Intelligent reflecting surface assisted secure and covert communication design method

Abstract

The application discloses a kind of intelligent reflecting surface assisted security and covert communication design method, belong to wireless communication technical field;The application constructs the optimization problem with the goal of maximizing system security rate.We design an algorithm based on Dinkelbach method to solve the optimization problem.In this algorithm, we first convert the initial non-convex problem into a more easily handled convex problem according to Dinkelbach and semi-positive relaxation method, and obtain the optimal transmit power (Alice) and intelligent reflecting surface (IRS) reflection matrix coefficient, then use Gaussian randomization to restore the solution that satisfies the rank-one constraint.The simulation results of the algorithm show that, compared with the case without IRS, the security performance of the system is significantly improved with the assistance of IRS.

Description

Technical Field

[0001] This invention relates to the field of wireless communication technology, and in particular to a secure and covert communication design method assisted by an intelligent reflective surface. Background Technology

[0002] In recent years, communication based on intelligent reflectors (IRS) has attracted widespread attention due to its significant advantages in improving spectral and energy efficiency. Generally, an intelligent reflector (IRS) consists of a number of low-cost reflective components, each capable of reflecting incoming electromagnetic waves and adjusting their amplitude through reconfigurable phase shifts. By appropriately adjusting the phase shifts of the intelligent reflector (IRS) elements, the signal received at the target receiver can be enhanced while the eavesdropping signal can be weakened. Therefore, intelligent reflectors (IRS) have the potential to enhance received signal strength and improve security / privacy performance.

[0003] Based on the aforementioned advantages, intelligent reflective surfaces (IRS) have recently been applied to physical layer security to improve confidentiality performance. For example, some researchers have studied secure multiple-input multiple-output (MIMO) communication assisted by intelligent reflective surfaces (IRS), and the results show that the confidentiality performance of the intelligent reflective surface (IRS) assisted scheme is significantly better than that of the scheme without intelligent reflective surfaces (IRS). Compared with physical layer security, covert communication aims to hide the transmitter's transmission behavior, thereby providing a high level of security and privacy. Similarly, some studies have shown that with the assistance of intelligent reflective surfaces (IRS), wireless communication systems can improve the system's transmission capacity while meeting the covert requirements.

[0004] Currently, there is limited research on whether it is possible to improve the physical layer security transmission rate of a wireless communication system while maintaining its stealth requirements. Some scholars have explored achieving this with the aid of artificial noise; however, whether this can be achieved with the aid of intelligent reflective surfaces (IRS) remains unknown. Therefore, this invention proposes a secure and stealthy communication design method assisted by intelligent reflective surfaces. Summary of the Invention

[0005] The purpose of this invention is to provide a communication design method based on intelligent reflective surface (IRS) that can improve system security speed while satisfying concealment constraints.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] A communication design method assisted by an intelligent reflective surface that satisfies security and concealment performance specifically includes the following steps:

[0008] S1. Construct a three-dimensional system model: Select the transmitter (Alice), receiver (Bob), listener (Willie, Eve) and intelligent reflector (IRS) as the entity research objects of the three-dimensional system model, and use the Cartesian coordinate system to represent the spatial position information of each entity object;

[0009] S2. Solve for the minimum total detection error probability of the eavesdropper (Willie): The eavesdropper (Willie) determines whether the transmitter (Alice) sends a hidden message to the receiver (Bob) based on the signal samples it observes. It counts the signals received by the eavesdropper (Willie) within the symbol period and combines the cases where the transmitter (Alice) did not send information and did send information in the received signals to calculate the minimum total detection error probability of the eavesdropper (Willie).

