Signal Reconstruction Performance Under Quantized Noisy Compressed Sensing

We study rate-distortion (RD) performance of various single-sensor compressed sensing (CS) schemes for acquiring sparse signals via quantized/encoded noisy linear measurements, motivated by low-power sensor applications. For such a quantized CS (QCS) context, the paper combines and refines our recent advances in algorithm designs and theoretical analysis. Practical symbol-by-symbol quantizer based QCS methods of different compression strategies are proposed. The compression limit of QCS - the remote RDF - is assessed through an analytical lower bound and a numerical approximation method. Simulation results compare the RD performances of different schemes.