基于Golay互补序列的压缩感知稀疏信道估计算法

姚志强; 李广龙; 王仕果; 游志宏

doi:10.11999/JEIT150594

基于Golay互补序列的压缩感知稀疏信道估计算法

doi: 10.11999/JEIT150594

基金项目:

国家自然科学基金(61372127)，湖南省自然科学基金(13JJ3065)

计量
- 文章访问数: 1408
- HTML全文浏览量: 160
- PDF下载量: 649
- 被引次数: 0
出版历程
- 收稿日期: 2015-05-18
- 修回日期: 2015-09-16
- 刊出日期: 2016-02-19

Compressed Sensing Channel Estimation Algorithm Based on Deterministic Sensing with Golay Complementary Sequences

Funds:

The National Natural Science Foundation of China (61372127), The Natural Science Foundation of Hunan Province (13JJ3065)

摘要

摘要: 该文针对现有基于压缩感知的信道估计方法峰均功率比高、计算量大等问题，使用确定性格雷(Golay)序列作为训练序列对稀疏信道进行信道估计，在接收端实现了对信道冲激响应的估计，给出了估计模型和具体的算法推演，推导了该方法的峰均功率比，并与基于随机高斯序列的压缩感知信道估计方法的性能、峰均功率比和计算量进行了比较。仿真实验表明：格雷序列以及随机高斯序列两种序列都可以重构出稀疏信道非零抽头系数，但是格雷序列对稀疏信道冲激响应的确定性观测估计值的均方误差(MSE)和匹配度性能(Match Rate, MR)均优于随机高斯序列，计算量降低了许多，且在OFDM系统中峰均功率比大大降低。
- 信道估计 /
- 压缩感知 /
- Golay互补序列 /
- 稀疏信道
Abstract: To deal with problems of large Peak-to-Average Power Ratio (PAPR) and computation complexity in existing channel estimation algorithm based on compressed sensing, Golay complementary sequence is utilized to estimate sparse channel as a deterministic training sequence. Estimation model and algorithm are provided in detail. The PAPR of this method is deduced and its performance, PAPR and computation complexity are compared with Gaussian random sequence. The simulation result indicates that Golay sequence and Gaussian random sequence can reconstruct nonzero tap coefficients. But Golay sequence outperforms Gaussian random sequence both in the Mean Square Error (MSE) and Match Rate (MR) for estimating sparse channel impulse response. And the computation and PAPR are reduced significantly in the OFDM system.
- Channel estimation /
- Compressed Sensing (CS) /
- Golay complementary sequences /
- Sparse multipath

HTML全文

参考文献(23)

叶新荣, 朱卫平, 张爱清, 等. OFDM系统双选择性慢衰落信道的压缩感知估计[J].电子与信息学报, 2015, 37(1): 169-174. doi: 10.11999/JEIT140247.

YE Xinrong, ZHU Weiping, ZHANG Aiqing, et al. Compressed sensing based on doubly-selective slow-fading channel estimation in OFDM systems[J]. Journal of Electronics Information Technology, 2015, 37(1): 169-174. doi: 10.11999/JEIT140247.

TAUBOCK G and HLAWATSCH F. A compressed sensing technique for OFDM channel estimation in mobile environments: Exploiting channel sparsity for reducing pilots[C]. IEEE International Conference on Acoustics Speech and Signal Processing, Las Vegas, NV, 2008: 2885-2888.

DONOHO D L. Compressed sensing[J]. IEEE Transactions on Information Theory, 2006, 52(4): 1289-1306.

TSAIG Y and DONOHO D L. Extensions of compressed sensing[J]. Signal Processing, 2006, 86(3): 549-571.

CANDES E and ROMBERG J. Robust signal recovery from incomplete observations[C]. IEEE International Conference on Image Processing, Atlanta, GA, 2006: 1281-1284.

CANDES E, ROMBERG J, and TAO T. Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information[J]. IEEE Transactions on Information Theory, 2006, 52(3): 489-509.

