基于独立分量分析的盲分离及盲多用户检测技术研究
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摘要
盲源分离(Blind Source Separation, BSS)是近年来人工神经网络、统计信号处理、信息理论等领域共同研究与发展的重要课题,作为BSS重要手段的独立分量分析(Independent Component Analysis, ICA)也得到了大力发展。此外,多用户检测(Multi-User Detection , MUD)技术作为第三代移动通信系统克服多址干扰(Multiple Access Interference, MAI)的重要技术越来越受到人们的重视。盲自适应多用户检测不需要训练序列也不需要其它干扰用户的扩频信息,因而成为今后多用户检测技术中的一个研究热点。
基于上述研究背景,本课题研究了独立分量分析算法及其在盲多用户检测中的应用,提出了一种新的非线性ICA算法和一种基于ICA的带有动量项的盲多用户检测算法。另外,还研究了非高斯信道噪声下基于分数低阶统计量的盲多用户检测技术。全文共分为五章,课题的核心算法分别分布在第二、三、四章内,下面对各章节的内容加以概述。
第一章为绪论,此部分主要阐述了本课题的研究背景、研究现状以及本文的主要研究内容、结构安排、贡献及创新。
第二章中提出了一种非线性ICA盲源分离算法。非线性ICA是对线性ICA的扩展,更加符合实际情况,但因其数学理论复杂且许多实际的非线性系统在仅假设源信号统计独立的条件下不能完全分离出源信号,所以非线性盲源分离的成熟研究结果较少,本课题在Mono非线性混合模型下提出了一种基于互信息最小的盲分离算法,通过对比提出算法和Almeida的互信息分离(Mutual Information Separation, MISEP)算法对Mono非线性混合信号的分离性能,证明了提出算法的有效性。
第三章提出了一种基于ICA的多用户检测算法和基于ICA的带有动量项的多用户检测算法。多址干扰是直扩码分多址(Direct Sequence-Code Division Multiple Access , DS-CDMA)系统中的一项重要干扰,严重影响系统的性能。ICA是一种基于高阶统计量(Higher Order Statistics,HOS)的方法,比传统的基于二阶统计量(Second Order Statistics,SOS)的信号处理方法能更好地利用信号的特性。ICA只要求源信号相互统计独立,DS-CDMA系统满足这一条件,将ICA与传统的信号检测技术相结合可以减少MAI,使得系统的性能更加稳健,计算效率更高。
第四章中提出了一种基于分数低阶统计量的广义恒模算法(Fractional Lower Order Statistics based Generalized Constant Modulus Algorithm , FLOS-GCMA)以解决非高斯信道噪声下的盲MUD问题。以DS-CDMA系统为例,将基于分数低阶统计量的广义恒模盲多用户检测算法与传统恒模盲多用户检测算法、分数低阶统计量恒模盲多用户检测算法进行对比,实验仿真结果表明:FLOS-GCMA无论在高斯白噪声下还是在α稳定分布噪声下均可有效地抑制多址接入干扰和噪声的影响,并且具有更快的收敛速度,从而使该算法具有更广泛的适用性。
第五章为全文总结,总结了本文的主要工作内容,指出了进一步需要研究的问题。
In recent years, blind source separation (BSS) has become a hot spot for researchers in many fields, such as artificial neural networks, statistical signal processing, information theory, and so on. As a kind of important method for BSS, independent component analysis (ICA) has been studied widely and deeply. Additionally, multi-user detection (MUD) technology, which is a key technology of the third generation mobile communication to combat multiple access interference (MAI), draws more and more attention from researchers. Blind adaptive MUD does not need the train sequence and spread sequence of other interfering users, and so becomes a research topic of MUD in the future.
Based on the above background, this dissertation studies ICA and blind MUD, and presents a new algorithm for non-linear ICA and a new algorithm for MUD based on ICA and ICA with momentum term. In addition, the technology of blind MUD based on fraction lower order statistics (FLOS) in non-Gaussian channel noise is also studied. The whole dissertation consists of five chapters, and the second, third and fourth chapter is the main parts. The following is an abstract of every chapter. Chapter one is an introduction of the research background, present research situation, main contents and structure of the dissertation, the contribution and innovation of this dissertation is also pointed out.
A new algorithm for non-linear ICA is presented in chapter two. Non-linear ICA is an extention of ICA and more fit for the practical environment, however, there is little mature algorithm for it because of the mathematical complex and nonseparability of many real systems just under the condition of statistical independence of the sources. In this chapter, a separation algorithm based on mutual information minimization for mono-nonlinear model is proposed, and its efficacy is proved by the contrast with the classical mutual information separation (MISEP) algorithm proposed by Almeida.
