An IRS joint AN assisted UAV air-ground covert communication performance optimization method

Abstract

The application provides a performance optimization method for IRS combined with AN assisted UAV air-ground covert communication, comprising the following steps: S1, starting the method; S2, initializing parameters; S3, establishing a system channel model, an illegal detection model and a system covert performance maximization model; S4, optimizing an IRS phase shift matrix; S5, optimizing a UAV trajectory; S6, optimizing UAV transmission power and ground user transmission AN interference power; S7, judging whether the system covert performance is maximized based on the output result of step S6, if yes, executing step S8, otherwise, after optimizing the UAV transmission power and the ground user transmission AN interference power, jumping to step S4 until the system covert performance is maximized; and S8, outputting the result. The application can effectively improve the covert transmission rate of the communication system, resist detection by illegal detectors, and is conducive to reducing the conditions of covert constraints and enhancing the concealment of the system.

Description

Technical Field

[0001] The present invention relates to the field of communication technology, and in particular to a performance optimization method for an IRS combined with an AN to assist UAV in air-to-ground covert communication. Background Art

[0002] Currently, there are two main approaches to addressing wireless covert communication security: network-layer encryption and physical-layer security. However, these two approaches primarily focus on maintaining confidentiality in the content of transmitted information, overlooking a practical issue: once a malicious node detects a transmitter's transmission, it can be tracked and monitored, leaking its location and still posing a security risk to the network. Wireless covert communication technology, as a more rigorous wireless communication security solution, can address this issue.

[0003] Compared to terrestrial wireless communications, UAV (Unmanned Aerial Vehicle) air-to-ground links have a high probability of line-of-sight. Therefore, UAV-to-ground information transmissions are more susceptible to detection by unauthorized probes, reducing the security level of air-to-ground communications. However, due to the high maneuverability and ease of deployment of UAVs, their flexibility can be leveraged to counter unauthorized probes and improve communication stealth. For example, by optimizing the UAV's power control, hovering position, and trajectory, the UAV can find a more suitable location to transmit signals, thereby increasing the probability of unauthorized probes failing to detect them. Furthermore, to increase the system's temporal stealth, random noise can be introduced. By introducing external friendly jammers, artificial noise (AN) can be actively generated to enhance communication stealth and reduce the detection performance of unauthorized probes. However, these solutions may incur additional resource consumption and increase the burden of system deployment. Furthermore, the link between the UAV and the user may be interrupted by obstacles such as buildings. Intelligent Reflecting Surfaces (IRS) can reconfigure channel gain by adjusting the phase shift matrix. They can be deployed near transmitters to improve communication quality and security in severely attenuated channels.

[0004] Although the performance optimization method for UAV covert communication based on AN cooperative interference and the performance optimization method for IRS-assisted UAV air-to-ground covert communication have good effects in enhancing the concealment performance of the UAV air-to-ground communication system, the inventors of this patent have found at least the following technical problems in the process of implementing the technical methods of the embodiments of the present invention:

[0005] 1) Using AN cooperative jamming to counter illegal probes can increase the time concealment of UAV covert communications and address the uncertainty of signal sample statistics under limited code length. However, this requires the UAV to have a low transmit power to meet the covert constraints, resulting in a low covert transmission rate for UAV-to-ground communications.

[0006] 2) The introduction of IRS can reconstruct the channel propagation environment between UAVs and ground users, but the problem of UAV air-to-ground covert communication assisted by IRS and AN has not been reported. In addition, the problem is more challenging due to the different system models and complex integrated management of wireless and communication resources in the fusion of IRS and AN collaborative interference strategies.

[0007] 3) Under the constraints of concealment level and UAV transmission power, the IRS reflection coefficient, UAV flight trajectory and transmission power are jointly optimized to maximize the AN interference power transmitted by the ground user. The optimization problem of the transmission rate of UAV air-to-ground covert communication is a non-convex optimization problem and is difficult to solve directly.

