Switching in the Rain: Predictive Wireless x-haul Network Reconfiguration
Switching in the Rain: Predictive Wireless x-haul Network Reconfiguration
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DOI：
10.1145/3570616
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发表时间：
2022-03
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影响因子：
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通讯作者：
I. Kadota;Dror Jacoby;H. Messer;G. Zussman;J. Ostrometzky
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作者：
I. Kadota;Dror Jacoby;H. Messer;G. Zussman;J. Ostrometzky
4G、5G和智慧城市网络通常依赖于微波和毫米波x-haul链路。与这些高频链路相关的一个主要挑战是它们对天气条件的敏感性。特别是，降水可能导致严重的信号衰减，这显著降低了网络性能。在本文中，我们开发了一个预测网络重构（PNR）框架，该框架使用历史数据来预测每个链路的未来状况，然后提前为即将发生的干扰做好网络准备。PNR框架具有两个组件：（i）衰减预测（AP）机制;以及（ii）多步网络重构（MSNR）算法。AP机制采用编码器-解码器长短期记忆（LSTM）模型来预测每个链路的未来衰减水平的序列。MSNR算法利用这些预测来动态优化路由和准入控制决策，旨在最大化网络利用率，同时保持使用网络的节点之间的最大-最小公平性（例如，基站）并防止可能由切换路由引起的瞬时拥塞。我们使用包含从真实世界城市规模回程网络收集的超过200万个测量数据的数据集来训练、验证和评估PNR框架。结果表明，该框架：（i）预测衰减精度高，预测范围为50秒，RMSE小于0.4 dB;（ii）与无法利用未来干扰信息的无功网络重构算法相比，可以将瞬时网络利用率提高200%以上。
4G, 5G, and smart city networks often rely on microwave and millimeter-wave x-haul links. A major challenge associated with these high frequency links is their susceptibility to weather conditions. In particular, precipitation may cause severe signal attenuation, which significantly degrades the network performance. In this paper, we develop a Predictive Network Reconfiguration (PNR) framework that uses historical data to predict the future condition of each link and then prepares the network ahead of time for imminent disturbances. The PNR framework has two components: (i) an Attenuation Prediction (AP) mechanism; and (ii) a Multi-Step Network Reconfiguration (MSNR) algorithm. The AP mechanism employs an encoder-decoder Long Short-Term Memory (LSTM) model to predict the sequence of future attenuation levels of each link. The MSNR algorithm leverages these predictions to dynamically optimize routing and admission control decisions aiming to maximize network utilization, while preserving max-min fairness among the nodes using the network (e.g., base-stations) and preventing transient congestion that may be caused by switching routes. We train, validate, and evaluate the PNR framework using a dataset containing over 2 million measurements collected from a real-world city-scale backhaul network. The results show that the framework: (i) predicts attenuation with high accuracy, with an RMSE of less than 0.4 dB for a prediction horizon of 50 seconds; and (ii) can improve the instantaneous network utilization by more than 200% when compared to reactive network reconfiguration algorithms that cannot leverage information about future disturbances.