Transfer learning enabled transformer-based generative adversarial networks for modeling and generating terahertz...

Area of Science:

• Wireless Communications
• Signal Processing
• Artificial Intelligence

Background:

• Terahertz (THz) communications are key for future wireless systems, offering Terabits-per-second (Tbps) data rates.
• Accurate channel modeling is essential for realizing THz spectrum potential but current methods require extensive, costly measurements.

Purpose of the Study:

• To develop an efficient and accurate terahertz channel modeling technique.
• To reduce reliance on time-consuming and expensive physical measurements in THz channel characterization.

Main Methods:

• A generative adversarial network (GAN) was employed to generate channel parameters.
• A transformer structure with self-attention mechanisms was integrated into the GAN for enhanced accuracy.
• Transfer learning was applied to address the discrepancy between the model and real-world measurements.

Main Results:

• The proposed model achieves high accuracy in terahertz channel modeling.
• The method significantly reduces the amount of measurement data required.
• The approach demonstrates a promising alternative to traditional measurement-intensive techniques.

Conclusions:

• The transfer learning-enabled transformer-based GAN offers an efficient solution for terahertz channel modeling.
• This AI-driven approach complements existing methods by minimizing measurement needs.
• The technique paves the way for accelerated development and deployment of 6G and beyond wireless systems.