[0008] Therefore, how to utilize limited resources to improve the covert transmission rate of UAV air-to-ground covert communication and reduce the requirements for covert constraints has become an urgent problem to be solved in this field. Summary of the Invention

[0009] The embodiment of the present invention provides a method for assisting UAV air-to-ground covert communication using an intelligent reflective surface combined with artificial noise, which solves the problem that the existing technology is unable to utilize limited resources, improve the covert transmission rate of UAV air-to-ground covert communication, and reduce the requirements for covert constraints.

[0010] An embodiment of the present invention provides a performance optimization method for IRS-assisted AN-assisted UAV air-to-ground covert communication, including:

[0011] Step S1: method starts;

[0012] Step S2: Initialize parameters;

[0013] Step S3: Establishing a system channel model, an illegal detection model, and a system concealment performance maximization model;

[0014] Step S4: Given the initialized UAV trajectory Q, UAV transmission power and ground user transmission AN interference power, optimize the IRS phase shift matrix ;

[0015] Step S5: Given the optimized IRS phase shift matrix and initialize the UAV transmission power and the ground user transmission AN interference power to optimize the UAV trajectory Q;

[0016] Step S6: Given the optimized IRS phase shift matrix and UAV trajectory Q, optimizing the UAV transmission power and the ground user transmission AN interference power;

[0017] Step S7: Based on the output of step S6, determine whether the system concealment performance is maximized. If so, proceed to step S8. Otherwise, after optimizing the UAV transmission power and the ground user AN interference power, jump to step S4 until the system concealment performance is maximized.

[0018] Step S8: Output the result.

[0019] Optionally, the parameters of step S2 include but are not limited to the locations of ground users, ground illegal detectors and IRSs ( ,0),( ,0) and ( , h ), UAV service time T , UAV flight altitude H , the initial and final horizontal positions of the UAV and , UAV maximum flight speed , UAV initial trajectory Q 0 , UAV transmission power , Initial value of AN interference power transmitted by ground users , iteration accuracy .

[0020] Optionally, the step S3 specifically includes: the step S3 specifically includes: using the channel power gain between the ground user and the illegal detector, the channel power gain between the UAV and the ground user / illegal detector, the channel power gain between the UAV and the IRS, and the channel power gain between the IRS and the ground user / ground illegal detector as the , , , , express; order and They represent the private message transmitted by the UAV and the AN sent by the ground user, respectively. The received signals received by the ground user and the illegal detector are:

[0021] ;

[0022] ;

[0023] Assuming that the illegal detector uses energy detection to compare the received signal with a threshold to determine whether the UAV is sending information to the ground, the binary hypothesis made by the illegal detector to distinguish whether the UAV is sending information to the user is:

[0024] ;

[0025] Considering the case of finite code length, the effective throughput of the system is used to measure the concealment performance of the system. Since the effective throughput of the system is an increasing function of the ground user's received signal-to-interference-and-noise ratio, the received signal-to-interference-and-noise ratios of the ground user and the illegal detector after reflection from the IRS are expressed as:

[0026] ;

[0027] ;

[0028] Therefore, the maximum concealment performance model of the system under limited code length is:

[0029]

[0030] .

[0031] Optionally, the step S4 specifically includes: given the initialized UAV trajectory Q and transmission power and the ground user AN interference power, since the IRS cannot obtain the small-scale fading between itself and the illegal detector, the optimal value of the IRS phase shift is when the ground user's receiving rate is maximized. The solution to this problem can be directly derived, that is, n Time slot IRS phase shift matrix ,in , .

[0032] Optionally, the step S5 specifically includes: the step S5 specifically includes: given an optimized IRS phase shift matrix The UAV transmission power and the ground user AN interference power are initialized. Then, the non-convex expression C6 in the model formula for maximizing the system concealment performance under finite code length in step S3 is converted into a convex expression through the SCA method by introducing slack variables, and then solved using a convex optimization algorithm.

[0033] Optionally, the step S6 specifically includes: the step S6 specifically includes: given the optimized IRS phase shift matrix and UAV trajectory Q, according to the Dinkelbach algorithm theory, by introducing the variable factor The fractional optimization problem is transformed into a linear programming problem and then solved using a convex optimization algorithm.

