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.

该论文阐述了 EC-MARL 在未来 6G 无线网络中的重要性，该方法赋予网络实体自主决策能力，解决自动驾驶、机器人导航、飞行基站网络规划和智能城市应用等复杂任务，提供 EC-MARL 算法的概述、设计准则以及研究机会。