When UAV Swarm Meets IRS: Collaborative Secure Communications in Low-altitude Wireless Networks

2025-10-24

AI Summary

In low-altitude wireless networks (LAWNs), swarms of unmanned aerial vehicles (UAVs) face severe physical-layer security threats from both known and unknown eavesdroppers, degrading secrecy and reliability. To address this, we propose a UAV-coordinated intelligent reflecting surface (IRS) framework for physical-layer security enhancement. Our method jointly optimizes UAV trajectories, IRS phase shifts, and excitation weights to construct a dynamic virtual antenna array, enabling deep integration of active beamforming (via UAVs) and passive beamforming (via IRS). We formulate the problem as a heterogeneous Markov decision process and design a heterogeneous multi-agent control architecture (HMCA), solved via a distributed multi-agent soft actor-critic algorithm. Simulation results demonstrate significant improvements in secrecy rate and energy efficiency, effective suppression of sidelobe leakage, and progressive enhancement of security performance with increasing UAV count.

Technology Category

• Intelligent Robots: Multi-Robot Systems
• Multiagent Systems: Multiagent Systems under Uncertainty
• Search and Optimization: Learning to Search

Application Category

• Security and Privacy: Security and privacy of machine learning and AI applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Low-altitude wireless networks (LAWNs) represent a promising architecture that integrates unmanned aerial vehicles (UAVs) as aerial nodes to provide enhanced coverage, reliability, and throughput for diverse applications. However, these networks face significant security vulnerabilities from both known and potential unknown eavesdroppers, which may threaten data confidentiality and system integrity. To solve this critical issue, we propose a novel secure communication framework for LAWNs where the selected UAVs within a swarm function as a virtual antenna array (VAA), complemented by intelligent reflecting surface (IRS) to create a robust defense against eavesdropping attacks. Specifically, we formulate a multi-objective optimization problem that simultaneously maximizes the secrecy rate while minimizing the maximum sidelobe level and total energy consumption, requiring joint optimization of UAV excitation current weights, flight trajectories, and IRS phase shifts. This problem presents significant difficulties due to the dynamic nature of the system and heterogeneous components. Thus, we first transform the problem into a heterogeneous Markov decision process (MDP). Then, we propose a heterogeneous multi-agent control approach (HMCA) that integrates a dedicated IRS control policy with a multi-agent soft actor-critic framework for UAV control, which enables coordinated operation across heterogeneous network elements. Simulation results show that the proposed HMCA achieves superior performance compared to baseline approaches in terms of secrecy rate improvement, sidelobe suppression, and energy efficiency. Furthermore, we find that the collaborative and passive beamforming synergy between VAA and IRS creates robust security guarantees when the number of UAVs increases.

Problem

• Addresses security vulnerabilities in low-altitude wireless networks against eavesdroppers
• Maximizes secrecy rate while minimizing sidelobe levels and energy consumption
• Solves joint optimization of UAV trajectories, excitation weights and IRS phases

Innovation

• UAV swarm forms virtual antenna array for security
• Intelligent reflecting surface enhances anti-eavesdropping beamforming
• Multi-agent reinforcement learning optimizes heterogeneous network control