A Smart Reflector-Assisted Covert Communication System for Unmanned Aerial Vehicles Based on Non-Orthogonal Multiple Access

Abstract

This invention discloses a covert communication system for unmanned aerial vehicles (UAVs) based on a non-orthogonal multiple access (NOMA) intelligent reflector. A covert UAV model based on NOMA is established, the minimum error detection probability is calculated, and the maximum covert transmission rate is optimized. The intelligent reflector reflects the combined signals of the UAV to both the NOMA public signal receiver and the NOMA covert signal receiver, allowing eavesdroppers to monitor the presence of the covert signal. A public link interference mechanism is designed to monitor the covert link. Using the transmission power of the NOMA public signal, the transmission power of the NOMA covert signal, the UAV's three-dimensional position, and the intelligent reflector's reflection coefficient matrix as variables, the maximum covert transmission rate is optimized under multiple constraints, including ensuring the communication performance of the public link and the covert link's concealment and decoding performance. This effectively improves the covert transmission rate of the UAV system based on a non-orthogonal multiple access intelligent reflector.

Description

Technical Field

[0001] This invention pertains to wireless communication technology, specifically to a smart reflective surface-assisted covert communication system for unmanned aerial vehicles based on non-orthogonal multiple access. Background Technology

[0002] With the rapid development of communication technology for Unmanned Aerial Vehicles (UAVs), their flexible deployment and high mobility have led to their widespread application in scenarios such as emergency communication, remote area coverage, and military reconnaissance. However, the limited spectrum resources for UAV airborne communication pose a significant challenge, and the broadcast nature of wireless networks can easily expose communications. To mitigate these risks, covert communication technology has gradually become an important research direction for ensuring the security of UAV communications. Its goal is to ensure reliable communication quality while making communication activities difficult for monitors to detect.

[0003] Intelligent Reflecting Surface (IRS) technology reconstructs the wireless propagation environment in a low-power, programmable manner, becoming an effective means to enhance stealth performance. Combined with the high mobility of UAVs, it can further dynamically construct stealthy transmission paths in different locations and environments, thereby effectively improving the flexibility and stealth of communication. With the growth in the number of users and the increasing demand for spectrum, how to improve transmission efficiency while ensuring stealth has become a new challenge.

[0004] Non-Orthogonal Multiple Access (NOMA) technology, by multiplexing multiple user signals on the same time-frequency resources, can effectively improve spectrum utilization and introduce a new dimension of interference control in covert communication. By combining NOMA with UAV and IRS systems, near-end users can receive covert information, while far-end users receive common signals, thus creating legitimate interference. This can effectively confuse eavesdroppers and enhance the covertness of communication links.

[0005] Patent CN115002800A discloses a UAV-assisted NOMA backscatter communication system and a rate maximization method. Patent CN116600396B discloses a reconfigurable smart surface-assisted nonorthogonal multiple access network resource allocation method, but it does not introduce a monitor detection model in the system modeling, thus failing to consider the issue of communication link concealment protection. Patent CN119519768B discloses a device-to-device concealed communication system and method jointly assisted by UAV and smart reflector, which considers the joint concealed communication model of UAV and IRS, but does not consider the impact of NOMA technology power allocation and cooperative interference mechanism on concealment in multi-user scenarios, taking into account both interference effectiveness and communication concealment.

[0006] Therefore, this study investigates a covert communication method and system for unmanned aerial vehicles (UAVs) based on non-orthogonal multiple access (NOTA) intelligent reflector-assisted UAVs, while ensuring the transmission power of the covert signal is within acceptable limits. Transmission power of public signals 3D positioning of the Alice drone and IRS reflection coefficient matrix As variables, it is necessary and feasible to construct a joint optimization problem to calculate the maximum covert transmission rate under seven constraints, including the communication performance of the public link, the covert link's concealment and decoding performance, the transmission power of the covert signal and the public signal, the IRS reflection coefficient matrix, and the UAV Alice altitude. Summary of the Invention

[0007] Purpose of the Invention: The purpose of this invention is to address the shortcomings of existing technologies and provide a non-orthogonal multiple access-based intelligent reflector-assisted UAV covert communication system. This invention uses the minimum error detection probability to measure the system's covertness constraints. Under the seven constraints of simultaneously satisfying the communication performance of the common link, the covert link's covert and decoding performance, the transmission power of the covert and common signals, the IRS reflection coefficient matrix, and the UAV Alice altitude, joint optimization obtains the maximum covert transmission rate. Under the non-orthogonal multiple access-based intelligent reflector-assisted UAV covert communication, the system's covert transmission rate is effectively improved.

