A method for analyzing integrated sensing mutual information, a terminal and a storage medium

Abstract

This invention discloses a method, terminal, and storage medium for analyzing sensing mutual information in a sensory-integrated system, belonging to the field of wireless communication technology. The method includes: acquiring the basic physical parameters of a target sensory-integrated system, where the basic physical parameters are those at the time of initial construction of the target sensory-integrated system or at a specified time; calculating target system constants based on the basic physical parameters, and simplifying the definition formula of sensing mutual information into a closed-form analytical formula based on the target system constants; when a target trigger condition is met, acquiring system operating parameters in real time, and calculating the real-time sensing mutual information value of the target sensory-integrated system based on the system operating parameters, the target system constants, and the closed-form analytical formula; and performing real-time evaluation of the target sensory-integrated system based on the real-time sensing mutual information value to guide system resource allocation or waveform configuration. This invention achieves a fundamental leap from "theoretical definition" to "real-time calculable engineering" of sensing mutual information.

Description

Technical Field

[0001] This invention relates to the field of wireless communication technology, and in particular to a method, terminal and storage medium for integrated sensing and mutual information parsing. Background Technology

[0002] Integrated Sensing and Communication (ISAC) is one of the core technologies of sixth-generation mobile communication systems. Its core idea is to simultaneously implement communication and sensing functions on the same hardware device. Scientifically evaluating its integrated performance is crucial for system design. Perceptual mutual information, as a core metric in information theory, can accurately characterize a system's ability to extract target state information from received signals. However, the theoretical definition of this metric involves high-dimensional complex calculations, resulting in extremely high computational complexity. It remains primarily at the theoretical analysis and simulation stage, making it difficult to translate into an evaluation module that can be embedded in actual communication protocol stacks and computed online. Consequently, it cannot provide direct, quantitative feedback for dynamic optimizations such as real-time resource scheduling and waveform adaptive adjustment.

[0003] Therefore, how to transform perceived mutual information into an engineering metric that can be computed in real time has become a technical problem that urgently needs to be solved in this field.

[0004] Therefore, existing technologies still need to be improved and enhanced. Summary of the Invention

[0005] The main objective of this invention is to provide a method, terminal, and storage medium for analyzing sensing mutual information in a syntactic and perceptual system, aiming to solve the problem that existing technologies have not yet achieved constant-level complexity calculation of sensing mutual information, thus enabling theoretical measurements to truly have online application value.

[0006] To achieve the aforementioned objective, a first aspect of the present invention provides a method for parsing inter-sensory information in a synesthetic system, wherein the method comprises: Obtain the basic physical parameters of the target synesthetic system, which are the physical parameters at the time of initial construction of the target synesthetic system or at a specified time of the target. The target system constants are calculated based on the aforementioned basic physical parameters, and the definition formula of the sensing mutual information is simplified into a closed analytical formula based on the target system constants. When the target triggering condition is met, the system operating parameters are acquired in real time, and the real-time sensing mutual information value of the target integrated sensing system is calculated based on the system operating parameters, the target system constant, and the closed-form analytical formula. The target integrated sensing system is evaluated in real time based on the real-time sensing mutual information value to guide system resource allocation or waveform configuration.

[0007] In one implementation, the basic physical parameters include antenna configuration parameters, spectrum configuration parameters, transmit power parameters, and channel environment parameters; The system operating parameters include: number of receiving antennas, number of resource blocks, effective signal-to-noise ratio or resource allocation factor, spatial size of the sensing range, and sensing spatial dimension.

[0008] In one implementation, calculating the target system constants based on the fundamental physical parameters includes: Within the sensing range of the target sensing integrated system, a pre-sensing set is selected, and the channel parameters of each sensing point in the pre-sensing set are measured to obtain the target parameter set. Based on the basic physical parameters and the target parameter set, calculate the Fischer information matrix of each sensing point in the pre-sensing set; Obtain the target minimum value, which is the minimum ratio of the minimum eigenvalue of the Fischer information matrix of each sensing point in the pre-sensing set to the geometric mean of all eigenvalues; Obtain the target perception range and target perception dimension, and calculate the target system constant based on the target minimum value, the target perception range, and the target perception dimension. The target perception range is the spatial size of the perception range of the target synesthetic system, and the target perception dimension is the perception spatial dimension of the perception range of the target synesthetic system.

[0009] In one implementation, calculating the target system constants based on the fundamental physical parameters includes: The constants of the target system are calculated based on the constant calculation formula, which is as follows: ; in, Let be the constant of the target system. For the range of perception, Indicates the spatial size of the perception range. Indicates in Top Accumulate points. The minimum value of the target. Let q be the parameter to be sensed, and q be the channel parameter. right The gradient operator, where T is the matrix transpose symbol. For subcarrier spacing, This represents the number of distinguishable propagation paths in the channel.

