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
Computer Science · Engineering · Mathematics · #Advanced Wireless Communication Technologies #Algorithm #Artificial intelligence #Base station #Beamforming #Computer network #Computer science #Efficient energy use #Electrical engineering #Energy (signal processing) #Engineering #FOS: Computer and information sciences #FOS: Electrical engineering #Information Theory (cs.IT) #Machine Learning (cs.LG) #Mathematical optimization #Mathematics #Maximization #Noma #Optical Wireless Communication Technologies #Reinforcement learning #Signal Processing (eess.SP) #Star (game theory) #Telecommunications #Telecommunications link #Transmission (telecommunications) #UAV Applications and Optimization #Wireless #Wireless network #cs.IT #cs.LG #eess.SP #electronic engineering #information engineering #math.IT

paper · pdf · doi:10.48550/arxiv.2111.15464

published in arXiv (Cornell University) (Cornell University)

arxiv created 2021/11/30 · openalex publication_date 2021/11/30 · arxiv updated 2021/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/08

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
• A Brief Survey of Deep Reinforcement Learning

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

Related

• Meta-Learning for Resource Allocation in Uplink Multi-Active STAR-RIS-aided NOMA System
• Uplink NOMA For STAR-RIS Networks
• Active RIS-aided EH-NOMA Networks: A Deep Reinforcement Learning Approach
• A Reinforcement Learning Approach for an IRS-assisted NOMA Network
• Resource Allocation for RIS-Assisted CoMP-NOMA Networks using Reinforcement Learning