In chapter three, a MUD algorithm based on ICA and ICA with momentum term is introduced. Multiple access interference is one of the important interferences in DS-CDMA system, which damages the performance of the system deeply. ICA , which is a method based on higher order statistics, can use the characteristics of mixed signals more efficiently than the traditional signal processing method based on second order statistics. ICA only needs the statistical independence of the source signals, and DS-CDMA system satisfies this point, so, combining ICA with the traditional signal detection technology can make the system more robust and efficient.
Chapter four proposes a fractional lower order statistics based generalized constant modulus algorithm (FLOS-GCMA) to solve the problem of blind MUD in non-Gaussian channel noise. The comparison of FLOS-GCMA with the traditional constant modulus algorithm (CMA) and fractional lower order statistics based constant modulus algorithm (FLOS-CMA) in direct sequence code division multiple access (DS-CDMA) system shows that FLOS-GCMA has good performance both inα-stable distribution noise and Gaussian noise channel, which makes FLOS-GCMA has wide application.
Chapter five is a conclusion of the whole dissertation and points out the future work to do.
基于上述研究背景,本课题研究了独立分量分析算法及其在盲多用户检测中的应用,提出了一种新的非线性ICA算法和一种基于ICA的带有动量项的盲多用户检测算法。另外,还研究了非高斯信道噪声下基于分数低阶统计量的盲多用户检测技术。全文共分为五章,课题的核心算法分别分布在第二、三、四章内,下面对各章节的内容加以概述。
第一章为绪论,此部分主要阐述了本课题的研究背景、研究现状以及本文的主要研究内容、结构安排、贡献及创新。
第二章中提出了一种非线性ICA盲源分离算法。非线性ICA是对线性ICA的扩展,更加符合实际情况,但因其数学理论复杂且许多实际的非线性系统在仅假设源信号统计独立的条件下不能完全分离出源信号,所以非线性盲源分离的成熟研究结果较少,本课题在Mono非线性混合模型下提出了一种基于互信息最小的盲分离算法,通过对比提出算法和Almeida的互信息分离(Mutual Information Separation, MISEP)算法对Mono非线性混合信号的分离性能,证明了提出算法的有效性。
第三章提出了一种基于ICA的多用户检测算法和基于ICA的带有动量项的多用户检测算法。多址干扰是直扩码分多址(Direct Sequence-Code Division Multiple Access , DS-CDMA)系统中的一项重要干扰,严重影响系统的性能。ICA是一种基于高阶统计量(Higher Order Statistics,HOS)的方法,比传统的基于二阶统计量(Second Order Statistics,SOS)的信号处理方法能更好地利用信号的特性。ICA只要求源信号相互统计独立,DS-CDMA系统满足这一条件,将ICA与传统的信号检测技术相结合可以减少MAI,使得系统的性能更加稳健,计算效率更高。
第四章中提出了一种基于分数低阶统计量的广义恒模算法(Fractional Lower Order Statistics based Generalized Constant Modulus Algorithm , FLOS-GCMA)以解决非高斯信道噪声下的盲MUD问题。以DS-CDMA系统为例,将基于分数低阶统计量的广义恒模盲多用户检测算法与传统恒模盲多用户检测算法、分数低阶统计量恒模盲多用户检测算法进行对比,实验仿真结果表明:FLOS-GCMA无论在高斯白噪声下还是在α稳定分布噪声下均可有效地抑制多址接入干扰和噪声的影响,并且具有更快的收敛速度,从而使该算法具有更广泛的适用性。
第五章为全文总结,总结了本文的主要工作内容,指出了进一步需要研究的问题。
In recent years, blind source separation (BSS) has become a hot spot for researchers in many fields, such as artificial neural networks, statistical signal processing, information theory, and so on. As a kind of important method for BSS, independent component analysis (ICA) has been studied widely and deeply. Additionally, multi-user detection (MUD) technology, which is a key technology of the third generation mobile communication to combat multiple access interference (MAI), draws more and more attention from researchers. Blind adaptive MUD does not need the train sequence and spread sequence of other interfering users, and so becomes a research topic of MUD in the future.
Based on the above background, this dissertation studies ICA and blind MUD, and presents a new algorithm for non-linear ICA and a new algorithm for MUD based on ICA and ICA with momentum term. In addition, the technology of blind MUD based on fraction lower order statistics (FLOS) in non-Gaussian channel noise is also studied. The whole dissertation consists of five chapters, and the second, third and fourth chapter is the main parts. The following is an abstract of every chapter. Chapter one is an introduction of the research background, present research situation, main contents and structure of the dissertation, the contribution and innovation of this dissertation is also pointed out.
A new algorithm for non-linear ICA is presented in chapter two. Non-linear ICA is an extention of ICA and more fit for the practical environment, however, there is little mature algorithm for it because of the mathematical complex and nonseparability of many real systems just under the condition of statistical independence of the sources. In this chapter, a separation algorithm based on mutual information minimization for mono-nonlinear model is proposed, and its efficacy is proved by the contrast with the classical mutual information separation (MISEP) algorithm proposed by Almeida.
