A physical layer data communication encryption method, device and equipment for data transmission

Abstract

The application relates to a physical layer data communication encryption method, device and equipment for data transmission, which comprises the following steps: acquiring communication data and constraint parameters of a communication system and constructing a first communication data optimization model; gradually optimizing the first communication data optimization model by adopting a zero space construction mode to obtain a seventh communication data optimization model; setting iteration parameters, inputting the iteration parameters, the communication data and the constraint parameters into the seventh communication data optimization model for iteration calculation, and obtaining an optimal beam vector for transmitting data by a communication base station to each terminal user. The method is optimized by constructing the first communication data optimization model, the seventh communication data optimization model approximated by the zero space is obtained, and iteration calculation is performed on the seventh communication data optimization model, so that the optimal beam vector for transmitting data by the communication base station to each terminal user is obtained. The optimal beam vector is used for data transmission between the communication base station and the terminal user, the security of data transmission is improved, and the information is prevented from being eavesdropped by an eavesdropping user.

Description

Technical Field

[0001] This application relates to the field of data communication technology, and in particular to a physical layer data communication encryption method, apparatus, and device for 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 physical layer data communication encryption method, apparatus, and device for data transmission, which solves 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 communication encryption method for data transmission, applied to a communication system, the communication system including a communication base station, M terminal users, and one eavesdropping user, the communication base station being equipped with K antennas, the physical layer data communication encryption method comprising the following steps:

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

[0007] The first communication data optimization model is gradually optimized using a null space construction method to obtain the seventh communication data optimization model.

[0008] Set the iteration parameters, input the iteration parameters, the communication data, and the constraint parameters into the seventh communication data optimization model for iterative calculation, and obtain the optimal beam vector for the communication base station to transmit data 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 sending 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 the minimum eavesdropping rate of the eavesdropping user and the maximum transmit power of the communication base station. The iteration parameters include an iteration threshold and an initial iterative beam vector set, wherein the initial iterative beam vector set includes beam vectors sent by the communication base station to M terminal users.

[0010] Preferably, the first communication data optimization model is progressively optimized using a null-space construction method to obtain the seventh communication data optimization model, which includes:

[0011] The first communication data optimization model is optimized by maximizing the beam vector decomposition of the interference signal transmitted by the communication base station to obtain the second communication data optimization model.

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

[0013] The third communication data optimization model is optimized using an approximation optimization method to obtain the fourth communication data optimization model;

[0014] The fourth communication data optimization model is iteratively optimized using an iterative algorithm to obtain the fifth communication data optimization model that converges optimally.

[0015] The fifth communication data optimization model is optimized using a semidefinite programming algorithm to obtain the sixth communication data optimization model;

[0016] The sixth communication data optimization model is solved using a semidefinite relaxation algorithm to obtain the seventh communication data optimization model with null space approximation.

[0017] Preferably, the expression for the first communication data optimization model is:

[0018]

[0019]

[0020]

[0021] The expression for the second communication data optimization model is:

[0022]

[0023]

[0024]

[0025] The expression for the third communication data optimization model is:

[0026]

[0027]

[0028]

[0029]

[0030] The expression for the fourth communication data optimization model is:

[0031]

[0032]

[0033]

[0034]

[0035] The expression for the fifth communication data optimization model is:

[0036]

[0037]

[0038]

[0039] The expression for the sixth communication data optimization model is:

[0040]

[0041]

[0042]

[0043] Rank(Q) = 1

[0044] qq H =Q

[0045] The expression for the seventh communication data optimization model is:

[0046]

[0047]

[0048]

[0049] In the formula, U and q are the beam vector decomposition parameters of the interference signal transmitted by the communication base station, respectively, 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. nw 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 t be the beam vector sent by the base station to the m-th terminal user. mn h is an auxiliary variable. m Let be the channel vector matrix of the m-th terminal user, where l is a natural number, R is the data of the real part of the vector, and σ is the channel vector matrix. 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.

[0050] Preferably, optimizing the third communication data optimization model using an approximation optimization method to obtain the fourth communication data optimization model includes: reducing the expression of the third communication data optimization model using a constraint set to obtain the fourth communication data optimization model;

[0051] The constraint set is as follows:

[0052]

[0053] Preferably, inputting the iteration parameters, the communication data, and the constraint parameters into the seventh communication data optimization model for iterative calculation to obtain the optimal beam vector transmitted by the communication base station to each terminal user includes: inputting the iteration parameters, the communication data, and the constraint parameters into the seventh communication data optimization model for iterative calculation until the comparison value between the beam vectors of two adjacent communication base stations transmitting interference information is not greater than the iteration threshold. At this point, the beam vector transmitted by the communication base station to each terminal user is taken as the optimal beam vector.

