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

TL;DR

This paper introduces a secure communication framework for low-altitude wireless networks using UAV swarms and IRS, optimizing multiple objectives to enhance security, efficiency, and robustness against eavesdropping.

Contribution

It proposes a novel joint optimization approach employing a heterogeneous MDP and multi-agent control for UAV-IRS collaboration in secure communications.

Findings

HMCA outperforms baseline methods in secrecy rate and energy efficiency.

Collaborative VAA and IRS significantly improve security with more UAVs.

Passive beamforming enhances robustness against eavesdroppers.

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.