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

• URL: http://arxiv.org/abs/2309.06021v1
• Date: Tue, 12 Sep 2023 07:40:53 GMT
• Title: Emergent Communication in Multi-Agent Reinforcement Learning for Future Wireless Networks
• Authors: Marwa Chafii, Salmane Naoumi, Reda Alami, Ebtesam Almazrouei, Mehdi Bennis, Merouane Debbah
• Abstract summary: Multi-agent reinforcement learning with emergent communication (EC-MARL) is a promising solution to address high dimensional continuous control problems. 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.
• Score: 30.678152524314225
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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