Covert communication method and device of movable antenna, medium and product

Abstract

The invention provides a covert communication method and device for a movable antenna, a medium and a product, and relates to the technical field of wireless communication, and the method comprises the steps: building a concealment constraint model according to the probability that a listener mistakenly detects a communication behavior between a base station and a user; maximizing the sum of communication rates of all users as a target, and taking a concealment constraint model as a constraint condition; and the beamforming vector and the position of the movable antenna are obtained, so that the hidden transmission capability of communication is improved.

Description

Technical Field

[0001] The present application relates to the field of wireless communication technology, and in particular to a concealed communication method, device, medium and product of a movable antenna. Background Art

[0002] As people's demand for the transmission of massive heterogeneous data increases, multiple-input multiple-output (MIMO) technology has been recognized as a key enabling technology. However, traditional MIMO systems usually use fixed-position antennas, which limits the freedom of antennas to utilize continuous spatial domains, thereby limiting the spatial multiplexing gain.

[0003] In recent years, a study has proposed a movable antenna that can fully utilize the spatial freedom by flexibly adjusting its position. The advantages and potential of movable antennas have been applied in various wireless communication systems, such as ground transmission, drone networks, and satellite communications. However, the openness of wireless propagation can easily make communication transmission vulnerable to attacks. The use of movable antennas can increase the difference between the received signal strength in the desired direction and the undesired direction, thereby enhancing covert communication. However, at present, the covert communication technology enhanced by movable antennas is still in the early stages of development.

[0004] Therefore, the present application provides a covert communication method, device, medium and product with a movable antenna to enhance the covert communication transmission capability. Summary of the invention

[0005] The present application provides a covert communication method, device, medium and product of a movable antenna, which are used to improve the covert communication transmission capability of the movable antenna.

[0006] In a first aspect, the present application provides a concealed communication method of a movable antenna, which is used in a base station including multiple movable antennas, and the method includes:

[0007] Establishing a channel model enhanced by a movable antenna, including: establishing a position matrix of the movable antenna; obtaining channel vectors between the base station and the user, and between the base station and the listener according to the position matrix;

[0008] Establishing a concealment constraint model according to a channel vector between the base station and the listener and a probability that the listener falsely detects a communication behavior between the base station and the user;

[0009] An expression for the sum of the communication rates of all the users is obtained according to the channel vector between the base station and the user, and a joint optimization model of the beamforming of the movable antenna and the antenna position is constructed by taking maximizing the sum of the communication rates as the objective function and taking the concealment constraint model as the constraint condition;

[0010] The joint optimization model is solved to obtain the position matrix and the beamforming vector of the base station.

[0011] In a possible design, the concealment constraint model is established according to the channel vector between the base station and the listener and the probability that the listener misdetects the communication behavior between the base station and the user; including:

[0012] Assuming that the listener uses energy detection technology to perform binary hypothesis testing to detect whether the base station is transmitting, the binary hypothesis testing model can be modeled as:

[0013]

[0014] in, represents the noise when the base station is silent; Indicates the noise when the base station is transmitting; is the average power of the signal received by the listener, is the Gaussian white noise at the listener, w k is the beamforming vector of the base station; assuming that the Gaussian white noise at the listener obeys a logarithmic uniform distribution, its density function is given by the following formula:

[0015]

[0016] In the above formula, τ≥1 is used to measure the uncertainty of noise. is the rated noise power at the listener, then the listener can set the decision threshold for the binary hypothesis problem to The corresponding minimum false detection probability is given by:

[0017]

[0018] In order to ensure the high security of the system, it is necessary to ensure that the minimum error detection probability satisfies ξ * ≥1-ε, where ε is a small positive number, by definition The hidden constraint model can be expressed as

[0019]

[0020] In one possible design, the joint optimization model includes:

[0021]

[0022] in, is the objective function, γ k is the signal-to-interference-noise ratio of the kth user, w kis the beamforming vector of the base station corresponding to the kth user, W = [w1,w2,...,w K ],T=[t1,t2,...,t N ] is the position matrix of the movable antenna, t n represents the position of the nth movable antenna, expressed as (x n ,y n ) is the coordinate point of the nth movable antenna, Indicates the movable area of ​​the antenna with a size of A×A;

[0023] in, Indicates that the power of the base station cannot exceed its upper limit p max , Indicates that the movable antenna moves within a given area, and the distance between the two movable antennas must be greater than d min , is the concealment constraint model, h0(T) is the channel vector between the base station and the listener, and the threshold is defined as in is the rated noise power at the listener, and ε represents a very small positive number.

[0024] In one possible design, solving the joint optimization model to obtain the position matrix and the beamforming vector of the base station includes:

[0025] After equivalent transformation of the joint optimization model, a first equivalent transformation model is obtained, where the first equivalent transformation model is:

[0026]

[0027] in, and is the auxiliary vector variable introduced, is the function introduced in the calculation process, and its expression is: in To take the real part, σ 2 is the ambient noise power at the user.