[0008] Technical Solution: This invention discloses a non-orthogonal multiple access (NOMA) intelligent reflector-assisted covert communication system for unmanned aerial vehicles (UAVs), comprising the following entities: a NOMA transmitter UAV Alice, a relay device IRS installed on a high-rise building, NOMA receivers Bob and Carol, and an eavesdropper Willie; Under the monitoring of the eavesdropper Willie, UAV Alice transmits a combined signal consisting of a common signal and a covert signal to the IRS; the IRS reflects the combined signal from UAV Alice to NOMA receivers Bob and Carol, where NOMA receiver Bob aims to receive the covert signal, and NOMA receiver Carol aims to receive the common signal; the eavesdropper Willie monitors the existence of the covert signal between UAV Alice and NOMA receiver Bob via the IRS; and the common signal is designed to interfere with the monitoring of the eavesdropper Willie.

[0009] There are three types of links between the entities: the covert link UAV→IRS→Bob between IRS-assisted drone Alice and NOMA receiver Bob, NOMA receiver Carol and eavesdropper Willie; the public link UAV→IRS→Carol; and the monitoring link UAV→IRS→Willie.

[0010] The covert link UAV→IRS→Bob is used to transmit covert signals. The eavesdropper Willie monitors the transmission of covert signals through the monitoring link UAV→IRS→Willie. The common link UAV→IRS→Carol is designed to interfere with the eavesdropper Willie's monitoring.

[0011] Furthermore, Bob, the NOMA receiver, is a NOMA near-user, and Carol, the NOMA receiver, is a NOMA far-user; Willie, the eavesdropper, is a power-type detector; Alice, the drone, flies above a tall building; Alice, Bob, Carol, and Willie, the eavesdropper, all have single antennas.

[0012] IRS equipped There are 10 reflective elements; the IRS reflection coefficient matrix of each reflective element is... ;

[0013] ;

[0014] j is represented by a complex number. It is the reflection amplitude coefficient. It is the first Phase shift of each IRS reflective element, , ;

[0015] Then consider a more severe eavesdropping scenario: when both the detection threshold of the eavesdropper Willie and the channel state information of the monitoring link are unknown, the minimum false detection probability is used. To measure system stealth and ensure higher security for covert links, this is where... Calculate the concealment constraints of the covert link UAV→IRS→Bob, where It is a hidden tolerance factor. ;

[0016] Finally, the transmission power of the concealed signal... Transmission power of public signals 3D positioning of the Alice drone and IRS reflection coefficient matrix As variables, the maximum covert transmission rate is obtained by optimization under seven constraints: communication performance of the public link, covert link covert performance, transmission power of covert and public signals, IRS reflection coefficient matrix, and UAV Alice altitude.

[0017] Furthermore, the construction process of the covert link UAV→IRS→Bob, the public link UAV→IRS→Carol, and the monitoring link UAV→IRS→Willie is as follows:

[0018] The transmission power of the Alice drone's combined signal is The transmission power of the concealed signal is The transmission power of the public signal is ,satisfy ;

[0019] NOMA receiver Carol directly decodes the public signal reflected by the IRS to obtain the communication performance constraints of the public link. ;

[0020] The NOMA receiver, Bob, first decodes and removes common signal interference using serial interference cancellation to obtain the decoding performance constraints of the covert link. Then decode the hidden signal reflected by the IRS;

[0021] in For NOMA receiver Carol, the signal-to-interference ratio This is the equivalent concatenation path for the common link UAV→IRS→Carol. For Carol, the NOMA receiver, the received noise power is... For Bob, the NOMA receiver, decoding the signal-to-interference ratio (SIR) of Carol's signal from the NOMA receiver. This is the equivalent concatenated channel for the covert link UAV→IRS→Bob. This represents the received noise power of Bob, the NOMA receiver. The minimum signal-to-interference ratio threshold is used; the eavesdropper Willie performs detection on the received signal to determine if the hidden signal exists;

[0022] Among them, channel The model is as follows:

[0023] ;

[0024] In the above formula, This is an index variable used to identify the corresponding signal propagation path between different entities. For the drone Alice, For IRS, Bob, the NOMA receiver, For NOMA receiver Carol, For the eavesdropper Willie, It is the signal propagation path between the drone Alice and the IRS. It is the signal propagation path between the IRS and the NOMA receiver Bob. It is the signal propagation path between the IRS and the NOMA receiver Carol. It is the signal propagation path between the IRS and the eavesdropper Willie; yes The three-dimensional Euclidean distance; For reference distance Path loss at point m; It is the path loss index; It is the Rice factor; Corresponding to the line-of-sight component, The corresponding non-line-of-sight components are independent of each other and follow the same distribution.

