Deep learning acceleration for channel estimation in terahertz band MIMO communication systems
太赫兹频段MIMO通信系统的信道估计深度学习加速

摘要

本文探讨了基于深度学习加速的太赫兹频段MIMO通信系统的信道估计技术。在高速数据传输和低延迟通信的需求下，太赫兹通信技术逐渐成为未来无线通信的关键。太赫兹频段的高频特性使得信道估计成为一项复杂且挑战性巨大的任务。为了应对这一挑战，本文提出了一种基于深度学习的信道估计方法，通过引入神经网络模型来提高估计精度，并加速传统估计算法的性能。实验结果表明，所提出的方法在信道估计精度和计算效率方面均优于传统技术，为太赫兹频段MIMO通信系统的实际应用提供了有力支持。

Abstract

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.