Communication sensing security joint beam forming method for smart granary

Abstract

The invention discloses a communication perception security joint beam forming method for an intelligent granary. The method comprises the following steps: constructing a completely known communication perception integrated system model considering channel state information of an eavesdropper; establishing a communication performance index and a sensing performance index of the ISAC system model; constructing an optimization problem by taking the sum of the minimum communication beam power and the artificial noise power as a target; splitting the optimization problem into three sub-problems by adopting a block coordinate descent method; and solving the sub-problem 1 by using an algorithm combining fractional programming and positive semidefinite relaxation, modeling the sub-problem 2 into positive semidefinite programming and solving, and solving the sub-problem 3 according to generalized Rayleigh entropy to obtain respective optimal solutions of a sensor beam forming vector, an artificial noise covariance matrix and a base station receiving filter vector. According to the method, the safety communication of the system is ensured under the condition that the requirements of communication quality and sensing precision are met, and a high-robustness optimization approach is provided for the intelligent granary which is a multi-node, multi-target and multi-interference-source complex system.

Description

Technical Field

[0001] This invention relates to the field of integrated communication and sensing technology, and in particular to a joint beamforming method for communication, sensing and security for smart grain warehouses. Background Technology

[0002] In recent years, as the boundaries between communication and radar sensing in spectrum usage have become increasingly blurred, the problem of spectrum resource scarcity has intensified, and integrated communication and sensing technology has gradually become an important development direction for 6G wireless networks. This technology utilizes the same base station and antenna array to perform both communication and target sensing simultaneously, effectively improving spectrum efficiency and reducing system complexity.

[0003] As a crucial component of the national food security project, smart grain warehouses require long-term, reliable monitoring of grain conditions, with real-time wireless data transmission to a management platform. Simultaneously, to prevent grain spoilage, pest spread, or storage equipment malfunctions, they also need the ability to proactively sense and locate specific areas or equipment within the warehouse. Grain warehouses contain numerous sensors, and communication frequency bands are already extremely limited; independently deploying radar sensing frequency bands would be a significant waste of electromagnetic resources. The warehouse structure is often a closed steel-framed space with numerous internal supports, making the installation of multiple antenna arrays and sensing devices not only difficult but also costly and maintenance-intensive. Heat spots and pest infestations within the grain warehouse need to be detected and addressed promptly. If communication and sensing are separated, two systems must work together to assess risks, resulting in slow response times, insufficient reliability, and potential safety hazards. For these reasons, smart grain warehouses urgently need a technology that can simultaneously perform communication and sensing tasks using a single antenna structure.

[0004] The sharing of waveform resources between communication and sensing systems also brings new security challenges. Radar detection signals contain communication information, which must be propagated to the sensing target for echo detection. Therefore, communication data is very vulnerable to malicious eavesdropping, and the sensed target may use the communication information carried by the sensing signal to conduct eavesdropping. This means that transmitters need to make certain trade-offs regarding security issues in integrated communication and sensing systems: on the one hand, they hope to improve sensing reliability by concentrating power in the target direction; on the other hand, they need to limit the leakage of communication information in the target direction. Existing research only considers communication service quality or radar pattern constraints, or uses a single beam design method such as SCA or WMMSE, which makes it difficult to balance performance indicators under multi-user, multi-target conditions. Physical layer security research based on artificial noise rarely focuses on system energy efficiency and has a single eavesdropping target. Summary of the Invention

[0005] In view of the above problems, this invention provides a joint beamforming method for communication-aware security for smart grain warehouses. This method is based on beamforming design with known channel state information of eavesdroppers. Under the constraints of sensor communication, eavesdropper constraints and radar perception constraints, a joint optimization problem model for the base station transmit beamforming vector is established to minimize the sum of communication beam power and artificial noise power. The optimization problem is solved by block coordinate descent, fractional programming and semidefinite relaxation algorithms.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows: In a first aspect, the present invention provides a joint beamforming method for communication-aware security in smart grain warehouses, comprising the following steps: Step 1: Construct a communication and sensing integrated system model that considers the eavesdropper's channel state information to be fully known. The model includes a base station with both radar and communication functions, a sensor that legally communicates with the base station, a radar target that is sensed by the base station, and an eavesdropper attempting to eavesdrop on the sensor's communication signals. Step 2: Establish the communication performance indicators and sensing performance indicators of the ISAC system model. The communication performance indicators include the signal-to-interference-plus-noise ratio (SNR) of legitimate eavesdroppers and the SNR of illegal eavesdroppers on the sensors. The sensing performance indicators include the radar SNR of the radar echo signal received by the base station. Step 3: With the goal of minimizing the sum of communication beam power and artificial noise power, and with constraints such as the communication signal-to-noise ratio of legitimate sensors, the eavesdropping signal-to-noise ratio of the sensors, and the radar signal-to-noise ratio of the radar echo signal received by the base station, jointly optimize the beamforming matrix of the base station to construct an optimization problem. Step 4: The optimization problem is divided into three sub-problems using the block coordinate descent method. Sub-problem 1 is to optimize the sensor beamforming vector with a fixed artificial noise covariance matrix and a fixed base station receiving filter vector. Sub-problem 2 is to optimize the artificial noise covariance matrix with a fixed sensor beamforming vector and a fixed base station receiving filter vector. Sub-problem 3 is to optimize the base station receiving filter vector with a fixed artificial noise covariance matrix and a fixed sensor beamforming vector. Step 5: Solve subproblem 1 using an algorithm combining fractional programming and semidefinite relaxation. Model subproblem 2 as a semidefinite programming problem and solve it. Solve subproblem 3 using the generalized Rayleigh entropy to obtain the optimal solutions for the sensor beamforming vector, the artificial noise covariance matrix, and the base station receiving filter vector.

[0007] In one embodiment of the present invention, the model in step 1 includes One sensor, radar targets and An eavesdropper, the base station is equipped with One transmitting antenna, One receiving antenna, simultaneously responsible for... Each sensor provides communication services and... Detection is performed in one direction by radar.

[0008] In one embodiment of the present invention, the process of obtaining the signal-to-interference-plus-noise ratio (SNR) of the legitimate eavesdropper and the SNR of the illegal eavesdropper on the sensor in step 2 is as follows: the base station's transmitted signal is... , Artificial noise added to prevent information leakage, and ; in, It is a beamforming matrix. Representing the Beamforming vectors of a valid sensor, It is an information symbol vector that satisfies And assume the communication symbol vector and artificial noise vector They are independent of each other; Covariance matrix of transmitted signal ; The signal received by the sensor is , This represents the channel between the base station and the sensor. For sensors Noise at the location; Based on the expression of the received signal, the sensor The signal-to-interference-plus-noise ratio is expressed as:

[0009]

[0010] in, ; No. The received signal of the eavesdropper is expressed as follows: , It is additive white Gaussian noise; No. The eavesdropper on the first The SINR of each sensor is as follows:

[0011] In one embodiment of the present invention, the process of obtaining the radar signal-to-noise ratio corresponding to the radar echo signal received by the base station in step 2 is as follows: the base station transmits a signal. At the same time, it also receives radar echo signals; the radar echo signals received by the base station are ,in The target response matrix is ​​represented as follows:

[0012] in It is a complex target amplitude that satisfies ;vector It is the radar antenna transmitting array in Direction steering vector, It is the steering vector of the receiving array; The azimuth of the target; The signal received by the base station is ; in, This represents noise composed of additive white Gaussian noise and residual self-interference. To improve radar sensing performance, the base station uses a set of receiving filters. To receive radar echo signals, the filtered signal is Therefore, the SINR for the perception of target n is:

[0013]

[0014] in, .

[0015] In one embodiment of the present invention, the optimization problem constructed in step 3 is:

[0016] ,

[0017] ,

[0018]

[0019] In the formula For the SINR requirement of the k-th valid sensor, A predefined threshold to achieve the desired target detection performance.

[0020] In one embodiment of the present invention, step 5, which uses an algorithm combining fractional programming and semidefinite relaxation to solve subproblem 1, specifically includes: fixed , ,right Optimize: Subproblem 1 is:

[0021]

[0022]

[0023]

[0024] First, the FP algorithm is used to handle communication constraints, and auxiliary variables are introduced. Perform multidimensional direct FP reconstruction:

[0025] The rank-one Hermitian positive semidefinite matrix defined earlier , It should meet the following requirements:

[0026]

[0027] Next, define all The set of Viermitian positive semidefinite matrices is ; Atomic Problem 1 is transformed into:

[0028]

[0029]

[0030]

