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

Abstract

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.

References in corpus (4)

• Artificial intelligence enabled radio propagation for communications-Part I: Channel characterization and antenna-channel optimization
• Orbital Angular Momentum Multiplexing in Highly Reverberant Environments
• Enabling FDD Massive MIMO through Deep Learning-based Channel Prediction
• A Comprehensive Survey of Machine Learning Based Localization with Wireless Signals