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<?Pub Dtl?>Reliable channel estimation and effective interference cancellation are essential for enhancing the performance of multiple-input–multiple-output (MIMO) underwater acoustic communication (UAC) systems. In this paper, an efficient user-parameter-free Bayesian approach, referred to as sparse learning via iterative minimization (SLIM), is presented. SLIM provides good channel estimation...
This paper focuses on mobile multi-input multi-output (MIMO) underwater acoustic communications (UAC) over double-selective channels suffering from both inter-symbol interference and Doppler scaling effects (stretching or compression). Temporal resampling is implemented to effectively convert the Doppler scaling effects to Doppler frequency shifts. Then an extension of the sparse learning via iterative...
Effective training sequences and reliable channel estimation algorithms are essential for enhancing the performance of multi-input multi-output (MIMO) underwater acoustic communications (UAC). Also, effective interference cancellation schemes are crucial for reliable symbol detection. In this paper, the problem of designing MIMO training sequences is considered. Moreover, we present a sparse learning...
We consider improving the performance of orthogonal frequency-division multiplexing (OFDM)-based mobile broadband wireless communication (MBWC) systems over time-varying frequency-selective fading channels. To battle the problem related to frequency-selective fading, we can use space-time block coding (STBC) on each OFDM subcarrier. To battle the problem related to time varying as well as that related...
This paper focuses on the channel estimation and symbol detection problems in multi-input multi-output (MIMO) underwater acoustic (UWA) communications. To enhance channel estimation performance, a cyclic approach for designing training sequences and an iterative adaptive approach (IAA) for estimating the channel taps are presented. Sparse channel estimates can be obtained. by combining IAA with the...
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