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

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

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Article number55
JournalProceedings of the ACM on Measurement and Analysis of Computing Systems
Issue number3
StatePublished - 8 Dec 2022
Externally publishedYes

Bibliographical note

Funding Information:
We thank our shepherd Prof. Suzan Bayhan and the anonymous referees for their helpful comments. This research was supported in part by NSF-BSF grant CNS-1910757 and NSF grants CNS-2148128, EEC-2133516.

Publisher Copyright:
© 2022 ACM.


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


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