[0010] In one implementation, the target triggering condition includes a timer expiration or a resource allocation scheme update event; When the target triggering condition is not activated, the real-time sensing mutual information value of the target integrated sensing system is not calculated.

[0011] In one implementation, the closed-form analytical formula expresses the perceptual mutual information as a functional relationship between the spatial size of the sensing range of the target integrated sensing system, the number of receiving antennas, the number of resource blocks, the square root of the effective signal-to-noise ratio, the sensing spatial dimension, the target system constant, and the confidence parameter; wherein, the perceptual mutual information is positively correlated with the spatial size of the sensing range, positively correlated with the power of the perceptual spatial dimension of the product of the number of receiving antennas, the number of resource blocks, and the square root of the effective signal-to-noise ratio, and negatively correlated with the correlation function of the target system constant and the confidence parameter.

[0012] In one implementation, the closed-form analytical formula is: ; in, The real-time perceived mutual information value. M The number of receiving antennas in the target integrated sensing system. N The number of resource blocks in the target integrated sensor system. As a resource allocation factor, Represents the parameter to be sensed The number of dimensions, Let be the constant of the target system. This is the confidence correlation function.

[0013] In one implementation, the real-time evaluation of the target sensory integration system based on real-time perceived mutual information values ​​includes: The real-time sensing mutual information value is compared with a preset threshold. When it is lower than the preset threshold, a resource adjustment command is triggered. The resource adjustment command includes increasing the number of receiving antennas, increasing the number of resource blocks, or increasing the transmission power. When the value exceeds the preset threshold, a preset proportion of resources are released for communication services to achieve collaborative optimization of sensing resources.

[0014] In a second aspect, the present invention provides a terminal, wherein the terminal includes: a memory, a processor, and a synesthetic and sensory mutual information parsing program stored in the memory and executable on the processor, wherein the synesthetic and sensory mutual information parsing program, when executed by the processor, implements the steps of the synesthetic and sensory mutual information parsing method as described above.

[0015] A third aspect of the present invention provides a computer-readable storage medium, wherein the computer storage medium stores one or more programs that can be executed by one or more processors to implement the steps of the integrated sensing mutual information parsing method described in any of the preceding claims.

[0016] Beneficial Effects: Compared with existing technologies, this invention provides a method, terminal, and storage medium for analyzing the mutual information of sensory integration. The method involves acquiring the basic physical parameters of the target sensory integration system (either during initial construction or at a specified time), calculating target system constants based on these parameters, and simplifying the mutual information definition formula into a closed-form analytical formula. When the target trigger condition is met, system operating parameters are acquired in real time, and the real-time mutual information value of the target sensory integration system is calculated based on these parameters, the target system constants, and the closed-form analytical formula. This allows for real-time evaluation of the target sensory integration system based on the real-time mutual information value, guiding system resource allocation or waveform configuration. This invention provides a method for analyzing the mutual information of sensory integration, solving the problem in existing technologies where constant-level complexity calculations of mutual information are not yet implemented, thus enabling theoretical measurements to have real-time application value. In this invention, a fundamental leap from "theoretical definition" to "real-time calculable engineering information" is achieved through the method of "phased calculation + closed-form analytical formula + on-demand triggering", enabling the integrated sensing system to evaluate sensing performance online and dynamically optimize resource allocation accordingly. Attached Figure Description

[0017] Figure 1 A flowchart illustrating an embodiment of the synesthetic and sensory mutual information parsing method provided by the present invention; Figure 2 A flowchart illustrating the mutual information calculation process of the integrated sensing mutual information parsing method provided by this invention; Figure 3 This is a system architecture diagram of the integrated sensing mutual information parsing method provided by the present invention; Figure 4 A schematic diagram of the operating environment of an embodiment of the terminal provided by the present invention. Detailed Implementation

[0018] To make the objectives, technical solutions, and effects of this invention clearer and more explicit, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0019] Those skilled in the art will understand that, unless specifically stated otherwise, the singular forms “a,” “an,” “the,” and “the” used herein may also include the plural forms. It should be further understood that the term “comprising” as used in this specification means the presence of the stated features, integers, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof. It should be understood that when we say an element is “connected” or “coupled” to another element, it can be directly connected or coupled to the other element, or there may be intermediate elements. Furthermore, “connected” or “coupled” as used herein can include wireless connections or wireless coupling. The term “and / or” as used herein includes all or any units and all combinations of one or more associated listed items.

[0020] It will be understood by those skilled in the art that, unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. It should also be understood that terms such as those defined in general dictionaries should be understood to have the same meaning as in the context of the prior art, and should not be interpreted in an idealized or overly formal sense unless specifically defined as herein.