[0031]

[0032]

[0033] Using the SDR algorithm, the rank-one constraint is discarded, and the problem is relaxed to:

[0034]

[0035]

[0036]

[0037]

[0038] This is a semidefinite programming problem, which can be solved efficiently using CVX; since the rank-one constraint is temporarily ignored, the optimal objective value of the problem is only used as a lower bound; after obtaining... After that, if obtained If the rank-one constraint is satisfied, then eigenvalue decomposition is used to obtain the optimal solution. Otherwise, Gaussian randomization is needed to transform the high-rank solution of the problem into a row-rank-one solution; then, based on the obtained... renew :

[0039] Each iteration update , until convergence.

[0040] In one embodiment of the present invention, step 5, which involves modeling subproblem 2 as a semidefinite programming problem and solving it, specifically includes: fixed , Covariance of artificial noise Optimize; Subproblem 2 is expressed as:

[0041]

[0042]

[0043]

[0044]

[0045] This is also a semidefinite programming problem, because... Since the matrix itself is a positive semi-definite matrix and has no rank constraint, this problem is a convex optimization problem and can be solved directly using CVX.

[0046] In one embodiment of the present invention, step 5, which involves solving subproblem 3 based on the generalized Rayleigh entropy, specifically includes: fixed , ,optimization ; Subproblem 3 is only related to the radar SINR constraint, and the optimization problem is formulated as follows:

[0047] This is a typical generalized Rayleigh entropy, whose optimal solution is a matrix. The generalized eigenvector corresponding to the largest eigenvalue.

[0048] In a second aspect, the present invention provides a computer-readable storage medium storing computer instructions which are executed by a processor using the method described above.

[0049] Thirdly, the present invention provides a computer program product, the computer program product storing computer instructions, the computer instructions being executed by a processor using the method described above.

[0050] The beneficial effects achieved by this invention are as follows: 1. This invention addresses the challenges of strong metal reflection, complex propagation paths, and high eavesdropping risks within smart grain warehouses. It integrates communication links, radar sensing links, and security mechanisms into a unified design framework. Through an alternating BCD-FP-SDR optimization method, it effectively solves the coupling problem between multiple types of non-convex partial constraints. Compared to traditional schemes that only optimize communication or only optimize sensing, this invention reduces total system power consumption while ensuring communication quality and sensing accuracy, and significantly improves communication coverage and data transmission stability. This invention transforms complex non-convex problems into solvable convex optimization forms, enabling stable, energy-efficient communication in closed and complex environments such as smart grain warehouses.

[0051] 2. In smart grain warehouses, wireless signals are easily intercepted by eavesdroppers. Traditional integrated communication and sensing systems often only guarantee communication service quality or radar sensing performance, making it difficult to address physical layer security. This invention constructs a triple joint optimization index for communication SINR, radar sensing SINR, and eavesdropper SINR. Through artificial noise-assisted secure beamforming and a joint communication-sensing directional transmission design, it effectively suppresses the leakage of communication information towards eavesdropping while meeting the communication rate of legitimate sensors and the accuracy of radar target detection. Furthermore, this invention introduces a receiving filter at the radar receiver, which can further improve the signal-to-interference-plus-noise ratio of the target echo, thereby achieving synergistic optimization of secure communication and high-precision sensing.

[0052] 3. This invention first decomposes the high-dimensional coupling problem into three sub-problems: beamforming optimization, artificial noise optimization, and radar filter optimization using the BCD method. Then, it employs the FP multidimensional reconstruction method to handle communication and security fractional constraints. Finally, it uses SDR to relax the rank constraint and recovers a high-quality feasible solution through eigenvalue decomposition and Gaussian randomization. Compared with existing technologies, this invention improves the sensing SINR in the radar target direction and maintains fast and stable convergence even in multi-sensor dense scenarios, providing a highly robust optimization approach for complex systems like smart grain warehouses with multiple nodes, multiple targets, and multiple interference sources. Attached Figure Description

[0053] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the embodiments of the present invention will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the drawings without creative effort.