In chapter three, a MUD algorithm based on ICA and ICA with momentum term is introduced. Multiple access interference is one of the important interferences in DS-CDMA system, which damages the performance of the system deeply. ICA , which is a method based on higher order statistics, can use the characteristics of mixed signals more efficiently than the traditional signal processing method based on second order statistics. ICA only needs the statistical independence of the source signals, and DS-CDMA system satisfies this point, so, combining ICA with the traditional signal detection technology can make the system more robust and efficient.
Chapter four proposes a fractional lower order statistics based generalized constant modulus algorithm (FLOS-GCMA) to solve the problem of blind MUD in non-Gaussian channel noise. The comparison of FLOS-GCMA with the traditional constant modulus algorithm (CMA) and fractional lower order statistics based constant modulus algorithm (FLOS-CMA) in direct sequence code division multiple access (DS-CDMA) system shows that FLOS-GCMA has good performance both inα-stable distribution noise and Gaussian noise channel, which makes FLOS-GCMA has wide application.
Chapter five is a conclusion of the whole dissertation and points out the future work to do.
引文
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[2]啜钢,王文博,常永宇等.移动通信原理与应用.北京邮电大学出版社.北京. 2002: 142.
[3]胡捍英,杨峰义.第三代移动通信.人民邮电出版社.北京. 2001: 7-12.
[4] M. Shao, C. L. Nikias. Signal processing with fractional lower order moments:Stable processes and their applications. Proceedings of the IEEE, 1993, VOL. 81, NO.7, 986-1010.
[5] J. Herault, C. Jutten. Space of time adaptive signal processing by neural network models. In Denker ed. Neural Network for Computing. AIP Conferrnce Proceedings, 151, 1986, Ameriacan Institute of Physics. 206-211.
[6] G. B. Giannakis, A. Swami. New results on state-space and input-output identification of non-Gaussian processes using cumulants [C]//Proc SPIE’87. San Diego: [S.N.], 1987:199-205.
[7] C. Jutten, J. Herault. Blind separation of sources, part 1: an adaptive algorithm based on neuromimetic architecture [J]. Signal Processing, 1991, 24(1):1-10.
[8] E. Sorouchyari. Blind separation of sources, partⅢ: stability analysis [J]. Signal Processing, 1991, 24(1): 21-29.
[9] P. Comon. Blind separation of sources, partⅡ: problem statement [J]. Signal Processing, 1991, 24(1): 11-20.
[10] P. Comon. Independent component analysis, a new concept? Signal Processing, 1994, 36: 287-314.
[11] A. J. Bell, T. J. Sejnowski. An information-maximization approach to blind separation and blind deconvolution. Neural Computation, 1995, 7(6): 1004-1034.
[12] J. F. Cardoso, B. H. Laheld. Equivariant adaptive source separation. IEEE Transactions Signal Processing, 1996, 44(12): 3017-3030.
[13] S. Amari. Natural gradient works efficiently in learning. Neural Computation, 1998, 10:251-276.
[14] C. Platt, F. Faggin. Networks for the separation of sources that are super-imposed and delayed. Advances In Neural Information Processing Systems, 1991, 730-737.
[15] D. Yellin, E. Wensten. Criteria for multichannel signal separation. IEEE Transactions on Signal Processing, 1994, 42(8): 2158-2168.
[16] H. N. Thi, C. Jutten. Blind source separation for convolutive mixtures. Signal Processing, 1995, 45(2): 209-229.
[17] T. W. Lee, A. J. Bell, R. H. Lambert. Blind separation of delayed and convolved sources. Advances In Neural Information Processing System. Cambridge MA: MIT Press, 1997, 758-764.
[18] G. Burel. Blind separation of sources: A nonlinear neural algorithm, Neural Networks, 1992, 5(6), 937-947.
[19] L. C. Parra. Sympletic nonlinear component analysis, Advances in Neural Information Processing Systems, MA: MIT Press, 1996, 8, 437-443.
[20] P. Pajunen, A. Hyvarinen, J. Karhunen. Nonlinear blind source separation by self-organization maps, in Progress in Neural Information Processing, Berlin, Springer, 1996, 2, 1207-1210.
[21] H. H. Yang, S. Amari, A. Cichocki. Information back-propagation for blind separation of sources from non-linear mixture, in Proc. ICNN, Houston, 1997, 2141-2146.
[22] A. Taleb, C. Jutten. Source separation in post nonlinear mixtures: An entropy-based algorithm, in Proc. Of ICASSP, Seattle, Washington, 1998, 2089-2092.
[23]张贤达,保铮.盲信号分离.电子学报, 29(12): 1766-1771, 2001.
[24]张雪峰,刘建强,冯大政.一种快速的频域盲语音分离系统, 2005, 21(5): 434-438.
[25]谭丽丽,韦岗.卷积混合信号的最小互信息量盲分离算法,上海交通大学学报, 1999, 33(5): 566-569.
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