Optimal Approximate entropy detection for Dynamic Spectrum Access in Cognitive Radio

Sensing the radio frequency bands is an open problem for dynamic spectrum access (DSA) in cognitive radio networks. To address this, numerous signal processing algorithms have been described based on entropy measurement. However, most of the methods lack connectivity between signal detection and appropriateness of the selected entropy. In this work, we propose an optimal detection method which estimates the degree of chaotic nature of Digital Television (DTV) signal based on Approximate entropy (ApEn). The optimal required input parameter of rt is estimated based on heuristic search algorithm to maximize the detection probability. The results reveal that the optimal ApEn with adaptive threshold can be used as an efficient method for spectrum sensing.