Data signals for deep learning applications in Terahertz communications

Abstract

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.