Perception-assisted communication interference integrated robust beam forming design method

Abstract

The invention provides a perception-assisted communication interference integrated robust beam forming design method, and relates to the technical field of wireless communication. The method comprises the following steps: a base station estimates position information of an eavesdropping user through a sensing means, establishes a sensing performance index based on the Cramer-Rao bound, and deduces a relationship between sensing time and a channel error; constructing a joint optimization problem, and performing joint optimization on the communication beam forming matrix, the interference beam forming matrix and the sensing time; processing the non-convexity of the optimization problem by using positive semidefinite relaxation and S lemma; and decoupling the plurality of optimization variables by adopting an alternating optimization algorithm, and outputting the optimized sensing time, communication beam forming matrix and interference beam forming matrix. According to the method, the sensing time and the channel error are coupled, and the resource allocation of the sensing stage and the communication interference stage is optimized, so that the communication interference performance is ensured, and meanwhile, the resource overhead is remarkably reduced.

Description

Technical Field

[0001] This application relates to the field of wireless communication technologies, and in particular, to a robust beamforming design method for integrated communication and interference with sensing assistance. Background Art

[0002] With the rapid growth in the number of communication devices, the scarcity and competition of spectrum resources have become a widely concerned issue, and inevitably, scenarios where friendly users and eavesdropping users coexist will occur. Existing research mainly focuses on physical layer security assisted by artificial noise, aiming to add artificial noise to the transmitted signal to confuse eavesdroppers. However, increasingly complex environments, such as electronic warfare scenarios, also pose new challenges to communication, requiring the base station to ensure both communication and interference performance simultaneously. To ensure the communication quality of friendly users and effectively interfere with the eavesdropping and communication of eavesdropping users, integrated communication and interference have also begun to receive attention.

[0003] In a wireless communication system, signal transmission faces problems such as multipath fading, interference, and noise. The traditional omnidirectional antenna transmission method is difficult to meet the communication requirements of high speed and high reliability. Beamforming technology can effectively improve the signal quality, increase the transmission distance, reduce interference, and improve the spectrum utilization rate by transmitting signals directionally. This also means that the base station needs to know the location information of users in advance. Generally speaking, due to the cooperation between the base station and friendly users, we can know the location information of friendly users in advance; however, there is no cooperation between the base station and eavesdropping users, and the location information of eavesdropping users needs to be obtained through sensing means.

[0004] Most existing research is based on the assumption of perfect location information, ignoring the error in location estimation. The magnitude of this error is related to the sensing time. The longer the sensing time, the smaller the location error, and correspondingly, the smaller the channel error, and the better the interference performance. However, in the case of limited resources, how to balance the resource allocation and performance trade-off among sensing, communication, and interference is a problem worthy of in-depth study. Summary of the Invention

[0005] This application aims to solve at least one of the technical problems in the related technologies to some extent.

[0006] To this end, the first objective of this application is to propose a robust beamforming design method for integrated communication and interference with sensing assistance, aiming to solve the problem of how to allocate resources when the sensing time affects the location error and thus affects the communication and interference performance in the case of limited resources.

[0007] The second objective of this application is to propose a device for robust beamforming design for integrated communication and interference with sensing assistance.

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

[0009] The fourth object of the present application is to propose a computer-readable storage medium.

[0010] The fifth object of the present application is to propose a computer program product.

[0011] To achieve the above object, an embodiment of the first aspect of the present application proposes a perception-assisted communication jamming integrated robust beamforming design method, including: Controlling the base station to estimate the location information of the eavesdropping user through perception means, establishing a perception performance index based on the Cramer-Rao bound, and deriving the relationship between the perception time and the channel error; Constructing a joint optimization problem, aiming to minimize the transmission power of the base station, optimizing the communication beamforming matrix, the interference beamforming matrix and the perception time, while satisfying the communication rate constraint, the interference signal-to-noise ratio constraint and the perception time range constraint; Performing an equivalent transformation on the joint optimization problem, and using semidefinite relaxation and the S-lemma to handle the non-convexity constraints of the beamforming matrix, so as to transform it into a solvable convex optimization problem; Adopting an alternating optimization algorithm, and performing step-by-step optimization through the block coordinate descent method. First, fix the perception time, optimize the communication beamforming matrix and the interference beamforming matrix, and then fix the communication beamforming matrix and the interference beamforming matrix, optimize the perception time, and iterate and update until convergence; Outputting the optimized perception time, communication beamforming matrix and interference beamforming matrix.

