Method and system for uplink anti-jamming secure communication based on movable antenna array

Abstract

The application discloses an uplink anti-interference safe communication method and system based on a movable antenna array, and belongs to the wireless communication field. The method comprises the following steps: acquiring communication scene data, and constructing an optimization problem model with a receiving beamforming and an antenna position as optimization variables according to the communication scene data; a target transmitting user is a transmitting user with the minimum signal-to-interference-and-noise ratio among all transmitting users; the optimization problem model is solved to obtain an optimized receiving beamforming vector and an antenna position vector, which are used for uplink signal receiving of a base station; and the optimization target of the optimization problem model is to maximize the signal-to-interference-and-noise ratio of the target transmitting user. Compared with the prior art, the application can effectively cooperatively control the antenna position adjustment and the receiving beamforming, and guarantee the fairness among multiple users in an interference environment.

Description

Technical Field

[0001] This invention relates to the field of wireless communication technology, and more specifically, to an uplink anti-interference secure communication method and system based on a movable antenna array. Background Technology

[0002] In traditional uplink anti-jamming communication systems, base stations typically employ fixed-position antenna arrays. The anti-jamming capability of such systems primarily relies on beamforming at the receiver to suppress interference signals in the direction of interference. However, the physical location of fixed antennas cannot be changed once deployed, limiting their spatial freedom. When the direction of the interference source changes or the spatial distribution of multiple legitimate users differs, beamforming alone cannot simultaneously enhance the signal for all users and effectively suppress interference, thus affecting fairness among users.

[0003] In recent years, by adjusting the physical position of antenna elements, systems can dynamically reconstruct the channel environment and optimize signal reception conditions in the spatial dimension. However, in multi-user uplink scenarios with malicious interference, base stations face new control challenges: adjusting the antenna position simultaneously alters the channel response of all users and jammers, thus affecting the designed receive beamforming; conversely, updating beamforming places new demands on the optimality of the antenna position. Current technology has not yet provided a method for effectively coordinating antenna position adjustment and receive beamforming to ensure fairness among multiple users in interference environments. Summary of the Invention

[0004] The summary section of this application is intended to provide a brief overview of the concepts, which will be described in detail in the detailed description section below. This summary section is not intended to identify key or essential features of the claimed technical solutions, nor is it intended to limit the scope of the claimed technical solutions.

[0005] Some embodiments of this application propose an uplink anti-interference secure communication method and system based on a movable antenna array to solve the technical problems mentioned in the background section above.

[0006] As a first aspect of this application, some embodiments of this application provide an uplink anti-interference secure communication method based on a movable antenna array, comprising the following steps:

[0007] Acquire communication scenario data; the communication scenario data includes the location information of each transmitting user, the transmission power of each transmitting user, the location information of the jammer, the transmission power of the jammer, the location information of the base station, and the noise power of the base station; the base station is equipped with an antenna array; the antenna array includes multiple movable antenna elements; Based on communication scenario data, an optimization problem model is constructed with receive beamforming and antenna position as optimization variables; receive beamforming is used to enhance the signal from the target transmitting user and suppress interference signals; the target transmitting user is the transmitting user with the lowest signal-to-interference-plus-noise ratio among all transmitting users; Solve the optimization problem model to obtain the optimized receive beamforming vector and antenna position vector, which are used by the base station for uplink signal reception; The optimization objective of the optimization problem model is to maximize the signal-to-interference-plus-noise ratio (SIR) of the target transmitting user.

[0008] Furthermore, the signal-to-interference-plus-noise ratio (SIR) of the transmitting user is the ratio of the transmitting user's signal power to the total interference power; Total interference power includes the interference power caused by other transmitting users to the transmitting user, the interference power caused by the jammer to the transmitting user, and the noise power of the base station.

[0009] Furthermore, the signal-to-interference-plus-noise ratio (SIR) of the transmitting user is expressed as: ; In the formula: For the number of users to launch; The serial number of the transmitting user, with a value range of 100. ; For the first The receive beamforming vector corresponding to each transmitting user; Let be the antenna position vector. This refers to the number of movable antenna elements. For the first The position coordinates of each antenna element; For the first A channel vector from the transmitting user to the base station; Indicates the first Angle of arrival for the transmitting user signal; For the first A channel vector from a transmitting user to a base station, ; Indicates the first Angle of arrival for the transmitting user signal; This is the channel vector from the jammer to the base station; Indicates the angle of arrival of the interference signal; For the first Transmit power of each transmitting user; For the first Transmit power of each transmitting user; This refers to the jammer's transmission power. This represents the conjugate transpose operation; For the first The signal-to-interference-plus-noise ratio of each transmitting user.

[0010] Furthermore, the constraints for optimizing the problem model include: the position of each antenna element can be adjusted within a preset range.

