Graduate School and Research Center in Digital Sciences

Deterministic and Bayesian blind and semi-blind channel identification for wireless communications

Omar, Samir Mohamad


During the last two decades there has been a great interest in blind and semiblind channel estimation due to the advantages offered by these techniques over training-based ones. The most prominent is the augmentation of the throughput as a result of reducing the length of the training sequence/pilots required to estimate the channel at the receiver. Moreover, semi-blind techniques have the potential to estimate the channel in some situations where the training-based techniques fail. There exists a slew of algorithms that exploit either the second order statistics (SOS) or the higher order statistics (HOS) that have been derived and analyzed in the literature. Recently, this topic has been treated in the context of Space Time Block Coding (STBC), neural networks, multiuser scenario and cognitive radio, to name a few. In the first part of this thesis, we treat the blind channel estimation in the context of SIMO and MIMO cyclic prefix (CP) systems. We propose a novel approach to structure the sample covariance matrix, which in turn leads to a significant enhancement in the estimation quality, even when there is only a single OFDM symbol available at the receiver. On the other hand, we provide an analytical performance analysis of some CP SOS-based algorithms that permits to highlight some features of these algorithms and inspires the derivation of enhanced versions. At the end of this part, we introduce the variational Bayesian approach to carry out the joint ML/MAP estimation of channel and symbols.   In the second part, we introduce and elaborate a classical Bayesian approach to estimate the channel and the symbols in the context of blind and semiblind SIMO systems. As a consequence, six different ML/MAP estimators are derived and their performances are compared numerically by conducting Monte-Carlo simulations. Furthermore, we derive the corresponding Cramer-Rao Bounds (CRBs) for the various scenarios of these estimators. At the end of this part, we propose a novel quasi-Bayesian approach that exploits the knowledge of the power delay profile (PDP) to estimate only part of the channel taps while neglecting the rest. This approach can be applied to various deterministic algorithms that exist in the literature, allowing their extension to a point that is midway between deterministic and Bayesian approaches. We show by simulations and by deriving the corresponding CRBs how this approach leads to a considerable improvement in the performance of many deterministic algorithms in terms of both Normalized Mean Squared Error (NMSE) and Symbol Error Probability (SER). Finally, in the third part we focus on the performance of Zero-Forcing (ZF) Linear Equalizers (LEs) or Decision-Feedback Equalizers (DFEs) for fading channels when they are based on (semi-)blind channel estimates. Although it has been known that various (semi-)blind channel estimation techniques have a receiver counterpart that is matched in terms of symbol knowledge hypotheses, we show here that these (semi-)blind techniques and corresponding receivers also match in terms of diversity order: the channel becomes (semi-)blindly unidentifiable whenever its corresponding receiver structure goes in outage. In the case of mismatched receiver and (semi-blind) channel estimation technique, the lower diversity order dominates. Various cases of (semi-) blind channel estimation and corresponding receivers are considered in detail.

Document Bibtex

Title:Deterministic and Bayesian blind and semi-blind channel identification for wireless communications
Department:Communication systems
Eurecom ref:3529
Copyright: © Université de Nice. Personal use of this material is permitted. The definitive version of this paper was published in Thesis and is available at :
Bibtex: @phdthesis{EURECOM+3529, year = {2011}, title = {{D}eterministic and {B}ayesian blind and semi-blind channel identification for wireless communications}, author = {{O}mar, {S}amir {M}ohamad}, school = {{T}hesis}, month = {10}, url = {} }
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