One-Bit Spectrum Sensing Based on Statistical Covariances: Eigenvalue Moment Ratio Approach

Abstract

One-bit analog-to-digital converter (ADC), performing signal sampling as an extreme simple comparator, is an overwhelming technology for spectrum sensing due to its low-cost, low-power consumptions and high sampling rate. In this letter, we propose a novel one-bit sensing approach based on the eigenvalue moment ratio (EMR), which has been proved to be highly efficient for conventional multi-antenna spectrum sensing in <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\infty $ </tex-math></inline-formula> -bit situation. The statistical covariances of the one-bit samples inherent the statistical correlation properties, thus allow us to detect the existence of primary user (PU) though the test for sphericity. Particularly, we verify the element-by-element independence of one-bit sample covariance matrix (SCM) in the absence of signal, which allows us to asymptotically determine the null distributions of statistical covariance based spectrum sensing techniques. On this basis, we formulate the asymptotic distribution of one-bit EMR under null hypothesis via the central limited theorem (CLT) and perform spectrum sensing with one-bit samples directly. Theoretical and simulation results show the new approach can provide superior sensing performance at a low hardware cost.