Switching in the Rain: Predictive Wireless x-haul Network Reconfiguration

Igor Kadota, Dror Jacoby, Hagit Messer, Gil Zussman, Jonatan Ostrometzky

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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. The full paper associated with this abstract can be found at https://doi.org/10.1145/3570616.

Original languageEnglish
Title of host publicationSIGMETRICS 2023 - Abstract Proceedings of the 2023 ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems
PublisherAssociation for Computing Machinery, Inc
Pages101-102
Number of pages2
ISBN (Electronic)9798400700743
DOIs
StatePublished - 19 Jun 2023
Event2023 ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems, SIGMETRICS 2023 - Orlando, United States
Duration: 19 Jun 202323 Jun 2023

Publication series

NameSIGMETRICS 2023 - Abstract Proceedings of the 2023 ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems

Conference

Conference2023 ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems, SIGMETRICS 2023
Country/TerritoryUnited States
CityOrlando
Period19/06/2323/06/23

Funding

FundersFunder number
NSF-BSFCNS-1910757
National Science FoundationCNS-2148128, EEC-2133516

    Keywords

    • 5g
    • backhaul
    • fronthaul
    • machine learning
    • millimeter-wave
    • rain attenuation
    • routing
    • wireless networks

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