[0025] Furthermore, the minimum false detection probability is adopted. To measure the system's concealment in more severe eavesdropping environments, calculate the concealment constraints of the covert link UAV→IRS→Bob. The detailed method is as follows:

[0026] First, calculate the false detection probability. ;

[0027] For Willie, the eavesdropper, the false alarm probability. The probability of Willie, the eavesdropper, being missed;

[0028] ;

[0029] ;

[0030] in, This refers to the probability of the corresponding event occurring; It is the received noise power of the eavesdropper Willie. It is the detection threshold for the eavesdropper Willie. It is the equivalent concatenated channel of the monitoring link UAV→IRS→Willie;

[0031] Then, calculate as follows:

[0032] ;

[0033] in, It is the average noise power of the eavesdropper Willie. Quantifying the noise uncertainty range of the eavesdropper Willie;

[0034] Next, the minimum detection error probability is calculated. as follows:

[0035] ;

[0036] Finally, Substitution The transformation yields hidden constraints. .

[0037] Furthermore, with the transmission power of the concealed signal Transmission power of public signals 3D positioning of the Alice drone and IRS reflection coefficient matrix As variables, under seven constraints—communication performance of the public link, concealment and decoding performance of the covert link, transmission power of the covert and public signals, IRS reflection coefficient matrix, and the altitude of the UAV Alice—a joint optimization problem is constructed to calculate the maximum covert transmission rate, as follows:

[0038] ;

[0039] ,

[0040] ,

[0041] ,

[0042] ,

[0043] ,

[0044] ,

[0045] ,

[0046] in, This is the actual altitude at which the drone Alice flies. This is the minimum altitude at which the drone Alice can fly. This is the maximum altitude at which the Alice drone flies.

[0047] The above is a multivariable coupled, non-convex optimization problem, which is difficult to solve directly. Further design incorporates techniques such as semidefinite relaxation, Gaussian randomization, and successive convex approximation to transform the original non-convex problem into a solvable convex problem. The specific steps are as follows:

[0048] Step (1): Use semi-definite relaxation techniques to transform problem P1 into problem P1.1:

[0049]

[0050]

[0051]

[0052]

[0053]

[0054] ,

[0055] ,

[0056]

[0057] in, The reflection coefficient matrix of the IRS after processing with semidefinite relaxation technique (the reflection coefficient matrix is ​​a defined variable, and passive beamforming is the optimization objective). This is the equivalent concatenated channel for the covert link UAV→IRS→Bob. For the trace operation of a matrix, To monitor the equivalent concatenated channel of the link UAV→IRS→Willie, This is the equivalent concatenated channel for the common link UAV→IRS→Carol. For matrix The diagonal elements, It is a column vector of dimension M, consisting entirely of one elements. For matrix The rank of the IRS reflection coefficient matrix is ​​determined by using Gaussian randomization to solve problem P1.1, yielding the optimal solution. ;

[0058] Step (2): Optimal solution based on the IRS reflection coefficient matrix obtained in step (1) Furthermore, problem P1.1 is transformed into problem P1.2:

[0059]

[0060]

[0061]

[0062]

[0063] The CVX optimization tool was used to solve problem P1.2, yielding the optimal solution for the transmission power of the concealed signal. Optimal solution for the transmission power of the common signal .

[0064] Step (3): Optimal solution based on the IRS reflection coefficient matrix obtained in step (1) The optimal solution for the covert signal transmission power obtained in step (2) Optimal solution for the transmission power of the common signal By using the successive convex approximation technique, problem P1.2 is transformed into problem P1.3:

[0065]

[0066]

[0067]

[0068]

[0069]

[0070] in, This represents the small-scale fading vector between the UAV Alice and the IRS. This is the conjugate transpose of the small-scale fading vector between the IRS and the eavesdropper Willie. It is the conjugate transpose of the small-scale fading vector between the IRS and the NOMA receiver Carol. It is the conjugate transpose of the small-scale fading vector between the IRS and the NOMA receiver Carol. The optimal solution for the three-dimensional position of the UAV Alice is obtained by iteratively solving problem P1.3. Ultimately obtained The optimal solution is thus obtained to achieve the maximum covert transmission rate.

[0071] Beneficial Effects: This invention optimizes the maximum covert transmission rate of the UAV→IRS→Bob covert link under multiple constraints, including the communication performance of the public link, the concealment and decoding performance of the covert link, the transmission power of the covert and public signals, the IRS reflection coefficient matrix, and the altitude of the UAV Alice. Furthermore, it effectively improves the system's communication performance through intelligent reflector-assisted UAV covert communication based on non-orthogonal multiple access. Specific advantages are as follows:

[0072] 1. The drone Alice of this invention sends a combined signal to NOMA receivers Bob and Carol via IRS under the monitoring of eavesdropper Willie. The relay device IRS reflects the combined signal to NOMA receivers Bob and Carol by changing the angle of the combined signal through a reflective element. The NOMA receiver Carol is designed to interfere with the monitoring of eavesdropper Willie.

