Application of ICA adaptive kernel estimation in multi-user detection

doi:10.3778/j.issn.1002-8331.2010.31.065

Computer Engineering and Applications ›› 2010, Vol. 46 ›› Issue (31): 232-234.DOI: 10.3778/j.issn.1002-8331.2010.31.065

• 工程与应用 • Previous Articles Next Articles

Application of ICA adaptive kernel estimation in multi-user detection

WAN Ming-gao^1，2，LI Xiao-hui¹

1.School of Electronic Science and Technology，Anhui University，Hefei 230039，China
2.Department of Mechanical and Engineering，Chizhou College，Chizhou，Anhui 247000，China

Received:2009-03-26 Revised:2009-05-20 Online:2010-11-01 Published:2010-11-01
Contact: WAN Ming-gao

ICA自适应核估计在多用户检测中的应用

宛明高^1，2，李晓辉¹

1.安徽大学电子科学与技术学院，合肥 230039
2.池州学院机电工程系，安徽池州 247000

通讯作者: 宛明高

Abstract

Abstract: ICA（Independent Component Analysis） algorithm of adaptive kernel estimation is applied to DS-CDMA system in multi-user detection.When not having aliasing signal distribution supposition，according to the signal statistical property，the appraisal function is estimated automatically and the random aliasing signal is separated blindly by using the output of the adaptive kernel density estimation ICA algorithm detector to initialize the independent component analysis iteration.By comparing the simulation results of the traditional matched filtering detection，the ICA post-processing matches filtering detection and this method，the results show that this algorithm in DS-CDMA multi-user detection has feasibility and superiority.

Key words: Code Division Multiple Access（CDMA）, adaptive kernel estimation, Independent Component Analysis（ICA）, multi-user detection

摘要： 将自适应核估计的ICA算法应用于DS-CDMA系统的多用户检测中，在没有关于混叠信号的分布假设时，根据信号的统计特性自动估计评价函数，采用自适应核密度估计ICA算法检测器的输出初始化独立分量分析的迭代，对任意混叠信号进行盲分离。通过与传统匹配滤波检测和ICA后处理匹配滤波检测进行仿真比对，证明了该算法在DS-CDMA多用户检测中的可行性和优越性。

关键词: 码分多址, 自适应核密度估计, 独立分量分析, 多用户检测

CLC Number:

TN911.23

WAN Ming-gao^1，2，LI Xiao-hui¹. Application of ICA adaptive kernel estimation in multi-user detection[J]. Computer Engineering and Applications, 2010, 46(31): 232-234.

宛明高^1，2，李晓辉¹. ICA自适应核估计在多用户检测中的应用[J]. 计算机工程与应用, 2010, 46(31): 232-234.

[1]	LI Jiaqing1, LI Haifang1, BAI Yifan1, YIN Guimei2, SUN Liting1. Research on recognizing and removing ocular artifact automatically from EEG signals [J]. Computer Engineering and Applications, 2018, 54(13): 148-152.
[2]	HE Anling, HE Xuansen. Improved Fast-ICA algorithms with rational nonlinearities [J]. Computer Engineering and Applications, 2018, 54(1): 251-255.
[3]	ZHAO Liquan, WANG Xin. Independent component analysis based on improved artificial bee colony algorithm [J]. Computer Engineering and Applications, 2017, 53(18): 204-207.
[4]	CHEN Meng, HE Xuansen. Improved Fast-ICA algorithm based on eighth-order convergence of Newton’s iterative method [J]. Computer Engineering and Applications, 2017, 53(11): 178-181.
[5]	EN De1, CHEN Yake1, MAO Zhelong2. Discontinuous transmission of voice in L-PLC under low SNR based on FastICA [J]. Computer Engineering and Applications, 2016, 52(9): 108-111.
[6]	ZHANG Yanfei, OUYANG Jianfei, YAO Lifeng. Physiological signals extraction based on ICA and EMD [J]. Computer Engineering and Applications, 2016, 52(6): 167-171.
[7]	ZHANG Zhifu, PEI Jun. Kalman blind adaptive multiuser detector based on QR factor [J]. Computer Engineering and Applications, 2015, 51(15): 202-205.
[8]	ZHANG Jie, LIU Hui, OU Lunwei. Research on improved FastICA algorithms [J]. Computer Engineering and Applications, 2014, 50(6): 210-212.
[9]	XU Chengqian, HAO Hongjie. Improved intelligent optimization algorithm used in multiuser detection [J]. Computer Engineering and Applications, 2013, 49(3): 143-146.
[10]	FENG Fan1，2, WANG Jianhua1, ZHANG Zhengbao2, WANG Huiping1, QI Hongjun1, ZHANG Hailong1. Blind extraction algorithm of embedded messages based on CSR-ICA [J]. Computer Engineering and Applications, 2013, 49(24): 70-73.
[11]	BAI Xiahong, LI Hui, XING Gang. Independent Component Analysis based denoising for blind multiuser detector [J]. Computer Engineering and Applications, 2013, 49(24): 210-213.
[12]	ZHANG Yujie, LI Zhiming, SONG Guangyu, LING Jiaxi. New independent component analysis method based on fish swarm algorithm [J]. Computer Engineering and Applications, 2013, 49(13): 187-190.
[13]	WANG Xiaowei1, SHI Linsuo1, YANG Long2, LU Mi3. Application of wavelet denoising and ICA in separating speech signals [J]. Computer Engineering and Applications, 2012, 48(3): 120-121.
[14]	GUO Jinyu, LIU Yuqin. Application of robust principal component analysis to palmprint recognition [J]. Computer Engineering and Applications, 2012, 48(19): 8-10.
[15]	LUO Lianling1, WANG Xiuxin1，2, NONG Jinghui1, LIANG Zongjing1, TANG Guyun1. Remote sensing forest classification with texture based on ICA and SVM [J]. Computer Engineering and Applications, 2012, 48(13): 227-229.

Application of ICA adaptive kernel estimation in multi-user detection

ICA自适应核估计在多用户检测中的应用

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics