Affiliation:
1. Columbia University, New York, NY, USA
2. Tel Aviv University, Tel Aviv, Israel
Abstract
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.
Publisher
Association for Computing Machinery (ACM)
Subject
Computer Networks and Communications,Hardware and Architecture,Safety, Risk, Reliability and Quality,Computer Science (miscellaneous)
Reference62 articles.
1. Content Caching and Scheduling in Wireless Networks With Elastic and Inelastic Traffic
2. Centralized and Distributed Algorithms for Routing and Weighted Max-Min Fair Bandwidth Allocation
3. J. Andersson J. Olsson R. van de Beek and J. Hansryd. 2022. OpenMRG: Open data from Microwave links Radar and Gauges for rainfall quantification in Gothenburg Sweden. Earth System Science Data Discussions (2022) 1--26. J. Andersson J. Olsson R. van de Beek and J. Hansryd. 2022. OpenMRG: Open data from Microwave links Radar and Gauges for rainfall quantification in Gothenburg Sweden. Earth System Science Data Discussions (2022) 1--26.
4. A. Balachandran , V. Sekar , A. Akella , S. Seshan , I. Stoica , and H. Zhang . 2013. Developing a predictive model of quality of experience for Internet video . In Proc. ACM SIGCOMM. A. Balachandran, V. Sekar, A. Akella, S. Seshan, I. Stoica, and H. Zhang. 2013. Developing a predictive model of quality of experience for Internet video. In Proc. ACM SIGCOMM.
5. L. Bao , J. Hansryd , T. Danielson , G. Sandin , and U. Noser . 2015. Field trial on adaptive modulation of microwave communication link at 6.8 GHz . In Proc. IEEE EuCAP. L. Bao, J. Hansryd, T. Danielson, G. Sandin, and U. Noser. 2015. Field trial on adaptive modulation of microwave communication link at 6.8 GHz. In Proc. IEEE EuCAP.
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