This two-part paper investigates the application of artificial intelligence (AI) and in particular machine learning (ML) to the study of wireless propagation channels. In Part I, we introduced AI and ML as well as provided a comprehensive survey on ML enabled channel characterization and antenna-channel optimization, and in this part (Part II) we review state-of-the-art literature on scenario identification and channel modeling here. In particular, the key ideas of ML for scenario identification and channel modeling/prediction are presented, and the widely used ML methods for propagation scenario identification and channel modeling and prediction are analyzed and compared. Based on the state-of-art, the future challenges of AI/ML-based channel data processing techniques are given as well.
@article{arxiv.2111.12228,
title = {Artificial intelligence enabled radio propagation for communications-Part II: Scenario identification and channel modeling},
author = {Chen Huang and Ruisi He and Bo Ai and Andreas F. Molisch and Buon Kiong Lau and Katsuyuki Haneda and Bo Liu and Cheng-Xiang Wang and Mi Yang and Claude Oestges and Zhangdui Zhong},
journal= {arXiv preprint arXiv:2111.12228},
year = {2022}
}