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  • Research Article
  • Open Access

Optimal Superimposed Training Sequences for Channel Estimation in MIMO-OFDM Systems

EURASIP Journal on Advances in Signal Processing20102010:140506

  • Received: 7 June 2009
  • Accepted: 10 February 2010
  • Published:


In this work an iterative time domain Least Squares (LS) based channel estimation method using superimposed training (ST) for a Multiple Input Multiple Output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) system over time varying frequency selective fading channels is proposed. The performance of the channel estimator is analyzed in terms of the Mean Square Estimation Error (MSEE) and its impact on the uncoded Bit Error Rate (BER) of the MIMO-OFDM system is studied. A new selection criterion for the training sequences that jointly optimizes the MSEE and the BER of the OFDM system is proposed. Chirp based sequences are proposed and shown to satisfy the same. These are compared with the other sequences proposed in the literature and are found to yield a superior performance. The sequences, one for each transmitting antenna, offers fairness through providing equal interference in all the data carriers unlike earlier proposals. The effectiveness of the mathematical analysis presented is demonstrated through a comparison with the simulation studies. Experimental studies are carried out to study and validate the improved performance of the proposed scheme. The scheme is applied to the IEEE 802.16e OFDM standard and a case is made with the required design of the sequence.


  • Orthogonal Frequency Division Multiplex
  • Channel Estimation
  • Input Multiple Output
  • Training Sequence
  • OFDM System

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Authors’ Affiliations

G.S. Sanyal School of Telecommunications, IIT, Kharagpur, 721302, India
Department of Electronics and Electrical Communication, IIT, Kharagpur, 721302, India


© J. P. Nair and R. V. R. Kumar. 2010

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.