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

TL;DR

This paper reviews how artificial intelligence and machine learning techniques are applied to identify wireless propagation scenarios and model channels, highlighting current methods, challenges, and future directions in AI-driven radio communication modeling.

Contribution

It provides a comprehensive review of AI/ML methods for scenario identification and channel modeling in wireless communications, emphasizing recent advances and future challenges.

Findings

Analysis of ML methods for scenario identification

Comparison of ML techniques for channel modeling

Discussion of future challenges in AI/ML-based channel data processing

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.