Two channel estimation methods are often opposed:training sequence methods which use the information coming from known symbols and blind methods which use the information coming from the received signal without integrating the possible knowledge of symbols. Semi-blind methods combine both informations and appear more powerful than both methods separately. Two Maximum-Likelihood approaches to semi-blind SIMO channel esti-mation are presented, one based on a deterministic model and another on a Gaussian model. Their asymptotic per-formance are studied and compared to the Cramer-Rao Bounds. The superiority of semi-blind over blind and train-ing sequence methods, and of the Gaussian approach is demonstrated.
Asymptotic performance of ML methods for semi-blind channel estimation
ASILOMAR 1997, 31st IEEE Annual Asilomar conference on signals, systems & computers, November 2-5, 1997, Pacific Grove, USA
Systèmes de Communication
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