An improved channel estimation algorithm for OFDM systems with virtual carriers

  • Authors:

  • Jiandong Li;Yang Zhang;Lihua Pang;Chuan Li

  • Affiliations:

  • Broadband Wireless Communications Laboratory, Information Science Institute,State Key Laboratory of Integrated Service Networks, Xidian University, Xi'an, China;Broadband Wireless Communications Laboratory, Information Science Institute,State Key Laboratory of Integrated Service Networks, Xidian University, Xi'an, China;Broadband Wireless Communications Laboratory, Information Science Institute,State Key Laboratory of Integrated Service Networks, Xidian University, Xi'an, China;Broadband Wireless Communications Laboratory, Information Science Institute,State Key Laboratory of Integrated Service Networks, Xidian University, Xi'an, China

  • Venue:
  • WiCOM'09 Proceedings of the 5th International Conference on Wireless communications, networking and mobile computing
  • Year:
  • 2009

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Abstract

In this paper, we propose an improved channel estimation algorithm for orthogonal frequency division multiplexing (OFDM) systems with virtual carriers. As conventional estimator can not estimate the channel transfer function (CTF) at virtual carriers, resulting in channel energy leakage after inverse discrete Fourier transform (IDFT), time-domain filtering method is not directly applicable. To circumvent this problem, we derive least squares (LS) method to estimate the CTF at virtual carriers by using the assumption of limitation of channel impulse response (CIR). Further, by exploiting the noise correlation between signal subspace and noise subspace, we use maximum a posterior (MAP) criterion to estimate the noise in signal subspace and then suppress the estimation error brought by it without the knowledge of channel statistical parameters. In addition to the training mode, the proposed method can also be extended and used in the tracking mode with decision-aided feedback. Simulation results show that the improved method is free of symbol error rate (SER) floor and its SER attains 2dB improvement in comparison with that of regular LS estimator.