A method, apparatus, and equipment for preventing physical layer data theft during data transmission.

Abstract

This application relates to a method, apparatus, and device for preventing physical layer data theft during data transmission. The method includes acquiring communication data and constraint parameters of a communication system and constructing a first optimization problem model; performing stepwise optimization on the first optimization problem model to obtain a fourth optimization problem model; setting iteration parameters, inputting the iteration parameters, communication data, and constraint parameters into the fourth optimization problem model for iterative calculation, and obtaining the optimal beam vector for data transmission from the communication base station to each terminal user. This method optimizes the constructed first optimization problem model to obtain a fourth optimization problem model of a semi-definite fractional programming approximation problem, and iteratively calculates it to obtain the optimal beam vector for data transmission from the communication base station to each terminal user. By using the optimal beam vector for data transmission between the communication base station and the terminal user, the security of data transmission is improved, preventing eavesdropping users from intercepting information.

Description

Technical Field

[0001] This application relates to the field of data communication technology, and in particular to a method, apparatus and device for preventing physical layer data theft during data transmission. Background Technology

[0002] In recent years, in the field of communications, traditional upper-layer encryption algorithms for data transmission cannot completely guarantee absolute information security, especially with the continuous improvement of eavesdroppers' computing and decoding capabilities. Compared with upper-layer encryption technologies, the security of information transmission can also be achieved from the perspective of information theory. For example, secure communication in illegal eavesdropping scenarios can be solved at the physical layer without using conventional key encryption. Summary of the Invention

[0003] This application provides a method, apparatus, and device for preventing physical layer data theft in data transmission, which addresses the technical problem that existing communication encryption technologies cannot completely prevent eavesdroppers from stealing communication information, thus posing a communication security risk.

[0004] To achieve the above objectives, the embodiments of this application provide the following technical solutions:

[0005] A physical layer data anti-eavesdropping method for data transmission is applied to a communication system with a known eavesdropping channel. The communication system includes a communication base station, M terminal users, and one eavesdropping user. The communication base station is equipped with K antennas. The physical layer data anti-eavesdropping method includes the following steps:

[0006] Obtain communication data and constraint parameters of the communication system, and construct a first optimization problem model based on the communication data and constraint parameters;

[0007] The first optimization problem model is subjected to stepwise optimization to obtain the fourth optimization problem model;

[0008] Set the iteration parameters, input the iteration parameters, the communication data and the constraint parameters into the fourth optimization problem model, and use the semi-definite fractional programming approximation algorithm to perform iterative calculations to obtain the optimal beam vector transmitted by the communication base station to each terminal user.

[0009] The communication data includes beam vectors sent by the communication base station to the terminal user, beam vectors of the communication base station transmitting interference information, channel vectors of the eavesdropping user, Gaussian white noise of the communication base station, channel vectors of the terminal user, Gaussian white noise of the terminal user, and minimum signal-to-interference-plus-noise ratio of the terminal user. The constraint parameters include minimizing the eavesdropping rate and the maximum transmit power of the communication base station. The iteration parameters include an iteration threshold and an initial iterative beam vector set, and the initial iterative beam vector set includes beam vectors sent by the communication base station to M terminal users.

[0010] Preferably, the first optimization problem model is subjected to stepwise optimization processing to obtain a fourth optimization problem model, including:

[0011] The first optimization problem model is transformed using a fractional programming algorithm to obtain the second optimization problem model;

[0012] The second optimization problem model is optimized using auxiliary variables to obtain the third optimization problem model;

[0013] The third optimization problem model is transformed using a semidefinite programming algorithm to obtain a fourth optimization problem model approximated by semidefinite fractional programming.

[0014] Preferably, the expression of the first optimization problem model is:

[0015]

[0016]

[0017]

[0018] The expression for the second optimization problem model is:

[0019]

[0020]

[0021]

[0022] The expression for the third optimization problem model is:

[0023]

[0024]

[0025]

[0026]

[0027] The expression for the fourth optimization problem model is:

[0028]

[0029]

[0030]

[0031]

[0032] In the formula, V is the matrix of beam vectors transmitting interference information from the communication base station, and w n P is the beam vector sent by the communication base station to the nth terminal user. max w is the maximum transmit power of the communication base station. i Let σ be the beam vector sent by the communication base station to the i-th terminal user. m For end users, Gaussian white noise. Let γ be the transpose of the channel vector of the m-th terminal user. n w represents the minimum signal-to-interference-plus-noise ratio for the nth terminal user. m Let g be the beam vector sent by the communication base station to the m-th terminal user, g be the channel vector of the eavesdropping user, t be an auxiliary variable, and h be the beam vector sent by the base station to the m-th terminal user. m Let be the channel vector matrix of the m-th terminal user, l be the number of iterations (a natural number), R be the data of the real part of the vector, and σ be the channel vector matrix of the m-th terminal user. e Let v be the Gaussian white noise of the communication base station, v be the beam vector of the interference information transmitted by the communication base station, and u be the signal vector. l The value is updated iteratively.

