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OFDM系统中一种低复杂度的信道估计算法 被引量：1

Low-Complexity Channel Estimation Method for OFDM Systems
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摘要 提出一种适用于正交频分复用(Orthogonal frequency division multiplexing,OFDM)系统的低复杂度信道时域响应估计算法。在发送端,算法基于最小均方误差(Mean-square error,MSE)准则以及给定的导频分布对导频功率及相位进行设计;在接收端,算法直接对接收到的导频符号进行求和,通过对求和结果进行简单加权后得到信道时域响应的估计。由于该算法在估计信道时只需进行很少量的复数乘法运算,因此接收端的计算复杂度非常低。理论分析与仿真结果表明,当信道时不变或慢时变时,该算法具有较小的估计均方误差。 A low-complexity channel estimation method for orthogonal frequency division multiplexing（OFDM） systems is proposed.At the receiver,the power and phases of the pilots are designed based on the minimum mean-square error（MSE） criteria and the given pilot placement.At the receiver,the estimation of the channel impulse response is obtained by directly adding the received pilots and then multiplying the sum by a weighted factor.Since only a small number of complex multiplications are needed in the estimation,the computational complexity at the receiver is very low.Theoretical analysis and simulation results show that,when the channel is time-invariant or slowly time-varying,the method has a relatively small estimation MSE.
作者 胡蝶 周小林
机构地区 复旦大学通信科学与工程系
出处 《数据采集与处理》 CSCD 北大核心 2010年第6期677-682,共6页 Journal of Data Acquisition and Processing
基金 国家自然科学基金(60902007 60802011)资助项目 高等学校博士学科点专项科研基金(200802461141)资助项目 上海市教育委员会和上海市教育发展基金会"晨光计划"(09CG01)资助项目 国家科技重大专项(2009ZX03002-003)资助项目
关键词 正交频分复用 信道估计 导频设计 orthogonal frequency division multiplexing（OFDM） channel estimation pilot design
分类号 TN911.23 [电子电信—通信与信息系统]
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参考文献9

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同被引文献12

  • 1Bajwa W U, Haupt J, Sayeed A M, et al. Com- pressed channel sensing: A new approach to estima- ting sparse multipath channels [J]. Proceedings of the IEEE, 2010, 98(6):1058-1076.
  • 2Shu F, Lee J, Wu L N, et al. Time-frequency chan- nel estimation for digital amplitude modulation broadcasting systems based on OFDM [J]. IEE Pro- ceedings Communications, 2003, 150(4):259-264.
  • 3Cotter S F, Rao B D. Sparse channel estimation via matching pursuit with application to equalization [J]. Communications, IEEE Transactions on, 2002, 50(3) :374-377.
  • 4Kocic M, Brady D, Merriam S. Reduced-complexity RLS estimation for shallow-water channels [C]// Autonomous Underwater Vehicle TechnOlogy, 1994. AUV'94, Proceedings of the 1994 Symposium on.[S. 1. ]:IEEE, 1994:165-170.
  • 5Raghavendra M R, Bhashyam S, Giridhar K. Impro- ving channel estimation in OFDM systems sparse multipath channels [C]//Signal Processing Advances in Wireless Communications, 2004 IEEE 5th Work- shop on. [S. 1.]:IEEE, 2004:106-109.
  • 6Tarighat A, Sayed A H. Joint compensation of transmitter and receiver impairments in OFDM sys- tems[J]. Wireless Communications, IEEE Trans- actions on, 2007, 6(1) :240-247.
  • 7Lin H, Yamashita K. Time domain blind I/Q imbal- ance compensation based on real-valued filter [J]. Wireless Communications, IEEE Transactions on, 2012,11(12) :4342 -4350.
  • 8Beheshti M, Omidi M J, Doost-Hoseini A M. Joint compensation of transmitter and receiver IQ imbal- ance for MIMO-OFDM over doubly selective chan- nels [J]. Wireless Personal Communications, 2013, 70(2):537-559.
  • 9Rhee M Y. Mobile communication systems and secu- rity [M]. New York: John Wiley & Sons, 2009: 133-166.
  • 10Zibulevsky M, Elad M. L1-L2 optimization in signal and image processing [J]. Signal Processing Maga- zine, IEEE, 2010, 27(3)..76-88.

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数据采集与处理
2010年 第6期

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