Blind reconstruction and automatic modulation classifier for non-uniform sampling based wideband communication receivers

Abstract

Electromagnetic spectrum is a limited natural resource and needs to be used efficiently. However, various measurements conducted worldwide have observed poor spectrum utilization. Software defined radios (SDRs) and cognitive radios (CRs) technologies allow efficient utilization of spectrum by empowering mobile devices to change their transmission parameters like frequency band, sampling rate, modulation scheme, etc. to meet the desired quality of service for different channel conditions. These mobile devices require smart receiver to detect transmission parameters of received signal. Such smart receivers, also known as multi-standard wireless communication receivers (MWCRs), must be capable of digitizing wideband signal ranging from 400 MHz to few GHz to support wide variety of data intensive services. Limited reconfigurability of analog front-end and unavailability of high rate analog to digital converters (ADCs) have generated significant interest in non-uniform (sub-Nyquist) sampling (NUS) and digital reconstruction based MWCRs. Existing reconstruction approaches require prior knowledge of sparsity which may not be available in dynamic spectrum environment. To alleviate this problem, a blind adaptive orthogonal matching pursuit (AOMP) reconstruction approach has been proposed and is the first contribution of this thesis. Novelty of AOMP is the use of online learning algorithm to find spectrum occupancy (i.e. sparsity). Simulation results show that the average reconstruction error of AOMP is 29.7% lower than other approaches. To validate the usefulness of proposed approach in real life applications, performance of cumulant and machine learning based automatic modulation classifier (AMC) is analyzed for the wideband signal digitized using the proposed approach. The simulation results are further verified on the proposed USRP testbed in real radio environment. Simulation and experimental results show that the accuracy of NUS based AMC approaches the accuracy of uniform sampling based AMC for higher values of SNR and proposed AOMP is superior to others. Also, the performance of AMC does not degrade significantly with NUS given that wideband signal is sparse in frequency