[0054] This application also provides a physical layer data communication encryption device for data transmission, applied to a communication system, the communication system including a communication base station, M terminal users and one eavesdropping user, the communication base station being equipped with K antennas, the physical layer data communication encryption device including: a model building module, an optimization processing module and an iterative calculation module;

[0055] The model building module is used to acquire communication data and constraint parameters of the communication system, and to build a first communication data optimization model based on the communication data and constraint parameters.

[0056] The optimization processing module is used to perform stepwise optimization processing on the first communication data optimization model using a zero-space construction method to obtain a seventh communication data optimization model.

[0057] The iterative calculation module is used to set iterative parameters, input the iterative parameters, the communication data, and the constraint parameters into the seventh communication data optimization model for iterative calculation, and obtain the optimal beam vector for data transmission from the communication base station to each terminal user.

[0058] The communication data includes beam vectors sent by the communication base station to the terminal 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 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 the minimum eavesdropping rate of the eavesdropping user and the maximum transmit power of the communication base station. The iteration parameters include an iteration threshold and an initial iterative beam vector set, wherein the initial iterative beam vector set includes beam vectors sent by the communication base station to M terminal users.

[0059] Preferably, the optimization processing module includes a decomposition optimization submodule, an auxiliary optimization submodule, an approximation optimization submodule, an iterative optimization submodule, a semidefinite programming optimization submodule, and a semidefinite relaxation optimization submodule;

[0060] The decomposition and optimization submodule is used to optimize the first communication data optimization model based on the beam vector decomposition maximization of the interference signal transmitted by the communication base station, so as to obtain the second communication data optimization model.

[0061] The auxiliary optimization submodule is used to optimize the second communication data optimization model using auxiliary variables to obtain the third communication data optimization model.

[0062] The approximation optimization submodule is used to optimize the third communication data optimization model using an approximation optimization method to obtain a fourth communication data optimization model.

[0063] The iterative optimization submodule is used to perform iterative optimization on the fourth communication data optimization model using an iterative algorithm to obtain the fifth communication data optimization model with the best iterative convergence.

[0064] The semidefinite programming optimization submodule is used to optimize the fifth communication data optimization model using a semidefinite programming algorithm to obtain the sixth communication data optimization model.

[0065] The semidefinite relaxation optimization submodule is used to solve the sixth communication data optimization model using a semidefinite relaxation algorithm to obtain a seventh communication data optimization model with null space approximation.

[0066] Preferably, the expression for the first communication data optimization model is:

[0067]

[0068]

[0069]

[0070] The expression for the second communication data optimization model is:

[0071]

[0072]

[0073]

[0074] The expression for the third communication data optimization model is:

[0075]

[0076]

[0077]

[0078]

[0079] The expression for the fourth communication data optimization model is:

[0080]

[0081]

[0082]

[0083]

[0084] The expression for the fifth communication data optimization model is:

[0085]

[0086]

[0087]

[0088] The expression for the sixth communication data optimization model is:

[0089]

[0090]

[0091]

[0092] Rank(Q) = 1

[0093] qq H =Q

[0094] The expression for the seventh communication data optimization model is:

[0095]

[0096]

[0097]

[0098] In the formula, U and q are the beam vector decomposition parameters of the interference signal transmitted by the communication base station, respectively, 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, and t be the beam vector sent by the base station to the m-th terminal user. mn h is an auxiliary variable. m Let be the channel vector matrix of the m-th terminal user, where l is a natural number, R is the data of the real part of the vector, and σ is the channel vector matrix. 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.

[0099] Preferably, the iterative calculation module is further used to input the iterative parameters, the communication data, and the constraint parameters into the seventh communication data optimization model for iterative calculation until the comparison value between the beam vectors of the interference information sent by two adjacent communication base stations is not greater than the iterative threshold. At this time, the beam vector transmitted by the communication base station to each terminal user is taken as the optimal beam vector.

[0100] This application also provides a terminal device, including a processor and a memory;

[0101] The memory is used to store program code and transmit the program code to the processor;

[0102] The processor is configured to execute the physical layer data communication encryption method for data transmission as described above, according to the instructions in the program code.

