2017/11/24 by Yeon Geun Lim, Yae Jee Cho, Lim, Yeon-Geun +9 · 1 citation
Agricultural and Biological Sciences · Engineering · #FOS: Computer and information sciences #FOS: Electrical engineering #Information Theory (cs.IT) #Microwave Engineering and Waveguides #Millimeter-Wave Propagation and Modeling #Plant Pathogens and Resistance #Signal Processing (eess.SP) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.1711.09052
openalex publication_date 2017/11/24 · openalex created_date 2019/07/23 · openalex updated_date 2026/07/28
Compared to the current wireless communication systems, millimeter wave (mm-Wave) promises a wide range of spectrum. As viable alternatives to existing mm-Wave channel models, various map-based channel models with different modeling methods have been widely discussed. Map-based channel models are based on a ray-tracing algorithm and include realistic channel parameters in a given map. Such parameters enable researchers to accurately evaluate novel technologies in the mm-Wave range. Diverse map-based modeling methods result in different modeling objectives, including the characteristics of channel parameters and different complexities of the modeling procedure. This article outlines an overview of map-based mm-Wave channel models and proposes a concept of how they can be utilized to integrate a hardware testbed/sounder with a software testbed/sounder. In addition, we categorize map-based channel parameters and provide guidelines for hybrid modeling. Next, we share the measurement data and the map-based channel parameters with the public. Lastly, we evaluate a machine learning-based beam selection algorithm through the shared database. We expect that the offered guidelines and the shared database will enable researchers to readily design a map-based channel model.