Deep learning for physical-layer 5g wireless techniques: Opportunities, challenges and solutions

Hongji Huang, Song Guo, Guan Gui, Zhen Yang, Jianhua Zhang, Hikmet Sari, Fumiyuki Adachi

Research output: Contribution to journalArticlepeer-review

255 Citations (Scopus)


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.

Original languageEnglish
Article number8786074
Pages (from-to)214-222
Number of pages9
JournalIEEE Wireless Communications
Issue number1
Publication statusPublished - 2020 Feb


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