[0033] Preferably, transforming the first optimization problem model using a fractional programming algorithm includes:

[0034] The objective function of the first optimization problem model is transformed using a fractional programming algorithm to obtain a transformed objective function with the optimal value;

[0035] The transformation objective function is minimized to obtain the processing objective function;

[0036] The objective function is decomposed based on the optimal value of the first optimization problem model to obtain the decomposed objective function;

[0037] The objective function of the first optimization problem model is:

[0038]

[0039] The objective function for transformation is:

[0040]

[0041]

[0042] The objective function for processing is:

[0043]

[0044] The objective function for decomposition is:

[0045]

[0046] In the formula, u * This represents the optimal value for the first optimization problem model. and v * All of these are the optimal solutions to the first optimization problem model.

[0047] Preferably, the physical layer data anti-theft method for data transmission includes: inputting the iteration parameters, the communication data, and the constraint parameters into the fourth optimization problem model and performing iterative calculations using a semi-definite fractional programming approximation algorithm until the absolute value of the comparison between the beam vector matrices of two adjacent communication base stations transmitting interference information is not greater than the iteration threshold, and the fourth optimization problem model outputs the optimal beam vector transmitted by the communication base station to each terminal user.

[0048] This application also provides a physical layer data anti-theft device for data transmission, applied to a communication system with a known eavesdropping channel. The communication system includes a communication base station, M terminal users and one eavesdropping user. The communication base station is equipped with K antennas. The physical layer data anti-theft device includes: a model building module, an optimization processing module and an iterative calculation module.

[0049] The model building module is used to acquire communication data and constraint parameters of the communication system, and to build a first optimization problem model based on the communication data and constraint parameters.

[0050] The optimization processing module is used to perform step-by-step optimization processing on the first optimization problem model to obtain a fourth optimization problem model;

[0051] The iterative calculation module is used to set the iterative parameters, input the iterative parameters, the communication data, and the constraint parameters into the fourth optimization problem model, and perform iterative calculation using a semi-definite fractional programming approximation algorithm to obtain the optimal beam vector transmitted by the communication base station to each terminal user.

[0052] The communication data includes beam vectors sent by the communication base station to the terminal user, beam vectors of the communication base station transmitting interference information, channel vectors of the eavesdropping user, Gaussian white noise of the communication base station, channel vectors of the terminal user, Gaussian white noise of the terminal user, and minimum signal-to-interference-plus-noise ratio of the terminal user. The constraint parameters include minimizing the eavesdropping rate and the maximum transmit power of the communication base station. The iteration parameters include an iteration threshold and an initial iterative beam vector set, and the initial iterative beam vector set includes beam vectors sent by the communication base station to M terminal users.

[0053] Preferably, the optimization processing module includes a first conversion submodule, an auxiliary optimization submodule, and a second conversion submodule;

[0054] The first transformation submodule is used to transform the first optimization problem model using a fractional programming algorithm to obtain the second optimization problem model;

[0055] The auxiliary optimization submodule is used to optimize the second optimization problem model using auxiliary variables to obtain the third optimization problem model.

[0056] The second transformation submodule is used to transform the third optimization problem model using a semidefinite programming algorithm to obtain a fourth optimization problem model approximated by semidefinite fractional programming.

[0057] Preferably, the expression of the first optimization problem model is:

[0058]

[0059]

[0060]

[0061] The expression for the second optimization problem model is:

[0062]

[0063]

[0064]

[0065] The expression for the third optimization problem model is:

[0066]

[0067]

[0068]

[0069]

[0070] The expression for the fourth optimization problem model is:

[0071]

[0072]

[0073]

[0074]

[0075] In the formula, V is the matrix of beam vectors transmitting interference information from the communication base station, and w nP is the beam vector sent by the communication base station to the nth terminal user. max w is the maximum transmit power of the communication base station. i Let σ be the beam vector sent by the communication base station to the i-th terminal user. m For end users, Gaussian white noise. Let γ be the transpose of the channel vector of the m-th terminal user. n w represents the minimum signal-to-interference-plus-noise ratio for the nth terminal user. m Let g be the beam vector sent by the communication base station to the m-th terminal user, g be the channel vector of the eavesdropping user, t be an auxiliary variable, and h be the beam vector sent by the base station to the m-th terminal user. m Let be the channel vector matrix of the m-th terminal user, l be the number of iterations (a natural number), R be the data of the real part of the vector, and σ be the channel vector matrix of the m-th terminal user. e Let v be the Gaussian white noise of the communication base station, v be the beam vector of the interference information transmitted by the communication base station, and u be the signal vector. l The value is updated iteratively.

