Theory and Design of a Quadrature Analog-to-Information Converter for Energy-Efficient Wideband Spectrum Sensing

A flexible bandwidth, blind sub-Nyquist sampling approach referred to as the quadrature analog-to-information converter (QAIC) is proposed. The QAIC relaxes the analog front-end bandwidth requirements at the cost of some added complexity compared to the modulated wideband converter (MWC) for an overall improvement in sensitivity and energy consumption. An approach for detailed frequency domain analysis of the proposed system with linear impairments is developed. A process for selecting QAIC parameter values is illustrated through examples. The benefits of the QAIC are highlighted with cognitive radio use cases where a wide range of spectrum is observed at various resolution bandwidth settings. We demonstrate that the energy consumption of the QAIC is potentially two orders of magnitude lower than the swept-tuned spectrum analyzer (STSA) and an order of magnitude lower than the MWC. We also demonstrate that the QAIC significantly improves upon the sensitivity performance delivered by the MWC.