Open AccessIRS Element Selection Using LSTM-Based Deep Learning for UAV Communications
Author(s) -
Sobia Jangsher,
Arafat Al-Dweik,
Emad Alsusa
Publication year - 2023
Publication title -
ieee wireless communications letters
Language(s) - English
Resource type - Journals
eISSN - 2162-2345
pISSN - 2162-2337
DOI - 10.1109/lwc.2023.3304477
Subject(s) - communication, networking and broadcast technologies , computing and processing , signal processing and analysis
This letter proposes using deep learning (DL) for intelligent reflecting surface (IRS) element selection to reduce the bit error rate (BER) of unmanned aerial vehicle (UAV) communications affected by imperfect phase estimation and compensation. In the presence of phase errors, increasing the number of elements does not necessarily reduce the BER. In contrast, muting certain IRS elements can improve BER. However, solving the optimization problem heuristically is extremely complex because it requires evaluating BER expressions numerically for an enormous number of cases. Consequently, a long shortterm memory (LSTM)-based element selection (ES) technique is proposed to reduce the substantial complexity inherent in the conventional solution. A supervised learning approach with offline training is adopted where the decision of ES is made based on the phase estimation error parameter j. The obtained results show that the computation time of the proposed technique is 100 times less than that of state-of-the-art algorithms.