[0034] One or more technical solutions provided in the embodiments of the present invention have at least the following technical effects or advantages:

[0035] The UAV transmits private messages to the target user via the IRS, resolving the low covert transmission rate problem of the communication system due to the need to meet covert constraints. To enhance the system's covertness, a full-duplex ground user transmits an AN jamming signal to degrade the detection of unauthorized ground probes. Subject to the covert constraints, the IRS phase shift matrix, UAV path trajectory, UAV transmit power, and ground user AN jamming power are jointly optimized to maximize the ground user's signal-to-interference-noise ratio. To address the non-convexity of the proposed problem, an alternating iterative optimization algorithm based on fractional programming is proposed. This algorithm achieves a suboptimal solution by alternately optimizing three sub-objectives.

[0036] The key technical points of the present invention are:

[0037] 1. In order to improve the concealment performance of UAV air-to-ground covert communication, the optimization of the IRS phase shift matrix to change the channel is considered. At the same time, full-duplex ground users are used to transmit AN interference signals to deteriorate the ground illegal detector channel. Combined with the optimization of UAV trajectory and transmission power, the detection probability of illegal detectors is effectively reduced, thereby improving the concealment performance of the system.

[0038] 2. An alternating iterative optimization algorithm based on fractional programming is proposed, which divides the original problem into three sub-problems and is solved using fractional programming, continuous convex approximation method and convex optimization method. In addition, by reconstructing the channel through IRS and transmitting AN interference signal by ground users, the received signal-to-interference-noise ratio of ground users can be improved, effectively enhancing the concealment performance of air-to-ground wireless communication.

[0039] 3. The system's concealed transmission performance curve is obtained under different concealment level parameters, different numbers of IRS elements, different AN interference powers, and different total numbers of symbols in communication blocks, and the results are analyzed.

[0040] 4. Provide multiple concealment performance optimization solutions, including a pre-determined IRS-free solution, an AN-free solution, and an alternating iterative optimization solution based on fractional programming. The system's concealment transmission rate is maximized, thereby maximizing the system's concealment performance.

[0041] The advantages of the present invention are:

[0042] 1. The present invention improves the covert transmission rate of the UAV air-to-ground covert communication system by optimizing the receiving signal-to-noise ratio of ground users, reduces the conditions of covert constraints, and meets the covert requirements of the system.

[0043] 2. By using full-duplex ground users to transmit artificial noise interference signals to deteriorate the channel of illegal ground detectors, and using the reflection of intelligent reflective surfaces to make this interference effect more significant, the concealment of the UAV air-to-ground covert communication system is effectively enhanced;

[0044] 3. The alternating iterative optimization scheme based on fractional programming can effectively enhance the receiving signal-to-noise ratio of ground users and improve the concealment performance of the UAV air-to-ground covert communication system. It is more advantageous than the traditional scheme without intelligent reflective surface and artificial noise.

[0045] 4. The present invention adopts an alternating iterative optimization algorithm based on fractional programming to jointly optimize multiple variables, thereby effectively enhancing the covert communication performance of the UAV air-to-ground covert communication system. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 This is a flow chart of a performance optimization method for an IRS combined with an AN to assist UAV in air-to-ground covert communication in one embodiment of the present invention;

[0047] Figure 2 This is a model diagram of an IRS combined with an AN-assisted UAV air-to-ground covert communication system in one embodiment of the present invention;

[0048] Figure 3 Schematic diagram of the receiver signal-to-noise ratio when the AN-IRS, no IRS, and no AN schemes vary with AN interference power in one embodiment of the present invention;

[0049] Figure 4 Schematic diagram of the receiver signal-to-noise ratio when the number of IRS reflective elements changes in three schemes: AN-IRS, no IRS, and no AN in one embodiment of the present invention;

[0050] Figure 5 Schematic diagram of the receiver signal-to-noise ratio when the IRS position changes in three schemes: AN-IRS, no IRS, and no AN in one embodiment of the present invention;

[0051] Figure 6 Schematic diagram of the receiver's received signal-to-noise ratio when the three schemes, AN-IRS, no IRS, and no AN, vary with the concealment performance level in one embodiment of the present invention;

[0052] Figure 7 Schematic diagram of the received signal-to-noise ratio of a receiver when the total number of symbols in a communication block changes for the three schemes of AN-IRS, no IRS, and no AN in one embodiment of the present invention. DETAILED DESCRIPTION

[0053] The embodiment of the present invention provides a method for assisting UAV air-to-ground covert communication using an intelligent reflective surface combined with artificial noise, which solves the problem that the existing technology is unable to utilize limited resources, improve the covert transmission rate of UAV air-to-ground covert communication, and reduce the requirements for covert constraints.