[0011] Furthermore, the optimization problem model is expressed as: ; ; In the formula: For the number of users to launch; The serial number of the transmitting user; For the first The receive beamforming vector corresponding to each transmitting user; Let be the antenna position vector. This refers to the number of movable antenna elements. For the first The position coordinates of each antenna element; For the first Transmit power of each transmitting user; This refers to the jammer's transmission power. For the first Signal-to-interference-plus-noise ratio for each transmitting user; The total span of the antenna array. This represents the minimum spacing between adjacent antennas. This is the maximum transmit power threshold.

[0012] Furthermore, the optimization problem model is solved, including using alternating iteration to jointly optimize the received beamforming vector and the antenna position vector, repeating the alternating iteration process until the convergence condition is met, and obtaining the optimized received beamforming vector and antenna position vector; The alternating iterative process includes: While keeping the antenna position vector unchanged, find the receive beamforming vector that maximizes the signal-to-interference-plus-noise ratio (SIR) for the user with the minimum SIR. The obtained received beamforming vector is substituted into the optimization problem model to update the antenna position vector, resulting in the updated antenna position vector.

[0013] Furthermore, while keeping the antenna position vector unchanged, the receiving beamforming vector that maximizes the signal-to-interference-plus-noise ratio (SIR) for the user with the minimum SIR is solved, specifically including: Introducing slack variables The optimization problem model is transformed into: ; ; With fixed antenna position vector Under the condition of minimizing mean square error, the calculation is performed to make Maximize the received beamforming vector .

[0014] Furthermore, the obtained received beamforming vector is substituted into the optimization problem model to obtain the updated antenna position vector, which specifically includes: Substituting the obtained received beamforming vector into the optimization problem model, we obtain a non-convex optimization problem that is only related to the antenna position. The continuous convex approximation method is used to perform a first-order Taylor expansion of the non-convex optimization problem at the current antenna position to obtain a convex optimization objective function. The updated antenna position vector is obtained by solving the optimization objective function.

[0015] As a second aspect of this application, some embodiments of this application provide an uplink anti-interference secure communication system based on a movable antenna array, including a base station, the base station comprising: A movable antenna array contains multiple movable antenna elements; a movable antenna array is used to change the environment in which signals are received by adjusting the position of each antenna element. The data acquisition module is used to acquire communication scenario data. The communication scenario data includes the location information of each transmitting user, the transmission power of each transmitting user, the location information of the jammer, the transmission power of the jammer, the location information of the base station, and the noise power of the base station. The base station is equipped with an antenna array, which includes multiple movable antenna elements. The model building module is used to construct an optimization problem model with receive beamforming and antenna position as optimization variables based on communication scenario data. Receive beamforming is used to enhance the signal from the target transmitting user and suppress interference signals. The target transmitting user is the transmitting user with the lowest signal-to-interference-plus-noise ratio among all transmitting users. The solution module is used to solve the optimization problem model and obtain the optimized receive beamforming vector and antenna position vector, which are used by the base station for uplink signal reception. The optimization objective of the optimization problem model is to maximize the signal-to-interference-plus-noise ratio (SIR) of the target transmitting user.

[0016] As a third aspect of this application, some embodiments of this application provide an electronic device, including: One or more processors; Memory, used to store one or more programs; When one or more programs are executed by one or more processors, the one or more processors implement the above-described uplink anti-interference secure communication method based on a movable antenna array.

[0017] Compared with the prior art, the advantages of this invention are: (1) This scheme acquires communication scenario data and constructs an optimization problem model with receive beamforming and antenna position as optimization variables. Receiver beamforming is used to enhance the signal of the target transmitting user and suppress interference signals. It considers the antenna position and receive beamforming, which were originally mutually influential, together, avoiding the problem of mutual coupling and difficulty in coordinated control of antenna position and receive beamforming in the prior art. At the same time, the optimization objective of the optimization problem model in this scheme is to maximize the signal-to-interference-plus-noise ratio of the target transmitting user. The target transmitting user is the transmitting user with the smallest signal-to-interference-plus-noise ratio among all transmitting users, thereby ensuring that the user with the worst channel conditions can still obtain a guaranteed communication quality under malicious interference, thus achieving user fairness. Finally, by solving the optimization problem model, the optimized receive beamforming vector and antenna position vector are obtained. The base station receives the signal according to the optimized receive beamforming vector and antenna position vector, thereby improving anti-interference capability and user fairness in dynamic interference environment.

