Cascaded learning system for SR noise induced spectrum sensing in cognitive radio network

H. Reda, S. Shin
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引用次数: 1

Abstract

In this paper artificial neural network (ANN) based cascaded learning for stochastic resonance (SR) noise driven spectrum sensing is proposed for cognitive radio (CR). In the first phase of a two-stage cascaded learning system Backpropagation gradient ascent learning is used to estimate optimal noise standard deviation which has an outstanding performance improvement for the discovery of the existence or absence of PU signal in a specified channel. In the second stage, based on results of local detection, binary classification method is used to finally predict the behaviour of the PU channel as either idle or busy channel. The non-robustness of energy detectors (ED) to common noise uncertainty and unable to detect in fading environments is improved by use of adaptive SR noise through learning so that high detection probability is achieved with minimum sensing time under low SNR environment. Therefore, through the cascaded learning system performance parameters such as probability of detection, detection time and false alarm probability of ED in multiple antenna aided CR systems is improved significantly. Moreover, simulation results of our proposed system reveal that throughput and spectral efficiency of the CR (aka secondary user) is improved as compared to the conventional ED algorithms.
认知无线电网络中SR噪声诱导频谱感知的级联学习系统
本文提出了基于人工神经网络(ANN)的级联学习随机共振(SR)噪声驱动频谱感知的认知无线电(CR)算法。在两阶段级联学习系统的第一阶段,采用反向传播梯度上升学习估计最优噪声标准差,对于发现指定信道中存在或不存在PU信号有显著的性能提高。在第二阶段,基于局部检测结果,采用二值分类方法最终预测PU通道的空闲或繁忙状态。通过学习,利用自适应SR噪声改善能量检测器对常见噪声不确定性的非鲁棒性以及在衰落环境下无法检测的问题,从而在低信噪比环境下以最小的感知时间获得较高的检测概率。因此,通过级联学习,可以显著提高多天线辅助CR系统中ED的检测概率、检测时间和虚警概率等性能参数。此外,我们提出的系统的仿真结果表明,与传统的ED算法相比,CR(即次要用户)的吞吐量和频谱效率得到了提高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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