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

2026-07-27

AI Summary

This study addresses the challenge of jointly achieving security and energy efficiency in low-altitude aerial communications involving unmanned aerial vehicles (UAVs) and intelligent reflecting surfaces (IRSs). To maximize secrecy energy efficiency, the authors propose a joint optimization framework that simultaneously designs beamforming vectors, IRS phase shifts, and UAV trajectories. The non-convex fractional objective is tackled via Dinkelbach’s transformation, while semi-definite relaxation and slack variable techniques are employed to decompose the problem. Furthermore, a novel D3QN-PER algorithm—integrating Dueling Double Deep Q-Network (Dueling Double DQN) with prioritized experience replay—is developed to accelerate convergence and enhance stability in trajectory optimization. Simulation results demonstrate that the proposed approach significantly outperforms existing methods, confirming the superiority of D3QN-PER in IRS-assisted secure low-altitude communications.

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.

Problem

• Secrecy Energy Efficiency
• Low-Altitude Communications
• UAV-IRS Integration
• Physical Layer Security
• Energy-Efficient Communication

Innovation

• Intelligent Reflecting Surface (IRS)
• Secrecy Energy Efficiency (SEE)
• D3QN-PER
• UAV trajectory optimization
• Alternating optimization