[0018] (2) In view of the high non-convexity of the optimization problem model, this scheme solves the problem by alternating iteration. First, fix the antenna position and calculate the optimal receiving beamforming under the current antenna position. Then, substitute the optimal receiving beamforming into the objective function of optimization, transform the problem into an optimization problem that is only related to the antenna position, and solve it by continuous convex approximation to obtain the updated antenna position, and obtain the optimized receiving beamforming and antenna position. Attached Figure Description

[0019] Figure 1 This is a flowchart of an uplink anti-interference secure communication method based on a movable antenna array according to an embodiment of the present invention; Figure 2 This is a schematic diagram of an uplink multi-user anti-interference communication scenario in one embodiment of the present invention; Figure 3 This is a flowchart of an uplink anti-interference secure communication method based on a movable antenna array according to another embodiment of the present invention; Figure 4 This is a convergence curve diagram of an uplink anti-interference secure communication method based on a movable antenna array under different antenna number configurations in one embodiment of the present invention; in the figure: Iteration number represents the number of iterations, and Minimum SINR represents the signal-to-interference-plus-noise ratio (unit: dB) of the weakest user. Figure 5 This is a performance comparison curve of an uplink anti-interference secure communication method based on a movable antenna array and a traditional fixed antenna method in one embodiment of the present invention; in the figure: Number of antennas represents the number of antenna elements, and MinimumSINR represents the signal-to-interference-plus-noise ratio (unit: dB) of the weakest user. Detailed Implementation

[0020] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments.

[0021] The technical solution of this invention is applied to an uplink multi-user anti-interference communication scenario. In this scenario, multiple transmitting users simultaneously send information to the base station; at the same time, a malicious jammer transmits interference signals to the base station, attempting to disrupt the communication of legitimate users.

[0022] To address this scenario, this invention constructs a simulation environment in a computer. In this simulation environment, by combining the positional relationships of the transmitting user, the jammer, and the movable antenna array, as well as various constraints, the antenna position and receiving beamforming are optimized. The optimal antenna position and receiving beamforming are found to achieve the best communication quality for the target transmitting user. Finally, the optimization results are applied to the actual base station's receiving process.

[0023] like Figure 1 As shown, this invention provides an uplink anti-interference secure communication method based on a movable antenna array, comprising the following steps: S1. Obtain communication scenario data.

[0024] Before performing anti-interference optimization, the base station first needs to obtain the current communication scenario data.

[0025] The communication scenario data includes the location information of each transmitting user, the transmission power of each transmitting user, the location information of the jammer, the transmission power of the jammer, the location information of the base station, and the noise power of the base station.

[0026] Among them, the location information of each transmitting user is used to determine the direction of arrival of each user's signal, the location information of the jammer is used to determine the direction of arrival of the jamming signal, and the location information of the base station is used as a reference point to calculate the direction of arrival.

[0027] Specifically, the base station is equipped with a movable antenna array. In this embodiment, the movable antenna array is a linear array, comprising multiple movable antenna elements. The position of each antenna element can be independently adjusted within a preset range, thereby changing the spatial configuration of the array and optimizing signal reception conditions.

[0028] like Figure 2 As shown, The position coordinates of each antenna element are represented as an antenna position vector: ,in For the first The position coordinates of each antenna element , This indicates the transpose operation.

[0029] In a specific embodiment, the location of the transmitting user and the jammer can be obtained through several methods: The base station can measure the direction of arrival of each signal in real time by estimating the angle of arrival; or in a cooperative communication scenario, the user can actively report their location information; or the base station can obtain the location of jammers (such as fixed jamming devices) with known locations through pre-configuration.

[0030] In a specific embodiment, the transmit power and interference power can be obtained through various methods such as communication protocol negotiation, power detection, or pre-configuration.

[0031] By acquiring current communication scenario data, the base station can determine the direction of arrival of each signal and understand the current communication environment.

[0032] S2. Based on the communication scenario data, construct an optimization problem model.

[0033] A base station is deployed with multiple antenna elements, and the signals received by each antenna element include signals from various transmitting users and jammers. Because the propagation path length of the signal to different antenna elements is different, the same signal will produce phase differences on each antenna element, and the spatial direction information of the signal is reflected by the phase differences.

[0034] To extract useful signals from specific directions from these signals with phase differences, the base station needs to combine the signals received by each antenna element. Specifically, to adjust the amplitude and phase of the signal, the base station uses receive beamforming—that is, multiplying the signal of each antenna element by a complex coefficient, and then adding the weighted signals together to obtain a total output signal. This allows for in-phase superposition to enhance the signal in the desired direction, while out-of-phase superposition to weaken signals from other directions.

[0035] Specifically, receive beamforming is used to enhance the signal from the target transmitting user and suppress interference signals. The target transmitting user is the one with the lowest signal-to-interference-plus-noise ratio (SINR) among all transmitting users. Since the signals from different transmitting users come from different directions, the base station designs a set of complex coefficients for each transmitting user, forming the receive beamforming vector corresponding to that user. Each element of the receive beamforming vector corresponds to the weighting coefficient of an antenna element and satisfies normalization constraints.

