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中文摘要：
针对现有基于聚类算法的信号调制识别在低信噪比时识别率低的缺点,文中采用聚类算法提取信号特征参数,通过变梯度Polak-Ribiere BP修正算法对神经网络进行训练,以提高收敛速度,改善在低信噪比条件下网络识别性能,实现对基于星座图调制方式信号的调制识别,仿真结果表明,在低信噪比条件下,调制识别率和单独采用聚类算法或基于BP算法的神经网络识别时比较提高30%以上,在信噪比为4d B条件下识别率可达到90%,且系统易于实现,在信号调制识别中具有广泛的应用前景。
英文摘要：
To improve the recognition rate of the signal,a modulation recognition method is proposed based on the clustering algorithm under the low SNR.The characteristic parameter of the signal is extrac-ted by using a clustering algorithm,neural network is trained by using the algorithm of variable gradient correction BP so as to enhance the rate of convergence.The performance of recognition under the low SNR is improved,and the modulation recognition of the signal is realized based on the modulation system of the constellation diagram.Simulation results show that the recognition rate based on this algorithm is enhanced over 30% compared with methods of adopting clustering algorithm or neural network based on BP algorithm alone under the low SNR.The recognition rate can reach 90%when the SNR is 4 dB,and the method is easy to be achieved so that it has broad application prospect in the modulating recognition.
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Outage probability analysis of partial AF relay selection in spectrum-sharing scenario with multiple
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