STAR-RIS-assisted Collaborative Beamforming for Low-altitude Wireless Networks

2025-10-24

AI Summary

To address severe signal attenuation in Low-Altitude UAV Networks (LAWNs) operating in dense urban environments due to obstructing buildings and structures, this paper proposes a joint optimization framework integrating omnidirectional reconfigurable beamforming enabled by Simultaneously Transmitting and Reflecting Reconfigurable Intelligent Surfaces (STAR-RIS) with cooperative UAV beamforming. We introduce the Heterogeneous Multi-Agent Cooperative Dynamic (HMCD) optimization framework: it employs Simulated Annealing to optimize STAR-RIS transmission/reflection coefficients, and incorporates a multi-agent deep reinforcement learning algorithm enhanced with self-attention–based state evaluation and adaptive velocity control to jointly maximize spectral efficiency and minimize energy consumption. Experimental results demonstrate that HMCD significantly outperforms baseline methods in convergence speed, average achievable rate, and energy efficiency. Moreover, system throughput consistently improves with increasing numbers of UAVs and STAR-RIS elements, validating the framework’s effectiveness and scalability in complex urban scenarios.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Intelligent Robots: Learning & Optimization for ROB
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

While low-altitude wireless networks (LAWNs) based on uncrewed aerial vehicles (UAVs) offer high mobility, flexibility, and coverage for urban communications, they face severe signal attenuation in dense environments due to obstructions. To address this critical issue, we consider introducing collaborative beamforming (CB) of UAVs and omnidirectional reconfigurable beamforming (ORB) of simultaneous transmitting and reflecting reconfigurable intelligent surfaces (STAR-RIS) to enhance the signal quality and directionality. On this basis, we formulate a joint rate and energy optimization problem (JREOP) to maximize the transmission rate of the overall system, while minimizing the energy consumption of the UAV swarm. Due to the non-convex and NP-hard nature of JREOP, we propose a heterogeneous multi-agent collaborative dynamic (HMCD) optimization framework, which has two core components. The first component is a simulated annealing (SA)-based STAR-RIS control method, which dynamically optimizes reflection and transmission coefficients to enhance signal propagation. The second component is an improved multi-agent deep reinforcement learning (MADRL) control method, which incorporates a self-attention evaluation mechanism to capture interactions between UAVs and an adaptive velocity transition mechanism to enhance training stability. Simulation results demonstrate that HMCD outperforms various baselines in terms of convergence speed, average transmission rate, and energy consumption. Further analysis reveals that the average transmission rate of the overall system scales positively with both UAV count and STAR-RIS element numbers.

Problem

• Enhancing signal quality in obstructed low-altitude UAV networks
• Optimizing transmission rate while minimizing UAV swarm energy consumption
• Solving non-convex NP-hard beamforming with multi-agent reinforcement learning

Innovation

• STAR-RIS with omnidirectional reconfigurable beamforming enhances signals
• Multi-agent deep reinforcement learning optimizes UAV swarm control
• Simulated annealing dynamically adjusts reflection and transmission coefficients