Joint channel estimation, equalization, and data detection for OFDM systems in the presence of very high mobility

  • Authors:

  • Erdal Panayirci;Habib Şenol;H. Vincent Poor

  • Affiliations:

  • Department of Electronics Engineering, Kadir Has University, Cibali, Istanbul, Turkey and Department of Electrical Engineering, Princeton University, Princeton, NJ;Department of Computer Engineering, Kadir Has University, Cibali, Istanbul, Turkey;Department of Electrical Engineering, Princeton University, Princeton, NJ

  • Venue:
  • IEEE Transactions on Signal Processing
  • Year:
  • 2010

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Abstract

This paper is concerned with the challenging and timely problem of joint channel estimation, equalization, and data detection for uplink orthogonal frequency division multiplexing (OFDM) systems in the presence of frequency selective and very rapidly time varying channels. The resulting algorithm is based on the space alternating generalized expectation maximization (SAGE) technique which is particularly well suited to multicarrier signal formats leading to a receiver structure that also incorporates interchannel interference (ICI) cancelation. In order to reduce the computational complexity of the algorithm, band-limited, discrete cosine orthogonal basis functions are employed to represent the rapidly time-varying fading channel by the discrete cosine serial expansion coefficients. It is shown that, depending on the normalized Doppler frequency, only a small number of expansion coefficients is sufficient to approximate the channel perfectly and there is no need to know the correlation function of the input signal. In this way, the resulting reduced dimensional channel coefficients are estimated and the data symbols detected iteratively with tractable complexity. The proposed SAGE joint detection algorithm updates the data sequences serially and the channel parameters are updated in parallel, leading to a receiver structure that also incorporates ICI cancelation. Computer simulations show that the cosine transformation represents the time-varying channel very effectively and the proposed algorithm has excellent symbol error rate and channel estimation performance even with a very small number of channel expansion coefficients employed in the algorithm, resulting in substantial reduction of the computational complexity.