Compressed Sensing is an emerging approach exploiting the sparsity feature of a signal to give accurate waveform representation at reduced sampling rate, below the Shannon-Nyquist conditions, thus leading to efficient radar and communication systems, with reduced complexity and cost.

The aim of this special issue is to provide an international forum for experts and researchers working in the area of Compressed Sensing applied to radar and communication contexts, in order to explore the state-of-the-art of such techniques as well as to present new advanced concepts and results.

Potential topics include, but are not limited to:

• Compressed Sensing for signal processing
• Compressed Sensing for synthetic aperture radar (SAR)
• Compressed Sensing for MIMO architectures
• Compressed Sensing for through-the-wall radar applications
• Compressed Sensing for high-resolution radars
• Compressed Sensing in wireless communications
• Compressed Sensing for ultrawideband communications
• Compressed Sensing for distributed networks
• Computational aspects of compressive sensing

Practical demonstrations and actual measurements are highly encouraged. Full openness of all source code and data (following reproducible research principles) is required to allow readers the chance to reproduce results.