Joint data and channel estimation using semi-blind MLSE algorithms
Chia-Lung Wu, Ming Bo Xiao, Mikael Skoglund · 2013
Operation-intensive exhaustive checking is the unique optimal method for performing joint maximum-likelihood (JML) data and channel estimation. Thus, several suboptimal methods incorporating conventional maximum-likelihood sequence estimation (MLSE) or its extensions have been proposed in order to reduce complexity. However, most of them suffer from either long convergence delay or unsatisfactory performance. Therefore, based on a two-phase MLSE algorithm designed for partially coherent receivers, we propose a novel low-complexity and low-latency batch algorithm for JML process. The proposed algorithm first searches for several of the best candidate training sequences using priority-first search, and then applies the training sequences to the two-phase MLSEs to obtain the maximum-likelihood data sequence. Simulation results show that when the candidate training sequence length is adequate, the proposed algorithm can achieve the performance of optimal exhaustive checking.