[0103] As can be seen from the above technical solutions, the embodiments of this application have the following advantages: The physical layer data communication encryption method, apparatus, and device for data transmission includes: acquiring communication data and constraint parameters of a communication system; constructing a first communication data optimization model based on the communication data and constraint parameters; progressively optimizing the first communication data optimization model using a null-space construction method to obtain a seventh communication data optimization model; setting iteration parameters; inputting the iteration parameters, communication data, and constraint parameters into the seventh communication data optimization model 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 communication encryption method for data transmission optimizes the first communication data optimization model constructed from the data of the communication system to obtain a seventh communication data optimization model that is a null-space approximation problem. Iterative calculations are performed in the seventh communication data optimization 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 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. Attached Figure Description

[0104] 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.

[0105] Figure 1 This is a flowchart illustrating the steps of the physical layer data communication encryption method for data transmission described in the embodiments of this application;

[0106] Figure 2 This is a schematic diagram of the communication system in the physical layer data communication encryption method for data transmission described in the embodiments of this application;

[0107] Figure 3 This is a framework diagram of a physical layer data communication encryption device for data transmission according to an embodiment of this application. Detailed Implementation

[0108] 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.

[0109] This application provides a physical layer data communication encryption method, apparatus, and device for data transmission, which solves the technical problem that existing communication encryption technologies cannot completely prevent eavesdroppers from stealing communication information, thus posing a communication security risk. This physical layer data communication encryption method, apparatus, and device prioritizes security rate and therefore uses interception rate as the optimization objective.

[0110] Example 1:

[0111] Figure 1 This is a flowchart illustrating the steps of the physical layer data communication encryption method for data transmission described in an embodiment of this application. Figure 2 This is a schematic diagram of the communication system in the physical layer data communication encryption method for data transmission described in the embodiments of this application.

[0112] like Figure 1 and Figure 2 As shown in the figure, this application provides a physical layer data communication encryption method for data transmission, which is applied to a communication system. 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.

[0113] 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.

[0114] In this embodiment of the application, the expression for the communication base station transmitting information x is:

[0115]

[0116] 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. mThe expression is:

[0117]

[0118] 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:

[0119]

[0120]

[0121] 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.

[0122] In this embodiment of the application, the eavesdropping information of the user in the communication system is completely unknown. For the eavesdropping user, the communication information y received by the eavesdropping user... e，m The expression can be:

[0123]

[0124] In the formula, For the channel vector of the eavesdropping user, n e To eavesdrop on external interference information received by the user, it is a mean value with a value of 0. Let S = {u1, u2, ..., u...} M} represents an ordered set of interference from eavesdropping users to end users. End users closer to the communication base station have stronger channel conditions, and the channel with the stronger signal is assigned a larger index. Therefore, for index n ≤ m, u m It is capable of decoding the information of the m-th terminal user. The conditions that must be met for decoding the terminal user's information are: In the formula γ n The minimum signal-to-interference-plus-noise ratio for the nth terminal user. The expression is:

[0125]

[0126] In the formula, w n w is the beam vector sent by the communication base station to the nth terminal user. i Let C be the beam vector sent by the communication base station to the i-th terminal user. For an eavesdropping user to eavesdrop on the n-th terminal user's information, the signal-to-interference-plus-noise ratio (SINORN) is C. e，n The expression is:

[0127]

[0128] In the formula, σ e The noise used in the communication base station is Gaussian white noise, and its value can be 1. To more easily handle eavesdropping issues in communication systems, a total eavesdropping rate is set, and the rate of eavesdropped information R after introducing the total eavesdropping rate is... e The expression is:

[0129]

[0130] In the formula, g is the channel vector of the eavesdropping user.

[0131] The physical layer data communication encryption method for this data transmission includes the following steps:

[0132] S1. Obtain the communication data and constraint parameters of the communication system, and construct the first communication data optimization model based on the communication data and constraint parameters.

[0133] 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 sending 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 the minimum eavesdropping rate of the eavesdropping user and the maximum transmission power of the communication base station, etc. In step S1, the first communication data optimization model is mainly constructed based on the acquired communication data and constraint parameters. In this embodiment, since the eavesdropping user is harmful in the communication system and its specific location is difficult to obtain, a perfect eavesdropping channel does not exist. When the channel of the eavesdropping user is completely unknown, step S1 is an optimization problem of constructing the first communication data optimization model in the physical layer data communication encryption method for data transmission based on minimizing the eavesdropping rate of the eavesdropping user and the constraints of the transmission power of the communication base station and the decoding constraints of the non-orthogonal multiple access technology of the communication system.

[0134] In this embodiment of the application, the expression for the first communication data optimization model is:

[0135]

[0136]

[0137]

[0138] S2. The first communication data optimization model is gradually optimized using a zero-space construction method to obtain the seventh communication data optimization model.

