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      Tracking A Dynamic Sparse Channel Via Differential Orthogonal Matching Pursuit

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          Abstract

          This paper considers the problem of tracking a dynamic sparse channel in a broadband wireless communication system. A probabilistic signal model is firstly proposed to describe the special features of temporal correlations of dynamic sparse channels: path delays change slowly over time, while path gains evolve faster. Based on such temporal correlations, we then propose the differential orthogonal matching pursuit (D-OMP) algorithm to track a dynamic sparse channel in a sequential way by updating the small channel variation over time. Compared with other channel tracking algorithms, simulation results demonstrate that the proposed D-OMP algorithm can track dynamic sparse channels faster with improved accuracy.

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          Greed is Good: Algorithmic Results for Sparse Approximation

          J.A. Tropp (2004)
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            An Interior-Point Method for Large-Scale -Regularized Least Squares

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              Bayesian Compressive Sensing

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                Author and article information

                Journal
                1507.05363

                Numerical methods,Information systems & theory
                Numerical methods, Information systems & theory

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