一种基于压缩感知的信道缩短滤波器设计
PDF下载 (570)刘小青,李有明*,朱 星,陈 斌,雷 鹏,季 彪.一种基于压缩感知的信道缩短滤波器设计[J].宁波大学学报(理工版),2015,28(01):47-51.DOI:
LIU Xiao-qing,LI You-ming*,ZHU Xing,CHEN Bing,LEI Peng,JI Biao.A Design of Channel Shortening Filter Based on Compressed Sensing[J].Journal of Ningbo University(Natural Science & Engineering Edition),2015,28(01):47-51.DOI:
| Title: | A Design of Channel Shortening Filter Based on Compressed Sensing |
| 作者: | 刘小青, 李有明*, 朱 星, 陈 斌, 雷 鹏, 季 彪 |
| Author(s): | LIU Xiao-qing, LI You-ming*, ZHU Xing, CHEN Bing, LEI Peng, JI Biao |
| 关键词: | 压缩感知; 信道缩短; 子空间追踪; 稀疏滤波器 稀疏度自适应文献码: A 宽带通信信道的冲激响应较长, 会延伸至数10个符号周期, 并引起严重的码间干扰(Inter Symbol Interference, ISI); 在正交频分复用(Ortho- gonal Frequency Division Multiplexing, OFDM)系统中, 需要在每个符号前加入循环前缀以减小码间干扰, 并且循环前缀的长度应不小于信道冲激响应的长度. 宽带通信信道下需要较长的循环前缀, 这会严重降低系统的传输速率[1]. 为此, 在接收前端加入一个滤波器, 使信道和滤波器的联合冲激响应缩短, 从而缩短循环前缀的长度, 降低由循环前缀所造成的传输速率的损失. 然而系统复杂度与滤波器中非零抽头个数的平方成正比, 因此如何减少滤波器的非零抽头个数, 即稀疏滤波器的设计越来越受到人们的关注. |
| Keywords: | compressed sensing; channel shorten; subspace pursuit; sparse filter; sparsity adaptive |
| 分类号: | TN713 |
| 文献标识码: | A |
| 摘要: | 鉴于使用信道缩短滤波器的系统的复杂度会随着滤波器中非零抽头的增加而快速增大, 运用盲自适应子空间追踪(Blind Adaptive Subspace Pursuit, BASP)算法设计了一种稀疏滤波器来减少非零抽头. 首先在最小均方误差(Minimum Mean Square Error, MMSE)准则下, 将信道缩短问题转化为稀疏滤波器的设计问题, 然后通过加入稀疏度盲估计过程, 使滤波器可以自适应地改变稀疏度, 最后在子空间追踪算法(Subspace Pursuit, SP)的框架下实现非零抽头不连续的稀疏滤波器设计. 仿真结果表明, 设计的稀疏滤波器有效地实现了信道缩短, 运用该滤波器的系统可在更低的复杂度下获得更高的精度. |
| Abstract: | The computing complexity of the system grows fast with the number of filter’s nonzero taps. Therefore, a sparse filter is designed in this paper based on the Blind Adaptive Subspace Pursuit algorithm to reduce the number of the nonzero taps. Firstly, the channel shortening problem is transformed into a sparse filter design problem based on the Minimum Mean Square Error criterion. Then, the filter’s sparsity is adaptively changed according to a sparsity blind estimation method. Finally, a sparse filter with noncontiguous nonzeros taps is achieved under the frame of Subspace Pursuit algorithm. The simulation results demonstrate that the designed filter can shorten the channel efficiently. Furthermore, comparing with the existing sparse filters, the system using the designed filter obtains higher accuracy with lower complexity |
| 参考文献 /References: | [1].Chen Shaoping, Zhu Cuitao. ICI and ISI analysis and mitigation for OFDM systems with insufficient cyclic prefix in time-varying channels[J]. IEEE Transactions on Consumer Electronics, 2004, 50(1):78-83. [2].Melvasalo M, Janis P, Koivunen V. Sparse equalization in high data rate WCDMA systems[C]//IEEE 8th Workshop on Signal Processing Advances in Wireless Communications (SPAWC), 2007:1-5. [3].Kutz G, Raphaeli D. Determination of tap positions for sparse equalizers[J]. IEEE Transaction on Communication, 2007, 55(9):1712-1724. [4].Sui H, Masry E, Rao B. Chip-level DS-CDMA downlink interference suppression with optimized finger placement [J]. IEEE Trans on Signal Process, 2006, 54(10):3908- 3921. [5].Baran T, Wei D, Oppenheim A. Linear programming algorithms for sparse filter design[J]. IEEE Transaction on Signal Processing, 2010, 58(3):1605-1617. [6].Gomaa A, Dhahir N. Low-complexity sparse FIR channel shortening[C]//IEEE on Global Telecommunications Conference (GLOBECOM), 2010:1-5. [7].Wei D, Sestok C K, Oppenheim A V. Sparse filter design under a quadratic constraint: Low-complexity algorithms [J]. IEEE Transactions on Signal Processing, 2013, 61(4): 857-870. [8].Wei D, Oppenheim A V. A branch-and-bound algorithm for quadratically-constrained sparse filter design[J]. IEEE Transactions on Signal Processing, 2013, 61(4):1006- 1018. [9].Gomaa A, Al-Dhahir N. A new design framework for sparse FIR MIMO equalizers[J]. IEEE Transactions on Communications, 2011, 59(8):2132-2140. [10].Tropp J, Gilbert A. Signal recovery from random measurements via orthogonal matching pursuit[J]. IEEE Transactions on Information Theory, 2007, 53(12):4655- 4666. [11].Dai W, Milenkovic O. Subspace pursuit for compressive sensing signal reconstruction[J]. IEEE Transactions on Information Theory, 2009, 55(5):2230-2249. [12].杨成, 冯巍, 冯辉, 等. 一种压缩采样中的稀疏度自适应子空间追踪算法[J]. 电子学报, 2010, 8(38):19141917. [13].田文飚, 付争, 芮国胜. 基于分治试探的盲自适应匹配追踪重构算法[J]. 通信学报, 2013, 34(4):180-186. |
| 备注/Memo: | 收稿日期: 2013?12?05. 宁波大学学报（理工版）网址: http://journallg.nbu.edu.cn/ 基金项目: 国家自然科学基金（60874083）; 浙江省教育厅科研项目（Y200907622）; 宁波大学科技学院预研项目（003-21021003）. 第一作者: 刘小青（1991－）, 男, 湖北襄阳人, 在读硕士研究生, 主要研究方向: 压缩感知技术及其应用. E-mail: 1025006979@qq.com *通信作者: 李有明（1963－）, 男, 陕西扶风人, 博导/教授, 主要研究方向: 宽带接入技术. E-mail: liyouming@nbu.edu.cn 宁波大学学报（理工版）网址：http://journallg.nbu.edu.cn/ |
