Low-Complexity Stochastic Gradient Pursuit Algorithm and Architecture for Robust Compressive Sensing Reconstruction

Abstract

Compressive sensing (CS) is able to measure sparse signals with fewer measurements than Nyquist rate. The reconstruction of sparse signals in CS is an underdetermined problem. For realistic applications, the sparsity information may not be available, the measurements are used to be disturbed by noise, and efficient hardware realization is important. Existing CS reconstruction algorithms are tradeoff between noise tolerance and low complexity. In this paper, we propose a novel reconstruction algorithm called stochastic gradient pursuit (SGP). The stochastic gradient approach is applied to replace least square process of well-known orthogonal matching pursuit (OMP) algorithm. Since stochastic gradient approach iterates toward a minimum mean square error solution, proposed algorithm shows robustness against noise. Furthermore, proposed algorithm inherits the low-complexity property of OMP and shows feasibility of hardware implementation. The SGP algorithm can achieve 36% higher success rate as well as 27% less complexity than OMP when SNR is 20 dB. Finally, we implement the algorithm with designed hardware sharing architecture in TSMC 90 nm technology. The postlayout result shows that the core size is only 1.08 mm 2 at 150 MHz operation frequency. Compared with state-of-the-art ASIC designs, the proposed engine achieves 29% higher throughput-to-area ratio.