Achieving low-latency h2m communications through predicting bandwidth demand: A comparative study of statistical prediction and machine learning techniques

Abstract

Bandwidth demand prediction is crucial in reducing uplink latency of human-tomachine traffic in future converged networks. A comprehensive review of existing statistical prediction and state-of-the-art machine learning techniques is presented.