A secure transmission method for UAV ground-to-air communication system assisted by intelligent reflective surface

Abstract

The present invention relates to a secure transmission method for an unmanned aerial vehicle ground-to-air communication system assisted by an intelligent reflective surface. In the present invention, for a ground-to-air UAV communication system with multiple aerial eavesdroppers, the wireless propagation environment is cleverly reconstructed by deploying an intelligent reflective surface IRS on the facade of a building, thereby enhancing the signal strength at the legitimate user and reducing the signal strength at the eavesdropper. The transmit power, active and passive beamforming, and UAV three-dimensional trajectory are jointly optimized to maximize the average confidentiality rate. Due to the non-convexity of the optimization problem, the overall optimization problem is divided into four sub-problems and iterated using the block coordinate descent method. The non-convex sub-problems are solved using continuous convex approximation and semi-positive definite relaxation. Compared with the method without IRS assistance, the present invention significantly improves the physical layer security performance of the UAV communication system.

Description

Technical Field

[0001] The present invention belongs to the technical field of wireless communications, and in particular relates to a secure transmission method for a ground-to-air communication system of an unmanned aerial vehicle (UAV) assisted by an intelligent reflective surface. Background Art

[0002] In recent years, with the rapid growth in demand for mobile communications services, traditional fixed infrastructure communication networks can no longer meet people's growing communication needs. Consequently, unmanned aerial vehicles (UAVs) have been widely used in wireless communications due to their high maneuverability, low cost, on-demand deployment, and line-of-sight channels. While these characteristics make UAVs promising for future applications, they also make them vulnerable to malicious eavesdroppers, leading to the leakage of confidential information. Therefore, security performance is an essential consideration in UAV communication systems. Traditional upper-layer encryption technologies require high computational complexity and are unsuitable for energy-constrained UAV platforms. In particular, the emergence of powerful computing platforms such as quantum computers has significantly reduced the efficiency of upper-layer encryption technologies.

[0003] As a supplement to encryption algorithms, physical layer security (PLS) technology has lower computational complexity and energy consumption. By combining physical layer security technology with drone communications, it is possible to protect the secure information transmission of drone communication systems from an information theory perspective, utilizing the dynamic physical characteristics of the transmission channel and combining coding and transmission scheme design. For drone communication systems with relatively low security threats, secure communication can be achieved by applying methods such as beamforming, power control, or trajectory planning. When legitimate channels face more serious security threats, technologies such as artificial noise and cooperative relaying can further improve confidentiality performance, but this will consume more transmission power and require additional RF chain costs.

[0004] Therefore, the present invention considers introducing (Intelligent Reflecting Surface, IRS) into the design of the UAV secure communication system. As one of the most promising disruptive technologies in future 6G communications, IRS has the advantages of low cost, low energy consumption and large-scale deployment. A large number of low-cost passive reflection units can be integrated on the IRS, and the incident signal is adjusted in amplitude and phase by an intelligent controller before reflection, which can achieve fine passive 3D beamforming. Therefore, the IRS can be used to appropriately modify the wireless propagation environment, and by superimposing its reflected signal with the signal from other paths, the desired signal can be enhanced or the undesired signal can be suppressed. Therefore, it is urgent to invent a UAV communication system secure transmission method that can utilize IRS technology and combine other security measures to resist multiple aerial eavesdroppers with line-of-sight link advantages in a power-constrained UAV system. Summary of the Invention

[0005] Technical problems to be solved

[0006] In order to avoid the shortcomings of the prior art, the present invention proposes a secure transmission method for a UAV ground-to-air communication system assisted by an intelligent reflective surface.

[0007] Technical Solution

[0008] A method for secure transmission of a UAV ground-to-air communication system assisted by an intelligent reflective surface, characterized by the following steps:

[0009] Step 1: Assume that the IRS-UAV ground-to-air communication system consists of a ground base station, an IRS deployed on a building facade, an aerial UAV user, and multiple UAV eavesdroppers hovering in the air. The channels in this communication system all follow the Rician fading model. In addition to the conventional line-of-sight link and scattering link between the ground base station and the aerial node, there is also a reflection link between the ground base station, the IRS, and the UAV. The ground base station is equipped with multiple antennas, the IRS array is equipped with multiple reflection units, and all other nodes in the system are equipped with single antennas.

