SPECTRUM SENSING USING NEYMAN-PEARSON BASED MATCHED FILTER DETECTION IN COGNITIVE RADIO NETWORKS

In the last two decades, the number of wireless communication users and its applications increasing rapidly. With this rapid growth there exists spectrum scarcity. One of the best solutions to overcome spectrum scarcity is cognitive radio with its dynamic spectrum sensing. Cognitive radio is a promising technology, which is used to sense the unused spectrum in an appropriate manner and then the unlicensed users are allowed to communicate using these spectrum temporarily. When the primary (licensed) user present, the unlicensed user should vacate the spectrum immediately. Various methods have been proposed in the past, such as energy detection, feature detection, matched filter and so on. Different techniques serve different purpose based on their advantages and disadvantages. However, all possible cognitive radio operation scenarios cannot be accommodating by individual method. The performance of spectrum sensing can be improved by proper selection of threshold. In this paper, it is suggested an approach of dynamic threshold based spectrum sensing using matched filter with Neyman Pearson criterion, which increases the detection probability of spectrum. The energy detection and matched filter detector with adaptive threshold are compared and analyzed under different SNR values by using MATLAB.