Bayesian blind and semiblind channel estimation

Blind and semiblind channel estimation techniques are developed and usually evaluated for a given channel realization, i.e. with a deterministic channel model. Such blind channel estimates, especially those based on subspaces in the data, are often only partial and ill-conditioned. 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 (as e.g. in 3G WCDMAsystems) the pure pilot-based (deterministic) channel estimate is of limited quality. The fading in wireless communications leads indeed often to a poor data to parameter ratio. In this paper we introduce a Bayesian approach to (semi-)blind channel estimation, exploiting a priori information on fading channels. Two case can be considered, either given prior information or joint estimation of channel and prior. In the second case there are still identifiability issues whereas in the first case there are typically none. However, the identifiability issues can be resolved with a reduced amount of training in a semiblind approach. Various models/parameterizations for the channel correlation structure and the fading process can be considered.