Performance study of least-squares channel estimation based on hard decisions

The main purpose of this paper is to study the performance of the bootstrap channel estimation scheme, which consists in this paper in using the hard-decided outputs of the channel decoder/equalizer in order to extend the training sequence. Through a simple asymptotic analysis, we derive the expression of the channel estimation MSE in order to evaluate the impact of using wrong decisions on the Least-Squares channel estimation performance. In particular, it is showed that it depends on the first and second moments 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 impact of using bootstrap channel estimation on the overall receiver performance is studied through an SNR analysis.