Artificial intelligence enabled radio propagation for communications—part II: scenario identification and channel modeling

Huang, C. et al. (2022) Artificial intelligence enabled radio propagation for communications—part II: scenario identification and channel modeling. IEEE Transactions on Antennas and Propagation, 70(6), pp. 3955-3969. (doi: 10.1109/TAP.2022.3149665)

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.

• Item Type: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Liu, Dr Bo
• Authors: Huang, C., He, R., Ai, B., Molisch, A. F., Lau, B. K., Haneda, K., Liu, B., Wang, C.-X., Yang, M., Oestges, C., and Zhong, Z.
• College/School: College of Science and Engineering > School of Engineering > Autonomous Systems and Connectivity
• Journal Name: IEEE Transactions on Antennas and Propagation
• Publisher: IEEE
• ISSN: 0018-926X
• ISSN (Online): 1558-2221
• Published Online: 14 February 2022
• Copyright Holders: Copyright © 2021 IEEE
• First Published: First published in IEEE Transactions on Antennas and Propagation 70(6): 3955-3969
• Publisher Policy: Reproduced in accordance with the publisher copyright policy