Emergent Communication in Multi-Agent Reinforcement Learning for Future Wireless Networks

TL;DR

This paper explores how emergent communication in multi-agent reinforcement learning can enable autonomous decision-making in future 6G wireless networks, addressing complex tasks like navigation and network planning.

Contribution

It provides an overview of EC-MARL algorithms, discusses their design, and highlights research opportunities for applying emergent communication in wireless networks.

Findings

EC-MARL facilitates cooperative decision-making in complex wireless scenarios.

Emergent communication protocols improve efficiency in high-dimensional control tasks.

The paper identifies key research directions for EC-MARL in 6G networks.

Abstract

In different wireless network scenarios, multiple network entities need to cooperate in order to achieve a common task with minimum delay and energy consumption. Future wireless networks mandate exchanging high dimensional data in dynamic and uncertain environments, therefore implementing communication control tasks becomes challenging and highly complex. Multi-agent reinforcement learning with emergent communication (EC-MARL) is a promising solution to address high dimensional continuous control problems with partially observable states in a cooperative fashion where agents build an emergent communication protocol to solve complex tasks. This paper articulates the importance of EC-MARL within the context of future 6G wireless networks, which imbues autonomous decision-making capabilities into network entities to solve complex tasks such as autonomous driving, robot navigation, flying base stations network planning, and smart city applications. An overview of EC-MARL algorithms and their design criteria are provided while presenting use cases and research opportunities on this emerging topic.