Spatio-Temporal Spectrum Sensing in Cognitive Radio Networks Using Beamformer-Aided SVM Algorithms
Spatio-Temporal Spectrum Sensing in Cognitive Radio Networks Using Beamformer-Aided SVM Algorithms
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DOI：
10.1109/access.2018.2825603
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发表时间：
2018
期刊：
影响因子：
3.9
通讯作者：
O. Awe;Anastasios Deligiannis;S. Lambotharan
中科院分区：
文献类型：
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作者：
O. Awe;Anastasios Deligiannis;S. Lambotharan
本文使用支持向量机（SVM）算法解决多天线认知无线电系统中的频谱感知问题。首先，我们将多个主用户场景下的频谱感知问题表述为多状态信号检测问题。接下来，我们提出了一种新颖的波束形成器辅助特征实现策略，用于增强 SVM 在单主用户和多个主用户条件下进行信号分类的能力。然后，我们研究了基于纠错输出码的多类支持向量机算法，并为解决多状态谱感知问题提供了多个独立模型替代方案。所提出的检测器的性能根据检测概率、误报概率、接收器操作特性 (ROC)、ROC 曲线下面积和总体分类精度进行量化。仿真结果表明，所提出的检测器对于认知无线电网络中频谱空洞的时间和联合时空检测具有鲁棒性。
This paper addresses the problem of spectrum sensing in multi-antenna cognitive radio system using the support vector machine (SVM) algorithms. First, we formulated the spectrum sensing problem under multiple primary users scenarios as a multiple state signal detection problem. Next, we propose a novel beamformer-aided feature realization strategy for enhancing the capability of the SVM for signal classification under both single and multiple primary users conditions. Then, we investigate the error correcting output codes-based multi-class SVM algorithms and provide a multiple independent model alternative for solving the multiple state spectrum sensing problem. The performance of the proposed detectors is quantified in terms of probability of detection, probability of false alarm, receiver operating characteristics (ROC), area under ROC curves, and overall classification accuracy. Simulation results show that the proposed detectors are robust to both temporal and joint spatio-temporal detection of spectrum holes in cognitive radio networks.