# Towards A Learning-Based Framework for Self-Driving Design of Networking Protocols

arXiv: https://arxiv.org/abs/2009.02128 · HTML: https://mlsanity.com/paper/2009.02128/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Sep 2020.

## Built on by (most cited first, 5 of 5)

- [The Emergence of Wireless MAC Protocols with Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2108.07144/) (2021)
- [Learning Generalized Wireless MAC Communication Protocols via Abstraction](https://mlsanity.com/paper/2206.06331/) (2022)
- [Scalable Joint Learning of Wireless Multiple-Access Policies and their Signaling](https://mlsanity.com/paper/2206.03844/) (2022)
- [ML Framework for Wireless MAC Protocol Design](https://mlsanity.com/paper/2402.07208/) (2024)
- [Evaluation of Automated Speech Recognition Systems for Conversational Speech: A Linguistic Perspective](https://mlsanity.com/paper/2211.02812/) (2022)

## Abstract (arXiv)

Networking protocols are designed through long-time and hard-work human efforts. Machine Learning (ML)-based solutions have been developed for communication protocol design to avoid manual efforts to tune individual protocol parameters. While other proposed ML-based methods mainly focus on tuning individual protocol parameters (e.g., adjusting contention window), our main contribution is to propose a novel Deep Reinforcement Learning (DRL)-based framework to systematically design and evaluate networking protocols. We decouple a protocol into a set of parametric modules, each representing a main protocol functionality that is used as DRL input to better understand the generated protocols design optimization and analyze them in a systematic fashion. As a case study, we introduce and evaluate DeepMAC a framework in which a MAC protocol is decoupled into a set of blocks across popular flavors of 802.11 WLANs (e.g., 802.11 b/a/g/n/ac). We are interested to see what blocks are selected by DeepMAC across different networking scenarios and whether DeepMAC is able to adapt to network dynamics.