A method for integrating covert communication and energy transmission based on intelligent reflective surfaces

Abstract

The present invention relates to an integrated method for covert communication and energy transmission based on an intelligent reflective surface. The method mainly utilizes the characteristics of flexible deployment, low cost, and controllable phase shift of the intelligent reflective surface. In complex environments such as battlefields, the line-of-sight link of the system is blocked. The intelligent reflective surface is used to allow the signal to bypass the obstacle to perform wireless energy transmission for the passive device. At the same time, the signal is reflected during the uplink data transmission of the passive device to interfere with the eavesdropper's monitoring. The phase shift matrix of the intelligent reflective surface and the time slot allocation of energy and data transmission are jointly designed through the proposed iterative algorithm to maximize the uplink data transmission throughput of the passive device. The present invention jointly optimizes the covert communication of the device and the time slot allocation of energy and data transmission, ensuring that the throughput of the device's uplink data transmission is increased under certain concealment requirements; the present invention is also applicable when the eavesdropper's sampling rate is limited or unlimited.

Description

Technical Field

[0001] The present invention relates to the field of communication technology, and in particular to a method for integrating covert communication and intelligent transmission based on an intelligent reflecting surface. Background Art

[0002] Digital energy transmission systems are increasingly being used in modern wireless communication networks. They use transmitted wireless radio frequency signals to power other passive devices in the environment while also transmitting data, making charging and controlling devices more convenient and breaking away from traditional wired connections. In addition, in complex environments such as battlefields, disaster areas, mountains, and jungles, it is often difficult to maintain equipment through wired connections or frequent battery replacement. Therefore, digital energy transmission systems can be used to achieve remote power supply and control, making devices work more intelligently in complex environments.

[0003] However, complex environments present challenges beyond the difficulty of human intervention. Unpredictable changes and various obstacles also significantly hinder signal transmission. Signals reaching receiving devices through such complex channels often suffer from poor quality. Therefore, a smart reflective surface technology has been proposed to significantly improve wireless communication signal strength. This reflective surface is primarily composed of a large number of reflective units, each of which can control the phase of the incoming signal. For obstructions in line-of-sight links, the smart reflective surface can effectively bypass these obstacles and improve communication quality.

[0004] With the rapid development of wireless communication technology, communication security has received increasing attention. Information encryption sometimes fails to meet security requirements, as information leakage could occur if an eavesdropper were to crack the encryption. Covert communication technology, however, allows eavesdroppers to eavesdrop on communications with minimal probability. This means the eavesdropper is unaware of the communication process, making further decryption difficult. In covert communication models, the signal strength of both communicating parties and the covert communication's concealment performance are often in conflict. Greater signal strength increases the likelihood of eavesdropping. Therefore, optimizing resource allocation is crucial in covert communication technology.

[0005] Leveraging the characteristics of smart reflective surfaces, the phase-shift matrix of reflective units can be designed to meet concealment requirements. Furthermore, for data transmission systems, smart reflective surfaces can enhance signal quality. Existing research primarily focuses on single-information transmission or covert communications assisted by smart reflective surfaces, and has not examined the integration of data transmission systems and covert communications in complex environments. However, in scenarios as varied, complex, and requiring high security as the battlefield, not only is the application of covert communications crucial, but the demand for wireless data transmission is also substantial.

[0006] It should be noted that the information disclosed in the above background technology section is only used to enhance the understanding of the background of the present disclosure, and therefore may include information that does not constitute prior art known to ordinary technicians in the field. Summary of the Invention

[0007] The purpose of the present invention is to overcome the shortcomings of the prior art and provide an integrated method of covert communication and energy transmission based on an intelligent reflective surface, thereby solving the deficiencies in existing research.

[0008] The object of the present invention is achieved through the following technical solution: a method for integrating covert communication and energy transmission based on an intelligent reflective surface, the integrated method comprising:

[0009] S1. Set up the system model, and with the assistance of the smart reflective surface, the passive device first receives the energy uploaded by reusing it;

[0010] S2. Establish a model of stealth constraints and perform different processing methods based on the different sampling rates of the eavesdropper;

[0011] S3. Setting the optimization problem of maximizing the uplink data transmission throughput of the passive device and various constraints in the model;

[0012] S4. Decompose the optimization problem into two sub-problems, and solve the two sub-problems through semi-definite relaxation and convex optimization methods, and then iterate the solutions of the sub-problems to obtain the final solution.

[0013] The optimization problem is decomposed into two sub-problems, and the two sub-problems are solved by semi-positive relaxation and convex optimization methods, and the solutions of the sub-problems are iterated to obtain the final solution. Specifically, the following steps are performed:

[0014] A1. Initialize the system parameters, the position of each node, and the convergence accuracy ρ, and the uplink data transmission time of the given variable An initial value, and set the number of iterations i to 0;

[0015] A2. For a given initial value The optimization problem is converted into a problem with only one optimization variable Q. Through semi-positive relaxation and Gaussian randomization, an optimal smart reflector phase shift matrix Q is obtained. i ;

[0016] A3. According to the obtained phase shift matrix Q i Convert the hidden constraint to T under this condition U The constraints are updated to the optimization problem;

[0017] A4, Q iBring it into the optimization problem, and the optimization problem is transformed into only the optimization variable T U The optimal uplink data transmission time is solved by convex optimization method.

[0018] A5. According to the obtained Q i and Substitute the throughput expression to obtain the final solution for this iteration Comparison of convergence accuracy ρ and If the latter is greater than the convergence accuracy, the number of iterations i=i+1 is updated and the process returns to step A2; otherwise, the iteration is terminated.

[0019] The method of establishing a concealment constraint model and performing different processing according to different sampling rates of the eavesdropper specifically includes:

[0020] If the sampling rate of the eavesdropper is S per unit time, then the signal strength received by the eavesdropper in a short period of time is the mean value of the n samples. According to the central limit theorem, the mean value T(y) obeys the Gaussian distribution. Among them, H0 and H1 represent the situation where the passive device is not transmitting information and is transmitting information respectively. At this time, the eavesdropper will make a binary decision after receiving the signal. Where c represents the eavesdropper's decision threshold, D1 represents the eavesdropper's belief that the passive device is transmitting, and D0 represents the eavesdropper's belief that the passive device is not transmitting. The eavesdropper's detection error probability ξ is equal to its false alarm rate. Plus the missed detection rate The concealment constraint is ξ≥1-∈, and the optimal decision threshold when the detection error probability ξ of the eavesdropper is minimized is set to c, then c is the horizontal coordinate of the intersection of the two Gaussian distribution PDFs;

[0021] If the sampling rate of the eavesdropper is infinite per unit time, then the signal strength received by the eavesdropper in a short period of time is an accurate average value. At this time, the energy node can interfere with the eavesdropper's judgment by using random transmission power in different time slots. According to Pinsker's inequality, the lower bound of the eavesdropper's detection error probability ξ is is the corresponding KL divergence. In this case, we use As a hidden constraint of the system.

[0022] Each reflective unit of the intelligent reflective surface can adjust the phase θ n , where n∈N={1,…,N} represents the set of reflection units, then the phase shift matrix of the smart reflection surface is written as Q=diag{q1,q2,…,q N},in Represents an imaginary number.

[0023] The path loss model of the energy signal reaching the passive device is set as It includes the part where the signal reaches the passive device through the smart reflector and the part where the signal reaches the passive device directly. Q represents the phase shift matrix of the smart reflector, and the subscript of h represents two specific nodes. represents the equivalent channel between different nodes, is the large-scale path fading, where β0 represents the path loss at the reference distance d0 = 1m, d ab Represents the distance between different nodes, α ab represents the path loss factor. In this model Obeys Rayleigh distribution.

[0024] The energy collected by the passive device through the energy signal is E A =ηP EA T D , where η represents the energy conversion efficiency, P EA =P E H EA Represents the corresponding wireless energy transmission power, P E represents the energy node transmission power, T D Indicates the time of downlink energy transmission, H EA Represents the path loss model for the energy signal to reach the passive device.

