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

arXiv: https://arxiv.org/abs/2510.22117 · HTML: https://mlsanity.com/paper/2510.22117/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 40 references that are in the map.
- First posted on arXiv 25 Oct 2025.

## Builds on (most cited first, 16 of 16)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments](https://mlsanity.com/paper/1706.02275/) (2017)
- [Soft Actor-Critic Algorithms and Applications](https://mlsanity.com/paper/1812.05905/) (2018)
- [Actor-Attention-Critic for Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1810.02912/) (2018)
- [Towards Smart and Reconfigurable Environment: Intelligent Reflecting Surface Aided Wireless Network](https://mlsanity.com/paper/1905.00152/) (2019)
- [Intelligent Reflecting Surface Aided Wireless Communications: A Tutorial](https://mlsanity.com/paper/2007.02759/) (2020)
- [Joint Task Offloading and Resource Allocation in Aerial-Terrestrial UAV Networks with Edge and Fog Computing for Post-Disaster Rescue](https://mlsanity.com/paper/2309.16709/) (2023)
- [Beamforming Optimization for Wireless Network Aided by Intelligent Reflecting Surface with Discrete Phase Shifts](https://mlsanity.com/paper/1906.03165/) (2019)
- [DL-DRL: A double-level deep reinforcement learning approach for large-scale task scheduling of multi-UAV](https://mlsanity.com/paper/2208.02447/) (2022)
- [Multi-Objective Optimization for UAV Swarm-Assisted IoT with Virtual Antenna Arrays](https://mlsanity.com/paper/2308.01511/) (2023)
- [Aerial Reliable Collaborative Communications for Terrestrial Mobile Users via Evolutionary Multi-Objective Deep Reinforcement Learning](https://mlsanity.com/paper/2502.05824/) (2025)
- [From Ground to Sky: Architectures, Applications, and Challenges Shaping Low-Altitude Wireless Networks](https://mlsanity.com/paper/2506.12308/) (2025)
- [UAV Swarm-enabled Collaborative Secure Relay Communications with Time-domain Colluding Eavesdropper](https://mlsanity.com/paper/2310.01980/) (2023)
- [Rate Maximization for Fluid Antenna System Assisted Semantic Communication](https://mlsanity.com/paper/2506.22943/) (2025)
- [Predictive Control over Low-Altitude Wireless Networks: Joint Trajectory Design and Resource Allocation](https://mlsanity.com/paper/2507.02374/) (2025)

## Abstract (arXiv)

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.