[0054] Figure 1 This is a flowchart of the method of the present invention; Figure 2 This is a model diagram of the ISAC system in this invention; Figure 3 This is a comparison chart of the convergence performance of the method of the present invention under different communication SINR conditions; Figure 4 This is a comparison chart of the total power of the method of the present invention and the comparative method in a multi-user scenario; Figure 5 This is a comparison chart of the total power of the method of the present invention and the comparative method under different radar SINR. Detailed Implementation

[0055] The embodiments of the technical solution of the present invention will now be described in detail with reference to the accompanying drawings. These embodiments are merely illustrative of the technical solution of the present invention and are therefore intended to limit the scope of protection of the present invention.

[0056] Unless otherwise defined, all technical and scientific terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains; the terminology used in this invention is for the purpose of describing particular embodiments only and is not intended to limit the invention; the terms “comprising” and “having”, and any variations thereof, in the specification, claims and foregoing description of the drawings are intended to cover non-exclusive inclusion.

[0057] In the description of the embodiments of this invention, technical terms such as "first" and "second" are used only to distinguish different objects and should not be construed as indicating or implying relative importance or implicitly specifying the number, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this invention, "multiple" means two or more, unless otherwise explicitly defined.

[0058] In this invention, the reference to "embodiment" means that a specific feature, structure, or characteristic described in connection with an embodiment may be included in at least some embodiments of the invention. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described in this invention can be combined with other embodiments.

[0059] In the description of the embodiments of this invention, the term "and / or" is merely a description of the relationship between associated objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, in this invention, the character " / " generally indicates that the preceding and following associated objects have an "or" relationship.

[0060] like Figure 1 As shown, this invention provides a joint beamforming method for communication-aware security in smart grain warehouses, comprising the following steps: Step 1: Construct a communication and sensing integrated system model that considers the eavesdropper's channel state information to be fully known. The model includes a base station with both radar and communication functions, a sensor that legally communicates with the base station, a radar target that is sensed by the base station, and an eavesdropper attempting to eavesdrop on the sensor's communication signals. Step 2: Establish the communication performance indicators and sensing performance indicators of the ISAC system model. The communication performance indicators include the signal-to-interference-plus-noise ratio (SNR) of legitimate eavesdroppers and the SNR of illegal eavesdroppers on the sensors. The sensing performance indicators include the radar SNR of the radar echo signal received by the base station. Step 3: With the goal of minimizing the sum of communication beam power and artificial noise power, and with constraints such as the communication signal-to-noise ratio of legitimate sensors, the eavesdropping signal-to-noise ratio of the sensors, and the radar signal-to-noise ratio of the radar echo signal received by the base station, jointly optimize the beamforming matrix of the base station to construct an optimization problem. Step 4: The optimization problem is divided into three sub-problems using the block coordinate descent method. Sub-problem 1 is to optimize the sensor beamforming vector with a fixed artificial noise covariance matrix and a fixed base station receiving filter vector. Sub-problem 2 is to optimize the artificial noise covariance matrix with a fixed sensor beamforming vector and a fixed base station receiving filter vector. Sub-problem 3 is to optimize the base station receiving filter vector with a fixed artificial noise covariance matrix and a fixed sensor beamforming vector. Step 5: Solve subproblem 1 using an algorithm combining fractional programming and semidefinite relaxation. Model subproblem 2 as a semidefinite programming problem and solve it. Solve subproblem 3 using the generalized Rayleigh entropy to obtain the optimal solutions for the sensor beamforming vector, the artificial noise covariance matrix, and the base station receiving filter vector.

[0061] like Figure 2 As shown, the model in step 1 includes a base station (BS). One sensor, radar targets and One eavesdropper, whose base station is equipped with One transmitting antenna, One receiving antenna, simultaneously responsible for... Each sensor provides communication services and... The radar detects in one direction; these antennas are arranged at half-wavelength intervals to form a uniform linear array ULA.

[0062] All symbols used below conform to the following definitions, with lowercase and uppercase letters representing vectors and matrices, respectively. express A set of complex matrices of dimension 1; express 3D identity matrix; Representation matrix It is a Hermitian positive semidefinite matrix; Representation matrix trace operation; Representation matrix Rank. This indicates that the mean is 0 and the variance is 0. A circularly symmetric complex Gaussian random vector. This represents the complex conjugate transpose operation. Representing complex numbers The modulus, Representing vectors The Euclidean norm, This indicates the operation of the real part.

[0063] In some embodiments, the process of obtaining the signal-to-interference-plus-noise ratio (SNR) of the legitimate eavesdropper and the eavesdropping SNR of the illegal eavesdropper on the sensor in step 2 is as follows: The base station's transmitted signal is , Artificial noise added to prevent information leakage, and .

