signaling message. For instance, a network device can send a Radio Resource Control Reconfiguration (RRCReconfiguration) message to a sensing node, which can include sensing configuration parameters and reference signal resources. That is, the network device can first execute S02 and S03, and then combine the execution of S01 and S04.

[0236] S05, the sensing node performs this sensing task based on the reference signal resources configured in the network device.

[0237] In a V2X integrated sensing system, sensing nodes can transmit directional beam reference signals, such as SRS and / or PRS, and utilize the reflection and scattering characteristics of these signals to achieve real-time sensing of the physical environment within the coverage area. It is understandable that the reference signal resources configured on the network devices will differ depending on the sensing scenario, the sensing node, and the sensing task. Each sensing node can perform its sensing task based on the reference signal resources configured for it by the network devices.

[0238] like Figure 3 As shown, the network device is an RSU 240, and the sensing node is the vehicle's OBU. The OBU can transmit SRS signals directionally on the configured SRS resources according to the instructions of the RSU 240, then capture the echo signals through the receiving antenna, and subsequently execute a series of algorithms to extract effective sensing information, such as calculating the target's radial velocity based on the Doppler effect, calculating the target's azimuth angle based on the beam's transmission direction, and calculating the target's distance based on the signal round-trip time delay. Afterwards, the OBU fuses the "trace" information extracted from multiple beams and pulses to complete real-time modeling of the physical environment.

[0239] like Figure 4 As shown, the network device is base station 220, and the sensing node is RSU 240. RSU 240 uses a massive MIMO antenna and beamforming technology to focus the PRS signal into a narrow beam and transmit it in a preset direction. The echo signal is then captured by the receiving antenna. Subsequently, the signal processing module of the baseband unit executes a series of algorithms to extract effective sensing information, such as radial velocity, target azimuth, and target distance. Afterward, RSU 240 fuses the "trace" information extracted from multiple beams and pulses to complete real-time modeling of the physical environment.

[0240] S06, the sensing node reports the sensing performance evaluation parameters for performing this sensing task to the network device.

[0241] like Figure 3As shown, after the OBU executes this sensing task, it can also evaluate the sensing performance during the task, calculate the sensing performance evaluation parameters, and report these parameters to the RSU 240. Correspondingly, the network device updates the stored historical sensing performance evaluation parameters.

[0242] like Figure 4 As shown, after RSU 240 performs this sensing task, it can also evaluate the sensing performance during the task, calculate the sensing performance evaluation parameters, and report these parameters to base station 220. Correspondingly, the network device updates the stored historical sensing performance evaluation parameters.

[0243] The aforementioned perception performance evaluation parameters can be used to optimize reference signal resources when performing subsequent perception tasks (such as the next perception task).

[0244] In some embodiments, the perception performance evaluation parameters may include the false detection rate, excluding Doppler frequency shift and target scattering matrix eigenvalues. The false detection rate may include the false alarm rate and / or the missed detection rate. Based on the description of the above embodiments, the network device may first base its evaluation on historical false detection rates. Determine the lower limit of confidence level Then, based solely on the lower confidence level... Set the confidence level L. For example, network devices can directly set the lower limit of the confidence level. Set the confidence level to L.

[0245] In other embodiments, the perception performance evaluation parameters may include not only the false detection rate, but also at least one of Doppler frequency shift and target scattering matrix eigenvalues. According to the description of the above embodiments, the network device can first assess the historical false detection rate... Determine the lower limit of confidence level Then, at the lower confidence level Based on this, the confidence level L is determined by combining at least one of historical Doppler spectral consistency (DSC) and historical polarization scattering entropy (H). For example, network devices can first calculate the weighted Doppler spectral consistency. and polarization scattering entropy Then; determine whether it is satisfied. ,and ;like ,and Then based on the lower confidence level Increase the confidence level L; if ,or Then the lower limit of the confidence level will be... Set the confidence level to L.

[0246] It is understood that false alarm rate, false negative rate, Doppler shift and target scattering matrix eigenvalues ​​are merely illustrative examples. In actual implementation, the perception performance evaluation parameters may also include other parameters used to adjust the confidence level and reference signal resources.

