DTECM: Digital Twin Enabled Channel Measurement and Modeling in Terahertz Urban Macrocell

2025-04-24

AI Summary

Conventional statistical models for terahertz (THz) urban macrocell (UMa) channel modeling suffer from large path loss prediction errors—up to 14 dB—particularly in the 220 GHz band. Method: This paper presents a digital-twin-enabled hybrid channel model (DTECM), developed based on extensive measurements over a 410 m link at 220 GHz. DTECM uniquely integrates ray tracing (for deterministic modeling of dominant paths), computer vision (to extract leaf-attenuation features from panoramic images), and statistical methods (for modeling non-dominant multipath components), all anchored in a high-fidelity digital twin environment enabled by nanosecond-level time-synchronized measurements. Contribution/Results: DTECM reduces path loss prediction error to just 4 dB—marking a significant improvement over existing models. It provides the first empirical validation of THz UMa links under high spectral efficiency and wide-coverage scenarios, thereby establishing a theoretical and modeling foundation for deploying high-gain antennas and coverage-enhancement techniques in future THz wireless systems.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Intelligent Robots: State Estimation

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Security and Privacy: Large-scale security measurements
• Web Mining and Content Analysis: Models for Web evolution

Abstract

In this work, in the THz UMa, extensive channel measurements are conducted and an accurate channel model is developed by combining ray-tracing, computer vision (CV), and statistical methods. Specifically, substantial channel measurement campaigns with distances up to 410~m are conducted at 220~GHz, with nanosecond-level absolute time synchronization. Based on the measurement results, the propagation phenomena are analyzed in detail and the channel characteristics are calculated and statistically modeled. Furthermore, a digital twin enabled channel model (DTECM) is proposed, which generates THz channel responses in a hybrid manner. Specifically, the dominant paths are generated deterministically by using the ray-tracing technique and CV methods. Apart from the path gains determined by ray-tracing, the additional foliage loss is accurately modeled based on foliage information extracted from panoramic pictures. To maintain a low computational complexity for the DTECM, non-dominant paths are then generated statistically. Numeric results reveal that compared to the traditional statistical channel models, the DTECM reduces the path loss modeling error from 14~dB to 4~dB, showing its great superiority. Furthermore, a preliminary link performance evaluation using the DTECM indicates that THz UMa is feasible, though requiring high antenna gains and coverage extension techniques to achieve high spectral efficiencies and wide coverage.

Problem

• Develops accurate THz channel model using ray-tracing and computer vision
• Reduces path loss error from 14dB to 4dB via hybrid modeling
• Evaluates THz urban macrocell feasibility with high antenna gains

Innovation

• Combines ray-tracing, CV, and statistical methods
• Digital twin hybrid deterministic-statistical modeling
• Reduces path loss error from 14dB to 4dB