[0021] The present invention provides a method for parsing the mutual information of integrated sensing and communication, which can be applied to terminals with computing capabilities. The terminal can execute the method for parsing the mutual information of integrated sensing and communication provided by the present invention to calculate the mutual information of sensing and communication to perform real-time evaluation of the integrated sensing and communication system.

[0022] Example 1 This embodiment provides a method for analyzing sensing mutual information in a syntactic and perceptual system, belonging to the field of wireless communication technology. The method for analyzing sensing mutual information in a syntactic and perceptual system provided in this embodiment achieves constant-level complexity calculation of sensing mutual information by constructing a deterministic mapping from the original system parameters to the closed-form solution of sensing mutual information, making the theoretical measurement truly valuable for online applications.

[0023] In existing technologies, Integrated Sensing and Communication (ISAC) is one of the core technologies of sixth-generation mobile communication systems. Its core idea is to simultaneously realize communication and radar sensing functions on the same set of hardware equipment and the same spectrum resources. How to scientifically and uniformly evaluate the comprehensive performance of an integrated sensing and communication system has become a key issue in system design and optimization.

[0024] Information theory provides a solid theoretical foundation for the performance evaluation of sensor-integrated systems. Perceptual mutual information (also known as perceptual entropy reduction), as a core metric in information theory, can accurately characterize the system's ability to extract target state information from received signals from the perspective of information content. In sensor-integrated systems, the definition of perceptual mutual information involves integration over a high-dimensional observation space and an optimized search for state shifts; its expression is: ; in, This represents the parameters to be sensed in the system (position, velocity, etc.), and its sensing range is... , This represents the mean of the detection error. Indicates receiving signal, To receive signals The posterior probability, Representing sensing parameters The probability of perceptual mutual information. The definition of perceptual mutual information is theoretically rigorous, but due to its extremely high computational complexity (usually exponential), it cannot be directly applied in the real-time processing of practical systems, which greatly limits the guiding value of this metric for the online optimization of synesthetic systems.

[0025] In existing technologies, there are some performance optimization methods for integrated sensing systems. For example, iterative optimization of angle parameters is achieved using compressed sensing and gradient descent, and some methods aim to maximize sensing mutual information while satisfying communication constraints. However, none of these methods can solve the problem of efficient computation of sensing mutual information itself.

[0026] Therefore, how to transform the theoretically sound performance metric of mutual information perception into an evaluation module that can be embedded in actual communication protocol stacks and computed online, so as to provide direct and quantitative feedback for dynamic optimization such as real-time resource scheduling and waveform adaptive adjustment of the system, has become a technical problem that urgently needs to be solved in this field.

[0027] In this embodiment, to bridge the gap between theoretical measurement and engineering practice, a specific and operable method for calculating perceptual mutual information is provided. By establishing an explicit model of this method with a series of key system parameters, high-performance synesthetic evaluation is no longer an offline theoretical tool, but becomes a core engine for online intelligent system optimization. Compared to the complex process of solving perceptual mutual information through high-dimensional numerical integration and nonlinear iterative optimization in existing technologies, this embodiment presents an explicit closed-form solution for this metric for the first time. This reduces the computational complexity from exponential levels, which cannot be processed in real time, to constant levels. It abandons the path of directly performing complex numerical calculations and instead derives a closed-form analytical solution for this metric that is only related to a finite number of native system parameters.

[0028] Specifically, such as Figure 1As shown, one embodiment of the integrated sensing mutual information parsing method provided in this embodiment includes the following steps: S100. Obtain the basic physical parameters of the target integrated sensor system. The basic physical parameters are the physical parameters at the time of initial construction of the target integrated sensor system or at the time specified in the target.

[0029] The basic physical parameters include antenna configuration parameters, spectrum configuration parameters, transmit power parameters, and channel environment parameters.

[0030] Specifically, in this embodiment, in order to solve the problem of moving from theoretical framework to engineering implementation of integrated sensing measurement, a deterministic mapping from the system's native parameters to the closed-form solution of perceptual mutual information is constructed.

[0031] First, a direct correlation model between system parameters and information-theoretic metrics is established. This is achieved through joint analysis of the signal model and channel model of the sensing system. Specifically, in this embodiment, key physical layer and control layer parameters affecting sensing performance, including but not limited to the number of receiving antennas, are analyzed. Number of resource blocks Effective signal-to-noise ratio or resource allocation factor and the prior perception space of the target state. With spatial dimension This is identified as the core input variable. Subsequently, the parameterized closed-form expression for the perceived mutual information is derived.

