Efficient Parallel Artificial Bee Colony Algorithm for Cooperative Spectrum Sensing Optimization

In this paper, the multiuser linear cooperative spectrum sensing optimization problem in cognitive radio system where the primary user (PU) and cognitive radio (CR) employ multiple antennas is investigated. By optimizing the different weights assigned on the statistic of each CR given a targeted probability of false alarm, the cooperative spectrum sensing optimization focuses on maximizing the probability of detection. Statistical characteristics of parameters in cooperative spectrum sensing for the PU with a single antenna and the CR with multiple antennas (SPMC) system, the PU with multiple antennas and the CR with a single antenna (MPSC) system, both the PU and the CR with multiple antennas (MPMC) system as well as the PU with Alamouti coding system have been investigated. Due to the non-convex characteristic of the cooperative spectrum sensing problem, an efficient parallel artificial bee colony (PABC) method motivated by the intelligent foraging behavior of a honeybee colony is introduced to address the problem without approximations and convexity constraints. Furthermore, classical benchmark functions are presented to validate the searching ability of the proposed algorithm. Simulation results showed that the PABC shows a superior ability when applied in both the typical test functions and the cooperative spectrum sensing scenario. Also, the reliability of spectrum sensing can be significantly promoted with the use of multiple antennas and Alamouti coding.