[0028] According to the expression The first equivalent transformation model is iteratively solved to obtain the position matrix and the beamforming vector of the base station, wherein: include:

[0029]

[0030] Where m+1 represents the number of current iterations.

[0031] In one possible design, the basis Iteratively solving the first equivalent transformation model to obtain the position matrix and the beamforming vector of the base station includes:

[0032] according to and Get and The closed-form solution of

[0033] Will Transformed into the second equivalent model, the second equivalent model is:

[0034]

[0035] Among them, A k is the introduced parameter, By using a close distance algorithm, the second equivalent model can be transformed into a third equivalent transformation model, and the third equivalent transformation model is:

[0036]

[0037] in, is the penalty factor, Represents point x to set distance.

[0038]

[0039] The optimal solution obtained by solving the third equivalent model according to the Lagrange multiplier method is:

[0040]

[0041] By continuously increasing the penalty factor value and iteratively updating the beamforming vector using the above formula until convergence, we can obtain The suboptimal solution of

[0042] According to the obtained β (m+1) and W (m+1) ,Will Equivalently converted into the fourth equivalent model, the fourth equivalent model is:

[0043]

[0044] in,

[0045] Solve the fourth equivalent model according to a solution method for non-convex problems to obtain the positions of all the movable antennas;

[0046] Return to the basis and Get and The steps of the closed-form solution are iterated until convergence to obtain the position of the movable antenna and the beamforming vector of the base station.

[0047] In one possible design, acquiring a channel vector between a base station and a user according to the position matrix includes:

[0048] Obtaining a normalized pointing direction from the base station to each of the users in a preset channel receiving path;

[0049] Acquire a channel corresponding vector between the movable antenna and the user according to the normalized pointing direction;

[0050] The channel vector between the base station and the user is acquired according to the channel corresponding vector and the position matrix.

[0051] In a second aspect, the present application provides a concealed communication device with a movable antenna, the device comprising:

[0052] The first module is used to establish a channel model enhanced by a movable antenna, including: establishing a position matrix of the movable antenna; obtaining channel vectors between the base station and the user, and between the base station and the listener according to the position matrix;

[0053] The second module is used to establish a concealment constraint model according to the channel vector between the base station and the listener and the probability that the listener misdetects the communication behavior between the base station and the user;

[0054] The third module is used to obtain an expression of the sum of the communication rates of all the users according to the channel vector between the base station and the user, and to construct a joint optimization model of the beamforming and antenna position of the movable antenna by taking maximizing the sum of the communication rates as the objective function and taking the concealment constraint model as the constraint condition;

[0055] The fourth module is used to solve the joint optimization model to obtain the position matrix and the base station

[0056] In a third aspect, the present application provides an electronic device, comprising: a processor, and a memory communicatively connected to the processor;

[0057] The memory stores computer-executable instructions;

[0058] The processor executes the computer-executable instructions stored in the memory to implement the above-mentioned concealed communication method of the movable antenna.

[0059] In a fourth aspect, the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are executed by a processor, they are used to implement the above-mentioned covert communication method of a movable antenna.

[0060] In a fifth aspect, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the above-mentioned covert communication method of a movable antenna.

[0061] The present application provides a covert communication method, device, medium and product for a movable antenna. A concealment constraint model is established based on the probability that an eavesdropper incorrectly detects the communication behavior between a base station and a user. The concealment constraint model is used as a constraint condition with the goal of maximizing the sum of the communication rates of all users. The beamforming vector and the position of the movable antenna are obtained, thereby improving the concealed transmission capability of the communication. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0063] Figure 1 A schematic diagram of an application scenario of a concealed communication method with a movable antenna provided in an embodiment of the present application;

[0064] Figure 2 A schematic diagram of a concealed communication method for a movable antenna provided in an embodiment of the present application Figure 1 ;

[0065] Figure 3 A schematic diagram of a concealed communication method for a movable antenna provided in an embodiment of the present application Figure 2 ;

[0066] Figure 4 A schematic diagram of the variation curve of the maximum transmission power-total communication rate under different methods provided in this application;

[0067] Figure 5 A schematic diagram of the change curve of the number of antennas-total communication rate under different methods provided in this application;

[0068] Figure 6 A schematic diagram of a curve showing the change of the channel error-communication rate sum under different methods provided in this application;

[0069] Figure 7A schematic diagram of the structure of a device provided in an embodiment of the present application;

[0070] Figure 8 A schematic diagram of the hardware structure of an electronic device provided in an embodiment of the present application.

