Transfer Generative Adversarial Networks (T-GAN)-based Terahertz Channel Modeling

TL;DR

This paper introduces a T-GAN-based method for modeling THz channels, leveraging transfer learning to improve accuracy with limited measurement data, outperforming traditional models in fidelity and efficiency.

Contribution

The paper presents a novel T-GAN approach that combines GANs and transfer learning for accurate THz channel modeling using limited measurement data.

Findings

T-GAN accurately models PDP distributions aligning with measurements.

T-GAN outperforms traditional 3GPP models with 9 dB RMSE improvement.

Transfer learning enhances model generalization with limited data.

Abstract

Terahertz (THz) communications are envisioned as a promising technology for 6G and beyond wireless systems, providing ultra-broad bandwidth and thus Terabit-per-second (Tbps) data rates. However, as foundation of designing THz communications, channel modeling and characterization are fundamental to scrutinize the potential of the new spectrum. Relied on physical measurements, traditional statistical channel modeling methods suffer from the problem of low accuracy with the assumed certain distributions and empirical parameters. Moreover, it is time-consuming and expensive to acquire extensive channel measurement in the THz band. In this paper, a transfer generative adversarial network (T-GAN) based modeling method is proposed in the THz band, which exploits the advantage of GAN in modeling the complex distribution, and the benefit of transfer learning in transferring the knowledge from a source task to improve generalization about the target task with limited training data. Specifically, to start with, the proposed GAN is pre-trained using the simulated dataset, generated by the standard channel model from 3rd generation partnerships project (3GPP). Furthermore, by transferring the knowledge and fine-tuning the pre-trained GAN, the T-GAN is developed by using the THz measured dataset with a small amount. Experimental results reveal that the distribution of PDPs generated by the proposed T-GAN method shows good agreement with measurement. Moreover, T-GAN achieves good performance in channel modeling, with 9 dB improved root-mean-square error (RMSE) and higher Structure Similarity Index Measure (SSIM), compared with traditional 3GPP method.