[0036] After obtaining the communication scenario data through step S1, the base station needs to determine how to adjust the antenna position and receiving beam to ensure the communication quality of all transmitting users in an interference environment.

[0037] Since adjusting the antenna position changes the propagation channel of all signals, thus affecting the optimality of the receiving beamforming, and conversely, adjusting the receiving beamforming also changes the optimal configuration of the antenna position, this step constructs an optimization problem model with receiving beamforming and antenna position as optimization variables.

[0038] In multi-user communication scenarios, the core metric for measuring user communication quality is the signal-to-interference-plus-noise ratio (SINR). For a given transmitting user, the SINR is the ratio of the transmitting user's signal power to the total interference power.

[0039] Total interference power includes the interference power caused by other transmitting users to that transmitting user, the interference power caused by the jammer to that transmitting user, and the noise power of the base station. The higher the signal power and the lower the total interference power, the higher the signal-to-interference-plus-noise ratio and the better the communication quality.

[0040] Improving the signal-to-interference-plus-noise ratio (SNR) of only a single transmitter could allocate all resources to that transmitter, but this would degrade the performance of other transmitters; jammers often target transmitters with the worst channel conditions for suppression. Therefore, this scheme maximizes the SNR of the transmitter with the worst SNR among all transmitters, thus ensuring fairness among them. In this way, regardless of the transmitter's location or the degree of interference, each transmitter can obtain relatively balanced communication quality.

[0041] Therefore, the optimization objective of the optimization problem model is to maximize the signal-to-interference-plus-noise ratio (SIR) of the target transmitting user.

[0042] The signal propagation channel depends on the antenna position vector and the signal arrival angle of the transmitting user. The phase of the signal arriving at each antenna element in the same direction is different, and the signal propagation channel response is also different.

[0043] Specifically, the receive beamforming vector used by the base station for a given transmitting user suppresses signals from other directions (including the interference power caused by the jammer to that transmitting user and the base station's noise power). Furthermore, the interference power caused by the jammer to that transmitting user depends on the jammer's transmit power and the interference channel (determined by the antenna position and the angle of arrival of the interference signal). The base station's noise power also depends on the base station's transmit power and the interference channel (determined by the antenna position and the angle of arrival of the interference signal).

[0044] Specifically, the formula for calculating the signal-to-interference-plus-noise ratio (SIR) of the transmitting user is expressed as follows: ; In the formula: For the number of users to launch; The serial number of the transmitting user, with a value range of 100. ; For the first The receive beamforming vector corresponding to each transmitting user; This is the antenna position vector; The number of movable antenna elements; For the first The position coordinates of each antenna element; For the first A channel vector from the transmitting user to the base station; Indicates the first Angle of arrival for the transmitting user signal; For the first A channel vector from a transmitting user to a base station, ; Indicates the first Angle of arrival for the transmitting user signal; This is the channel vector from the jammer to the base station; Indicates the angle of arrival of the interference signal; For the first Transmit power of each transmitting user; For the first Transmit power of each transmitting user; This refers to the jammer's transmission power. This represents the conjugate transpose operation; For the first The signal-to-interference-plus-noise ratio of each transmitting user.

[0045] Among them, the receiving beamforming vector Essentially, these are the weighted combining coefficients of the signals received by each antenna element of the base station. This is achieved by adjusting the beamforming vector. This can enhance the effect from the first It transmits signals from one user while suppressing signals from other users and jammers.

[0046] Specifically, Satisfy normalization constraints: This ensures that beamforming does not introduce additional power gain or attenuation, making the signal-to-interference-plus-noise ratio comparable.

[0047] In practical applications, base stations are also subject to the following constraints: (1) Each antenna element can only move within a preset range. Let the total span of the antenna array be... Then the preset range .

[0048] (2) Adjacent antennas should not be too close together to avoid coupling effects between them. Therefore, the distance between any two adjacent antenna elements must not be less than the preset minimum distance, i.e., satisfy the following conditions. ,in This is the preset minimum spacing.

[0049] (3) The transmission power of each transmitting user and the transmission power of the jammer shall not exceed the maximum transmission power threshold. .Right now .

[0050] Based on the above optimization objective and constraints, the optimization problem model can be expressed as: ; ; In the formula: The total span of the antenna array. This represents the minimum spacing between adjacent antennas. This is the maximum transmit power threshold.

[0051] Thus, an optimization problem model is constructed.

[0052] S3. Solve the optimization problem model to obtain the optimized receive beamforming vector and antenna position vector, which are used by the base station for uplink signal reception.