[0012] Optionally, the controlling the base station to estimate the location information of the eavesdropping user through perception means, establishing a perception performance index based on the Cramer-Rao bound, and deriving the relationship between the perception time and the channel error includes: Initializing the number of base station antennas , the number of friendly users , the number of eavesdropping users , the azimuth angle and error of the eavesdropping user , the frame length , the perception time , the signal-to-noise ratio of the perception signal and the noise power , as the initial system parameters; Based on the initial system parameters, establishing a Cramer-Rao bound model, and calculating the Cramer-Rao bound of the azimuth angle estimation error of the eavesdropping user , and its expression is:

[0013] Obtaining the communication rate of the friendly user as:

[0014] Obtain the average interference signal-to-noise ratio of the wiretapped user It is:

[0015] According to the Cramér-Rao bound of the azimuth estimation error of the wiretapped user, deduce the relationship between the sensing time and the channel estimation error, and obtain the channel between the base station and the wiretapped user Regarding the azimuth estimator Closed-form solution:

[0016]

[0017] Among them, the channel uncertainty range Is given by the following expression:

[0018] Construct a sensing error constraint set , which characterizes the uncertainty range of the channel between the base station and the wiretapped user, to provide the constraint conditions for robust beamforming optimization.

[0019] Optionally, the construction of the joint optimization problem aims to minimize the transmit power of the base station, 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:

[0020] Make an equivalent representation of the power optimization problem, define the communication beamforming matrix And the interference beamforming matrix , obtain the equivalent representation of the joint optimization problem, the formula is:

[0021] Among them, Is the communication beamforming vector of the th friendly user, Is the interference beamforming vector of the th wiretapped user; Among them, the constraint Requires that the communication rate of each friendly user cannot be lower than the minimum value , the constraint Requires that the interference signal-to-noise ratio of each wiretapped user cannot be lower than the minimum value , the constraint Requires that the sensing time is between the minimum value And the maximum value between, constraint guarantee and is the covariance matrix, constraint guarantee and .

[0022] Optionally, the equivalent transformation of the joint optimization problem is performed, and the non-convexity constraint of the beamforming matrix is processed by semi-definite relaxation and the S-lemma to convert it into a solvable convex optimization problem, including: For the communication rate constraint perform an equivalent transformation and rewrite it as:

[0023] where is constrained by the following inequality:

[0024] For the interference signal-to-noise ratio constraint perform an equivalent transformation and rewrite it as:

[0025] where is constrained by the following inequality:

[0026] Using the S-lemma, the equivalent transformation of the communication rate constraint is expressed as the following matrix inequality:

[0027] where is the Lagrange multiplier; Using the S-lemma, the equivalent transformation of the interference signal-to-noise ratio constraint is expressed as the following matrix inequality:

[0028] where is the Lagrange multiplier; Ignoring the constraint , the optimization problem is converted by semi-definite relaxation to make it a standard convex optimization problem, and the expression is:

[0029] where is the slack variable, , , .

[0030] Optionally, the alternating optimization algorithm is adopted and optimized step by step through the 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 iteration is updated until convergence, including: S1. Set the initial number of iterations , the maximum number of iterations , and set the initial feasible solution ; S2. Fix , solve the optimization problem regarding , and update to obtain ; S3. Fix , solve the optimization problem regarding , and update to obtain ; S4. Determine whether is satisfied. If it is satisfied, output ; if not, update the number of iterations , and repeat steps S1 to S4.