[0073] 2. This invention considers more severe eavesdropping scenarios: when both the eavesdropper's detection threshold and the channel state information of the monitoring link are unknown, the minimum false detection probability is used. Measure the system's stealth to ensure that the hidden links have higher security.

[0074] 3. In optimizing the acquisition of the maximum covert transmission rate, this invention uses the transmission power of the covert signal. Transmission power of public signals 3D positioning of the Alice drone and IRS reflection coefficient matrix As variables, a joint optimization problem is constructed under seven constraints: the communication performance of the public link, the concealment and decoding performance of the covert link, the transmission power of the covert signal and the public signal, the IRS reflection coefficient matrix, and the altitude of the UAV Alice. The maximum covert transmission rate is then calculated.

[0075] 4. This invention can effectively improve the covert transmission rate of the UAV based on non-orthogonal multiple access intelligent reflective surface-assisted covert communication system, achieve a balance between covertness and rate, and has the triple advantages of cooperative interference, reconfigurable transmission path and flexible deployment. Attached Figure Description

[0076] Figure 1 This is a schematic diagram of the overall covert communication model of the present invention.

[0077] Figure 2 This is the overall flowchart of the present invention.

[0078] Figure 3 This is a flowchart of the minimum error detection probability calculation for the eavesdropper Willie in this invention.

[0079] Figure 4 This is a flowchart of the signal-to-interference ratio calculation for NOMA receivers Bob and Carol in this invention.

[0080] Figure 5 This is a flowchart of the covert transmission rate calculation of the present invention.

[0081] Figure 6 The x-axis represents the different eavesdroppers Willie in the embodiments. Simulation diagram showing the relationship between covert transmission rate and the altitude of different UAVs (Alice).

[0082] Figure 7 This is a simulation diagram showing the relationship between the concealed transmission rate and the number of IRS reflective elements in this invention.

[0083] Figure 8 This is a comparative simulation diagram of the covert communication scheme of the present invention. Detailed Implementation

[0084] The technical solution of the present invention will be described in detail below, but the scope of protection of the present invention is not limited to the embodiments.

[0085] like Figure 1 and Figure 2 As shown, the present invention relates to a non-orthogonal multiple access (NOMA)-assisted intelligent reflector-assisted covert communication system for unmanned aerial vehicles (UAVs), comprising the following entities: a NOMA transmitter UAV Alice, a relay device IRS installed on a high-rise building, NOMA receivers Bob and Carol, and an eavesdropper Willie; under the monitoring of the eavesdropper Willie, UAV Alice transmits a combined signal consisting of a common signal and a covert signal to the IRS; the IRS reflects the combined signal from UAV Alice to NOMA receivers Bob and Carol; NOMA receiver Bob aims to receive the covert signal; NOMA receiver Carol aims to receive the common signal; the eavesdropper Willie monitors the existence of the covert signal between UAV Alice and NOMA receiver Bob via the IRS; and the common signal is designed to interfere with the monitoring of the eavesdropper Willie.

[0086] There are three types of links between the entities, mainly including: the covert link UAV→IRS→Bob between IRS-assisted drone Alice and NOMA receiver Bob, NOMA receiver Carol and eavesdropper Willie; the public link UAV→IRS→Carol; and the monitoring link UAV→IRS→Willie.

[0087] The covert link UAV→IRS→Bob is used to transmit covert signals. The eavesdropper Willie monitors the transmission of covert signals through the monitoring link UAV→IRS→Willie. The common link UAV→IRS→Carol is designed to interfere with the eavesdropper Willie's monitoring.

[0088] Among them, Bob, the NOMA receiver, is a NOMA near user, and Carol, the NOMA receiver, is a NOMA far user; Willie, the eavesdropper, is a power detector; Alice, the drone, flies above a tall building; Alice, Bob, Carol, and Willie, the eavesdropper, all have single antennas.

[0089] IRS equipped There are 10 reflective elements; the IRS reflection coefficient matrix of each reflective element is... ;

[0090] ;

[0091] j is represented by a complex number. It is the reflection amplitude coefficient. It is the first Phase shift of each IRS reflective element, , ;

[0092] Then consider a more severe eavesdropping scenario: when both the detection threshold of the eavesdropper Willie and the channel state information of the monitoring link are unknown, the minimum false detection probability is used. To measure system stealth and ensure higher security for covert links, this is where... Calculate the concealment constraints of the covert link UAV→IRS→Bob, where It is a hidden tolerance factor. ;

[0093] Finally, the transmission power of the concealed signal... Transmission power of public signals 3D positioning of the Alice drone and IRS reflection coefficient matrix As variables, the maximum covert transmission rate is obtained by optimization under seven constraints: communication performance of the public link, covert link covert performance, transmission power of covert and public signals, IRS reflection coefficient matrix, and UAV Alice altitude.

