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Abstract
Spectrum sensing has been a major issue while dealing with cognitive radio (CR) networks. Predominantly the situation arises where noise is falsely interpreted as primary user signal called as probability of false alarm (Pfa). The user presence is estimated based on the parameters Pfa, probability of detection (PD) and receiver operating characteristics (ROC). Matched filter detection (MFD) and Neyman Pearson (NP) observer approaches are existing methods used to identify the Pfa. MFD measured ROC with different algorithms and suggests NP observer to improve the PD and minimise the Pfa. This paper proposes a novel method of matched filter detection with inverse covariance matrix-based spectrum sensing (MDI-SS). The ROC is measured, compared between MDI-SS and MFD. Next by comparing NP observer and MDI-SS, the affected samples of Pfa are identified, tabulated for different SNR levels. Finally, comparative analysis has been proposed between MDI-SS with NP observer for PD and Pfa.
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