AUTHOR=Jiao Shiyu , Xie Ximing , Ding Zhiguo 
  
TITLE=Deep Reinforcement Learning-Based Optimization for RIS-Based UAV-NOMA Downlink Networks (Invited Paper)
  
JOURNAL=Frontiers in Signal Processing
  
VOLUME=Volume 2 - 2022
  
YEAR=2022
  
URL=https://www.frontiersin.org/journals/signal-processing/articles/10.3389/frsip.2022.915567
  
DOI=10.3389/frsip.2022.915567
  
ISSN=2673-8198
  
ABSTRACT=This paper investigates the application of deep deterministic policy gradient (DDPG) to reconfigurable intelligent surfaces (RIS) based unmanned aerial vehicles (UAV) assisted non-orthogonal multiple access (NOMA) downlink networks. The deployment of the UAV equipped with a RIS is important, as the UAV increases the flexibility of the RIS significantly, especially for the case of users who have no line of sight (LoS) path to the base station (BS). Therefore, the aim of this paper is to maximize the sum rate by jointly optimizing the power allocation of the BS, the phase shifting of the RIS and the horizontal position of the UAV. Because the formulated problem is not convex, the DDPG algorithm is utilized to solve it. The computer simulation results are provided to show the superior performance of the proposed DDPG based algorithm.