Optimal Handover Policy for mmWave Cellular Networks: A Multi-Armed Bandit Approach
Optimal Handover Policy for mmWave Cellular Networks: A Multi-Armed Bandit Approach
复制标题
DOI：
10.1109/globecom38437.2019.9014079
复制
发表时间：
2019-12
期刊：
影响因子：
--
通讯作者：
Li Sun;Jing Hou;Tao Shu
中科院分区：
文献类型：
--
作者：
Li Sun;Jing Hou;Tao Shu
毫米波（mmWave）由于其丰富的带宽资源而在5G通信中是一种很有前途的技术。然而，其严重的路径衰减和易受视线（LOS）阻塞的影响，导致比传统技术更不可预测的中断。这种特殊的传播特性对毫米波蜂窝网络中的移动性管理提出了重大挑战。特别地，传统的切换策略纯粹依赖于信号强度的测量。如果直接应用在毫米波蜂窝网络中，它们会由于频繁的障碍物造成的短期LOS阻塞而导致大量不必要的拥塞，从而在网络上施加高信令和能量开销。在本文中，我们提出了一种新型的切换机制，通过仔细决定用户应该切换到的下一个基站（BS）来减少毫米波蜂窝网络中不必要的切换，以便切换后新的用户-BS连接可以持续尽可能长的时间。显然，做出这样的最佳决策需要关于用户的切换后移动性轨迹和LOS阻塞的一些知识，其实现不能在切换时刻被假设。所提出的切换机制通过利用用户切换后轨迹和LOS阻塞的经验分布来解决这一挑战，该经验分布通过多臂强盗框架在线学习，目的是最大化每次切换后用户-BS连接时间的期望。数值实验的结果表明，所提出的切换策略优于现有的同行在减少并发症，特别是在用户的移动性遵循规则模式的场景。
Millimeter wave (mmWave) is a promising technology in 5G communication due to its abundant bandwidth re- source. However, its severe path attenuation and vulnerability to line-of-sight (LOS) blockage result in much more unpredictable outages than traditional technologies. This special propagation property raises a significant challenge to the mobility management in mmWave cellular networks. In particular, conventional handover policies purely rely on the measurement of signal strength. If being applied directly in mmWave cellular networks, they would cause a large number of unnecessary handovers due to the frequent short-term LOS blockage by obstacles, imposing high signaling and energy overhead on the network. In this paper, we propose a novel handover mechanism to reduce unnecessary handovers in a mmWave cellular network by carefully deciding the next base station (BS) a user should handover to, so that the new user-BS connection after the handover can last as long as possible. Clearly, making such an optimal decision requires some knowledge on the users’ post-handover mobility trajectory and LOS blockage, whose realization cannot be assumed at the moment of handover. The proposed handover mechanism addresses this challenge by exploiting the empirical distribution of users’ post-handover trajectory and LOS blockage, learned online through a multi-armed bandit framework, with the intention to maximize the expectation of the user-BS connection time after each handover. The results of numerical experiments demonstrate that the proposed handover policy outperforms existing counterparts on reducing handovers, especially in the scenarios where users’ mobility follows regular patterns.