[0139] It should be noted that in step S2, the first communication data optimization model is gradually optimized using a null-space construction method until the seventh communication data optimization model is obtained. In this embodiment, due to the presence of eavesdropping users in the communication system and the unknown channel of these users, the optimization problem of the first communication data optimization model is a non-convex function, making it very difficult to solve. Therefore, a special beam vector is set to eliminate interference in the communication system network, ensuring that all terminal users are not affected by interference information transmitted by the communication base station, and preventing interference from eavesdropping users when the eavesdropping users are unknown. A seventh communication data optimization model capable of outputting a zero-forcing beam vector is needed for a perfect channel for all terminal users.

[0140] S3. Set the iteration parameters. Input the iteration parameters, communication data, and constraint parameters into the seventh communication data optimization model for iterative calculation to obtain the optimal beam vector for data transmission from the communication base station to each terminal user. The iteration parameters include an iteration threshold and an initial set of iteration beam vectors. The initial set of iteration beam vectors includes the beam vectors transmitted by the communication base station to M terminal users.

[0141] It should be noted that in step S3, the iteration parameters, communication data, and constraint parameters are used as input parameters for the seventh communication data optimization model. The constraint parameters are used as the initial values ​​for the iterative calculation of the seventh communication data optimization model. In the seventh communication data optimization model, the beam vector and v sent by the communication base station to M terminal users are calculated and output. l+1 =tr(U H Q l+ 1 U).

[0142] In this embodiment of the application, the physical layer data communication encryption method for data transmission further includes: inputting iteration parameters, communication data and constraint parameters into the seventh communication data optimization model for iterative calculation until the comparison value between the beam vectors of the interference information sent by two adjacent communication base stations is not greater than the iteration threshold. At this time, the beam vector transmitted by the communication base station to each terminal user is taken as the optimal beam vector.

[0143] It should be noted that if the constraint parameter is l=0, v0=tr(UH Q 0 U) Input the data into the seventh communication data optimization model to obtain the calculation result. The calculation results replace Re-enter the seventh communication data optimization model, repeat the iterative calculation with l = l + 1, until v l-1 -v l If the beam vector v is not greater than the iteration threshold, an optimal beam vector v for transmitting interference information from the communication base station is obtained. l+1 =tr(U H Q l+1 U), and output through the seventh communication data optimization model.

[0144] This application provides a physical layer data communication encryption method for data transmission. The method includes acquiring communication data and constraint parameters of a communication system; constructing a first communication data optimization model based on the communication data and constraint parameters; progressively optimizing the first communication data optimization model using a null-space construction method to obtain a seventh communication data optimization model; setting iteration parameters; inputting the iteration parameters, communication data, and constraint parameters into the seventh communication data optimization model 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 communication encryption method optimizes the first communication data optimization model constructed from the communication system data to obtain a seventh communication data optimization model, which is a null-space approximation problem. Iterative calculations are performed in the seventh communication data optimization model using iteration parameters to obtain the optimal beam vector for data transmission from the communication base station to each terminal user. Using the optimal beam vector for data transmission between the communication base station and the terminal user improves the security of communication data transmission and prevents eavesdropping. It solves the technical problem that existing communication encryption technologies cannot completely prevent eavesdroppers from stealing communication information, thus posing a communication security risk.

[0145] In one embodiment of this application, the first communication data optimization model is progressively optimized using a null space construction method to obtain the seventh communication data optimization model, which includes:

[0146] The first communication data optimization model is optimized by maximizing the beam vector decomposition of the interference signal transmitted by the communication base station to obtain the second communication data optimization model.

[0147] The second communication data optimization model is optimized using auxiliary variables to obtain the third communication data optimization model.

[0148] The third communication data optimization model is optimized using an approximation optimization method to obtain the fourth communication data optimization model;

[0149] An iterative algorithm is used to iteratively optimize the fourth communication data optimization model, resulting in the fifth communication data optimization model that converges optimally.

[0150] The fifth communication data optimization model was optimized using a semidefinite programming algorithm to obtain the sixth communication data optimization model;

[0151] The semidefinite relaxation algorithm is used to solve the sixth communication data optimization model to obtain the seventh communication data optimization model with null space approximation.

