基于改进粒子群优化的快速协作式频谱感知算法

为了改进算法的计算效率和感知性能，提出了一种新的线性协作式频谱感知算法.在新算法中，通过动态地改变粒子群优化（PSO）方法在每次迭代过程中的迭代系数，以获取最优的权重向量，从而最大化算法的检测概率.采用时变迭代系数后，基于PSO的协作式频谱感知算法在粒子飞行的初期具有很好的全局探索能力，而随着迭代次数的增加，算法具有很好的局部搜索能力.仿真结果表明，新算法相比基于传统PSO的频谱感知算法具有更快的收敛速度，相比传统的基于修正系数和基于传统PSO的协作式感知算法具有更好的性能.不同场景下的仿真结果验证了新算法的有效性.

Fast Cooperative Spectrum Sensing Method Based on Improved Particle Swarm Optimization

To improve the computation efficiency and the sensing performance,a new linear cooperative spectrum sensing algorithm is developed for cognitive radio.In the proposed method,the improved particle swarm optimization (PSO) is utilized to obtain the optimal weight vector to maximize the probability of detection by dynamically changing the inertia weight in each iteration.Due to using the of time varying inertia weight,the proposed PSO based cooperative spectrum sensing algorithm has good global exploration capability at the beginning of the swarm flight,and with the increase in the number of iterations,especially in the late swarm flight,has good local search capability.The simulation results illustrate that the proposed method has faster convergence speed than the method based on the traditional PSO.At the same time,it shows better detection performance than both the method based on modified deflection coefficient and the method based on traditional PSO.The simulation results under different sensing scenarios validate the effectiveness of the proposed cooperative sensing algorithm.