Published Paper Details:

This research delves into the optimization of cooperative spectrum sensing in multiuser multiple-input-multiple-output (MIMO) systems, where both the primary user (PU) and the cognitive radio (CR) are endowed with multiple antennas. The focal point of optimization lies in determining optimal weights assigned to the received signals of CRs. The objective is to maximize the probability of detection while adhering to a predefined probability of false alarm. Statistical characteristics of parameters in MIMO cooperative spectrum sensing systems have been ascertained for various scenarios, including the PU with a single antenna and the CR with multiple antennas, the PU with multiple antennas and the CR with a single antenna, as well as both the PU and the CR equipped with multiple antennas. Given the non-convex nature of the optimization problem, an alternative approach utilizing a genetic algorithm (GA) is proposed. This deviation from convex methods allows for the exploration of optimal weight vectors without imposing solution domain restrictions or convexity constraints. Additionally, various classical GA crossover operators are examined to assess their impact on sensing performance. Simulation results underscore that the reliability of spectrum sensing in cooperative spectrum sensing systems experiences significant enhancement with the integration of multiple antennas. Moreover, the GA method emerges as a promising approach for addressing the challenges associated with cooperative spectrum sensing.