Special Issue on Artificial Intelligence in Radio Propagation for Communications
JUNE 2022 | VOLUME 70 | NUMBER 6
The Special Issue showcases how AI acts as a key enabler for wireless communications by providing new and innovative solutions for the complex problem of communication system design while also benefitting radio propagation characterization and wireless channel modeling. It includes 16 research and overview articles from established research teams around the globe.
Guest Editorial: Artificial Intelligence in Radio Propagation for CommunicationsR. He, B. K. Lau, C. Oestges, K. Haneda, and B. Liu
Artificial Intelligence Enabled Radio Propagation for Communications—Part I: Channel Characterization and Antenna-Channel Optimization (Invited Paper)C. Huang, R. He, B. Ai, A. F. Molisch, B. K. Lau, K. Haneda, B. Liu, C.-X. Wang, M. Yang, C. Oestges, and Z. Zhong
Artificial Intelligence Enabled Radio Propagation for Communications—Part II: Scenario Identification and Channel Modeling (Invited Paper)R. He, B. Ai, A. F. Molisch, B. K. Lau, K. Haneda, B. Liu, C.-X. Wang, M. Yang, C. Oestges, and Z. Zhong
An Overview of Machine Learning Techniques for Radiowave Propagation ModelingA. Seretis and C. D. Sarris
A General Method for Calibrating Stochastic Radio Channel Models With KernelsA. Bharti, F.-X. Briol, and T. Pedersen
An Atmospheric Data-Driven Q-Band Satellite Channel Model With Feature SelectionL. Bai, Q. Xu, Z. Huang, S. Wu, S. Ventouras, G. Goussetis, and X. Cheng
SVM-Assisted Adaptive Kernel Power Density Clustering Algorithm for Millimeter Wave ChannelsF. Du, X. Zhao, Y. Zhang, Y. Wen, Z. Fu, S. Geng, P. Qin, Z. Zhou, C. Xu, Y. Liu, and W. Fan
Machine Learning-Based Multipath Components Clustering and Cluster Characteristics Analysis in High-Speed Railway ScenariosT. Zhou, Y. Qiao, S. Salous, L. Liu, and C. Tao
Regional Refined Long-Term Predictions Method of Usable Frequency for HF Communication Based on Machine Learning Over AsiaJ. Wang, C. Yang, and W. An
Predictive Modeling of Millimeter-Wave Vegetation- Scattering Effect Using Hybrid Physics-Based and Data-Driven ApproachP. Zhang, C. Yi, B. Yang, H. Wang, C. Oestges, and X. You
A Framework of Mahalanobis-Distance Metric With Supervised Learning for Clustering Multipath Components in MIMO Channel AnalysisY. Chen, C. Han, J. He, and G. Wang
Semi-Deterministic Dynamic Millimeter-Wave Channel Modeling Based on an Optimal Neural Network ApproachX. Zhao, Z. Fu, W. Fan, Y. Zhang, S. Geng, F. Du, P. Qin, Z. Zhou, and L. Zhang
Machine Learning-Based Urban Canyon Path Loss Prediction Using 28 GHz Manhattan MeasurementsA. Gupta, J. Du, D. Chizhik, R. A. Valenzuela, and M. Sellathurai
Toward Physics-Based Generalizable Convolutional Neural Network Models for Indoor PropagationA. Seretis and C. D. Sarris
Deep Learning of Transferable MIMO Channel Modes for 6G V2X CommunicationsL. Cazzella, D. Tagliaferri, M. Mizmizi, D. Badini, C. Mazzucco, M. Matteucci, and U. Spagnolini
EM DeepRay: An Expedient, Generalizable, and Realistic Data-Driven Indoor Propagation ModelS. Bakirtzis, J. Chen, K. Qiu, J. Zhang, and I. Wassell
3.5 GHz Outdoor Radio Signal Strength Prediction With Machine Learning Based on Low-Cost Geographic FeaturesY. Liu, J. Dong, W. Huangfu, J. Liu, and K. Long