[0076] Preferably, the iterative calculation module is further used to input the iterative parameters, the communication data, and the constraint parameters into the fourth optimization problem model and perform iterative calculation using a semi-definite fractional programming approximation algorithm until the absolute value of the comparison between the beam vector matrices of the interference information sent by two adjacent communication base stations is not greater than the iteration threshold. The fourth optimization problem model outputs the optimal beam vector transmitted by the communication base station to each terminal user.

[0077] This application also provides a terminal device, including a processor and a memory;

[0078] The memory is used to store program code and transmit the program code to the processor;

[0079] The processor is configured to execute the physical layer data anti-theft method for data transmission as described above, according to the instructions in the program code.

[0080] As can be seen from the above technical solutions, the embodiments of this application have the following advantages: The physical layer data anti-theft method, apparatus, and device for data transmission includes acquiring communication data and constraint parameters of a communication system; constructing a first optimization problem model based on the communication data and constraint parameters; performing stepwise optimization processing on the first optimization problem model to obtain a fourth optimization problem model; setting iteration parameters; inputting the iteration parameters, communication data, and constraint parameters into the fourth optimization problem model and performing iterative calculation using a semi-definite fractional programming approximation algorithm to obtain the optimal beam vector for data transmission from the communication base station to each terminal user. This physical layer data anti-theft method for data transmission optimizes the first optimization problem model constructed from the data of the communication system to obtain a fourth optimization problem model of a semi-definite fractional programming approximation problem. Iterative calculation is performed in the fourth optimization problem model using iteration parameters to obtain the optimal beam vector for data transmission from the communication base station to each terminal user. Data transmission between the communication base station and the terminal user is performed using the optimal beam vector, which improves the security of communication data transmission and prevents eavesdropping users from eavesdropping on information. This solves the technical problem that existing communication encryption technologies cannot completely prevent eavesdroppers from stealing communication information, thus addressing the communication security issue. Attached Figure Description

[0081] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0082] Figure 1 This is a flowchart illustrating the steps of the physical layer data anti-theft method for data transmission described in the embodiments of this application;

[0083] Figure 2 This is a schematic diagram of the communication system in the physical layer data anti-theft method for data transmission described in the embodiments of this application;

[0084] Figure 3 This is a framework diagram of a physical layer data anti-theft device for data transmission according to an embodiment of this application. Detailed Implementation

[0085] To make the inventive objectives, features, and advantages of this application more apparent and understandable, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described below are only some embodiments of this application, and not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0086] This application provides a method, apparatus, and device for preventing physical layer data theft during data transmission, addressing the technical problem that existing communication encryption technologies cannot completely prevent eavesdroppers from stealing communication information, thus posing a communication security risk. This method, apparatus, and device prioritizes security efficiency, therefore employing interception rate as the optimization objective.

[0087] Example 1:

[0088] Figure 1 This is a flowchart illustrating the steps of the physical layer data anti-theft method for data transmission described in this application embodiment. Figure 2 This is a schematic diagram of the communication system in the physical layer data anti-theft method for data transmission described in the embodiments of this application.

[0089] like Figure 1 and Figure 2 As shown, this application provides a physical layer data anti-theft method for data transmission, which is applied to a communication system with a known eavesdropping channel. The communication system includes a communication base station, M terminal users and one eavesdropping user, and the communication base station is equipped with K antennas.

[0090] It should be noted that both the end user and the eavesdropping user use a single antenna. The end user primarily decodes the information they need, while the eavesdropping user aims to intercept the communication information of all end users. The end user can be a smartwatch, mobile phone, or iPad, etc.

[0091] In this embodiment of the application, the expression for the communication base station transmitting information x is:

[0092]

[0093] In the formula, Let s be the beam vector transmitted by the communication base station to the m-th terminal user. m To receive communication information from the communication base station for the m-th terminal user, The beam vector s for transmitting interference information to a communication base station e Let C be the set of complex numbers and K be the total number of antennas, representing the interference information transmitted by the communication base station. Then, the m-th terminal user receives all the communication information y. m The expression is:

[0094]

[0095] In the formula, Let n be the channel vector of the m-th terminal user. m The external interference information received by the m-th terminal user is a mean, which can be 0; the variance is... Let H be the Gaussian white noise of the m-th end user, and H be its transpose. To perform continuous interference cancellation at the end user, a decoding sequence needs to be established, which depends on the power level of the end user. Therefore, assume... It follows the Rician channel model, therefore, the channel vector h of the m-th terminal user is... m The expression is:

[0096]

[0097]

[0098] In the formula, β m To calculate 1 / (d) m ) η The obtained values, ξ, are parameters greater than 0, u m Let be a mean value of 0, a(θ) be the starting angle of the m-th terminal user, and d be the mean value of 0. m Let θ be the distance between the m-th terminal user and the communication base station. m Let λ be the deviation angle of the m-th terminal user, j be an imaginary number, and λ be the path loss exponent.