[0032] Specifically, in this embodiment, the target sensing integrated system has an M-row ULA antenna array, where the spacing between the antenna elements is... , If the system operating wavelength is given, then the channel model of the target sensing integrated system is modeled as follows: ; in, Representation matrix The m Line number n Column elements, The target sensing integrated system shows the parameters to be sensed (position, velocity, etc.), and its sensing range is... ; The channel matrix is ​​represented as follows: The function, M The number of receiving antennas in the target integrated sensing system. N The number of resource blocks in the target integrated sensing system; They represent the first The channel gain, propagation delay, and angle of arrival for each path. Indicates the subcarrier spacing; This represents the sum of gains from multiple reflection paths, where each element follows a mean of 0 and a variance of . Gaussian distribution; This represents the system noise, where the variance of each element is 1. .

[0033] S200. Calculate the target system constants based on the basic physical parameters, and simplify the definition formula of the sensing mutual information into a closed analytical formula based on the target system constants.

[0034] Reference Figure 2 The calculation of the target system constants based on the fundamental physical parameters includes: S210. Select a pre-sensing set within the sensing range of the target sensing integrated system, measure the channel parameters of each sensing point in the pre-sensing set, and obtain the target parameter set; S220. Calculate the Fischer information matrix of each sensing point in the pre-sensing set based on the basic physical parameters and the target parameter set. S230. Obtain the target minimum value, wherein the target minimum value is the minimum ratio of the minimum eigenvalue of the Fischer information matrix of each sensing point in the pre-sensing set to the geometric mean of all eigenvalues. S240. Obtain the target perception range and target perception dimension, and calculate the target system constant based on the target minimum value, the target perception range and the target perception dimension. The target perception range is the spatial size of the perception range of the target synesthetic system, and the target perception dimension is the perception spatial dimension of the perception range of the target synesthetic system.

[0035] Specifically, within the range of perception Within this set, several representative sensing points are selected to form the pre-sensing set. The system's channel measurement function is used to obtain the channel parameters corresponding to each sensing point in the pre-sensing set. The target parameter set is obtained, which includes information such as channel gain, propagation delay, and angle of arrival for each path.

[0036] Then, based on the basic physical parameters configured during system initialization, including the initial number of antennas (the number is...), ), initial number of subcarriers (number of which is ), initial transmit power (expressed as ) and subcarrier spacing (represented as Combined with the target parameter set, a one-time or low-frequency calculation is performed to determine and store the target system constants. This is for reuse in subsequent real-time calculations.

[0037] Specifically, the target system constants are determined based on the basic physical parameters configured during system initialization and the target parameter set. ,include: Based on the basic physical parameters and the target parameter set, calculate the Fischer information matrix for each sensing point in the pre-sensing set.

[0038] Based on the system initial parameters and the pre-sensing set obtained in step 1 The channel parameters of all sensing points are used to calculate the pre-sensing set. Fisher information matrix of all sensing points .in, It is The matrix is ​​calculated as follows: ; in , .

[0039] For the pre-sensing set FISHER information matrix of all sensing points Calculate the eigenvalues ​​of each matrix and obtain the target minimum value. ,in Representation matrix The smallest eigenvalue, Representation matrix The geometric mean of all eigenvalues.

[0040] Then, the constants of the target system are calculated based on the constant calculation formula, which is: ; in, Let be the constant of the target system. For the range of perception, Indicates the spatial size of the perception range. Indicates in Integrate over P. The minimum value of the target. Let q be the parameter to be sensed, and q be the channel parameter. For the gradient operator of P, T is the matrix transpose symbol. For subcarrier spacing, This represents the number of distinguishable propagation paths in the channel.

[0041] S300. When the target triggering condition is met, the system operating parameters are acquired in real time, and the real-time sensing mutual information value of the target integrated sensing system is calculated based on the system operating parameters, the target system constant, and the closed-form analytical formula.

[0042] The system operating parameters include: number of receiving antennas, number of resource blocks, effective signal-to-noise ratio or resource allocation factor, spatial size of the sensing range, and spatial dimension of the sensing space. The target triggering conditions include timer expiration or resource allocation scheme update events; When the target triggering condition is not activated, the real-time sensing mutual information value of the target integrated sensing system is not calculated.

[0043] Specifically, in this embodiment, it is determined in real time whether to update the resource allocation scheme of the sensing system (including power allocation scheme, antenna allocation scheme, subcarrier allocation scheme, etc.). The triggering condition can be timed or event-triggered (such as a resource allocation scheme update event). If it is not triggered, the sensing performance calculation module does not work; if it is triggered, the next operation is performed.

[0044] When the target trigger condition is met, the real-time sensing mutual information value is obtained.

[0045] First, the system operating parameters are acquired in real time, including but not limited to the number of antennas. Number of resource blocks Effective signal-to-noise ratio or resource allocation factor and the dimension of perceived space Output the results.