[0031] Optionally, the fixing , solving the optimization problem regarding , and updating to obtain , including: Fix , and obtain the optimization problem regarding , expressed as:

[0032] Ignore , and obtain the convex optimization problem regarding , and update to obtain through the convex optimization tool.

[0033] Optionally, the fixing , solving the optimization problem regarding , and updating to obtain , including: Fix , and obtain the convex optimization problem regarding , expressed as:

[0034] Update to obtain through the convex optimization tool.

[0035] To achieve the above object, the second aspect embodiment of the present application proposes a sensing-assisted communication interference integrated robust beamforming design device, including: The perception information acquisition module is used to control the base station to estimate the location information of eavesdropping users through perception means, establish a perception performance index based on the Cramer-Rao bound, and deduce the relationship between the perception time and the channel error; The joint optimization construction module is used to construct a joint optimization problem, aiming to minimize the transmit power of the base station, optimize the communication beamforming matrix, the interference beamforming matrix and the perception time, while satisfying the communication rate constraint, the interference signal-to-noise ratio constraint and the perception time range constraint; The optimization problem transformation module is used to perform an equivalent transformation on the joint optimization problem, and use semidefinite relaxation and the S-lemma to handle the non-convexity constraints of the beamforming matrix, so as to transform it into a solvable convex optimization problem; The alternating optimization calculation module is used to adopt an alternating optimization algorithm, and perform step-by-step optimization through the block coordinate descent method. First, fix the perception time, optimize the communication beamforming matrix and the interference beamforming matrix, and then fix the communication beamforming matrix and the interference beamforming matrix, optimize the perception time, and iterate and update until convergence; The optimization result output module is used to output the optimized perception time, communication beamforming matrix and interference beamforming matrix.

[0036] To achieve the above object, an embodiment of the third aspect of the present application proposes an electronic device, including: a processor, and a memory communicatively connected to the processor; The memory stores computer execution instructions; The processor executes the computer execution instructions stored in the memory to implement the method described in any one of the first aspect.

[0037] To achieve the above object, an embodiment of the fourth aspect of the present application proposes a computer-readable storage medium, in which computer execution instructions are stored, and when the computer execution instructions are executed by a processor, they are used to implement the method described in any one of the first aspect.

[0038] To achieve the above object, an embodiment of the fifth aspect of the present application proposes a computer program product, and when the computer program is executed by a processor, it implements the method described in any one of the first aspect.

[0039] The technical solutions provided by the embodiments of the present application at least bring the following beneficial effects: 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 behaviors of eavesdropping users with unknown locations while ensuring the communication quality of friendly users, and to solve the problem of how to allocate resources when the perception time affects the position error and thus affects the communication interference performance in the case of limited resources, significantly reducing the power consumption at the base station side.

[0040] Additional aspects and advantages of the present application will be given in part in the following description, become apparent in part from the following description, or be learned through the practice of the present application. Description of the Drawings

[0041] The above-mentioned and / or additional aspects and advantages of the present application will become apparent and be readily understood from the following description of embodiments in conjunction with the drawings, where: Figure 1 It is a schematic flow chart of a perception-assisted integrated communication and interference robust beamforming design method provided by an embodiment of the present application; Figure 2 It is a schematic diagram of an integrated communication and interference robust beamforming scenario provided by an embodiment of the present application; Figure 3 It is a schematic diagram of a perception-assisted communication and interference frame structure provided by an embodiment of the present application; Figure 4 It is a schematic diagram of the change of base station power consumption with the number of iterations provided by an embodiment of the present application; Figure 5 It is a schematic diagram of the change of base station power consumption with the proportion of the sensing stage provided by an embodiment of the present application; Figure 6 It is a schematic diagram of the change of base station power consumption with the number of antennas provided by an embodiment of the present application; Figure 7 It is a schematic diagram of the change of base station power consumption with the average interference signal-to-noise ratio provided by an embodiment of the present application; Figure 8 It is a schematic diagram of the change of base station power consumption with the minimum communication rate of users provided by an embodiment of the present application; Figure 9 It is a schematic structural diagram of a perception-assisted integrated communication and interference robust beamforming design device provided by an embodiment of the present application. Detailed Embodiments

[0042] Embodiments of the present application will be described in detail below. Examples of the embodiments are shown in the drawings, where the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the drawings are exemplary and are intended to explain the present application and should not be construed as limiting the present application.

