DISCRIMINATING STATISTICAL FEATURE FOR WIDEBAND SPECTRUM SENSING
Keywords:Cognitive radio, Low signal-to-noise ratio (SNR), Variable noise floor, Variance of multi-scale moving average, Wideband sensing
Spectrum-aware devices and cognitive radios with wideband spectrum sensing will be an integral part of 5G or beyond wireless broadband. They must be fast and energy efficient for opportunistic dynamic access to the licensed spectrum. Compressed sensing (CS) methods can implement wideband sensing with reduced time and power consumption but are inaccurate at low SNR. Eigen methods are one of the best among non-CS methods but are high in computational cost. In this paper, we present a simple feature named the variance of multi-scale moving average (VMMA) that can be directly used as a decision statistic, discriminating signal from noise very accurately, even at a low signal-to-noise ratio (SNR). VMMA computes variance in a specific way over the entire band after comparing the short-term and long-term moving averages. Tests on experimental spectrum data and numerical simulations show that the proposed algorithms are not only fast but also have higher detection probability than the algorithms developed in the literature. Analytical expressions for the probability of detection and false alarm, along with the complexities of the algorithms, are also derived.
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