DS/CDMA通信系统中的智能多用户检测算法研究
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摘要
在DS／CDMA通信系统中，多用户检测是将各用户发送的信号做联合检测，缓解了远近效应问题，并且有效地消除了多址干扰，使系统容量得到提高。本文将几种智能信号处理方法应用于多用户检测，得到了一些有意义的结果。本文的主要工作可以概括如下：
◆提出了基于免疫算法的多用户检测方法，这种方法大大降低了最佳多用户检测器的计算复杂度，同时达到了次最佳的检测效果。
◆提出了基于自适应子波网络的多用户检测方法。这种方法的抗远近效应和抗噪声能力较之传统的单用户检测方法有很大提高。
◆提出了基于自适应子波网络与空时匹配滤波方法，这种方法使用子波网络对阵列天线波束形成之后的输出进行后续处理，是一种单用户的检测方法。
◆系统地评述了独立分量分析(ICA)的新近进展，分析了各算法的特点，并且在此基础上提出了两类基于ICA算法和前馈神经网络结构的多用户检测方法。
本文工作得到了国家“863”计划和国家自然科学基金(60073053)的资助。
As a key technique in CDMA communication system, the multiuser detection can jointly detect the signals transmitted by all the users, thereby alleviate the "near-far" effect, cancel the MAI and increase the system capacity. In this paper by applying intelligent signal processing algorithms to multiuser detection, several DS/CDMA signal receiving algorithms are proposed.
The main works of this paper can be summarized as follows:
A multiuser detection method based on immune algorithm is proposed. This method decreases greatly computation complexity of optimal multiuser detector and achieves suboptimal performance.
A multiuser detection method based on adaptive wavelet network is proposed. This method can effectively eliminate MAI and white noise.
A novel space-time matched filter combined with adaptive wavelet network in asynchronous multipath CDMA channels is proposed. It is shown this filter is far superior to the conventional space-time matched filter in eliminating near-far effect and white noise with theoretical analysis and computer simulations.
The latest progress in independent component analysis (1CA) is reviewed systematically, then two novel classes of multiuser detection methods based on ICA algorithms and feedforward neural networks are proposed.
This paper is supported by the National Natural Science Foundation of China (Grant No. 60073053) and the Natoinal "863" Project.
◆提出了基于免疫算法的多用户检测方法，这种方法大大降低了最佳多用户检测器的计算复杂度，同时达到了次最佳的检测效果。
◆提出了基于自适应子波网络的多用户检测方法。这种方法的抗远近效应和抗噪声能力较之传统的单用户检测方法有很大提高。
◆提出了基于自适应子波网络与空时匹配滤波方法，这种方法使用子波网络对阵列天线波束形成之后的输出进行后续处理，是一种单用户的检测方法。
◆系统地评述了独立分量分析(ICA)的新近进展，分析了各算法的特点，并且在此基础上提出了两类基于ICA算法和前馈神经网络结构的多用户检测方法。
本文工作得到了国家“863”计划和国家自然科学基金(60073053)的资助。
As a key technique in CDMA communication system, the multiuser detection can jointly detect the signals transmitted by all the users, thereby alleviate the "near-far" effect, cancel the MAI and increase the system capacity. In this paper by applying intelligent signal processing algorithms to multiuser detection, several DS/CDMA signal receiving algorithms are proposed.
The main works of this paper can be summarized as follows:
A multiuser detection method based on immune algorithm is proposed. This method decreases greatly computation complexity of optimal multiuser detector and achieves suboptimal performance.
A multiuser detection method based on adaptive wavelet network is proposed. This method can effectively eliminate MAI and white noise.
A novel space-time matched filter combined with adaptive wavelet network in asynchronous multipath CDMA channels is proposed. It is shown this filter is far superior to the conventional space-time matched filter in eliminating near-far effect and white noise with theoretical analysis and computer simulations.
The latest progress in independent component analysis (1CA) is reviewed systematically, then two novel classes of multiuser detection methods based on ICA algorithms and feedforward neural networks are proposed.
This paper is supported by the National Natural Science Foundation of China (Grant No. 60073053) and the Natoinal "863" Project.
