Spectrum sensing, which helps to resolve the coexistence issue and optimize spectrum efficiency, plays an important role in future wireless communication systems. However, the upcoming 5G communication involves diversified scenarios with different characteristics and diverse requirements. This tendency makes it difficult for spectrum sensing methods to flexibly serve various applications while maintaining satisfactory performance. Motivated by such a challenge, this article combines the reinforcement learning concept with spectrum sensing technique, seeking a feasible way to adaptively deploy spectrum sensing configurations so as to optimize system performance under multifarious scenarios in 5G communications. In this article, we first categorize several major optimization targets for spectrum sensing in future communications. Then we elaborate the full details of the proposed sensing technique. Three dedicated modes with respective optimization objectives are designed thereafter. Numerical results manifest that the proposed sensing technique has the capability of adapting to various scenarios, which is appealing in practice.

中文翻译：

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智能频谱感应：当强化学习遇到5G通信中的自动重复感应时

频谱感测有助于解决共存问题并优化频谱效率，在未来的无线通信系统中扮演着重要的角色。但是，即将到来的5G通信涉及具有不同特征和不同要求的多种场景。这种趋势使得频谱感测方法难以灵活地服务于各种应用，同时又不能保持令人满意的性能。受此挑战的启发，本文将强化学习的概念与频谱感测技术相结合，寻求一种可行的方式来自适应地部署频谱感测配置，从而在5G通信的多种情况下优化系统性能。在本文中，我们首先对未来通信中频谱感知的几个主要优化目标进行了分类。然后，我们详细阐述了所提出的传感技术。此后设计具有各自优化目标的三种专用模式。数值结果表明，所提出的传感技术具有适应各种场景的能力，在实际中具有吸引力。