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Electrical Engineering and Systems Science > Signal Processing

arXiv:2207.06064 (eess)
[Submitted on 13 Jul 2022]

Title:DDPG Learning for Aerial RIS-Assisted MU-MISO Communications

Authors:Aly Sabri Abdalla, Vuk Marojevic
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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.
Comments: This paper has been accepted for presentation in 2022 IEEE 33rd Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)
Subjects: Signal Processing (eess.SP); Information Theory (cs.IT)
Cite as: arXiv:2207.06064 [eess.SP]
  (or arXiv:2207.06064v1 [eess.SP] for this version)
  https://doi.org/10.48550/arXiv.2207.06064

Submission history

From: Aly Sabri Abdalla [view email]
[v1] Wed, 13 Jul 2022 09:19:37 UTC (2,841 KB)
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