认知无线电网络中利用FEC和DE的多节点频谱感知算法

针对认知无线电网络(CRN)中空闲频谱感知困难的问题,本文提出了基于前向纠错和差分进化算法的多节点频谱感知算法。首先,利用基于差分进化算法的协同检测完成信号感知；然后,研究了信道噪声对频谱感知性能的影响；最后,分析了前向纠错技术在信道存在噪声时对频谱感知性能的影响。仿真实验将纠错和无纠错控制信道的不同信噪比作为依据,采用三种不同的检测方法评估了本文算法。仿真实验结果表明,在存在噪声的认知无线电网络中,本文算法提高了系统的性能和检测概率,且协同感知算法的性能随着节点数目的增加而提高,该算法适合应用于实时性要求较高的应用程序。

Multi-nodes cooperative spectrum sensing algorithms using FEC and DE in cognitive radio networks

For the issue of the free spectrum in cognitive radio networks (CRN), multi-nodes sensing algorithms based on forward error correction (FEC) and differential evolution (DE) algorithm is proposed. Firstly, cooperative detection based on DE algorithm is used to predict the presence of signal. Then the effect of noise in control channel is studied on the spectrum sensing capabilities. Finally, the effect of forward error correction technique in the noisy control channel on the sensing capabilities is analyzed. Our algorithm is evaluated through three different detection methods and different signal-to-noise ratio in control channel with and without error correction is set as evaluate basis. The simulate results show that detection probability or the system efficiency can be increased with error correction technique and the performance of the cooperative sensing algorithm improves with increase in number of nodes in noisy cognitive radio network. Proposed algorithm is suitable for applications with high real-time demand.