Generalized Analog-to-Information Converter With Analysis Sparse Prior

Conventional analog-to-information converter (AIC) frameworks employ a discrete-time synthesis sparse model to deal with analog signals, which, however, induces a challenging basis mismatch problem. In this paper, we propose a novel AIC framework, called generalized AIC (G-AIC), to tackle this issue. In the new method, an analysis sparse model is taken, for the first time, as the prior information of analog signals being sampled at sub-Nyquist rate. Through the joint optimization for the discretization operator and its analysis sparse operator, the G-AIC removes the model error between an analog signal and its equivalent discrete samples. To validate the G-AIC framework, we design a single channel G-AIC system based on switched-capacitor (SC) circuits. The circuit design is presented at the theoretical-level, the system-level, and the transistor-level. Numerical simulations demonstrate the G-AIC system can well restore an analog signal from its sub-Nyquist measurements, even though its sparse basis is unknown. Compared with two state-of-the-art AIC systems, the new design can achieve at least 2dB reconstruction gain. In brief, the proposed method provides a promising alternative to exploit analog signals in sub-Nyquist sampling systems.