Iterative Decomposed OFDM Channel Estimation Algorithm Over Highly Mobile Channels

  • Authors:

  • Qilin Guo;Muqing Wu;Qinjuan Zhang;Xiaofang Hao

  • Affiliations:

  • Laboratory of Network System Architecture and Convergence, Beijing University of Posts and Telecommunications, Beijing, China 100876;Laboratory of Network System Architecture and Convergence, Beijing University of Posts and Telecommunications, Beijing, China 100876;Laboratory of Network System Architecture and Convergence, Beijing University of Posts and Telecommunications, Beijing, China 100876;Laboratory of Network System Architecture and Convergence, Beijing University of Posts and Telecommunications, Beijing, China 100876

  • Venue:
  • Wireless Personal Communications: An International Journal
  • Year:
  • 2013

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Abstract

In orthogonal frequency-division multiplexing systems, the temporal channel gains to estimate are much more than the observable data over highly mobile channels. The basis expansion model (BEM) has been employed to reduce the number of these channel parameters. In the absence of channel statistics, generalized complex-exponential BEM (GCE-BEM) is popular for its fast algebra operation and easy generation of basis matrix. However, there is still much potential for performance improvement by modeling error reduction. In this paper, the factors affecting the modeling error are analyzed and an iterative decomposed estimation algorithm is proposed to improve the modeling accuracy. The proposed algorithm decomposes each tap into the linear part and the non-linear part. The linear part with two parameters (the middle value and the slope) is initialized by estimation in linearly time-varying channel models. And the non-linear part is addressed by the conventional least-squares (LS) method based on GCE-BEM and then the slopes of the linear part are updated for the next iteration by two distinct slope update methods. The simulations show that the proposed algorithm outperforms the conventional estimation methods with significantly reduced modeling error under both high signal to noise ratio and Doppler shift conditions.