Sample-Efficient Spatio-Spectral Whitespace Detection Using Least Matching Pursuit

Abstract

Multi-antenna wireless communication improves spectral efficiency by reusing frequencies at different locations in space using beamforming and spatial multiplexing. In the past, research has extensively focused on dynamically reusing unused frequency bands to optimize spectrum usage, but methods that identify unused resources in space appear to be unexplored. In this paper, we propose a sample-efficient whitespace detection pipeline for multi-antenna radio-frequency (RF) transceivers that detects unused resources in both frequency and space. Our spatio-spectral whitespace detection pipeline relies on multi-antenna nonuniform wavelet sampling, which identifies unused frequencies in space at sub-Nyquist sampling rates. We demonstrate the efficacy of our approach via system simulations and show that reliable spatio-spectral whitespace detection is possible with $16 \times $ lower sampling rates than methods relying on Nyquist sampling.

Keywords

• Coherence
• compressive sensing (CS)
• least matching pursuit (LMP)
• multi-antenna communication
• nonuniform wavelet sampling (NUWS)
• spatio-spectral sensing