# Sum Rate Maximization in STAR-RIS-UAV-Assisted Networks: A CA-DDPG Approach for Joint Optimization

arXiv: https://arxiv.org/abs/2512.01202 · HTML: https://mlsanity.com/paper/2512.01202/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods.
- Builds on 17 of its 60 references that are in the map.
- First posted on arXiv 1 Dec 2025.

## Placed in

- Deep reinforcement learning methods / [RL for robot navigation and UAV control](https://mlsanity.com/deeprl/rl-for-robot-navigation-and-uav-control/)

## Builds on (most cited first, 17 of 17)

- [A Joint Learning and Communications Framework for Federated Learning over Wireless Networks](https://mlsanity.com/paper/1909.07972/) (2019)
- [A Vision of 6G Wireless Systems: Applications, Trends, Technologies, and Open Research Problems](https://mlsanity.com/paper/1902.10265/) (2019)
- [On the Road to 6G: Visions, Requirements, Key Technologies and Testbeds](https://mlsanity.com/paper/2302.14536/) (2023)
- [Reconfigurable Intelligent Surface Assisted Multiuser MISO Systems Exploiting Deep Reinforcement Learning](https://mlsanity.com/paper/2002.10072/) (2020)
- [Towards Smart and Reconfigurable Environment: Intelligent Reflecting Surface Aided Wireless Network](https://mlsanity.com/paper/1905.00152/) (2019)
- [Reconfigurable Intelligent Surfaces: Principles and Opportunities](https://mlsanity.com/paper/2007.03435/) (2020)
- [Weighted Sum-Rate Maximization for Reconfigurable Intelligent Surface Aided Wireless Networks](https://mlsanity.com/paper/1912.11999/) (2019)
- [Hybrid Beamforming for Reconfigurable Intelligent Surface based Multi-user Communications: Achievable Rates with Limited Discrete Phase Shifts](https://mlsanity.com/paper/1910.14328/) (2019)
- [A Survey on Model-based, Heuristic, and Machine Learning Optimization Approaches in RIS-aided Wireless Networks](https://mlsanity.com/paper/2303.14320/) (2023)
- [Joint Design for Simultaneously Transmitting And Reflecting (STAR) RIS Assisted NOMA Systems](https://mlsanity.com/paper/2106.03001/) (2021)
- [A joint precoding framework for wideband reconfigurable intelligent surface-aided cell-free network](https://mlsanity.com/paper/2002.03744/) (2020)
- [Reconfigurable Intelligent Surface-Based Index Modulation: A New Beyond MIMO Paradigm for 6G](https://mlsanity.com/paper/1904.06704/) (2019)
- [RIS-Aided MIMO Systems with Hardware Impairments: Robust Beamforming Design and Analysis](https://mlsanity.com/paper/2209.11425/) (2022)
- [Robust Beamforming Design for RIS-aided Cell-free Systems with CSI Uncertainties and Capacity-limited Backhaul](https://mlsanity.com/paper/2305.05348/) (2023)
- [Joint Reflecting and Precoding Designs for SER Minimization in Reconfigurable Intelligent Surfaces Assisted MIMO Systems](https://mlsanity.com/paper/1906.11466/) (2019)
- [Hybrid Precoding Design for Reconfigurable Intelligent Surface aided mmWave Communication Systems](https://mlsanity.com/paper/1912.00040/) (2019)
- [RIS-Aided Integrated Sensing and Communication: Joint Beamforming and Reflection Design](https://mlsanity.com/paper/2302.11249/) (2023)

## Abstract (arXiv)

With the rapid advances in programmable materials, reconfigurable intelligent surfaces (RIS) have become a pivotal technology for future wireless communications. The simultaneous transmitting and reflecting reconfigurable intelligent surfaces (STAR-RIS) can both transmit and reflect signals, enabling comprehensive signal control and expanding application scenarios. This paper introduces an unmanned aerial vehicle (UAV) to further enhance system flexibility and proposes an optimization design for the spectrum efficiency of the STAR-RIS-UAV-assisted wireless communication system. We present a deep reinforcement learning (DRL) algorithm capable of iteratively optimizing beamforming, phase shifts, and UAV positioning to maximize the system's sum rate through continuous interactions with the environment. To improve exploration in deterministic policies, we introduce a stochastic perturbation factor, which enhances exploration capabilities. As exploration is strengthened, the algorithm's ability to accurately evaluate the state-action value function becomes critical. Thus, based on the deep deterministic policy gradient (DDPG) algorithm, we propose a convolution-augmented deep deterministic policy gradient (CA-DDPG) algorithm that balances exploration and evaluation to improve the system's sum rate. The simulation results demonstrate that the CA-DDPG algorithm effectively interacts with the environment, optimizing the beamforming matrix, phase shift matrix, and UAV location, thereby improving system capacity and achieving better performance than other algorithms.