[0053] Because the optimization problem model is highly nonconvex, it cannot be solved directly using conventional optimization methods. This step proposes a solution method based on alternating iteration, which jointly optimizes the received beamforming vector and the antenna position vector, repeating the alternating iteration process until the convergence condition is met, thus obtaining the optimized received beamforming vector and antenna position vector. Specifically, the alternating iteration process includes: (1) While keeping the antenna position vector unchanged, find the receiving beamforming vector that maximizes the signal-to-interference-plus-noise ratio (SIR) for the user with the minimum SIR.

[0054] When the antenna position vector is fixed At that time, all channels respond , , All of these become known values. At this point, the original optimization problem is transformed into a problem relating only to the received beamforming vector. The optimization problem.

[0055] To facilitate the solution, we first introduce a slack variable. The optimization objective of maximizing the minimum signal-to-interference-plus-noise ratio (SIR) is transformed into: ensuring that the SIR of all transmitting users is no less than [value missing]. and maximize .Right now: ; ; In the formula: This represents the current fixed antenna position vector. At this point, Only depend on For a given , It is relative to The broad definition of Rayleigh.

[0056] To solve for the problem at a fixed antenna position The optimal receiving beamforming vector is first introduced by an intermediate variable. , defined as the first The interference plus noise covariance matrix for each user: ; In the formula: The covariance matrix representing the interference signal caused to this transmitting user by other transmitting users is determined by the transmit power of each of the other transmitting users. and its channel vector The weighted sum of the outer products is obtained; The covariance matrix representing the interference signal caused by the jammer is determined by the jammer's transmit power. and its channel vector The outer product is obtained; yes The identity matrix corresponds to the normalized noise power; where This represents the number of antenna elements.

[0057] To solve for the problem at a fixed antenna position Under the condition of linear maximum root mean square error (MMSE), make Maximize the optimal receive beamforming vector It can be represented as: ; Will Substituting into the formula for calculating the signal-to-interference-plus-noise ratio (SIR), we can obtain the new SIR expression under optimal receiving beamforming, denoted as: : ; at this time, Only with the antenna position vector Related to, and related to the received beamforming vector This is irrelevant and ensures that the signal-to-interference-plus-noise ratio of the transmitting user reaches its maximum under a fixed antenna position.

[0058] (2) Substitute the obtained receiving beamforming vector into the optimization problem model to update the antenna position vector and obtain the updated antenna position vector.

[0059] Substitute the optimal receiving beamforming vector obtained in step (1) into the optimization problem model, and transform it into a problem that only relates to the antenna position vector. The relevant optimization problem model is represented as follows: ; ; in, It is the signal-to-interference-plus-noise ratio expression after substituting the optimal receiving beamforming.

[0060] For ease of subsequent processing, let: ; It is important to note that since the channel vector contains trigonometric functions of the antenna position, the resulting problem is a non-convex optimization problem that depends only on the antenna position. To solve this problem, this scheme employs the Successive Convex Approximation (SCA) method, which applies the non-convex constraints to the vicinity of the current iteration point. Approximately convex constraints are used to solve for the next iteration point, gradually approaching the optimal solution of the optimization problem model.

[0061] Specifically, in the first In this iteration, the current antenna position is known. Using the properties of Hermitian matrices and Taylor expansions of convex functions, non-convex matrices can be transformed. exist Expanding at this point, we obtain its convex approximation function. Therefore, the optimization problem model is transformed into the following convex optimization objective function: ; ; At this point, standard convex optimization tools (such as the CVX toolkit in Matlab) can be used to solve the problem, and the updated antenna position vector obtained from the solution can be used. As the starting point for the next iteration.

[0062] Specifically, the convergence condition can be: the objective function value The change is less than a preset threshold, the change of the antenna position vector is less than a preset threshold, or the preset maximum number of iterations is reached.

[0063] After the convergence condition is met, the optimized receive beamforming vector and antenna position vector are obtained, which are used by the base station for uplink signal reception.

[0064] Through the above iterative process, this scheme decomposes the complex non-convex optimization problem into two easily handled sub-problems: using the Rayleigh quotient property to obtain the MMSE closed-form solution; and using the continuous convex approximation to transform the non-convex problem into a convex problem, requiring only one convex optimization objective function to be solved in each iteration, thereby achieving the coordinated optimization of the movable antenna array and the receiving beamforming.

[0065] Based on the above-described uplink anti-interference secure communication method based on a movable antenna array, the present invention also provides an uplink anti-interference secure communication system based on a movable antenna array, including a base station.

[0066] Specifically, the base station includes: A movable antenna array comprising multiple movable antenna elements; the movable antenna array is used to change the environment for receiving signals by adjusting the position of each antenna element; The data acquisition module is used to acquire communication scenario data.

