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\begin{document}

\title{Improved D2D Millimeter Wave Communications for 5G Networks Using Deep
Learning}



\author[1]{Ahmed abdelreheem }%
\author[2]{Ahmed S. A. Mubarak }%
\author[2]{Osama A. Omer }%
\author[2]{Hamada Esmaiel }%
\author[2]{Usama S. Mohamed }%
\affil[1]{Aswan University}%
\affil[2]{Affiliation not available}%


\vspace{-1em}



  
  \date{May 22, 2024}


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\begin{abstract}
Mode selection is normally used in conjunction with Device-to-Device
(D2D) millimeter wave (mmWave) communications in 5G networks to overcome
the low coverage area, poor reliability and vulnerable to path blocking
of mmWave transmissions. Thus, producing a high-efficient D2D mmWave
using mode selection based on select the optimal mode with low
complexity turns to be a big challenge towards ubiquitous D2D mmWave
communications. In this paper, low complexity and high-efficient mode
selection in D2D mmWave communications based on deep learning is
introduced utilizing the artificial intelligence. In which, deep
learning is used to estimate the optimal mode y in the case of blocking
of mmWave transmission or low coverage area of mmWave communications.
Then, the proposed deep learning model is based on training the model
with almost use cases in offline phase to predict the optimal mode for
data relaying high-reliability communication in online phase. In mode
selection process, the potential D2D transmitter select the mode to
transmit the data either based on dedicated D2D communication or through
the cellular uplink using the base station (BS) as a relay based on
several criteria. The proposed deep learning model is developed to
overcome the challenges of selected the optimal mode with low complexity
and high efficiency. The simulation analysis show that the proposed mode
selection algorithms outperform the conventional techniques in D2D
mmWave communication in the spectral efficiency, energy efficiency and
coverage probability.%
\end{abstract}%



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