[0071] Description of reference numerals:

[0072] 110-base station; 111-movable antenna; 120-listener; 130-user;

[0073] 70 - device; 701 - first module; 702 - second module; 703 - third module; 704 - fourth module; 80 - electronic device; 801 - processor; 802 - memory; 803 - communication component; 804 - bus. DETAILED DESCRIPTION

[0074] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. On the contrary, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the attached claims, rather than all embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0075] It should be noted that in the embodiments of the present application, words such as "exemplary" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary" or "for example" in the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a specific way.

[0076] It should be noted that the terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein, for example.

[0077] It should be noted that the “at…” in the embodiments of the present application may be the instant when a certain situation occurs, or may be a period of time after the occurrence of a certain situation, and the embodiments of the present application do not specifically limit this.

[0078] In the existing multi-input multi-output (MIMO) technology, fixed-position antennas are often used. The openness of wireless propagation makes it easy for communication transmission to be attacked. The use of movable antennas can increase the gap between the received signal strength in the desired direction and the undesired direction, thereby enhancing covert communication. However, at present, the covert communication technology of movable antennas is still in the early stages of development, and its covert transmission capability needs to be improved.

[0079] The present application provides a concealed communication method with a movable antenna, aiming to solve the above technical problems in the prior art.

[0080] The technical solution of the present application and how the technical solution of the present application solves the above-mentioned technical problems are described in detail below with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present application will be described below in conjunction with the accompanying drawings.

[0081] Figure 1 A schematic diagram of an application scenario of the covert communication method of the movable antenna 111 provided in an embodiment of the present application, such as Figure 1 As shown, a base station 110, multiple users 130 and an eavesdropper 120 are specifically selected to apply the application scenario of the present method. The base station 110 provided by the present application includes multiple movable antennas 111; multiple movable antennas 111 and movable areas are located in the same plane, and it is assumed that there is an eavesdropper 120 that monitors the base station 110 through a monitoring link. The present application is used to establish a concealment constraint model in the above-mentioned application scenario based on the probability of determining that the eavesdropper 120 incorrectly detects the communication behavior between the base station 110 and the user 130; with the goal of maximizing the sum of the communication rates of all users 130 and the concealment constraint model as a constraint condition; obtain the beamforming vector and the position of the movable antenna 111; and improve the concealed transmission capability of the communication.

[0082] Figure 2 A schematic diagram of a concealed communication method for a movable antenna provided in an embodiment of the present application Figure 1 ;like Figure 2 As shown, the method includes:

[0083] S210, establishing a channel model enhanced by a movable antenna, including: establishing a position matrix of the movable antenna; and obtaining channel vectors between a base station and a user, and between a base station and a listener according to the position matrix.

[0084] Specifically, assume that in the above scenario, there are K users, the base station includes N movable antennas and 1 listener. According to the assumed position coordinates of the movable antenna and the receiving path of each user, the channel field vector between each movable antenna and each user is obtained, thereby determining the channel vectors between the base station and the user, and between the base station and the listener.

[0085] S220: Establish a concealment constraint model according to the channel vector between the base station and the listener and the probability that the listener mistakenly detects the communication behavior between the base station and the user.

[0086] Specifically, combined with the channel vector between the base station and the listener, it is assumed that the listener uses energy detection technology to perform binary hypothesis testing to detect whether the base station is transmitting to obtain the decision threshold of the binary hypothesis testing model. In order to ensure the high security of the system, it is necessary to ensure the minimum error detection probability, and further determine the concealment constraint model based on the minimum error detection probability.

[0087] S230. Obtain an expression for the sum of the communication rates of all users according to the channel vector between the base station and the user, take maximizing the sum of the communication rates as the objective function, take the concealment constraint model as the constraint condition, and construct a joint optimization model of the beamforming of the movable antenna and the antenna position.

[0088] Specifically, for the communication between multiple users and multiple movable antennas, the joint optimization model takes maximizing the sum of communication rates as the objective function and the concealment constraint model as the constraint condition; its purpose is to obtain the optimal position matrix and beamforming vector of the base station.

[0089] S240. Solve the joint optimization model to obtain the position matrix and the beamforming vector of the base station.

[0090] Specifically, the above joint optimization model is equivalently transformed, and an iterative solution method based on block coordinate descent is adopted to obtain the optimal solution of the position matrix and the beamforming vector of the base station to improve the concealed transmission capability of the movable antenna.

[0091] The embodiment of the present application provides a covert communication method for a movable antenna. A concealment constraint model is established based on the probability of determining the communication behavior between a base station and a user by an eavesdropper. The concealment constraint model is used as a constraint condition with the goal of maximizing the sum of the communication rates of all users. The beamforming vector and the position of the movable antenna are obtained to improve the concealed transmission capability of the communication.

