Direction modulation based covert secure transmission method and communication base station

Abstract

The application relates to a direction modulation-based covert secure transmission method and a communication base station, and belongs to the technical field of wireless communication. In view of the case that channel state information is non-ideal and distortion noise exists, a robust direction modulation technology is adopted, a continuous convex approximation algorithm is used to optimize and select a transmitting end, a combination with low secrecy capacity is eliminated, the secrecy capacity is improved, the possibility of eavesdropping by a eavesdropping user is reduced to a certain extent, and physical layer secure communication is further ensured.

Description

Technical Field

[0001] This invention belongs to the field of wireless communication technology, specifically relating to a covert and secure transmission technology based on directional modulation. Background Technology

[0002] In recent years, user demand for wireless services has been increasing, bringing with it increasingly significant security issues. In practical communication environments, to achieve covert, secure, and reliable transmission, traditional solutions utilize directional beamforming to create nulls in beam and power in a specific direction, based on the user's ideal channel state information. However, due to unavoidable estimation errors, quantization errors, and feedback delays, base stations struggle to obtain the user's ideal channel state information. When the channel estimation error is minor, the base station can obtain partial channel state information, leading to signal leakage and the failure of covert and secure transmission. Therefore, wireless communication system design requires a more robust signal transmission method to reduce the impact of channel state information estimation errors. Traditional wireless communication methods are based on ideal conditions; however, in practical applications, transceivers incur hardware losses. These remaining hardware losses lead to distortion noise, adversely affecting the system and reducing its reliability and security. To meet the demands for high security and high reliability, a more robust communication method is needed. For complex and variable electromagnetic environments and eavesdropping techniques, robust directional modulation methods offer a new solution.

[0003] Currently, the main directional modulation techniques include beam directional modulation and directional modulation techniques with superimposed artificial noise.

[0004] Beam direction modulation: directs the beam towards the desired user, but its disadvantage is insufficient user privacy capacity.

[0005] Directional modulation technique with superimposed artificial noise: This technique combines artificial noise with directional modulation. While sending secure signals, it also transmits artificial noise of a certain power to achieve secure communication. The disadvantage is that it requires additional power from the transmitter. Summary of the Invention

[0006] The technical problem to be solved by this invention is:

[0007] To overcome the shortcomings of non-ideal channel state information and distortion noise in practical applications, this invention provides a robust directional modulation method for mobile communication information transmission. By utilizing a beamforming vector optimization algorithm, the method maximizes the system's security capacity, reduces the impact of non-ideal channel state information and distortion noise on the performance of beamforming communication systems, and meets the requirements of high-security communication.

[0008] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows:

[0009] A communication base station is characterized by comprising a transmitter and a receiver. The transmitter is provided with a uniform linear array, which consists of N antennas, each antenna being connected to a radio frequency link, the radio frequency link consisting of a power amplifier and a phase shifter. The receiver includes a target user and an eavesdropping user, both of which receive data via a single antenna.

[0010] A covert and secure transmission method based on directional modulation, characterized by the following steps:

[0011] Step 1: Definition and These are the channel state information from the base station to the legitimate user and the eavesdropping user, respectively; assuming that the channel state information is non-ideal, h BU and h BE It is modeled as a deterministic error model, denoted as and Where, ||Δh BU ||<ε u ,||Δh BE ||<ε e , and Δh represents the estimated channel vector. BU and Δh BE ε represents the corresponding channel estimation error vector. u and ε e This indicates the range of the uncertainty region in the channel estimation;

[0012] Step 2: The base station's transmitted signal is represented as x = ws + m t ,in, It is the beamforming vector; where s represents a confidential independent Gaussian data symbol, satisfying E[|s| 2 ] = 1; It is an independent Gaussian transmission distortion noise, and the power of the distortion noise on the transmitting antenna is proportional to the power of its transmitted signal, that is... μ≥0 is the ratio of transmitted distortion noise power to transmitted signal power;

[0013] Step 3: The signal received at the legitimate user's location is in, It is the AWGN at the legitimate user's location; then, the data rate obtained by the legitimate user is given as... in, The sum of distortion noise and AWGN power for legitimate users; the signal received by the eavesdropping user is in, The AWGN is located at the eavesdropping user's location; the data rate obtained by the eavesdropping user is... in, To eavesdrop on the user's distortion noise and the sum of the AWGN power; using the formula C = min(R U -R E The worst-case confidentiality capacity C is obtained.