[0043] In view of the problems existing in the prior art, an embodiment of the present application provides a perception-assisted integrated communication and interference robust beamforming design method. Figure 1 It is a schematic flow chart of a perception-assisted integrated communication and interference robust beamforming design method provided by an embodiment of the present application. As Figure 1 shown, the method includes the following steps: Step 101: 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 deduces the relationship between the sensing time and the channel error.

[0044] In the embodiment of the present application, the method proposed in the present application relies on the scenario of integrated robust beamforming for sensing-assisted communication interference.

[0045] In a possible embodiment, an integrated robust beamforming scenario for communication interference is as Figure 2 shown. In this scenario: There are multiple friendly users and multiple eavesdropping users distributed around a multi-antenna base station. The task of the base station is to communicate with the friendly users and interfere with the eavesdropping users at the same time. The location information of the friendly users is the prior information known to the base station, and the location information of the eavesdropping users is unknown to the base station and needs to be estimated through sensing means. The base station constructs a channel through the location information and simultaneously completes the communication with the friendly users and the interference with the eavesdropping users through the robust beamforming method.

[0046] In a possible embodiment, consider a downlink multi-user scenario, including a base station equipped with root uniform linear array antennas, single-antenna friendly users and single-antenna suspicious targets, the carrier frequency is , with as the reference distance path loss , Rice factor , noise power . The signal-to-noise ratio of the sensing signal , the length of each frame , the minimum rate of each friendly user , the minimum effective interference signal-to-noise ratio of each eavesdropping user . The task of the base station is to communicate with the friendly users and interfere with the eavesdropping at the same time. 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 users and the eavesdropping users need to be known in advance. Generally speaking, there is cooperation between the base station and the friendly users so that the azimuth angle of the friendly users can be known in advance, but there is no cooperation between the base station and the suspicious targets. Therefore, in the embodiment of the present application, before interfering with the suspicious targets, the azimuth angle of the suspicious targets needs to be obtained through sensing means in order to effectively interfere with the suspicious targets.

[0047] As Figure 3 shown, in order to meet the requirements of communication and interference, the embodiment of the present application designs a sensing-assisted communication and interference frame structure. A complete frame structure includes a sensing phase and a communication and interference phase, and the total length is , the lengths of the sensing phase and the communication and jamming phase are respectively and . In the sensing phase, the base station obtains the azimuth and distance of the eavesdropping user through radar detection technology; in the communication and jamming phase, the base station simultaneously communicates and jams according to the known location information and of the legitimate users and the location information and of the eavesdropping user obtained by sensing.

[0048] In the sensing phase, the embodiment of the present application can obtain the azimuth of the eavesdropping user through means such as target detection. The sensing echo signal received by the base station can be expressed as:

[0049] where represents the array steering vector and can be expressed as:

[0050] where represents the wavelength, represents the antenna spacing. Due to hardware limitations, the non - cooperation of the eavesdropping user, etc., there will be errors in estimating the azimuth of the eavesdropping user, which can be expressed as:

[0051] where represents the estimated value of the azimuth of the eavesdropping user, represents the estimation error of the azimuth of the eavesdropping user. Assuming that the estimation error follows a Gaussian distribution, and the Cramer - Rao bound is used to measure the variance of the estimation error, which is expressed as:

[0052] where represents the estimated value of the azimuth of the eavesdropping user, represents the estimation error of the azimuth of the eavesdropping user.

[0053] In the communication and jamming phase, the task of the base station is to communicate with the legitimate users while jamming the eavesdropping users. The received signals of the k - th legitimate user and the j - th eavesdropping user can be expressed as:

[0054] where and represent the communication symbol and the jamming symbol respectively. Assuming that the symbol energy is 1, there is .