[0054] like Figure 1As shown, a performance optimization method for IRS-assisted AN-assisted UAV air-to-ground covert communication includes 8 steps S1-S8.

[0055] Step S1: The method starts.

[0056] Step S2: Initialize parameters.

[0057] In the specific implementation process of step S2, some parameters need to be assigned. and the end point , the location of the ground user ( , 0), and the position of the illegal detector on the ground ( , 0). UAV has limited service time T Fixed height inside H For ease of calculation, T is divided into N time slots of equal length, i.e. ,in is the time slot length. Then the position of the UAV in the nth time slot can be expressed as ,in In addition, the maximum speed of the UAV is Therefore, the maximum flight distance of the UAV in each time slot is The IRS is fixed to the surface of the building by M The position of IRS is fixed at ( , h), where . h is the height of the IRS center. n The phase shift matrix of the time slot IRS is expressed as ,in , .

[0058] S3: Establish system channel model, illegal detection model, and system concealment performance maximization model.

[0059] During the specific implementation process, step S3 is divided into the following three sub-steps.

[0060] 1) Establishment of the system channel model. The channel power gains between ground users and illegal detectors, the channel power gains between UAVs and ground users / illegal detectors, the channel power gains between UAVs and IRSs, and the channel power gains between IRSs and ground users / illegal detectors are:

[0061] ;

[0062] ;

[0063] ;

[0064] , , ;

[0065] in, Indicates reference distance The channel gain at is a random scattering component independently modeled by a complex Gaussian random variable with zero mean and unit variance, Indicates in n The cosine of the angle of arrival from the UAV to the IRS in the time slot, , Represents the cosine of the departure angle of the signal from the IRS to the ground user / illegal detector, and In time slots n The distance from Alice to the ground user / illegal detector and IRS, and They are the distance between IRS and ground users / illegal detectors and the distance between ground users and illegal detectors, λ is the frequency wavelength of the carrier, κ is the corresponding path loss exponent, d is the distance between two consecutive reflective units of the IRS.

[0066] The mobility constraints of the UAV are expressed as:

[0067] ;

[0068] ;

[0069] make and They represent the private message transmitted by the UAV and the AN sent by the ground user, respectively. Both obey the complex Gaussian distribution with a mean of 0 and a variance of 1. Then the received signals received by the ground user and the illegal detector are:

[0070] ;

[0071] ;

[0072] in, and is additive white Gaussian noise, , , Indicates time slot n The UAV's transmission power, assuming that the power must also meet the average power and peak power limit ,Right now:

[0073] ;

[0074] .

[0075] 2) Establishment of the illegal detection model. Assuming that the illegal detector uses energy detection to compare the received signal with a threshold to determine whether the UAV is sending information to the ground, the binary hypothesis made by the illegal detector to distinguish whether the UAV is sending information to the user is:

[0076] ;

[0077] in, is represented as the null hypothesis, and Indicates the alternative hypothesis.

[0078] According to the above formula, the detection error probability of an illegal detector is given by the false alarm probability and the probability of missed alarms Indicates that , , and represents the illegal detector’s decision on whether the UAV sends a message to the user. The total false detection probability of the illegal detector is:

[0079] ;

[0080] in, and express and The prior emission probability of . The lower bound of is used to measure the detection performance of illegal detectors, namely:

[0081] ;

[0082] in, yes arrive The KL divergence of can be derived as:

[0083] ;

[0084] 3) Establish a model to maximize the system's concealment performance. Considering the case of finite code length, the system's effective throughput is used to measure the system's concealment performance. Since the system's effective throughput is an increasing function of the ground user's received signal-to-interference-and-noise ratio (SINR), the SINRs of the ground user and the illegal detector after reflection from the IRS are expressed as:

[0085] ;

[0086] .

