Secrecy Energy Efficiency for IRS-Assisted Low-Altitude Communications: A D3QN-PER Based Approach

• URL: http://arxiv.org/abs/2607.24183v1
• Date: Mon, 27 Jul 2026 09:05:35 GMT
• Title: Secrecy Energy Efficiency for IRS-Assisted Low-Altitude Communications: A D3QN-PER Based Approach
• Abstract summary: Security and energy challenges in low-altitude economy (LAE) wireless communications are addressed.<n>We develop a secure synergistic network integrating unmanned aerial vehicle (UAV) and intelligent surface (IRS)<n>We show that the developed D3Q-PERN-based learning approaches outperforms existing state-of-the-art learning approaches.
• Score: 25.752091807817816
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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