[0247] In the method provided in this application, the network device can dynamically adjust the confidence level based on the error detection rate. For example, in complex scenarios with strong interference, the network device can increase the confidence level to a higher level based on the historical error detection rate to ensure the reliability of the sensing results; in an ideal environment, the network device can appropriately decrease the confidence level based on the historical error detection rate to save resources. This adaptive adjustment method enhances the robustness and reliability of ISAC in different scenarios.

[0248] In scenarios such as intelligent transportation, smart cities, industrial internet, emergency rescue, border security, drone control, marine and satellite communication sensing, or other possible scenarios based on ISAC, sensing tasks face complex and ever-changing environmental interference. Network devices can quantify the reference signal resources required to perform this sensing task based on the confidence level, configure sensing resources, and dynamically adjust the accuracy and resource usage of the sensing system from a macroscopic perspective, so that the reference signal resources can adapt to the dynamic environment.

[0249] The above embodiments describe specific implementation methods for network devices to dynamically configure the reference signal resources required for sensing tasks based on confidence levels, and do not limit the scope of this application. In other embodiments, network devices can also dynamically configure the reference signal resources required for sensing tasks based on other parameters.

[0250] For example, Figure 13 This is a flowchart illustrating another resource configuration method provided in an embodiment of this application.

[0251] like Figure 13 As shown, the method may include the following steps S11 to S17.

[0252] S11, the network device sends sensing configuration parameters to the sensing node.

[0253] The perception configuration parameters are a set of configurations in the ISAC system that define perception task parameters, resource allocation, and signal rules. Their function is to transform the upper-layer perception requirements into signal and algorithm parameters that can be executed by the perception nodes.

[0254] For the specific implementation of S11, please refer to the description of S01 above, which will not be repeated here.

[0255] S12, the network device determines the confidence level based on the historical error detection rate.

[0256] The false detection rate refers to the probability of erroneously detected events out of the total number of detected events when performing a perception task. It represents the overall error level of the perception decisions made by the perception node. For example, the false detection rate can include one or more of the false alarm rate and the false detection rate. The false alarm rate is the probability of incorrectly determining "the presence of a target" when no real target exists; that is, the ratio of the number of false alarm events to the total number of detections without a target. The false detection rate is the probability of correctly determining "the presence of a target" when a real target exists.

[0257] Historical error detection rate refers to the error detection rate reported by the sensing node to the network device before executing the current sensing task. For example, the historical error detection rate can include the error detection rate of the previous J sensing tasks executed by the sensing node before executing the current sensing task, where J is an integer greater than or equal to 2.

[0258] The historical weighted error detection rate of the previous J perception tasks was calculated. Subsequently, network devices can base their decisions on historical weighted error detection rates. Determine the lower limit of confidence level Determine the lower limit of the confidence level. Subsequently, network devices can also be configured based on the lower limit of the confidence level. Set the confidence level.

[0259] In the first implementation, the network device directly sets the lower limit of the confidence level to the confidence level, that is, the confidence level is equal to the lower limit of the confidence level.

[0260] In the second implementation, the network device can set a value greater than the lower confidence level as the confidence level. This value can be a preset value greater than the lower confidence level.

[0261] In the third implementation, the network device can determine the confidence level based on the lower limit of the confidence level, combined with at least one of historical Doppler spectral consistency and historical polarimetric scattering entropy. Here, Doppler spectral consistency represents the degree of matching or discrepancy between the Doppler frequency shift distribution of the target's reflected signal and the predicted radial velocity of the moving target. Polarimetric scattering entropy is an indicator used to quantify the complexity of the target's scattering mechanism. Its core idea is to decompose the total scattering power of the target into the contributions of multiple independent scattering mechanisms, and measure the randomness and complexity of the scattering behavior through the power proportion distribution of each scattering mechanism.

[0262] For the specific implementation of S12, please refer to the description of S02 above, which will not be repeated here.

[0263] S13, the network device determines the coherent processing interval (CPI) based on the historical error detection rate.