[0010] S3. Construct an optimization problem: Select the safe rate as the system performance metric and construct an optimization problem based on maximizing the safe rate;

[0011] S4. Simplify the optimization problem: Simplify the optimization problem constructed in S3, transforming it into a more manageable optimization problem, and then use the CVX toolkit to solve for the optimal transmit power P of the transmitter (Alice). a The optimal coupling variable M between the reflection coefficient v of the intelligent reflective surface;

[0012] S5. Set the placement position of each node:

[0013] Preferably, the use of a Cartesian coordinate system to represent the spatial location information of the transmitter (Alice), receiver (Bob), listeners (Willie, Eve), and intelligent reflector (IRS) in S1 specifically includes the following:

[0014] The spatial location information of the transmitter (Alice) is represented as q. a =[0, 0, 0] T The spatial location information of the receiver (Bob) is represented as q. b =[70, 0, 0] T The spatial location information of the eavesdropper Willie is represented as q. w = [50, -10, 0] T The spatial location information of the listener Eve is represented as q. e =[50, 10, 0] T The spatial location information of the intelligent reflective surface IRS is represented as q r =[50, 0, 3] T The channels from transmitter to receiver, transmitter to listener Willie, transmitter to listener Eve, and transmitter to smart reflector are respectively represented by h.ab h aw h ae h ar The channels from the smart reflector to the receiver, from the smart reflector to the eavesdropper Willie, and from the smart reflector to the eavesdropper Eve are represented by h, respectively. rb. h rw h re This indicates that it is assumed that all channels are Rayleigh fading channels.

[0015] Preferably, the process of solving for the minimum total detection error probability of the eavesdropper (Willie) in S2 specifically includes the following:

[0016] A1. In covert communication, the eavesdropper (Willie) relies on observed signal samples... Determine whether the transmitter (Alice) sent covert information to the receiver (Bob), where y w [i] represents the signal received by the listener (Willie) in the i-th symbol period, which can be represented as:

[0017]

[0018] In the formula, n w This represents the noise at the location of the listener, Willie. and These indicate that the transmitter (Alice) has not sent any information to the receiver (Bob), and has sent information to the receiver (Bob) respectively.

[0019] A2. Assume the false alarm probability is represented by α, and the false alarm probability is represented by β, respectively, by... and Given that D1 and D0 represent the binary decision made by the eavesdropper regarding whether the transmitter (Alice) should transmit a message, the total detection error probability of the eavesdropper is obtained as follows:

[0020] ξ=α+β (1)

[0021] A4. The eavesdropper Willie primarily minimizes the total detection error probability ξ by finding the optimal detection threshold τ, using ξ... * The minimum value of ξ is represented by the following formula:

[0022]

[0023] in, A parameter representing the level of measurement noise uncertainty. v represents the vector consisting of the diagonal elements of the diagonal matrix Θ; w = diag(h H rw )h ar ; This indicates the noise power at Willie;

[0024] Using the safety rate as a metric for system performance, and under the premise of satisfying concealment constraints, the transmit power P of the transmitter (Alice) is jointly optimized. a Given the reflection coefficient v of the intelligent reflective surface (IRS), the optimization problem is:

[0025]

[0026] Among them, the formula It is a requirement of concealment, formula This represents the reflection coefficient constraint of the intelligent reflective surface (IRS).

[0027] Preferably, the simplification and optimization problem described in S4 specifically includes the following:

[0028] B1. First, process the quadratic form in the optimization problem:

[0029] First, construct a new matrix:

[0030] u = [v H 1] H ,

[0031] at this time, Again The original problem then becomes:

[0032]

[0033] Where, m n This represents the nth component of m;

[0034] B2. Based on the content described in B1, optimization problem (5) can be transformed into a more manageable optimization problem:

[0035]

[0036] Where M = mm H The optimization problem (10) and optimization problem (12) are equivalent and have the same optimal solution;

[0037] B3. For optimization problem (6), since the objective function is in linear fractional form and is still non-convex, we use the Dinkelbach method to transform it into the following linear form:

[0038]

[0039] in, β represents the Dinkelbach variable, which is determined by... During updates and iterations, id is used as the iteration index; after convergence, a Gaussian randomization method is used to recover a high-quality rank-1 solution.