[0025] Under the condition of meeting the concealment constraint, the optimization problem of maximizing the uplink data transmission throughput of the passive device is as follows:

[0026] Optimization problem:

[0027] Constraints:

[0028] ξ min ≥1-ε,

[0029] T D +T U ≤T

[0030] where R U Indicates the throughput of uplink data transmission, uplink data transmission time T U The phase shift matrix Q of the smart reflector is the variable to be optimized. The first constraint is the constraint of the phase adjustment of the smart reflector, and the second constraint is the concealment constraint. If the eavesdropper sampling rate is considered to be infinite, it should be changed to The third constraint is the uplink and downlink time constraint.

[0031] The present invention has the following advantages: a method for integrating covert communication and energy transmission based on an intelligent reflecting surface, which utilizes the intelligent reflecting surface to assist in transmitting energy signals, thereby increasing the energy collected by passive devices when there is obstruction in the line-of-sight link; jointly optimizing the covert communication of the device and the time slot allocation for energy and data transmission, thereby ensuring that the throughput of the device's uplink data transmission is increased under certain concealment requirements; the algorithm is also applicable when the eavesdropper's sampling rate is limited or unlimited, and the effectiveness of the algorithm is also verified under different numbers of reflective units on the intelligent reflecting surface, different transmission powers of energy signals, and different concealment requirements. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 The system model and energy and data transmission time slot diagram of the present invention;

[0033] Figure 2 It is the algorithm flow chart of the present invention;

[0034] Figure 3 This is a simulation diagram comparing the signal-to-noise ratio of an eavesdropper and a legitimate receiver under different numbers of reflective units on the smart reflector.

[0035] Figure 4 This is a simulation diagram of the uplink data transmission throughput under different numbers of reflective units on the smart reflector when the eavesdropper's sampling rate is limited;

[0036] Figure 5 This is a simulation diagram of the uplink data transmission throughput under different numbers of reflective units on the smart reflector when the eavesdropper's sampling rate is infinite. DETAILED DESCRIPTION

[0037] In order to make the purpose, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. The components of the embodiments of the present application generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the detailed description of the embodiments of the present application provided below in conjunction with the drawings is not intended to limit the scope of protection of the present application for which protection is claimed, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work are within the scope of protection of the present application. The present invention is further described below in conjunction with the drawings.

[0038] This invention specifically relates to a method for integrating covert communication and energy transmission based on intelligent reflective surfaces. This method leverages the flexible deployment, low cost, and controllable phase shift characteristics of intelligent reflective surfaces. In complex environments such as battlefields, where line-of-sight links are obstructed, the intelligent reflective surfaces enable wireless energy transmission to passive devices by routing signals around obstacles. Furthermore, the surfaces reflect signals during uplink data transmission from passive devices, disrupting eavesdroppers. A proposed iterative algorithm combines the design of the intelligent reflective surface's phase shift matrix with the allocation of energy and data transmission time slots to maximize the uplink data throughput of passive devices. The effectiveness of the algorithm is verified using reasonable data simulations.

[0039] Figure 1 This is a system model diagram of the covert communication and digital energy transmission system assisted by the intelligent reflective surface of the present invention. As shown in the figure, there is a passive device A in a complex environment such as a battlefield. Its task is to monitor certain variables in the environment and regularly upload data to the receiver B. Therefore, an energy node E and an intelligent reflective surface are deployed in the environment to assist the energy collection of the passive device A. At the same time, there is an eavesdropper W, which needs to eavesdrop on the uplink data transmission from A to B. A transmission cycle of the system model is divided into two stages. The first stage is the energy collection stage of the passive device A. At this time, the passive device A needs to collect energy from the radio frequency energy signal emitted by the energy node; the second stage is the uplink data transmission stage of the passive device A. At this time, the passive device A uses the energy collected in the previous stage to transmit uplink data to the receiver B. The eavesdropper always tries to eavesdrop on the communication from A to B, but it is always interfered by the signal emitted by the energy node E.

