认知无线传感器网络新型SVM频谱感知策略

A New SVM Spectrum Sensing Strategy Based on Cognitive Wireless Sensor Networks

阐述了基于认知无线传感器网络背景运用支持向量机的可行性。针对低信噪比噪声复杂性高的无线环境,单一的识别方法难以获得相对准确的结果。基于隐马尔可夫模型(Hidden Markov Model,HMM)对传统SVM频谱感知算法进行了优化,采用多个分类器集成降低识别错误和增强识别鲁棒性。采用最小二乘法将线性不等式约束转化为线性约束得到最优超平面来分割主信号和噪声干扰,对主用户状态进行决策,最后与传统能量检测算法比较性能。仿真结果表明,基于SVM频谱感知性能更接近理论值,比能量检测更为可靠与准确,错误率为1.6%,在低SNR下检测概率比能量检测高出18%,具有更优的检测性能与鲁棒性。

This paper explains the feasibility of applying support vector machine based on cognitive wireless sensor network.Under condition of the wireless environment of low SNR and complex noise,aimed at the problems that single identification method fails to reach relatively accurate results,based on Hidden Markov Model,HMM,this paper optimizes the traditional spectrum sensing algorithm of SVM by adopting multiple classifiers ensemble to reduce identification error and strengthen identification robustness,and by adopting least square method to turn linear inequality constraints into linear constraints so as to get optimal hyperplane to distinguish primary signal from noise and then decide primary user state.Finally,its performance is compared with traditional energy detecting algorithm.The simulation results show that the spectrum sensing performance based on SVM is closer to the theoretical value,is more reliable and accurate than that of the energy detection,the error rate is 1.6%,the detection probability is 18 percent higher than the energy detection under condition of low SNR,and has more favorable detection performance and robustness.