[0064] in, It is a beamforming matrix. Representing the Beamforming vectors of a valid sensor, It is an information symbol vector that satisfies And assume the communication symbol vector and artificial noise vector They are independent of each other.

[0065] Covariance matrix of transmitted signal .

[0066] The signal received by the sensor is , This represents the channel between the base station and the sensor. For sensors Noise at that location.

[0067] Based on the expression of the received signal, the sensor The signal-to-interference-plus-noise ratio can be expressed as:

[0068]

[0069] in, .

[0070] No. The received signal of an eavesdropper can be expressed as: , It is additive white Gaussian noise.

[0071] No. The eavesdropper on the first The SINR of each sensor is as follows:

[0072] In some embodiments, the process of obtaining the radar signal-to-noise ratio corresponding to the radar echo signal received by the base station in step 2 is as follows: the base station transmits a signal. At the same time, it also receives radar echo signals. The radar echo signals received by the base station are... ,in The target response matrix can be represented as follows:

[0073] in It is a complex target amplitude that satisfies .vector It is the radar antenna transmitting array in Direction steering vector, It is the steering vector of the receiving array. The azimuth of the target is represented by the direction of the target in radar-related literature. The direction of the target is usually known to the transmitter because it can be easily estimated from previous observations or given by the angle center of the sector of interest.

[0074] The signal received by the base station is .

[0075] in, This represents noise composed of additive white Gaussian noise and residual self-interference.

[0076] To improve radar sensing performance, the base station uses a set of receiving filters. To receive radar echo signals, the filtered signal is Therefore, the SINR for the perception of target n can be written as:

[0077]

[0078] in, .

[0079] In some embodiments, the optimization problem constructed in step 3 is: to reduce the transmitted signal power of the integrated system while ensuring secure communication of the system under the requirements of communication quality and sensing accuracy, a model is established to minimize the sum of communication beam power and artificial noise power based on the above signal-to-interference-plus-noise ratio expression.

[0080] ,

[0081] ,

[0082]

[0083] In the formula For the SINR requirement of the k-th valid sensor, A predefined threshold to achieve the desired target detection performance.

[0084] In some embodiments, step 4 uses block coordinate descent to break down the optimization problem into three sub-problems, sub-problem 1 being described as follows: Subproblem 2 is described as follows: Subproblem 3 is described as follows: .

[0085] In some embodiments, step 5, solving subproblem 1 using an algorithm combining fractional programming and semidefinite relaxation, specifically includes: fixed , ,right Optimize: Subproblem 1 can be expressed as:

[0086]

[0087]

[0088]

[0089] First, the FP algorithm is used to handle communication constraints, and auxiliary variables are introduced. Perform multidimensional direct FP reconstruction:

[0090] The rank-one Hermitian positive semidefinite matrix defined earlier , It should meet the following requirements:

[0091]

[0092] Next, define all The set of Viermitian positive semidefinite matrices is .

[0093] Atomic Problem 1 is transformed into:

[0094]

[0095]

[0096]

[0097]

[0098]

[0099] Clearly, the rank-one constraint significantly hinders direct solutions. Therefore, using the SDR algorithm, we discard the rank-one constraint and relax the problem to:

[0100]

[0101]

[0102]

[0103]

[0104] This is a semidefinite programming problem that can be solved efficiently using CVX. Since the rank-one constraint is temporarily ignored, the optimal objective value is only considered as a lower bound. After obtaining... After that, if obtained If the rank-one constraint is satisfied, then eigenvalue decomposition is used to obtain the optimal solution. Otherwise, Gaussian randomization is needed to transform the high-rank solution of the problem into a feasible rank-1 solution. Then, based on the obtained... renew :

[0105] Each iteration update , until convergence.

[0106] In some embodiments, step 5, modeling subproblem 2 as a semi-positive definite programming problem and solving it specifically includes: fixed , Covariance of artificial noise Optimize; Subproblem 2 can be expressed as:

[0107]

[0108]

[0109]

[0110]

[0111] This is also a semidefinite programming problem, because... Since the matrix itself is a positive semi-definite matrix and has no rank constraint, this problem is a convex optimization problem and can be solved directly using CVX.

