In a cognitive radio system, efficient wideband spectrum estimation is a basic component of dynamic spectrum access. The systems high sampling rate is the main challenge in the frontend. In this paper, wideband power spectrum sensing is studied based on sub-Nyquist sampling instead of signal recovery. Compared to other spectrum sensing methods based on sub-Nyquist sampling, the proposed scheme is suitable for both sparse and nonsparse signals. A low complexity, adaptive resolution frequency averaging scheme is proposed to exploit the cross-power spectrum between the outputs of different channels. Spectrum reconstruction presents only a simple least square without any sparse constraint. The normalized mean square error is computed to demonstrate estimation performance.
['Lebing Pan', 'Shiliang Xiao', 'Xiao-bing Yuan']
Wideband Power Spectrum Sensing for Cognitive Radios Based on Sub-Nyquist Sampling