Wideband and Narrowband Spectrum Sensing Methods Using Software Defined Radios

Abstract

The ability to accurately sense the surrounding wireless spectrum, without having any prior information about the type of signals present, is an important aspect for dynamic spectrum access and cognitive radio. Energy detection is one viable method, however its performance is limited at low SNR and must adhere to Nyquist sampling theorem. Compressive sensing has emerged as a potential method to recover wideband signals using sub-Nyquist sampling rates, under the presumption that the signals are sparse in a certain domain. In this study, the performance and some of the practical limitations of energy detection and compressive sensing are compared via simulation, and also implementation using the Universal Software Radio Peripheral (USRP) software defined radio (SDR) platform. The usefulness and simplicity of the USRP and GNU Radio software toolkit for simulation and experimentation, as well as some other application areas of compressive sensing and SDR, is also discussed