Neighbor Discovery and Selection in Millimeter Wave D2D Networks Using Stochastic MAB

Sherief Hashima, Kohei Hatano, Eiji Takimoto, Ehab Mahmoud Mohamed

研究成果: Contribution to journalArticle査読

9 被引用数 (Scopus)

抄録

The propagation characteristics of millimeter-wave (mmWaves), encourages its use in the device to device (D2D) communications for fifth-generation (5G) and future beyond 5G (B5G) networks. However, due to the use of beamforming training (BT), there is a tradeoff between exploring neighbor devices for best device selection and the required overhead. In this letter, using a tool of machine learning, joint neighbor discovery and selection (NDS) in mmWave D2D networks is formulated as a stochastic budget-constraint multi-armed bandit (MAB) problem. Hence, a modified Thomson sampling (TS) and variants of upper confidence bound (UCB) based algorithms are proposed to address the topic while considering the residual energies of the surrounding devices. Simulation analysis demonstrates the effectiveness of the proposed techniques over the conventional approaches concerning average throughput, energy efficiency, and network lifetime.

本文言語英語
論文番号9082651
ページ(範囲)1840-1844
ページ数5
ジャーナルIEEE Communications Letters
24
8
DOI
出版ステータス出版済み - 8 2020

All Science Journal Classification (ASJC) codes

  • モデリングとシミュレーション
  • コンピュータ サイエンスの応用
  • 電子工学および電気工学

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