基于多符号BEM的OFDM系统时变信道估计

doi:10.11959/j.issn.1000-436x.2017060

通信学报 ›› 2017, Vol. 38 ›› Issue (3): 45-52.doi: 10.11959/j.issn.1000-436x.2017060

基于多符号BEM的OFDM系统时变信道估计

丁勇^1,²,欧阳缮^1,²,谢跃雷²,蒋俊正³,陈小毛³

¹ 西安电子科技大学电子工程学院，陕西西安 710071
² 桂林电子科技大学广西无线宽带通信与信号处理重点实验室，广西桂林 541004
³ 桂林电子科技大学认知无线电与信息处理省部共建教育部重点实验室，广西桂林 541004

修回日期:2016-12-09 出版日期:2017-03-01 发布日期:2017-04-13
作者简介:丁勇（1977-），男，湖南祁阳人，桂林电子科技大学讲师，西安电子科技大学博士生，主要研究方向为通信信号处理、OFDM信道估计。|欧阳缮（1960-），男，江西安福人，博士，桂林电子科技大学教授、博士生导师，主要研究方向为通信信号处理、超宽带雷达技术及信号处理等。|谢跃雷（1975-），男，河北邯郸人，桂林电子科技大学副教授、硕士生导师，主要研究方向为通信信号处理、阵列信号处理等。|蒋俊正（1983-），男，浙江金华人，博士，桂林电子科技大学副教授、硕士生导师，主要研究方向为多速率信号处理、FBMC优化设计。|陈小毛（1976-），男，湖北咸宁人，博士，桂林电子科技大学副教授、硕士生导师，主要研究方向为通信和雷达信号处理。
基金资助:
国家自然科学基金资助项目(61371186);国家自然科学基金资助项目(61461015);国家自然科学基金资助项目(61261032);国家自然科学基金资助项目(61261018);广西区自然科学基金资助项目(2013GXNSFFA019004);广西区自然科学基金资助项目(2015GXNSFAA139302);广西无线宽带通信与信号处理重点实验室基金资助项目(GXKL06160101)

Time-varying channel estimation in OFDM systems based on multiple symbols BEM

Yong DING^1,²,Shan OUYANG^1,²,Yue-lei XIE²,Jun-zheng JIANG³,Xiao-mao CHEN³

¹ School of Electronic Engineering,Xidian University,Xi’an 710071,China
² Guangxi Key Laboratory of Wireless Wideband Communication and Signal Processing,Guilin University of Electronic Technology,Guilin 541004,China
³ Key Laboratory of Cognitive Radio and Information Processing,Ministry of Education,Guilin University of Electronic Technology,Guilin 541004,China

Revised:2016-12-09 Online:2017-03-01 Published:2017-04-13
Supported by:
The National Natural Science Foundation of China(61371186);The National Natural Science Foundation of China(61461015);The National Natural Science Foundation of China(61261032);The National Natural Science Foundation of China(61261018);The Natural Science Foundation of Guangxi(2013GXNSFFA019004);The Natural Science Foundation of Guangxi(2015GXNSFAA139302);Foundation of Guangxi Key Laboratory of Wireless Wideband Communication and Signal Processing(GXKL06160101)

摘要/Abstract

摘要：

摘要：针对基于扩展模型(BEM)的正交频分复用(OFDM)系统时变信道估计中频谱利用率不高的问题，提出了一种多符号BEM方法。相对于传统的单符号BEM方法，提出的方法减少了导频的子载波，并且通过基线倾斜技术抑制了傅里叶级数展开时出现的Gibbs现象。理论分析表明，基线倾斜技术引入后的信道模型误差在归一化多普勒频偏不大于 $\frac{\frac{1}{2} - \frac{1}{M}}{1 + \frac{N_{g}}{N}}$ 时均有减小。数值仿真显示在相应的时变信道条件下，提出的方法的估计误差性能和误比特率(BER)性能相对传统方法有明显改善。

关键词: 正交频分复用, 信道估计, 基扩展模型, 多符号

Abstract:

In view of the problem of low frequency spectrum utilization for the time-varying channel estimation in orthogonal frequency division multiplexing(OFDM) systems based on basis expansion model (BEM),a new method based on multiple symbols BEM was proposed.Compared with the conventional method based on single symbol,the proposed method reduced the subcarriers for pilots.And by means of the baseline tilting technique,the new method mitigated the Gibbs phenomenon caused by Fourier series expansion.The theoretical analysis reveals that the channel model error is reduced due to the introduction of the baseline tilting technique when the normalized Doppler frequency is no more than $\frac{\frac{1}{2} - \frac{1}{M}}{1 + \frac{N_{g}}{N}}$ .Finally,the numerical simulation shows that compared with conventional methods,the proposed algorithm achieves significant improvement on the estimation error performance and bit error rate (BER) performance under the conditions of the corresponding time-varying channel.

Key words: orthogonal frequency division multiplexing, channel estimation, basis expansion model (BEM), multiple symbol

中图分类号:

TN929.5

丁勇,欧阳缮,谢跃雷,蒋俊正,陈小毛. 基于多符号BEM的OFDM系统时变信道估计[J]. 通信学报, 2017, 38(3): 45-52.

Yong DING,Shan OUYANG,Yue-lei XIE,Jun-zheng JIANG,Xiao-mao CHEN. Time-varying channel estimation in OFDM systems based on multiple symbols BEM[J]. Journal on Communications, 2017, 38(3): 45-52.

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参考文献 17

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M	f_dT上限
2	0
4	0.242
6	0.323
8	0.364
10	0.388
12	0.404
14	0.416
16	0.424
18	0.431
20	0.436
22	0.441
24	0.444
26	0.448
28	0.450
30	0.453
32	0.455
34	0.456
36	0.458

方法	每个OFDM符号中所需最少导频数目	最大子载波利用率
单符号CCE/GCE -BEM(L_p=3)	99	80.66%
单符号CCE/GCE -BEM(L_p=5)	55	89.26%
多符号CCE-BEM	22	95.70%

基于多符号BEM的OFDM系统时变信道估计

Time-varying channel estimation in OFDM systems based on multiple symbols BEM

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