[0087] in, is the expectation operator, and , is the self-interference factor of the ground user, where , and are the additive white Gaussian noise at the illegal detector and the ground user respectively.

[0088] Therefore, the maximum concealment performance model of the system under limited code length is:

[0089]

[0090] .

[0091] S4: Given the initial UAV trajectory Q, UAV transmission power and ground user transmission AN interference power, optimize the IRS phase shift matrix .

[0092] In the specific implementation process of step S4, the initialization parameters of step S2 are used for iterative optimization. Given the initialized UAV trajectory Q and transmission power and the ground user's AN interference power, since the IRS cannot obtain the small-scale fading between itself and the illegal detector, the optimal value of the IRS phase shift is when the ground user's receiving rate is maximized. The solution to this problem can be directly derived, that is, n Time slot IRS phase shift matrix ,in , .

[0093] S5: Given the optimized IRS phase shift matrix And initialize the UAV transmission power and the ground user transmission AN interference power, and optimize the UAV trajectory Q.

[0094] First, given the optimized IRS phase shift matrix The UAV transmission power and the ground user AN interference power are initialized. Then, the non-convex expression C6 in the model formula for maximizing the system concealment performance under finite code length in step S3 is converted into a convex expression through the SCA method by introducing slack variables, and then solved using a convex optimization algorithm.

[0095] S6: Given the optimized IRS phase shift matrix and UAV trajectory Q, optimizing the UAV transmission power and the ground user transmission AN interference power.

[0096] In the specific implementation process of step S6, firstly, the optimized IRS phase shift matrix is ​​given and UAV trajectory Q, according to the Dinkelbach algorithm theory, by introducing the variable factor The fractional optimization problem is transformed into a linear programming problem and then solved using a convex optimization algorithm.

[0097] S7: Based on the output of step S6, determine whether the system concealment performance is maximized. If so, execute step S8. Otherwise, after optimizing the UAV transmission power and the ground user AN interference power, jump to step S4 until the system concealment performance is maximized.

[0098] S8: Output results.

[0099] In the specific implementation process of step S8, the concealment performance of the system is maximized after alternate iterative optimization, and the result is output, and the process ends here.

[0100] like Figure 2 As shown in the figure, in an IRS-assisted AN-assisted UAV air-to-ground covert communication system, a source UAV attempts to transmit covert information to a ground user, Bob. Assume that their locations are known. Due to the open and broadcast nature of wireless communications, the UAV-to-ground communication could be detected by a ground user, Willie. This could lead to security attacks. Therefore, to enhance the system's covertness, an IRS is introduced to change the channel to improve the performance of the UAV air-to-ground covert communication. At the same time, a ground user operating in full-duplex mode transmits an AN while receiving the UAV's private information, thereby compromising the detection of the unauthorized probe.

[0101] Next, the effectiveness of the performance optimization method of IRS combined with AN to assist UAV air-to-ground covert communication in this application is evaluated through simulation results. The horizontal positions of IRS, Bob, and Willie are set to (0,0), (0,30), and (50,30), respectively, in meters (m). The initial and final positions of the UAV are set to , ,high H =100m, maximum speed , average and peak transmit power dBm, dBm, flight time T=200s, each time slot The height of IRS is h = 10m, the distance between two consecutive elements of IRS is ,in is the carrier frequency. In addition, dBm, , dBm, They are the power of noise, the path loss index of each link, the total reference channel gain and the iteration accuracy.

