Scientific Development Research

This paper explores deep learning-accelerated channel estimation techniques for terahertz band MIMO communication systems. Driven by the demands for high-speed data transmission and low-latency communication, terahertz communication technology has gradually emerged as a key enabler for future wireless communications. However, the high-frequency characteristics of the terahertz band make channel estimation a complex and highly challenging task. To address this challenge, this paper proposes a deep learning-based channel estimation method that incorporates neural network models to enhance estimation accuracy and accelerate the performance of traditional estimation algorithms. Experimental results demonstrate that the proposed method outperforms traditional techniques in both channel estimation accuracy and computational efficiency, providing strong support for the practical application of terahertz band MIMO communication systems.

Terahertz band; MIMO communication; Channel estimation; Deep learning; Acceleration