BandSeer: Bandwidth Prediction for Cellular Networks

In the context of cellular networks, such as with 5G and upcoming 6G networks, the available bandwidth of a connection is inherently dynamic. Accurate prediction of future bandwidth availability within a link is essential for latency-sensitive and mission-critical applications such as video streaming or remote driving. Bandwidth prediction ensures efficient utilization of a link and thus prevents delays. This paper introduces BandSeer, a stacked Bi-LSTM-based approach for bandwidth prediction in LTE and 5G cellular networks. BandSeer captures complex correlations in historical metrics better than prior work and outperforms SotA baselines. It achieves reductions of up to 18.32% in RMSE and 26.87% in MAE on the Berlin V2X dataset, and reductions of up to 12.43% in RMSE and 28.45% in MAE on the Beyond 5G dataset compared to the SotA Informer baseline. Furthermore, we argue that any bandwidth algorithm must be resource efficient to enable for development on various devices. Our evaluations show that BandSeer consumes one order of magnitude fewer resources and needs roughly a quarter to half the inference time of its closest competitor, the Informer model.