Energy Detection in Cognitive Radio applications using Logarithmic Square Adaptive Learning

To avoid spectrum scarcity problems in wireless communications, cognitive radio concept used as reliable and effective solution. To use proper exploitation of white sources in cognitive radios required accurate, fast and robust methods. In this paper, we proposed new method for detecting white spaces in spectrum. Based on this strategy, cognitive radio performs spectrum sensing via energy detection technique. Main novelty of this paper is adaptive algorithm i.e., error normalized least mean logarithmic square (ENLMLS), it contains the information of primary user presence or absence. Identification of white spaces depends on entity which is able to improve deflection coefficient significantly related with detector when compared to other adaptive algorithms. Simulation results shows that proposed ENLMLS algorithm performs well compared to LMS algorithm by means of convergence. Further by using clipping function, it reduces noise levels and yields missed detection probability is smaller by SNR values and predefined threshold value.