引文
[1]张贤达等．通信信号处理．北京：国防工业出版社，2001．
[2]郭梯云等．数字移动通信．北京：人民邮电出版社，1996．
[3]吴伟陵．移动通信中的关键技术．北京：北京邮电大学出版社，2000．
[4]曾兴雯等．通信中的扩展频谱技术．西安：西安电子科技大学出版社，1995．
[5]Verdu S. Multiuser Detection. Cambridge, UK: Cambridge University Press, 1998.
[6]Verdu S. Optimum Multiuser Asymptotic Efficiency. IEEE Trans. Commun., 1986, 34(9): 890.
[7]Schneider, K. S. Optimum detection of code-division multiplexed signal. IEEE Trans. Aerop. Electron. Syst., 1979, 15:: 181.
[8]Verdu S. Minimum probabilty of error for asynchronous Gaussian multiple-access chann-el. IEEE Trans. Inform. Theory, 1986, 32(1): 86.
[9]Lupas R, et al. Linear multiuser detectors for synchronous code division access channels. IEEE Trans. Inform. Theory, 1989, 35(1): 123.
[10]Madhow U, et al. MMSE interference suppression for direct-sequence spread-spectrum CDMA. IEEE Trans. Commun., 1994, 42(12): 3178.
[11]Varanasi M, et al. Multistage dectection in asynchronous code division multiple access communications. IEEE Trans. Commun., 1990, 38(4): 509.
[12]Patel P, et al. Analysis of a simple successive interference cancellation scheme in a DS/CDMA system. IEEE JCAS, 1994, 12(5): 796.
[13]Divsalar D, et al. Improved parallel interference cancellation for CDMA. IEEE Trans. Commun., 1998, 46(2): 258.
[14]Aazhang B, et al. Neural network for multiuser detection in CDMA communications, IEEE J. Select. Areas Commun., 1992, 40(7): 1212.
[15]焦李成等。智能多用户检测与自适应处理。国家“863”计划研究报告。西安电子科技大学雷达信号处理国家重点实验室，2000，12．
[16]潘进等．用遗传算法确定短序列正交多子波．自然科学进展，2000，10(3)：266．
[17]杨新星等．多子波神经网络的预滤波，自然科学进展，2000，10(4)：354．
[18]陈俊丽等．基于多子波包变换的码分多址方式．自然科学进展．2001，11(3)：322
[19]Honig M, et al. Blind adaptive multiuser detection. IEEE Trans. Information Theory, 1995, 41(4): 944.
[20]Wang X D, Blind multiuser detection: A Subspace approach. IEEE Trans. Inform. Theory, 1998, 44(1): 91.
[21]Lang T, et al. Blind identification and equalization based on second order statistics: A time domain approach. IEEE Trans. Inform. Theory, 1994, 40(3): 340.
[22]Shen J Q, et al. Direct blind MMSE channel equalization based on second order statistics. IEEE Trans. Signal Processing, 2000, 48(4): 1015.
[23]Xu Z Y, et al. Blind Channel estimation for long code multiuser CDMA systems. IEEE Trans. Signal Processing, 2000, 48(4): 988.
[24]Liu H, et al. Blind equalization in antenna arrqay CDMA systems. IEEE Trans. Signal Processing, 1997, 45(1): 161.
[25]Tastsanis M K, et al. Constrained optimization methods for direct blind equalization. IEEE J. Select. Areas Commun., 1999, 17(3): 424.
[26]陈国良等．遗传算法及其应用．北京：人民邮电出版社，1996．
[27]王磊等．免疫算法．电子学报，2000，28(7)：74—78．
[28]Ergun C, et al. Multiuser detection using a genetic algorithm in CDMA communications systems. IEEE Trans. Commun., 2000, 48(8): 1374.
[29]杨海兵．智能信号处理在移动通信中的应用．西安电子科技大学硕士论文，2000．
[30]臧小刚．基于进化网络的多用户检测．西安电子科技大学硕士论文，2000．
[31]丁志刚．智能信号处理在多用户检测中的应用．西安电子科技大学硕士论文，2001．
[32]屈炳云．移动通信中的多用户检测算法．西安电子科技大学硕士论文，2001．
[33]张艳宁．智能目标识别方法研究．西安电子科技大学博士后研究工作报告．1999．
[34]学术论文选编：神经网络与子波．西安电子科技大学雷达信号处理重点实验室．1994．
[35]焦李成．神经网络系统理论．西安电子科技大学出版社，1990．
[36]Mitra U, et al. Neural network techniques for adaptive multiuser demodulation. IEEE JSAC, 1994, 12(9): 1460.
