Compressive sensing spectrum recovery from quantized measurements in 28nm SOI CMOS

Spectral activity detection of wideband radio-frequency (RF) signals for cognitive radios typically requires expensive and energy-inefficient analog-to-digital converters (ADCs). Fortunately, the RF spectrum is-in many practical situations-sparsely populated, which enables the design of so called analog-to-information (A2I) converters. A2I converters are capable of acquiring and extracting the spectral activity information at low cost and low power by means of compressive sensing (CS). In this paper, we present a high-throughput spectrum recovery stage for CS-based wideband A2I converters. The recovery stage is designed for a CS-based signal acquisition front-end that performs pseudo-random subsampling in combination with coarse quantization. High-throughput spectrum activity detection from such coarsely quantized and compressive measurements is achieved by means of a massively-parallel VLSI design of a novel accelerated sparse signal dequantization (ASSD) algorithm. The resulting design is implemented in 28 nm SOI CMOS and able to reconstruct 215-point frequency-sparse RF spectra at a rate of more than 7.6 k reconstructions/second.