  Wireless x-haul networks rely on microwave and millimeter-wave links between
4G and/or 5G base-stations to support ultra-high data rate and ultra-low
latency. 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 base-stations sharing the network and
preventing transient congestion that may be caused by re-routing. 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.