[0152] In this embodiment of the application, during the optimization process of the first communication data optimization model based on the beam vector decomposition maximization of the interference signal transmitted by the communication base station, a K×M matrix H is first set. m H m =[h1,…,…h M It also includes the channel vectors between all communication base stations and end users, and decomposes the beam vector of the interference signal transmitted by the communication base station into v = Uq, where U is located in matrix H. m If a vector exists in the null space, then the matrix H... m The decomposition expression is: In the formula, It is matrix H m The matrix composed of the first M channel vectors, It is matrix H m The orthogonal basis. It represents the channel vector with eigenvalues ​​of 0, and is a matrix. The matrix formed by the last KM channel vectors in the formula.

[0153] It should be noted that the physical layer data communication encryption method for this data transmission sets up all interference information s transmitted by the communication base station. e If all the interference caused can be eliminated by the end user, then the signal-to-interference-plus-noise ratio (SIR) of the end user can be represented by the first update expression, which is:

[0154]

[0155] The eavesdropping rate expression for the communication system is then updated to:

[0156]

[0157] In communication systems, maximizing the security rate can be approximately equivalent to minimizing the total rate of eavesdropping, R. e Since the location of the eavesdropping user is completely unknown, the goal is to minimize the total eavesdropping information rate R. eInterference with eavesdropping users can be achieved by maximizing v = Uq. Therefore, the second communication data optimization model is obtained by optimizing the first communication data optimization model using beam vector decomposition maximization based on the interference signal emitted by the communication base station.

[0158] In this embodiment of the application, the expression for the second communication data optimization model is:

[0159]

[0160]

[0161]

[0162] In the formula, U and q are the beam vector decomposition parameters of the interference signal transmitted by the communication base station, respectively, 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 Let be the minimum signal-to-interference-plus-noise ratio for the nth terminal user.

[0163] It should be noted that the expression in the second communication data optimization model represents a non-convex problem. Therefore, the objective function in the second communication data optimization model is also a non-convex function, and the rank of the expression in the second communication data optimization model cannot be guaranteed to be 1. Therefore, it is necessary to introduce an auxiliary variable t. mn The second communication data optimization model is optimized to obtain the third communication data optimization model.

[0164] In the embodiments of this application, the expression for the third communication data optimization model is:

[0165]

[0166]

[0167]

[0168]

[0169] It should be noted that the expression in the third communication data optimization model represents a non-convex problem, and therefore the third communication data optimization model is also a non-convex function problem.

[0170] In the embodiments of this application, a constraint set is used to reduce the expression of the third communication data optimization model to obtain the fourth communication data optimization model. The constraint set is:

[0171]

[0172] It should be noted that the physical layer data communication encryption method for this data transmission uses the first formula during the optimization process of the third communication data optimization model. To constrain the nonconvexity of the third communication data optimization model, set the initial iterative beam vector set. As an initial point in the constraint set, optimization yields the second expression, which is:

[0173]

[0174] The third expression is obtained by narrowing down the third communication data optimization model using the second expression. The third expression is:

[0175]

[0176] Therefore, after optimizing the third communication data optimization model, a more stringent constraint problem is obtained, and an initial iterative beam vector set is set. We select an initial point for the constraint set in the third communication data optimization model to obtain the fourth communication data optimization model. l represents the number of iterations.

[0177] In this embodiment of the application, the expression for the fourth communication data optimization model is:

[0178]

[0179]

[0180]

[0181]

[0182] It should be noted that in the fourth communication data optimization model, a result can be obtained. and Where l = l + 1, then when Get Substituting these values ​​back into the fourth communication data optimization model, we obtain an iterative algorithm in this manner. This iterative algorithm is as follows:

[0183]

[0184] Among them, v l It is a monotonically non-increasing continuous number. In this embodiment, vl ≥v l+1 This is satisfied for all l=1, ... . In the fourth communication data optimization model, an initial point is given within the constraint set of the optimization problem. Substituting this into the expression of the fourth communication data optimization model, since the objective function of the expression of the fourth communication data optimization model is tr(||Uq|| 2 When maximizing tr(||Uq||) and optimizing based on the observation constraints of the fourth communication data optimization model, it can be found that when tr(||Uq||) is maximized, 2 (to grow bigger, in a disguised form) The value will decrease, thus leading to the iterative interference vector algorithm. Through the iterative interference vector algorithm, it can be seen that the iterative process of optimizing the fourth communication data optimization model can converge to a local optimum, thus yielding the fifth communication data optimization model.

[0185] The observation constraints are:

[0186]

[0187] The iterative interference vector algorithm is as follows:

[0188]

[0189] In this embodiment of the application, the expression for the fifth communication data optimization model is:

[0190]

[0191]

[0192]

[0193] It should be noted that the expression of the fifth communication data optimization model is also a non-convex function. During the optimization process of the fifth communication data optimization model, a semidefinite programming algorithm is used. H =Q optimizes the fifth communication data optimization model into the sixth communication data optimization model.