[0046] Then, extract the target system constants. and the system parameter group output by the system parameter input module. Calculate the perceived performance of the system under this resource configuration scheme.

[0047] Specifically, the real-time sensing mutual information value of the target integrated sensing system is calculated based on the system operating parameters, the target system constants, and the closed-form analytical formula.

[0048] First, determine the parameter to be sensed. Dimensions The value of . If Then the confidence correlation function ,in Standard Gaussian The inverse function of a function; if ,but .

[0049] Based on the obtained confidence correlation function In addition to the input parameters of other modules, the system's sensing performance under this resource configuration scheme is calculated based on the closed-form analytical formula. The closed-form analytical formula expresses the sensing mutual information as a functional relationship between the spatial size of the sensing range of the target integrated sensing system, the number of receiving antennas, the number of resource blocks, the square root of the effective signal-to-noise ratio, the sensing spatial dimension, the target system constant, and the confidence parameter. Among them, the sensing mutual information is positively correlated with the spatial size of the sensing range, positively correlated with the power of the sensing spatial dimension (the product of the number of receiving antennas, the number of resource blocks, and the square root of the effective signal-to-noise ratio), and negatively correlated with the function relating the target system constant and the confidence parameter.

[0050] Specifically, the closed-form analytical formula is: ; in, The real-time perceived mutual information value. M The number of receiving antennas in the target integrated sensing system. N The number of resource blocks in the target integrated sensor system. As a resource allocation factor, Represents the parameter to be sensed The number of dimensions, Let be the constant of the target system. This is the confidence correlation function.

[0051] It should be noted that in this embodiment, it is based on and This example is used for illustration, but it is not limited to this in many embodiments. For In this case, the parameter to be sensed This will include more dimensional state information (e.g., adding a height dimension in three-dimensional space). At this point, some parameters (such as system constants) will be affected. , The calculation method can be adaptively adjusted based on the geometric characteristics of the high-dimensional space, and the specific calculation method can be determined according to the actual application scenario. As long as the core idea of ​​mapping system parameters to closed-form solutions proposed in this invention is followed, it falls within the protection scope of this invention.

[0052] S400. Based on the real-time sensing mutual information value, the target integrated sensing system is evaluated in real time to guide system resource allocation or waveform configuration.

[0053] The real-time evaluation of the target sensory integrated system based on real-time perceived mutual information values ​​includes: The real-time sensing mutual information value is compared with a preset threshold. When it is lower than the preset threshold, a resource adjustment command is triggered. The resource adjustment command includes increasing the number of receiving antennas, increasing the number of resource blocks, or increasing the transmission power. When the value exceeds the preset threshold, a preset proportion of resources are released for communication services to achieve collaborative optimization of sensing resources.

[0054] Obtain the real-time sensing mutual information value Subsequently, in this embodiment, it also includes based on Perform real-time assessment and resource optimization: The real-time perceived mutual information value With the preset threshold Comparison: when When this occurs, it indicates that the current system's sensing capability is insufficient and cannot meet business needs. The system then triggers resource adjustment instructions, including but not limited to: increasing the number of receiving antennas, increasing the number of resource blocks, or increasing the transmission power to improve the effective signal-to-noise ratio.

[0055] when When this occurs, it indicates that the current system's sensing capabilities are excessive. The system then releases a preset proportion of resources for communication services, achieving collaborative optimization of sensing resources and improving overall spectrum efficiency.

[0056] Specifically, in this embodiment, the operation can be based on a sensory integration and mutual information analysis system, referring to... Figure 3 First, the system configuration module performs one-time or low-frequency calculations based on the system's fundamental physical parameters to determine and store the core system constants. When a triggering condition module (such as a timer expiration or resource allocation scheme update event) is activated, it will drive the system input module to collect or receive the latest system operating parameters (including the number of receiving antennas) in real time. Number of resource blocks Effective signal-to-noise ratio or resource allocation factor Prior perception space of the target state With spatial dimension Finally, the perception performance calculation module synchronously calls the constants pre-stored in the system configuration module. In addition to the real-time parameters provided by the system input module, the closed-form analytical formula is executed. Instantly outputs accurate perceived mutual information values This enables instantaneous and efficient calculation and evaluation of the system's perceived performance, guiding system resource allocation or waveform configuration.

[0057] Furthermore, in this embodiment, an efficient closed-form analytical formula for perceptual mutual information is first proposed. This closed-form analytical formula is the first to analytically express the core metric of perceptual mutual information as an explicit function of a finite set of native parameters that can be directly obtained from the system's physical and resource management layers.

