Improved superimposed training-based channel estimation method for closed-loop multiple input multiple output orthogonal frequency division multiplexing systems

Abstract

This study pertains to the use of superimposed training (ST) in closed-loop multiple input multiple output (MIMO) orthogonal frequency division multiplexing (OFDM) systems. A novel method consisting of a technique to refine the channel estimate at the transmitter in an ST-based closed-loop MIMO-OFDM system is proposed. The estimate is refined at the transmitter by making use of the previous data that was transmitted. This significantly reduces the data interference in the channel estimate and hence improves performance. The mean squared estimation error (MSEE) of the refined channel estimate is mathematically analysed and it is shown that in the limit, the accuracy of the refined channel estimate is identical to the pilot-assisted case with the additional advantage of the bandwidth efficiency inherent in ST-based systems. Two factors in general affect all transmitter side techniques that make use of the fed back channel coefficients. One is the quantisation error affecting the channel estimate. The other is the channel variations that may render the fed back channel coefficients outdated for further use. The effect of these factors on the proposed refined channel estimation scheme is discussed in detail. Simulation results are presented in terms of the MSEE and the bit error rate performance demonstrating the usefulness of the proposed scheme.