AI 中文总结
研究 IRS 辅助低空通信的保密能量效率问题，构建安全协同网络，建立信道模型并制定优化问题，利用多种方法解耦求解，提出 D3QN - PER 算法优化 UAV 轨迹，数值模拟验证该算法在提升保密能量效率上优于现有方法。
AI 中文摘要
为应对低空经济（LAE）无线通信中的安全和能量效率挑战，我们构建了一个整合无人机（UAV）和智能反射面（IRS）的安全协同网络，着重于最大化下行传输场景的保密能量效率（SEE）。首先建立了 UAV - IRS 辅助 LAE 通信网络的信道传输模型，接着针对 SEE 最大化制定了一个非凸分数优化问题，该问题涉及波束成形、IRS 相位和 UAV 轨迹三个紧密耦合变量。利用 Dinkelbach 方法和等效变换重新构造目标函数，通过交替优化策略将其解耦并分解为三个独立子问题迭代求解。采用松弛变量和半定松弛（SDR）凸化波束成形和 IRS 相移优化子问题以获得最优解。对于 UAV 轨迹优化子问题，提出了一种 D3QN - PER 算法，该算法将决斗双深度 Q 网络与优先经验回放相结合，以解决传统深度 Q 网络（DQN）固有的收敛慢和训练不稳定问题。数值模拟验证了所提联合优化方案的性能，比较结果表明基于 D DQN - PER 的算法优于现有最先进的学习方法，证实了其在提高 UAV - IRS 辅助 LAE 无线通信网络 SEE 方面的优越性。
英文摘要
To address the security and energy efficiency challenges in low-altitude economy (LAE) wireless communications, we develop a secure synergistic network integrating unmanned aerial vehicle (UAV) and intelligent reflecting surface (IRS), with an emphasis on maximizing secrecy energy efficiency (SEE) for downlink transmission scenarios. In particular, firstly, we establish the channel transmission models for UAV-IRS assisted LAE communications network. Then, we formulate a non-convex fractional optimization problem for SEE maximization, involving three tightly coupled variables, i.e., the beamforming, IRS phase and UAV trajectory. To tackle the fractional structure and variable coupling, Dinkelbach's method and equivalent transformations are leveraged to reformulate the objective function, which is then decoupled and decomposed into three independent subproblems via an alternating optimization strategy for iterative resolution. Slack variables and Semidefinite Relaxation (SDR) are further employed to convexify the subproblems of beamforming and IRS phase shift optimization, thereby obtaining their optimal solutions. For the UAV trajectory optimization subproblem, we propose a D3QN-PER algorithm, which integrates a Dueling Double Deep Q-Network with Prioritized Experience Replay, to tackle the slow convergence and training instability inherent in conventional Deep Q-Network (DQN). Numerical simulations validate the performance for our proposed joint optimization scheme. Comparative results demonstrate that the developed D3QN-PER-based algorithm outperforms existing state-of-the-art learning approaches which verifies its superiority in improving SEE for UAV-IRS-assisted LAE wireless communications network.
