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• W2766141972 abstract "In this paper, we propose a maximum likelihood (ML) detection technique for underwater acoustic (UWA) communication in oceanic medium. The multi-path fading and additive noise in UWA channel is modeled by Nakagami distribution and mixture of Generalized Gaussian (GG) distribution, respectively. In the system model, we also consider multiple receive antennas to take advantage of spatial diversity. In order to design an efficient and computationally inexpensive detector, we apply Expectation Maximization (EM) algorithm to decompose the additive noise distribution in terms of finite weighted Gaussian components. In this context, we propose decision boundary for ML detection of binary modulated signal. A discussion on variation in decision boundary under various signal to noise ratio (SNR) levels is also presented. Finally, we compare the performance of proposed ML detection technique with conventional maximal ratio combining (MRC) technique, and validate the proposed approach by showing improvement in performance." @default.
• W2766141972 created "2017-11-10" @default.
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• W2766141972 date "2017-03-01" @default.
• W2766141972 modified "2023-09-25" @default.
• W2766141972 title "A ML detection for UWA communication with Nakagami fading and GG noise" @default.
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• W2766141972 doi "https://doi.org/10.1109/ncc.2017.8077050" @default.
• W2766141972 hasPublicationYear "2017" @default.
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