Machine Learning Empowered Spectrum Sharing in Intelligent Unmanned Swarm Communication Systems: Challenges, Requirements and Solutions

Abstract

The unmanned swarm system (USS) has been seen as a promising technology, and will play an extremely important role in both the military and civilian fields such as military strikes, disaster relief and transportation business. As the ''nerve center'' of USS, the unmanned swarm communication system (USCS) provides the necessary information transmission medium so as to ensure the system stability and mission implementation. However, challenges caused by multiple tasks, distributed collaboration, high dynamics, ultra-dense and jamming threat make it hard for USCS to manage limited spectrum resources. To tackle with such problems, the machine learning (ML) empowered intelligent spectrum management technique is introduced in this paper. First, based on the challenges of the spectrum resource management in USCS, the requirement of spectrum sharing is analyzed from the perspective of spectrum collaboration and spectrum confrontation. We found that suitable multi-agent collaborative decision making is promising to realize effective spectrum sharing in both two perspectives. Therefore, a multi-agent learning framework is proposed which contains mobile-computing-assisted and distributed structures. Based on the framework, we provide case studies. Finally, future research directions are discussed. INDEX TERMS Unmanned swarm system, spectrum sharing, machine learning, multi-agent learning, game theory.

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