无人机辅助的反向散射通信系统安全容量优化

Optimization of the Security Capacity of Unmanned Aerial Vehicle Assisted Backscatter Communication Systems

  • 摘要: 反向散射通信技术具有低成本、低功耗等特点,可以广泛应用于大规模物联网中。物联网的传感器分布广泛,有些偏僻区域移动网络无法覆盖,在无线网络中引入无人机可以改善网络覆盖性能,提高频谱利用率。由于无线通信信号容易被窃听,用户的信息会被泄露,因此本文针对无人机辅助反向散射通信系统的安全问题,提出了能量采集与反向散射通信安全容量的联合优化算法。在能量和反向散射通信的安全性能约束条件下,构建了最小反向散射安全容量最大化的优化问题。由于该优化问题为非凸优化问题,因此本文采用块坐标下降算法(Block Coordinate Descent,BCD)的全局迭代算法进行求解,将原优化问题分解为三个优化子问题。对于时间分配子问题,采用线性规划进行求解。对于非凸无人机轨迹优化子问题,首先利用变量替换对该子问题进行简化。随后利用连续凸逼近法(Successive Convex Approximation, SCA)进行求解。对于非凸的地面用户调度子问题,由于待求解的优化变量是整数变量,因此采用分支定界法进行求解。最后通过全局迭代分别获得时间分配、无人机轨迹以及地面反向散射设备调度的全局最优解。仿真结果表明,本文算法具有较好的收敛性,并且与列举的其余两种参考算法相比,本文提出的算法性能更好,可以有效地提高系统的安全容量。

     

    Abstract: ‍ ‍Backscatter communication technology is characterized by low cost and low power consumption, and it exhibits significant potential for widespread use in large-scale Internet of Things applications. Although Internet of Things sensors are widely distributed, some remote areas cannot be covered by mobile networks. Introducing unmanned aerial vehicle (UAV) into wireless networks can enhance network coverage performance and increase spectrum utilization. However, wireless communication signals are easily eavesdropped on, which raises concerns regarding the potential leakage of user information. This study proposes a joint optimization algorithm for energy harvesting and backscatter communication security capacity to address the security issues associated with UAV-assisted backscatter communication systems. Under the constraints of the energy and security performance for backscatter communication, an optimization problem was constructed to maximize the minimum backscatter security capacity. As this optimization problem is non-convex, this study employed the block coordinate descent (BCD) global iterative algorithm to solve it, decomposing the original optimization problem into three optimization sub-problems. Linear programming was utilized to solve the time allocation sub-problem. For the non-convex UAV trajectory optimization sub-problem, the sub-problem was first simplified by substituting the variable and then solved using successive convex approximation (SCA). Solving the non-convex ground user scheduling sub-problem necessitated the application of the branch and bound method as the optimization variables to be solved were integer variables. Finally, the global optimal solutions for time allocation, UAV trajectory, and ground backscatter equipment scheduling were obtained via global iteration. The simulation results demonstrated that the algorithm proposed in this study has good convergence. Compared with the other two reference algorithms listed, the algorithm proposed in this study had better performance and could effectively improve the security capacity of the system.

     

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