[0055] Then the communication rate of the k-th friendly user within each frame length can be expressed as:

[0056] Furthermore, in the embodiments of the present application, the interference signal-to-noise ratio is considered to measure the interference performance of the base station. Then, the average interference signal-to-noise ratio of the j-th eavesdropping user within each frame length can be expressed as:

[0057] Considering that there are errors in the channels between the base station and the friendly users and the eavesdropping users, it can be expressed as:

[0058] Assuming that the estimation error is bounded, then there is . Since there is cooperation between the base station and the friendly users, for example, the friendly users can send pilots to the base station, and the base station can know the azimuth angle and error range of the friendly users in advance. At the same time, the base station has no prior information about the eavesdropping users. To effectively interfere with the eavesdropping users, the base station needs to perform sensing to obtain the azimuth angle of the eavesdropping users. Assuming that the azimuth angle estimation error obeys a Gaussian distribution, the probability that it is distributed within is 0.9973, which is sufficient to approximate the bound of using . Thus, the following expression holds:

[0059] where represents the steering vector error caused by .

[0060] After some algebraic transformations, can be rewritten as:

[0061] where, .

[0062] Due to the coupling of various trigonometric functions, it is difficult to obtain a closed-form solution. For this reason, the embodiments of the present application consider the case where is sufficiently small and perform a first-order Taylor expansion on , and then there is:

[0063] Furthermore, by using the second-order Taylor expansion at 0, the present application can deduce that:

[0064] where 。

[0065] After the above approximation of trigonometric functions, the present application can obtain the closed-form solution of the channel between the base station and the eavesdropping user with respect to the azimuth angle estimator The expression is:

[0066]

[0067] where the channel uncertainty range is given by the following expression:

[0068] Finally, the embodiment of the present application can construct a perception error constraint set to characterize the uncertainty range of the channel between the base station and the eavesdropping user, so as to provide constraint conditions for robust beamforming optimization.

[0069] Step 102: Construct a joint optimization problem with the goal of minimizing the transmit power of the base station, and 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.

[0070] In the embodiment of the present application, a joint optimization problem can be constructed based on various formulas proposed in step 101.

[0071] 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 goal is modeled as a base station power minimization problem, and its mathematical representation is as follows:

[0072] To further simplify the optimization problem, in the embodiment of the present application, an equivalent representation of the power optimization problem is made, and the communication beamforming matrix and the interference beamforming matrix are defined.

[0073] Thus, an equivalent representation of the joint optimization problem can be obtained, and the formula is:

[0074] where is the communication beamforming vector of the th friendly user, is the interference beamforming vector for the th eavesdropping user.

[0075] In the embodiments of the present application, the constraint requires that the communication rate of each friendly user cannot be lower than the minimum value , and the constraint requires that the interference signal-to-noise ratio of each eavesdropping user cannot be lower than the minimum value , and the constraint requires that the sensing time is between the minimum value and the maximum value , and the constraint ensures that and are covariance matrices, and the constraint ensures that and .

[0076] It can be understood that the establishment of the joint optimization problem provides a mathematical basis for subsequent equivalent transformation and solution, enabling the optimization problem to obtain a near-optimal solution while ensuring that the computational complexity is controllable.

[0077] Step 103: Perform an equivalent transformation on the joint optimization problem, and use semidefinite relaxation and the S-lemma to handle the non-convex constraints of the beamforming matrix, so as to transform it into a solvable convex optimization problem.

[0078] In the embodiments of the present application, step 103 involves performing an equivalent transformation on the joint optimization problem, and using semidefinite relaxation (SDR) and the S-lemma to handle the non-convex constraints of the beamforming matrix, so as to transform it into a solvable convex optimization problem. Through mathematical transformation, the communication rate constraint and the interference signal-to-noise ratio (JSNR) constraint are transformed into a convex optimization form for subsequent solution.

