# Active RIS-aided EH-NOMA Networks: A Deep Reinforcement Learning Approach

arXiv: https://arxiv.org/abs/2304.12184 · HTML: https://mlsanity.com/paper/2304.12184/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 22 of its 47 references that are in the map.
- First posted on arXiv 11 Apr 2023.

## Builds on (most cited first, 22 of 22)

- [Applications of Deep Reinforcement Learning in Communications and Networking: A Survey](https://mlsanity.com/paper/1810.07862/) (2018)
- [Intelligent Reflecting Surface Enhanced Wireless Network via Joint Active and Passive Beamforming](https://mlsanity.com/paper/1810.03961/) (2018)
- [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)
- [Rayleigh Fading Modeling and Channel Hardening for Reconfigurable Intelligent Surfaces](https://mlsanity.com/paper/2009.04723/) (2020)
- [Intelligent Reflecting Surface-Assisted Millimeter Wave Communications: Joint Active and Passive Precoding Design](https://mlsanity.com/paper/1908.10734/) (2019)
- [Active RIS vs. Passive RIS: Which Will Prevail in 6G?](https://mlsanity.com/paper/2103.15154/) (2021)
- [Deep Reinforcement Learning for Optimizing RIS-Assisted HD-FD Wireless Systems](https://mlsanity.com/paper/2110.04859/) (2021)
- [RIS Enhanced Massive Non-orthogonal Multiple Access Networks: Deployment and Passive Beamforming Design](https://mlsanity.com/paper/2001.10363/) (2020)
- [A joint precoding framework for wideband reconfigurable intelligent surface-aided cell-free network](https://mlsanity.com/paper/2002.03744/) (2020)
- [Physics-based Modeling and Scalable Optimization of Large Intelligent Reflecting Surfaces](https://mlsanity.com/paper/2004.12957/) (2020)
- [Channel Estimation for RIS-Empowered Multi-User MISO Wireless Communications](https://mlsanity.com/paper/2008.01459/) (2020)
- [Joint Resource Management for MC-NOMA: A Deep Reinforcement Learning Approach](https://mlsanity.com/paper/2103.15371/) (2021)
- [Cache-Aided NOMA Mobile Edge Computing: A Reinforcement Learning Approach](https://mlsanity.com/paper/1906.08812/) (2019)
- [Active RIS Versus Passive RIS: Which Is Superior with the Same Power Budget?](https://mlsanity.com/paper/2112.07510/) (2021)
- [MIMO-NOMA Networks Relying on Reconfigurable Intelligent Surface: A Signal Cancellation Based Design](https://mlsanity.com/paper/2003.02117/) (2020)
- [Power Minimization for Multi-cell Uplink NOMA with Imperfect SIC](https://mlsanity.com/paper/2007.10001/) (2020)
- [Present and Future of Reconfigurable Intelligent Surface-Empowered Communications](https://mlsanity.com/paper/2105.00671/) (2021)
- [Wireless Communication Aided by Intelligent Reflecting Surface: Active or Passive?](https://mlsanity.com/paper/2106.10963/) (2021)
- [Deep Reinforcement Learning Based Multidimensional Resource Management for Energy Harvesting Cognitive NOMA Communications](https://mlsanity.com/paper/2109.09503/) (2021)
- [Placement and Resource Allocation of Wireless-Powered Multiantenna UAV for Energy-Efficient Multiuser NOMA](https://mlsanity.com/paper/2204.09350/) (2022)
- [Deep Reinforcement Learning for RIS-Assisted FD Systems: Single or Distributed RIS?](https://mlsanity.com/paper/2208.07424/) (2022)

## Abstract (arXiv)

An active reconfigurable intelligent surface (RIS)-aided multi-user downlink communication system is investigated, where non-orthogonal multiple access (NOMA) is employed to improve spectral efficiency, and the active RIS is powered by energy harvesting (EH). The problem of joint control of the RIS's amplification matrix and phase shift matrix is formulated to maximize the communication success ratio with considering the quality of service (QoS) requirements of users, dynamic communication state, and dynamic available energy of RIS. To tackle this non-convex problem, a cascaded deep learning algorithm namely long short-term memory-deep deterministic policy gradient (LSTM-DDPG) is designed. First, an advanced LSTM based algorithm is developed to predict users' dynamic communication state. Then, based on the prediction results, a DDPG based algorithm is proposed to joint control the amplification matrix and phase shift matrix of the RIS. Finally, simulation results verify the accuracy of the prediction of the proposed LSTM algorithm, and demonstrate that the LSTM-DDPG algorithm has a significant advantage over other benchmark algorithms in terms of communication success ratio performance.