When the transmission scenario includes a training sequence or pilots, semi-blind channel estimation techniques have shown a tendency to fully exploit the information available from the received signal if they are correctly implemented. This feature leads semiblind channel estimation performance to exceed that of the schemes based on the blind part or the training sequence only. Moreover, in some situations they can estimate the channel when the other
techniques fail. Semi-blind channel estimation techniques were developed and usually evaluated for a given channel realization, i.e. with a deterministic channel model. On the other hand, in wireless communications the channel is typically modeled as Rayleigh fading, i.e. with a Gaussian (prior) distribution expressing variances of and correlations between channel coefficients. In recent years, such prior information on the channel has started to get exploited in pilot-based channel estimation, since often the pure pilot-based (deterministic) channel estimate is of limited quality due to limited pilots. In this paper we provide a performance comparison between ML/MAP algorithms that use Bayesian and deterministic
approaches in semi-blind channel estimation.