Energy-Efficient Design for a NOMA assisted STAR-RIS Network with Deep Reinforcement Learning

2021/11/30 by Yi Guo, Guo, Yi, Fang Fang +5 · 3 citations
Engineering · #Advanced Wireless Communication Technologies #FOS: Computer and information sciences #FOS: Electrical engineering #Information Theory (cs.IT) #Machine Learning (cs.LG) #Optical Wireless Communication Technologies #Signal Processing (eess.SP) #UAV Applications and Optimization #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2111.15464

openalex publication_date 2021/11/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Simultaneous transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) has been considered as a promising auxiliary device to enhance the performance of the wireless network, where users located at the different sides of the surfaces can be simultaneously served by the transmitting and reflecting signals. In this paper, the energy efficiency (EE) maximization problem for a non-orthogonal multiple access (NOMA) assisted STAR-RIS downlink network is investigated. Due to the fractional form of the EE, it is challenging to solve the EE maximization problem by the traditional convex optimization solutions. In this work, a deep deterministic policy gradient (DDPG)-based algorithm is proposed to maximize the EE by jointly optimizing the transmission beamforming vectors at the base station and the coefficients matrices at the STAR-RIS. Simulation results demonstrate that the proposed algorithm can effectively maximize the system EE considering the time-varying channels.

Citations

• Continuous control with deep reinforcement learning
• Deep Reinforcement Learning: A Brief Survey

Cited by

• STAR-RIS-assisted Collaborative Beamforming for Low-altitude Wireless Networks
• Using STAR-IRS to Secure Indoor Communications Through Symbol-Level Random Phase Modulation
• RIS-Assisted NOMA with Partial CSI and Mutual Coupling: A Machine Learning Approach

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