计算机应用 ›› 2013, Vol. 33 ›› Issue (06): 1508-1514.DOI: 10.3724/SP.J.1087.2013.01508

• 网络与通信 • 上一篇    下一篇

基于自适应阈值SAMP算法的OFDM稀疏信道估计

姜杉,仇洪冰,韩旭   

  1. 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
  • 收稿日期:2012-12-05 修回日期:2013-02-02 出版日期:2013-06-01 发布日期:2013-06-05
  • 通讯作者: 姜杉
  • 作者简介:姜杉(1988-),女,湖北武汉人,硕士研究生,主要研究方向:无线通信、信道估计;仇洪冰(1963-),男,江苏如皋人,教授,博士生导师,博士,主要研究方向:移动通信、超宽带无线通信、宽带通信网络、通信信号处理;韩旭(1988-),男,河北石家庄人,硕士研究生,主要研究方向:无线通信、信道估计。
  • 基金资助:

    国家科技重大专项(2012ZX03006002)

Sparsity adaptive matching pursuit algorithm based on adaptive threshold for OFDM sparse channel estimation

JIANG Shan, QIU Hongbing,HAN Xu   

  1. Guangxi Key Laboratory of Wireless Wideband Communication and Signal Processing,Guilin University of Electronic Technology, Guilin Guangxi 541004, China
  • Received:2012-12-05 Revised:2013-02-02 Online:2013-06-05 Published:2013-06-01
  • Contact: JIANG Shan

摘要: 为了降低重构算法的复杂度,提高重构的精确度,提出一种自适应阈值的稀疏度自适应匹配追踪算法(SAMP),并将其运用在OFDM稀疏信道估计中。蒙特卡洛仿真证明,改进后的算法相比于原算法在CPU运行时间上减少了44.7%,并且在较低的信噪比下也能达到较好的估计效果。此外,针对OFDM稀疏信道估计问题,结合压缩感知理论中观测矩阵的构造方法,提出一种新的导频图案分布设计方法,仿真证明该导频图案设计方法比现有方法在估计精确度方面提高2~4dB。

关键词: 正交频分多址, 信道估计, 压缩感知, 稀疏度自适应匹配追踪算法, 导频分布图案

Abstract: In order to reduce the complexity of the reconstruction algorithm and improve the precision of estimation, the authors proposed a new Sparsity Adaptive Matching Pursuit (SAMP) algorithm by using the adaptive threshold applied in the OFDM (Orthogonal Frequency Division Multiplexing) sparse channel estimation. The Monte Carlo simulation results show that, compared with the traditional method, the CPU run time decreased by 44.7%. And in lower SNR (SignaltoNoise Ratio), the performance achieved obvious improvements. Besides, in OFDM sparse channel estimation, a new design of pilot pattern was presented based on the mutual coherence of the measurement matrix in Compressive Sensing (CS) theory. The Monto Carlo simulation results show that, the precision of channel is increased by 2-4 dB with the new pilot pattern.

Key words: Orthogonal Frequency Division Multiplexing (OFDM), channel estimation, Compressive Sensing (CS), Sparsity Adaptive Matching Pursuit (SAMP) algorithm, pilot pattern

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