Biograph: Guan Gui (M’11-SM’17) received the Dr. Eng degree in Information and Communication Engineering from University of Electronic Science and Technology of China (UESTC), Chengdu, China, in 2012. From 2009 to 2014, he joined the wireless signal processing and network laboratory (Prof. Adachi laboratory), Department of Communications Engineering, Graduate School of Engineering, Tohoku University as for research assistant as well as postdoctoral research fellow, respectively. From 2014 to 2015, he was an Assistant Professor in Department of Electronics and Information System, Akita Prefectural University. Since 2015, he has been a professor with Nanjing University of Posts and Telecommunications (NJUPT), Nanjing, China.

He is currently engaged in research of deep learning, compressive sensing and advanced wireless techniques. Dr. Gui has published more than 200 international peer-reviewed journal/conference papers. He received Member and Global Activities Contributions Award in IEEE ComSoc and eight best paper awards, such as, ICC 2017, ICC 2014 and VTC 2014-Spring. He was also selected as for Jiangsu Specially-Appointed Professor (2016), Jiangsu High-level Innovation and Entrepreneurial Talent (2016), Jiangsu Six Top Talent (2018), Nanjing Youth Award (2018). Dr. Gui was an Editor of Security and Communication Networks (2012~2016). He has been the Editor of IEEE Transactions on Vehicular Technology, since 2017, the Editor of IEEE Access, since 2018, the Editor of Physical Communication, since 2019, the Editor of Wireless Network, since 2019, the Editor of KSII Transactions on Internet and Information Systems since 2017, the Editor of Journal of Communications, since 2019. He is IEEE Senior Member.

Title: Deep Learning for Next-Generation Physical Layer Wireless Communications

Abstract: 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 learning-based communication methods are presented along with the research opportunities and challenges. In particular, novel communication frameworks of non-orthogonal multiple access (NOMA), massive multiple-input multiple-output (MIMO), and millimeter wave (mmWave) are investigated, and their superior performances are demonstrated. We vision 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.