Map-based Millimeter-Wave Channel Models: An Overview, Hybrid Modeling, Data, and Learning

TL;DR

This paper provides an overview of map-based millimeter-wave channel models, discusses their methodologies, shares data, and demonstrates a machine learning application for beam selection, aiming to facilitate future research and development.

Contribution

It categorizes map-based channel parameters, proposes hybrid modeling guidelines, shares measurement data publicly, and evaluates a machine learning algorithm for beam selection.

Findings

Shared measurement data and channel parameters facilitate research.

Hybrid modeling guidelines improve model design.

ML-based beam selection shows promising results.

Abstract

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.