[0014] Step 4: Design the objective function for the optimization problem, find the maximum value of the worst-case confidentiality capacity C, and take the precoding vector w corresponding to the maximum value as the optimal precoding vector w.

[0015] A further technical solution of the present invention: Step 4 is as follows:

[0016] Step 41: Initialize the precoding vector w (1) Determine the maximum number of iterations n max The initial value for the iteration is n=1;

[0017] Step 42: Introduce auxiliary variable ω, l = [l1, l2, l3] T And m = [m1, m2, m3] T The objective function of the optimization problem is: The constraint is log2(1+l3)-log(1+m3)≥ω. and

[0018] Step 43: Handle Constraints and Robust least squares method is used to approximate it as a second-order cone constraint form, i.e. and

[0019] Step 44: Use the continuous convex approximation method to handle the constraints. and The lower bound on the left is equivalently replaced by a first-order Taylor approximation, expressed as: Where W = w (n) w H +ww (n),H -w (n) w (n),H w (n) The solution obtained in the nth iteration; according to Constraints Transform into By applying the S-procedure method, constraints are... Equivalently converted to linear matrix inequality form Similarly, Equivalent to linear matrix inequality form

[0020] Step 45: Handle Constraints and constraint Transform the constraint into a second-order cone constraint form: ||(m3-b) / 2, m1||2≤(m3+b) / 2; by introducing auxiliary variables. and ι, Transformed into two constraints and Convert to second-order cone constraint form and Approximated by a first-order Taylor series: Finally, addressing the non-convex constraint log2(1+l3)-log(1+m3)≥ω, at point (m3) (n) Using the first-order Taylor approximation, the second term on the left-hand side of log2(1+l3)-log(1+m3)≥ω is linearized. Then, an auxiliary variable t is introduced, and the constraint log2(1+l3)-log(1+m3)≥ω is transformed into log2(1+l3)≥t and

[0021] Step 46: The equivalent convex transformation of the optimization problem in the (n+1)th iteration is expressed as:

[0022]

[0023]

[0024]

[0025]

[0026]

[0027]

[0028] ||(m3-b) / 2,m1||2≤(m3+b) / 2

[0029]

[0030]

[0031]

[0032] log2(1+l3)≥t,

[0033]

[0034] The optimization problem described above is convex. Using the CVX toolbox in MATLAB, we calculate the precoding vector w and its corresponding worst-case security capacity C. We then compare the magnitudes of the worst-case security capacity C and select the precoding vector w corresponding to the maximum value of the worst-case security capacity C as w. (n+1) ;

[0035] Step 47: Repeat step 46 until n > n max When the iteration terminates, the optimal precoding vector w is obtained.

[0036] A computer system is characterized by comprising: one or more processors, and a computer-readable storage medium for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors cause the one or more processors to implement the method described above.

[0037] A computer-readable storage medium is characterized by storing computer-executable instructions, which, when executed, are used to implement the above-described method.

[0038] The beneficial effects of this invention are as follows:

[0039] This invention provides a covert and secure transmission method based on directional modulation. In the case of non-ideal channel state information and the presence of distortion noise, it adopts robust directional modulation technology and uses a continuous convex approximation algorithm to optimize the selection of the transmitter, eliminating combinations with low security capacity and improving the security capacity. This reduces the possibility of eavesdropping to a certain extent and further ensures secure communication at the physical layer. Attached Figure Description

[0040] The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Throughout the drawings, the same reference numerals denote the same parts.

[0041] Figure 1 This is a model diagram of a directional modulation system.

[0042] Figure 2 This is a simulation comparison diagram of the method and the non-robust method in this invention. Detailed Implementation

[0043] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this invention described below can be combined with each other as long as they do not conflict with each other.