[0102] like Figure 3 As shown in the figure, as the AN interference power increases, the concealed transmission performance of the scheme containing AN also increases, and then tends to be flat. This is because AN will also interfere with the ground users themselves. When the AN interference power is small, it is of limited help in meeting the concealment of the UAV air-to-ground concealed communication system. The concealment of the scheme without IRS begins to be lower than that of the scheme without AN. However, with the continuous increase of AN, the concealment of the system can be better met, and the concealment performance also increases. This also shows that when the AN interference power is relatively large, there is a significant gain in improving the concealment performance of the UAV air-to-ground concealed system, mainly because it can suppress the receiving signal-to-interference-noise ratio of illegal detection nodes. From Figure 3 It can be seen that the proposed AN-IRS combined solution has better system concealment performance than the solution without IRS, indicating that the introduction of IRS can significantly improve the system's concealment performance. Secondly, as the AN interference power increases to 4dBm, both the AN-IRS solution and the solution without IRS perform better than the solution without AN. This is mainly because when the AN interference power is too low, the AN does little to reduce the system's concealment, while the IRS can directly reflect some undesirable signals to interfere with illegal detectors. Therefore, the received signal-to-interference-noise ratio of the solution without IRS is lower than that of the solution without AN.

[0103] Given a concealment performance level, Figure 4 It can be seen that with the increase of IRS reflective elements, the covert transmission performance of the system containing the IRS scheme also increases accordingly, and the covert transmission performance of the proposed AN-IRS optimization scheme is slightly higher than that of the scheme without AN, which shows that AN plays an important role in assisting covert communication and worsening illegal detectors. The covert performance of the AN-IRS scheme is much higher than that of the scheme without IRS, which also shows that IRS and AN can dually assist covert communication and enhance the covert transmission rate.

[0104] Given concealment performance level , number of IRS reflective elements , the initial coordinates are set to (0,0,10)m, and then the IRS moves in the positive direction of the x-axis. Figure 5As can be seen, regardless of the IRS's location, the proposed AN-IRS scheme outperforms the other two baseline schemes. As the IRS gets closer to the unauthorized probe, the covert transmission performance of the various schemes gradually decreases. However, the decline is slower when the IRS is closer to Bob, and increases when it is closer to Willie. This is primarily because the communication link quality between the IRS and Bob is better when the IRS is closer to Bob. When the IRS is closer to Willie, Willie is more likely to eavesdrop on legitimate information, making it more difficult for the system to meet the covert constraints. This also provides a theoretical reference for IRS location deployment.

[0105] from Figure 6 It can be seen that with the level of concealment performance As the concealment performance level increases, the concealment transmission performance of the three optimization schemes increases. This is because as the concealment performance level increases, the freedom of the system increases, and the concealment constraint is easier to satisfy. Therefore, the concealment transmission performance of the user increases with But from Figure 6 As can be seen, thanks to the assistance of the IRS and AN, the proposed AN-IRS optimization scheme significantly improves the ability to meet covert constraints, resulting in better covert communication performance. This also leads to the conclusion that both the IRS and AN help reduce the probability of information leakage during covert communication, effectively preventing eavesdroppers from eavesdropping, and thus providing dual support for covert communication.

[0106] from Figure 7 It can be seen that as the total number of symbols in the communication block As the concealed transmission performance of the ground users of the three schemes decreases, the main reason is that with the increase of Increasing the number of channels will make the requirements for meeting the concealment constraints more stringent, thus affecting the covert transmission performance of the UAV air-to-ground system. At the same time, under the same code length conditions, the proposed optimization scheme using IRS and AN assistance significantly outperforms the schemes without AN and without IRS. Therefore, under limited channel conditions, IRS and AN are both very helpful in improving the covert transmission performance of the UAV air-to-ground covert system.

