Evolution of MAC Protocols in the Machine Learning Decade: A
Comprehensive Survey
Abstract
The last decade, (2012 - 2022), saw an unprecedented advance in machine
learning (ML) techniques, particularly deep learning (DL). As a result
of the proven capabilities of DL, a large amount of work has been
presented and studied in almost every field. Since 2012, when the
convolution neural networks have been reintroduced in the context of
\textit{ImagNet} competition, DL continued to achieve
superior performance in many challenging tasks and problems. Wireless
communications, in general, and medium access control (MAC) techniques,
in particular, were among the fields that were heavily affected by this
improvement. MAC protocols play a critical role in defining the
performance of wireless communication systems. At the same time, the
community lacks a comprehensive survey that collects, analyses, and
categorizes the recent work in ML-inspired MAC techniques. In this work,
we fill this gap by surveying a long line of work in this era. We
solidify the impact of machine learning on wireless MAC protocols. We
provide a comprehensive background to the widely adopted MAC techniques,
their design issues, and their taxonomy, in connection with the famous
application domains. Furthermore, we provide an overview of the ML
techniques that have been considered in this context. Finally, we
augment our work by proposing some promising future research directions
and open research questions that are worth further investigation.