Data signals for deep learning applications in Terahertz communications

The Terahertz (THz) band (0.1–10 THz) is projected to enable broadband wireless communications of the future, and many envision deep learning as a solution to improve the performance of THz communication systems and networks. However, there are few available datasets of true THz signals that could enable testing and training of deep learning algorithms for the research community. In this paper, we provide an extensive dataset of 120,000 data frames for the research community. All signals were transmitted at 165 GHz but with varying bandwidths (5 GHz, 10 GHz, and 20 GHz), modulations (4PSK, 8PSK, 16QAM, and 64QAM), and transmit amplitudes (75 mV and 600 mV), resulting in twenty-four distinct bandwidth-modulation-power combinations each with 5,000 unique captures. The signals were captured after down conversion at an intermediate frequency of 10 GHz. This dataset enables the research community to experimentally explore solutions relating to ultrabroadband deep and machine learning applications.

太赫兹（THz）频段（0.1 - 10太赫兹）预计将用于未来的宽带无线通信，许多人设想将深度学习作为一种提高太赫兹通信系统和网络性能的解决方案。然而，可供研究团体用于测试和训练深度学习算法的真实太赫兹信号数据集却很少。在本文中，我们为研究团体提供了一个包含120,000个数据帧的广泛数据集。所有信号都在165吉赫兹发射，但具有不同的带宽（5吉赫兹、10吉赫兹和20吉赫兹）、调制方式（4相移键控、8相移键控、16正交幅度调制和64正交幅度调制）以及发射幅度（75毫伏和600毫伏），从而产生了24种不同的带宽 - 调制 - 功率组合，每种组合都有5000次独特的采集。这些信号是在经过下变频到10吉赫兹的中频后采集的。这个数据集使研究团体能够通过实验探索与超宽带深度学习和机器学习应用相关的解决方案。