[0194] In this embodiment of the application, the expression for the sixth communication data optimization model is:

[0195]

[0196]

[0197]

[0198] Rank(Q) = 1.

[0199] It should be noted that in the sixth communication data optimization model, since the rank of Rank(Q) = 1 is 1, the sixth communication data optimization model is also a non-convex problem. The physical layer data communication encryption method for this data transmission uses a semi-definite relaxation algorithm to optimize the non-convex problem of the sixth communication data optimization model, resulting in the seventh communication data optimization model. During the optimization process, Q is set... * Q is the optimal solution for the sixth communication data optimization model. * =q * (q * ) H , then q * This is the optimal solution for the fifth communication data optimization model. For example: Suppose X is a positive semi-definite N×N complex Hermitian matrix with rank R, and A and B are both N×N Hermitian matrices, then there exists a rank-1 decomposition. and

[0200] In this embodiment of the application, the expression for the seventh communication data optimization model is:

[0201]

[0202]

[0203]

[0204] It should be noted that the seventh communication data optimization model, which approximates null space, is obtained by progressively optimizing the first communication data optimization model, and an initial set of iterative beam vectors is provided by the iterative parameters. Substituting this into the seventh communication data optimization model, we obtain a new solution. With a new solution replace Substitute the data back into the seventh communication data optimization model for iterative calculation until the termination condition (v) of the alternative iterative calculation is met. l-1 -v l (Not greater than the iteration threshold), the seventh communication data optimization model outputs the optimal beam vector for data transmission from the communication base station to each terminal user.

[0205] Example 2:

[0206] Figure 3 This is a flowchart illustrating the framework of the physical layer data communication encryption device for data transmission as described in the embodiments of this application.

[0207] like Figure 3As shown, this application provides a physical layer data communication encryption device for data transmission, which is applied to a communication system. 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 communication encryption device includes: a model building module 10, an optimization processing module 20 and an iterative calculation module 30.

[0208] Model building module 10 is used to acquire communication data and constraint parameters of the communication system, and to build a first communication data optimization model based on the communication data and constraint parameters.

[0209] The optimization processing module 20 is used to perform stepwise optimization processing on the first communication data optimization model using a zero-space construction method to obtain the seventh communication data optimization model;

[0210] The iterative calculation module 30 is used to set the iterative parameters, input the iterative parameters, communication data and constraint parameters into the seventh communication data optimization model for iterative calculation, and obtain the optimal beam vector for the communication base station to transmit data to each terminal user.

[0211] 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 the minimum eavesdropping rate of the eavesdropping user 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.

[0212] In this embodiment of the application, the optimization processing module 20 includes a decomposition optimization submodule, an auxiliary optimization submodule, an approximation optimization submodule, an iterative optimization submodule, a semidefinite programming optimization submodule, and a semidefinite relaxation optimization submodule;

[0213] The decomposition and optimization submodule is used to optimize the first communication data optimization model based on the beam vector decomposition maximization of the interference signal transmitted by the communication base station, so as to obtain the second communication data optimization model.

[0214] The auxiliary optimization submodule is used to optimize the second communication data optimization model using auxiliary variables to obtain the third communication data optimization model.

[0215] The approximation optimization submodule is used to optimize the third communication data optimization model using an approximation optimization method to obtain the fourth communication data optimization model.

[0216] The iterative optimization submodule is used to iteratively optimize the fourth communication data optimization model using an iterative algorithm to obtain the fifth communication data optimization model that converges to the optimal value.

[0217] The semidefinite programming optimization submodule is used to optimize the fifth communication data optimization model using a semidefinite programming algorithm to obtain the sixth communication data optimization model.

[0218] The semidefinite relaxation optimization submodule is used to solve the sixth communication data optimization model using a semidefinite relaxation algorithm to obtain the seventh communication data optimization model with null space approximation.

