# ML Framework for Wireless MAC Protocol Design

arXiv: https://arxiv.org/abs/2402.07208 · HTML: https://mlsanity.com/paper/2402.07208/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 8 of its 16 references that are in the map.
- First posted on arXiv 11 Feb 2024.

## Placed in

- Deep reinforcement learning methods / [RL for energy, networks and operations](https://mlsanity.com/deeprl/rl-for-energy-networks-and-operations/)

## Builds on (most cited first, 8 of 8)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Trust Region Policy Optimization](https://mlsanity.com/paper/1502.05477/) (2015)
- [Wi-Fi Meets ML: A Survey on Improving IEEE 802.11 Performance with Machine Learning](https://mlsanity.com/paper/2109.04786/) (2021)
- [The Emergence of Wireless MAC Protocols with Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2108.07144/) (2021)
- [What Will Wi-Fi 8 Be? A Primer on IEEE 802.11bn Ultra High Reliability](https://mlsanity.com/paper/2303.10442/) (2023)
- [Contention Window Optimization in IEEE 802.11ax Networks with Deep Reinforcement Learning](https://mlsanity.com/paper/2003.01492/) (2020)
- [Towards A Learning-Based Framework for Self-Driving Design of Networking Protocols](https://mlsanity.com/paper/2009.02128/) (2020)
- [Scalable Joint Learning of Wireless Multiple-Access Policies and their Signaling](https://mlsanity.com/paper/2206.03844/) (2022)

## Built on by (most cited first, 2 of 2)

- [Learning Power Control Protocol for In-Factory 6G Subnetworks](https://mlsanity.com/paper/2505.05967/) (2025)
- [LLM-Empowered Agentic MAC Protocols: A Dynamic Stackelberg Game Approach](https://mlsanity.com/paper/2510.10895/) (2025)

## Abstract (arXiv)

Adaptivity, reconfigurability and intelligence are key features of the next-generation wireless networks to meet the increasingly diverse quality of service (QoS) requirements of the future applications. Conventional protocol designs, however, struggle to provide flexibility and agility to changing radio environments, traffic types and different user service requirements. In this paper, we explore the potential of deep reinforcement learning (DRL), in particular Proximal Policy Optimization (PPO), to design and configure intelligent and application-specific medium access control (MAC) protocols. We propose a framework that enables the addition, removal, or modification of protocol features to meet individual application needs. The DRL channel access policy design empowers the protocol to adapt and optimize in accordance with the network and radio environment. Through extensive simulations, we demonstrate the superior performance of the learned protocols over legacy IEEE 802.11ac in terms of throughput and latency.