Non-Supervised Learning for Spread Spectrum Signal Pseudo-Noise Sequence Acquisition

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摘要 Anideaofestimatingthedirectsequencespreadspectrum(DSSS)signalpseudo-noise(PN)sequenceispresented.WithouttheapriorityknowledgeabouttheDSSSsignalinthenon-cooperationcondition,weproposeaself-organizingfeaturemap(SOFM)neuralnetworkalgorithmtodetectandidentifythePNsequence.Anon-supervisedlearningalgorithmisproposedaccordingtheKohonenruleinSOFM.TheblindalgorithmcanalsoestimatethePNsequenceinalowsignal-to-noise(SNR)andcomputersimulationdemonstratesthatthealgorithmiseffective.Comparedwiththetraditionalcorrelationalgorithmbasedonslip-correlation,theproposedalgorithm’sbiterrorrate(BER)andcomplexityarelower.
机构地区 不详
出版日期 2015年01月11日(中国期刊网平台首次上网日期,不代表论文的发表时间)
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