[0264] CPI (Correlation Interval) is used to represent a continuous time window for the accumulation of coherent signals. The "correlation" in the coherent processing interval refers to the fact that the phase relationship of the signals is known and maintained during the accumulation process. However, it's not simply adding up the signal amplitudes; instead, it's vector superposition of the complex values ​​of the signals (including amplitude and phase, etc.) according to the correct phase relationship. Ultimately, the signal energy of multiple repeating pulses or sampling points within a CPI is vector superimposed. CPI can be used to improve the signal-to-noise ratio of sensing, provide high velocity resolution, increase radar detection range, distinguish targets with similar speeds, or filter out clutter, among other things.

[0265] In this embodiment of the application, the historical error detection probability of the target is calculated. Then, network devices can detect errors based on historical error probabilities. The length of CPI is dynamically adjusted. This applies to historical error detection probabilities. The specific calculation method can be referred to the description of the above embodiment S02, and will not be repeated here.

[0266] The following is combined with Figure 14 Provide a method based on historical error detection probability A scheme to dynamically adjust the length of CPI.

[0267] like Figure 14 As shown, two thresholds for error detection probability are preset: (referred to as the first threshold) (referred to as the second threshold), and It should be noted that, , Compared with the above embodiments Their sizes are not necessarily equal. They can be equal or unequal.

[0268] Network devices can pre-configure a basic CPI value based on the sensing scenario and sensing task requirements.

[0269] As an example, the base CPI value can be calculated using the following formula:

[0270] .

[0271] in, This represents the base CPI value, also known as the baseline CPI value. The pulse repetition interval, also known as the pulse repetition interval, refers to the time interval between two adjacent transmitted pulses and determines the maximum detection range and Doppler resolution of the sensing system. This represents the number of pulses. N and PRI can be preset.

[0272] For example, The minimum value can be calculated using the following formula:

[0273] .

[0274] in, represent The minimum value, The wavelength representing the radar. This represents the target velocity resolution.

[0275] The historical weighted error detection rate of the previous J perception tasks is calculated by referring to S02 of the above embodiment. Afterwards, network devices can compare historical weighted error detection rates. With threshold The size relationship, and based on the historical weighted error detection rate The interval to which it belongs determines the CPI for performing this perception task (referred to as the first CPI).

[0276] As an example, historical weighted false detection rate The relationship with CPI is as follows:

[0277]

[0278] in, The CPI value representing the performance of this sensing task; Represents the base CPI value; The offset value representing CPI is a preset value, such as... >0; Two pre-set thresholds for error detection probabilities, .

[0279] if Therefore, the detection accuracy of the system can be improved by increasing the value of CPI, thereby improving the system performance under high error detection probability conditions.

[0280] if Then set CPI to .

[0281] if If the CPI value is low, it indicates that the system performance is stable. By reducing the CPI value, some perceived performance is sacrificed to save system resources.

[0282] In the above scheme, the historical false detection probability of the target is calculated. Then, network devices can utilize historical error detection probabilities. The CPI value is dynamically adjusted. By dynamically adjusting the CPI, the coherence interval of signal processing can be adjusted, achieving a balance between sensing accuracy and resource overhead. When the system's sensing performance is poor, the CPI value can be increased to improve sensing accuracy. When the system's sensing performance is good, the CPI value can be decreased to save system resources.

[0283] S14, the network device configures the reference signal resources required for this sensing task based on the confidence level obtained through S12 and the CPI obtained through S13.

[0284] The aforementioned "reference signal" may include, but is not limited to, SRS and / or PRS.

[0285] As an example, after obtaining the confidence level through S12 and the CPI through S13, the network device can configure the reference signal resources required for this sensing task in the following manner.

[0286] First, network devices can calculate the resource allocation multiple using the following formula:

[0287] .

[0288] Where ρ represents the resource allocation multiple; L represents the confidence level calculated using the above SO2; This represents the CPI value used to perform this sensing task. and For the preset weights, All are greater than 0.

[0289] Then, the network device can calculate the reference signal resources required for this sensing task using the following formula:

[0290] .