[0099] The physical layer data interception prevention method for this data transmission includes the following steps:

[0100] S1. Obtain the communication data and constraint parameters of the communication system, and construct the first optimization problem model based on the communication data and constraint parameters.

[0101] It should be noted that the communication data includes the beam vector sent by the communication base station to the terminal user, the beam vector of the communication base station transmitting interference information, the channel vector of the eavesdropping user, the Gaussian white noise of the communication base station, the channel vector of the terminal user, the Gaussian white noise of the terminal user, and the minimum signal-to-interference-plus-noise ratio of the terminal user, etc. The constraint parameters include minimizing the eavesdropping rate and the maximum transmit power of the communication base station, etc. Step S1 mainly involves constructing a first optimization problem model based on the acquired communication data and constraint parameters. In this embodiment, in many communication systems, the eavesdropping channel in the communication system is completely known. This physical layer data anti-eavesdropping method for data transmission is based on the assumption that the eavesdropping user's eavesdropping channel is perfect and known, with no quantization error. Step S1 is an optimization problem in constructing the first optimization problem model of the physical layer data anti-eavesdropping method for data transmission based on minimizing the eavesdropping rate of the eavesdropping user and the constraints of the transmit power of the communication base station and the decoding constraints of the non-orthogonal multiple access technology of the communication system.

[0102] In this embodiment of the application, the expression of the first optimization problem model is:

[0103]

[0104]

[0105]

[0106] S2. The first optimization problem model is optimized step by step to obtain the fourth optimization problem model.

[0107] It should be noted that step S2 mainly involves progressively optimizing the first optimization problem model until a fourth optimization problem model is obtained. In this embodiment, the optimization problem of the first optimization problem model is a non-convex optimization problem, and the expression of the first optimization problem model is also a non-convex function, making it very difficult to solve. Therefore, the first optimization problem model is progressively optimized to obtain a fourth optimization problem model for the semi-definite fractional programming approximation problem.

[0108] S3. Set the iteration parameters, input the iteration parameters, the communication data and the constraint parameters into the fourth optimization problem model and use the semi-definite fractional programming approximation algorithm to perform iterative calculations to obtain the optimal beam vector for the communication base station to transmit data to each terminal user.

[0109] It should be noted that in step S3, the iteration parameters, communication data, and constraint parameters are used as input parameters for the fourth optimization problem model. The constraint parameters are used as the initial values ​​for the iterative calculation of the fourth optimization problem model. In the fourth optimization problem model, the output beam vectors sent by the communication base station to the M terminal users are calculated. The iteration parameters include the iteration threshold and the initial iterative beam vector set, which includes the beam vectors sent by the communication base station to the M terminal users.

[0110] In this embodiment of the application, the physical layer data anti-theft method for data transmission further includes: inputting the iteration parameters, communication data and constraint parameters into the fourth optimization problem model and performing iterative calculation using a semi-definite fractional programming approximation algorithm until the absolute value of the comparison between the beam vector matrices of the interference information sent by two adjacent communication base stations is not greater than the iteration threshold, and the fourth optimization problem model outputs the optimal beam vector transmitted by the communication base station to each terminal user.

[0111] It should be noted that if the initial iterative beam vector set of the constraint parameters is The input l=0 is used in the fourth optimization problem model to calculate the result. The calculation results replace Re-enter the fourth optimization problem model, repeat the iterative calculation with l = l + 1, until abs(V l -V l-1The optimal beam vector for data transmission from the communication base station to each terminal user is output through the fourth optimization problem model, with the result ≤ d. V is the matrix of beam vectors transmitting interference information from the communication base station, and d is the iteration threshold.

[0112] This application provides a physical layer data anti-theft method for data transmission. The method includes acquiring communication data and constraint parameters of a communication system; constructing a first optimization problem model based on the communication data and constraint parameters; performing stepwise optimization on the first optimization problem model to obtain a fourth optimization problem model; setting iteration parameters; inputting the iteration parameters, communication data, and constraint parameters into the fourth optimization problem model; and using a semi-definite fractional programming approximation algorithm for iterative calculation to obtain the optimal beam vector for data transmission from the communication base station to each terminal user. This physical layer data anti-theft method optimizes the first optimization problem model constructed from the communication system data to obtain a fourth optimization problem model, and uses iteration parameters to perform iterative calculations in the fourth optimization problem model to obtain the optimal beam vector for data transmission from the communication base station to each terminal user. By using the optimal beam vector for data transmission between the communication base station and the terminal user, the security of communication data transmission is improved, and eavesdropping users are prevented from intercepting information. This solves the technical problem that existing communication encryption technologies cannot completely prevent eavesdroppers from stealing communication information, thus posing a communication security risk.

[0113] In one embodiment of this application, the fourth optimization problem model is obtained by performing stepwise optimization on the first optimization problem model, including:

[0114] The first optimization problem model is transformed using a fractional programming algorithm to obtain the second optimization problem model;

[0115] The second optimization problem model is optimized using auxiliary variables to obtain the third optimization problem model;

[0116] The third optimization problem model is transformed using a semidefinite programming algorithm to obtain the fourth optimization problem model approximated by semidefinite fractional programming.

[0117] In this embodiment of the application, the transformation of the first optimization problem model using a fractional programming algorithm includes:

[0118] The objective function of the first optimization problem model is transformed using a fractional programming algorithm to obtain a transformed objective function with the optimal value;

[0119] The transformation objective function is minimized to obtain the processing objective function;

[0120] The objective function is decomposed based on the optimal value of the first optimization problem model to obtain the decomposed objective function;

[0121] The objective function of the first optimization problem model is:

[0122]

[0123] The objective function for transformation is:

[0124]

[0125]

[0126] The objective function is:

[0127]

[0128] The objective function is decomposed as follows:

[0129]

[0130] In the formula, u * This represents the optimal value for the first optimization problem model. and v * All are the optimal solutions to the first optimization problem model, w i Let g be the beam vector sent by the communication base station to the i-th terminal user, g be the channel vector of the eavesdropping user, and σ be the beam vector sent by the base station to the i-th terminal user. e Let v be the Gaussian white noise of the communication base station, and v be the beam vector of the interference information transmitted by the communication base station.

[0131] In this embodiment of the application, during the transformation process of the first optimization problem model, since the objective function of the first optimization problem model is a non-convex function, the objective function is transformed using a fractional programming algorithm. First, the objective function is transformed using a fractional programming algorithm, and the optimal value of the first optimization problem model is set to u. * The transformation objective function is obtained. and v * The optimal solution to the first optimization problem model is obtained by minimizing the transformation objective function to obtain the processing objective function; then the processing objective function is decomposed to obtain the decomposed objective function.

[0132] It should be noted that the physical layer data theft prevention method for this data transmission can determine the optimal beamforming vector for the original fractional programming problem using a fractional programming algorithm. The objective function can be obtained by solving the non-fractional decomposition problem. Therefore, the second optimization problem model can be obtained by transforming the first optimization problem model through fractional programming algorithm.

[0133] In this embodiment of the application, the expression for the second optimization problem model is:

[0134]

[0135]

[0136]

[0137] In the formula, w n P is the beam vector sent by the communication base station to the nth terminal user. max w is the maximum transmit power of the communication base station. i Let σ be the beam vector sent by the communication base station to the i-th terminal user. m For end users, Gaussian white noise. Let γ be the transpose of the channel vector of the m-th terminal user. n w represents the minimum signal-to-interference-plus-noise ratio for the nth terminal user. m Let g be the beam vector sent by the communication base station to the m-th terminal user, g be the channel vector of the eavesdropping user, and u be the beam vector sent by the base station to the m-th terminal user. l The value is updated iteratively, where v is the beam vector of the interference information transmitted by the communication base station. The value is updated iteratively. l The expression is:

[0138]

[0139] In the formula, σ e The signal is Gaussian white noise for the communication base station.

[0140] It should be noted that the optimization problems in the first and second optimization problem models are related. An auxiliary variable t needs to be introduced to optimize the second optimization problem model to obtain the third optimization problem model.

[0141] In the embodiments of this application, the expression of the third optimization problem model is:

[0142]

[0143]

[0144]

[0145]

[0146] It should be noted that R represents the real part of a vector, and the expression in the third optimization problem model represents a non-convex function problem. Therefore, the third optimization problem model... It is also a non-convex function problem. In the embodiments of this application, a semidefinite programming algorithm is used to transform the third optimization problem model into a fourth optimization problem model.

[0147] In this embodiment of the application, the expression for the fourth optimization problem model is:

[0148]

[0149]

[0150]

[0151]

[0152] In the formula, l is the number of iterations and is a natural number.

[0153] It should be noted that in the fourth optimization problem model, V = vv H In the fourth optimization problem model, when l = 0, the initial iterative beam vector set is used. The values ​​are used as input to the fourth optimization problem model for calculation, resulting in... Where when l = l + 1, then Using these values ​​as input to the fourth optimization problem model, and repeating this iterative process, we can obtain the iterative calculation formula, which is:

[0154]

[0155]

[0156]

[0157] Among them, u l It is a monotonically non-increasing function.

