A multi-resolution spectrum sensing (MRSS) scheme under measurement-based channel models in cognitive radio

Spectrum sensing is one of the key technologies in Cognitive Radios (CRs). Previous works are accomplished under simple channel models, which may lead to unreliable results when it applied to the over-the-air systems. In this paper, we investigate the performance of a Multi-Resolution Spectrum Sensing (MRSS) algorithm under measurement-based channel models in China. MRSS is a wavelet based algorithm which is suitable for non-stationary, wideband signal analysis. Using statistical modeling, measurement-based channel models are presented under typical urban and suburban scenarios in Shanghai, China. Then, the performance of the MRSS algorithm is evaluated under the measurement-based channel models. Simulation results show that, using MRSS, the performance is always better in the scenarios where Line-Of-Sight (LOS) path exist; also, in LOS scenarios, rich scattering effect helps to increase the performance.