Millimeter wave (mmWave) relaying has been introduced recently as a solution to extend the coverage of mmWave communication systems and to deal with the blockage problem as well. In order for the relay probing process to identify the most suitable relays, we should maintain an intelligent trade-off between the number of probed relays and the overhead due to beamforming training (BT). This letter leverages an online learning tool, namely sleeping contextual multi-armed bandits (S-CMAB), to effectively address this problem. Thanks to the multi-band capability of WiGig devices that supports both mmWave and WiFi, the characteristics of the WiFi signal centered at 5.25 GHz are used as contexts for the candidate WiGig relays operating at 60 GHz. Moreover, the sleeping relays that are unable to construct a WiGig link, due to blockages for instance, could be identified during the online learning process and accordingly excluded. Extensive simulations prove that the proposed S-CMAB approach integrated with the proposed sleeping linear upper confidence bound (S-LinUCB) algorithm outperform the legacy approaches and the context-free UCB algorithm in terms of both the average throughput and energy efficiency.

中文翻译：

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使用休眠上下文 Bandits 的 WiGig 设备到设备网络中的两跳中继探测

最近引入了毫米波 (mmWave) 中继，作为扩展毫米波通信系统覆盖范围和处理阻塞问题的解决方案。为了让中继探测过程识别最合适的中继，我们应该在探测到的中继数量和波束成形训练 (BT) 的开销之间保持智能权衡。这封信利用在线学习工具，即睡眠上下文多臂强盗 (S-CMAB) 来有效解决这个问题。由于支持毫米波和 WiFi 的 WiGig 设备的多频段功能，以 5.25 GHz 为中心的 WiFi 信号的特性被用作工作在 60 GHz 的候选 WiGig 中继的上下文。此外，由于阻塞等原因，无法构建 WiGig 链路的休眠中继，可以在在线学习过程中被识别并相应地排除在外。大量模拟证明，所提出的 S-CMAB 方法与所提出的睡眠线性置信上限 (S-LinUCB) 算法相结合，在平均吞吐量和能源效率方面均优于传统方法和上下文无关 UCB 算法。