Asymptotic Diversity Analysis of Alamouti Transmit Diversity with Quasi-ML Decoding Algorithm in Time-Selective Fading Channels
نویسنده
چکیده
This paper addresses the asymptotic diversity performance of Alamouti transmit diversity technique especially in time-selective fading channels. In particular, a linear quasi-maximum-likelihood (QML) decoding method is employed for the Alamouti transmit diversity system to solve the error floor problem induced by the conventional linear ML decoding method. By judiciously utilizing the derived asymptotic closed-form formula of symbol pairwise error rate (SPER), it is theoretically verified that the asymptotic diversity orders achieved by the QML decoding algorithm become 2 and 1 in quasi-static and time-selective fading channels, respectively.
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