Vision and Causal Learning Based Channel Estimation for THz Communications

TL;DR

This work tackles the challenge of accurate THz channel estimation in dynamic urban environments by fusing computer vision with causality-driven learning. It introduces a MulT-based environmental feature extractor combined with a structural causal model and variational causal dynamics (VCD), enabling causal inference and sparse adaptation to environmental changes. The approach demonstrates up to two-fold gains in estimation accuracy and robust generalization to unseen layouts, particularly in NLoS conditions, validated through CARLA/MATLAB ray-tracing simulations. The framework offers interpretable, physics-aligned channel estimation with potential impact on beamforming, RIS control, and 6G THz network reliability.

Abstract

The use of terahertz (THz) communications with massive multiple input multiple output (MIMO) systems in 6G can potentially provide high data rates and low latency communications. However, accurate channel estimation in THz frequencies presents significant challenges due to factors such as high propagation losses, sensitivity to environmental obstructions, and strong atmospheric absorption. These challenges are particularly pronounced in urban environments, where traditional channel estimation methods often fail to deliver reliable results, particularly in complex non-line-of-sight (NLoS) scenarios. This paper introduces a novel vision-based channel estimation technique that integrates causal reasoning into urban THz communication systems. The proposed method combines computer vision algorithms with variational causal dynamics (VCD) to analyze real-time images of the urban environment, allowing for a deeper understanding of the physical factors that influence THz signal propagation. By capturing the complex, dynamic interactions between physical objects (such as buildings, trees, and vehicles) and the transmitted signals, the model can predict the channel with up to twice the accuracy of conventional methods. This model improves estimation accuracy and demonstrates superior generalization performance. Hence, it can provide reliable predictions even in previously unseen urban environments. The effectiveness of the proposed method is particularly evident in NLoS conditions, where it significantly outperforms traditional methods such as by accounting for indirect signal paths, such as reflections and diffractions. Simulation results confirm that the proposed vision-based approach surpasses conventional artificial intelligence (AI)-based estimation techniques in accuracy and robustness, showing a substantial improvement across various dynamic urban scenarios.

Figures (9)

• Figure 1: System model of the proposed framework.
• Figure 2: Multi-task learning framework for environmental feature extraction.
• Figure 3: Environmental views from the BS to target vehicles in different scenarios(The target vehicle is in the red box).
• Figure 4: Example of DAG for the channel estimation.
• Figure 5: Channel estimation performance comparison across different environmental features in the same scenario (scenario 1).