[0044] This invention provides a communication base station. The transmitting end has a uniform linear array consisting of N antennas, each connected to a radio frequency (RF) link. The RF link consists of a power amplifier and a phase shifter. It is assumed that there is one desired user and one eavesdropping user as receivers, both using a single antenna.

[0045] This invention provides a robust directional modulation method that optimizes the precoding vector at the base station transmitter using a continuous convex approximation algorithm to maximize the confidentiality capacity of the desired user and meet the needs of high-security communication.

[0046] Step 1: Definition and These are the channel state information from the base station to the legitimate user and the eavesdropping user, respectively. Assume that the channel state information is non-ideal, h BU and h BE It is modeled as a deterministic error model, denoted as and Where, ||Δh BU ||<ε u ,||Δh BE ||<ε e , and Δh represents the estimated channel vector. BU and Δh BE ε represents the corresponding channel estimation error vector. u and ε e This represents the range of the uncertainty region in the channel estimation. Simultaneously, the maximum number of iterations, n, is determined. max The initial value for the iteration is n=1;

[0047] Step 2: The base station's transmitted signal is represented as x = ws + m t ,in, It is the beamforming vector. Here, s represents a confidential, independent Gaussian data symbol, satisfying E[|s|]. 2 ] = 1. It is an independent Gaussian transmission distortion noise, and the power of the distortion noise on the transmitting antenna is proportional to the power of its transmitted signal, that is... μ≥0 is the ratio of transmit distortion noise power to transmit signal power. Initialize the precoding vector w. (1) ;

[0048] Step 3: The signal received at the legitimate user's location is in, It is the AWGN at the legitimate user's location. Then, the data rate obtained by the legitimate user is given as... in, The summation of distortion noise and AWGN power for legitimate users. The signal received by the eavesdropping user is... in, It is the AWGN at the eavesdropping user's location. The data rate obtained by the eavesdropping user is... in, To calculate the sum of the distortion noise and AWGN power of the eavesdropping user, the formula is C = min(R). U -R E The worst-case confidentiality capacity C is obtained.

[0049] Step 4: Introduce auxiliary variable ω, l = [l1, l2, l3] T And m = [m1, m2, m3] T The objective function of the optimization problem is: The constraint is log2(1+l3)-log(1+m3)≥ω. and

[0050] Step 5: Handle constraints and Robust least squares method is used to approximate it as a second-order cone constraint form, i.e. and

[0051] Step 6: Use the continuous convex approximation method to handle the constraints. and The lower bound on the left is equivalently replaced by a first-order Taylor approximation, expressed as: Where W = w (n) w H +ww (n),H -w (n) w (n),H w (n) This is the solution obtained in the nth iteration. According to... Constraints Transform into By applying the S-procedure method, constraints are... Equivalently converted to linear matrix inequality form Similarly, Equivalent to linear matrix inequality form

[0052] Step 7: Handle Constraints and constraint This is converted to a second-order cone constraint form: ||(m3-b) / 2, m1||2≤(m3+b) / 2. This is achieved by introducing auxiliary variables. and ι, Transformed into two constraints and Convert to second-order cone constraint form and Approximated by a first-order Taylor series: Finally, addressing the non-convex constraint log2(1+l3)-log(1+m3)≥ω, at point (m3) (n) Using the first-order Taylor approximation, the second term on the left-hand side of log2(1+l3)-log(1+m3)≥ω is linearized. Then, an auxiliary variable t is introduced, and the constraint log2(1+l3)-log(1+m3)≥ω is transformed into log2(1+l3)≥t and

[0053] Step 8: The equivalent convex transformation of the optimization problem in the (n+1)th iteration is expressed as:

[0054]

[0055]

[0056]

[0057]

[0058]

[0059]

[0060] ||(m3-b) / 2,m1||2≤(m3+b) / 2

[0061]

[0062]

[0063]

[0064] log2(1+l3)≥t,

[0065]

[0066] The optimization problem described above is convex. Using the CVX toolbox in MATLAB, we calculate the precoding vector w and its corresponding worst-case security capacity C. We then compare the magnitudes of the worst-case security capacity C and select the precoding vector w corresponding to the maximum value of the worst-case security capacity C as w. (n+1) .

