  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.