[0067] The model building module is used to construct an optimization problem model with receive beamforming and antenna position as optimization variables based on communication scenario data. Receive beamforming is used to enhance the signal from the target transmitting user and suppress interference signals; the target transmitting user is the transmitting user with the lowest signal-to-interference-plus-noise ratio among the transmitting users.

[0068] The solution module is used to solve the optimization problem model and obtain the optimized receive beamforming vector and antenna position vector, which are used by the base station for uplink signal reception.

[0069] The communication scenario data includes the location information of each transmitting user, the transmission power of each transmitting user, the location information of the jammer, the power of the jammer, and the location information of the base station; the base station is equipped with an antenna array; the antenna array includes multiple movable antenna elements.

[0070] Specifically, the optimization objective of the optimization problem model is to maximize the signal-to-interference-plus-noise ratio (SIR) of the target transmitting user.

[0071] Figure 3 Another flowchart of this scheme is shown. (See diagram below.) Figure 3 As shown, the steps of this solution are as follows: Step 1: Construct a scenario in which multiple transmitting users and a single jammer communicate with a base station with a movable antenna array; Step 2: Based on the constructed communication scenario, model the channels between multiple transmitting users and the jammer and base station respectively. Through channel modeling, the expression of the received signal at the base station can be obtained, and the signal-to-interference-plus-noise ratio of the instantaneous received signal can be calculated. Step 3: Based on the pre-given positions of the transmitter and jammer and the signal arrival angle from the transmitter to the base station, construct a problem model with the receiving beamforming and the position of the movable antenna array as variables, and maximize the worst-case base station received signal-to-interference-plus-noise ratio as the objective optimization function. Step 4: Further introduce slack variables The objective function is further transformed into maximizing To address the problem, a continuous convex approximation algorithm was finally adopted to iteratively optimize the receiving beamforming and the position of the movable antenna array. Step 5: Given the known positions of the antenna elements, find the maximum value of the linear maximum root mean square error receiving beamforming using the Rayleigh quotient algorithm; Step 6: Substitute the above step 5 into step 4, and the optimization objective is transformed into an optimization function that is only related to the position of the movable antenna array. Using the continuous convex approximation, based on the properties of the Hermitian matrix, Taylor expansion of convex functions and other techniques, the optimization objective of the position of the movable antenna array is iteratively optimized, so that the optimization problem is transformed from a non-convex problem to a convex problem. The CVX toolkit in Matlab is used for solving. Step 7: Repeat steps 4 through 6 above to obtain the optimal receive beamforming and antenna array unit position to maximize the objective function. This achieves the goal of secure uplink communication with anti-interference based on a movable antenna array.

[0072] The underlying principle is explained in the above embodiment and will not be repeated here.

[0073] based on Figure 3 In a specific embodiment, to verify the effectiveness of the method of the present invention, the following simulation experiment was conducted, with the simulation parameters set as follows: Minimum spacing between adjacent antenna elements ; The carrier wavelength is normalized to 1. Noise power normalized to ; The arrangement range of the movable antenna array is set to movable antenna array range ; Path loss index ; Transmit power of all transmitting users and jammers ; Path loss index ; The angle of arrival of the signal transmitted by the user to the base station is , The angle of arrival of the signal transmitted by the jammer to the base station is set as... ,and .

[0074] like Figure 4 As shown, the method exhibits significant convergence after several iterations. Figure 4 The horizontal axis, "Iteration number," represents the number of iterations, and the vertical axis, "Minimum SINR," represents the signal-to-interference-plus-noise ratio (SNR) of the weakest user (in dB). Within the array range, the SNR of the target user continuously increases with the increase in the number of antennas. This is because increasing the number of movable antennas allows for full utilization of spatial degrees of freedom, thereby achieving higher receive diversity gain. Simultaneously, as the number of iterations increases, the SNR of the target user eventually converges to a stable steady-state value. This is because, within a given range of movement of the movable antenna array, when the antenna array's position reaches its optimal configuration, the receive gain of the antennas within the array area will tend to saturate.

[0075] In one specific embodiment, the simulation parameters are set as follows: , ,user Both the node and the jammer have a transmit power of 10 dBm, and the movable antenna array range is... , ;when At that time, the angle of arrival of the signal sent by the user to the BS is ,when At that time, the angle of arrival of the signal sent by the user to the BS is .

