Training Interval Length Optimization for MIMO Flat Fading Channels Using Decision-Directed Channel Estimation

In this paper, we address the problem of optimization of the training sequence interval for MIMO (multiple-input multiple-output) flat fading channels when an iterative receiver composed of a likelihood generator and a maximum a posteriori (MAP) decoder is used. At each iteration of the receiver, the channel is estimated using the hard decisions on the transmitted symbols at the output of the decoder. The optimal length of the training interval is found by maximizing an effective signal-to-noise ratio (SNR) taking into account the data throughput loss due to the use of pilot symbols.