[0058] Secondly, the quantitative influence mechanism of each system parameter on sensing performance in the closed-form solution is revealed. The closed-form analytical formula clearly reveals: sensing performance With the total amount of resources ( and Effective signal-to-noise ratio (SNR) and the size of the space within the perception range. The increase is due to the increase in the state dimension, and the rate of increase is due to the increase in the state dimension. This decision provides a direct and quantitative theoretical basis for system optimization (such as resource allocation and waveform design).

[0059] Furthermore, an efficient and modular computing system based on closed-form solutions was constructed. To implement the above formulas, this embodiment designed a system architecture in which a system configuration module, a triggering and input module, and a core computing module work together. This architecture decouples the relatively fixed constant calculations from the real-time changing parameter processing, ensuring instantaneous and accurate evaluation of perceived performance in constant-time complexity, thus making theoretical measurements truly valuable for online applications.

[0060] Finally, a complete technical system of "theoretical definition - efficient computation" was formed. Specifically, this embodiment represents a breakthrough in the efficient and feasible calculation method for the theoretical indicator of "perceptual entropy reduction". The combination of the two constitutes a complete integrated sensory performance evaluation solution, from a unified metric theory to a real-time computing engine.

[0061] In summary, this embodiment provides a method for resolving sensing mutual information in a synesthetic system. When evaluating a synesthetic system in real time, it acquires the basic physical parameters of the target synesthetic system. These basic physical parameters are those at the time of initial construction of the target synesthetic system or at a specified target time. Then, it calculates the target system constants based on these basic physical parameters and simplifies the sensing mutual information definition formula into a closed-form analytical formula based on these target system constants. When the target trigger condition is met, it acquires the system operating parameters in real time and calculates the real-time sensing mutual information value of the target synesthetic system based on these system operating parameters, the target system constants, and the closed-form analytical formula. This allows for real-time evaluation of the target synesthetic system based on the real-time sensing mutual information value, guiding system resource allocation or waveform configuration. This embodiment provides a method for resolving sensing mutual information in a synesthetic system, solving the problem in existing technologies where constant-level complexity calculation of sensing mutual information has not yet been achieved, thus enabling theoretical measurements to have real-world application value. In this embodiment, the method of "phased calculation + closed-form analytical formula + on-demand triggering" is used to achieve a fundamental leap from "theoretical definition" to "real-time calculable engineering information" for sensing mutual information, enabling the integrated sensing system to evaluate sensing performance online and dynamically optimize resource allocation accordingly.

[0062] It should be understood that although the steps in the flowcharts shown in the accompanying drawings are displayed sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of the steps in this invention, and these steps can be executed in other orders. Moreover, at least a portion of the steps in this invention may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least a portion of the sub-steps or stages of other steps.

[0063] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program using signal-related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided by this invention can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM). ROM Programmable ROM ( PROM ), electrically programmable ROM ( EPROM Electrically erasable programmable ROM ( EEPROM ) or flash memory. Volatile memory may include random access memory (RAM) RAM Alternatively, an external cache memory. This is for illustrative purposes only and not as a limitation. RAM It can be obtained in various forms, such as static RAM ( SRAM ),dynamic RAM ( DRAM ),synchronous DRAM ( SDRAM ), double data rate SDRAM ( DDRSDRAM ), Enhanced SDRAM ( ESDRAM ), Synchronization Link ( Synchlink ), DRAM ( SLDRAM ), memory bus ( Rambus )direct RAM ( RDRAM ), Direct Memory Bus Dynamics RAM ( DRDRAM ), and memory bus dynamics RAM ( RDRAM )wait.

[0064] Example 2 like Figure 4 As shown, based on the above-mentioned integrated sensing mutual information parsing method, the present invention also provides a terminal, which includes a processor 10, a memory 20 and a display 30. Figure 4 Only some of the terminal components are shown; however, it should be understood that it is not required to implement all of the components shown, and more or fewer components may be implemented instead.

[0065] In some embodiments, the memory 20 may be an internal storage unit of the terminal, such as the terminal's hard drive or memory. In other embodiments, the memory 20 may also be an external storage device of the terminal, such as a plug-in hard drive or smart memory card equipped on the terminal. SmartMedia Card , SMC ), Secure Digital ( Secure Digital , SD ) card, flash memory card ( Flash Card Furthermore, the memory 20 may include both internal storage units and external storage devices of the terminal. The memory 20 is used to store application software and various types of data installed on the terminal, such as the program code installed on the terminal. The memory 20 can also be used to temporarily store data that has been output or will be output. In one embodiment, the memory 20 stores a synesthetic integration sensing mutual information parsing program 40, which can be executed by the processor 10 to implement the synesthetic integration sensing mutual information parsing method of this application.

[0066] In some embodiments, the processor 10 may be a central processing unit (CPU). Central Processing Unit , CPU (a microprocessor or other data processing chip) is used to run the program code stored in the memory 20 or process data, such as executing the integrated sensing mutual information parsing method.

