# Evolution of MAC Protocols in the Machine Learning Decade: A Comprehensive Survey

arXiv: https://arxiv.org/abs/2302.13876 · HTML: https://mlsanity.com/paper/2302.13876/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 102 references that are in the map.
- First posted on arXiv 24 Jan 2023.

## Builds on (most cited first, 14 of 14)

- [A Brief Survey of Deep Reinforcement Learning](https://mlsanity.com/paper/1708.05866/) (2017)
- [A Tutorial on Principal Component Analysis](https://mlsanity.com/paper/1404.1100/) (2014)
- [Deep Learning in Mobile and Wireless Networking: A Survey](https://mlsanity.com/paper/1803.04311/) (2018)
- [Deep Learning-Based Channel Estimation](https://mlsanity.com/paper/1810.05893/) (2018)
- [Deep-Reinforcement Learning Multiple Access for Heterogeneous Wireless Networks](https://mlsanity.com/paper/1712.00162/) (2017)
- [Application of Machine Learning in Wireless Networks: Key Techniques and Open Issues](https://mlsanity.com/paper/1809.08707/) (2018)
- [Learning Decentralized Wireless Resource Allocations with Graph Neural Networks](https://mlsanity.com/paper/2107.01489/) (2021)
- [Can Shallow Neural Networks Beat the Curse of Dimensionality? A mean field training perspective](https://mlsanity.com/paper/2005.10815/) (2020)
- [An Efficient Deep Learning Framework for Low Rate Massive MIMO CSI Reporting](https://mlsanity.com/paper/1912.10608/) (2019)
- [Unsupervised Deep Learning for Massive MIMO Hybrid Beamforming](https://mlsanity.com/paper/2007.00038/) (2020)
- [Massive MIMO Adaptive Modulation and Coding Using Online Deep Learning Algorithm](https://mlsanity.com/paper/2105.12827/) (2021)
- [A Scalable Hybrid MAC Protocol for Massive M2M Networks](https://mlsanity.com/paper/1301.4315/) (2013)
- [Design of A Scalable Hybrid MAC Protocol for Heterogeneous M2M Networks](https://mlsanity.com/paper/1405.6360/) (2014)
- [A Learning Framework for Bandwidth-Efficient Distributed Inference in Wireless IoT](https://mlsanity.com/paper/2203.09631/) (2022)

## Abstract (arXiv)

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 textitImagNet 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.