[0092] Figure 3 A schematic diagram of a concealed communication method for a movable antenna provided in an embodiment of the present application Figure 2 ;like Figure 3 As shown, the method includes:

[0093] S310, establishing a channel model enhanced by a movable antenna, including: establishing a position matrix of the movable antenna; and obtaining channel vectors between a base station and a user, and between a base station and a listener according to the position matrix.

[0094] Specifically, assume that the application scenario includes K users, a base station equipped with N movable antennas, and a listener. Then, the position of the nth movable antenna can be expressed as in The size of the antenna movable area is A×A, and the position set of all movable antennas is T=[t1, t2, ..., t N ].

[0095] Specifically, assume that the number of channel receiving paths from the base station to user k is L k ,1≤k≤K, therefore, the normalized pointing direction from the base station to user k can be given by Given, where θ k,l and φ k,l are the horizontal angle of arrival and the elevation angle of arrival respectively.

[0096] Therefore, the channel field response vector between the nth movable antenna of the base station and user k can be defined as Where λ is the wavelength. The path response vector between the base station and user k is In this application, it is assumed that the channel between the base station and the user and the listener node is quasi-static slow fading. Therefore, the channel vector between the base station and user k can be modeled as:

[0097]

[0098] in,

[0099] Furthermore, similarly, the channel vector between the base station and the listener can be modeled as h0(T).

[0100] S320: Establish a concealment constraint model according to the channel vector between the base station and the listener and the probability that the listener mistakenly detects the communication behavior between the base station and the user.

[0101] Specifically, assuming that the listener uses energy detection technology to perform binary hypothesis testing to detect whether the base station is transmitting, the binary hypothesis testing model can be modeled as:

[0102]

[0103] in, represents the assumption that the base station is silent, that is, the base station is not transmitting, Indicates the assumption that the base station is transmitting. is the average power of the signal received by the listener, is the Gaussian white noise at the listener, w k is the beamforming vector of the base station. This application assumes that the Gaussian white noise at the listener obeys a logarithmic uniform distribution, and its density function is given by the following formula:

[0104]

[0105] In the above formula, τ≥1 is used to measure the uncertainty of noise. is the rated noise power at the listener, then the listener can set the decision threshold for the binary hypothesis problem to The corresponding minimum false detection probability is given by:

[0106]

[0107] In order to ensure the high security of the system, it is necessary to ensure that the minimum error detection probability satisfies ξ * ≥1-ε, where ε is a small positive number, by definition The hidden constraint model can be expressed as:

[0108]

[0109] S330. Obtain an expression for the sum of the communication rates of all users according to the channel vector between the base station and the user, take maximizing the sum of the communication rates as the objective function, take the concealment constraint model as the constraint condition, and construct a joint optimization model of the beamforming of the movable antenna and the antenna position.

[0110] Specifically, according to the channel vector between the base station and the user, the signal to interference noise ratio of user k is obtained as:

[0111]

[0112] Among them, σ 2 is the ambient noise power at the user.

[0113] Then, the joint optimization model of beamforming and antenna position for movable antenna is:

[0114]

[0115] in, Indicates that the power of the base station cannot exceed its upper limit p max , Indicates that the movable antenna moves within a given area, and the distance between the two movable antennas must be greater than d min , t n and t n′ represents the distance between two different movable antennas, It is a hidden constraint model.

[0116] S340, converting the joint optimization model into equivalent form to obtain a first equivalent conversion model, and obtaining an expression according to the first equivalent conversion model

[0117] Specifically, the joint optimization model is equivalently transformed into the first equivalent transformation model:

[0118]

[0119] in, and is the auxiliary vector variable introduced,

[0120] Among them, the first equivalent transformation model can be adopted as follows Solution:

[0121]

[0122] in, β (m) ,W (m) and T (m) is the solution obtained during the mth iteration, To optimize the sub-problem of beamforming at the base station, It is a subproblem of optimizing the positions of n movable antennas.

[0123] S350, according to and Get and The closed-form solution of

[0124] Specifically, and The closed-form solution of can be given by the following two equations.

[0125]

[0126] S360, The second equivalent model is transformed into a third equivalent transformation model by using a close distance algorithm. The third equivalent model is solved according to the Lagrange multiplier method to obtain the optimal solution. By continuously improving the value of the penalty factor and iteratively updating the beamforming vector using the above formula until convergence, the suboptimal solution of the beamforming can be obtained.

[0127] Specifically, in the (m+1)th inner loop, the value obtained in step 350 is fixed. β (m+1) , and the antenna position T (m) , the beamforming subproblem at the base station It can be modeled as a second equivalent model, the second equivalent model is:

[0128]

[0129] in:

[0130] in:

[0131] Through the close distance algorithm, the second equivalent model can be transformed into the third equivalent transformation model, and the third equivalent transformation model is:

[0132]

[0133] in, is the penalty factor, Represents point x to set Then, replacing the distance function with their upper bounds, we get:

[0134]

[0135] in, represents the projection of x onto the set χ, and their specific expressions are given by the following two equations:

[0136]

[0137] in, You can use a one-dimensional search until Satisfied. [X] :,j Indicates taking the jth column of the matrix, p max Indicates the maximum transmit power of the base station.

