Open AccessDeep Learning Methods in Communication Systems: A Review
Author(s) -
Feifan Liao,
Shengyun Wei,
Shun Zou
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1617/1/012024
Subject(s) - deep learning , computer science , communications system , communication theory , artificial intelligence , data science , human–computer interaction , telecommunications , communication , psychology
With the rapid development of modern communication systems, the amount of data has exploded, the system structure has become increasingly complex, and existing communication theories and technologies are facing huge challenges. The successful application of deep learning technology in the fields of images, speech, natural language processing, and games provides a possible solution for the theory and technology of communication systems that goes beyond traditional ideas and performance. This article mainly summarizes the application cases of deep learning methods in channel estimation, signal detection, and modulation recognition, and shows their outstanding performance compared to traditional communication theory and technology. Finally, we analyze the opportunities and challenges faced by deep learning-based communication technologies.