[0040] The present invention jointly designs the phase shift matrix of the smart reflector, energy, and data transmission time slot allocation to maximize the uplink data transmission throughput of the passive device while meeting the concealment requirements. Specifically, the present invention includes the following contents:

[0041] S1. Set up the system model, and with the assistance of the smart reflective surface, the passive device first receives the energy uploaded by reusing it;

[0042] S2. Establish a model of stealth constraints and perform different processing methods based on the different sampling rates of the eavesdropper;

[0043] S3. Setting the optimization problem of maximizing the uplink data transmission throughput of the passive device and various constraints in the model;

[0044] S4. Decompose the optimization problem into two sub-problems, and solve the two sub-problems through semi-definite relaxation and convex optimization methods, and then iterate the solutions of the sub-problems to obtain the final solution.

[0045] S5. Data simulation is performed based on the designed algorithm to verify the effectiveness of the proposed algorithm under different numbers of reflective units on the intelligent reflective surface, different transmission powers of energy signals, and different concealment requirements. The method is also applicable when the eavesdropper's sampling rate is finite and infinite.

[0046] Furthermore, the modeling and processing of stealth constraints should be handled differently when the eavesdropper has different sampling rates:

[0047] (1) In unit time, the eavesdropper's sampling rate is S. The signal strength he receives in a short period of time is the mean of the n samples. According to the central limit theorem, this mean T(y) obeys the Gaussian distribution. H0 and H1 represent the situation where the passive device is not transmitting information and is transmitting information respectively. In this case, the eavesdropper will make a binary decision after receiving the signal. Where c represents the eavesdropper's decision threshold, D1 represents the eavesdropper's belief that the passive device is transmitting, and D0 represents the eavesdropper's belief that the passive device is not transmitting. The eavesdropper's detection error probability ξ is equal to its false alarm rate Add missed detection rate The concealment constraint is ξ≥1-∈. Considering that the eavesdropper can find an optimal decision threshold c so that his detection error probability ξ is minimized, then c is the horizontal coordinate of the intersection of the two Gaussian distribution PDFs.

[0048] (2) In a unit of time, the sampling rate of the eavesdropper is infinite, so the signal strength he receives in a short period of time is an accurate average. In this case, the energy node uses random transmission power in different time slots to interfere with the eavesdropper's judgment. According to Pinsker's inequality, the lower bound of the eavesdropper's detection error probability ξ is is the corresponding KL divergence. In this case, we use As the hidden constraint of the system, this constraint is stricter than the original hidden constraint ξ≥1-∈.

[0049] Among them, each reflection unit of the smart reflection surface can adjust the phase θ n , where n∈N={1,…,N} represents the set of reflection units, then the phase shift matrix of the smart reflection surface can be written as Q=diag{q1,q2,…,q N},in

[0050] The path loss model of the energy signal reaching the passive device is: It includes the part where the signal reaches the passive device through the intelligent reflector and the part where the signal reaches the passive device directly. The subscript of h represents two specific nodes. represents the equivalent channel between different nodes, is the large-scale path fading, where β0 represents the path loss at the reference distance d0 = 1m, d ab Represents the distance between different nodes, α ab represents the path loss factor. In this model Obeys Rayleigh distribution. In addition, the path loss between other nodes adopts the above model.

[0051] The energy collected by the passive device through the energy signal is E A =ηP EA T D , where η represents the energy conversion efficiency, P EA =P E H EA Indicates the corresponding wireless energy transmission power, T D Indicates the time of downlink energy transmission.

[0052] Passive devices use the collected energy to transmit information uplink. For the receiver, the energy signal and channel information can be known in advance. Assuming that the interference of the energy signal to the receiver can be completely eliminated, the throughput Where T U Indicates the time of uplink data transmission, h AB represents the path loss between nodes, σ 2 Represents the noise power.

