On the Impact of Using Unreliable Data on the Bootstrap Channel Estimation Performance

In this paper, we study the performance of the bootstrap channel estimation scheme, which consists in this paper in using the hard-decided outputs of the channel decoder in order to extend the training sequence. Based on a simple large system analysis, we derive the expression of the channel estimation MSE in order to evaluate the impact of using wrong decisions on channel estimation performance. In particular, it is showed that it depends on the first and second moment of the number of errors per block of symbols. Interestingly, it is also proven that the bootstrap estimator does not always improve channel estimation accuracy. The performance of the Viterbi equalizer based on bootstrap channel estimation is assessed through a SNR analysis.