[0138] Therefore, the beamforming problem It can be transformed into the following problem:

[0139]

[0140] The above problem is an unconstrained quadratic programming problem, which can be solved by the Lagrange multiplier method. The optimal solution is given by the following formula:

[0141]

[0142] By continuously increasing the penalty factor value and iteratively updating the beamforming vector using the above formula until convergence, the beamforming can be obtained: The suboptimal solution.

[0143] S370, according to the obtained β (m+1) and W (m+1) ,Will The fourth equivalent model is equivalently converted to the fourth equivalent model, and the fourth equivalent model is solved according to the solution method of the non-convex problem to obtain the positions of all movable antennas.

[0144] Specifically, since the positions of different antennas are highly coupled and difficult to solve, this step solves the position of each movable antenna in turn.

[0145] Specifically, in the (m+1)th inner loop, the value obtained in step 403 is fixed. β (m+1) and the beamforming vector W obtained in step 404 (m+1) , and the antenna position {t n′ ,1≤n′≤N,n′≠n}, then the nth antenna position optimization subproblem Transformed into the fourth equivalent model, the fourth equivalent model is:

[0146]

[0147] In order to facilitate the calculation, the function Ψ is introduced k (t n ), whose expression is:

[0148]

[0149] Note that the above problem is still a non-convex problem and is difficult to solve. This application defines: Will Rewritten as:

[0150]

[0151] Where C1 is a constant, expressed by the following expression:

[0152]

[0153] Then, construct A convex upper bound of is as follows:

[0154]

[0155] Among them, the function ψ is introduced ub , whose expression is:

[0156]

[0157] Similarly, this application defines represents the conjugate of the complex gain of the lth path of user k, Rewritten as:

[0158]

[0159] in,

[0160]

[0161] Then, construct A concave lower bound is as follows:

[0162]

[0163] Among them, ψ lb It is expressed as:

[0164]

[0165] To handle non-convex constraints This application uses a first-order Taylor expansion to obtain:

[0166]

[0167] To handle non-convex constraints A similar approach can be used The way to get The convex upper bound of Therefore, the subproblem of optimizing the position of the nth antenna can be transformed into:

[0168]

[0169] The above problem is a convex problem and can be solved using a standard convex optimization solver. By sequentially setting n=1 to n=N, the suboptimal positions of the N movable antennas can be solved.

[0170] S380, return to step S350 to iterate until convergence, and obtain the position of the movable antenna and the beamforming vector of the base station.

[0171] The embodiment of the present application provides a concealed communication method for a movable antenna, which establishes a position matrix of the movable antenna; obtains the channel vectors between the base station and the user, and between the base station and the listener according to the position matrix; establishes a concealment constraint model according to the channel vector between the base station and the listener and the probability that the listener mistakenly detects the communication behavior between the base station and the user; obtains the expression of the sum of the communication rates of all users according to the channel vector between the base station and the user, and constructs a joint optimization model of the beamforming of the movable antenna and the antenna position with maximizing the sum of the communication rates as the objective function and the concealment constraint model as the constraint condition; solves the joint optimization model to obtain the position matrix and the beamforming vector of the base station. The following technical effects can be achieved:

[0172] The embodiments of the present application select base stations, users, and listeners as objects; with the goal of maximizing the sum of the communication rates of all users, a joint optimization problem of the beamforming and antenna position of the movable antenna is constructed; the beamforming and antenna position are jointly optimized to maximize the sum of the communication rates of all users, and a beamforming vector and a movable antenna position setting scheme are obtained, which can improve the covert communication transmission capability.

[0173] In order to reflect the advantages of this application, three methods are provided for comparison, namely "fixed antenna-beamforming method proposed in this application", "fixed antenna-zero-forcing beamforming method", and "movable antenna-zero-forcing beamforming method". For specific comparison data, see Figures 4 to 6 .

[0174] like Figure 4 The variation of the sum of user communication rates with the maximum transmission power under several different methods is shown; Figure 4 It can be seen that the total communication rate of the method proposed in the embodiment of the present application at any maximum transmission power is better than that of the other three comparison methods. The embodiment of the present application can improve the total communication rate at any maximum transmission power.

[0175] Figure 5 The variation of the sum of user communication rates with the number of antennas under several different methods is shown; Figure 5 It can be seen that the total communication rate of the method proposed in the embodiment of the present application under any number of antennas is better than that of the other three comparison methods. The embodiment of the present application can improve the total communication rate under any number of antennas.