[0291] Where S represents the reference signal resources configured for this sensing task; ρ represents the resource allocation factor. This represents a pre-configured base resource value for performing a perception task. Different types of perception tasks may be pre-configured with the same base resource value, or different types of perception tasks may be pre-configured with different base resource values.

[0292] It's understandable that when the required confidence level is low, the value of ρ can be appropriately reduced, for example... This saves system resources. When a higher confidence level is required, the value of ρ can be appropriately increased, for example... This improves the accuracy of perception.

[0293] It should be noted that the above embodiments are illustrative examples of calculating the reference signal resources required for the current sensing task based on the confidence level and CPI, and do not limit the scope of this application. In actual implementation, the network device may also calculate the reference signal resources required for the current sensing task solely based on the CPI, such as by first using a relational expression. Calculate the resource allocation multiple, and then use the relational formula. Calculate the reference signal resources required for this sensing task.

[0294] S15, the network device sends instruction information to the sensing node.

[0295] Accordingly, the sensing node receives the instruction information.

[0296] The above indication information is used to indicate reference signal resources.

[0297] For the specific implementation of S15, please refer to the description of S04 above, which will not be repeated here.

[0298] S16, the sensing node performs this sensing task based on the reference signal resources configured in the network device.

[0299] For the specific implementation of S16, please refer to the description of S05 above, which will not be repeated here.

[0300] S17, the sensing node reports the sensing performance evaluation parameters for performing this sensing task to the network device.

[0301] In some embodiments, the perception performance evaluation parameters may include the false detection rate, excluding Doppler shift and target scattering matrix eigenvalues. The false detection rate may include the false alarm rate and / or the missed detection rate.

[0302] In other embodiments, the sensing performance evaluation parameters may include not only the false detection rate, but also at least one of the Doppler frequency shift and target scattering matrix eigenvalues.

[0303] For the specific implementation of S17, please refer to the description of S06 above, which will not be repeated here.

[0304] In the method provided in this application, the historical error detection probability of the target is calculated. Then, network devices can detect errors based on historical error probabilities. The CPI is dynamically adjusted. By dynamically adjusting the CPI, the coherence interval of signal processing can be adjusted, achieving a balance between sensing accuracy and resource overhead. When the system's sensing performance is poor, the CPI value can be increased to improve sensing accuracy. When the system's sensing performance is good, the CPI value can be decreased to save system resources.

[0305] In scenarios such as intelligent transportation, smart cities, industrial internet, emergency rescue, border security, drone control, marine and satellite communication sensing, or other possible scenarios based on ISAC, sensing tasks face complex and ever-changing environmental interference. Network devices can quantify the reference signal resources required to perform this sensing task based on confidence level and CPI, configure sensing resources, and dynamically adjust the accuracy and resource usage of the sensing system from a macroscopic perspective, so that the reference signal resources can adapt to the dynamic environment.

[0306] It should be understood that Figures 1 to 14 The flowcharts or scene diagrams shown are for illustrative purposes only and are not intended to limit the embodiments of this application to the examples illustrated. In fact, those skilled in the art can interpret the embodiments based on... Figures 1 to 14 The examples in the document can be transformed into equivalent ways to obtain more implementations.

[0307] Figure 15 This is a schematic block diagram of a communication device provided in an embodiment of this application. The notification device 150 may be a chip, chip system, or processor, etc., in a network device or sensing node that implements the above-described method. The notification device 150 can be used to implement the method described in the above-described method embodiments, and for details, please refer to the description in the above-described method embodiments.

[0308] like Figure 15 As shown, the notification device 150 may include one or more processors 151, which may also be referred to as processing units or processing modules, and can implement certain control functions. The processor 151 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 notification device 150 (e.g., a base station, baseband chip, user, user chip), execute software programs, and process data from the software programs.

[0309] In one possible implementation, the processor 151 may also store instructions and / or data, which can be executed by the processor 151 to cause the notification device 150 to perform the method described in the above method embodiments.

[0310] In another possible implementation, the notification device 150 may include a communication interface 152 for implementing receiving and sending functions. For example, the communication interface 152 may be a transceiver circuit, interface, interface circuit, or transceiver. The transceiver circuit, interface, interface circuit, or transceiver for implementing receiving and sending 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.

