Hashima Sherief, Hatano Kohei, Kasban Hany, Mahmoud Mohamed Ehab
RIKEN-Advanced Intelligent Project, Computational Learning Theory Team, Fukuoka 819-0395, Japan.
Engineering and Scientific Equipment's Department, Egyptian Atomic Energy Authority, Cairo 13759, Egypt.
Sensors (Basel). 2021 Apr 17;21(8):2835. doi: 10.3390/s21082835.
The unique features of millimeter waves (mmWaves) motivate its leveraging to future, beyond-fifth-generation/sixth-generation (B5G/6G)-based device-to-device (D2D) communications. However, the neighborhood discovery and selection (NDS) problem still needs intelligent solutions due to the trade-off of investigating adjacent devices for the optimum device choice against the crucial beamform training (BT) overhead. In this paper, by making use of multiband (μW/mmWave) standard devices, the mmWave NDS problem is addressed using machine-learning-based contextual multi-armed bandit (CMAB) algorithms. This is done by leveraging the context information of Wi-Fi signal characteristics, i.e., received signal strength (RSS), mean, and variance, to further improve the NDS method. In this setup, the transmitting device acts as the player, the arms are the candidate mmWave D2D links between that device and its neighbors, while the reward is the average throughput. We examine the NDS's primary trade-off and the impacts of the contextual information on the total performance. Furthermore, modified energy-aware linear upper confidence bound (EA-LinUCB) and contextual Thomson sampling (EA-CTS) algorithms are proposed to handle the problem through reflecting the nearby devices' withstanding battery levels, which simulate real scenarios. Simulation results ensure the superior efficiency of the proposed algorithms over the single band (mmWave) energy-aware noncontextual MAB algorithms (EA-UCB and EA-TS) and traditional schemes regarding energy efficiency and average throughput with a reasonable convergence rate.
毫米波（mmWave）的独特特性促使其在未来基于第五代/第六代（B5G/6G）的设备到设备（D2D）通信中得到应用。然而，由于在为选择最佳设备而调查相邻设备与关键的波束成形训练（BT）开销之间需要进行权衡，邻域发现和选择（NDS）问题仍需要智能解决方案。在本文中，通过利用多频段（μW/mmWave）标准设备，使用基于机器学习的上下文多臂赌博机（CMAB）算法来解决毫米波NDS问题。这是通过利用Wi-Fi信号特征的上下文信息，即接收信号强度（RSS）、均值和方差，来进一步改进NDS方法。在这种设置中，发送设备充当玩家，臂是该设备与其邻居之间的候选毫米波D2D链路，而奖励是平均吞吐量。我们研究了NDS的主要权衡以及上下文信息对整体性能的影响。此外，还提出了改进的能量感知线性上置信界（EA-LinUCB）和上下文汤普森采样（EA-CTS）算法，以通过反映附近设备的电池承受水平来处理该问题，从而模拟实际场景。仿真结果确保了所提出的算法在能量效率和平均吞吐量方面比单频段（毫米波）能量感知非上下文MAB算法（EA-UCB和EA-TS）以及传统方案具有更高的效率，且收敛速度合理。
