ieee antennas propagation society engineers education students

antenna signal processing radio astronomy engineering space communication

wireless mobile satellite telecommunications applied optics electromagnetic waves

menu

ieee-logo-black2

Special Issue on Artificial Intelligence in Radio Propagation for Communications

JUNE 2022 | VOLUME 70 | NUMBER 6

Special Issue on Artificial Intelligence in Radio Propagation for Communications

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