[0040] Compared with existing technologies, this invention provides a secure and covert communication design method assisted by an intelligent reflective surface, which has the following beneficial effects:

[0041] (1) This invention can make full use of the constraint relationship between variables, and the solution complexity is low;

[0042] (2) This invention couples the transmitter (Alice) transmit power and the reflection coefficient of the intelligent reflector (IRS) into a single variable for optimization;

[0043] (3) This invention demonstrates that, with the assistance of a smart reflective surface (IRS), a wireless communication system can improve the physical layer security of the system while satisfying the requirement of covert communication. Attached Figure Description

[0044] Figure 1 This is a schematic diagram of a system model for a smart reflective surface-assisted secure and covert communication design method proposed in this invention;

[0045] Figure 2 This is a schematic diagram of the algorithm flow for a smart reflective surface-assisted secure and covert communication design method proposed in this invention;

[0046] Figure 3 This is a schematic diagram showing the change in the security rate as a function of the number of reflective elements in a smart reflective surface-assisted secure and covert communication design method proposed in this invention.

[0047] Figure 4 This is a schematic diagram showing the change in the security rate as a function of noise uncertainty in a smart reflective surface-assisted secure and covert communication design method proposed in this invention. Detailed Implementation

[0048] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0049] Example 1:

[0050] Please see Figure 1-2 A secure and covert communication design method assisted by an intelligent reflective surface, specifically including the following steps:

[0051] S1. Constructing a 3D System Model: Select the transmitter (Alice), receiver (Bob), listeners (Willie, Eve), and intelligent reflector (IRS) as the entities to be studied in the 3D system model. Use a Cartesian coordinate system to represent the spatial position information of each entity, specifically including the following:

[0052] The spatial location information of the transmitter (Alice) is represented as q. a =[0, 0, 0] T The spatial location information of the receiver (Bob) is represented as q. b =[70, 0, 0] T The spatial location information of the eavesdropper Willie is represented as q. w = [50, -10, 0] T The spatial location information of the listener Eve is represented as q. e =[50, 10, 0] T The spatial location information of the intelligent reflective surface IRS is represented as q r =[50, 0, 3] T The channels from transmitter to receiver, transmitter to listener Willie, transmitter to listener Eve, and transmitter to smart reflector are respectively represented by h. ab h aw h ae h ar The channels from the smart reflector to the receiver, from the smart reflector to the eavesdropper Willie, and from the smart reflector to the eavesdropper Eve are represented by h, respectively. rb. h rw h re This indicates that it is assumed all channels are Rayleigh fading channels;

[0053] S2. Solving for the minimum total detection error probability of the eavesdropper (Willie): The eavesdropper (Willie) determines whether the transmitter (Alice) has sent covert information to the UAV based on the signal samples it observes. It statistically analyzes the signals received by the eavesdropper (Willie) within the symbol period, and combines the received signals with those from signals where the transmitter (Alice) has not sent information and those from signals that have been sent, to calculate the minimum total detection error probability of the eavesdropper (Willie). The solution for the minimum total detection error probability includes the following:

[0054] Specifically, it includes the following:

[0055] A1. In covert communication, the eavesdropper (Willie) relies on observed signal samples... Determine whether the transmitter (Alice) sent covert information to the receiver (Bob), where y w[i] represents the signal received by the listener (Willie) in the i-th symbol period, which can be represented as:

[0056]

[0057] In the formula, n w H0 represents the noise at the location of the listener Willie; H1 and H0 represent the transmitter (Alice) not sending and the transmitter (Bob) sending information to the receiver (Bob), respectively.

[0058] A2. Assume the false alarm probability is represented by α, and the false alarm probability is represented by β, respectively, by... and Given that D1 and D0 represent the binary decision made by the eavesdropper regarding whether the transmitter (Alice) should transmit a message, the total detection error probability of the eavesdropper is obtained as follows:

[0059] ξ=α+β (1)

[0060] A4. The eavesdropper Willie primarily minimizes the total detection error probability ξ by finding the optimal detection threshold τ, using ξ... * The minimum value of ξ is represented by the following formula:

[0061]

[0062] in, A parameter representing the level of measurement noise uncertainty. v represents the vector consisting of the diagonal elements of the diagonal matrix Θ; w = diag(h H rw )h ar ; This represents the noise power at Willie.

