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

arXiv: https://arxiv.org/abs/2510.22108 · HTML: https://mlsanity.com/paper/2510.22108/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 58 references that are in the map.
- First posted on arXiv 25 Oct 2025.

## Builds on (most cited first, 19 of 19)

- [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)
- [The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Games](https://mlsanity.com/paper/2103.01955/) (2021)
- [WiFo: Wireless Foundation Model for Channel Prediction](https://mlsanity.com/paper/2412.08908/) (2024)
- [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)
- [UAV Swarm-enabled Collaborative Secure Relay Communications with Time-domain Colluding Eavesdropper](https://mlsanity.com/paper/2310.01980/) (2023)
- [Learning a Gaussian Mixture Model from Imperfect Training Data for Robust Channel Estimation](https://mlsanity.com/paper/2301.06488/) (2023)
- [QoE Maximization for Multiple-UAV-Assisted Multi-Access Edge Computing via an Online Joint Optimization Approach](https://mlsanity.com/paper/2406.11918/) (2024)
- [Joint Optimization of UAV-Carried IRS for Urban Low Altitude mmWave Communications with Deep Reinforcement Learning](https://mlsanity.com/paper/2501.02787/) (2025)
- [Securing the Sky: Integrated Satellite-UAV Physical Layer Security for Low-Altitude Wireless Networks](https://mlsanity.com/paper/2506.23493/) (2025)
- [Energy-Efficient Design for a NOMA assisted STAR-RIS Network with Deep Reinforcement Learning](https://mlsanity.com/paper/2111.15464/) (2021)
- [Digital Twin-Assisted Space-Air-Ground Integrated Multi-Access Edge Computing for Low-Altitude Economy: An Online Decentralized Optimization Approach](https://mlsanity.com/paper/2411.09712/) (2024)
- [Online Collaborative Resource Allocation and Task Offloading for Multi-access Edge Computing](https://mlsanity.com/paper/2501.02952/) (2025)
- [Near-Field Integrated Sensing and Communications for Secure UAV Networks](https://mlsanity.com/paper/2502.01003/) (2025)
- [Aerial Secure Collaborative Communications under Eavesdropper Collusion in Low-altitude Economy: A Generative Swarm Intelligent Approach](https://mlsanity.com/paper/2503.00721/) (2025)
- [Cooperative UAV-mounted RISs-assisted Energy-efficient Communications](https://mlsanity.com/paper/2509.11533/) (2025)

## Abstract (arXiv)

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.