# Deep Reinforcement Learning Powered IRS-Assisted Downlink NOMA

arXiv: https://arxiv.org/abs/2104.01414 · HTML: https://mlsanity.com/paper/2104.01414/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 38 references that are in the map.
- First posted on arXiv 3 Apr 2021.

## Builds on (most cited first, 18 of 18)

- [An Introduction to Deep Reinforcement Learning](https://mlsanity.com/paper/1811.12560/) (2018)
- [Reconfigurable Intelligent Surface Assisted Multiuser MISO Systems Exploiting Deep Reinforcement Learning](https://mlsanity.com/paper/2002.10072/) (2020)
- [Wireless Communications Through Reconfigurable Intelligent Surfaces](https://mlsanity.com/paper/1906.09490/) (2019)
- [Deep Reinforcement Learning Based Intelligent Reflecting Surface for Secure Wireless Communications](https://mlsanity.com/paper/2002.12271/) (2020)
- [Deep Reinforcement Learning for Intelligent Reflecting Surfaces: Towards Standalone Operation](https://mlsanity.com/paper/2002.11101/) (2020)
- [Power-Domain Non-Orthogonal Multiple Access (NOMA) in 5G Systems: Potentials and Challenges](https://mlsanity.com/paper/1609.06261/) (2016)
- [Wireless Communications with Reconfigurable Intelligent Surface: Path Loss Modeling and Experimental Measurement](https://mlsanity.com/paper/1911.05326/) (2019)
- [Intelligent Reflecting Surface-Assisted Millimeter Wave Communications: Joint Active and Passive Precoding Design](https://mlsanity.com/paper/1908.10734/) (2019)
- [Intelligent Reflecting Surface Enhanced Wireless Network: Joint Active and Passive Beamforming Design](https://mlsanity.com/paper/1809.01423/) (2018)
- [Reconfigurable Intelligent Surfaces: Three Myths and Two Critical Questions](https://mlsanity.com/paper/2006.03377/) (2020)
- [The Shift to 6G Communications: Vision and Requirements](https://mlsanity.com/paper/2010.07993/) (2020)
- [A Survey of Intelligent Reflecting Surfaces (IRSs): Towards 6G Wireless Communication Networks](https://mlsanity.com/paper/1907.04789/) (2019)
- [A Simple Design of IRS-NOMA Transmission](https://mlsanity.com/paper/1907.09918/) (2019)
- [Sum Rate Maximization for IRS-assisted Uplink NOMA](https://mlsanity.com/paper/2004.10791/) (2020)
- [Smart and Secure Wireless Communications via Reflecting Intelligent Surfaces: A Short Survey](https://mlsanity.com/paper/2006.14519/) (2020)
- [Intelligent Reflecting Surface Assisted Non-Orthogonal Multiple Access](https://mlsanity.com/paper/1907.03133/) (2019)
- [Pilot-Based Channel Estimation Design in Covert Wireless Communication](https://mlsanity.com/paper/1908.00226/) (2019)
- [Intelligent Reflecting Surface Enhanced Millimeter-Wave NOMA Systems](https://mlsanity.com/paper/2005.01562/) (2020)

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

- [On the Robustness of Deep Reinforcement Learning in IRS-Aided Wireless Communications Systems](https://mlsanity.com/paper/2107.08293/) (2021)

## Abstract (arXiv)

In this work, we examine an intelligent reflecting surface (IRS) assisted downlink non-orthogonal multiple access (NOMA) scenario with the aim of maximizing the sum rate of users. The optimization problem at the IRS is quite complicated, and non-convex, since it requires the tuning of the phase shift reflection matrix. Driven by the rising deployment of deep reinforcement learning (DRL) techniques that are capable of coping with solving non-convex optimization problems, we employ DRL to predict and optimally tune the IRS phase shift matrices. Simulation results reveal that IRS assisted NOMA based on our utilized DRL scheme achieves high sum rate compared to OMA based one, and as the transmit power increases, the capability of serving more users increases. Furthermore, results show that imperfect successive interference cancellation (SIC) has a deleterious impact on the data rate of users performing SIC. As the imperfection increases by ten times, the rate decreases by more than 10%.