A perception-assisted communication-interference integrated robust beamforming design method

Abstract

The present application proposes a perception-assisted communication interference integrated robust beamforming design method, which relates to the field of wireless communication technology. The method includes: the base station estimates the location information of the eavesdropping user through perception means, and establishes a perception performance index based on the Cramer-Rao bound, and derives the relationship between the perception time and the channel error; constructs a joint optimization problem, and jointly optimizes the communication beamforming matrix, the interference beamforming matrix and the perception time; uses semi-positive relaxation and the S lemma to deal with the non-convexity of the optimization problem; uses an alternating optimization algorithm to decouple multiple optimization variables, and outputs the optimized perception time, communication beamforming matrix and interference beamforming matrix. The present application significantly reduces resource overhead while ensuring communication interference performance by coupling the perception time with the channel error and optimizing the resource allocation of the two stages of perception and communication interference.

Description

Technical Field

[0001] The present application relates to the field of wireless communication technology, and in particular to a perception-assisted communication interference integrated robust beamforming design method. Background Art

[0002] With the rapid growth in the number of communication devices, the scarcity and competition of spectrum resources have become a widely recognized issue. Furthermore, scenarios where friendly users and eavesdroppers coexist are inevitable. Existing research focuses on artificial noise-assisted physical layer security, aiming to add artificial noise to transmitted signals to confuse eavesdroppers. However, increasingly complex environments, such as electronic warfare scenarios, pose new challenges to communications, requiring base stations to ensure both communication and jamming performance. To ensure communication quality for friendly users while effectively disrupting the eavesdropping and communication of eavesdroppers, integrated communication and jamming methods are gaining attention.

[0003] In wireless communication systems, signal transmission faces challenges such as multipath fading, interference, and noise. Traditional omnidirectional antenna transmission methods struggle to meet the demands of high-speed, high-reliability communications. Beamforming technology, through directional signal transmission, can effectively improve signal quality, increase transmission range, reduce interference, and increase spectrum efficiency. This also means that the base station needs to know the user's location information in advance. Typically, due to the cooperative nature of the base station and friendly users, we can know the location of friendly users in advance. However, there is no collaborative nature between the base station and the eavesdropping user, and the eavesdropping user's location information must be obtained through sensing methods.

[0004] Most existing research assumes perfect location information and ignores position estimation errors. The magnitude of this error is related to the sensing time. Longer sensing times reduce the position error, which in turn reduces the channel error and improves interference performance. However, given limited resources, how to balance sensing, communication, and interference among resource allocation and performance tradeoffs is a question worthy of further study. Summary of the Invention

[0005] The present application aims to solve one of the technical problems in the related art at least to a certain extent.

[0006] To this end, the first purpose of this application is to propose a perception-assisted communication interference integrated robust beamforming design method, which aims to solve the problem of how to allocate resources when resources are limited and the perception time affects the position error, thereby affecting the communication interference performance.

[0007] The second objective of this application is to propose a perception-assisted communication interference integrated robust beamforming design device.

[0008] The third objective of this application is to provide an electronic device.

[0009] The fourth object of this application is to provide a computer-readable storage medium.

[0010] A fifth object of this application is to provide a computer program product.

[0011] To achieve the above objectives, the first embodiment of the present application proposes a perception-assisted communication-interference integrated robust beamforming design method, including:

[0012] The control base station estimates the location information of the eavesdropping user through sensing means, establishes a sensing performance index based on the Cramer-Rao bound, and derives the relationship between sensing time and channel error;

[0013] A joint optimization problem is constructed to minimize the base station transmit power, optimize the communication beamforming matrix, interference beamforming matrix, and sensing time, while satisfying the communication rate constraint, interference signal-to-noise ratio constraint, and sensing time range constraint.

[0014] Performing an equivalent transformation on the joint optimization problem, using semi-definite relaxation and S-lemma to deal with the non-convexity constraint of the beamforming matrix, so as to transform it into a solvable convex optimization problem;

[0015] An alternating optimization algorithm is used, which uses a block coordinate descent method to optimize in steps. First, the sensing time is fixed, and the communication beamforming matrix and the interference beamforming matrix are optimized. Then, the communication beamforming matrix and the interference beamforming matrix are fixed, and the sensing time is optimized. The iterative update is carried out until convergence.

[0016] Output the optimized sensing time, communication beamforming matrix and interference beamforming matrix.

