Can Online Learning Increase the Reliability of Extreme Mobility Management?
Can Online Learning Increase the Reliability of Extreme Mobility Management?
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DOI：
10.1109/iwqos52092.2021.9521264
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发表时间：
2021-06
期刊：
影响因子：
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通讯作者：
Yuanjie Li;Esha Datta;Jiaxin Ding;N. Shroff;Xin Liu
中科院分区：
文献类型：
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作者：
Yuanjie Li;Esha Datta;Jiaxin Ding;N. Shroff;Xin Liu
在高速列车和车辆上，对用户极端移动性下的无缝互联网接入提出了很高的要求。然而，现有的移动的网络（例如，4G LTE和5G NR）无法可靠地满足这一需求，在200-350 km/h时切换失败率为5.5%-12.6%。根本原因是，4G/5G切换必须平衡探索更多测量以实现满意切换和在快速移动的用户离开覆盖范围之前及时切换的利用。我们设计了BaTT，这是一种在线学习解决方案，用于在极端移动性下可靠切换。BaTT将探索-开发权衡分解为两个多臂强盗问题。它使用二进制搜索来优化服务小区的信号强度阈值，以启动具有$\mathcal{O}（\log J\log T）$遗憾的切换。它进一步采用机会性Thompson采样来优化所测量的靶细胞的序列，以获得可靠的杂交结果。BaTT可以使用最新的开放无线电接入网络（O-RAN）框架在可操作的4G LTE和5G NR中实现。我们对中国高速铁路运营LTE网络数据集的评估显示，在200-350 km/h的速度下，切换失败减少了29.1%。
Seamless Internet access under extreme user mobility is highly demanded on high-speed trains and vehicles. However, existing mobile networks (e.g., 4G LTE and 5G NR) cannot reliably satisfy this demand, with a 5.5%-12.6% handover failure ratio at 200–350 km/h. A root cause is that, the 4G/5G handovers have to balance the exploration of more measurements for satisfactory handover and the exploitation for timely handover before the fast-moving user leaves the coverage.We design BaTT, an online learning solution for reliable handovers in extreme mobility. BaTT decomposes the explorationexploitation tradeoff into two multi-armed bandit problems. It uses ϵ-binary-search to optimize the threshold of a serving cell’s signal strength to initiate the handover with $\mathcal{O}(\log J\log T)$ regrets. It further adopts opportunistic Thompson sampling to optimize the sequence of target cells measured for reliable handovers. BaTT can be implemented using the recent Open Radio Access Network (O-RAN) framework in operational 4G LTE and 5G NR. Our evaluations over a dataset from operational LTE networks on the Chinese high-speed rails show a 29.1% handover failure reduction at the speed of 200-350 km/h.