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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 (AI) techniques. In this two-part article, we investigate the application of AI and, in particular, machine learning (ML) to the study of wireless propagation channels. It first provides a comprehensive overview of ML for channel characterization and ML-based antenna&#x2013;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 this article.

Artificial Intelligence Enabled Radio Propagation for Communications—Part I: Channel Characterization and Antenna-Channel Optimization