The new demands for high-reliability and ultra-high capacity wireless communication have led to extensive research into 5G communications. However, current communication systems, which were designed on the basis of conventional communication theories, significantly restrict further performance improvements and lead to severe limitations. Recently, the emerging deep learning techniques have been recognized as a promising tool for handling the complicated communication systems, and their potential for optimizing wireless communications has been demonstrated. In this article, we first review the development of deep learning solutions for 5G communication, and then propose efficient schemes for deep learning-based 5G scenarios. Specifically, the key ideas for several important deep learning-based communication methods are presented along with the research opportunities and challenges. In particular, novel communication frameworks of NOMA, massive multiple-input multiple-output (MIMO), and millimeter wave (mmWave) are investigated, and their superior performances are demonstrated. We envision that the appealing deep learning- based wireless physical layer frameworks will bring a new direction in communication theories and that this work will move us forward along this road.

中文翻译：

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物理层5G无线技术的深度学习：机遇，挑战和解决方案

对高可靠性和超高容量无线通信的新要求导致对5G通信的广泛研究。但是，基于传统通信理论设计的当前通信系统极大地限制了进一步的性能改进并导致了严重的局限性。最近，新兴的深度学习技术被认为是用于处理复杂的通信系统的有前途的工具，并且已经证明了其优化无线通信的潜力。在本文中，我们首先回顾用于5G通信的深度学习解决方案的开发，然后针对基于深度学习的5G场景提出有效的方案。特别，提出了几种基于深度学习的重要交流方法的关键思想以及研究机遇和挑战。特别是，研究了NOMA，大规模多输入多输出（MIMO）和毫米波（mmWave）的新型通信框架，并展示了其优越的性能。我们设想有吸引力的基于深度学习的无线物理层框架将为通信理论带来新的方向，并且这项工作将使我们沿着这条道路前进。