Compressed Sensing for Electronic Radio Frequency Receiver: Detection, Sensitivity, and Implementation

The current state-of the-art for digital receiver bandwidth coverage is now reaching multi-GHz. The conventional wideband digital receiver design is based on the Nyquist information theory, and its bandwidth coverage is limited by the Nyquist sampling rate. Therefore, receiver performance highly depends on the high speed analog-to-digital (ADC) technology and computation hardware such as FPGA. Having proved a fundamental theory that Nyquist waveform can be restored with a reduced sampling rate under certain situations, compressed sensing (CS) technique becomes an attractive solution to wideband digital receiver development. In this dissertation, performance analysis of the compressed sensing in receiver application is conducted. The compressed sensing receiver uses two modulations and sampling schemes: 1) Pseudo Random Code (PRC), a uniform sampling approach, and 2) a proposed non-uniform sampling (NUS) approach. Three algorithms are used to process the compressed signals: 1) Orthogonal Matching Pursuit (OMP), 2) Parameter Estimation (PE), and 3) Nesterov\u27s algorithm (NESTA). Signal detection thresholds for the compressed sensing receivers are determined by Additive white Gaussian noise (AWGN) distribution through probability density function (PDF) using the best fitting analog function for a false alarm rate of 10-7. Remedy algorithms are developed to solve frequency-misread problem caused by CS modulations. Signal detection and sensitivity of the compressed sensing receivers are measured and presented. GPU-accelerated parallel computing is adopted to process the compressed signals. Computing results of OMP and PE from NVIDIA Telsa K-40 GPU are presented