ABC Algorithm Based Minimization of The Detaction Error in Cooperative Sensing

In the recent wireless communication trends, the radio frequency spectrum is the prime concern for effective utilization as there are many radio frequency channels simultaneously used by many users. Cognitive radio (CR) can be the most useful new technique to utilize the radio frequency spectrum effectively and efficiently. Multiple users of the cognitive radio can be detected by sensing the spectrum in cooperative mode and vacant spectrum space is detected from these cognitive radio networks (CRN) for new users. Information of these CR users jointly utilized and combined at the common receiver level either by conventional or soft combining technique. Hence most attention is required in sensing the used cooperative spectrum with a minimum of error. This paper focuses on the optimization technique called Artificial Bees Colony (ABC) under the MINI-MAX criterion to minimize the probability of the error of spectrum energy level weighting coefficient. ABC algorithm generates the weighting coefficients vector which in turn minimizes the probability of error during sensing. Comparative analysis of the performance of this proposed algorithm and traditional soft decision fusion (SDF) methods like Equal Gain Combining (EGC) and hard decision fusion (HDF) methods like Majority, AND, OR etc. is done in this paper and simulation results shows that proposed technique have a minimum of error in detection.