[0158] In this embodiment of the application, during the iterative calculation process using iterative parameters in the fourth optimization problem model, l is first set to 0, and the numerical value u is updated iteratively. l The expression is calculated to obtain u. 0 The value, then adopted u l Substitute the values ​​into the fourth optimization problem model and solve until the termination condition abs(V) is obtained in the iterative calculation. l -V l-1 The fourth optimization problem model outputs the optimal beam vector transmitted by the communication base station to each terminal user for data transmission, where d ≤ d. It can also be used to calculate the numerical value u when l = l + 1, which is updated iteratively. l The expression is calculated to obtain u. l+1 The value.

[0159] Example 2:

[0160] Figure 3 This is a flowchart illustrating the framework of the physical layer data anti-theft device for data transmission described in the embodiments of this application.

[0161] like Figure 3 As shown, this application embodiment provides a physical layer data anti-theft device for data transmission, which is applied to a communication system with a known eavesdropping channel. The communication system includes a communication base station, M terminal users and one eavesdropping user. The communication base station is equipped with K antennas. The physical layer data anti-theft device includes: a model building module 10, an optimization processing module 20 and an iterative calculation module 30.

[0162] Model building module 10 is used to acquire communication data and constraint parameters of the communication system, and to build a first optimization problem model based on the communication data and constraint parameters;

[0163] The optimization processing module 20 is used to perform step-by-step optimization processing on the first optimization problem model to obtain the fourth optimization problem model.

[0164] The iterative calculation module 30 is used to set the iterative parameters, input the iterative parameters, communication data and constraint parameters into the fourth optimization problem model and use the semi-definite fractional programming approximation algorithm for iterative calculation to obtain the optimal beam vector transmitted by the communication base station to each terminal user.

[0165] The communication data includes beam vectors sent by the communication base station to the end user, beam vectors of the communication base station sending interference information, channel vectors of the eavesdropping user, Gaussian white noise of the communication base station, channel vectors of the end user, Gaussian white noise of the end user, and minimum signal-to-interference-plus-noise ratio of the end user. The constraint parameters include minimizing the eavesdropping rate and the maximum transmit power of the communication base station. The iteration parameters include the iteration threshold and the initial iteration beam vector set, which includes beam vectors sent by the communication base station to M end users.

[0166] In this embodiment of the application, the optimization processing module 20 includes a first conversion submodule, an auxiliary optimization submodule, and a second conversion submodule;

[0167] The first transformation submodule is used to transform the first optimization problem model using a fractional programming algorithm to obtain the second optimization problem model;

[0168] The auxiliary optimization submodule is used to optimize the second optimization problem model using auxiliary variables to obtain the third optimization problem model.

[0169] The second transformation submodule is used to transform the third optimization problem model using a semidefinite programming algorithm to obtain the fourth optimization problem model approximated by semidefinite fractional programming.

[0170] In this embodiment of the application, the expression of the first optimization problem model is:

[0171]

[0172]

[0173]

[0174] The expression for the second optimization problem model is:

[0175]

[0176]

[0177]

[0178] The expression for the third optimization problem model is:

[0179]

[0180]

[0181]

[0182]

[0183] The expression for the fourth optimization problem model is:

[0184]

[0185]

[0186]

[0187]

[0188] In the formula, V is the matrix of beam vectors transmitting interference information from the communication base station, and w n P is the beam vector sent by the communication base station to the nth terminal user. max w is the maximum transmit power of the communication base station. i Let σ be the beam vector sent by the communication base station to the i-th terminal user. m For end users, Gaussian white noise. Let γ be the transpose of the channel vector of the m-th terminal user. n w represents the minimum signal-to-interference-plus-noise ratio for the nth terminal user. m Let g be the beam vector sent by the communication base station to the m-th terminal user, g be the channel vector of the eavesdropping user, t be an auxiliary variable, and h be the beam vector sent by the base station to the m-th terminal user. m Let be the channel vector matrix of the m-th terminal user, l be the number of iterations (a natural number), R be the data of the real part of the vector, and σ be the channel vector matrix of the m-th terminal user. e Let v be the Gaussian white noise of the communication base station, v be the beam vector of the interference information transmitted by the communication base station, and u be the signal vector.l The value is updated iteratively.

[0189] In this embodiment, the iterative calculation module 30 is further used to input the iterative parameters, communication data and constraint parameters into the fourth optimization problem model and perform iterative calculation using a semi-definite fractional programming approximation algorithm until the absolute value of the comparison between the beam vector matrices of the interference information sent by two adjacent communication base stations is not greater than the iteration threshold. The fourth optimization problem model outputs the optimal beam vector transmitted by the communication base station to each terminal user.

[0190] It should be noted that the modules in the device of Embodiment 2 correspond to the steps in the method of Embodiment 1. The content of the physical layer data anti-theft method for data transmission has been described in detail in Embodiment 1, and the content of the modules in the device will not be described in detail in this Embodiment 2.

