Experimental Study of Multi-Resolution Spectrum Opportunity Detection Using Wavelet Analysis

Spectrum sensing is one of the crucial aspects in Cognitive Radio (CR). Fast and accurate spectrum opportunity detection provides interference avoidance to other/licensed users. At the same time, it offers more efficient spectrum utilization by providing accurate sensing information as an input to the intelligent dynamic resource allocation process. Wideband spectrum sensing has been introduced due to the higher bandwidth demand and increasing spectrum scarcity since it provides better chance of detecting spectrum opportunity. In this paper, the application of wavelet transform techniques for wideband spectrum opportunity detection in CRs is documented. Wavelet analysis is used in two-step process detection or multi-resolution opportunity detection proposed here. Edge detection using wavelet analysis is employed in the first step to indentify possibly available subband(s). The fine analysis is done in the second step for each chosen subband(s) using wavelet transform in order to detect any non-stationary signal, which may present in the chosen subband(s). With this two-step process, detection time could be reduced and at the same time providing detection accuracy. The paper presents research approach and the experimental study, which involves the development of the test platform used to obtain real-time spectrum sensing results and the software tool used for the opportunity detection. The experimental results are provided, which prove the practicality and accuracy of the approach.