[0076] Based on the above parameters, such as Figure 5 As shown, the minimum signal-to-interference-plus-noise ratio (SINR) performance of the method of this invention and the traditional fixed-location antenna method are compared under different antenna and user configurations. In the figure, the horizontal axis "Number of antennas" represents the number of antenna elements, and the vertical axis "Minimum SINR" represents the SINR (in dB) of the weakest user. The two solid lines (M=3 and M=4) correspond to the results of this method, and the two dashed lines correspond to the results of the traditional fixed-location antenna method. With the increase of the number of antennas, the performance of both the fixed-location antenna method and the method of this invention is improved. Furthermore, Figure 5The maximum and worst signal-to-interference-plus-noise ratio (SNR) values ​​for two schemes are also given. When the number of antennas is fixed, the performance of the method of this invention is significantly better than that of the traditional fixed-position antenna method, indicating that the movable antenna can make full use of spatial degrees of freedom to improve channel gain. In contrast, the antenna position and transmit / receive beamforming angle of the fixed-position antenna method are fixed values, so its system performance indicators remain constant. The above results confirm that, compared with the fixed-position antenna method, the uplink system anti-interference performance of the method of this invention by introducing a movable antenna is significantly improved. On the other hand, when the number of antennas... When the number of users remains constant, As the noise level increases, the minimum signal-to-interference-plus-noise ratio (SIR) of the method of this invention tends to decrease. Clearly, The maximum-minimum fairness optimization result is better than The reason for this situation is: when When there are fewer users, the signal-to-interference-plus-noise ratio is relatively higher, and beamforming optimization is easier; while when At this time, it has higher rank and more degrees of freedom, and can support more users to transmit in parallel, but the performance of a single user will be significantly reduced.

[0077] Table 1 shows the target transmit user signal-to-interference-plus-noise ratio (SINNR) for both the proposed method and the fixed-position antenna method under different configurations. In each group... Under the given configuration, the system's minimum signal-to-interference-plus-noise ratio varies with the number of antennas. It continues to improve with the increase in the number of users, and as the number of users increases... The increase in the number of antennas leads to a continuous decrease. When fixed, the performance of this method is significantly better than that of traditional fixed-position antenna methods, a conclusion that also verifies... Figure 4 The experimental results. It is worth noting that when... At that time, the improvement rate of this method compared with the traditional method was as high as 81.4%; however, as the number of antennas increased... With further increases in [specific parameters], the rate of performance improvement gradually slows down. This indicates that within a given antenna array range... Within, increase the number of antennas in the movable antenna array. It will be limited by the strong coupling effect between adjacent antennas. Therefore, although in With this configuration, the minimum signal-to-interference-plus-noise ratio is still improved, but the improvement rate of this method relative to the traditional method drops to 75.7%. Furthermore, when the number of antennas... Keeping the number of users constant, increasing the number of users from 3 to 4 will cause a decrease in the system's minimum signal-to-interference-plus-noise ratio (SINNR). Figure 5 The experimental results presented are consistent.

[0078] Table 1 Comparison of Signal-to-Interference-Ratio (SIR) of Target Transmitters and Users

[0079] The invention and its embodiments have been described above illustratively. This description is not restrictive, and the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. The accompanying drawings are only one embodiment of the invention, and the actual structure is not limited thereto. No reference numerals in the claims should limit the scope of the claims. Therefore, if a person skilled in the art is inspired by this description and designs a similar structure and embodiment without departing from the spirit of the invention, such design should fall within the scope of protection of this patent. Furthermore, the word "comprising" does not exclude other elements or steps, and the word "a" preceding an element does not exclude the inclusion of "a plurality" of that element. Multiple elements stated in the product claims may also be implemented by a single element through software or hardware. The terms "first," "second," etc., are used to indicate names and do not indicate any specific order.

Claims

1. An uplink anti-interference secure communication method based on a movable antenna array, characterized in that, Includes the following steps: Acquire communication scenario data; the communication scenario data includes the location information of each transmitting user, the transmission power of each transmitting user, the location information of the jammer, the transmission power of the jammer, the location information of the base station, and the noise power of the base station; the base station is equipped with an antenna array; The antenna array includes multiple movable antenna elements; Based on the communication scenario data, an optimization problem model is constructed with receive beamforming and antenna position as optimization variables; the receive beamforming is used to enhance the signal from the target transmitting user and suppress interference signals; the target transmitting user is the transmitting user with the lowest signal-to-interference-plus-noise ratio among all transmitting users; Solving the optimization problem model yields the optimized receive beamforming vector and antenna position vector, which are used by the base station for uplink signal reception. The optimization objective of the optimization problem model is to maximize the signal-to-interference-plus-noise ratio (SIR) of the target transmitting user.

2. The uplink anti-interference secure communication method based on a movable antenna array according to claim 1, characterized in that, The signal-to-interference-plus-noise ratio (SIR) of the transmitting user is the ratio of the transmitting user's signal power to the total interference power; The total interference power includes the interference power caused by other transmitting users to the transmitting user, the interference power caused by the jammer to the transmitting user, and the noise power of the base station.

