What Should We Learn? Special Issue on Machine Learning for Cognition in Radio Communications and Radar

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What Should We Learn? Special Issue on Machine Learning for Cognition in Radio Communications and Radar

By: 
Yang Li

Signal processing is always at the heart of the technology that differentiates today’s generations from those of the past is reaffirmed once again by the special issue on Machine Learning for Cognition in Radio Communications and Radar in IEEE Journal of Selected Topics in Signal Processing, February 2018.

Machine learning is the technological disruptor of our time, achieving groundbreaking success in self-driving cars, gaming and virtual reality, natural language processing and business analytics. This special issue articulates its impact on signal processing research in radio communications and radar by showcasing a stunning diversity of research problems addressed by means of machine learning. The editors aimed to show the variety of topics outlined in the call for papers. It appears that despite the lack of big data in communications and radar, the momentum from the deep learning revolution has had a spill-over effect, inspiring new and creative approaches to signal processing problems in these fields. The editors believe that further advancement of machine learning based cognition in communications and radar would benefit the most from a publicly available signal processing knowledge base of RF signals. Hope that this special issue on Machine Learning for Cognition in Radio Communications and Radar helped demonstrate the value of such an open-access repository.

 

 

 

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