Optimal training sequence length for soft iterative channel estimation

In this paper, we consider the problem of optimization of the training sequence length when a turbo-detector composed of a maximum a posteriori (MAP) equalizer and a MAP decoder is used. The initial channel estimate based on the training sequence is iteratively improved using the Expectation Maximization (EM) algorithm. In order to “unbias” the EM estimates, a modified version of the EM estimator is used. The optimal length of the training sequence 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.