Design and exploration of low-power sub-nyquist processing with Modulated Wideband Converter

This study proposes a practical method to optimize Modulated Wideband Converter (MWC) used in analog-to-digital conversion based on sparse Bernoulli algorithm. MWC is a multi-channel parallel architecture with Analog-to-Information Converter (AIC) to achieve sub-Nyquist sampling for the wideband multi-frequency signals. However, the dense Bernoulli measurement matrix in MWC will cause redundancy between the measurement value and increase complexity of circuit. This study uses sparse Bernoulli algorithm instead which reduces the relevance between measurement values and reduces the complexity of the circuit design and power consumption. Numerical simulation results show that the proposed MWC can perform sub-Nyquist sampling and accurately reconstruct the original signal.