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In an amplify-and-forward cooperative network, a closed-form expression of the a priori distribution of the complex-valued gain of the global relay channel is intractable, so that a priori information is often not exploited for estimating this gain. Here, we present two iterative channel gain and noise variance estimation algorithms that make use of a priori channel information and exploit the presence of not only pilot symbols but also unknown data symbols. These algorithms are approximations of maximum a posteriori estimation and linear minimum mean-square error estimation, respectively. A substantially reduced frame error rate is achieved as compared to the case where only pilot symbols are used in the estimation.

Exploiting a priori information for iterative channel estimation in block-fading amplify-and-forward cooperative networks