[0176] Figure 6 The variation of the sum of user communication rates with channel error under several different methods is shown; Figure 6 It can be seen that the total communication rate of the method proposed in the embodiment of the present application under any channel error is better than that of the other three comparison methods, and can improve the total communication rate under any channel error.

[0177] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily required by the present application.

[0178] It should be further noted that, although the various steps in the flow chart are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps is not strictly limited in order, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the flow chart may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these sub-steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

[0179] The embodiment of the present invention can divide the electronic device or the main control device into functional modules according to the above method example. For example, each functional module can be divided according to each function, or two or more functions can be integrated into one processing unit. The above integrated unit can be implemented in the form of hardware or in the form of software functional modules. It should be noted that the division of modules in the embodiment of the present invention is schematic and is only a logical function division. There may be other division methods in actual implementation.

[0180] Figure 7 A schematic diagram of the structure of a concealed communication device with a movable antenna provided in an embodiment of the present application; Figure 7 As shown, the device 70 includes:

[0181] The first module 701 is used to establish a channel model for movable antenna enhancement, including: establishing a position matrix of the movable antenna; obtaining channel vectors between the base station and the user, and between the base station and the listener according to the position matrix;

[0182] The second module 702 is used to establish a concealment constraint model according to the channel vector between the base station and the listener and the probability that the listener misdetects the communication behavior between the base station and the user;

[0183] The third module 703 is used to obtain an expression of the sum of the communication rates of all users according to the channel vector between the base station and the user, and to construct a joint optimization model of the beamforming of the movable antenna and the antenna position with maximizing the sum of the communication rates as the objective function and the concealment constraint model as the constraint condition;

[0184] The fourth module 704 is used to solve the joint optimization model to obtain the position matrix and the beamforming vector of the base station.

[0185] Furthermore, the second module 702 is specifically used for:

[0186] Assuming that the listener uses energy detection technology to perform binary hypothesis testing to detect whether the base station is transmitting, the binary hypothesis testing model can be modeled as:

[0187]

[0188] in, Indicates the noise when the base station is silent; Indicates the noise when the base station is transmitting; is the average power of the signal received by the listener, is the Gaussian white noise at the listener, w k is the beamforming vector of the base station; assuming that the Gaussian white noise at the listener obeys a logarithmic uniform distribution, its density function is given by the following formula:

[0189]

[0190] In the above formula, τ≥1 is used to measure the uncertainty of noise. is the rated noise power at the listener, then the listener can set the decision threshold for the binary hypothesis problem to The corresponding minimum false detection probability is given by:

[0191]

[0192] In order to ensure the high security of the system, it is necessary to ensure that the minimum error detection probability satisfies ξ * ≥1-ε, where ε is a small positive number, by definition The hidden constraint model can be expressed as

[0193]

[0194] Furthermore, the joint optimization model includes:

[0195]

[0196] in, is the objective function, γ k is the signal-to-interference-noise ratio of the kth user, w k is the beamforming vector of the base station corresponding to the kth user, W = [w1,w2,...,w K ],T=[t1,t2,...,t N ] is the position matrix of the movable antenna, t n represents the position of the nth movable antenna, expressed as (x n ,y n ) is the coordinate point of the nth movable antenna, Indicates the movable area of ​​the antenna with a size of A×A;

[0197] in, Indicates that the power of the base station cannot exceed its upper limit p max , Indicates that the movable antenna moves within a given area, and the distance between the two movable antennas must be greater than d min , is the concealment constraint model, h0(T) is the channel vector between the base station and the listener, and the threshold is defined as in is the rated noise power at the listener, and ε represents a very small positive number.

[0198] Furthermore, the third module 703 is specifically used for:

[0199] After equivalent transformation of the joint optimization model, the first equivalent transformation model is obtained. The first equivalent transformation model is:

[0200]

[0201] in, and It is introduced

[0202] Auxiliary vector variables, is the function introduced in the calculation process, and its expression is: in To take the real part, σ 2 is the ambient noise power at the user.

[0203] According to the expression The first equivalent transformation model is iteratively solved to obtain the position matrix and the beamforming vector of the base station, where: include:

[0204]

[0205] Where m+1 represents the number of current iterations.

[0206] Furthermore, the third module 703 is specifically used for:

[0207] according to and

[0208] Get and The closed-form solution of

[0209] Will Transformed into the second equivalent model, the second equivalent model is:

[0210]

[0211] in, Through the close distance algorithm, the second equivalent model can be transformed into the third equivalent transformation model, and the third equivalent transformation model is:

[0212]

[0213] in, is the penalty factor, Represents point x to set distance.