[0010] Step 2: Within a fixed time duration, T, the UAV user performs a three-dimensional maneuver from the starting point to the destination point. For ease of processing, T is divided into N time blocks of equal length. Subject to satisfying constraints, four transmission methods are jointly adopted within each time block to counteract eavesdroppers: First, the ground base station uses beamforming technology to focus the encrypted signal beam toward the UAV user and the IRS. Second, the ground base station controls its transmit power, increasing it when the legitimate channel dominates and reducing or setting it to zero when the eavesdropping channel dominates. Third, the IRS controller intelligently controls the IRS phase shift matrix in real time, i.e., passive beamforming, to focus the reflected signal toward the UAV user. Fourth, the UAV three-dimensional trajectory is planned, where the UAV actively approaches the ground base station and IRS during flight and moves away from the eavesdropper.

[0011] Step 3: The average confidentiality rate in this communication scenario is in the form of the subtraction of two logarithmic functions. Maximizing the average confidentiality rate is a non-convex NP-hard problem with strong coupling between variables, making it difficult to directly solve it to obtain the optimal transmission strategy. Therefore, a block coordinate descent (BCD) algorithm was developed. This algorithm decomposes the optimization problem into four subproblem blocks and iteratively optimizes and solves them in the direction of minimizing the confidentiality rate. Since each subproblem is solved in the coordinate descent direction, the convergence of the algorithm is guaranteed. The objective function of the average confidentiality rate optimization problem can be expressed as:

[0012]

[0013] Among them, R sec is the average confidentiality rate, is the set of K air eavesdroppers, R B [n] and R Ek [n] represents the receiving rate of the legitimate UAV at time n and the receiving rate of the kth eavesdropping UAV, respectively. P[n], Θ[n], and w[n] represent the transmit power, IRS phase shift matrix, and beamforming vector at time n, respectively. AB [n], H AR 、h RB [n], Represent the channel gain vector / matrix from base station to UAV user, base station to k-th eavesdropping UAV, base station to IRS, IRS to UAV user, and IRS to k-th eavesdropping UAV, respectively. represent the legitimate channel noise power and the eavesdropping channel noise power respectively.

[0014] Preferably, in step 3, for solving each non-convex subproblem, the semi-definite relaxation SDR and the continuous convex approximation SCA algorithms are used to obtain their suboptimal solutions.

[0015] A computer system, characterized in that it includes: one or more processors, and a computer-readable storage medium for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the above-mentioned method.

[0016] A computer-readable storage medium is characterized by storing computer-executable instructions, which are used to implement the above method when executed.

[0017] A computer program, characterized by comprising computer executable instructions, wherein the instructions are used to implement the above method when executed.

[0018] Beneficial effects

[0019] The present invention proposes a method for secure transmission of a UAV ground-to-air communication system assisted by an intelligent reflective surface. Compared with the existing UAV communication system secure transmission scheme, for ground-to-air UAV communication systems with multiple aerial eavesdroppers, the IRS is deployed on the facade of the building to cleverly reconstruct the wireless propagation environment, enhance the signal strength at the legitimate user and reduce the signal strength at the eavesdropper. The average confidentiality rate is maximized by jointly optimizing the transmit power, active and passive beamforming, and the three-dimensional trajectory of the UAV. Due to the non-convexity of the optimization problem, the overall optimization problem is divided into four sub-problems and iterated using the block coordinate descent method. The non-convex sub-problems are solved using continuous convex approximation and semi-positive relaxation. Compared with the method without IRS assistance, the present invention significantly improves the physical layer security performance of the UAV communication system. It has the following advantages:

[0020] (1) Fully save system energy and reduce hardware costs.

[0021] To address the more serious security threats in UAV communication systems, traditional secure communication technologies, such as artificial noise and cooperative relaying, require additional energy consumption and expensive radio frequency links. This invention introduces IRS, which improves system confidentiality without requiring additional energy consumption. IRS is also cost-effective to deploy, making it more suitable for large-scale deployment in future UAV communication networks with short wavelengths, high density, and high data rates.

