认知无线网络中基于粒子群算法的功率和网络容量同步优化共信道干扰约束频谱分配

Pratik Tiwari, Seemanti Saha
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引用次数: 6

摘要

针对认知无线网络(CRN)中多个辅助用户(SU)对和一个主用户(PU)对以频谱底层方式构成的系统模型,提出了一种基于粒子群优化(PSO)的协同信道干扰约束下的高效低复杂度频谱分配方案。提出了一种新的加权适应度函数,该函数的约束条件是所有主用户和辅助用户都有各自的目标信噪比。MATLAB仿真结果表明,适当选择适应度函数的权比,可以使网络容量基本保持不变,但总传输功率(TPPN)降低,因此,在实时情况下,通过参数化研究预先确定权比,可以避免多目标优化计算量大的问题。
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Co-channel interference constrained spectrum allocation with simultaneous power and network capacity optimization using PSO in Cognitive Radio Network
This paper presents a Particle Swarm Optimization (PSO) based co-channel interference constrained efficient and low-complexity novel spectrum allocation scheme with simultaneous power and network capacity optimization for a Cognitive Radio Network (CRN) with a system model of multiple secondary user (SU) pairs and one primary user (PU) pair in a spectrum underlay fashion. A novel weighted fitness function is proposed subject to the constraint that all primary users and secondary users are supported with their target signal-to-interference-noise ratio (SINR). MATLAB simulation shows that total transmitted-power-per- node (TPPN) decreases while the network capacity remains almost constant for proper choice of weight ratio of the fitness function involved and hence, in the real-time situation, we can avoid the high computational burden of multi-objective optimization by predetermining the weight ratio through parametric study.
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