DDPG Learning for Aerial RIS-Assisted MU-MISO Communications

Aly Sabri Abdalla; Vuk Marojevic

DDPG Learning for Aerial RIS-Assisted MU-MISO Communications

Signal Processing 2022-07-14 v1 Information Theory math.IT

Authors: Aly Sabri Abdalla , Vuk Marojevic

Abstract

This paper defines the problem of optimizing the downlink multi-user multiple input, single output (MU-MISO) sum-rate for ground users served by an aerial reconfigurable intelligent surface (ARIS) that acts as a relay to the terrestrial base station. The deep deterministic policy gradient (DDPG) is proposed to calculate the optimal active beamforming matrix at the base station and the phase shifts of the reflecting elements at the ARIS to maximize the data rate. Simulation results show the superiority of the proposed scheme when compared to deep Q-learning (DQL) and baseline approaches.

Cite

@article{arxiv.2207.06064,
  title  = {DDPG Learning for Aerial RIS-Assisted MU-MISO Communications},
  author = {Aly Sabri Abdalla and Vuk Marojevic},
  journal= {arXiv preprint arXiv:2207.06064},
  year   = {2022}
}

Comments

This paper has been accepted for presentation in 2022 IEEE 33rd Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

DDPG Learning for Aerial RIS-Assisted MU-MISO Communications

Abstract

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