Multi-Agent Reinforcement Learning in Wireless Distributed Networks for 6G

TL;DR

This paper reviews the integration of multi-agent reinforcement learning with wireless distributed networks to meet 6G requirements, analyzing structures, challenges, and future research directions.

Contribution

It provides a comprehensive overview of MARL-assisted wireless networks for 6G, including mathematical foundations, network structures, challenges, and emerging techniques.

Findings

Analysis of homogeneous and heterogeneous network structures

Identification of key challenges in MARL-assisted networks

Discussion of future research directions and techniques

Abstract

The introduction of intelligent interconnectivity between the physical and human worlds has attracted great attention for future sixth-generation (6G) networks, emphasizing massive capacity, ultra-low latency, and unparalleled reliability. Wireless distributed networks and multi-agent reinforcement learning (MARL), both of which have evolved from centralized paradigms, are two promising solutions for the great attention. Given their distinct capabilities, such as decentralization and collaborative mechanisms, integrating these two paradigms holds great promise for unleashing the full power of 6G, attracting significant research and development attention. This paper provides a comprehensive study on MARL-assisted wireless distributed networks for 6G. In particular, we introduce the basic mathematical background and evolution of wireless distributed networks and MARL, as well as demonstrate their interrelationships. Subsequently, we analyze different structures of wireless distributed networks from the perspectives of homogeneous and heterogeneous. Furthermore, we introduce the basic concepts of MARL and discuss two typical categories, including model-based and model-free. We then present critical challenges faced by MARL-assisted wireless distributed networks, providing important guidance and insights for actual implementation. We also explore an interplay between MARL-assisted wireless distributed networks and emerging techniques, such as information bottleneck and mirror learning, delivering in-depth analyses and application scenarios. Finally, we outline several compelling research directions for future MARL-assisted wireless distributed networks.