[0214]

[0215] According to the Lagrange multiplier method, the optimal solution for solving the third equivalent model is:

[0216]

[0217] By continuously increasing the penalty factor value and iteratively updating the beamforming vector using the above formula until convergence, we can obtain The suboptimal solution of

[0218] According to the obtained β (m+1) and W (m+1) ,Will Equivalent conversion to the fourth equivalent model, the fourth equivalent model is:

[0219]

[0220] in,

[0221] The fourth equivalent model is solved according to the solution method of non-convex problems to obtain the positions of all movable antennas;

[0222] Return according to and

[0223] Get and The steps of the closed-form solution are iterated until convergence to obtain the position of the movable antenna and the beamforming vector of the base station.

[0224] Specifically, the first module 701 is further used for:

[0225] Obtaining a normalized pointing direction from the base station to each user in a preset channel receiving path;

[0226] Obtaining a channel corresponding vector between the movable antenna and the user according to the normalized pointing direction;

[0227] The channel vector between the base station and the user is obtained according to the channel response vector and the position matrix.

[0228] The present embodiment provides a concealed communication device with a movable antenna, which can execute the concealed communication method with a movable antenna described above. Its implementation principle and technical effects are similar, and will not be described in detail in the present embodiment.

[0229] In the specific implementation of the aforementioned concealed communication device with a movable antenna, each module can be implemented as a processor, and the processor can execute computer-executable instructions stored in the memory, so that the processor executes the above-mentioned concealed communication method with a movable antenna.

[0230] Figure 8 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. Figure 8 As shown, the electronic device 80 includes: at least one processor 801 and a memory 802. The electronic device 80 also includes a communication component 803. The processor 801, the memory 802 and the communication component 803 are connected via a bus 804.

[0231] In a specific implementation process, at least one processor 801 executes the computer-executable instructions stored in the memory 802, so that at least one processor 801 executes the covert communication method of the movable antenna executed by the electronic device side as above.

[0232] The specific implementation process of the processor 801 can be found in the above method embodiment, and its implementation principle and technical effect are similar, so this embodiment will not be repeated here.

[0233] In the above embodiments, it should be understood that the processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), etc. A general-purpose processor may be a microprocessor or any conventional processor. The steps of the method disclosed in the invention may be directly implemented as being executed by a hardware processor, or may be executed by a combination of hardware and software modules in the processor.

[0234] The memory may include a high-speed RAM memory, and may also include a non-volatile storage NVM, such as at least one disk storage.

[0235] The bus may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, the bus in the drawings of the present application is not limited to only one bus or one type of bus.

[0236] The above-mentioned functions implemented by the electronic device and the main control device introduce the scheme provided by the embodiment of the present invention. It can be understood that in order to implement the above-mentioned functions, the electronic device or the main control device includes a hardware structure and / or software module corresponding to the execution of each function. In combination with the units and algorithm steps of each example described in the embodiment disclosed in the embodiment of the present invention, the embodiment of the present invention can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is executed in the form of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the technical solution of the embodiment of the present invention.

[0237] The present application also provides a computer-readable storage medium, in which computer-executable instructions are stored. When a processor executes the computer-executable instructions, the covert communication method of the movable antenna as described above is implemented.

[0238] The computer-readable storage medium mentioned above can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk. The readable storage medium can be any available medium that can be accessed by a general or special-purpose computer.

[0239] An exemplary readable storage medium is coupled to a processor so that the processor can read information from the readable storage medium and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can be located in an application specific integrated circuit (Application Specific Integrated Circuits, referred to as: ASIC). Of course, the processor and the readable storage medium can also exist as discrete components in an electronic device or a main control device.

[0240] The present application also provides a computer program product, which includes: a computer program, which is stored in a readable storage medium. At least one processor of an electronic device can read the computer program from the readable storage medium, and at least one processor executes the computer program so that the electronic device executes the solution provided by any of the above embodiments.

[0241] Those skilled in the art can understand that all or part of the steps of implementing the above-mentioned method embodiments can be completed by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, the steps of the above-mentioned method embodiments are executed; and the aforementioned storage medium includes: ROM, RAM, disk or optical disk and other media that can store program codes.

[0242] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A concealed communication method with a movable antenna, characterized in that: For a base station including a plurality of movable antennas, the method comprising: Establishing a channel model enhanced by a movable antenna, including: establishing a position matrix of the movable antenna; obtaining channel vectors between the base station and the user, and between the base station and the listener according to the position matrix; Establishing a concealment constraint model according to a channel vector between the base station and the listener and a probability that the listener falsely detects a communication behavior between the base station and the user; An expression for the sum of the communication rates of all the users is obtained according to the channel vector between the base station and the user, and a joint optimization model of the beamforming of the movable antenna and the antenna position is constructed by taking maximizing the sum of the communication rates as the objective function and taking the concealment constraint model as the constraint condition; The joint optimization model is solved to obtain the position matrix and the beamforming vector of the base station.