[0311] Optionally, the notification device 150 may include one or more memories 153, which may store instructions that can be executed on the processor 151, causing the notification device 150 to perform the methods described in the above method embodiments. Optionally, the memories 153 may also store data. Optionally, the processor 151 may also store instructions and / or data. The processor 151 and the memories 153 may be provided separately or integrated together.

[0312] It should be understood that, in one possible implementation, the steps in the method embodiments provided in this application can be completed by integrated logic circuits in the processor hardware or by instructions in software form. The steps of the method disclosed in the embodiments of this application can be directly implemented by a hardware processor, or 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.

[0313] In one implementation, the notification device 150 may correspond to the terminal device in the above method embodiments, and may be used to execute the various steps and / or processes executed by the terminal device in the above method embodiments. The processor 151 may be used to execute instructions stored in the memory 153, and when the processor 151 executes the instructions stored in the memory, the processor 151 is used to execute the various steps and / or processes of the above method embodiments corresponding to the terminal device.

[0314] In another implementation, the notification device 150 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 151 may be used to execute instructions stored in the memory 153, and when the processor 151 executes the instructions stored in the memory, the processor 151 is used to execute the various steps and / or processes of the above method embodiments corresponding to the network device.

[0315] It should be understood that the aforementioned processor can be one or more chips. For example, the processor can be a field-programmable gate array (FPGA), an application-specific integrated circuit (ASIC), a system-on-a-chip (SoC), a central processing unit (CPU), a network processor (NP), a digital signal processor (DSP), a microcontroller unit (MCU), a programmable logic device (PLD), or other integrated chips.

[0316] 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.

[0317] 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, 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. The chip system may include input circuitry or interfaces for transmitting information or data, and output circuitry or interfaces for receiving information or data.

[0318] 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 in any of the foregoing method embodiments.

[0319] 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 perform the various steps or processes in any of the foregoing method embodiments.

[0320] 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.

[0321] 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.

[0322] 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. A computer program product includes one or more computer instructions. When the computer instructions are loaded and executed on a computer, all or part of the flow or function according to the embodiments of this application is generated.

[0323] 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.

[0324] 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. In summary, the above are merely preferred embodiments of the technical solutions of this application and are 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 resource allocation method, characterized in that, The method includes: The confidence level is determined based on the historical error detection rate, which is the error detection rate reported by the sensing node before it performs the current sensing task. The error detection rate refers to the probability of an error detection event out of the total number of detection events. The system sends an instruction to the sensing node, the instruction being used to indicate the reference signal resources required to perform this sensing task, the reference signal resources being configured according to the confidence level.

2. The method according to claim 1, characterized in that, The determination of confidence level based on historical error detection rate includes: Based on the historical error detection rate, determine the lower limit of the confidence level; The confidence level is set according to the lower limit of the confidence level.

3. The method according to claim 2, characterized in that, The step of determining the lower limit of the confidence level based on the historical error detection rate includes: Based on the numerical relationship between the historical error detection rate and the M thresholds, determine the interval to which the historical error detection rate belongs; The preset value corresponding to the interval to which the historical error detection rate belongs is determined as the lower limit of the confidence level; Different intervals correspond to different preset values, and M is a positive integer.

4. The method according to claim 2, characterized in that, Setting the confidence level based on the lower limit of the confidence level includes: Under the condition that the preset conditions are met, the confidence level is set based on the lower limit of the confidence level, and the confidence level is higher than the lower limit of the confidence level; If the preset conditions are not met, the confidence level is set to the lower limit of the confidence level; The preset conditions include at least one of the following: The weighted Doppler spectral consistency is less than or equal to a first threshold. The weighted Doppler spectral consistency is used to reflect the degree of matching between the Doppler frequency shift distribution of the target reflected signal and the predicted radial velocity of the target when the sensing node performs the first J sensing tasks. J is an integer greater than or equal to 2. The weighted polarization scattering entropy is greater than or equal to the second threshold. The weighted polarization scattering entropy is used to reflect the distribution of target scattering matrix eigenvalues ​​measured by the dual-polarized antenna when the sensing node performs the first J sensing tasks.