[37]Kechriotis G I, et al. Hopfield neural network implementation of the optimal CDMA communications. IEEE Trans. Commoun., 1996, 7(1): 131.
[38]陈强等．多径条件下的空时解相关接收机研究．国防科技大学学报．2001，23(1)：73．
[39]Wang X D, et al. Space-Time Multiuser Detection in Multipath CDMA Channels. IEEE Trans. Signal Processing, 1999, 47(9): 2356.
[40]Madhow U. Blind adaptive interference suppression for direct-sequence CDMA. Proc. of IEEE, 1998, 86(10): 2049.
[41]张洪渊等．一种任意信号源盲分离的高效算法，电子学报，2001，29(10)：1392．
[42]Comon P. Independent component analysis, a new concept? Signal Processing, 1994, 36(3): 287.
[43]Jutten C, et al. Blind separation of sources, part Ⅰ: an adaptive algorithm based on neuromimetic architecture. Signal Processing, 1991, 24(1): 1.
[44]Douglas S, et al. Neural networks for blind decorrelation of signals. IEEE Trans. Signal Processing, 1997, 45(11): 2829.
[45]Silva F M, et al. A distributed solution for data orthonormalization. Proc. Int. Conf. Artificial Neural Networks, Espoo, Finland. 1991, 1: 943.
[46]Cichocki A, et al. Robust neural networks with on-line learning for blind identification and blind separation of sources. IEEE Trans. Circuits syst. I, 1996, 43(11): 894.
[47]Cruces-Alvarez S, et al. An iterative inversion approach to blind source separation. IEEE Trans on Neural Networks, 2000, 11(6): 1423.
[48]Cardoso J, et al. Equivariant adaptive source separation. IEEE Trans on Signal Processing, 1996, 44(12): 3017.
[2]郭梯云等．数字移动通信．北京：人民邮电出版社，1996．
[3]吴伟陵．移动通信中的关键技术．北京：北京邮电大学出版社，2000．
[4]曾兴雯等．通信中的扩展频谱技术．西安：西安电子科技大学出版社，1995．
[5]Verdu S. Multiuser Detection. Cambridge, UK: Cambridge University Press, 1998.
[6]Verdu S. Optimum Multiuser Asymptotic Efficiency. IEEE Trans. Commun., 1986, 34(9): 890.
[7]Schneider, K. S. Optimum detection of code-division multiplexed signal. IEEE Trans. Aerop. Electron. Syst., 1979, 15:: 181.
[8]Verdu S. Minimum probabilty of error for asynchronous Gaussian multiple-access chann-el. IEEE Trans. Inform. Theory, 1986, 32(1): 86.
[9]Lupas R, et al. Linear multiuser detectors for synchronous code division access channels. IEEE Trans. Inform. Theory, 1989, 35(1): 123.
[10]Madhow U, et al. MMSE interference suppression for direct-sequence spread-spectrum CDMA. IEEE Trans. Commun., 1994, 42(12): 3178.
[11]Varanasi M, et al. Multistage dectection in asynchronous code division multiple access communications. IEEE Trans. Commun., 1990, 38(4): 509.
[12]Patel P, et al. Analysis of a simple successive interference cancellation scheme in a DS/CDMA system. IEEE JCAS, 1994, 12(5): 796.
[13]Divsalar D, et al. Improved parallel interference cancellation for CDMA. IEEE Trans. Commun., 1998, 46(2): 258.
[14]Aazhang B, et al. Neural network for multiuser detection in CDMA communications, IEEE J. Select. Areas Commun., 1992, 40(7): 1212.
