A Pragmatic Look at Some Compressive Sensing Architectures With Saturation and Quantization (original) (raw)
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In this work we combing two novelty in the area of Analog Information Converter based on Compressed Sensing. A new architecture, the Spread Spectrum Random Modulation Pre-Integration and a new design flow, the rakeness based design of a Compressed Sensing system. We demonstrate that combining these approaches produces a strong reduction of the internal chipping frequency in the sensing coupled with a high compression ratio with respect to standard Analog to Digital Converter.
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Analog-to-digital conversion comprises of two fundamental discretization steps: sampling and quantization. Recent results in compressive sensing (CS) have overhauled the conventional wisdom related to the sampling step, by demonstrating that sparse or compressible signals can be sampled at rates much closer to their sparsity rate, rather than their bandwidth. This work further overhauls the conventional wisdom related to the quantization step by demonstrating that quantizer overflow can be treated differently in CS and by exploiting the tradeoff between quantization error and overflow. We demonstrate that contrary to classical approaches that avoid quantizer overflow, a better finite-range scalar quantization strategy for CS is to amplify the signal such that the finite range quantizer overflows at a pre-determined rate, and subsequently reject the overflowed measurements from the reconstruction. Our results further suggest a simple and effective automatic gain control strategy which uses feedback from the saturation rate to control the signal gain.