[0017] Optionally, the control base station estimates the location information of the eavesdropping user through a sensing method, establishes a sensing performance index based on the Cramer-Rao bound, and derives the relationship between the sensing time and the channel error, including:

[0018] Initialize the number of base station antennas , number of friendly users , Number of eavesdropping users , eavesdropping user azimuth and error , frame length , perception of time , Perception Signal-to-Noise Ratio and noise power , as the initial system parameters;

[0019] Based on the initial system parameters, a Cramer-Rao bound model is established to calculate the Cramer-Rao bound of the azimuth estimation error of the eavesdropping user. , whose expression is:

[0020]

[0021] Get the communication rate of friendly users for:

[0022]

[0023] Get the average interference signal-to-noise ratio of the eavesdropping user for:

[0024]

[0025] According to the Cramer-Rao bound of the azimuth estimation error of the eavesdropping user, the relationship between the sensing time and the channel estimation error is derived, and the channel between the base station and the eavesdropping user is obtained. About the azimuth angle estimator The closed-form solution of :

[0026]

[0027]

[0028] Among them, the channel uncertainty range is given by the following expression:

[0029]

[0030] Constructing a set of perceptual error constraints , characterizes the uncertainty range of the base station to the eavesdropped user channel to provide constraints for robust beamforming optimization.

[0031] Optionally, constructing a joint optimization problem to minimize base station transmit power, optimize the communication beamforming matrix, the interference beamforming matrix, and the sensing time while satisfying a communication rate constraint, an interference signal-to-noise ratio constraint, and a sensing time range constraint, includes:

[0032] Construct the base station power minimization problem, the formula is:

[0033]

[0034] For the power optimization problem, an equivalent expression is made and the communication beamforming matrix is ​​defined as and interference beamforming matrix , we get the equivalent expression of the joint optimization problem, the formula is:

[0035]

[0036] in, For the The communication beamforming vectors of friendly users, For the The jamming beamforming vector of the eavesdropping user;

[0037] Among them, the constraints Requires that the communication rate of each friendly user cannot be lower than the minimum value ,constraint The interference signal-to-noise ratio of each eavesdropping user must not be lower than the minimum value ,constraint Requires the perception time to be at a minimum and maximum value Between, constraints ensure and is the covariance matrix, the constraint ensure and .

[0038] Optionally, performing an equivalent transformation on the joint optimization problem, using semi-positive relaxation and the S lemma to process the non-convexity constraint of the beamforming matrix to transform it into a solvable convex optimization problem, includes:

[0039] Communication rate constraints Perform an equivalent transformation and rewrite it as:

[0040]

[0041] in, The constraints are given by the following inequalities:

[0042]

[0043] Interference signal-to-noise ratio constraints Perform an equivalent transformation and rewrite it as:

[0044]

[0045] in, The constraints are given by the following inequalities:

[0046]

[0047] Using the S lemma, the communication rate is constrained The equivalent transformation is expressed as the following matrix inequality:

[0048]

[0049] in, is the Lagrange multiplier;

[0050] Using the S lemma, the interference signal-to-noise ratio constraint The equivalent transformation is expressed as the following matrix inequality:

[0051]

[0052] in, is the Lagrange multiplier;

[0053] Ignore constraints , the optimization problem is transformed into a standard convex optimization problem by semi-positive relaxation, which is expressed as:

[0054]

[0055] in, is the slack variable, , , .

[0056] Optionally, the alternating optimization algorithm is used to optimize in steps through a block coordinate descent method. First, the sensing time is fixed, the communication beamforming matrix and the interference beamforming matrix are optimized, and then the communication beamforming matrix and the interference beamforming matrix are fixed, the sensing time is optimized, and iterative updates are performed until convergence, including:

[0057] S1. Set the initial number of iterations , maximum number of iterations , set the initial feasible solution ;

[0058] S2, fixed , solve about The optimization problem is updated to ;

[0059] S3, fixed , solve about The optimization problem is updated to ;

[0060] S4. Determine whether If satisfied, then output ; If not satisfied, update the number of iterations , and repeat steps S1 to S4.

[0061] Optionally, the fixing , solve about The optimization problem is updated to ,include:

[0062] fixed , get about The optimization problem is expressed as:

[0063]

[0064] neglect , get about The convex optimization problem is obtained by updating the convex optimization tool .

[0065] Optionally, the fixing , solve about The optimization problem is updated to ,include:

[0066] fixed , get about The convex optimization problem is expressed as:

[0067]

[0068] Updated by convex optimization tool .

[0069] To achieve the above objectives, the second embodiment of the present application proposes a perception-assisted communication-interference integrated robust beamforming design device, including:

[0070] The sensing information acquisition module is used to control the base station to estimate the location information of the eavesdropping user through sensing means, establish a sensing performance index based on the Cramer-Rao bound, and derive the relationship between the sensing time and the channel error;

[0071] A joint optimization building block is used to construct a joint optimization problem to minimize the base station transmit power, optimize the communication beamforming matrix, the interference beamforming matrix, and the sensing time, while satisfying the communication rate constraint, the interference signal-to-noise ratio constraint, and the sensing time range constraint.

