Abstract
Sub-Nyquist spectrum sensing and learning are
investigated from theory to practice as a promising approach
enabling cognitive and intelligent radios and wireless systems
to work in GHz channel bandwidth. These techniques
would be helpful for future electromagnetic spectrum sensing
in sub-6 GHz, millimetre-wave, and Terahertz frequency
bands. However, challenges such as computation complexity,
real-time processing and theoretical sampling limits still
exist. We issued a challenge with a reference sub-Nyquist
algorithm, open data set and awards up to 10,000 USD to
stimulate novel approaches and designs on sub-Nyquist spectrum
sensing and learning algorithms to promote relative research
and facilitate the theory-to-practice process of promising
ideas.