[0022] (2) Improved transmission security.

[0023] By reflecting the signal off the IRS panel and superimposing it with the direct link signal, the signal strength at the legitimate UAV user is enhanced, while the signal strength at the eavesdropping UAV is weakened. Simultaneously, the combined use of beamforming, power control, and UAV trajectory planning techniques maximizes the overall confidentiality rate of the communication system. Theoretical analysis and simulation results demonstrate that this invention can achieve confidential transmission even when multiple aerial eavesdroppers have the advantage of a ground-to-air line-of-sight link. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] The accompanying drawings are only for the purpose of illustrating particular embodiments and are not to be considered limiting of the present invention. Like reference symbols denote like parts throughout the drawings.

[0025] Figure 1 This is a model diagram of the secure transmission of the UAV ground-to-air communication system assisted by the intelligent reflective surface proposed in the present invention under Ricean channel conditions.

[0026] Figure 2 FIG. 4 is a comparison chart of the convergence performance of the secure transmission method proposed in the present invention as the flight time T changes.

[0027] Figure 3It is the three-dimensional flight trajectory of the UAV user with and without IRS assistance using the secure transmission method proposed in the present invention as the flight time T changes.

[0028] Figure 4 This is a comparison chart of the average confidentiality rate that can be achieved by the secure transmission method proposed in the present invention and multiple benchmark methods as the number of reflective units on the IRS panel changes.

[0029] Figure 5 This is a comparison chart of the average confidentiality rate that can be achieved by the secure transmission method proposed in the present invention and multiple benchmark methods as the deployment height of the IRS panel changes. DETAILED DESCRIPTION

[0030] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are only intended to illustrate the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.

[0031] Reference Figure 1 Suppose that the IRS-UAV communication system consists of a ground base station BS, an IRS deployed on the facade of a building, a UAV user, and multiple hovering aerial eavesdroppers. In addition to the direct link between the base station and the aerial node, there is also a reflection link from the ground base station to the IRS to the aerial node. The ground base station is equipped with a uniform linear array with multiple elements, the IRS array is equipped with multiple reflection units, and the other nodes in the system are all equipped with a single antenna. Divide the flight time T into N time blocks of equal length, that is: T = Nδ t The system's optimal secure communication solution consists of the best solution for N time blocks, each of which contains four strategies: base station beamforming, transmitter power control, IRS phase shift matrix design, and UAV three-dimensional trajectory planning.

[0032] Definition: q0, q B [n] and q F z0, z[n] and z[n] represent the initial and final horizontal coordinates of the UAV user at time 0, time n and time N, respectively. F Represent the initial and final height coordinates of the UAV user at time 0, time n, and time N, respectively. Ek represents the horizontal coordinate of the kth aerial eavesdropper, z Ek represents the height coordinate of the kth aerial eavesdropper, D h 、D v and D min They represent the maximum horizontal and vertical displacement of the UAV user in a single time block and the minimum safety distance between UAVs, respectively.max and H min Represent the upper and lower limits of the UAV flight altitude, and P max Represent the average transmit power value and the maximum transmit power value of the base station respectively, w[n] is the beamforming vector of the L-element transmit linear array antenna, is the phase shift of the mth reflection unit in the M-element IRS array. Then, for the IRS-UAV ground-to-air communication system of the present invention, the optimization problem of the secure communication strategy can be specifically expressed as:

[0033]

[0034]

[0035] ||q B [1]-q0||≤D h ,q B [N] = q F (1b)

[0036]

[0037] |z[1]-z0|≤D v ,z[N]=z F (2b)

[0038]

[0039]

[0040]

[0041] 0≤P[n]≤P max (5b)

[0042] w H [n]w[n]≤P max (6)

[0043]

[0044] Constraints (1) to (7) represent the maximum horizontal displacement constraint of the UAV within a single time block, the maximum vertical displacement constraint of the UAV within a single time block, the UAV flight altitude constraint, the anti-collision distance constraint between the UAV user and the UAV eavesdropper, the transmission power constraint, the beamforming power constraint, and the IRS reflection coefficient constant modulus constraint, respectively.