[0072] An optimization problem conversion module is used to perform an equivalent transformation on the joint optimization problem, using semi-positive relaxation and S lemma to process the non-convexity constraints of the beamforming matrix, so as to convert it into a solvable convex optimization problem;

[0073] An alternating optimization calculation module is used to adopt an alternating optimization algorithm and perform step-by-step optimization through a block coordinate descent method. First, the sensing time is fixed and the communication beamforming matrix and the interference beamforming matrix are optimized. Then, the communication beamforming matrix and the interference beamforming matrix are fixed and the sensing time is optimized. The optimization is performed iteratively until convergence.

[0074] The optimization result output module is used to output the optimized perception time, communication beamforming matrix and interference beamforming matrix.

[0075] To achieve the above-mentioned purpose, a third embodiment of the present application provides an electronic device, comprising: a processor, and a memory communicatively connected to the processor;

[0076] The memory stores computer-executable instructions;

[0077] The processor executes the computer-executable instructions stored in the memory to implement the method as described in any one of the first aspects.

[0078] To achieve the above-mentioned purpose, the fourth embodiment of the present application proposes a computer-readable storage medium, which stores computer-executable instructions. When the computer-executable instructions are executed by a processor, they are used to implement the method as described in any one of the first aspects.

[0079] To achieve the above-mentioned objectives, the fifth embodiment of the present application proposes a computer program product, which implements any one of the methods in the first aspect when executed by a processor.

[0080] The technical solutions provided by the embodiments of this application bring at least the following beneficial effects:

[0081] By coupling the perception time with the channel error and optimizing the resource allocation in the two stages of perception and communication interference, it is possible to interfere with the communication and eavesdropping behavior of eavesdropping users with unknown locations while ensuring the communication quality of friendly users. It also solves the problem of how to allocate resources when the perception time affects the location error and thus affects the communication interference performance under limited resources, significantly reducing the power consumption at the base station end.

[0082] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0083] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0084] Figure 1 A flowchart of a perception-assisted communication-interference integrated robust beamforming design method provided in an embodiment of the present application;

[0085] Figure 2 A schematic diagram of a communication-interference integrated robust beamforming scenario provided in an embodiment of the present application;

[0086] Figure 3 Schematic diagram of the perception-assisted communication and interference frame structure provided in an embodiment of the present application;

[0087] Figure 4 A schematic diagram showing how base station power consumption varies with the number of iterations provided in an embodiment of the present application;

[0088] Figure 5 A schematic diagram showing the power consumption of a base station according to the proportion of the sensing phase provided in an embodiment of the present application;

[0089] Figure 6 A schematic diagram showing how base station power consumption varies with the number of antennas provided in an embodiment of the present application;

[0090] Figure 7 A schematic diagram showing the relationship between base station power consumption and average interference signal-to-noise ratio provided in an embodiment of the present application;

[0091] Figure 8 A schematic diagram showing how base station power consumption varies with user minimum communication rate, provided in an embodiment of the present application;

[0092] Figure 9 A schematic structural diagram of a perception-assisted communication interference integrated robust beamforming design device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0093] The following describes in detail embodiments of the present application, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0094] In order to solve the problems existing in the prior art, the present invention provides a method for designing a robust beamforming system with integrated communication and interference. Figure 1 This is a flow chart of a perception-assisted communication interference integrated robust beamforming design method provided in an embodiment of the present application. Figure 1 As shown, the method includes the following steps:

[0095] Step 101: The base station is controlled to estimate the location information of the eavesdropping user through sensing means, establish a sensing performance index based on the Cramer-Rao bound, and deduce the relationship between the sensing time and the channel error.

[0096] In an embodiment of the present application, the method proposed in the present application relies on a perception-assisted communication interference integrated robust beamforming scenario.

[0097] In a possible embodiment, a communication interference integrated robust beamforming scenario is as follows: Figure 2As shown in the figure, in this scenario, a multi-antenna base station is surrounded by multiple friendly users and multiple eavesdropping users. The base station's task is to communicate with the friendly users while simultaneously jamming the eavesdropping users. The friendly users' locations are known a priori to the base station, while the eavesdropping users' locations are unknown and must be estimated through sensing. The base station uses this location information to establish a channel and employs robust beamforming to simultaneously communicate with the friendly users and jam the eavesdropping users.