[0191] Example 3:

[0192] This application provides a terminal device, including a processor and a memory;

[0193] Memory is used to store program code and transfer the program code to the processor;

[0194] The processor is used to execute the physical layer data anti-theft method for the aforementioned data transmission according to the instructions in the program code.

[0195] It should be noted that the processor is used to execute the steps in the above-described embodiment of a physical layer data anti-theft method for data transmission according to the instructions in the program code. Alternatively, when the processor executes the computer program, it implements the functions of each module / unit in the above-described system / device embodiments.

[0196] For example, a computer program can be divided into one or more modules / units, one or more of which are stored in memory and executed by a processor to complete this application. One or more modules / units can be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in a terminal device.

[0197] Terminal devices can be computing devices such as desktop computers, laptops, handheld computers, and cloud servers. Terminal devices may include, but are not limited to, processors and memory. Those skilled in the art will understand that this does not constitute a limitation on the terminal device, which may include more or fewer components than illustrated, or combinations of certain components, or different components. For example, a terminal device may also include input / output devices, network access devices, buses, etc.

[0198] The processor referred to can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor.

[0199] Memory can be an internal storage unit of a terminal device, such as a hard drive or RAM. Memory can also be an external storage device, such as a plug-in hard drive, SmartMedia Card (SMC), Secure Digital (SD) card, or Flash Card. Furthermore, memory can include both internal and external storage units. Memory is used to store computer programs and other programs and data required by the terminal device. Memory can also be used for temporary storage of data that has been output or will be output.

[0200] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0201] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection between apparatuses or units through some interfaces, and may be electrical, mechanical, or other forms.

[0202] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0203] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0204] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0205] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A physical layer data anti-eavesdropping method for data transmission, applied to a communication system with known eavesdropping channel, the communication system comprising a communication base station, M terminal users and an eavesdropping user, the communication base station being provided with K antennas, characterized in that, The physical layer data anti-eavesdropping method comprises the following steps: Obtaining communication data and constraint parameters of a communication system, and constructing a first optimization problem model according to the communication data and the constraint parameters; Performing problem step-by-step optimization processing on the first optimization problem model to obtain a fourth optimization problem model; Setting an iteration parameter, inputting the iteration parameter, the communication data and the constraint parameter into the fourth optimization problem model, and performing iterative calculation by using a semi-definite fractional programming approximation algorithm to obtain an optimal beam vector of a communication base station for transmitting data to each terminal user; The communication data comprises a beam vector of the communication base station for sending to a terminal user, a beam vector of the communication base station for sending interference information, a channel vector of an eavesdropping user, a Gaussian white noise of the communication base station, a channel vector of the terminal user, a Gaussian white noise of the terminal user and a minimum signal-to-interference-and-noise ratio of the terminal user, the constraint parameter comprises a minimum eavesdropping rate and a maximum transmission power of the communication base station, and the iteration parameter comprises an iteration threshold value and an initial iteration beam vector set, wherein the initial iteration beam vector set comprises the beam vector of the communication base station for sending to M terminal users; The iteration parameter, the communication data and the constraint parameter are input into the fourth optimization problem model, and iterative calculation is performed by using the semi-definite fractional programming approximation algorithm until a comparison absolute value between beam vector matrices of adjacent communication base stations for sending interference information is not greater than the iteration threshold value, and the fourth optimization problem model outputs the optimal beam vector of the communication base station for transmitting data to each terminal user.

2. The physical layer data anti-tampering method for data transmission according to claim 1, wherein, The problem step-by-step optimization processing on the first optimization problem model comprises: Converting the first optimization problem model by using a fractional programming algorithm to obtain a second optimization problem model; Optimizing the second optimization problem model by using an auxiliary variable to obtain a third optimization problem model; Converting the third optimization problem model by using a semi-definite programming algorithm to obtain a semi-definite fractional programming approximation fourth optimization problem model.

3. The physical layer data anti-tampering method for data transmission according to claim 2, wherein, The expression of the first optimization problem model is: ； ； ； The expression of the second optimization problem model is: ； ； ； The expression of the third optimization problem model is: ； ； ； ； The expression of the fourth optimization problem model is: ； ； ； ； where V is a matrix of beam vectors sent by the communication base station to interfere with the information, w n is a beam vector sent by the communication base station to the nth terminal user, P max is the maximum transmission power of the communication base station, w i is a beam vector sent by the communication base station to the ith terminal user, is the Gaussian white noise of the terminal user, is the transpose matrix of the channel vector of the mth terminal user, is the minimum signal-to-interference-and-noise ratio of the nth terminal user, w m is a beam vector sent by the communication base station to the mth terminal user, g is the channel vector of the eavesdropping user, t is an auxiliary variable, h m is the channel vector matrix of the mth terminal user, l is the number of iterations and is a natural number, and R is the data of the real part of the vector, is the Gaussian white noise of the communication base station, v is a beam vector sent by the communication base station to interfere with the information, u l is the updated value of the iteration, w n l is a beam vector sent by the communication base station to the nth terminal user in the lth iteration.

