Wireless MAC Protocol Synthesis and Optimization with Multi-Agent Distributed Reinforcement Learning

TL;DR

This paper introduces a novel multi-agent deep reinforcement learning framework for designing adaptive wireless MAC protocols, enabling decentralized decision-making by network nodes to improve performance in various scenarios.

Contribution

It presents the first distributed MADRL framework integrated with ns-3 for autonomous MAC protocol synthesis and optimization.

Findings

MADRL outperforms legacy MAC protocols in simulations

Framework enables adaptive, environment-specific MAC design

Demonstrates significant QoS improvements for wireless networks

Abstract

In this letter, we propose a novel Multi-Agent Deep Reinforcement Learning (MADRL) framework for Medium Access Control (MAC) protocol design. Unlike centralized approaches, which rely on a single entity for decision-making, MADRL empowers individual network nodes to autonomously learn and optimize their MAC based on local observations. Leveraging ns3-ai and RLlib, as far as we are aware of, our framework is the first of a kind that enables distributed multi-agent learning within the ns-3 environment, facilitating the design and synthesis of adaptive MAC protocols tailored to specific environmental conditions. We demonstrate the effectiveness of the MADRL MAC framework through extensive simulations, showcasing superior performance compared to legacy protocols across diverse scenarios. Our findings highlight the potential of MADRL-based MAC protocols to significantly enhance Quality of Service (QoS) requirements for future wireless applications.