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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. [ABSTRACT FROM AUTHOR]

We give an overview of recent developments in the modeling of radiowave propagation, based on machine learning (ML) algorithms. We identify the input and output specification and the architecture of the model as the main challenges associated with ML-driven propagation models. Relevant papers are discussed and categorized based on their approach to each of these challenges. Emphasis is given on presenting the prospects and open problems in this promising and rapidly evolving area. [ABSTRACT FROM AUTHOR]

This August issue contains two sets of articles: the Special Issue on Artificial Intelligence: New Frontiers in Real-Time Inverse Scattering and Electromagnetic Imaging and Regular Papers. This Special Issue is motivated by the fact that, in recent years, it has become evident that artificial intelligence provides new innovative solutions for complex problems. Therefore, the Editorial Board of the IEEE Transactions on Antennas and Propagation invited applications for special issues related to how artificial intelligence, machine learning, and deep learning can assist with challenges in the field of antennas and propagation. Three special issues were accepted and this one focuses on a unified vision for the application of artificial intelligence in inverse scattering and electromagnetic imaging. [ABSTRACT FROM AUTHOR]

This June issue contains two sets of articles: the Special Issue on Artificial Intelligence in Radio Propagation for Communications and Regular Papers. This Special Issue is motivated by the fact that in recent years it has become evident that artificial intelligence provides new innovative solutions for complex problems. Therefore, the Editorial Board of the IEEE Transactions on Antennas and Propagation invited applications for special issues related to how artificial intelligence, machine learning, and deep learning can assist with challenges in the field of antennas and propagation. Three special issues were accepted and this one focuses on a unified vision for the application of artificial intelligence in the propagation of electromagnetic waves for applications related to communications. [ABSTRACT FROM AUTHOR]