[0079] First, perform an equivalent transformation on the communication rate constraint and rewrite it as:

[0080] where is constrained by the following inequality:

[0081] Then, perform an equivalent transformation on the interference signal-to-noise ratio constraint and rewrite it as:

[0082] where is constrained by the following inequality:

[0083] In the embodiments of the present application, the S-lemma is further utilized to express the equivalent transformation of the communication rate constraint as the following matrix inequality:

[0084] where is the Lagrange multiplier; Similarly, using the S-lemma, the equivalent transformation of the interference signal-to-noise ratio constraint is expressed as the following matrix inequality:

[0085] where is the Lagrange multiplier; To ensure that the optimization problem can be effectively solved, in the embodiments of the present application, the constraint is ignored, and the optimization problem is transformed through semidefinite relaxation to make it a standard convex optimization problem, and the expression is:

[0086] where is the slack variable, , , .

[0087] In the embodiments of the present application, through the above equivalent transformation, the original non-convex optimization problem is successfully transformed into a standard convex optimization problem. This transformation not only reduces the difficulty of solving but also ensures that the optimization problem can be efficiently solved by existing convex optimization tools, thereby further improving the communication performance and anti-eavesdropping ability of the system.

[0088] Step 104, adopt the alternating optimization algorithm, and perform step-by-step optimization through the block coordinate descent method. First, fix the sensing time, optimize the communication beamforming matrix and the interference beamforming matrix, and then fix the communication beamforming matrix and the interference beamforming matrix, and optimize the sensing time, and iterate and update until convergence.

[0089] In the embodiments of the present application, step 104 involves adopting the alternating optimization algorithm and performing step-by-step optimization through the block coordinate descent (BCD) method. The core idea of this method is: first, fix the sensing time and optimize the communication beamforming matrix and the interference beamforming matrix; then fix the beamforming matrix and optimize the sensing time; continuously alternate and iterate until convergence to ensure that the optimization problem can be effectively solved.

[0090] Specifically, the optimization steps are as follows: S1. Set the initial number of iterations , the maximum number of iterations , set the initial feasible solution .

[0091] S2. Fix , solve the optimization problem with respect to , and update to obtain .

[0092] In the embodiment of the present application, first fix , and obtain the optimization problem with respect to , which is expressed as:

[0093] Then ignore , and obtain the convex optimization problem with respect to , and update to obtain .

[0094] S3. Fix , solve the optimization problem with respect to , and update to obtain .

[0095] In the embodiment of the present application, first fix , and obtain the convex optimization problem with respect to , which is expressed as:

[0096] Then, update to obtain .

[0097] S4. Judge whether it satisfies . If it satisfies, output ; if it does not satisfy, update the iteration number , and repeat steps S1 to S4

[0098] In the embodiment of the present application, the alternating optimization algorithm iterates continuously until the convergence condition is satisfied: If the termination condition is satisfied or the optimization objective converges, output the final optimization result ; If the termination condition is not satisfied, update the iteration number: , and return to execute steps S2 to S4 to continue iterative optimization

[0099] In the embodiments of the present application, the alternating optimization algorithm combines the block coordinate descent method to gradually optimize the beamforming matrix and the sensing time, enabling the optimization problem to converge efficiently to a near-optimal solution, improving the computational efficiency and the solution accuracy. While ensuring the communication quality, this method further reduces the transmission power of the base station and enhances the interference effect on eavesdropping users, improving the security and reliability of the system.

[0100] Step 105: Output the optimized sensing time, communication beamforming matrix, and interference beamforming matrix.

[0101] In the embodiments of the present application, Step 105 involves outputting the optimized sensing time, communication beamforming matrix, and interference beamforming matrix. The finally output optimized parameters can be used for beamforming in an actual communication system to achieve efficient transmission to friendly users and effective interference on eavesdropping users by the base station, ensuring the security and energy efficiency of wireless communication.

[0102] In addition, to verify the effectiveness of the proposed optimization algorithm, the embodiments of the present application analyze the convergence and performance of the algorithm of the present application under different parameter settings through simulation experiments and compare it with the baseline method.