[0094] The relationships between the communication links in this embodiment are as follows: Figure 1 As shown, all communication links follow Rician fading. The channels and distances between the entities are represented as follows: the distance between the UAV Alice and the IRS corresponds to... and The IRS and the NOMA receiver Bob correspond to respectively and The IRS and NOMA receiver Carol correspond to respectively and The IRS and the eavesdropper Willie correspond to each other. and .

[0095] The specific implementation process of the above-mentioned intelligent reflective surface-assisted UAV covert communication system based on non-orthogonal multiple access is completed in the following steps.

[0096] Step 1: Combining Figure 1 and Figure 2The process of constructing the covert link UAV→IRS→Bob, the common link UAV→IRS→Carol, and the monitoring link UAV→IRS→Willie for intelligent reflective surface-assisted UAV covert communication based on non-orthogonal multiple access is as follows:

[0097] In this embodiment, the transmission power of the Alice combined signal from the UAV is... The transmission power of the concealed signal is The transmission power of the public signal is ,satisfy The NOMA receiver, Carol, directly decodes the public signal reflected by the IRS to obtain the communication performance constraints of the public link. The NOMA receiver, Bob, first decodes and removes common signal interference using serial interference cancellation to obtain the decoding performance constraints of the covert link. Then, the hidden signal reflected by the IRS is decoded. For NOMA receiver Carol, the signal-to-interference ratio This is the equivalent concatenation path for the common link UAV→IRS→Carol. For Carol, the NOMA receiver, the received noise power is... For Bob, the NOMA receiver, decoding the signal-to-interference ratio (SIR) of Carol's signal from the NOMA receiver. This is the equivalent concatenated channel for the covert link UAV→IRS→Bob. This represents the received noise power of Bob, the NOMA receiver. The minimum signal-to-interference ratio threshold is used; the eavesdropper Willie performs detection on the received signal to determine if the hidden signal exists.

[0098] Among them, channel The model is as follows:

[0099] ;

[0100] In the above formula, This is an index variable used to identify the corresponding signal propagation path between different entities. For the drone Alice, For IRS, Bob, the NOMA receiver, For NOMA receiver Carol, For the eavesdropper Willie, It is the signal propagation path between the drone Alice and the IRS. It is the signal propagation path between the IRS and the NOMA receiver Bob. It is the signal propagation path between the IRS and the NOMA receiver Carol. It is the signal propagation path between the IRS and the eavesdropper Willie; yes The three-dimensional Euclidean distance; For reference distance Path loss at point m; It is the path loss index; It is the Rice factor; Corresponding to the line-of-sight component, The corresponding non-line-of-sight components are independent of each other and follow the same distribution.

[0101] Step Two, Combining Figure 3 The minimum error detection probability is used in the middle. To measure the system's concealment in more severe eavesdropping environments, calculate the concealment constraints of the covert link UAV→IRS→Bob. The detailed method is as follows:

[0102] First, calculate the false detection probability. ;

[0103] For Willie, the eavesdropper, the false alarm probability. The probability of Willie, the eavesdropper, being missed;

[0104] ;

[0105] ;

[0106] in, This refers to the probability of the corresponding event occurring; It is the received noise power of the eavesdropper Willie. It is the detection threshold for the eavesdropper Willie. It is the equivalent concatenated channel of the monitoring link UAV→IRS→Willie;

[0107] Then, calculate as follows:

[0108] ;

[0109] in, It is the average noise power of the eavesdropper Willie. Quantifying the noise uncertainty range of the eavesdropper Willie;

[0110] Next, the minimum detection error probability is calculated. as follows:

[0111] ;

[0112] Finally, Substitution The transformation yields hidden constraints. .

[0113] Step 3, Combining Figure 2 , Figure 3 , Figure 4 and Figure 5 Complete the transmission power of the concealed signal. Transmission power of public signals 3D positioning of the Alice drone and IRS reflection coefficient matrix As variables, under seven constraints—communication performance of the public link, concealment and decoding performance of the covert link, transmission power of the covert and public signals, IRS reflection coefficient matrix, and the altitude of the UAV Alice—a joint optimization problem is constructed to calculate the maximum covert transmission rate, as follows:

[0114]

[0115] ,

[0116] ,

[0117] ,

[0118] ,

[0119] ,

[0120] ,

[0121] ,

[0122] in, This is the actual altitude at which the drone Alice flies. This is the minimum altitude at which the drone Alice can fly. This is the maximum altitude at which the drone Alice flies; the above is a multivariable coupled, non-convex optimization problem, which is difficult to solve directly.

