David E. Bellasi

TL;DR: This paper presents two generic very-large-scale integration (VLSI) architectures that implement the approximate message passing (AMP) algorithm for sparse signal recovery and shows that AMP-T is superior to AMp-M with respect to silicon area, throughput, and power consumption, whereasAMP-M offers more flexibility.

TL;DR: This paper presents the first very-large-scale integration (VLSI) design of a monolithic wideband CS-based A2I converter that includes a signal acquisition stage capable of acquiring RF signals having large bandwidths and a high-throughput spectral activity detection unit.

TL;DR: This paper considers the complete signal chain from acquisition to reconstruction, with particular attention to the effects of quantization, and shows that the two schemes differ significantly in encoder precision, measurement resolution, compression ratio, and reconstruction quality.

TL;DR: It is found that conventional CS acquisition can be made more energy-efficient as it tolerates a certain amount of random puncturing, and that more substantial power savings can be achieved when estimation is the target and undersampling is optimized by a suitable algorithm.

TL;DR: A novel compressive sensing (CS)-based analog front-end, which is able to sample sparse wideband RF signals at low cost and low power, and can be recovered off-line via novel sparse signal dequantization algorithms.