[0067] The display 30 may be, in some embodiments, LED Monitors, LCD monitors, touch LCD monitors and OLED ( Organic Light - Emitting Diode The display 30 includes components such as organic light-emitting diodes (OLEDs) and touchscreens. It is used to display information on the terminal and to display a visual user interface. The components 10-30 of the terminal communicate with each other via a system bus.

[0068] In one embodiment, when the processor 10 executes the integrated sensing mutual information parsing program 40 in the memory 20, the following steps are performed: Obtain the basic physical parameters of the target synesthetic system, which are the physical parameters at the time of initial construction of the target synesthetic system or at a specified time of the target. The target system constants are calculated based on the aforementioned basic physical parameters, and the definition formula of the sensing mutual information is simplified into a closed analytical formula based on the target system constants. When the target triggering condition is met, the system operating parameters are acquired in real time, and the real-time sensing mutual information value of the target integrated sensing system is calculated based on the system operating parameters, the target system constant, and the closed-form analytical formula. The target integrated sensing system is evaluated in real time based on the real-time sensing mutual information value to guide system resource allocation or waveform configuration.

[0069] The basic physical parameters include antenna configuration parameters, spectrum configuration parameters, transmit power parameters, and channel environment parameters; The system operating parameters include: number of receiving antennas, number of resource blocks, effective signal-to-noise ratio or resource allocation factor, spatial size of the sensing range, and sensing spatial dimension.

[0070] The calculation of the target system constants based on the fundamental physical parameters includes: Within the sensing range of the target sensing integrated system, a pre-sensing set is selected, and the channel parameters of each sensing point in the pre-sensing set are measured to obtain the target parameter set. Based on the basic physical parameters and the target parameter set, calculate the Fischer information matrix of each sensing point in the pre-sensing set; Obtain the target minimum value, which is the minimum ratio of the minimum eigenvalue of the Fischer information matrix of each sensing point in the pre-sensing set to the geometric mean of all eigenvalues; Obtain the target perception range and target perception dimension, and calculate the target system constant based on the target minimum value, the target perception range, and the target perception dimension. The target perception range is the spatial size of the perception range of the target synesthetic system, and the target perception dimension is the perception spatial dimension of the perception range of the target synesthetic system.

[0071] The calculation of the target system constants based on the aforementioned fundamental physical parameters includes: The constants of the target system are calculated based on the constant calculation formula, which is as follows: ; in, Let be the constant of the target system. For the range of perception, Indicates the spatial size of the perception range. Indicates in Top Accumulate points. The minimum value of the target. For the parameters to be sensed, For channel parameters, right The gradient operator, where T is the matrix transpose symbol. For subcarrier spacing, This represents the number of distinguishable propagation paths in the channel.

[0072] The target triggering conditions include timer expiration or resource allocation scheme update events; When the target triggering condition is not activated, the real-time sensing mutual information value of the target integrated sensing system is not calculated.

[0073] The closed-form analytical formula expresses the perceptual mutual information as a functional relationship between the spatial size of the sensing range of the target integrated sensing system, the number of receiving antennas, the number of resource blocks, the square root of the effective signal-to-noise ratio, the sensing spatial dimension, the target system constant, and the confidence parameter. The perceptual mutual information is positively correlated with the spatial size of the sensing range, positively correlated with the product of the number of receiving antennas, the number of resource blocks, and the square root of the effective signal-to-noise ratio (the sensing spatial dimension raised to the power of the product), and negatively correlated with the correlation function of the target system constant and the confidence parameter.

[0074] The closed-form analytical formula is as follows: ; in, The real-time perceived mutual information value. M The number of receiving antennas in the target integrated sensing system. N The number of resource blocks in the target integrated sensor system. As a resource allocation factor, Represents the parameter to be sensed The number of dimensions, Let be the constant of the target system. This is the confidence correlation function.

[0075] The real-time evaluation of the target sensing integrated system based on real-time perceived mutual information values ​​includes: The real-time sensing mutual information value is compared with a preset threshold. When it is lower than the preset threshold, a resource adjustment command is triggered. The resource adjustment command includes increasing the number of receiving antennas, increasing the number of resource blocks, or increasing the transmission power. When the value exceeds the preset threshold, a preset proportion of resources are released for communication services to achieve collaborative optimization of sensing resources.

[0076] Example 3 The present invention also provides a computer-readable storage medium having stored thereon one or more programs that can be executed by one or more processors to implement the steps of the integrated sensing mutual information parsing method described in the above embodiments.