[0123] This embodiment combines semidefinite relaxation techniques, Gaussian randomization methods, and successive convex approximation techniques to transform the original non-convex problem into a solvable convex problem. The specific steps are as follows:

[0124] Step (1): Use semi-definite relaxation techniques to transform problem P1 into problem P1.1:

[0125]

[0126]

[0127]

[0128]

[0129]

[0130] ,

[0131] ,

[0132]

[0133] in, The IRS reflection coefficient matrix is ​​obtained after processing with the positive semidefinite relaxation technique. This is the equivalent concatenated channel for the covert link UAV→IRS→Bob. For the trace operation of a matrix, To monitor the equivalent concatenated channel of the link UAV→IRS→Willie, This is the equivalent concatenated channel for the common link UAV→IRS→Carol. For matrix The diagonal elements, It is a column vector of dimension M, consisting entirely of one elements. For matrix The rank of the IRS reflection coefficient matrix is ​​determined by using Gaussian randomization to solve problem P1.1, yielding the optimal solution. .

[0134] Step (2): Optimal solution based on the IRS reflection coefficient matrix obtained in step (1) Furthermore, problem P1.1 is transformed into problem P1.2:

[0135]

[0136]

[0137]

[0138]

[0139] The CVX optimization tool was used to solve problem P1.2, and the optimal solution for the transmission power of the concealed signal was obtained. Optimal solution for the transmission power of the common signal .

[0140] Step (3): Optimal solution based on the IRS reflection coefficient matrix obtained in step (1) The optimal solution for the covert signal transmission power obtained in step (2) Optimal solution for the transmission power of the common signal By using the successive convex approximation technique, problem P1.2 is transformed into problem P1.3:

[0141]

[0142]

[0143]

[0144]

[0145]

[0146] in, This represents the small-scale fading vector between the UAV Alice and the IRS. This is the conjugate transpose of the small-scale fading vector between the IRS and the eavesdropper Willie. It is the conjugate transpose of the small-scale fading vector between the IRS and the NOMA receiver Carol. It is the conjugate transpose of the small-scale fading vector between the IRS and the NOMA receiver Carol. The optimal solution for the three-dimensional position of the UAV Alice is obtained by iteratively solving problem P1.3. .

[0147] Finally obtained The optimal solution is thus obtained to achieve the maximum covert transmission rate.

[0148] Example: To verify the performance of the method proposed in this invention, the following simulation experiments were conducted: Figures 6 to 8 As shown, the simulation environment is as follows: (Settings...) Path loss index .

[0149] Figure 6 Analysis of the horizontal position of different eavesdroppers Willie Alice altitude for different drones The impact of the covert transmission rate, where the eavesdropper Willie's vertical position Fixed at 10 meters. From Figure 6The conclusion is that the covert transmission rate exhibits a trend of first increasing and then decreasing, indicating the existence of an optimal drone altitude (Alice) that improves the quality of the covert link communication while limiting the monitoring capabilities of the eavesdropper, Willie. When the drone's altitude is too low, both the covert link and the public link are severely obstructed, resulting in insufficient signal transmission performance. When the altitude is too high, path loss increases, reducing the receiving gain of NOMA receivers Bob and Carol, and consequently decreasing the covert transmission rate. Furthermore, different... The curves show that the horizontal distance between the eavesdropper Willie and the drone Alice affects the coordinates and magnitude of the optimal point.

[0150] Figure 7 This demonstrates the relationship between the covert transmission rate and the number of IRS reflective elements at different heights and transmission powers, based on a closed-form analytical expression. The covert transmission rate first increases and then tends to saturate as the number of IRS reflective elements increases. Figure 7 This provides a practical upper limit for system performance and indicates the choice of the number of IRS reflective elements and the power configuration of the UAV Alice.

[0151] Figure 8 By comparing the performance of the technical solution of this invention with other solutions, the concealed transmission rate is analyzed as a function of different transmission powers. The changing pattern. Scheme 1 is the scheme of this invention. Existing scheme 1 is a concealment scheme based on this invention, introducing an IRS reflection random phase mechanism. Existing scheme 2 uses the random phase mechanism in this invention. Covert communication is achieved through concealed constraints. Existing technical solution 3 uses the random constraint mechanism of this invention. The covert constraints achieve covert communication, but a NOMA protocol between the sender and receiver is not used; all schemes include a covert tolerance factor. Set it to 0.1. For example... Figure 8 As shown, concealment performance varies with It increases with each increase, reaches its optimal value, and then gradually decreases. The maximum value appears at... =56dBm.

