A major task in cognitive radios (CRs) is spectrum sensing. In a wide-band regime, this is a challenging task requiring very high-speed analog-to-digital converters (ADCs), operating at or above the Nyquist rate. Compressed sensing is recognized as an effective technique to significantly reduce the sampling rate in wideband spectrum sensing, taking advantage of the sparsity of the spectrum. The recovery of the spectrum from the samples at sub-Nyquist rates is usually achieved through the so-called ℓ1-norm minimization. A more effective recovery technique for block sparse signals, called ℓ2/ℓ1-norm minimization, can be used as a replacement for ℓ1-norm minimization to reduce the sampling rate and consequently simplify the implementation of ADCs even further. In this paper, we propose two iterative ℓ2/ ℓ1-norm minimization algorithms for the recovery of block sparse spectrums. Similar to the standard ℓ2/ℓ1-norm minimization, the proposed algorithms require the side information about the boundaries of the spectral blocks. We evaluate the performance of the proposed algorithms both in the absence and in the presence of noise, and demonstrate that for both cases, the proposed algorithms significantly outperform the existing ℓ1- minimization-based and standard ℓ2/ℓ1 minimization recovery algorithms. The improvement in performance comes at a small cost in complexity

2012 IEEE International Conference on Communications, ICC 2012
Department of Systems and Computer Engineering

Zeinalkhani, Z. (Zeinab), & Banihashemi, A. (2012). Iterative recovery algorithms for compressed sensing of wideband block sparse spectrums. In IEEE International Conference on Communications (pp. 1630–1634). doi:10.1109/ICC.2012.6364377