[0103] In Figure 4 it shows the convergence of the optimization algorithm proposed in the present application 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 transmission power required by the base station decreases because more antennas can focus communication signals and interference signals more effectively, thus reducing the total power requirement. When the numbers of friendly users and eavesdropping users increase, the minimum transmission power required by the base station increases because more power is needed to meet the requirements of all users while ensuring communication and interference performance.

[0104] In Figure 5 it analyzes the variation of the base station transmission power under different numbers of antennas, friendly users, and eavesdropping users when the sensing time of the algorithm of the present application is not optimized. It can be observed that as the sensing time increases, the base station transmission power first decreases and then increases. When the sensing time is less than the optimal point, the channel error is large and has a significant impact on performance. Increasing the sensing time can reduce the base station transmission power; while when the sensing time is greater than the optimal point, the communication time and interference time decrease, resulting in a decline in overall performance. At this time, it is necessary to reduce the sensing time to optimize the base station transmission power.

[0105] In Figure 6In this application embodiment, the transmit power of the base station under different numbers of antennas is studied and compared with two baseline methods: For Baseline 1, this application embodiment does not optimize the sensing time and sets the sensing time to half of the entire frame length, that is ; For Baseline 2, this application embodiment assumes that the base station does not use sensing assistance, which means that the base station does not know the azimuth angle of the eavesdropping user. Therefore, the base station sends an omnidirectional interference signal to ensure the interference performance.

[0106] It can be observed that when the number of antennas increases, the transmit power of the base station decreases for all three methods. This indicates that increasing the antennas can more effectively concentrate the signal energy, thereby reducing power consumption.

[0107] In Figure 7 this application studies the transmit power of the base station under different interference signal-to-noise ratios (JSNR). When the JSNR increases, the transmit power of the base station increases for all three methods. This is because a stronger interference signal requires the base station to allocate more power to meet the interference performance requirements. It is worth noting that under all JSNR conditions, the transmit power of the base station of the optimization algorithm proposed in this application is lower than that of the baseline methods, indicating that the optimization algorithm can reduce power consumption while improving the interference performance. In addition, for Baseline 2 (omnidirectional interference signal), when the JSNR increases, the transmit power of the base station shows a significant increasing trend. This is because the omnidirectional interference signal is less efficient, and when the requirements for interference performance increase, it is necessary to significantly increase the power of the interference signal, resulting in a significant increase in the system power consumption.

[0108] In Figure 8 this application studies the transmit power of the base station under different minimum communication rates. When the minimum communication rate requirement of the user increases, the transmit power of the base station increases correspondingly for all three methods. This is because a higher communication rate requires the base station to provide a stronger signal power to meet the needs of all users. Under all communication rate requirements, the transmit power of the base station of the optimization algorithm proposed in this application is lower than that of the baseline methods, further verifying the superiority of the algorithm in this application in reducing power consumption.

[0109] In summary, the simulation experiment results show that the proposed integrated robust beamforming design method for sensing-assisted communication interference in this application has good convergence and optimization performance under different system parameters. Compared with the baseline methods, this method significantly reduces the transmit power of the base station and improves the energy efficiency of the system under the same communication and interference performance constraints, providing an effective solution for secure communication and energy-saving optimization.

[0110] To implement the above embodiments, this application also proposes an integrated robust beamforming design device for sensing-assisted communication interference.Figure 9 This is a schematic structural diagram of a perception-assisted communication jamming integrated robust beamforming design device provided by an embodiment of the present application. As Figure 9 shown, the device includes: A perception information acquisition module 100, configured to control the base station to estimate the location information of the eavesdropping user through perception means, establish a perception performance index based on the Cramer-Rao bound, and deduce the relationship between the perception time and the channel error; A joint optimization construction module 200, configured to construct a joint optimization problem, with the goal of minimizing the base station transmission power, optimize the communication beamforming matrix, the interference beamforming matrix, and the perception time, while satisfying the communication rate constraint, the interference signal-to-noise ratio constraint, and the perception time range constraint; An optimization problem conversion module 300, configured to perform an equivalent conversion on the joint optimization problem, and use semidefinite relaxation and the S-lemma to handle the non-convexity constraints of the beamforming matrix, so as to convert it into a solvable convex optimization problem; An alternating optimization calculation module 400, configured to adopt an alternating optimization algorithm, and perform step-by-step optimization through the block coordinate descent method. First, fix the perception time, optimize the communication beamforming matrix and the interference beamforming matrix, and then fix the communication beamforming matrix and the interference beamforming matrix, optimize the perception time, and iterate and update until convergence; An optimization result output module 500, configured to output the optimized perception time, communication beamforming matrix, and interference beamforming matrix.

