Channel Modeling for FR3 Upper Mid-band via Generative Adversarial Networks

TL;DR

This paper introduces a GAN-based model for channel modeling in the FR3 upper mid-band, enabling accurate simulation of complex radio environments to support 5G network design.

Contribution

It presents a novel GAN architecture and link state model specifically designed for FR3 channel modeling, improving data-driven simulation accuracy.

Findings

Model closely matches ray-tracing simulated data

GAN effectively captures complex channel relationships

Supports efficient network optimization for FR3 bands

Abstract

The upper mid-band (FR3) has been recently attracting interest for new generation of mobile networks, as it provides a promising balance between spectrum availability and coverage, which are inherent limitations of the sub 6GHz and millimeter wave bands, respectively. In order to efficiently design and optimize the network, channel modeling plays a key role since FR3 systems are expected to operate at multiple frequency bands. Data-driven methods, especially generative adversarial networks (GANs), can capture the intricate relationships among data samples, and provide an appropriate tool for FR3 channel modeling. In this work, we present the architecture, link state model, and path generative network of GAN-based FR3 channel modeling. The comparison of our model greatly matches the ray-tracing simulated data.