[0077] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for parsing mutual information in a synesthetic and sensory integration system, characterized in that, The integrated sensing mutual information parsing method includes: Obtain the basic physical parameters of the target synergistic system, which are the physical parameters at the time of initial construction of the target synergistic system or at a specified time of the target. The target system constants are calculated based on the aforementioned basic physical parameters, and the definition formula of the sensing mutual information is simplified into a closed analytical formula based on the target system constants. When the target triggering condition is met, the system operating parameters are acquired in real time, and the real-time sensing mutual information value of the target integrated sensing system is calculated based on the system operating parameters, the target system constant, and the closed-form analytical formula. The target integrated sensing system is evaluated in real time based on the real-time sensing mutual information value to guide system resource allocation or waveform configuration.

2. The method for parsing mutual information of integrated sensing according to claim 1, characterized in that, The basic physical parameters include antenna configuration parameters, spectrum configuration parameters, transmit power parameters, and channel environment parameters; The system operating parameters include: number of receiving antennas, number of resource blocks, effective signal-to-noise ratio or resource allocation factor, spatial size of the sensing range, and sensing spatial dimension.

3. The method for parsing mutual information of integrated sensing according to claim 2, characterized in that, The calculation of the target system constants based on the fundamental physical parameters includes: Within the sensing range of the target sensing integrated system, a pre-sensing set is selected, and the channel parameters of each sensing point in the pre-sensing set are measured to obtain the target parameter set. Based on the basic physical parameters and the target parameter set, calculate the Fischer information matrix of each sensing point in the pre-sensing set; Obtain the target minimum value, which is the minimum ratio of the minimum eigenvalue of the Fischer information matrix of each sensing point in the pre-sensing set to the geometric mean of all eigenvalues; Obtain the target perception range and target perception dimension, and calculate the target system constant based on the target minimum value, the target perception range, and the target perception dimension. The target perception range is the spatial size of the perception range of the target synesthetic system, and the target perception dimension is the perception spatial dimension of the perception range of the target synesthetic system.

4. The method for parsing mutual information of integrated sensing according to claim 3, characterized in that, The target system constants are calculated based on the aforementioned fundamental physical parameters, including: The constants of the target system are calculated based on the constant calculation formula, which is as follows: ； in, Let be the constant of the target system. For the range of perception, Indicates the spatial size of the perception range. Indicates in Top Accumulate points. The minimum value of the target. For the parameters to be sensed, For channel parameters, right The gradient operator, where T is the matrix transpose symbol. For subcarrier spacing, This represents the number of distinguishable propagation paths in the channel.

5. The method for parsing mutual information of integrated sensing according to claim 1, characterized in that, The target triggering conditions include timer expiration or resource allocation scheme update events; When the target triggering condition is not activated, the real-time sensing mutual information value of the target integrated sensing system is not calculated.

6. The method for parsing mutual information of integrated sensing according to claim 1, characterized in that, The closed-form analytical formula expresses the perceptual mutual information as a functional relationship between the spatial size of the sensing range of the target integrated sensing system, the number of receiving antennas, the number of resource blocks, the square root of the effective signal-to-noise ratio, the sensing spatial dimension, the target system constant, and the confidence parameter. Among them, the perceptual mutual information is positively correlated with the spatial size of the sensing range, positively correlated with the product of the number of receiving antennas, the number of resource blocks, and the square root of the effective signal-to-noise ratio (the sensing spatial dimension raised to the power of the product), and negatively correlated with the correlation function of the target system constant and the confidence parameter.

7. The method for parsing mutual information of integrated sensing according to claim 1, characterized in that, The closed-form analytical formula is: ； in, The real-time perceived mutual information value. M The number of receiving antennas in the target integrated sensing system. N The number of resource blocks in the target integrated sensor system. As a resource allocation factor, Represents the parameter to be sensed The number of dimensions, Let be the constant of the target system. This is the confidence correlation function.

8. The method for parsing mutual information of integrated sensing according to claim 1, characterized in that, The real-time evaluation of the target sensory integrated system based on real-time perceived mutual information values ​​includes: The real-time sensing mutual information value is compared with a preset threshold. When it is lower than the preset threshold, a resource adjustment command is triggered. The resource adjustment command includes increasing the number of receiving antennas, increasing the number of resource blocks, or increasing the transmission power. When the value exceeds the preset threshold, a preset proportion of resources are released for communication services to achieve collaborative optimization of sensing resources.

9. A smart terminal, characterized in that, The smart terminal includes a memory, a processor, and a sensory integration mutual information parsing program stored in the memory and executable on the processor. When the sensory integration mutual information parsing program is executed by the processor, it implements the steps of the sensory integration mutual information parsing method as described in any one of claims 1-8.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a syn-sensing integrated sensing mutual information parsing program, which, when executed by a processor, implements the steps of the syn-sensing integrated sensing mutual information parsing method as described in any one of claims 1-8.

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