CANDES E and TAO T. Decoding by linear programming[J]. IEEE Transactions on Information Theory, 2005, 51(12): 4203-4215.

BAJWA W U, HAUPT J, RAZ G, et al. Compressed channel sensing[C]. CISS 42nd Annual Conference on Information Sciences and Systems, Princeton, NJ, 2008: 5-10.

COHEN K M, ATTIAS C, and FARBMAN B. Channel estimation in UWB channels using compressed sensing[C]. 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Florence, 2014: 1966-1970.

WANG Weidong, YANG Junan, and ZHANG Chun. A novel compressed sensing ultra-wideband channel estimation method based on non-convex optimization[J]. International Journal of Communication Systems, 2015, 28(3): 472-482.

SHAKERI Z, BAJWA W U, et al. Deterministic selection of pilot tones for compressive estimation of MIMO-OFDM channels[C]. 49th Annual Conference on Information Sciences and Systems (CISS), Baltimore, MD, USA, 2015: 1-6.

KHOSRAVI M and MASHHADI S. Joint pilot power pattern design for compressive OFDM channel estimation[J]. IEEE Communications Letters, 2015, 19(1): 50-53.

CHEN Xin and FANG Yong. Compressed sensing based scattering channel estimation for OFDM system under the scenarios of High-speed Railway[C]. 2014 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC), Guilin, 2014, 539-543.

谢志斌,薛同思,田雨波,等.一种稀疏增强的压缩感知MIMO-OFDM信道估计算法[J]. 电子与信息学报, 2013, 35(3): 665-670. doi: 10.3724/SP.J.1146.2012.00860.

XIE Zhibin, XUE Tongsi, TIAN Yubo, et al. A sparsity enhanced channel estimation algorithm based on compressed sensing in MIMO-OFDM systems[J]. Journal of Electronics Information Technology, 2013, 35(3): 665-670. doi: 10.3724/ SP.J.1146.2012.00860.

BAJWA W U, JARVIS H, SAYEED A M, et al. Compressed channel sensing: A new approach to estimating sparse multipath channels[J]. Proceedings of the IEEE, 2010, 98(6): 1058-1076.

JARVIS H, BAJWA W U, and RAZ G. Toeplitz compressed sensing matrices with applications to sparse channel estimation[J]. IEEE Transactions on Information Theory, 2010, 56(11): 5862-5875.

GUAN G, QUN W, and WEI P. Sparse multipath channel estimation using compressive sampling matching pursuit algorithm[C]. IEEE Vehicular Technology Society Asia Pacific Wireless Communication Symposium, Piscataway, 2010: 19-22.

LI K, GAN L, and LING C. Convolutional compressed sensing using deterministic sequences[J]. IEEE Transactions on Signal Processing, 2012, 61(3): 740-752.

EIWEN D, TAUBOCK G, HLAWATSCH F, et al. Multichannel channel group sparsity methods for compressive channel stimation in doubly selective multicarrier MIMO systems[OL]. http//arxiv.org/abs/1407.3474, 2014.

GOLAY M J E. Complementary series[J]. IRE Transactions on Information Theory, 1961, 7(2): 82-87.

GRAY R M. Toeplitz and cimulant matrices: A review[J]. Communication and Information Theoy, 2006, 2(3): 155-239.

施引文献

资源附件(0)

访问统计

计量

文章访问数: 1408
HTML全文浏览量: 160
PDF下载量: 649
被引次数: 0

姓名
邮箱
手机号码
标题
留言内容
验证码

留言板

基于Golay互补序列的压缩感知稀疏信道估计算法

doi: 10.11999/JEIT150594

计量

Compressed Sensing Channel Estimation Algorithm Based on Deterministic Sensing with Golay Complementary Sequences

计量

目录

留言板

基于Golay互补序列的压缩感知稀疏信道估计算法

doi: 10.11999/JEIT150594

计量

出版历程

Compressed Sensing Channel Estimation Algorithm Based on Deterministic Sensing with Golay Complementary Sequences

计量

出版历程

目录