[0067] Step 9: Repeat step 8 until n > n maxWhen the iteration terminates, the optimal precoding vector w is obtained.

[0068] Example 1:

[0069] The transmitting base station has a uniform linear array consisting of four antennas, each connected to a radio frequency (RF) link composed of a power amplifier and a phase shifter. Assume there is one desired user and one eavesdropping user as receivers, both using a single antenna.

[0070] A covert and secure transmission method based on directional modulation based on the aforementioned base station includes the following steps:

[0071] Step 1: Definition and These are the channel state information from the base station to the legitimate user and the eavesdropping user, respectively. Assume that the channel state information is non-ideal, h BU and h BE It is modeled as a deterministic error model, denoted as and Where, ||Δh BU ||<ε u ,||Δh BE ||<ε e , and Δh represents the estimated channel vector. BU and Δh BE ε represents the corresponding channel estimation error vector. u and ε e This represents the range of the uncertainty region in the channel estimation. Simultaneously, the maximum number of iterations, n, is determined. max =500 and the initial value of the iteration is n=1;

[0072] Step 2: The base station's transmitted signal is represented as x = ws + m t ,in, It is the beamforming vector. Here, s represents a confidential, independent Gaussian data symbol, satisfying E[|s|]. 2 ] = 1. It is an independent Gaussian transmission distortion noise, and the power of the distortion noise on the transmitting antenna is proportional to the power of its transmitted signal, that is... μ≥0 is the ratio of transmit distortion noise power to transmit signal power. Initialize the precoding vector w. (1) ;

[0073] Step 3: The signal received at the legitimate user's location is in, It is the AWGN at the legitimate user's location. Then, the data rate obtained by the legitimate user is given as... in, The summation of distortion noise and AWGN power for legitimate users. The signal received by the eavesdropping user is... in, It is the AWGN at the eavesdropping user's location. The data rate obtained by the eavesdropping user is... in, To calculate the sum of the distortion noise and AWGN power of the eavesdropping user, the formula is C = min(R). U -R E The worst-case confidentiality capacity C is obtained.

[0074] Step 4: Introduce auxiliary variable ω, l = [l1, l2, l3] T And m = [m1, m2, m3] T The objective function of the optimization problem is: The constraint is log2(1+l3)-log(1+m3)≥ω. and

[0075] Step 5: Handle constraints and Robust least squares method is used to approximate it as a second-order cone constraint form, i.e. and

[0076] Step 6: Use the continuous convex approximation method to handle the constraints. and The lower bound on the left is equivalently replaced by a first-order Taylor approximation, expressed as: Where W = w (n) w H +ww (n),H -w (n) w (n),H w (n) This is the solution obtained in the nth iteration. According to... Constraints Transform into By applying the S-procedure method, constraints are... Equivalently converted to linear matrix inequality form Similarly, Equivalent to linear matrix inequality form

[0077] Step 7: Handle Constraints and constraint This is converted to a second-order cone constraint form: ||(m3-b) / 2, m1||2≤(m3+b) / 2. This is achieved by introducing auxiliary variables. and ι, Transformed into two constraints μl2 2 +σ 2 ≤ι and Convert to second-order cone constraint form and Approximated by a first-order Taylor series: Finally, addressing the non-convex constraint log2(1+l3)-log(1+m3)≥ω, at point (m3) (n Using the first-order Taylor approximation, the second term on the left-hand side of log2(1+l3)-log(1+m3)≥ω is linearized. Then, an auxiliary variable t is introduced, and the constraint log2(1+l3)-log(1+m3)≥ω is transformed into log2(1+l3)≥t and

[0078] Step 8: The equivalent convex transformation of the optimization problem in the (n+1)th iteration is expressed as:

[0079]

[0080]

[0081]

[0082]

[0083]

[0084]

[0085] ||(m3-b) / 2,m1||2≤(m3+b) / 2

[0086]

[0087]

[0088]

[0089] log2(1+l3)≥t,

[0090]

[0091] The optimization problem described above is convex. Using the CVX toolbox in MATLAB, we calculate the precoding vector w and its corresponding worst-case security capacity C. We then compare the magnitudes of the worst-case security capacity C and select the precoding vector w corresponding to the maximum value of the worst-case security capacity C as w. (n+1) .