[0112] In some embodiments, step 5, solving subproblem 3 based on the generalized Rayleigh entropy, specifically includes: fixed , ,optimization ; Subproblem 3 is only related to the radar SINR constraint, and the optimization problem is formulated as follows:

[0113] This is a typical generalized Rayleigh entropy, whose optimal solution is a matrix. The generalized eigenvector corresponding to the largest eigenvalue can be easily obtained.

[0114] The technical effects of the present invention will be further explained in detail below with reference to simulation experiments: In simulations, the comparison methods include the simplified BCD algorithm (which does not consider FP fractional reconstruction and only optimizes variables separately), the SCA algorithm (linearizes non-convex constraints), and the SDR algorithm (which relaxes all rank constraints into convex positive semidefinite constraints).

[0115] (1) Simulation parameter settings In the simulation experiment, the performance of the proposed algorithm was verified using Matlab simulation. In the simulation, the base station's transmit antenna was set to 8, the receive antenna to 6, the number of eavesdroppers was 2, and the number of targets was 4.

[0116] (2) Simulation content and result analysis Figure 3This chart compares the convergence performance of the proposed method under different communication SINRs. With the radar SINR fixed at 4dB, the chart shows that the proposed algorithm converges quickly and requires fewer iterations under different communication SINRs, all within 10 iterations. The objective function value increases with increasing communication weight SINR. This indicates that the BCD+FP+SDR algorithm is highly capable of handling non-convex problems and exhibits high efficiency and stability in multi-objective optimization.

[0117] Figure 4 This is a comparison chart of the total power of the proposed method and comparative methods in a multi-user scenario. As can be observed from the chart, the total transmission power of all methods increases with the number of sensor users, but the growth rate varies significantly. The BCD+FP+SDR method consistently exhibits the lowest total transmission power across the entire user range, and its total transmission power is significantly lower than the other three methods when the number of users reaches 10, demonstrating superior power efficiency. The total transmission power of the traditional SCA and simplified BCD methods is similar and higher than that of the BCD+FP+SDR method. The SDR method has lower power at low user counts, but its power increases rapidly with the number of users, eventually approaching that of the traditional SCA and simplified BCD methods. This indicates that the BCD+FP+SDR method has stronger power regulation capabilities and system resource utilization efficiency in multi-user scenarios, effectively reducing system energy consumption and improving overall energy efficiency.

[0118] Figure 5 This is a comparison chart of the total power of the proposed method and comparative methods under different radar SINRs. As can be observed from the chart, the total transmission power of all methods increases with the increase in radar SINR requirements, but the rate of increase varies significantly. The improved BCD+FP+SDR algorithm consistently exhibits the lowest total transmission power across the entire SINR range, and its power growth is extremely gradual at high SINR requirements (e.g., 8 dB), far lower than other comparative algorithms. In contrast, the power of the traditional SCA algorithm and the simplified BCD algorithm increases rapidly with SINR, especially accelerating significantly after SINR exceeds 4 dB; while the SDR algorithm, although having lower power at low SINR requirements, experiences a faster power increase as SINR requirements rise, eventually approaching the levels of the traditional SCA and simplified BCD algorithms. This indicates that the BCD+FP+SDR algorithm can effectively control system power consumption while meeting higher radar performance requirements, exhibiting superior power efficiency and robustness, fully demonstrating the advantages of this invention in balancing communication and radar performance and achieving efficient resource allocation in ISAC systems.

[0119] In summary, this invention provides a joint beamforming method for communication-sensing security in smart grain warehouses, comprising: constructing a communication-sensing integrated system model considering that the channel state information of an eavesdropper is fully known; establishing communication performance indicators and sensing performance indicators for the ISAC system model; constructing an optimization problem by jointly optimizing the beamforming matrix of the base station with the objective of minimizing the sum of communication beam power and artificial noise power, and constrained by the communication signal-to-noise ratio of legitimate sensors, the eavesdropper's eavesdropping signal-to-noise ratio of the sensors, and the radar signal-to-noise ratio of the radar echo signal received by the base station; and using the block coordinate descent method to decompose the optimization problem into three sub-problems. Subproblem 1 involves optimizing the sensor beamforming vector while fixing the artificial noise covariance matrix and the base station receiving filter vector. Subproblem 2 involves optimizing the artificial noise covariance matrix while fixing the sensor beamforming vector and the base station receiving filter vector. Subproblem 3 involves optimizing the base station receiving filter vector while fixing the artificial noise covariance matrix and the sensor beamforming vector. A combination of fractional programming and semidefinite relaxation algorithms is used to solve subproblem 1. Subproblem 2 is modeled as a semidefinite programming problem and solved. Subproblem 3 is solved using generalized Rayleigh entropy, yielding the optimal solutions for the sensor beamforming vector, the artificial noise covariance matrix, and the base station receiving filter vector. This invention ensures secure communication while meeting the requirements of communication quality and sensing accuracy, and reduces the power of the integrated system's transmitted signal. It provides a highly robust optimization approach for complex systems like smart grain warehouses, which involve multiple nodes, multiple targets, and multiple interference sources.