[0063] Using the safety rate as a metric for system performance, and under the premise of satisfying concealment constraints, the transmit power P of the transmitter (Alice) is jointly optimized. a Given the reflection coefficient v of the intelligent reflective surface (IRS), the optimization problem is:

[0064]

[0065] Among them, the formula It is a requirement of concealment, formula This represents the reflection coefficient constraint of the intelligent reflective surface (IRS);

[0066] S3. Construct an optimization problem: Select the safe rate as the system performance metric and construct an optimization problem based on maximizing the safe rate;

[0067] S4. Simplify the optimization problem: Simplify the optimization problem constructed in S3, transforming it into a more manageable optimization problem, and then use the CVX toolkit to solve for the optimal transmit power P of the transmitter (Alice). a The optimal coupling variable M between the intelligent reflector and the reflection coefficient v; specifically including the following:

[0068] B1. First, process the quadratic form in the optimization problem:

[0069] First, construct a new matrix:

[0070] u = [v H 1] H ,

[0071] at this time, Again The original problem then becomes:

[0072]

[0073] Where, m n This represents the nth component of m;

[0074] B2. Based on the content described in B1, optimization problem (5) can be transformed into a more manageable optimization problem:

[0075]

[0076] Where M = mm H The optimization problem (10) and optimization problem (12) are equivalent and have the same optimal solution;

[0077] B3. For optimization problem (6), since the objective function is in linear fractional form and is still non-convex, we use the Dinkelbach method to transform it into the following linear form:

[0078]

[0079] in, β represents the Dinkelbach variable, which is determined by... During updates and iterations, id is used as the iteration index; after convergence, a Gaussian randomization method is used to recover a high-quality rank-1 solution.

[0080] S5. Perform simulation according to the method proposed in S4, and set the placement position of each node based on the simulation results.

[0081] Example 2:

[0082] Please see Figure 1-4 Based on Example 1, but with the following differences:

[0083] This invention proposes a smart reflector-assisted secure and covert communication design method that can improve system security speed while satisfying covert constraints. Simulation verification is performed as follows:

[0084] like Figure 3 The figure shows the system security rate as a function of the number of smart reflective elements under different concealment levels. It can be seen that as the number of reflective elements N increases, the system security rate increases accordingly, and our proposed scheme is significantly better than the case without IRS assistance. Figure 4 The figure shows the curves of the system safety rate as a function of noise uncertainty level under different concealment levels. It can be seen that the system safety rate increases with the increase of noise uncertainty, and our proposed scheme is significantly better than the case without IRS assistance.

[0085] The simulation results above show that the proposed optimal design can achieve significant performance gains compared to the baseline scheme (i.e., the scheme without intelligent reflective surface assistance).

[0086] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A secure and covert communication design method assisted by an intelligent reflective surface, characterized in that, Specifically, the following steps are included: S1. Construct a three-dimensional system model: Select the transmitter, receiver, listener, and intelligent reflector as the entity research objects of the three-dimensional system model, and use the Cartesian coordinate system to represent the spatial position information of each entity object; S2. Solve for the minimum total detection error probability of the eavesdropper: The eavesdropper judges whether the transmitter sends hidden information to the receiver based on the signal samples it observes. It counts the signals received by the eavesdropper within the period and calculates the minimum total detection error probability of the eavesdropper by combining the cases of no information and information sent by the transmitter in the received signals. S3. Construct an optimization problem: Select the safe rate as the system performance metric and construct an optimization problem based on maximizing the safe rate; S4. Simplify the optimization problem: Simplify the optimization problem constructed in S3, transforming the non-convex problem of quadratic form into a linear convex problem, thereby obtaining the optimal transmit power of the transmitter. P a And optimal beamforming of intelligent reflective surfaces; S5. Perform simulation according to the method proposed in S4, and set the placement position of each node based on the simulation results.

