Sensing and Vision-aided Wireless Communication: Generalizable Deep Learning-Based Terahertz Channel Prediction for Indoor 6G Networks

Abstract

The evolution of 6G wireless networks requires robust and adaptive communication systems that can handle dynamic indoor environments. Accurate prediction of the terahertz (THz) channel is a key enabler of this adaptability, enabling proactive decisions such as beamforming and handover. Because traffic levels (i.e., user densities) fluctuate, the underlying channel statistics change over time, resulting in concept drift that can deteriorate the performance and generalization ability of deep learning (DL) models if they are tested against mismatched conditions (i.e., on a traffic level other than the one used for training). This paper introduces a novel framework for generalizable THz channel prediction, enabled by fusing environmental sensing with AI-assisted wireless communication to mitigate concept drift and maintain generalization. First, we investigate the use of DL-based channel prediction models tailored to specific traffic levels—light, moderate, and dense—and evaluate their performance under mismatched conditions. Results show up to 51% performance deterioration when models are exposed to mismatched traffic scenarios. To mitigate this issue, a traffic-aware channel prediction framework is proposed, comprising three stages: people counting using sensing technologies; quantization of the user count into traffic levels; and dynamic selection of the corresponding DL model. Simulation results demonstrate that integrating accurate sensing technologies, particularly vision-based systems, significantly reduces prediction deterioration to as low as 4%. The proposed framework’s adaptability ensures reliable channel prediction by aligning model selection with real-time traffic conditions, which highlights the potential of fusing environmental sensing with AI-assisted wireless communication to enhance the robustness of future 6G networks.

Topics & Concepts