[0107] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A performance optimization method for IRS-assisted AN-assisted UAV air-to-ground covert communication, characterized in that: include: Step S1: method starts; Step S2: Initialize parameters; Step S3: Establishing a system channel model, an illegal detection model, and a system concealment performance maximization model; Step S4: Given the initialized UAV trajectory , UAV transmission power and ground user transmission AN interference power, optimize the IRS phase shift matrix ; Step S5: Given the optimized IRS phase shift matrix Initialize UAV transmission power and ground user transmission AN interference power to optimize UAV trajectory ; Step S6: Given the optimized IRS phase shift matrix and UAV trajectories , optimize the UAV transmission power and the ground user transmission AN interference power; Step S7: Based on the output of step S6, determine whether the system concealment performance is maximized. If so, proceed to step S8. Otherwise, after optimizing the UAV transmission power and the ground user AN interference power, jump to step S4 until the system concealment performance is maximized. Step S8: output the result; The step S3 specifically includes: using the channel power gain between the ground user and the illegal detector, the channel power gain between the UAV and the ground user / illegal detector, the channel power gain between the UAV and the IRS, and the channel power gain between the IRS and the ground user / illegal detector as the channel power gain. , , , Indicates that, ;make and Respectively represent n The private message transmitted by the time slot UAV and the AN sent by the ground user, the received signal received by the ground user and the illegal detector is: ； ； Where, For the n The received signal received by the ground user in the time slot, For the n The received signal received by the illegal detector in the time slot, For the n Time slot IRS phase shift matrix, For the n Channel power gain of time slot UAV and IRS, is the self-interference factor of the ground user, For the time slot n The UAV's transmission power is For the time slot n Private messages sent by UAVs, For the time slot n When the ground user sends AN, and In time slots n Additive Gaussian white noise at the ground user and illegal detector when ; Assuming that the illegal detector uses energy detection to compare the received signal with a threshold to determine whether the UAV is sending information to the ground, the binary hypothesis made by the illegal detector to distinguish whether the UAV is sending information to the user is: ； Where, is the null hypothesis, represents the alternative hypothesis, For the time slot n The UAV's transmission power is For the time slot n Private messages sent by UAVs, For the time slot n When the ground user sends AN, For the time slot n The additive white Gaussian noise at the illegal detector when ; Considering the case of finite code length, the effective throughput of the system is used to measure the concealment performance of the system. Since the effective throughput of the system is an increasing function of the ground user's received signal-to-interference-and-noise ratio, the received signal-to-interference-and-noise ratios of the ground user and the illegal detector after reflection from the IRS are expressed as: ； ； Where, Time slot n The receiving signal-to-interference-and-noise ratio of the ground user is Time slot n When the illegal detector receives the signal to interference and noise ratio, is the total reference channel gain, is the noise power at the illegal detector, is the noise power at the ground user, For the time slot n The UAV's transmission power is is the expectation operator; Therefore, the maximum concealment performance model of the system under limited code length is: ； Where, is the UAV trajectory, is the IRS phase shift matrix, is the receiving signal-to-interference-and-noise ratio of the ground user, is the average transmit power of the UAV, is the peak transmit power of the UAV, For concealed performance level, Indicates time slot n The UAV's transmission power is is the initial horizontal position of the UAV, is the final horizontal position of the UAV, For time slots.

2. The method according to claim 1, wherein The parameters of step S2 include but are not limited to the locations of ground users, ground illegal detectors and IRS ( ,0),( ,0) and ( , h ), UAV service time T , UAV flight altitude H , the initial and final horizontal positions of the UAV and , UAV maximum flight speed , UAV initial trajectory , UAV transmission power , Initial value of AN interference power transmitted by ground users , iteration accuracy .

3. The method according to claim 1, wherein The step S4 specifically includes: given the initialized UAV trajectory and transmit power and the AN interference power transmitted by the ground user. Since the IRS cannot obtain the small-scale fading between itself and the illegal detector, the optimal value of the IRS phase shift is when the ground user's receiving rate is maximized. The solution to this problem can be directly derived, that is, n Time slot IRS phase shift matrix ,in , , where Indicates the n Time slot IRS phase shift matrix, is the number of IRS reflective elements, is the carrier wavelength, is the distance between two consecutive elements of IRS.

4. The method according to claim 1, wherein The step S5 specifically includes: given an optimized IRS phase shift matrix The UAV transmission power and the ground user AN interference power are initialized. Then, the non-convex expression C6 in the model formula for maximizing the system concealment performance under finite code length in step S3 is converted into a convex expression through the SCA method by introducing slack variables, and then solved using a convex optimization algorithm.

5. The method according to claim 1, wherein The step S6 specifically includes: given the optimized IRS phase shift matrix and UAV trajectories According to the Dinkelbach algorithm theory, by introducing the variable factor The fractional optimization problem is transformed into a linear programming problem and then solved using a convex optimization algorithm.

Images