received a B.Sc. and M.Sc. degree in electronics and communications engineering from the Arab Academy for Science, Technology, and Maritime Transport (AASTMT), Alexandria, Cairo, Egypt, in 2005 and 2021, respectively. His research interests include the fields of artificial intelligence, machine learning, deep learning, Python programming development, and data science. He is currently a Team leader Linux system engineer at Cairo Airport Company (CAC).
MIMO: multiple-input multiple-output technology uses multiple antennas to use reflected signals to provide channel robustness and throughput gains. It is advantageous in several applications like cellular systems, and users are distributed over a wide coverage area in various applications such as mobile systems, improving channel state information (CSI) processing efficiency in massive MIMO systems. This chapter proposes two channel-based deep learning methods to enhance the performance in a massive MIMO system and compares our proposed technique to the previous methods. The proposed technique is based on the channel state information network combined with the gated recurrent unit’s technique CsiNet-GRUs, which increases recovery efficiency. Besides, a fair balance between compression ratio (CR) and complexity is given using correlation time in training samples. The simulation results show that the proposed CsiNet-GRUs technique fulfills performance improvement compared with the existing literature techniques, namely CS-based methods Conv-LSTM CsiNet, LASSO, Tval3, and CsiNet.
Part of the book: Deep Learning and Reinforcement Learning
MIMO: Multiple-input multiple-output technology uses multiple antennas to use reflected signals to provide channel robustness and throughput gains. It is advantageous in several applications like cellular systems, users are distributed over a wide coverage area in various applications such as mobile systems, improving channel state information (CSI) processing efficiency in massive MIMO systems. This chapter proposes two channel-based deep learning methods gated recurrent unit and a Legendre memory unit to enhance the performance in a massive MIMO system and compares the complexity analysis to the previous methods, The complexity analysis is based on the channel state information network combined with gated recurrent units and Legendre memory units compared to indicator parameters which show the difference between literature-based techniques.
Part of the book: Machine Learning and Data Mining Annual Volume 2023