A rakeness-based design flow for Analog-to-Information conversion by Compressive Sensing

Classical design of Analog-to-Information converters based on Compressive Sensing uses random projection matrices made of independent and identically distributed entries. Leveraging on previous work, we define a complete and extremely simple design flow that quantifies the statistical dependencies in projection matrices allowing the exploitation of non-uniformities in the distribution of the energy of the input signal. The energy-driven reconstruction concept and the effect of this design technique are justified and demonstrated by simulations reporting conspicuous savings in the number of measurements needed for signal reconstruction that approach 50%.