Transfer Learning Enabled Transformer based Generative Adversarial Networks (TT-GAN) for Terahertz Channel Modeling and Generating

TL;DR

This paper introduces TT-GAN, a novel transfer learning-enabled transformer-based GAN that models Terahertz channels accurately with limited measurement data, advancing 6G wireless system development.

Contribution

It presents a new TT-GAN framework combining transformers and transfer learning to improve THz channel modeling accuracy with fewer measurements.

Findings

High accuracy in THz channel modeling achieved

Requires limited measurement data

Outperforms traditional measurement-based methods

Abstract

Terahertz (THz) communications, ranging from 100 GHz to 10 THz, are envisioned as a promising technology for 6G and beyond wireless systems. As foundation of designing THz communications, channel modeling and characterization are crucial to scrutinize the potential of the new spectrum. However, current channel modeling and standardization heavily rely on measurements, which are both time-consuming and costly to obtain in the THz band. Here, we propose a Transfer learning enabled Transformer based Generative Adversarial Network (TT-GAN) for THz channel modeling. Specifically, as a fundamental building block, a GAN is exploited to generate channel parameters, which can substitute measurements. To greatly improve the accuracy, the first T, i.e., a transformer structure with a self-attention mechanism is incorporated in GAN. Still incurring errors compared with ground-truth measurement, the second T, i.e., a transfer learning is designed to solve the mismatch between the formulated network and measurement. The proposed TT-GAN can achieve high accuracy in channel modeling, while requiring only rather limited amount of measurement, which is a promising complementary of channel standardization that fundamentally differs from the current techniques that heavily rely on measurement.