[1]孙君,高杰.基于压缩感知的自适应导频信道估计[J].计算机技术与发展,2016,26(10):184-187.
 SUN Jun,GAO Jie. Adaptive Pilot Channel Estimation Based on Compressive Sensing[J].,2016,26(10):184-187.
点击复制

基于压缩感知的自适应导频信道估计()
分享到:

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
26
期数:
2016年10期
页码:
184-187
栏目:
应用开发研究
出版日期:
2016-10-10

文章信息/Info

Title:
 Adaptive Pilot Channel Estimation Based on Compressive Sensing
文章编号:
1673-629X(2016)10-0184-04
作者:
 孙君高杰
 南京邮电大学 通信与信息工程学院
Author(s):
 SUN JunGAO Jie
关键词:
 压缩感知信道估计自适应导频
Keywords:
 compressive sensingchannel estimationadaptivepilot
分类号:
TP301
文献标志码:
A
摘要:
 在无线通信系统中,如何提升信道估计的准确度对提升无线通信的系统性能至关重要。在信道估计中,导频开销占据了较多的频谱资源,且传统的信道估计算法不能根据信道状态实时调整信道估计中所需要的导频数量。而压缩感知信道估计算法,可以利用无线信道的稀疏特性,提高信道估计的精确度,减少导频子载波的开销。基于此特点,将压缩感知与信道估计相结合,研究了基于压缩感知的稀疏度未知情况的信道估计,并提出一种适用于LTE-A系统的导频自适应信道估计算法。仿真结果表明:与传统的LS信道估计和LMMSE信道估计相比,所提出的导频自适应算法能够将导频数量减少40%左右,并能获得更准确的信道估计性能。
Abstract:
 In wireless communication system,how to improve the accuracy of channel estimation is very important for improvement of the performance for wireless communication system. In the channel estimation,the pilot overhead occupies a large amount of spectrum re-sources,and the traditional channel estimation algorithm cannot adjust the pilot channel estimation according to the channel state. The compressed sensing channel estimation algorithm,taking advantage of the sparse characteristic of wireless channel,improves the accuracy of channel estimation and decreases the pilot overhead. Based on these features,combined compressive sensing with channel estimation, investigating channel estimation with unknown channel sparsity based on compressive sensing,an adaptive pilot channel estimation algo-rithm is put forward for LTE-Advanced systems. Simulation shows that compared with the traditional LS and LMMSE,it can reduce the number of pilot by 40% and obtain more accurate channel estimation performance.

相似文献/References:

[1]张爱华 薄禄裕 盛飞 杨培.基于小波变换的压缩感知在图像加密中的应用[J].计算机技术与发展,2011,(12):145.
 ZHANG Ai-hua,BO Lu-yu,SHENG Fei,et al.Compressed Sensing Based on Single Layer Wavelet Transform for Image Encryption[J].,2011,(10):145.
[2]王韦刚 庄伟胤.基于NIOS Ⅱ的图像压缩感知[J].计算机技术与发展,2012,(04):12.
 WANG Wei-gang,ZHUANG Wei-yin.Compressed Sensing of Image Based on NIOS Ⅱ[J].,2012,(10):12.
[3]王韦刚 胡海峰.基于压缩感知的协作频谱检测[J].计算机技术与发展,2012,(12):241.
 WANG Wei-gang,HU Hai-feng.Collaborative Spectrum Detection Based on Compressed Sensing[J].,2012,(10):241.
[4]张晓咏,熊承义,胡开云,等.基于灰度纹理信息的图像压缩感知编码与重构[J].计算机技术与发展,2013,(01):47.
[5]刘洋,季薇,侯晓赟.一种改进的基于 OMP 重建的宽带频谱感知算法[J].计算机技术与发展,2013,(01):99.
 LIU Yang,JI Wei,HOU Xiao-yun.A Modified Spectrum Sensing Algorithm for Wideband Cognitive Radio Based on OMP[J].,2013,(10):99.
[6]彭钰,侯晓赟.基于二维压缩感知的双选信道估计[J].计算机技术与发展,2013,(10):220.
 PENG Yu,HOU Xiao-yun.Doubly Selective Channel Estimation Based on Two Dimension Compressed Sensing[J].,2013,(10):220.
[7]李熔.基于截尾估计的概率估计方法[J].计算机技术与发展,2014,24(02):101.
 LI Rong.Probability Estimation Method Based on Truncated Estimation[J].,2014,24(10):101.
[8]李燕,王博.基于压缩感知的数据压缩与检测[J].计算机技术与发展,2014,24(03):198.
 LI Yan,WANG Bo.Data Compression and Detection Based on Compressive Sensing[J].,2014,24(10):198.
[9]周飞飞,李雷.小波高频子带变换裁剪阈值SAMP算法研究[J].计算机技术与发展,2014,24(05):83.
 ZHOU Fei-fei,LI Lei.Research on Clipping Threshold SAMP Algorithm Based on High Frequency Sub-band Wavelet Transform[J].,2014,24(10):83.
[10]刘正其,季薇.一种改进的基于BOMP的宽带频谱感知算法[J].计算机技术与发展,2014,24(06):118.
 LIU Zheng-qi,JI Wei.A Modified Spectrum Sensing Algorithm for Wideband Cognitive Radio Based on BOMP[J].,2014,24(10):118.
[11]徐志坚,邱晓晖. 采用压缩感知的协作多点信道反馈算法研究[J].计算机技术与发展,2014,24(10):221.
 XU Zhi-jian,QIU Xiao-hui. Study on Channel Feedback Algorithm Using Compressed Sensing for Coordinated Multiple Point[J].,2014,24(10):221.
[12]柯家龙,李继楼. 压缩感知中的投影矩阵优化算法[J].计算机技术与发展,2015,25(03):95.
 KE Jia-long,LI Ji-lou. Algorithm of Optimization for Projection Matrix in Compressive Sensing[J].,2015,25(10):95.
[13]李尚靖[],朱琦[][],朱俊华[]. 基于压缩感知和正弦字典的语音编码新方案[J].计算机技术与发展,2015,25(04):188.
 LI Shang-jing[],ZHU Qi[][],ZHU Jun-hua[]. A New Scheme of Speech Coding Based on Compressed Sensing and Sinusoidal Dictionary[J].,2015,25(10):188.
[14]郭海亮. 基于GEP算法的压缩感知语音观测序列建模[J].计算机技术与发展,2015,25(05):46.
 GUO Hai-liang. Speech Signals Measurements Sequence Modeling in Compressed Sensing Based on GEP[J].,2015,25(10):46.
[15]郭青青,李雷. 基于SiT-ROMP算法的视频封装帧压缩重构研究[J].计算机技术与发展,2015,25(08):113.
 GUO Qing-qing,LI Lei. Research on Compressing and Reconstructing of Encapsulated Video Frame Based on Self-iterative Threshold ROMP Algorithm[J].,2015,25(10):113.
[16]李继楼,柯家龙. 基于压缩感知的WSN数据压缩与重构[J].计算机技术与发展,2015,25(09):111.
 LI Ji-lou,KE Jia-long. Data Compression and Recovery of WSN Based on Compressive Sensing[J].,2015,25(10):111.
[17]钱阳,李雷. 一种基于新型KPCA算法的视频压缩感知算法[J].计算机技术与发展,2015,25(10):101.
 QIAN Yang,LI Lei. A Video Compressed Sensing Algorithm Based on Novel KPCA[J].,2015,25(10):101.
[18]玲玲,齐丽娜. 特征字典与自适应联合的BCS-UWB信道估计[J].计算机技术与发展,2015,25(12):195.
 WANG Ling-ling,QI Li-na. Ultra-wideband Channel Estimation Based on Bayesian Compressive Sensing of Eigen-based Dictionary and Adaptive Joint[J].,2015,25(10):195.
[19]孙君,孙照伟. 基于压缩感知的信道互易性补偿方法[J].计算机技术与发展,2015,25(12):210.
 SUN Jun,SUN Zhao-wei. A Compensation Method for Channel Non-reciprocity Based on Compressive Sensing[J].,2015,25(10):210.
[20]于云,周伟栋. 基于压缩感知的鲁棒性说话人识别参数研究[J].计算机技术与发展,2016,26(03):18.
 YU Yun,ZHOU Wei-dong. Research on Robust Speaker Recognition Parameters Based on Compressed Sensing[J].,2016,26(10):18.

更新日期/Last Update: 2016-11-29