[0152] Specifically, Scheme 1 maintains the highest rate across the entire power range; while Scheme 2 is slightly lower than Scheme 1, it is still superior to both Scheme 1 and Scheme 3. Scheme 3 closely matches Scheme 1 in the low-power range, but falls rapidly behind in the medium-to-high power range, as its random phase cannot achieve directional reflection to effectively focus energy; Scheme 3 is the weakest performer, consistently ranking last across the entire range.

[0153] Figure 6 , Figure 7 and Figure 8Simulation results show that the present invention achieves this by using the transmission power of the concealed signal. Transmission power of public signals 3D positioning of the Alice drone and IRS reflection coefficient matrix As variables, a joint optimization problem is constructed under seven constraints: the communication performance of the public link, the concealment and decoding performance of the concealed link, the transmission power of the concealed signal and the public signal, the IRS reflection coefficient matrix, and the altitude of the UAV Alice. The maximum concealed transmission rate is then calculated. The concealed transmission rate of the UAV system assisted by a non-orthogonal multiple access intelligent reflector is effectively improved.

Claims

1. A covert communication system for unmanned aerial vehicles (UAVs) based on a non-orthogonal multiple access intelligent reflector surface, characterized in that, The following entities are involved: Alice, a NOMA transmitter drone; IRS, a relay device installed on a high-rise building; Bob and Carol, NOMA receivers; and Willie, an eavesdropper. Alice, under the monitoring of Willie, transmits a combined signal consisting of a public signal and a covert signal to the IRS. The IRS reflects Alice's combined signal to Bob and Carol, NOMA receivers. Bob receives the covert signal, Carol receives the public signal, and Willie monitors the existence of the covert signal between Alice and Bob via the IRS. The public signal is designed to interfere with Willie's monitoring. There are three types of links between the entities: the covert link UAV→IRS→Bob between IRS-assisted drone Alice and NOMA receiver Bob, NOMA receiver Carol and eavesdropper Willie; the public link UAV→IRS→Carol; and the monitoring link UAV→IRS→Willie. The covert link UAV→IRS→Bob is used to transmit covert signals. The eavesdropper Willie monitors the transmission of covert signals through the monitoring link UAV→IRS→Willie. The common link UAV→IRS→Carol is designed to interfere with the eavesdropper Willie's monitoring. Considering a severe eavesdropping scenario: when both the detection threshold of the eavesdropper Willie and the channel state information of the monitoring link are unknown, the minimum false detection probability is used. To measure the stealth of a system, at this point, we use Calculate the concealment constraints of the covert link UAV→IRS→Bob, where It is a hidden tolerance factor. ; Finally, the transmission power of the concealed signal... Transmission power of public signals 3D positioning of the Alice drone and IRS reflection coefficient matrix As variables, the maximum covert transmission rate is obtained by optimization under seven constraints: communication performance of the public link, covert link covert performance, transmission power of covert and public signals, IRS reflection coefficient matrix, and UAV Alice altitude. The construction process of the covert link UAV→IRS→Bob, the public link UAV→IRS→Carol, and the monitoring link UAV→IRS→Willie is as follows: The transmission power of the Alice drone's combined signal is The transmission power of the concealed signal is The transmission power of the public signal is ,satisfy ; The NOMA receiver, Carol, directly decodes the public signal reflected by the IRS to obtain the communication performance constraints of the public link. ; The NOMA receiver, Bob, first decodes and removes common signal interference using serial interference cancellation to obtain the decoding performance constraints of the covert link. Then decode the hidden signal reflected by the IRS; in For NOMA receiver Carol, the signal-to-interference ratio For Bob, the NOMA receiver, decoding the signal-to-interference ratio (SIR) of Carol's signal from NOMA receiver. The minimum signal-to-interference ratio threshold is used; the eavesdropper Willie performs detection on the received signal to determine whether the covert signal exists; Among them, channel The model is as follows: ； In the above formula, This is an index variable used to identify the corresponding signal propagation path between different entities. For the drone Alice, For IRS, Bob, the NOMA receiver, For NOMA receiver Carol, For the eavesdropper Willie, It is the signal propagation path between the drone Alice and the IRS. It is the signal propagation path between the IRS and the NOMA receiver Bob. It is the signal propagation path between the IRS and the NOMA receiver Carol. It is the signal propagation path between the IRS and the eavesdropper Willie; yes The three-dimensional Euclidean distance; For reference distance Path loss at point m; It is the path loss index; It is the Rice factor; Corresponding to the line-of-sight component, The corresponding non-line-of-sight components are independent of each other and follow the same distribution.

