# Terahertz channel modeling based on surface sensing characteristics

arXiv: https://arxiv.org/abs/2404.02661 · HTML: https://mlsanity.com/paper/2404.02661/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Apr 2024.

## Built on by (most cited first, 1 of 1)

- [Machine Learning for LiDAR-Based Indoor Surface Classification in Intelligent Wireless Environments](https://mlsanity.com/paper/2509.03813/) (2025)

## Abstract (arXiv)

The dielectric properties of environmental surfaces, including walls, floors and the ground, etc., play a crucial role in shaping the accuracy of terahertz (THz) channel modeling, thereby directly impacting the effectiveness of communication systems. Traditionally, acquiring these properties has relied on methods such as terahertz time-domain spectroscopy (THz-TDS) or vector network analyzers (VNA), demanding rigorous sample preparation and entailing a significant expenditure of time. However, such measurements are not always feasible, particularly in novel and uncharacterized scenarios. In this work, we propose a new approach for channel modeling that leverages the inherent sensing capabilities of THz channels. By comparing the results obtained through channel sensing with that derived from THz-TDS measurements, we demonstrate the method's ability to yield dependable surface property information. The application of this approach in both a miniaturized cityscape scenario and an indoor environment has shown consistency with experimental measurements, thereby verifying its effectiveness in real-world settings.