[0120] In some embodiments, the present invention provides a computer-readable storage medium storing computer instructions that are executed by a processor as described in any of the above embodiments, a communication-aware security joint beamforming method for smart grain warehouses.

[0121] Computer-readable storage media can take the form of any combination of one or more readable media. A readable medium can be a readable signal medium or a readable storage medium. A readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of readable storage media (not an exhaustive list) may include: electrical connections having one or more wires, portable disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD). ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0122] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, as well as combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0123] Embodiments of the present invention may also be computer program products, which include computer program instructions that, when executed by a processor, cause the processor to perform the steps in a communication-aware security joint beamforming method for smart grain warehouses according to various embodiments of the present invention, as described in the "Exemplary Methods" section above.

[0124] The steps of the method of the present invention are not limited to the specific order described above, unless otherwise specifically stated. Furthermore, in some embodiments, the invention may also be implemented as a program recorded on a recording medium, the program comprising machine-readable instructions for implementing the method according to the invention. Therefore, the invention also covers recording media storing programs for performing the method according to the invention.

[0125] Although the invention has been described with reference to preferred embodiments, various modifications can be made and components can be replaced with equivalents without departing from the scope of the invention. In particular, the technical features mentioned in the various embodiments can be combined in any manner as long as there is no structural conflict. The invention is not limited to the specific embodiments disclosed herein, but includes all technical solutions falling within the scope of the claims.

Claims

1. A joint beamforming method for communication, sensing, and security in smart grain warehouses, characterized in that, Includes the following steps: Step 1: Construct a communication and sensing integrated system model that considers the eavesdropper's channel state information to be fully known. The model includes a base station with both radar and communication functions, a sensor that legally communicates with the base station, a radar target that is sensed by the base station, and an eavesdropper attempting to eavesdrop on the sensor's communication signals. Step 2: Establish the communication performance indicators and sensing performance indicators of the ISAC system model. The communication performance indicators include the signal-to-interference-plus-noise ratio (SNR) of legitimate eavesdroppers and the SNR of illegal eavesdroppers on the sensors. The sensing performance indicators include the radar SNR of the radar echo signal received by the base station. Step 3: With the goal of minimizing the sum of communication beam power and artificial noise power, and with constraints such as the communication signal-to-noise ratio of legitimate sensors, the eavesdropping signal-to-noise ratio of the sensors, and the radar signal-to-noise ratio of the radar echo signal received by the base station, jointly optimize the beamforming matrix of the base station to construct an optimization problem. Step 4: The optimization problem is divided into three sub-problems using the block coordinate descent method. Sub-problem 1 is to optimize the sensor beamforming vector with a fixed artificial noise covariance matrix and a fixed base station receiving filter vector. Sub-problem 2 is to optimize the artificial noise covariance matrix with a fixed sensor beamforming vector and a fixed base station receiving filter vector. Sub-problem 3 is to optimize the base station receiving filter vector with a fixed artificial noise covariance matrix and a fixed sensor beamforming vector. Step 5: Solve subproblem 1 using an algorithm combining fractional programming and semidefinite relaxation. Model subproblem 2 as a semidefinite programming problem and solve it. Solve subproblem 3 using the generalized Rayleigh entropy to obtain the optimal solutions for the sensor beamforming vector, the artificial noise covariance matrix, and the base station receiving filter vector.

2. The communication-sensing-security joint beamforming method for smart grain warehouses according to claim 1, characterized in that, The model in step 1 includes One sensor, radar targets and An eavesdropper, the base station is equipped with One transmitting antenna, One receiving antenna, simultaneously responsible for... Each sensor provides communication services and... Detection is performed in one direction by radar.