2. The method according to claim 1, characterized in that The step of establishing a concealment constraint model according to the channel vector between the base station and the listener and the probability of the listener misdetecting the communication behavior between the base station and the user comprises: Assuming that the listener uses energy detection technology to perform binary hypothesis testing to detect whether the base station is transmitting, the binary hypothesis testing model can be modeled as: in, represents the noise when the base station is silent; Indicates the noise when the base station is transmitting; is the average power of the signal received by the listener, is the Gaussian white noise at the listener, w k is the beamforming vector of the base station; assuming that the Gaussian white noise at the listener obeys a logarithmic uniform distribution, its density function is given by the following formula: In the above formula, τ≥1 is used to measure the uncertainty of noise. is the rated noise power at the listener, then the listener can set the decision threshold for the binary hypothesis problem to The corresponding minimum false detection probability is given by: In order to ensure the high security of the system, it is necessary to ensure that the minimum error detection probability satisfies ξ * ≥1-ε, where ε is a small positive number, by definition The hidden constraint model can be expressed as 3. The method according to claim 1, characterized in that The joint optimization model includes: in, is the objective function, γ k is the signal-to-interference-noise ratio of the kth user, w k is the beamforming vector of the base station corresponding to the kth user, W = [w1,w2,...,w K ],T=[t1,t2,...,t N ] is the position matrix of the movable antenna, 1≤n≤N,t n represents the position of the nth movable antenna, expressed as 1≤n≤N;(x n ,y n ) is the coordinate point of the nth movable antenna, Indicates the movable area of ​​the antenna with a size of A×A; in, Indicates that the power of the base station cannot exceed its upper limit p max , Indicates that the movable antenna moves within a given area, and the distance between the two movable antennas must be greater than d min , is the concealment constraint model, h0(T) is the channel vector between the base station and the listener, and the threshold is defined as in is the rated noise power at the listener, and ε represents a very small positive number.

4. The method according to claim 3, characterized in that The step of solving the joint optimization model to obtain the position matrix and the beamforming vector of the base station includes: After equivalent transformation of the joint optimization model, a first equivalent transformation model is obtained, where the first equivalent transformation model is: in, and is the auxiliary vector variable introduced, is the function introduced in the calculation process, and its expression is: in To take the real part, σ 2 is the ambient noise power at the user; According to the expression The first equivalent transformation model is iteratively solved to obtain the position matrix and the beamforming vector of the base station, wherein: include: Where m+1 represents the number of current iterations.

5. The method according to claim 4, characterized in that The basis Iteratively solving the first equivalent transformation model to obtain the position matrix and the beamforming vector of the base station includes: according to and Get and The closed-form solution of Will Transformed into the second equivalent model, the second equivalent model is: in, By using a close distance algorithm, the second equivalent model can be transformed into a third equivalent transformation model, and the third equivalent transformation model is: in, is the penalty factor, dist(x,χ)=min y∈χ ||xy|| 2 Represents the distance from point x to set χ; The optimal solution obtained by solving the third equivalent model according to the Lagrange multiplier method is: By continuously increasing the penalty factor value and iteratively updating the beamforming vector using the above formula until convergence, we can obtain The suboptimal solution of According to the obtained β (m+1) and W (m+1) ,Will Equivalently converted into the fourth equivalent model, the fourth equivalent model is: in, Solve the fourth equivalent model according to a solution method for non-convex problems to obtain the positions of all the movable antennas; Return to the basis and Get and The steps of the closed-form solution are iterated until convergence to obtain the position of the movable antenna and the beamforming vector of the base station.

6. The method according to any one of claims 1 to 5, characterized in that The acquiring a channel vector between a base station and a user according to the position matrix includes: Obtaining a normalized pointing direction from the base station to each of the users in a preset channel receiving path; Acquire a channel corresponding vector between the movable antenna and the user according to the normalized pointing direction; The channel vector between the base station and the user is acquired according to the channel corresponding vector and the position matrix.

7. A concealed communication device with a movable antenna, characterized in that: include: The first module is used to establish a channel model for movable antenna enhancement, including: establishing a position matrix of the movable antenna; obtaining channel vectors between the base station and the user and between the base station and the listener according to the position matrix; The second module is used to establish a concealment constraint model according to the channel vector between the base station and the listener and the probability that the listener misdetects the communication behavior between the base station and the user; The third module is used to obtain an expression of the sum of the communication rates of all the users according to the channel vector between the base station and the user, and to construct a joint optimization model of the beamforming and antenna position of the movable antenna by taking maximizing the sum of the communication rates as the objective function and taking the concealment constraint model as the constraint condition; The fourth module is used to solve the joint optimization model to obtain the position matrix and the beamforming vector of the base station.

8. 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 6.

9. 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 6 when executed by a processor.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

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