Leveraging machine-learning for D2D communications in 5G/beyond 5G networks

Sherief Hashima, Basem M. Elhalawany, Kohei Hatano, Kaishun Wu, Ehab Mahmoud Mohamed

研究成果: ジャーナルへの寄稿学術誌査読

16 被引用数 (Scopus)


Device-to-device (D2D) communication is a promising paradigm for the fifth generation (5G) and beyond 5G (B5G) networks. Although D2D communication provides several benefits, including limited interference, energy efficiency, reduced delay, and network overhead, it faces a lot of technical challenges such as network architecture, and neighbor discovery, etc. The complexity of configuring D2D links and managing their interference, especially when using millimeter-wave (mmWave), inspire researchers to leverage different machine-learning (ML) techniques to address these problems towards boosting the performance of D2D networks. In this paper, a comprehensive survey about recent research activities on D2D networks will be explored with putting more emphasis on utilizing mmWave and ML methods. After exploring existing D2D research directions accompanied with their existing conventional solutions, we will show how different ML techniques can be applied to enhance the D2D networks performance over using conventional ways. Then, still open research directions in ML applications on D2D networks will be investigated including their essential needs. A case study of applying multi-armed bandit (MAB) as an efficient online ML tool to enhance the performance of neighbor discovery and selection (NDS) in mmWave D2D networks will be presented. This case study will put emphasis on the high potency of using ML solutions over using the conventional non-ML based methods for highly improving the average throughput performance of mmWave NDS.

ジャーナルElectronics (Switzerland)
出版ステータス出版済み - 1月 2 2021

!!!All Science Journal Classification (ASJC) codes

  • 制御およびシステム工学
  • 信号処理
  • ハードウェアとアーキテクチャ
  • コンピュータ ネットワークおよび通信
  • 電子工学および電気工学


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