# MAC Revivo: Artificial Intelligence Paves the Way

arXiv: https://arxiv.org/abs/2410.15820 · HTML: https://mlsanity.com/paper/2410.15820/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 15 references that are in the map.
- First posted on arXiv 21 Oct 2024.

## Builds on (most cited first, 5 of 5)

- [Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2003.08839/) (2020)
- [Wi-Fi Meets ML: A Survey on Improving IEEE 802.11 Performance with Machine Learning](https://mlsanity.com/paper/2109.04786/) (2021)
- [What Will Wi-Fi 8 Be? A Primer on IEEE 802.11bn Ultra High Reliability](https://mlsanity.com/paper/2303.10442/) (2023)
- [Statistical Delay and Error-Rate Bounded QoS Provisioning for AoI-Driven 6G Satellite-Terrestrial Integrated Networks Using FBC](https://mlsanity.com/paper/2406.05610/) (2024)
- [ReinWiFi: Application-Layer QoS Optimization of WiFi Networks with Reinforcement Learning](https://mlsanity.com/paper/2405.03526/) (2024)

## Built on by (most cited first, 1 of 1)

- [AI-Enhanced Distributed Channel Access for Collision Avoidance in Future Wi-Fi 8](https://mlsanity.com/paper/2509.23154/) (2025)

## Abstract (arXiv)

The vast adoption of Wi-Fi and/or Bluetooth capabilities in Internet of Things (IoT) devices, along with the rapid growth of deployed smart devices, has caused significant interference and congestion in the industrial, scientific, and medical (ISM) bands. Traditional Wi-Fi Medium Access Control (MAC) design faces significant challenges in managing increasingly complex wireless environments while ensuring network Quality of Service (QoS) performance. This paper explores the potential integration of advanced Artificial Intelligence (AI) methods into the design of Wi-Fi MAC protocols. We propose AI-MAC, an innovative approach that employs machine learning algorithms to dynamically adapt to changing network conditions, optimize channel access, mitigate interference, and ensure deterministic latency. By intelligently predicting and managing interference, AI-MAC aims to provide a robust solution for next generation of Wi-Fi networks, enabling seamless connectivity and enhanced QoS. Our experimental results demonstrate that AI-MAC significantly reduces both interference and latency, paving the way for more reliable and efficient wireless communications in the increasingly crowded ISM band.