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

arXiv: https://arxiv.org/abs/2111.15464 · HTML: https://mlsanity.com/paper/2111.15464/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 14 references that are in the map.
- First posted on arXiv 30 Nov 2021.

## Builds on (most cited first, 6 of 6)

- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [A Brief Survey of Deep Reinforcement Learning](https://mlsanity.com/paper/1708.05866/) (2017)
- [Reconfigurable Intelligent Surface Assisted Multiuser MISO Systems Exploiting Deep Reinforcement Learning](https://mlsanity.com/paper/2002.10072/) (2020)
- [AI based Service Management for 6G Green Communications](https://mlsanity.com/paper/2101.01588/) (2021)
- [Joint Design for Simultaneously Transmitting And Reflecting (STAR) RIS Assisted NOMA Systems](https://mlsanity.com/paper/2106.03001/) (2021)
- [Energy-Efficient Design of IRS-NOMA Networks](https://mlsanity.com/paper/2009.05344/) (2020)

## Built on by (most cited first, 1 of 1)

- [STAR-RIS-assisted Collaborative Beamforming for Low-altitude Wireless Networks](https://mlsanity.com/paper/2510.22108/) (2025)

## Abstract (arXiv)

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.