[15]焦李成等。智能多用户检测与自适应处理。国家“863”计划研究报告。西安电子科技大学雷达信号处理国家重点实验室，2000，12．
[16]潘进等．用遗传算法确定短序列正交多子波．自然科学进展，2000，10(3)：266．
[17]杨新星等．多子波神经网络的预滤波，自然科学进展，2000，10(4)：354．
[18]陈俊丽等．基于多子波包变换的码分多址方式．自然科学进展．2001，11(3)：322
[19]Honig M, et al. Blind adaptive multiuser detection. IEEE Trans. Information Theory, 1995, 41(4): 944.
[20]Wang X D, Blind multiuser detection: A Subspace approach. IEEE Trans. Inform. Theory, 1998, 44(1): 91.
[21]Lang T, et al. Blind identification and equalization based on second order statistics: A time domain approach. IEEE Trans. Inform. Theory, 1994, 40(3): 340.
[22]Shen J Q, et al. Direct blind MMSE channel equalization based on second order statistics. IEEE Trans. Signal Processing, 2000, 48(4): 1015.
[23]Xu Z Y, et al. Blind Channel estimation for long code multiuser CDMA systems. IEEE Trans. Signal Processing, 2000, 48(4): 988.
[24]Liu H, et al. Blind equalization in antenna arrqay CDMA systems. IEEE Trans. Signal Processing, 1997, 45(1): 161.
[25]Tastsanis M K, et al. Constrained optimization methods for direct blind equalization. IEEE J. Select. Areas Commun., 1999, 17(3): 424.
[26]陈国良等．遗传算法及其应用．北京：人民邮电出版社，1996．
[27]王磊等．免疫算法．电子学报，2000，28(7)：74—78．
[28]Ergun C, et al. Multiuser detection using a genetic algorithm in CDMA communications systems. IEEE Trans. Commun., 2000, 48(8): 1374.
[29]杨海兵．智能信号处理在移动通信中的应用．西安电子科技大学硕士论文，2000．
[30]臧小刚．基于进化网络的多用户检测．西安电子科技大学硕士论文，2000．
[31]丁志刚．智能信号处理在多用户检测中的应用．西安电子科技大学硕士论文，2001．
[32]屈炳云．移动通信中的多用户检测算法．西安电子科技大学硕士论文，2001．
[33]张艳宁．智能目标识别方法研究．西安电子科技大学博士后研究工作报告．1999．
[34]学术论文选编：神经网络与子波．西安电子科技大学雷达信号处理重点实验室．1994．
[35]焦李成．神经网络系统理论．西安电子科技大学出版社，1990．
[36]Mitra U, et al. Neural network techniques for adaptive multiuser demodulation. IEEE JSAC, 1994, 12(9): 1460.
[37]Kechriotis G I, et al. Hopfield neural network implementation of the optimal CDMA communications. IEEE Trans. Commoun., 1996, 7(1): 131.
[38]陈强等．多径条件下的空时解相关接收机研究．国防科技大学学报．2001，23(1)：73．
[39]Wang X D, et al. Space-Time Multiuser Detection in Multipath CDMA Channels. IEEE Trans. Signal Processing, 1999, 47(9): 2356.
[40]Madhow U. Blind adaptive interference suppression for direct-sequence CDMA. Proc. of IEEE, 1998, 86(10): 2049.
[41]张洪渊等．一种任意信号源盲分离的高效算法，电子学报，2001，29(10)：1392．
[42]Comon P. Independent component analysis, a new concept? Signal Processing, 1994, 36(3): 287.
[43]Jutten C, et al. Blind separation of sources, part Ⅰ: an adaptive algorithm based on neuromimetic architecture. Signal Processing, 1991, 24(1): 1.
[44]Douglas S, et al. Neural networks for blind decorrelation of signals. IEEE Trans. Signal Processing, 1997, 45(11): 2829.
[45]Silva F M, et al. A distributed solution for data orthonormalization. Proc. Int. Conf. Artificial Neural Networks, Espoo, Finland. 1991, 1: 943.
[46]Cichocki A, et al. Robust neural networks with on-line learning for blind identification and blind separation of sources. IEEE Trans. Circuits syst. I, 1996, 43(11): 894.
[47]Cruces-Alvarez S, et al. An iterative inversion approach to blind source separation. IEEE Trans on Neural Networks, 2000, 11(6): 1423.
[48]Cardoso J, et al. Equivariant adaptive source separation. IEEE Trans on Signal Processing, 1996, 44(12): 3017.
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