[0098] In one possible embodiment, consider a downlink multi-user scenario, including a A base station with uniform linear array antennas, Single antenna friendly user and A single-antenna suspicious target with a carrier frequency of ,by is the path loss at the reference distance , Rice factor , noise power . The signal-to-noise ratio of the perceived signal , each frame length , the minimum rate of each friendly user , the minimum effective interference signal-to-noise ratio of each eavesdropping user The mission of the base station is to communicate with the friendly user and interfere with the eavesdropping. In addition, the base station uses beamforming technology to achieve communication and interference, which means that the location information and channel information of the friendly user and the eavesdropping user must be known in advance. Generally speaking, there is a collaborative cooperation between the base station and the friendly user so that the azimuth of the friendly user can be known in advance. However, there is no cooperation between the base station and the suspicious target, so before interfering with the suspicious target, the embodiment of the present application needs to obtain the azimuth of the suspicious target through sensing means. , only then can we effectively interfere with suspicious targets.

[0099] like Figure 3 As shown, in order to meet the needs of communication and interference, the embodiment of the present application designs a communication and interference frame structure with sensing assistance. A complete frame structure includes the sensing stage and the communication and interference stage, with a total length of , the lengths of the perception phase and the communication and interference phase are and In the sensing phase, the base station uses radar detection technology to obtain the azimuth of the eavesdropping user. and distance ; In the communication and interference phase, the base station uses the known location information of friendly users 、 and the perceived eavesdropped user location information 、 , communicating and jamming simultaneously.

[0100] In the perception stage, the embodiment of the present application can obtain the azimuth of the eavesdropping user by means of target detection, etc. The sensing echo signal received by the base station can be expressed as:

[0101]

[0102] in, represents the array steering vector and can be expressed as:

[0103]

[0104] in represents the wavelength, represents the antenna spacing. Due to hardware limitations, the non-cooperation of the eavesdropping user, and other reasons, there will be errors in estimating the azimuth of the eavesdropping user, which can be expressed as:

[0105]

[0106] in represents the eavesdropping user's azimuth estimate, Represents the estimated error of the eavesdropping user's azimuth, assuming that the estimated error It obeys Gaussian distribution and uses Cramer-Rao bound to measure the variance of the estimation error, which is expressed as:

[0107]

[0108] in, represents the eavesdropping user's azimuth estimate, represents the estimation error of the eavesdropping user's azimuth.

[0109] In the communication and interference phase, the base station's task is to communicate with the friendly user while interfering with the eavesdropping user. The received signals of the kth friendly user and the jth eavesdropping user can be expressed as:

[0110]

[0111] in and Represent the communication symbol and interference symbol respectively. Assuming the symbol energy is 1, we have .

[0112] Then the communication rate of the k-th friendly user in each frame length is It can be expressed as:

[0113]

[0114] Furthermore, the embodiment of the present application considers using the interference signal to noise ratio to measure the interference performance of the base station, and the average interference signal to noise ratio of the jth eavesdropping user in each frame length is It can be expressed as:

[0115]

[0116] Considering that there are errors in the channels between the base station and the friendly user and the eavesdropping user, it can be expressed as:

[0117]

[0118] Assuming the estimation error is bounded, we have Because the base station cooperates with the friendly user, for example, the friendly user can send a pilot signal to the base station, and the base station can know the azimuth and error range of the friendly user in advance. At the same time, the base station does not have any prior information about the eavesdropping user. In order to effectively interfere with the eavesdropping user, the base station needs to sense and obtain the azimuth of the eavesdropping user. Assuming the azimuth estimation error Obeying Gaussian distribution, Distributed in The probability of being within the range is 0.9973, which is sufficient to use To approximate The following expression holds:

[0119]

[0120] in Representatives The steering vector error caused by

[0121] After some algebraic transformations, can be rewritten as:

[0122]

[0123] in, .

[0124] Due to the coupling of various trigonometric functions, It is difficult to obtain a closed-form solution. Therefore, the embodiment of the present application considers In sufficiently small cases and Perform a first-order Taylor expansion on , then we have:

[0125]

[0126] Furthermore, using the second-order Taylor expansion at 0, the present application can deduce:

[0127]

[0128] in .

[0129] Through the above approximation of trigonometric functions, this application can obtain the channel between the base station and the eavesdropping user. About the azimuth angle estimator The closed-form solution of is:

[0130]

[0131]

[0132] Among them, the channel uncertainty range is given by the following expression:

[0133]

[0134] Finally, the embodiment of the present application can construct a set of perceptual error constraints , characterizes the uncertainty range of the base station to the eavesdropped user channel to provide constraints for robust beamforming optimization.