[0053] Based on the system model described above, an optimization problem is proposed to maximize the uplink data transmission throughput of the passive device while satisfying the concealment constraint. The optimization problem is as follows:

[0054] Optimization problem:

[0055] Constraints:

[0056] ξ min <1-ε,

[0057] T D +T U ≤T

[0058] where R U Indicates the throughput of uplink data transmission, uplink data transmission time T U The phase shift matrix Q of the smart reflector is the variable to be optimized. The first constraint is the constraint of the phase adjustment of the smart reflector, and the second constraint is the concealment constraint. If the eavesdropper sampling rate is considered to be infinite, it should be changed to The third constraint is the uplink and downlink time constraint.

[0059] Furthermore, if Figure 2 As shown in Figure 1, the optimization problem is decomposed into two sub-problems, and the two sub-problems are solved by semi-positive relaxation and convex optimization methods. The solutions of the sub-problems are then iterated to obtain the final solution, which specifically includes the following:

[0060] A1. Initialize the system parameters, the position of each node, and the convergence accuracy ρ, and the uplink data transmission time of the given variable An initial value, and set the number of iterations i to 0;

[0061] A2. For a given initial value The optimization problem becomes a problem with only one optimization variable Q, where Define u = [q1, q2, ..., q N ] H , They are all transformation processes in semi-positive programming, that is, intermediate variables in the transformation process, then H EA =|u H a+h EA | 2 . Introduce an auxiliary variable t to satisfy |t| 2 =1, let represents the intermediate variable in the transformation process, then But this expression and the resulting unit module constraint are still non-convex, so we define represents the intermediate variables in the transformation process, then the optimization problem can be expressed as:

[0062]

[0063]

[0064] M≥0 (2)

[0065] rank(M)=1 (3)

[0066] When solving this problem, we first relax the rank 1 constraint, and then the problem can be solved by convex optimization tools such as CVX. Then, through the Gaussian randomization method, we can find a feasible solution Q that meets the relaxed rank 1 constraint. i

[0067] A3. The phase shift matrix Q obtained i The hidden constraint can be converted into the condition for T U The constraints are updated to the optimization problem;

[0068]

[0069] s·t·ξ min≤1-ε (1)

[0070] T U <T (2)

[0071] The treatment of the concealment constraint is slightly different when the eavesdropper's sampling rate is finite or infinite.

[0072] A31. If the eavesdropper's sampling rate is limited, then the mean value of the signal T(y) received in a short period of time follows a Gaussian distribution. After receiving the signal, the eavesdropper will make a binary decision. Assuming that the eavesdropper can find an optimal decision threshold c that minimizes its detection error probability ξ, then c is the horizontal coordinate of the intersection of the two Gaussian distribution PDFs. Finally, the eavesdropper's detection error probability should be the area of ​​the overlapping part of the two Gaussian distribution PDFs, that is, the area under the intersection point c, so Among them, μ1, μ2, There is only one variable T U function of T, ξ is T U The function of first increasing, then decreasing, and then increasing again can be solved by the given concealment requirement ε to obtain T U The feasible range of , updating the range to the constraints can continue to solve the optimization problem.

[0073] A32. If the eavesdropper's sampling rate is infinite, then the signal strength he receives in a short period of time is an accurate average. In this case, the energy node can interfere with the eavesdropper's judgment by using random transmission power in different time slots. As a hidden constraint of the system, where γ w is the signal-to-noise ratio at the eavesdropper, It follows γ w It is a monotonically increasing function, so from the given hiddenness requirement ε, we can find γ w Upper bound of The implicit constraint becomes in However, this constraint cannot be directly brought into the problem solution because both the numerator and denominator contain the variable T U , so the constraint is transformed into Then the conditions for convex optimization are met.

[0074] A4. Bring the updated hidden constraints into the original optimization problem, and the optimization problem becomes only the optimization variable T U The optimal uplink data transmission time can be solved by convex optimization method.

[0075] A5. Use the obtained Q i and Substitute the throughput expression to obtain the final solution for this iteration Comparison of convergence accuracy ρ and If the latter is greater than the convergence accuracy, the number of iterations i=i+1 is updated and the process returns to step A2; otherwise, the iteration is terminated.