3. The uplink anti-interference secure communication method based on a movable antenna array according to claim 2, characterized in that, The signal-to-interference-plus-noise ratio of the transmitting user is expressed as: ； In the formula: For the number of users to launch; The serial number of the transmitting user, with a value range of 100. ; For the first The receive beamforming vector corresponding to each transmitting user; Let be the antenna position vector. This refers to the number of movable antenna elements. For the first The position coordinates of each antenna element; For the first A channel vector from the transmitting user to the base station; Indicates the first Angle of arrival for the transmitting user signal; For the first A channel vector from a transmitting user to a base station, ; Indicates the first Angle of arrival for the transmitting user signal; This is the channel vector from the jammer to the base station; Indicates the angle of arrival of the interference signal; For the first Transmit power of each transmitting user; For the first Transmit power of each transmitting user; This refers to the jammer's transmission power. This represents the conjugate transpose operation; For the first The signal-to-interference-plus-noise ratio of each transmitting user.

4. The uplink anti-interference secure communication method based on a movable antenna array according to claim 1, characterized in that, The constraints of the optimization problem model include: the position of each antenna element can be adjusted within a preset range.

5. The uplink anti-interference secure communication method based on a movable antenna array according to claim 1, characterized in that, The optimization problem model is expressed as follows: ； ； In the formula: For the number of users to launch; The serial number of the transmitting user; For the first The receive beamforming vector corresponding to each transmitting user; Let be the antenna position vector. This refers to the number of movable antenna elements. For the first The position coordinates of each antenna element; For the first Transmit power of each transmitting user; This refers to the jammer's transmission power. For the first Signal-to-interference-plus-noise ratio for each transmitting user; The total span of the antenna array. This represents the minimum spacing between adjacent antennas. This is the maximum transmit power threshold.

6. The uplink anti-interference secure communication method based on a movable antenna array according to claim 5, characterized in that, Solving the optimization problem model includes using alternating iterations to jointly optimize the received beamforming vector and the antenna position vector, repeating the alternating iteration process until the convergence condition is met, and obtaining the optimized received beamforming vector and antenna position vector; The alternating iterative process includes: While keeping the antenna position vector unchanged, find the receiving beamforming vector that maximizes the signal-to-interference-plus-noise ratio (SIR) for the user with the minimum SIR. The obtained received beamforming vector is substituted into the optimization problem model to update the antenna position vector, resulting in the updated antenna position vector.

7. The uplink anti-interference secure communication method based on a movable antenna array according to claim 6, characterized in that, The step of finding the receive beamforming vector that maximizes the signal-to-interference-plus-noise ratio (SIR) for the user with the minimum SIR while keeping the antenna position vector unchanged specifically includes: Introducing slack variables The optimization problem model is transformed into: ； ； With the antenna position vector fixed Under the condition of minimizing mean square error, the calculation is performed to make Maximize the received beamforming vector .

8. The uplink anti-interference secure communication method based on a movable antenna array according to claim 6, characterized in that, The step of substituting the obtained received beamforming vector into the optimization problem model to obtain the updated antenna position vector specifically includes: Substituting the obtained received beamforming vector into the optimization problem model, we obtain a non-convex optimization problem that is only related to the antenna position. The continuous convex approximation method is used to perform a first-order Taylor expansion of the non-convex optimization problem at the current antenna position to obtain a convex optimization objective function. The updated antenna position vector is obtained by solving the optimization objective function.

9. An uplink anti-interference secure communication system based on a movable antenna array, characterized in that, Includes a base station, the base station comprising: A movable antenna array comprising multiple movable antenna elements; the movable antenna array is used to change the environment for receiving signals by adjusting the position of each antenna element; The data acquisition module is used to acquire communication scenario data; the communication scenario data includes the location information of each transmitting user, the transmission power of each transmitting user, the location information of the jammer, the transmission power of the jammer, the location information of the base station, and the noise power of the base station; the base station is equipped with an antenna array; the antenna array includes multiple movable antenna elements; The model building module is used to construct an optimization problem model with receive beamforming and antenna position as optimization variables based on the communication scenario data; the receive beamforming is used to enhance the signal from the target transmitting user and suppress interference signals; the target transmitting user is the transmitting user with the lowest signal-to-interference-plus-noise ratio among all transmitting users; The solution module is used to solve the optimization problem model to obtain the optimized receive beamforming vector and antenna position vector, which are used by the base station for uplink signal reception. The optimization objective of the optimization problem model is to maximize the signal-to-interference-plus-noise ratio (SIR) of the target transmitting user.

10. An electronic device, characterized in that, include: One or more processors; Memory, used to store one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the uplink anti-interference secure communication method based on a movable antenna array as described in any one of claims 1-8.

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