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.

翻译：在不同无线网络场景中，多个网络实体需要协同工作，以在最小延迟和能耗条件下完成共同任务。未来无线网络要求在动态和不确定环境中交换高维数据，因此通信控制任务的实现变得具有挑战性且高度复杂。具有涌现通信机制的多智能体强化学习（EC-MARL）是一种有前景的解决方案，能够以协作方式处理部分可观测状态下的高维连续控制问题，其中智能体通过构建涌现通信协议来完成复杂任务。本文阐述了EC-MARL在未来6G无线网络中的重要性——该技术赋予网络实体自主决策能力，以解决诸如自动驾驶、机器人导航、飞行基站网络规划和智慧城市应用等复杂任务。文章概述了EC-MARL算法及其设计准则，同时介绍了这一新兴课题的应用案例和研究机遇。