[0045] For the non-convex optimization problem (P1) for this confidentiality rate, a block coordinate descent (BCD) algorithm was developed. This algorithm decomposes the optimization problem into four sub-problems, each of which optimizes only one variable while assuming the other optimization variables are fixed. (P2) Optimizes the transmit power P; (P3) Optimizes the beamforming vector w; (P4) Optimizes the IRS phase shift matrix Θ; and (P5) Optimizes the UAV trajectory Q.

[0046] In (P2), the optimization problem is expressed as:

[0047] (P2):

[0048]

[0049] 0≤P[n]≤P max

[0050] in, Although the problem is non-convex, its optimal solution can be derived as:

[0051]

[0052] in, μ is the guarantee The non-negative parameter of , the value of μ can be obtained through one-dimensional binary search.

[0053] In (P3), the optimization problem is expressed as:

[0054]

[0055] stw H [n]w[n]≤P max

[0056] The present invention uses the SDR algorithm to solve the non-convex problem. B By converting the square term in [n], we can get:

[0057]

[0058] in, Tr(·) is the trace of the matrix. However, the problem is still non-convex, so the slack variable ζ[n] is introduced to relax R Ek [n], and use the SCA algorithm to perform Taylor expansion on log2(1+ζ[n]), with the expansion point being ζ0[n]. Then the optimization problem (P3) can be finally expressed as the following convex problem:

[0059] (P3.1):

[0060]

[0061] Tr(W[n])≤P max

[0062]

[0063] The convexity problem in (P3.1) can be solved using the CVX toolbox. However, a rank-1 solution (rank(W[n])=1) may not be possible. To obtain a rank-1 solution, the present invention employs a Gaussian randomization method to recover the beamforming vector w[n] from W[n].

[0064] In (P4), after introducing the slack variable ξ[n], the optimization problem can be expressed as:

[0065] (P4):

[0066]

[0067]

[0068] Similar to the phase processing in (P3) beamforming optimization, the optimization of the IRS reflection matrix in subproblem (P4) also involves phase processing. Therefore, in order to use the SDR method again, the following transformation is performed:

[0069]

[0070] in, v[n]=[v1[n],v2[n],…,v M [n],1] T Similarly, let in, So the subproblem can be reformulated as:

[0071] (P4.1):

[0072]

[0073]

[0074] (P4.1) can be converted to the SDR method:

[0075]

[0076] in, Applying the SCA method to log2(1+ξ[n]) and performing Taylor expansion, we can obtain the final convex problem form of this subproblem:

[0077] (P4.2):

[0078]

[0079]

[0080]

[0081] Similarly, after solving the V[n] matrix, Gaussian randomization can be used to obtain the corresponding rank 1 solution v[n], thereby obtaining the optimal phase shift matrix.

[0082] For the subproblem (P5) UAV user trajectory planning, the position of the aerial eavesdropper remains unchanged. Therefore, when other variables are constant, the confidentiality rate of the eavesdropping channel is also constant. Therefore, only the achievable rate of the legal channel needs to be optimized. Definition Where d represents the node distance and c represents the channel fading coefficient. Then the legal UAV user rate can be expressed as:

[0083]

[0084] in, In order to facilitate the solution of the angle variable using MATLAB's CVX toolbox, the present invention uses the iterative results of the previous round to calculate and approximate the gain matrix / vector of this round:

[0085]

[0086] Where j represents the number of iterations. We further introduce slack variables f[n], g[n], and γ[n], and perform Taylor expansion on them to obtain the final convex problem form of this subproblem:

[0087] (P5):

[0088]

[0089]

[0090]

[0091]

[0092]

[0093]

[0094]

[0095] ||q B [1]-q0||≤D h ,q B [N] = q F (1b)

[0096]

[0097] |z[1]-z0|≤D v ,z[N]=z F (2b)

[0098]

[0099] in, f0[n], g[n], and is the corresponding Taylor expansion point.

[0100] At this point, we have obtained solvable forms for the four subproblems. (P2) can be directly calculated, while (P3), (P4), and (P5) can be solved using the MATLAB CVX toolbox. By setting the iteration precision and maximum number of iterations, the convergence of the BCD algorithm of this invention can be guaranteed.

