An evaluation of machine learning techniques to support latency-sensitive human-to-machine (H2M) communications

Abstract

Summarizing state-of-the-art machine learning techniques, we review their technical details and their performance in allocating bandwidth resource in support of latency-sensitive H2M applications. An evaluation of different techniques, in terms of problem formulation, time and memory costs, bandwidth prediction performance, is also presented.