3. The communication-sensing-security joint beamforming method for smart grain warehouses according to claim 2, characterized in that, In step 2, the process of obtaining the signal-to-interference-plus-noise ratio (SNR) of the legitimate eavesdropper and the SNR of the illegal eavesdropper's eavesdropping on the sensor is as follows: the base station's transmitted signal is... , Artificial noise added to prevent information leakage, and ; in, It is a beamforming matrix. Representing the Beamforming vectors of a valid sensor, It is an information symbol vector that satisfies And assume the communication symbol vector and artificial noise vector They are independent of each other; Covariance matrix of transmitted signal ; The signal received by the sensor is , This represents the channel between the base station and the sensor. For sensors Noise at the location; Based on the expression of the received signal, the sensor The signal-to-interference-plus-noise ratio is expressed as: in, ; No. The received signal of the eavesdropper is expressed as follows: , It is additive white Gaussian noise; No. The eavesdropper on the first The SINR of each sensor is as follows: 。 4. The communication-sensing-security joint beamforming method for smart grain warehouses according to claim 3, characterized in that, In step 2, the process of obtaining the radar signal-to-noise ratio corresponding to the radar echo signal received by the base station is as follows: the base station transmits the signal. At the same time, it also receives radar echo signals; the radar echo signals received by the base station are ,in The target response matrix is ​​represented as follows: in It is a complex target amplitude that satisfies ;vector It is the radar antenna transmitting array in Direction steering vector, It is the steering vector of the receiving array; The azimuth of the target; The signal received by the base station is ; in, This represents noise composed of additive white Gaussian noise and residual self-interference. To improve radar sensing performance, the base station uses a set of receiving filters. To receive radar echo signals, the filtered signal is Therefore, the SINR for the perception of target n is: in, .

5. The communication-sensing-security joint beamforming method for smart grain warehouses according to claim 4, characterized in that, The optimization problem constructed in step 3 is: ， ， In the formula For the SINR requirement of the k-th valid sensor, A predefined threshold to achieve the desired target detection performance.

6. The communication-sensing-security joint beamforming method for smart grain warehouses according to claim 5, characterized in that, Step 5, which uses an algorithm combining fractional programming and semidefinite relaxation to solve subproblem 1, specifically includes: fixed , ,right Optimize: Subproblem 1 is: First, the FP algorithm is used to handle communication constraints, and auxiliary variables are introduced. Perform multidimensional direct FP reconstruction: The rank-one Hermitian positive semidefinite matrix defined earlier , It should meet the following requirements: Next, define all The set of Viermitian positive semidefinite matrices is ; Atomic Problem 1 is transformed into: Using the SDR algorithm, the rank-one constraint is discarded, and the problem is relaxed to: This is a semidefinite programming problem, which can be solved efficiently using CVX; since the rank-one constraint is temporarily ignored, the optimal objective value of the problem is only used as a lower bound; after obtaining... After that, if obtained If the rank-one constraint is satisfied, then eigenvalue decomposition is used to obtain the optimal solution. Otherwise, Gaussian randomization is needed to transform the high-rank solution of the problem into a row-rank-one solution; then, based on the obtained... renew : Each iteration update , until convergence.

7. A joint beamforming method for communication-awareness security for smart grain warehouses according to claim 6, characterized in that, In step 5, modeling subproblem 2 as a semidefinite programming problem and solving it specifically includes: fixed , Covariance of artificial noise Optimize; Subproblem 2 is expressed as: This is also a semidefinite programming problem, because... Since the matrix itself is a positive semi-definite matrix and has no rank constraint, this problem is a convex optimization problem and can be solved directly using CVX.

8. A joint beamforming method for communication-awareness security for smart grain warehouses according to claim 7, characterized in that, Step 5, in which the subproblem 3 is solved based on the generalized Rayleigh entropy, specifically includes: fixed , ,optimization ; Subproblem 3 is only related to the radar SINR constraint, and the optimization problem is formulated as follows: This is a typical generalized Rayleigh entropy, whose optimal solution is a matrix. The generalized eigenvector corresponding to the largest eigenvalue.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that are executed by a processor according to any one of claims 1 to 8.

10. A computer program product, characterized in that, The computer program product stores computer instructions, which are executed by a processor using the method as described in any one of claims 1 to 8.

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