TY - EJOU
AU - Mohamed, Ehab Mahmoud
AU - Hashima, Sherief
AU - Hatano, Kohei
AU - Kasban, Hani
AU - Rihan, Mohamed
TI - Millimeter-Wave Concurrent Beamforming: A Multi-Player Multi-Armed Bandit Approach
T2 - Computers, Materials \& Continua
PY - 2020
VL - 65
IS - 3
SN - 1546-2226
AB - The communication in the Millimeter-wave (mmWave) band, i.e., 30~300
GHz, is characterized by short-range transmissions and the use of antenna beamforming
(BF). Thus, multiple mmWave access points (APs) should be installed to fully cover a
target environment with gigabits per second (Gbps) connectivity. However, inter-beam
interference prevents maximizing the sum rates of the established concurrent links. In this
paper, a reinforcement learning (RL) approach is proposed for enabling mmWave
concurrent transmissions by finding out beam directions that maximize the long-term
average sum rates of the concurrent links. Specifically, the problem is formulated as a
multiplayer multiarmed bandit (MAB), where mmWave APs act as the players aiming to
maximize their achievable rewards, i.e., data rates, and the arms to play are the available
beam directions. In this setup, a selfish concurrent multiplayer MAB strategy is
advocated. Four different MAB algorithms, namely, *ϵ*-greedy, upper confidence bound
(UCB), Thompson sampling (TS), and exponential weight algorithm for exploration and
exploitation (EXP3) are examined by employing them in each AP to selfishly enhance its
beam selection based only on its previous observations. After a few rounds of interactions,
mmWave APs learn how to select concurrent beams that enhance the overall system
performance. The proposed MAB based mmWave concurrent BF shows comparable
performance to the optimal solution.
KW - Millimeter wave (mmWave)
KW - concurrent transmissions
KW - reinforcement learning
KW - multiarmed bandit (MAB)
DO - 10.32604/cmc.2020.011816