Long Short Term Memory Networks for Bandwidth Forecasting in Mobile Broadband Networks under Mobility

TL;DR

This paper introduces HINDSIGHT++, an AutoML-based framework using LSTM networks for accurate bandwidth forecasting in mobile broadband networks, demonstrating significant error reduction especially in 5G scenarios.

Contribution

The work presents HINDSIGHT++, a novel open-source R framework that automates data preprocessing and hyperparameter tuning for LSTM-based bandwidth forecasting in mobile networks.

Findings

Achieved near 30% reduction in MAE for 5G bandwidth forecasting.

Validated LSTM performance boundaries on 4G datasets.

Demonstrated the effectiveness of AutoML in optimizing hyperparameters.

Abstract

Bandwidth forecasting in Mobile Broadband (MBB) networks is a challenging task, particularly when coupled with a degree of mobility. In this work, we introduce HINDSIGHT++, an open-source R-based framework for bandwidth forecasting experimentation in MBB networks with Long Short Term Memory (LSTM) networks. We instrument HINDSIGHT++ following an Automated Machine Learning (AutoML) paradigm to first, alleviate the burden of data preprocessing, and second, enhance performance related aspects. We primarily focus on bandwidth forecasting for Fifth Generation (5G) networks. In particular, we leverage 5Gophers, the first open-source attempt to measure network performance on operational 5G networks in the US. We further explore the LSTM performance boundaries on Fourth Generation (4G) commercial settings using NYU-METS, an open-source dataset comprising of hundreds of bandwidth traces spanning different mobility scenarios. Our study aims to investigate the impact of hyperparameter optimization on achieving state-of-the-art performance and beyond. Results highlight its significance under 5G scenarios showing an average Mean Absolute Error (MAE) decrease of near 30% when compared to prior state-of-the-art values. Due to its universal design, we argue that HINDSIGHT++ can serve as a handy software tool for a multitude of applications in other scientific fields.