Low-complexity detection of zero blocks in wideband spectrum sensing

A low-complexity scheme for the reliable detection of zero blocks in a block sparse signal is proposed. The scheme is based on the application of verification based (VB) recovery algorithms in compressed sensing to block sparse signals, and is described in the context of wideband spectrum sensing (WSS). To apply VB algorithms to WSS, we devise a block sparse sensing matrix by designing a novel analog-to-information converter (AIC). The AIC, the sensing matrix and the VB algorithms are then optimized such that the largest number of zero blocks for a given number of measurements can be detected. This work introduces a new paradigm in the recovery of block sparse signals, where one is interested in partial detection of the complement of the support set, reliably, rather than the full recovery of the signal or its support. The analysis and simulations demonstrate significant improvement in performance/complexity over the existing block sparse recovery schemes within this new framework. An important application of the results would be in cognitive radios with limited computational resources.