[0101] The effect of the present invention can be further illustrated by the following simulation:

[0102] Assume that the coordinates of the base station, IRS, eavesdropper 1, eavesdropper 2, and UAV user’s starting and ending positions are: [0,120,0] T 、[0,-20,40] T 、[-400,120,120] T 、[400,120,120] T 、[-500,0,100] T and [-500,0,100] T Let N = 2T, the average transmission power of the base station Maximum power P max =1W. The initial point of the optimization variable is set to: initial power P 0 is the average power, the initial beamforming vector w 0 is the value of the maximum ratio transmission method, the initial IRS phase shift matrix Θ 0 is the unit matrix, the initial UAV trajectory Q 0 is a uniform line segment between the start and end positions. Unless otherwise specified, other parameters use the values ​​in Table 1.

[0103] Table 1

[0104]

[0105] The performance of the UAV ground-to-air communication system in the present invention under Ricean channel conditions is obtained by simulation. First, the convergence of the method proposed in the present invention is verified. Figure 2 As shown in the figure, when T = 40s, 41s and 45s, convergence is basically achieved after 7 to 8 rounds of iterations, which confirms the feasibility of the secure communication strategy.

[0106] Then, Figure 3 The image shows the flight paths of the UAV with and without an IRS. At T = 40 seconds, the UAV can only fly in a straight line. At T = 45 seconds, without an IRS, the UAV will first tilt downward toward the base station to approach the signal source and avoid eavesdropping. After reaching the minimum permitted altitude, it will continue to move horizontally toward the base station. At T = 45 seconds, with the IRS, due to the enhanced IRS signal, the UAV will first tilt downward toward the IRS. Similarly, after reaching the minimum permitted altitude, it will continue to move horizontally toward the IRS. At T = 41 seconds, the flight paths of both the IRS and non-IRS scenarios are similar to those at T = 45 seconds, but neither the IRS nor the base station is reached. In other words, the presence of an IRS forces the UAV to fly toward it, indirectly demonstrating the importance of IRS to secure UAV communication systems.

[0107] The average confidentiality rate change curve of the secure communication strategy proposed in this invention and other benchmark strategies is as follows: Figure 4 and 5 The secure communication strategy proposed in this invention is denoted as Scheme 1. For ease of comparison, other benchmark schemes are introduced: in Scheme 2, the power is the initial value, and the rest is the same as in Scheme 1; in Scheme 3, the beamforming vector is the initial value, and the rest is the same as in Scheme 1; in Scheme 4, the phase shift matrix is ​​the initial value, and the rest is the same as in Scheme 1; in Scheme 5, the UAV trajectory is the initial value, and the rest is the same as in Scheme 1.

[0108] exist Figure 4 In the scheme 4, the average security rate of schemes 1, 2, 3, and 5 will increase significantly with the increase of the number of IRSs. This is because the security capability of the phase shift matrix will increase with the increase of the number of reflective elements. However, the phase shift matrix in scheme 4 is randomly generated, so it fails to fully utilize the capability of the IRS phase shift matrix design. Figure 5 In the 0-50 meter range, as the IRS deployment altitude increases, the UAV can fly closer to the IRS and receive more confidential information. Therefore, the average confidentiality rate of these schemes can be improved. When the altitude exceeds 50 meters, although the UAV can still reach the vicinity of the IRS, the distance between the IRS and the eavesdropper is also closer, so the average confidentiality rate decreases with the increase of the IRS deployment altitude.

[0109] exist Figure 5In the ,using the parameter settings in Table 1, the average confidentiality rate of the proposed ,Scheme 1 is about 33% higher than the average confidentiality rate of ,Scheme 4 without IRS. This demonstrates the effectiveness of the ,secure communication strategy in this invention and the importance of ,IRS in

[0110] The above simulation analysis shows that, after multiple iterations, the optimal secure communication strategy for this IRS-UAV ground-to-air communication network scenario is to adopt the combined optimization strategy of power, beamforming, IRS phase shift matrix, and three-dimensional trajectory of Scheme 1. Specifically, the power is increased when the UAV is close to the ground base station and away from the eavesdropper, and reduced or even set to zero when the UAV is far from the base station and close to the eavesdropper. The main lobe of the transmitted beam is adjusted in real time to align with the direction of the UAV and the IRS. The reflected beam of the IRS is adjusted in real time to align with the direction of the UAV, and cooperates with the base station to offset the confidential signal at the eavesdropper. The optimal deployment altitude of the IRS is 50 meters. The UAV should lower its flight altitude as much as possible to distance itself from the eavesdropper within a certain timeframe and move horizontally closer to the base station.