[0111] Regarding the device in the above embodiment, the specific manners in which each module performs operations have been described in detail in the embodiment related to the method, and will not be elaborated here.

[0112] To implement the above embodiment, the present application also proposes an electronic device, including: a processor, and a memory communicatively connected to the processor; the memory stores computer execution instructions; the processor executes the computer execution instructions stored in the memory to implement the method provided in the foregoing embodiment.

[0113] To implement the above embodiment, the present application also proposes a computer-readable storage medium, in which computer execution instructions are stored, and when the computer execution instructions are executed by a processor, they are used to implement the method provided in the foregoing embodiment.

[0114] To implement the above embodiment, the present application also proposes a computer program product, including a computer program, and when the computer program is executed by a processor, it implements the method provided in the foregoing embodiment.

[0115] It should be understood that the various forms of processes shown above can be used, with steps reordered, added or deleted. For example, the steps recited in the present application can be executed in parallel, sequentially or in a different order, as long as the desired results of the technical solution of the present application can be achieved, and no limitation is made herein.

[0116] The above specific embodiments do not constitute a limitation on the scope of protection of the present application. Those skilled in the art should understand that various modifications, combinations, sub - combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principle of the present application shall be included within the scope of protection of the present 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 the sensing performance index based on the Cramer-Rao bound, and derives the relationship between the sensing time and the channel error; Construct a joint optimization problem to minimize the base station transmit power, optimize the communication beamforming matrix, interference beamforming matrix and sensing time, and meet the communication rate constraint, interference signal-to-noise ratio constraint and sensing time range constraint. The joint optimization problem is equivalently transformed, and the non-convexity constraint of the beamforming matrix is ​​processed by using semi-positive definite relaxation and S lemma to transform the joint optimization problem into a solvable convex optimization problem; An alternating optimization algorithm is used to optimize in steps through the block coordinate descent method. First, the perception 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 perception time is optimized, and iterative updates are made 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 eavesdropped user by 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 , perceive 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 error between the base station and the eavesdropping user is obtained. About the azimuth angle estimate The closed-form solution of is: Among them, the channel uncertainty range It 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, and meet 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: The power optimization problem is expressed equivalently 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 The communication rate of each friendly user must not 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, and 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 definite relaxation and S-lemma to process the non-convexity constraint of the beamforming matrix, so as to transform it into a solvable convex optimization problem, includes: Communication rate constraints Perform an equivalent transformation and rewrite it as: in, 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 of 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 of is expressed as the following matrix inequality: in, is the Lagrange multiplier; Ignore constraints , through the semi-positive definite relaxation transformation optimization problem, it becomes a standard convex optimization problem, the expression is: in, is the slack variable, , , .

5. The method according to claim 4, characterized in that 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: 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 can be expressed as: neglect , get about The convex optimization problem is updated by 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 can be 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 eavesdropped user through sensing means, establish the 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 module is used to build a joint optimization problem to minimize the base station transmit power, optimize the communication beamforming matrix, the interference beamforming matrix and the sensing time, and meet 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 equivalent conversion on the joint optimization problem, and use semi-positive definite relaxation and S lemma to process the non-convexity constraint of the beamforming matrix, so as to convert it into a solvable convex optimization problem; An alternating optimization calculation module is used for adopting an alternating optimization algorithm to optimize in steps 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, the sensing time is optimized, and iterative updates are made 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