[0076] During the simulation, the deployment positions of each node are fixed. In the three-dimensional coordinate system, E(0, 5, 5), A(50, 5, 0), B(60, 7.5, 0), IRS(50, 0, 5), W(45, 0, 0), the path loss is set to β0 = -30 dB, and the path loss factor α EI、AB、AW =2.4,α IA、IW =3,α EA、EW =4.2, the noise power is set to σ 2 =-100dBm, the specific simulation results are as follows:

[0077] like Figure 3 Figure 2 shows a simulation comparing the signal-to-noise ratio (SNR) of an eavesdropper and a legitimate receiver for different numbers of reflective units on a smart reflective surface. As can be seen from the figure, as the number of reflective units on the smart reflective surface increases, the receiver's SNR improves, while the eavesdropper's SNR decreases. On the one hand, increasing the number of reflective units on the smart reflective surface improves the energy collection performance of the passive device. Passive devices can use more energy when uploading information, resulting in a stronger signal and a higher SNR at the receiver. On the other hand, increasing the number of reflective units on the smart reflective surface increases the interference to the eavesdropper, increasing the interference noise and lowering the SNR at the eavesdropper's location. Therefore, the introduction of smart reflective surfaces can effectively improve information transmission performance while also enhancing the system's stealth.

[0078] like Figure 4 The figure below shows a simulation of uplink data transmission throughput for different numbers of reflective units on a smart reflective surface, when the eavesdropper's sampling rate is limited. As can be seen from the figure, uplink throughput increases with the number of reflective units on the smart reflective surface. This is because the introduction of the smart reflective surface effectively improves information transmission performance. Furthermore, for an eavesdropper, a higher sampling rate increases signal sensitivity. To achieve covert transmission, some information transmission performance must be sacrificed to meet the required concealment. Therefore, all other conditions being equal, the higher the eavesdropper's sampling rate or the higher the required concealment, the lower the uplink throughput.

[0079] like Figure 5Figure 2 shows a simulation of uplink data transmission throughput for different numbers of reflective units on a smart reflector when the eavesdropper's sampling rate is infinite. The figure shows that uplink throughput increases with the number of reflective units on the smart reflector. Higher transmit power at the energy node results in higher throughput, as the passive device collects more energy. However, higher concealment requirements lead to lower throughput, as some information transmission performance must be sacrificed to meet these requirements.

[0080] The foregoing description is merely a preferred embodiment of the present invention. It should be understood that the present invention is not limited to the form disclosed herein and should not be construed as excluding other embodiments. Rather, the present invention can be used in various other combinations, modifications, and environments and can be modified within the scope of the concept described herein through the above teachings or techniques or knowledge in the relevant field. Modifications and variations made by those skilled in the art that do not depart from the spirit and scope of the present invention are intended to be protected by the appended claims.

Claims

1. A method for integrating covert communication and energy transmission based on intelligent reflective surfaces, characterized by: The integrated method includes: S1. Set up the system model, and with the assistance of the smart reflective surface, the passive device first receives the energy uploaded by reusing it; S2. Establish a model of stealth constraints and perform different processing methods based on the different sampling rates of the eavesdropper; S3. Setting the optimization problem of maximizing the uplink data transmission throughput of the passive device and various constraints in the model; S4. Decompose the optimization problem into two sub-problems, solve the two sub-problems by semi-positive relaxation and convex optimization methods, and then iterate the solutions of the sub-problems to obtain the final solution; The method of establishing a concealment constraint model and performing different processing according to different sampling rates of the eavesdropper specifically includes: If the sampling rate of the eavesdropper is S per unit time, then the signal strength received by the eavesdropper in a short period of time is the mean value of the n samples. According to the central limit theorem, the mean value T(y) obeys the Gaussian distribution. , where H0 and H1 represent the situation where the passive device is not transmitting information and is transmitting information respectively. At this time, the eavesdropper will make a binary decision after receiving the signal. , where c represents the eavesdropper's decision threshold, D1 represents the eavesdropper's belief that the passive device is transmitting, and D0 represents the eavesdropper's belief that the passive device is not transmitting. The eavesdropper's detection error probability is Equal to his false alarm rate Plus the missed detection rate , the hidden constraint is , set the detection error probability considering the eavesdropper The optimal decision threshold at the minimum is c, then c is the horizontal coordinate of the intersection of the two Gaussian distribution PDFs; If the sampling rate of the eavesdropper is infinite per unit time, then the signal strength received by the eavesdropper in a short period of time is an accurate average value. At this time, the energy node can interfere with the eavesdropper's judgment by using random transmission power in different time slots. According to Pinsker's inequality, the detection error probability of the eavesdropper is The lower bound , is the corresponding KL divergence. In this case, we use As a hidden constraint of the system.

