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Improved soft fusion-based cooperative spectrum sensing using particle swarm optimization

机译:使用粒子群算法的改进的基于软融合的协作频谱感知

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References(7) Cited-By(2) In soft-decision fusion- (SDF-) based cooperative spectrum sensing, weighting the coefficients vector is the main factor affecting the detection performance of cognitive radio networks. In this paper, the use of particle swarm optimization (PSO) algorithm as a prominent technique is proposed to optimize the weighting coefficients vector. The proposed PSO-based scheme opts for the best weighting coefficients vector, leading to improved detection performance of the system. The performance of the proposed method is analyzed and compared with genetic algorithm- (GA-) based technique as well as other conventional SDF schemes through computer simulations. Simulation results validate the robustness of the proposed method over all other SDF techniques.
机译:参考文献(7)引用依据(2)在基于软决策融合(SDF-)的协作频谱感知中,加权系数向量是影响认知无线电网络检测性能的主要因素。在本文中,提出了使用粒子群算法(PSO)作为一种重要技术来优化加权系数向量。提出的基于PSO的方案选择了最佳的加权系数矢量,从而提高了系统的检测性能。通过计算机仿真,分析了该方法的性能,并将其与基于遗传算法(GA-)的技术以及其他常规SDF方案进行了比较。仿真结果证明了该方法相对于所有其他SDF技术的鲁棒性。

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