Wideband spectrum sensing method based on SVM and EN
Author:
Affiliation:
School of Communication and Information Engineering, Shanghai University, Shanghai 200072, China
Fund Project:
摘要
|
图/表
|
访问统计
|
参考文献
|
相似文献
|
引证文献
|
资源附件
|
文章评论
摘要:
频谱感知技术是认知无线电的核心技术之一,由于未来无线通信技术的发展对高速数据通信的需求,使得宽带频谱感知技术成为目前研究的重点方向。由于宽频带带宽较宽不能直接将整个频段划分为占用或者空闲,需要对宽频带进行细分。将信号划分为多个子频带,通过预处理将多元分类问题转化成二元分类问题。为了降低频谱感知的算法复杂度,提出了基于噪声估计（estimation of noise,EN）和支持向量机（support vector machine, SVM）的频谱感知算法,该算法利用检测性能较好的慢速感知算法作为噪声估计,再使用算法复杂度低的快速感知算法结合噪声估计的信息进行频谱感知。实验结果表明,在低信噪比下,该算法较传统的方法其检测性能有着明显的提高,在信噪比为-10 dB的无线环境中能够完全识别各个子信道的使用情况。
Abstract:
Spectrum sensing technology is one of the core technologies of cognitive radio. As the future development of wireless communication technology requires highspeed data communication, wideband spectrum sensing technology has become the focus of the current research. Wideband can’t be directly divided into occupied or idle, so it needs to be subdivided. The signal is divided into multiple subbands, and the problem of multivariate classification is transformed into binary classification problem by pretreatment. In order to reduce the complexity of the spectral sensing algorithm, a spectrum sensing algorithm based on estimation of noise(EN) and support vector machine(SVM) is proposed. The algorithm uses a slow sensing algorithm as estimation of noise and then combines it with low fast sensing algorithm for spectrum sensing. The experimental results show that the proposed algorithm has a significant improvement in the detection performance under the low SNR and can fully recognize the usage of each subchannel under SNR of -10 dB.