2. The method for integrating covert communication and energy transmission based on a smart reflective surface according to claim 1, characterized in that: The optimization problem is decomposed into two sub-problems, and the two sub-problems are solved by semi-positive relaxation and convex optimization methods, and the solutions of the sub-problems are iterated to obtain the final solution. Specifically, the following steps are performed: A1. Initialize the system parameters, the position of each node, and the convergence accuracy ρ, and the uplink data transmission time of the given variable An initial value, and set the number of iterations i to 0; A2. For a given initial value , the optimization problem is converted into a problem with only one optimization variable Q. Through semi-positive relaxation and Gaussian randomization, an optimal smart reflector phase shift matrix Q is obtained. i ; A3. According to the obtained phase shift matrix Q i Convert the hidden constraint to T under this condition U The constraints are updated to the optimization problem; A4, Q i Bring it into the optimization problem, and the optimization problem is transformed into only the optimization variable T U The optimal uplink data transmission time is solved by convex optimization method. ; A5. According to the obtained Q i and Substitute the throughput expression to obtain the final solution for this iteration , compare the convergence accuracy ρ and , if the latter is greater than the convergence accuracy, the number of iterations i=i+1 is updated and the process returns to step A2, otherwise the iteration ends.

3. The method for integrating covert communication and energy transmission based on a smart reflective surface according to claim 1, characterized in that: Each reflective unit of the intelligent reflective surface can adjust the phase ,in represents the set of reflection units, then the phase shift matrix of the smart reflection surface is written as ,in , j represents an imaginary number.

4. The method for integrating covert communication and energy transmission based on a smart reflective surface according to claim 1, characterized in that: The path loss model of the energy signal reaching the passive device is set as , which includes the part of the signal reaching the passive device through the smart reflector and the part of the signal reaching the passive device directly. Q represents the phase shift matrix of the smart reflector, and the subscript of h represents two specific nodes. represents the equivalent channel between different nodes, is the large-scale path fading, where Indicates the reference distance The path loss when Represents the distance between different nodes, represents the path loss factor. In this model Obeys Rayleigh distribution.

5. The method for integrating covert communication and energy transmission based on a smart reflective surface according to claim 1, characterized in that: The energy collected by the passive device through the energy signal is , where η represents the energy conversion efficiency, Represents the corresponding wireless energy transmission power, P E represents the energy node transmission power, T D Indicates the time of downlink energy transmission, H EA Represents the path loss model for the energy signal to reach the passive device.

6. The method for integrating covert communication and energy transmission based on a smart reflective surface according to claim 5, characterized in that: Under the condition of meeting the concealment constraint, the optimization problem of maximizing the uplink data transmission throughput of the passive device is as follows: Optimization problem: , Constraints: , , ， ， where R U Indicates the throughput of uplink data transmission, uplink data transmission time T U The phase shift matrix Q of the smart reflector is the variable to be optimized. The first constraint is the constraint of the phase adjustment of the smart reflector, and the second constraint is the concealment constraint. If the eavesdropper sampling rate is considered to be infinite, it should be changed to ,The third constraint is the uplink and downlink time constraint.

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