利用功率谱极值和几何平均的频谱感知算法
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摘要
为了提高基于功率谱的频谱感知算法抗噪声不确定性、抗频偏及低信噪比下检测性能,本文利用功率谱的部分样本平均估计最大值,以降低信号频偏对频谱感知性能影响;利用功率谱的最大值与最小值之差与功率谱几何平均之比作为判决统计量,以尽可能消除噪声影响及保留主用户信号;推导得到了检测门限表达式,表明该算法对噪声不确定性不敏感。加性高斯白噪声信道和瑞利衰落信道下的仿真结果表明:该算法频谱感知性能优于已有的基于功率谱的频谱感知算法,降低了未知载波频偏和噪声不确定性对频谱感知算法性能的影响,该算法能够有效检测实际信号。
The detection performance of spectrum sensing algorithms should be improved under low signal to noise radio,unknown carrier offset and noise uncertainty. The maximum value of signal 's power spectral is estimated by the part sample-averaging of power spectral to reduce the effect of frequency offset of signal on spectrum sensing performance. In order to remove the influence of noise and preserve primary user's signal features,the ratio of the difference of the average value of maximum and minimum power spectral to the power spectral geometric average is used as the decision statistics.The detection threshold expression is deducted,from which we can find that the proposed algorithm is insensitive to noise uncertainty. The simulation results in additive white Gaussian noise channel and Rayleigh fading channel show that the proposed algorithm is better than the existed algorithms based on power spectral,which reduces the influence of unknown carrier offset and noise uncertainty to the performance of spectrum sensing algorithms. The algorithm can detect practical signal very effectively.
The detection performance of spectrum sensing algorithms should be improved under low signal to noise radio,unknown carrier offset and noise uncertainty. The maximum value of signal 's power spectral is estimated by the part sample-averaging of power spectral to reduce the effect of frequency offset of signal on spectrum sensing performance. In order to remove the influence of noise and preserve primary user's signal features,the ratio of the difference of the average value of maximum and minimum power spectral to the power spectral geometric average is used as the decision statistics.The detection threshold expression is deducted,from which we can find that the proposed algorithm is insensitive to noise uncertainty. The simulation results in additive white Gaussian noise channel and Rayleigh fading channel show that the proposed algorithm is better than the existed algorithms based on power spectral,which reduces the influence of unknown carrier offset and noise uncertainty to the performance of spectrum sensing algorithms. The algorithm can detect practical signal very effectively.
引文
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[10]Penna F,Garello R,Spirito A M. Cooperative spectrum sensing based on the limiting eigenvalue ratio distribution in wishart matrices[J]. IEEE Communication Letters,2009,13(7):507-509.
[2]刘会衡,邓小鸿,陈伟.一种基于特征值比的频谱感知改进算法[J].电波科学学报,2015(2):282-288.Liu H H, Deng X H, Chen W. Improved spectrum sensing algorithm based on eigenvalue ratio[J].Journal of Radio Wave Science,2015(2):282-288.(in Chinese)
[3]齐佩汉,司江勃,李赞.新型抗噪声不确定度谱分段对消频谱感知算法[J].西安电子科技大学学报,2013,40(6):19-24.Qi P H,Si J B,Li Z. Novel anti-noise-uncertainty spectrum sensing algorithm based on power spectral density segment cancellation[J]. Journal of Xidian University,2013,40(6):19-24.(in Chinese)
[4]齐佩汉,司江勃,李赞.基于功率谱分段对消频谱感知算法研究及性能分析[J].电子与信息学报,2014,36(4):769-774.Qi P H,Si J B,Li Z. Research and performance analysis of spectrum sensing algorithm based on the power spectral density segment cancellation[J]. Journal of Electronics Information Technology,2014,36(4):769-774.(in Chinese)
[5]Gao R,Li Z,Qi P H,et al. A robust cooperative spectrum sensing method in cognitive radio networks[J].IEEE Communications Letters, 2014, 18(11):1987-1990.
[6]Pascal C,Sri H M,Aren J,et al. Frequency offset correction in speech without detecting pitch[C]∥IEEE International Conference on Acoustics,Shanghai,China,2013:7020-7024.
[7]Fan S H,Yu J J,Qian D Y,et al. A fast and efficient frequency offset correction technique for coherent optical orthogonal frequency division multiplexing[J]. Journal of Lightwave Technology,2011,29(13):1997-2004.
[8]吕曦.认知无线电中频谱感知算法的研究[D].杭州:杭州电子科技大学,2017.Lv X. Research on spectrum sensing algorithm in cognitive radio[D]. Hangzhou:Hangzhou Dianzi University,2017.(in Chinese)
[9]毛翊君.认知无线电频谱感知技术研究[D].杭州:杭州电子科技大学,2018.Mao Y J. Research on spectrum sensing algorithm in cognitive radio[D]. Hangzhou:Hangzhou Dianzi University,2018.(in Chinese)
[10]Penna F,Garello R,Spirito A M. Cooperative spectrum sensing based on the limiting eigenvalue ratio distribution in wishart matrices[J]. IEEE Communication Letters,2009,13(7):507-509.