Sub-Nyquist Wideband Spectrum Sensing Based on Analog to Information Converter for Cognitive Radio

Abstract With the development of wireless communication technology, higher spectrum efficiency is required for the 5G cellular networks. Cognitive radio (CR) has emerged as one of the most promising candidate solution to improve spectrum utilization and efficency. Wideband spectrum sensing has been the focal challenge point in cognitive radio technology, since existing techniques are reliant on analog to digital converters (ADC) with sampling at the Nyquist rate. However, in order to perform digitization of wideband RF signals at the Nyquist rate, a very high sampling frequency is required with primarily complex and energy inefficient designs. In this paper, a sub-Nyquist wideband spectrum sensing design is proposed. In the proposed design, an analog-to-information converter is presented. It utilizes Compressive Sensing which enables sampling far below the Nyquist rate, thus drastically reducing the power consumption, complexity and cost of the entire Receiver. The main biulding blocks in the proposed design which are: higher speed SAR analog to digital for random demodulator analog to information converter (AIC) were designed and simulated using cadance software for performance evaluation. The design was implemented in targeted technology of 130 nm standard CMOS technology. An optimization method suitable for VLSI implementation, termed Orthogonal Matching Pursuit (OMP), was utilized for spectrum recovery. Following comparative analyses with previously published studies, we demonstrate significant improvements in terms of speed and chip area of the SAR ADC design.