[1]冀少威,谢红,解武.基于DDST的MIMO时频双选信道的信道估计[J].应用科技,2010,37(04):43-46.[doi:10.3969/j.issn.1009-671X.2010.04.011]
 JI Shao-wei,XIE Hong,XIE Wu.Channel estimation of MIMO timefrequencyselective channels based on datadependent superimposed training[J].yykj,2010,37(04):43-46.[doi:10.3969/j.issn.1009-671X.2010.04.011]
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基于DDST的MIMO时频双选信道的信道估计(/HTML)
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《应用科技》[ISSN:1009-671X/CN:23-1191/U]

卷:
第37卷
期数:
2010年04期
页码:
43-46
栏目:
计算机技术与应用
出版日期:
2010-04-25

文章信息/Info

Title:
Channel estimation of MIMO timefrequencyselective channels  based on datadependent superimposed training
文章编号:
1009-671X (2010) 04-0043-04
作者:
冀少威 谢红解武
哈尔滨工程大学 信息与通信工程学院,黑龙江 哈尔滨 150001
Author(s):
JI Shao-wei XIE Hong XIE Wu
College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China
关键词:
基扩展信道模型时频双选信道MIMO系统信道估计均方误差
Keywords:
basis expansion model timefrequencyselective channel MIMO system channel estimation mean square error
分类号:
TN929.5
DOI:
10.3969/j.issn.1009-671X.2010.04.011
文献标志码:
A
摘要:
针对多输入多输出时频双选择性信道,提出了一种基于信息叠加训练序列和最小二乘相结合的信道估计方法.基于基扩展模型构建MIMO时频双衰落系统,采用DDST算法估计信道矩阵,对误码率进行分析. 训练序列采用等幅度的周期指数序列,同时在基于叠加训练序列估计的基础上叠加一个基于信息的周期序列. 实验表明,基于DDST的MIMO时频双选信道估计方法比传统的估计方法显著降低了信道估计的均方误差和误码率,并能保持较高的频带利用率.
Abstract:
A new channel estimating method which is based on datadependent superimposed training (DDST) and least square (LS) is proposed. Firstly the multiple input multiple output (MIMO) timefrequency fading system was built based on basis expansion model(BEM). Then the channel matrix was estimated using the DDST method, and the bit error rate was also analysed. The periodic exponential sequence with the same magnitude was used as the training sequence and another periodic sequence based on the information sequence was added to eliminate the effect of the unknown information sequence on the training sequence. The simulating experiment indicates that the estimating method proposed obviously reduces the mean square error and the bit error rate compared with the traditional superimposed training(ST) method, and does not waste the system bandwidth.

参考文献/References:

[1] GIANNAKIS G B, TEPEDELENLIOGLU C. Basis expansion models and diversity techniques for blind identification and equalization of timevarying channels[J]. Proceedings of the IEEE, 1998,86(10): 1969-1986.
[2] TUGNAIT J K , LUO Weilin. On channel estimation using superimposed training and firstorder statistics[J]. IEEE Communications, 2003, 9(7): 413-415.
[3] OROZCOLUGO A G, LARA M M, Des MCLERNON C. Channel estimation using implicit training[J]. IEEE Transactions on Signal Processing, 2004, 52 (1): 240-254. [4] GHOGHO M, MCLERNON D, HERNANDEZ E A, et al. Channel estimation and symbol detection for block transmission using datadependent superimposed training[J]. IEEE Signal Processing, 2005, 12(3): 226-229.
[5] ZHOU G T, MCKELVEY T. A firstorder statistical method for channel estimation[J]. IEEE Processing Letters, 2003, 10(3) : 57-60.
[6] TUGNAIT J K , HE S. Doublyselective channel estimation using datadependent superimposed training and exponential basis models[J]. IEEE Transactions on Wireless Communication, 2007, 11(6): 3877-3883.
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[8] MCLERNON D, HERNANDEZ E A, OROZCOLUGO A G. Implicitlytrained channel estimation and equalization with zero mean input data packets[C]// Proceedings of the Fourth IEEE International Symposium on Signal Processing and Information Technology. Rome,Italy,2004, 12: 136-139.

备注/Memo

备注/Memo:
作者简介:冀少威(1984-),男,硕士研究生,主要研究方向:通信与信息系统,E-mail:jishaowei1984@163.com.
更新日期/Last Update: 2010-05-05