[0135] Step 102: construct a joint optimization problem to minimize the base station transmit power, optimize the communication beamforming matrix, the interference beamforming matrix, and the sensing time, while satisfying the communication rate constraint, the interference signal-to-noise ratio constraint, and the sensing time range constraint.

[0136] In the embodiment of the present application, a joint optimization problem may be constructed based on the various formulas proposed in step 101 .

[0137] It should be noted that the total power consumption of the base station is determined by the communication beamforming power and the interference beamforming power. Therefore, in the embodiment of the present application, the optimization objective is modeled as a base station power minimization problem, which is mathematically expressed as follows:

[0138]

[0139] In order to further simplify the optimization problem, in the embodiment of the present application, the power optimization problem is equivalently expressed and the communication beamforming matrix is ​​defined. and interference beamforming matrix .

[0140] From this, we can get an equivalent expression of the joint optimization problem, which is:

[0141]

[0142] in, For the The communication beamforming vectors of friendly users, For the The interference beamforming vector of the eavesdropping user.

[0143] In the embodiment of this application, the constraint Requires that the communication rate of each friendly user cannot be lower than the minimum value ,constraint The interference signal-to-noise ratio of each eavesdropping user must not be lower than the minimum value ,constraint Requires the perception time to be at a minimum and maximum value Between, constraints ensure and is the covariance matrix, the constraint ensure and .

[0144] It can be understood that the establishment of the joint optimization problem provides a mathematical basis for subsequent equivalent transformation and solution, so that the optimization problem can obtain a near-optimal solution while ensuring that the computational complexity is controllable.

[0145] Step 103 , perform equivalent transformation on the joint optimization problem, and use semi-positive relaxation and S lemma to deal with the non-convexity constraint of the beamforming matrix, so as to transform it into a solvable convex optimization problem.

[0146] In this embodiment of the present application, step 103 involves equivalently transforming the joint optimization problem into a solvable convex optimization problem, utilizing semidefinite relaxation (SDR) and the S-lemma to address the non-convexity constraints of the beamforming matrix. Through mathematical transformations, the communication rate constraint and the interference signal-to-noise ratio (JSNR) constraint are converted into a convex optimization form for subsequent solution.

[0147] First, the communication rate constraint Perform an equivalent transformation and rewrite it as:

[0148]

[0149] in, The constraints are given by the following inequalities:

[0150]

[0151] Next, the interference signal to noise ratio constraint Perform an equivalent transformation and rewrite it as:

[0152]

[0153] in, The constraints are given by the following inequalities:

[0154]

[0155] In the embodiment of the present application, the S lemma is further used to constrain the communication rate The equivalent transformation is expressed as the following matrix inequality:

[0156]

[0157] in, is the Lagrange multiplier;

[0158] Similarly, using the S lemma, the interference signal to noise ratio constraint is The equivalent transformation is expressed as the following matrix inequality:

[0159]

[0160] in, is the Lagrange multiplier;

[0161] In order to ensure that the optimization problem can be solved effectively, in the embodiment of the present application, the constraint , the optimization problem is transformed into a standard convex optimization problem by semi-positive relaxation, which is expressed as:

[0162]

[0163] in, is the slack variable, , , .

[0164] In the embodiments of this application, the above equivalent transformation successfully converts the original non-convex optimization problem into a standard convex optimization problem. This transformation not only reduces the difficulty of solving the problem, but also ensures that the optimization problem can be efficiently solved using existing convex optimization tools, thereby further improving the system's communication performance and anti-eavesdropping capabilities.

[0165] Step 104 uses an alternating optimization algorithm to perform step-by-step optimization using a block coordinate descent method. First, the perception time is fixed, and the communication beamforming matrix and the interference beamforming matrix are optimized. Then, the communication beamforming matrix and the interference beamforming matrix are fixed, and the perception time is optimized. The optimization is performed iteratively until convergence.

[0166] In the embodiment of the present application, step 104 involves employing an alternating optimization algorithm, using the block coordinate descent (BCD) method for step-by-step optimization. The core concept of this method is: first, while fixing the sensing time, optimize the communication beamforming matrix and the interference beamforming matrix; then, while fixing the beamforming matrix, optimize the sensing time; and continuously alternating iterations until convergence, ensuring an effective solution to the optimization problem.

[0167] Specifically, the optimization steps are as follows:

[0168] S1. Set the initial number of iterations , maximum number of iterations , set the initial feasible solution .

[0169] S2, fixed , solve about The optimization problem is updated to .

[0170] In the embodiment of the present application, first fix , get about The optimization problem is expressed as:

[0171]

[0172] Then ignore , get about The convex optimization problem is solved by updating the convex optimization tool .

[0173] S3, fixed , solve about The optimization problem is updated to .

[0174] In the embodiment of the present application, first fix , get about The convex optimization problem is expressed as:

[0175]

[0176] Then, we update it through the convex optimization tool to get .

[0177] S4. Determine whether If satisfied, then output ; If not satisfied, update the number of iterations , and repeat steps S1 to S4.

[0178] In the embodiment of the present application, the alternating optimization algorithm is continuously iterated until the convergence condition is met:

[0179] If the termination condition is met Or if the optimization target converges, the final optimization result is output ;

[0180] If the termination condition is not met, update the number of iterations: , and return to execute steps S2 to S4 to continue iterative optimization.

[0181] In the embodiments of this application, an alternating optimization algorithm combined with a block coordinate descent method gradually optimizes the beamforming matrix and sensing time, enabling the optimization problem to converge efficiently to a near-optimal solution, improving computational efficiency and accuracy. While maintaining communication quality, this method further reduces the base station's transmit power and enhances the interference effect on eavesdropping users, thereby improving system security and reliability.

[0182] Step 105: Output the optimized sensing time, communication beamforming matrix, and interference beamforming matrix.

[0183] In an embodiment of the present application, step 105 involves outputting the optimized perception time, communication beamforming matrix, and interference beamforming matrix. The optimized parameters finally output can be used for beamforming in an actual communication system, enabling the base station to efficiently transmit to friendly users and effectively interfere with eavesdropping users, thereby ensuring the security and energy efficiency of wireless communications.

[0184] In addition, in order to verify the effectiveness of the proposed optimization algorithm, the embodiment of the present application analyzed the convergence and performance of the algorithm of the present application under different parameter settings through simulation experiments, and compared it with the baseline method.

[0185] exist Figure 4 The convergence of the optimization algorithm proposed in this application is demonstrated under different numbers of antennas, friendly users, and eavesdropping users. It can be observed that in all given cases, the algorithm can converge quickly within 8 iterations, indicating that the algorithm has good convergence efficiency. In addition, when the number of antennas increases, the minimum transmit power required by the base station decreases. This is because more antennas can more effectively focus communication signals and interference signals, thereby reducing the total power requirement. When the number of friendly users and eavesdropping users increases, the minimum transmit power required by the base station increases. This is because more power is required to meet the needs of all users while ensuring communication and interference performance.

[0186] exist Figure 5 In this paper, the algorithm of this application is analyzed in terms of incorrect perception time. Under the optimized condition, the base station transmission power changes under different antenna numbers, friendly users and eavesdropping users. It can be observed that as the sensing time As the sensing time increases, the base station transmit power decreases first and then increases. When the sensing time is less than the optimal value, the channel error is large, significantly impacting performance. Increasing the sensing time can reduce the base station transmit power. When the sensing time is greater than the optimal value, the communication time and interference time decrease, leading to a decrease in overall performance. In this case, reducing the sensing time is necessary to optimize the base station transmit power.

[0187] exist Figure 6 In this embodiment of the present application, the base station transmit power under different numbers of antennas is studied and compared with two baseline methods:

[0188] For baseline 1, the present embodiment does not perceive time Optimize and set the perception time to half of the entire frame length, that is, ;

[0189] For baseline 2, the embodiment of the present application assumes that the base station does not use sensing assistance, which means that the base station does not know the azimuth of the eavesdropping user, so the base station sends an omnidirectional interference signal to ensure interference performance.

[0190] It can be observed that the base station transmit power decreases when the number of antennas increases for all three methods. This indicates that adding antennas can more effectively concentrate signal energy, thereby reducing power consumption.

[0191] exist Figure 7 In this application, the base station transmit power under different interference signal to noise ratios (JSNR) is studied. When the JSNR increases, the base station transmit power of the three methods increases. This is because stronger interference signals require the base station to allocate more power to meet the interference performance requirements. It is worth noting that under all JSNR conditions, the base station transmit power of the optimization algorithm proposed in this application is lower than that of the baseline method, indicating that the optimization algorithm can reduce power consumption while improving interference performance. In addition, for baseline 2 (omnidirectional interference signal), when the JSNR increases, the base station transmit power shows a significant increase trend. This is because the omnidirectional interference signal is less efficient. When the interference performance requirements are increased, the power of the interference signal needs to be significantly increased, resulting in a significant increase in system power consumption.

[0192] exist Figure 8 In this study, we investigated base station transmit power at different minimum communication rates. As the user's minimum communication rate requirement increased, the base station transmit power increased accordingly for all three methods. This is because higher communication rates require the base station to provide stronger signal power to meet the needs of all users. At all communication rate requirements, the base station transmit power of the optimization algorithm proposed in this application was lower than that of the baseline method, further demonstrating the superiority of our algorithm in reducing power consumption.

[0193] In summary, simulation results demonstrate that the proposed perception-assisted communication-interference integrated robust beamforming design method exhibits good convergence and optimization performance under varying system parameters. Compared to baseline methods, this method significantly reduces base station transmit power and improves system energy efficiency under the same communication and interference performance constraints, providing an effective solution for secure communications and energy-saving optimization.

[0194] In order to implement the above embodiments, the present application also proposes a perception-assisted communication interference integrated robust beamforming design device. Figure 9 This is a schematic diagram of the structure of a perception-assisted communication interference integrated robust beamforming design device provided in an embodiment of the present application. Figure 9 As shown, the device includes:

[0195] The sensing information acquisition module 100 is used to control the base station to estimate the location information of the eavesdropped user through sensing means, establish a sensing performance index based on the Cramer-Rao bound, and derive the relationship between the sensing time and the channel error;

[0196] A joint optimization construction module 200 is used to construct a joint optimization problem to minimize the base station transmit power, optimize the communication beamforming matrix, the interference beamforming matrix, and the sensing time, while satisfying the communication rate constraint, the interference signal-to-noise ratio constraint, and the sensing time range constraint;

[0197] An optimization problem conversion module 300 is used to perform an equivalent conversion on the joint optimization problem, using semi-positive relaxation and the S lemma to process the non-convexity constraints of the beamforming matrix, so as to convert it into a solvable convex optimization problem;

[0198] An alternating optimization calculation module 400 is configured to employ an alternating optimization algorithm and perform step-by-step optimization using a block coordinate descent method. The module first optimizes the communication beamforming matrix and the interference beamforming matrix while fixing the sensing time. The module then fixes the communication beamforming matrix and the interference beamforming matrix while optimizing the sensing time, iterating until convergence is achieved.

[0199] The optimization result output module 500 is used to output the optimized sensing time, communication beamforming matrix and interference beamforming matrix.

[0200] Regarding the apparatus in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0201] In order to implement the above embodiments, the present application also proposes an electronic device, comprising: a processor, and a memory communicatively connected to the processor; the memory stores computer-executable instructions; the processor executes the computer-executable instructions stored in the memory to implement the method provided by the above embodiments.

[0202] In order to implement the above embodiments, the present application also proposes a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, they are used to implement the methods provided by the above embodiments.

[0203] In order to implement the above embodiments, the present application also proposes a computer program product, including a computer program, which implements the methods provided by the above embodiments when executed by a processor.

[0204] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in this application can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of this application can be achieved. This is not limited herein.

[0205] The above specific embodiments do not constitute a limitation on the scope of protection of this application. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application shall be included within the scope of protection of this application.

Claims

1. A perception-assisted communication-interference integrated robust beamforming design method, characterized in that: The following steps are involved: The control base station estimates the location information of the eavesdropping user through sensing means, establishes a sensing performance index based on the Cramer-Rao bound, and derives the relationship between sensing time and channel error; A joint optimization problem is constructed to minimize the base station transmit power, optimize the communication beamforming matrix, interference beamforming matrix, and sensing time, while satisfying the communication rate constraint, interference signal-to-noise ratio constraint, and sensing time range constraint. Performing an equivalent transformation on the joint optimization problem, using semi-positive relaxation and S-lemma to deal with the non-convexity constraint of the beamforming matrix, so as to transform it into a solvable convex optimization problem; An alternating optimization algorithm is used, which uses a block coordinate descent method to optimize in steps. First, the sensing time is fixed, and the communication beamforming matrix and the interference beamforming matrix are optimized. Then, the communication beamforming matrix and the interference beamforming matrix are fixed, and the sensing time is optimized. The iterative update is carried out until convergence. Output the optimized sensing time, communication beamforming matrix and interference beamforming matrix.

2. The method according to claim 1, characterized in that The control base station estimates the location information of the eavesdropping user through sensing means, establishes a sensing performance index based on the Cramer-Rao bound, and derives the relationship between the sensing time and the channel error, including: Initialize the number of base station antennas , number of friendly users , Number of eavesdropping users , eavesdropping user azimuth and error , frame length , perception of time , Perception Signal-to-Noise Ratio and noise power , as the initial system parameters; Based on the initial system parameters, a Cramer-Rao bound model is established to calculate the Cramer-Rao bound of the azimuth estimation error of the eavesdropping user. , whose expression is: Get the communication rate of friendly users for: Get the average interference signal-to-noise ratio of the eavesdropping user for: According to the Cramer-Rao bound of the azimuth estimation error of the eavesdropping user, the relationship between the sensing time and the channel estimation error is derived, and the channel between the base station and the eavesdropping user is obtained. About the azimuth angle estimator The closed-form solution of : Among them, the channel uncertainty range is given by the following expression: Constructing a set of perceptual error constraints , characterizes the uncertainty range of the base station to the eavesdropped user channel to provide constraints for robust beamforming optimization.

3. The method according to claim 2, characterized in that The joint optimization problem is constructed to minimize the base station transmit power, optimize the communication beamforming matrix, the interference beamforming matrix, and the sensing time, while satisfying the communication rate constraint, the interference signal-to-noise ratio constraint, and the sensing time range constraint, including: Construct the base station power minimization problem, the formula is: For the power optimization problem, an equivalent expression is made and the communication beamforming matrix is ​​defined as and interference beamforming matrix , we get the equivalent expression of the joint optimization problem, the formula is: in, For the The communication beamforming vectors of friendly users, For the The jamming beamforming vector of the eavesdropping user; Among them, the constraints Requires that the communication rate of each friendly user cannot be lower than the minimum value ,constraint The interference signal-to-noise ratio of each eavesdropping user must not be lower than the minimum value ,constraint Requires the perception time to be at a minimum and maximum value Between, constraints ensure and is the covariance matrix, the constraint ensure and .

4. The method according to claim 3, characterized in that The equivalent transformation of the joint optimization problem, using semi-positive relaxation and the S lemma to process the non-convexity constraint of the beamforming matrix, to transform it into a solvable convex optimization problem, includes: Communication rate constraints Perform an equivalent transformation and rewrite it as: in, is the noise power, The constraints are given by the following inequalities: Interference signal-to-noise ratio constraints Perform an equivalent transformation and rewrite it as: in, The constraints are given by the following inequalities: Using the S lemma, the communication rate is constrained The equivalent transformation is expressed as the following matrix inequality: in, is the Lagrange multiplier; Using the S lemma, the interference signal-to-noise ratio constraint The equivalent transformation is expressed as the following matrix inequality: in, is the Lagrange multiplier; Ignore constraints , the optimization problem is transformed into a standard convex optimization problem by semi-positive relaxation, which is expressed as: in, is the slack variable, , , .

5. The method according to claim 4, characterized in that The alternating optimization algorithm is optimized step by step through a block coordinate descent method. First, the sensing time is fixed, and the communication beamforming matrix and the interference beamforming matrix are optimized. Then, the communication beamforming matrix and the interference beamforming matrix are fixed, and the sensing time is optimized. The iterative update is performed until convergence, including: S1. Set the initial number of iterations , maximum number of iterations , set the initial feasible solution ; S2, fixed , solve about The optimization problem is updated to ; S3, fixed , solve about The optimization problem is updated to ; S4. Determine whether If satisfied, then output ; If not satisfied, update the number of iterations , and repeat steps S1 to S4.

6. The method according to claim 5, characterized in that The fixation , solve about The optimization problem is updated to ,include: fixed , get about The optimization problem is expressed as: neglect , get about The convex optimization problem is obtained by updating the convex optimization tool .

7. The method according to claim 6, characterized in that The fixation , solve about The optimization problem is updated to ,include: fixed , get about The convex optimization problem is expressed as: Updated by convex optimization tool .

8. A perception-assisted communication interference integrated robust beamforming design device, characterized in that: include: The sensing information acquisition module is used to control the base station to estimate the location information of the eavesdropping user through sensing means, establish a sensing performance index based on the Cramer-Rao bound, and derive the relationship between the sensing time and the channel error; A joint optimization building block is used to construct a joint optimization problem to minimize the base station transmit power, optimize the communication beamforming matrix, the interference beamforming matrix, and the sensing time, while satisfying the communication rate constraint, the interference signal-to-noise ratio constraint, and the sensing time range constraint. An optimization problem conversion module is used to perform an equivalent transformation on the joint optimization problem, using semi-positive relaxation and S lemma to process the non-convexity constraints of the beamforming matrix, so as to convert it into a solvable convex optimization problem; An alternating optimization calculation module is used to adopt an alternating optimization algorithm and perform step-by-step optimization through a block coordinate descent method. First, the sensing time is fixed and the communication beamforming matrix and the interference beamforming matrix are optimized. Then, the communication beamforming matrix and the interference beamforming matrix are fixed and the sensing time is optimized. The optimization is iteratively updated until convergence. The optimization result output module is used to output the optimized perception time, communication beamforming matrix and interference beamforming matrix.

9. An electronic device, characterized in that: include: a processor, and a memory communicatively connected to the processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory to implement the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer-executable instructions, which are used to implement the method according to any one of claims 1 to 7 when executed by a processor.

Images