[0219] In this embodiment of the application, the expression for the first communication data optimization model is:

[0220]

[0221]

[0222]

[0223] The expression for the second communication data optimization model is:

[0224]

[0225]

[0226]

[0227] The expression for the third communication data optimization model is:

[0228]

[0229]

[0230]

[0231]

[0232] The expression for the fourth communication data optimization model is:

[0233]

[0234]

[0235]

[0236]

[0237] The expression for the fifth communication data optimization model is:

[0238]

[0239]

[0240]

[0241] The expression for the sixth communication data optimization model is:

[0242]

[0243]

[0244]

[0245] Rank(Q) = 1

[0246] qq H =Q

[0247] The expression for the seventh communication data optimization model is:

[0248]

[0249]

[0250]

[0251] In the formula, U and q are the beam vector decomposition parameters of the interference signal transmitted by the communication base station, respectively, 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, and t be the beam vector sent by the base station to the m-th terminal user. mn h is an auxiliary variable. m Let be the channel vector matrix of the m-th terminal user, where l is a natural number, R is the data of the real part of the vector, and σ is the channel vector matrix. 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.

[0252] In this embodiment, the iterative calculation module 30 is further used to input the iterative parameters, communication data and constraint parameters into the seventh communication data optimization model for iterative calculation until the comparison value between the beam vectors of the interference information sent by two adjacent communication base stations is not greater than the iterative threshold. At this time, the beam vector transmitted by the communication base station to each terminal user is taken as the optimal beam vector.

[0253] 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 communication encryption 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.

[0254] Example 3:

[0255] This application provides a terminal device, including a processor and a memory;

[0256] Memory is used to store program code and transfer the program code to the processor;

[0257] A processor is used to execute the physical layer data communication encryption method for the aforementioned data transmission according to instructions in the program code.

[0258] It should be noted that the processor is used to execute the steps in the above-described embodiment of a physical layer data communication encryption method for data transmission according to the instructions in the program code. Alternatively, when the processor executes a computer program, it implements the functions of each module / unit in the above-described system / device embodiments.

[0259] 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.

[0260] 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.

[0261] 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.

[0262] 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.

[0263] 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.

[0264] 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.

[0265] 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.

[0266] 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.

[0267] 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.

[0268] 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 communication encryption method for data transmission, applied to a communication system, the communication system comprising a communication base station, M terminal users, and one eavesdropping user, wherein the communication base station is equipped with K antennas, characterized in that, The physical layer data communication encryption method includes the following steps: Obtain communication data and constraint parameters of the communication system, and construct a first communication data optimization model based on the communication data and constraint parameters; The first communication data optimization model is gradually optimized using a null space construction method to obtain the seventh communication data optimization model. Set the iteration parameters, input the iteration parameters, the communication data, and the constraint parameters into the seventh communication data optimization model for iterative calculation, and obtain the optimal beam vector for the communication base station to transmit data to each terminal user; The communication data includes beam vectors sent by the communication base station to the terminal 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 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 the minimum eavesdropping rate of the eavesdropping user and the maximum transmit power of the communication base station. The iteration parameters include an iteration threshold and an initial iterative beam vector set. The initial iterative beam vector set includes beam vectors sent by the communication base station to M terminal users. The first communication data optimization model is progressively optimized using a null-space construction method to obtain the seventh communication data optimization model, which includes: The first communication data optimization model is optimized by maximizing the beam vector decomposition of the interference signal transmitted by the communication base station to obtain the second communication data optimization model. The second communication data optimization model is optimized using auxiliary variables to obtain the third communication data optimization model; The third communication data optimization model is optimized using an approximation optimization method to obtain the fourth communication data optimization model; The fourth communication data optimization model is iteratively optimized using an iterative algorithm to obtain the fifth communication data optimization model that converges optimally. The fifth communication data optimization model is optimized using a semidefinite programming algorithm to obtain the sixth communication data optimization model; The sixth communication data optimization model is solved using a semidefinite relaxation algorithm to obtain the seventh communication data optimization model with null space approximation.

2. The physical layer data communication encryption method for data transmission according to claim 1, characterized in that, The expression for the first communication data optimization model is: The expression for the second communication data optimization model is: The expression for the third communication data optimization model is: The expression for the fourth communication data optimization model is: The expression for the fifth communication data optimization model is: The expression for the sixth communication data optimization model is: The expression for the seventh communication data optimization model is: In the formula, U and q are the beam vector decomposition parameters of the interference signal transmitted by the communication base station, respectively, 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 be the beam vector sent by the communication base station to the i-th terminal user. For end users, Gaussian white noise. Let be the transpose of the channel vector of the m-th terminal user. 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 t be the beam vector sent by the base station to the m-th terminal user. mn h is an auxiliary variable. m Let R be the channel vector matrix for the m-th terminal user, where l is a natural number and R is the data of the real part of the vector. 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.