2. The intelligent reflective surface-assisted covert communication system for unmanned aerial vehicles based on non-orthogonal multiple access as described in claim 1, characterized in that, The NOMA receiver Bob is a NOMA near user, the NOMA receiver Carol is a NOMA far user, the eavesdropper Willie is a power detector, the drone Alice flies above a tall building, and the drone Alice, the NOMA receiver Bob, the NOMA receiver Carol, and the eavesdropper Willie are all single antennas. The IRS is equipped with There are 10 reflective elements; the IRS reflection coefficient matrix of each reflective element is... ; ； In the above formula, j is represented by a complex number. It is the reflection amplitude coefficient. It is the first Phase shift of each IRS reflective element, , .

3. The intelligent reflective surface-assisted covert communication system for unmanned aerial vehicles based on non-orthogonal multiple access as described in claim 1, characterized in that, Using the minimum error detection probability To measure the system's concealment in more severe eavesdropping environments, calculate the concealment constraints of the covert link UAV→IRS→Bob. The detailed method is as follows: First, calculate the error detection probability. ; For Willie, the eavesdropper, the false alarm probability. The probability of Willie, the eavesdropper, being missed; ； ； in, This refers to the probability of the corresponding event occurring; It is the received noise power of the eavesdropper Willie. It is the detection threshold for the eavesdropper Willie. It is the equivalent concatenated channel of the monitoring link UAV→IRS→Willie; Then, calculate as follows: ； in, It is the average noise power of the eavesdropper Willie. Quantifying the noise uncertainty range of the eavesdropper Willie; Next, the minimum detection error probability is calculated. as follows: ； Finally, Substitution The transformation yields hidden constraints. .

4. The intelligent reflective surface-assisted covert communication system for unmanned aerial vehicles based on non-orthogonal multiple access as described in claim 3, characterized in that, With the power of the concealed signal transmission Transmission power of public signals 3D positioning of the Alice drone and IRS reflection coefficient matrix As variables, under seven constraints—communication performance of the public link, concealment and decoding performance of the covert link, transmission power of the covert and public signals, IRS reflection coefficient matrix, and the altitude of the UAV Alice—a joint optimization problem is constructed to calculate the maximum covert transmission rate, as follows: ； ； ； ； ； ； ； ； in, This is the actual altitude at which the drone Alice flies. This is the minimum altitude at which the drone Alice can fly. This is the maximum altitude at which the Alice drone flies.

5. The intelligent reflective surface-assisted covert communication system for unmanned aerial vehicles based on non-orthogonal multiple access as described in claim 4, characterized in that, Combining semidefinite relaxation techniques, Gaussian randomization methods, and successive convex approximation techniques to The original non-convex problem is transformed into a solvable convex problem. The specific steps are as follows: Step (1): Use semidefinite relaxation techniques to transform problem P1 into problem P1.1: ； ； ； ； ； ； ； ； in, The IRS reflection coefficient matrix is ​​obtained after processing with the positive semidefinite relaxation technique. This represents the received noise power of Bob at the NOMA receiver. The received noise power of the NOMA receiver Carol. For the equivalent concatenated channel of the covert link UAV→IRS→Bob, Tr() is the trace operation of the matrix. To monitor the equivalent concatenated channel of the link UAV→IRS→Willie, This is the equivalent concatenated channel for the common link UAV→IRS→Carol. For matrix The diagonal elements, It is a column vector of dimension M, consisting entirely of one elements. For matrix The rank of the IRS reflection coefficient matrix is ​​determined by using Gaussian randomization to solve problem P1.1, yielding the optimal solution. ; Step (2): Optimal solution based on the IRS reflection coefficient matrix obtained in step (1) Furthermore, problem P1.1 is transformed into problem P1.2: ； ； ； ； The CVX optimization tool was used to solve problem P1.2, yielding the optimal solution for the transmission power of the concealed signal. Optimal solution for the transmission power of the common signal ; Step (3): Optimal solution based on the IRS reflection coefficient matrix obtained in step (1) The optimal solution for the covert signal transmission power obtained in step (2) Optimal solution for the transmission power of the common signal By using the successive convex approximation technique, problem P1.2 is transformed into problem P1.3: ； ； ； ； ； in, This represents the small-scale fading vector between the UAV Alice and the IRS. This is the conjugate transpose of the small-scale fading vector between the IRS and the eavesdropper Willie. It is the conjugate transpose of the small-scale fading vector between the IRS and the NOMA receiver Carol. It is the conjugate transpose of the small-scale fading vector between the IRS and the NOMA receiver Carol; The optimal solution for the three-dimensional position of the UAV Alice is obtained by iteratively solving problem P1.

3. ; Finally obtained The optimal solution is thus obtained to achieve the maximum covert transmission rate.

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