近年来,水声信道估计主要是基于稀疏模型展开。水声介质的非均匀性等使声线以簇的形式传播,导致水声信道展现出块结构稀疏特性。本文针对信道的块结构稀疏特性,在OFDM通信系统中,提出使用改进的BOMP算法进行水声信道估计。BOMP算法一次筛选1个最大相关块,改进的算法一次挑选t个非零块,算法重构时间将降低t倍。仿真结果表明:改进的BOMP算法误码率和重构时间要优于传统的LS、基于压缩感知的OMP算法;在不降低BOMP算法重构精度的前提下,将重构时间降低t倍。
Recently, the underwater acoustic channel estimation is mainly based on the sparse model. Underwater acoustic medium inhomogeneity etc make voice spread in the form of cluster, which result the underwater acoustic channel show as block structure sparse features. In OFDM communication system, based on the block structure sparse characteristics. This article proposed to use the improved BOMP algorithm to estimate the underwater acoustic channel. At a time, the BOMP algorithm filtrate a maximum relative block, but the improved algorithm can select t non-zero block, which reduce the algorithm reconstruction time t Times. The simulation results show that the improved BOMP algorithm ber and reconstruction time are superior to the traditional LS, the OMP algorithm based on compression perception; without reducing BOMP algorithm reconstruction precision, reduce reconstruction time t Times.
2017,39(8): 156-159 收稿日期:2016-07-29
DOI:10.3404/j.issn.1672-7649.2017.08.033
分类号:TN911.5
基金项目:国家自然科学基金(11574120,61401180);江苏高校高技术船舶协同创新中心/江苏科技大学海洋装备研究院基金(HZ2016010);江苏科技大学深蓝人才工程青年学者计划基金等资助
作者简介:朱芹(1991-),女,硕士研究生,主要从事水声通信研究
参考文献:
[1] GUI G, XU L, SHAN L.Block bayesian sparse learning algorithms with application to estimating channels in OFDM systems[C]//International Symposium on Wireless Personal Multimedia Communications.IEEE, 2014: 238-242.
[2] SHAO J, ZHANG X, LIU Y.Channel estimation based on compressed sensing for high-speed underwater acoustic communication[C]//Image and Signal Processing (CISP), 20147th International Congress on.IEEE, 2015: 1017-1021.
[3] YU H, GUO S.Compressed sensing: optimized overcomplete dictionary for underwater acoustic channel estimation[J].Wireless Communication Over Zigbee for Automotive Inclination Measurement China Communications, 2012, 9(1): 40-48.
[4] LV X, BI G, WAN C.The Group Lasso for stable recovery of block-sparse signal representations[J].IEEE Transactions on Signal Processing, 2011, 59(4): 1371-1382.
[5] ELDAR Y C, KUPPINGER P, BÖLCSKEI H.Compressed sensing of block-sparse signals: uncertainty relations and efficient recovery[J].Mathematics, 2010, 58(6): 3042-3054.
[6] HUANG A, GUAN G, WAN Q, et al.A block orthogonal matching pursuit algorithm based on sensing dictionary[J].International Journal of Physical Sciences, 2011.
[7] BARANIUK R G, CEVHER V, DUARTE M F, et al.Model-based compressive sensing[J].IEEE Transactions on Information Theory, 2010, 56(4): 1982-2001.
[8] ZHAO Q, WANG J, HAN Y, et al.Compressive sensing of block-sparse signals recovery based on sparsity adaptive regularized orthogonal matching pursuit algorithm[C]//IEEE Fifth International Conference on Advanced Computational Intelligence.2012: 1141-1144.
[9] ELDAR Y C, MISHALI M.Block sparsity and sampling over a union of subspaces[C]//International Conference on Digital Signal Processing.2009: 1-8.
[10] 庄哲民, 吴力科, 李芬兰, 等.基于块稀疏信号的正则化自适应压缩感知算法[J].吉林大学学报(工学版), 2014, 44(1): 259-263.
[11] CAI T T, WANG L, XU G.New bounds for restricted isometry constants[J].Information Theory IEEE Transactions on, 2010, 56(9): 4388-4394.
[12] 刘芳, 武娇, 杨淑媛, 等.结构化压缩感知研究进展[J].自动化学报, 2013, 39(12): 1980-1995.