Deep Learning for Physical-Layer 5G Wireless Techniques: Opportunities, Challenges and Solutions

• Published in: IEEE wireless communications Vol. 27; no. 1; pp. 214 - 222
• Main Authors: Huang, Hongji, Guo, Song, Gui, Guan, Yang, Zhen, Zhang, Jianhua, Sari, Hikmet, Adachi, Fumiyuki
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.02.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: 5G mobile communication; Channel estimation; Communication; Communications systems; Deep learning; Machine learning; Millimeter waves; MIMO communication; NOMA; Wireless communication; Wireless communications
• ISSN: 1536-1284; 1558-0687
• DOI: 10.1109/MWC.2019.1900027

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.