[0092] Step 9: Repeat step 8 until n > 500, then terminate the iteration to obtain the optimal precoding vector w.

[0093] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the scope of the technology disclosed in the present invention, and such modifications or substitutions should all be covered within the scope of protection of the present invention.

Claims

1. A covert and secure transmission method based on directional modulation, applied to a communication base station, the communication base station comprising a transmitter and a receiver, the transmitter being equipped with a uniform linear array, the uniform linear array being composed of... The receiver consists of several antennas, each connected to a radio frequency link, the radio frequency link consisting of a power amplifier and a phase shifter; the receiver includes a target user and an eavesdropping user, both of whom receive data via a single antenna; its characteristic is... The steps are as follows: Step 1: Definition and These are the channel state information from the base station to the legitimate user and the eavesdropping user, respectively; it is assumed that the channel state information is non-ideal. and It is modeled as a deterministic error model, denoted as and ,in, , , and This represents the estimated channel vector. and This represents the corresponding channel estimation error vector. and This indicates the range of the uncertainty region in the channel estimation; Step 2: The base station's transmitted signal is represented as follows ,in, It is the beamforming vector; where Represents a confidential independent Gaussian data symbol that satisfies ; It is an independent Gaussian transmission distortion noise, and the power of the distortion noise on the transmitting antenna is proportional to the power of its transmitted signal, that is... , It is the ratio of transmitted distortion noise power to transmitted signal power; Step 3: The signal received at the legitimate user's location is ,in, It is the AWGN at the legitimate user's location; then, the data rate obtained by the legitimate user is given as... ,in, The sum of distortion noise and AWGN power for legitimate users; the signal received by the eavesdropping user is ,in, The AWGN is located at the eavesdropping user's location; the data rate obtained by the eavesdropping user is... ,in, To eavesdrop on the user's distortion noise and the sum of the AWGN power; using the formula Obtain the worst-case confidentiality capacity ; Step 4: Design the objective function for the optimization problem, and use a continuous convex approximation algorithm to obtain the worst-case confidentiality capacity. The maximum value, and the corresponding precoding vector at the maximum value. As the optimal precoding vector .

2. The covert and secure transmission method based on directional modulation according to claim 1, characterized in that: Step 4 is as follows: Step 41: Initialize the precoding vector Determine the maximum number of iterations. and the initial value of the iteration is ; Step 42: Introduce auxiliary variables , and The objective function of the optimization problem is: The constraints are , , , , , , and ; Step 43: Handle Constraints and The robust least squares method is used to approximate it as a second-order cone constraint form, i.e. and ; Step 44: Use the continuous convex approximation method to handle the constraints. and ; The lower bound on the left is equivalently replaced by a first-order Taylor approximation, expressed as: ,in, , For the first The solution obtained from the next iteration; based on Constraints Transform into By applying the S-procedure method, constraints are... Equivalently converted to linear matrix inequality form Similarly, Equivalent to linear matrix inequality form ; Step 45: Handle Constraints and ;constraint Convert to second-order cone constraint form By introducing auxiliary variables and , Transformed into two constraints and , Convert to second-order cone constraint form and ; Approximated by a first-order Taylor series: Finally, handle non-convex constraints. At point Using the first-order Taylor approximation The second term on the left is linearized, and then an auxiliary variable is introduced. ,constraint Convert to and ; Step 46: The optimization problem is in the... The equivalent convex transformation at the next iteration is expressed as: The optimization problem described above is convex. The precoding vector can be computed using the CVX toolbox in MATLAB. and the corresponding worst-case security capacity Compare the worst-case security capacity Size, including the worst-case confidentiality capacity The precoding vector corresponding to the maximum value As ; Step 47: Repeat step 46 until... When the iteration terminates, the optimal precoding vector is obtained. .

3. A computer system, characterized in that... include: One or more processors, a computer-readable storage medium, for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors cause the one or more processors to perform the method of any one of claims 1-2.

4. A computer-readable storage medium, characterized in that... The device stores computer-executable instructions, which, when executed, are used to implement the method described in any one of claims 1-2.

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