Improved Superimposed Training based Channel Estimation for MIMO-OFDM Systems with Quantized Feedback

This work 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 a 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. Two factors in general affect all transmitter side techniques that make use of the fed back channel coefficients. One is the quantization 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 The other is the channel variations that may render the fed back channel coefficients outdated for further use.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 mean squared estimation error (MSEE) demonstrating the usefulness of the proposed scheme.