Millimeter-wave (mmWave) communication is considered as a key enabler of ultra-high data rates in the future cellular and wireless networks. The need for directional communication between base stations (BSs) and users in mmWave systems, that is achieved through beamforming, increases the complexity of the channel estimation. Moreover, in order to provide better coverage, dense deployment of BSs is required which causes frequent handovers and increased association overhead. In this article, we present an approach that jointly addresses the beamforming and handover problems. Our solution entails an efficient beamforming method with a few number of pilots and a learning-based handover method supporting mobile scenarios. We use reinforcement learning algorithm to learn the optimal choices of the backup BSs in different locations of a mobile user. We show that our method provides an almost constant rate and reliability in all locations of the user’s trajectory with a small number of handovers. Simulation results in an outdoor environment based on narrow band cluster mmWave channel modeling and real building map data show the superior performance of our proposed solution in achievable instantaneous rate and trajectory rate.

中文翻译：

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移动毫米波网络中基于学习的切换

毫米波 (mmWave) 通信被认为是未来蜂窝和无线网络中超高数据速率的关键推动因素。毫米波系统中基站 (BS) 和用户之间的定向通信需要通过波束成形实现，这增加了信道估计的复杂性。此外，为了提供更好的覆盖，需要密集部署基站，这会导致频繁切换和增加关联开销。在本文中，我们提出了一种联合解决波束成形和切换问题的方法。我们的解决方案需要一种具有少量导频的高效波束成形方法和一种支持移动场景的基于学习的切换方法。我们使用强化学习算法来学习移动用户不同位置的备用基站的最佳选择。我们表明，我们的方法通过少量切换在用户轨迹的所有位置提供了几乎恒定的速率和可靠性。基于窄带集群毫米波信道建模和真实建筑地图数据的室外环境仿真结果表明，我们提出的解决方案在可实现的瞬时速率和轨迹速率方面具有卓越的性能。