An Approach to Blind Deconvolution Based on Second-order “soft” Statistics
نویسندگان
چکیده
In this paper we present a new blind equalizer that achieves identification of a channel by exploiting only second-order statistics of the observations. The novelty of the proposed approach is that the receiver accomplishes channel identification by using soft-statistics; roughly speaking, it consists of an Abend-Fritchman type [11] Maximum A Posteriori (MAP) equalizer that feeds a nonlinear Kalman-like channel-estimator with the softstatistics constituted by the A Posteriori Probabilities (APPs) of the channel-state sequence. So, since the receiver employs second-order statistics only, it achieves channel identification with fewer symbols than most techniques based on higher-order statistics.
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