Artificial intelligence enabled radio propagation for\n communications-Part II: Scenario identification and channel modeling

Abstract

This two-part paper investigates the application of artificial intelligence\n(AI) and in particular machine learning (ML) to the study of wireless\npropagation channels. In Part I, we introduced AI and ML as well as provided a\ncomprehensive survey on ML enabled channel characterization and antenna-channel\noptimization, and in this part (Part II) we review state-of-the-art literature\non scenario identification and channel modeling here. In particular, the key\nideas of ML for scenario identification and channel modeling/prediction are\npresented, and the widely used ML methods for propagation scenario\nidentification and channel modeling and prediction are analyzed and compared.\nBased on the state-of-art, the future challenges of AI/ML-based channel data\nprocessing techniques are given as well.\n