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arXiv · 2111.12227

Artificial intelligence enabled radio propagation for communications-Part I: Channel characterization and antenna-channel optimization

Chen Huang·Ruisi He·Bo Ai·Andreas F. Molisch·Buon Kiong Lau·Katsuyuki Haneda·Bo Liu·Cheng-Xiang Wang·Mi Yang·Claude Oestges·Zhangdui Zhong
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Abstract

To provide higher data rates, as well as better coverage, cost efficiency, security, adaptability, and scalability, the 5G and beyond 5G networks are developed with various artificial intelligence techniques. In this two-part paper, we investigate the application of artificial intelligence (AI) and in particular machine learning (ML) to the study of wireless propagation channels. It firstly provides a comprehensive overview of ML for channel characterization and ML-based antenna-channel optimization in this first part, and then it gives a state-of-the-art literature review of channel scenario identification and channel modeling in Part II. Fundamental results and key concepts of ML for communication networks are presented, and widely used ML methods for channel data processing, propagation channel estimation, and characterization are analyzed and compared. A discussion of challenges and future research directions for ML-enabled next generation networks of the topics covered in this part rounds off the paper.

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BibTeXRIS

Chen Huang, Ruisi He, Bo Ai, Andreas F. Molisch, Buon Kiong Lau, Katsuyuki Haneda, Bo Liu, Cheng-Xiang Wang, Mi Yang, Claude Oestges, Zhangdui Zhong. 2021-11-24. Artificial intelligence enabled radio propagation for communications-Part I: Channel characterization and antenna-channel optimization. https://doi.org/10.1109/tap.2022.3149663

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