5. The method according to claim 4, characterized in that, The Doppler spectrum consistency is determined based on the Doppler frequency shift value observed by the sensing node, the Doppler frequency shift value predicted based on the target trajectory, and the maximum Doppler frequency shift value.

6. The method according to claim 5, characterized in that, The Doppler spectral consistency is calculated using the following formula: ； DSC stands for Doppler spectral consistency; This represents the observed Doppler frequency shift value; This represents the Doppler frequency shift value predicted based on the target trajectory; This represents the maximum Doppler frequency shift value.

7. The method according to any one of claims 1 to 6, characterized in that, The historical error detection rate includes the error detection rate of the previous J sensing tasks performed before the current sensing task is performed at the sensing node. The error detection rate includes at least one of the false alarm rate and the missed detection rate, where J is an integer greater than or equal to 2.

8. The method according to claim 1, characterized in that, The method further includes: Configure the reference signal resources required for this sensing task based on the confidence level.

9. The method according to claim 8, characterized in that, The step of configuring the reference signal resources required for this sensing task according to the confidence level includes: Based on the confidence level, determine the resource allocation multiple; Multiply the resource allocation multiplier by the pre-configured base resource value to obtain the reference signal resource configured for the current sensing task.

10. The method according to claim 9, characterized in that, The step of determining the resource allocation multiple based on the confidence level includes: The resource allocation multiple is obtained by weighting the confidence level with the first coherent processing interval CPI.

11. The method according to claim 10, characterized in that, The method further includes: Based on the numerical relationship between the historical error detection rate and N thresholds, the interval to which the historical error detection rate belongs is determined, where N is a positive integer; Based on the range to which the historical error detection rate belongs, the first CPI is determined in combination with the base CPI value, which is determined according to the pulse repetition interval and the number of pulses.

12. The method according to claim 11, characterized in that, The determination of the first CPI based on the interval to which the historical error detection rate belongs, combined with the base CPI value, includes: If the historical error detection rate is greater than the first threshold, then the base CPI value is added to the offset value to obtain the first CPI; If the historical error detection rate is less than the second threshold, the first CPI is obtained by subtracting the offset value from the base CPI value. If the historical error detection rate is less than or equal to the first threshold and the historical error detection rate is greater than or equal to the second threshold, then the basic CPI value is used as the first CPI. Wherein, the second threshold is less than the first threshold.

13. A resource allocation method, characterized in that, The method includes: The system receives indication information from a network device. The indication information is used to indicate the reference signal resources required to perform this sensing task. The reference signal resources are configured according to a confidence level. The confidence level is determined based on the historical error detection rate. The historical error detection rate is the error detection rate reported to the network device before performing this sensing task. The error detection rate refers to the probability of an error detection event out of the total number of detection events. The current sensing task is performed based on the reference signal resources; The network device is reported with perception performance evaluation parameters for performing the current perception task, including the error detection rate.

14. The method according to claim 13, characterized in that, The error detection rate includes one or more of the false alarm rate and the false negative rate.

15. The method according to claim 13 or 14, characterized in that, The sensing performance evaluation parameters also include at least one of Doppler spectral consistency and polarization scattering entropy. The Doppler spectral consistency is used to reflect the degree of matching between the Doppler frequency shift distribution of the target reflected signal and the predicted radial velocity of the target when the sensing node performs the current sensing task. The polarization scattering entropy is used to reflect the distribution of the target scattering matrix eigenvalues ​​measured by the dual-polarized antenna when the sensing node performs the current sensing task.

16. A communication device, characterized in that, The communication device includes at least one processor coupled to a memory storing a program or instructions, the processor executing the program or instructions to cause the communication device to perform the method as described in any one of claims 1 to 15.

17. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program or instructions that, when executed, cause a computer to perform the method as described in any one of claims 1 to 15.

18. A communication system, characterized in that, The communication system includes the communication device as described in claim 16.

19. A computer program product, characterized in that, Includes a computer program that, when run, causes the method as described in any one of claims 1 to 15 to be performed.

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