3. The physical layer data communication encryption method for data transmission according to claim 2, characterized in that, The fourth communication data optimization model is obtained by optimizing the third communication data optimization model using an approximation optimization method, which includes: reducing the expression of the third communication data optimization model using a set of constraints. The constraint set is as follows: 。 4. The physical layer data communication encryption method for data transmission according to claim 1, characterized in that, The process of inputting the iteration parameters, the communication data, and the constraint parameters into the seventh communication data optimization model for iterative calculation to obtain the optimal beam vector transmitted by the communication base station to each terminal user includes: inputting the iteration parameters, the communication data, and the constraint parameters into the seventh communication data optimization model for iterative calculation until the comparison value between the beam vectors of two adjacent communication base stations transmitting interference information is not greater than the iteration threshold. At this point, the beam vector transmitted by the communication base station to each terminal user is taken as the optimal beam vector.

5. A physical layer data communication encryption device for data transmission, applied in a communication system, the communication system comprising a communication base station, M terminal users, and one eavesdropping user, wherein the communication base station is equipped with K antennas, characterized in that, The physical layer data communication encryption device includes: a model building module, an optimization processing module, and an iterative calculation module; The model building module is used to acquire communication data and constraint parameters of the communication system, and to build a first communication data optimization model based on the communication data and constraint parameters. The optimization processing module is used to perform stepwise optimization processing on the first communication data optimization model using a zero-space construction method to obtain a seventh communication data optimization model. The iterative calculation module is used to set iterative parameters, input the iterative parameters, the communication data, and the constraint parameters into the seventh communication data optimization model for iterative calculation, and obtain the optimal beam vector for data transmission from the communication base station to each terminal user. The communication data includes beam vectors sent by the communication base station to the terminal 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 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 the minimum eavesdropping rate of the eavesdropping user and the maximum transmit power of the communication base station. The iteration parameters include an iteration threshold and an initial iterative beam vector set. The initial iterative beam vector set includes beam vectors sent by the communication base station to M terminal users. The optimization processing module includes a decomposition optimization submodule, an auxiliary optimization submodule, an approximation optimization submodule, an iterative optimization submodule, a semidefinite programming optimization submodule, and a semidefinite relaxation optimization submodule. The decomposition and optimization submodule is used to optimize the first communication data optimization model based on the beam vector decomposition maximization of the interference signal transmitted by the communication base station, so as to obtain the second communication data optimization model. The auxiliary optimization submodule is used to optimize the second communication data optimization model using auxiliary variables to obtain the third communication data optimization model. The approximation optimization submodule is used to optimize the third communication data optimization model using an approximation optimization method to obtain a fourth communication data optimization model. The iterative optimization submodule is used to perform iterative optimization on the fourth communication data optimization model using an iterative algorithm to obtain the fifth communication data optimization model with the best iterative convergence. The semidefinite programming optimization submodule is used to optimize the fifth communication data optimization model using a semidefinite programming algorithm to obtain the sixth communication data optimization model. The semidefinite relaxation optimization submodule is used to solve the sixth communication data optimization model using a semidefinite relaxation algorithm to obtain a seventh communication data optimization model with null space approximation.

6. The physical layer data communication encryption device for data transmission according to claim 5, characterized in that, The expression for the first communication data optimization model is: The expression for the second communication data optimization model is: The expression for the third communication data optimization model is: The expression for the fourth communication data optimization model is: The expression for the fifth communication data optimization model is: The expression for the sixth communication data optimization model is: The expression for the seventh communication data optimization model is: In the formula, U and q are the beam vector decomposition parameters of the interference signal transmitted by the communication base station, respectively, 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 be the beam vector sent by the communication base station to the i-th terminal user. For end users, Gaussian white noise. Let be the transpose of the channel vector of the m-th terminal user. 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 t be the beam vector sent by the base station to the m-th terminal user. mn h is an auxiliary variable. m Let R be the channel vector matrix for the m-th terminal user, where l is a natural number and R is the data of the real part of the vector. 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.

7. The physical layer data communication encryption device for data transmission according to claim 5, characterized in that, The iterative calculation module is also used to input the iterative parameters, the communication data, and the constraint parameters into the seventh communication data optimization model for iterative calculation until the comparison value between the beam vectors of the interference information sent by two adjacent communication base stations is not greater than the iterative threshold. At this time, the beam vector transmitted by the communication base station to each terminal user is taken as the optimal beam vector.

8. A terminal device, characterized in that, Including the processor and memory; The memory is used to store program code and transmit the program code to the processor; The processor is configured to execute the physical layer data communication encryption method for data transmission as described in any one of claims 1-4, according to the instructions in the program code.

Images