2. The intelligent reflective surface-assisted secure and covert communication design method according to claim 1, characterized in that, The use of a Cartesian coordinate system to represent the spatial location information of the transmitter, receiver, listener, and smart reflector, as described in S1, specifically includes the following: The horizontal positions of the transmitter, receiver, Willie the eavesdropper, Eve the eavesdropper, and the smart reflector are respectively represented as... q a =[0, 0, 0] T , q b =[70, 0, 0] T , q w =[50, -10, 0] T , q e =[50, 10, 0] T , q r =[50, 0, 3] T The channels from transmitter to receiver, transmitter to eavesdropper Willie, transmitter to eavesdropper Eve, and transmitter to smart reflector are respectively... h ab , h aw , h ae , h ar The channels from the smart reflector to the receiver, from the smart reflector to the eavesdropper Willie, and from the smart reflector to the eavesdropper Eve are respectively represented by... h rb , h rw , h re This indicates that it is assumed that all channels are Rayleigh fading channels.

3. The intelligent reflective surface-assisted secure and covert communication design method according to claim 2, characterized in that, The solution to the minimum total detection error probability of the eavesdropper described in S2 specifically includes the following: A1. In covert communication, the eavesdropper relies on observed signal samples... To determine whether the transmitter is sending covert information to the drone, among other things... The signal received by the listener during the i-th symbol period is represented as: In the formula, n w H0 represents the noise at the listener Willie's location; H0 and H1 represent the transmitter not transmitting and the transmitter having sent information to the receiver, respectively. A2. Assume the false alarm error probability is used α The probability of a missed alarm is expressed as... β It means that, respectively by and Given, among which, and Let represent the binary decision made by the eavesdropper regarding whether the transmitter should transmit the message, and let represent the total error probability of the eavesdropper's detection. （1） A3. The eavesdropper Willie searches for the optimal detection threshold. τ To minimize the total detection error probability of 1111 ξ ,use ξ express ξ The minimum value of is calculated using the following formula: (2) in, A parameter representing the level of measurement noise uncertainty. ≥1; v represents a diagonal matrix A vector consisting of the diagonal elements; w= diag (h H rw )h ar ; This represents the noise power at Willie.

4. The intelligent reflective surface-assisted secure and covert communication design method according to claim 3, characterized in that, The construction optimization problem described in S3 specifically includes the following: In wireless communication, the system's secure rate is expressed as: （3） in, γ b , γ e These are the signal-to-noise ratios at the receiver and Eve, respectively. Using the safe rate as a system performance metric, and under the premise of satisfying concealment constraints and maximum artificial noise transmission power constraints, the transmitter's transmission power is jointly optimized. P a Given the reflection coefficient v of the smart reflective surface, the optimization problem is: （4） Among them, the formula For the sake of concealment, the formula This represents the constraint of the intelligent reflective surface.

5. The intelligent reflective surface-assisted secure and covert communication design method according to claim 4, characterized in that, The simplification and optimization problem described in S4 specifically includes the following: B1. Process the quadratic form in optimization problem (4): First, construct a new matrix: at this time, , Again The original problem then becomes: （5） in, m n express m The n One component; B2. Based on the content described in B1, the optimization problem (5) is transformed into a more manageable optimization problem: （6） Where M=mm H The optimization problem (6) is equivalent to the optimization problem (4) and has the same optimal solution; B3. For optimization problem (6), since the objective function is in linear fractional form and is still nonconvex, we can use the Dinkelbach method to transform it into the following linear form: in, ; β Represents the Dinkelbach variable, by During updates and iterations, id The index is used for iteration; after convergence, a high-quality rank-1 solution is recovered using Gaussian randomization.

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