Aly Sabri Abdalla - DDPG Learning for Aerial RIS-Assisted MU-MISO

Conference

A. S. Abdalla, V. Marojevic
2022 IEEE 33rd Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), 2022

DOI: 10.1109/PIMRC54779.2022.9978075

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APA Click to copy
Abdalla, A. S., & Marojevic, V. (2022). DDPG Learning for Aerial RIS-Assisted MU-MISO Communications. In 2022 IEEE 33rd Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC). https://doi.org/10.1109/PIMRC54779.2022.9978075

Chicago/Turabian Click to copy
Abdalla, A. S., and V. Marojevic. “DDPG Learning for Aerial RIS-Assisted MU-MISO Communications.” In 2022 IEEE 33rd Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), 2022.

MLA Click to copy
Abdalla, A. S., and V. Marojevic. “DDPG Learning for Aerial RIS-Assisted MU-MISO Communications.” 2022 IEEE 33rd Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), 2022, doi:10.1109/PIMRC54779.2022.9978075.

BibTeX Click to copy

@conference{a2022a,
  title = {DDPG Learning for Aerial RIS-Assisted MU-MISO Communications},
  year = {2022},
  journal = {2022 IEEE 33rd Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)},
  doi = {10.1109/PIMRC54779.2022.9978075},
  author = {Abdalla, A. S. and Marojevic, V.}
}

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.