Artificial Intelligence Enabled Radio Propagation for Communications-Part II: Scenario Identification and Channel Modeling - Huang, Chen; He, Ruisi; Ai, Bo; Molisch, Andreas F.; Lau, Buon Kiong; Haneda, Katsuyuki; Liu, Bo; Wang, Cheng-Xiang; Yang, Mi; Oestges, Claude; Zhong, Zhangdui (IEEE transactions on antennas and propagation, 2022)
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 of this article, we introduced AI and ML and provided a comprehensive survey on ML-enabled channel characterization and antenna-channel optimization, and in this part (Part II), we review the 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 the art, the future challenges of AI-/ML-based channel data processing techniques are given as well.