[0111] The above description is only a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with this technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed in the present invention, and these modifications or replacements should all be included in the scope of protection of the present invention.

Claims

1. A method for secure transmission of a UAV ground-to-air communication system assisted by an intelligent reflective surface, characterized in that Here are the steps: Step 1: Assume that there is a ground base station, an IRS deployed on the facade of a building, an aerial UAV user, and multiple UAV eavesdroppers hovering in the air in the IRS-UAV ground-to-air communication system; the channels in the UAV communication system all follow the Rice fading model. In addition to the line-of-sight link and scattering link between the ground base station and the aerial node, there is also a reflection link between the ground base station-IRS-UAV; the ground base station is equipped with multiple antennas, the IRS array is equipped with multiple reflection units, and the other nodes in the system are all equipped with single antennas; Step 2: Within a fixed time T, the UAV user performs three-dimensional maneuvers from the initial point to the destination point; T is divided into N time blocks of equal length; under the premise of satisfying the constraints, four transmission methods are jointly adopted in each time block to counter eavesdroppers: the first is that the ground base station uses beamforming technology to focus the encrypted signal beam on the UAV user and IRS; the second is that the ground base station controls its transmission power value, increases the transmission power when the legitimate channel is dominant, and reduces or sets the transmission power to zero when the eavesdropping channel is dominant; the third is to use the IRS controller to intelligently control the phase shift matrix of the IRS in real time, that is, passive passive beamforming, to focus the reflected signal on the UAV user; The fourth is UAV three-dimensional trajectory planning, where the UAV actively approaches the ground base station and IRS during flight and stays away from eavesdroppers; Step 3: The average confidentiality rate in the communication scenario is in the form of two logarithmic functions subtracted from each other. The average confidentiality rate maximization problem is a non-convex NP-hard problem, and the coupling between variables is very strong, making it difficult to directly solve to obtain the optimal transmission strategy. Therefore, a block coordinate descent BCD algorithm is developed to decompose the optimization problem into four sub-problem blocks, which are iteratively optimized and solved in the direction of minimizing the confidentiality rate. Since the solution direction of each sub-problem is the coordinate descent direction, the convergence of the algorithm is guaranteed. The objective function of the average confidentiality rate optimization problem can be expressed as: Among them, R sec is the average confidentiality rate, is the set of K air eavesdroppers, R B [n] and R Ek [n] represents the receiving rate of the legitimate UAV and the receiving rate of the kth eavesdropping UAV at time n, respectively. P[n], Θ[n], and w[n] represent the transmit power, IRS phase shift matrix, and beamforming vector at time n, respectively. AB [n], H AR 、h RB [n], Respectively represent the channel gain vector / matrix from the base station to the UAV user, the base station to the k-th eavesdropping UAV, the base station to the IRS, the IRS to the UAV user, and the IRS to the k-th eavesdropping UAV, Represent the legitimate channel noise power and the eavesdropping channel noise power respectively.

2. The method for secure transmission of a UAV ground-to-air communication system assisted by an intelligent reflective surface according to claim 1, characterized in that: In step 3, for each non-convex subproblem, the semi-positive definite relaxation SDR and continuous convex approximation SCA algorithms are used to obtain the suboptimal solution.

3. A computer system, characterized in that include: One or more processors, and a computer-readable storage medium for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors are enabled to implement the method of claim 1.

4. A computer-readable storage medium, characterized in that Computer executable instructions are stored, and when the instructions are executed, they are used to implement the method of claim 1.

5. A computer program, characterized in that The invention comprises computer executable instructions, which are used to implement the method of claim 1 when being executed.

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