4. The physical layer data anti-tampering method for data transmission according to claim 3, wherein, The conversion of the first optimization problem model by using the fractional programming algorithm comprises: Converting a target function of the first optimization problem model by using the fractional programming algorithm to obtain a converted target function with an optimal value; Minimizing the converted target function to obtain a processing target function; Decomposing the processing target function according to the optimal value of the first optimization problem model to obtain a decomposed target function; The target function of the first optimization problem model is: ； The converted target function is: ； ； The processing target function is: ； The decomposed target function is: ； wherein is the optimal value of the first optimization problem model, and are the optimal solutions of the first optimization problem model.

5. A physical layer data anti-eavesdropping device for data transmission, applied to a communication system with a known eavesdropping channel, the communication system comprising a communication base station, M terminal users and an eavesdropping user, the communication base station being provided with K antennas, characterized in that, The physical layer data anti-eavesdropping device comprises a model construction module, an optimization processing module and an iterative calculation module; The model construction module is configured to obtain communication data and constraint parameters of a communication system, and construct a first optimization problem model according to the communication data and the constraint parameters; The optimization processing module is configured to perform problem-by-problem optimization processing on the first optimization problem model to obtain a fourth optimization problem model. The iteration calculation module is configured to set an iteration parameter, input the iteration parameter, the communication data, and the constraint parameter into the fourth optimization problem model, and perform iteration calculation on the fourth optimization problem model by using a semi-definite fractional programming approximation algorithm to obtain optimal beam vectors of the communication base station for transmitting data to each terminal user. The communication data includes beam vectors of the communication base station for sending to the terminal user, beam vectors of the communication base station for sending interference information, channel vectors of a eavesdropping user, Gaussian white noise of the communication base station, channel vectors of the terminal user, Gaussian white noise of the terminal user, and minimum signal-to-interference-and-noise ratio of the terminal user. The constraint parameter includes minimizing eavesdropping rate and maximum transmission power of the communication base station. The iteration parameter includes an iteration threshold and an initial iteration beam vector set, and the initial iteration beam vector set includes beam vectors of the communication base station for sending to M terminal users. The iteration calculation module is further configured to input the iteration parameter, the communication data, and the constraint parameter into the fourth optimization problem model, and perform iteration calculation on the fourth optimization problem model by using the semi-definite fractional programming approximation algorithm until a comparison absolute value between beam vector matrices of adjacent communication base stations for sending interference information is not greater than the iteration threshold. The fourth optimization problem model outputs optimal beam vectors of the communication base station for transmitting data to each terminal user.

6. The physical layer data anti-theft device for data transmission according to claim 5, wherein, The optimization processing module includes a first conversion submodule, an auxiliary optimization submodule, and a second conversion submodule. The first conversion submodule is configured to convert the first optimization problem model by using a fractional programming algorithm to obtain a second optimization problem model. The auxiliary optimization submodule is configured to perform optimization processing on the second optimization problem model by using an auxiliary variable to obtain a third optimization problem model. The second conversion submodule is configured to convert the third optimization problem model by using a semi-definite programming algorithm to obtain a semi-definite fractional programming approximation fourth optimization problem model.

7. The physical layer data anti-theft device for data transmission according to claim 6, characterized in that, An expression of the first optimization problem model is as follows: ； ； ； An expression of the second optimization problem model is as follows: ； ； ； An expression of the third optimization problem model is as follows: ； ； ； ； An expression of the fourth optimization problem model is as follows: ； ； ； ； where V is a matrix of beam vectors sent by the communication base station to interfere with the mth terminal user, w n is a beam vector sent by the communication base station to the nth terminal user, P max is the maximum transmission power of the communication base station, w i is a beam vector sent by the communication base station to the ith terminal user, is the Gaussian white noise of the terminal user, is the transpose matrix of the channel vector of the mth terminal user, is the minimum signal-to-interference-and-noise ratio of the nth terminal user, w m is a beam vector sent by the communication base station to the mth terminal user, g is the channel vector of the eavesdropping user, t is an auxiliary variable, h m is the channel vector matrix of the mth terminal user, l is the number of iterative calculations and is a natural number, R is the data of the real part of the vector, is the Gaussian white noise of the communication base station, v is a beam vector sent by the communication base station to interfere with the mth terminal user, u l is the value of the iterative update, w n l is the beam vector sent by the communication base station to the nth terminal user in the lth iteration.

8. A terminal device, comprising: The device includes a processor and a memory. The memory is configured to store program code and transmit the program code to the processor. The processor is configured to execute the data transmission physical layer data anti-eavesdropping method according to instructions in the program code.

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