TY - GEN
T1 - Minimax Optimal Stochastic Strategy (MOSS) for Neighbor Discovery and Selection in Millimeter Wave D2D Networks
AU - Hashima, Sherief
AU - Hatano, Kohei
AU - Takimoto, Eiji
AU - Mohamed, Ehab Mahmoud
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/10/19
Y1 - 2020/10/19
N2 - The millimeter-waves (mmWaves) features promote its soon employment in device to device (D2D) communications. In D2D, neighbor discovery and selection (NDS) problem is a critical one due to the tradeoff between exploring more devices for the best choice and the expected beamforming training (BT) overhead. In this paper, mmWave D2D neighbor discovery and selection (NDS) problem is modeled as a budget-constrained multiarmed bandit (MAB). Specifically, an energy constrained minimax optimal stochastic strategy (E-MOSS) algorithm is proposed, which reflects the real network scenario by counting the remaining battery levels of the neighboring devices. Simulation results prove the efficiency of the proposed E-MOSS algorithm over the traditional NDS arrangements regards network lifetime, convergence rate, energy performance, and average throughput. Index Terms - MOSS, mmWave, D2D, Multiarmed Bandit (MAB).
AB - The millimeter-waves (mmWaves) features promote its soon employment in device to device (D2D) communications. In D2D, neighbor discovery and selection (NDS) problem is a critical one due to the tradeoff between exploring more devices for the best choice and the expected beamforming training (BT) overhead. In this paper, mmWave D2D neighbor discovery and selection (NDS) problem is modeled as a budget-constrained multiarmed bandit (MAB). Specifically, an energy constrained minimax optimal stochastic strategy (E-MOSS) algorithm is proposed, which reflects the real network scenario by counting the remaining battery levels of the neighboring devices. Simulation results prove the efficiency of the proposed E-MOSS algorithm over the traditional NDS arrangements regards network lifetime, convergence rate, energy performance, and average throughput. Index Terms - MOSS, mmWave, D2D, Multiarmed Bandit (MAB).
UR - http://www.scopus.com/inward/record.url?scp=85099578403&partnerID=8YFLogxK
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U2 - 10.1109/WPMC50192.2020.9309495
DO - 10.1109/WPMC50192.2020.9309495
M3 - Conference contribution
AN - SCOPUS:85099578403
T3 - International Symposium on Wireless Personal Multimedia Communications, WPMC
BT - WPMC 2020 - 23rd International Symposium on Wireless Personal Multimedia Communications
PB - IEEE Computer Society
T2 - 23rd International Symposium on Wireless Personal Multimedia Communications, WPMC 2020
Y2 - 19 October 2020 through 26 October 2020
ER -