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

The new demands for high-reliability and ultra-high capacity wireless communication have led to extensive research into 5G communications. However, the 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 learningbased communication methods are presented along with the research opportunities and challenges. H. Huang, G. Gui, Z. Yang, and H. Sari are with Key Lab of Broadband Wireless Communication and Sensor Network Technology (Nanjing University of Posts and Telecommunications), Ministry of Education, Nanjing 210003, China. S. Guo is with Department of Computing, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong (Email: song.guo@polyu.edu.hk). J. Zhang is with Beijing University of Posts and Telecommunication (BUPT), Beijing 100876, China (Email: jhzhang@bupt.edu.cn). F. Adachi is with Wireless Signal Processing Research Group, Research Organization of Electrical Communication (ROEC), Tohoku University, Sendai 980-8577, Japan (Email: adachi@ecei.tohoku.ac.jp).