Compressive sensing techniques can be employed to choose the locations of active tap-weights in the design of a sparse decision feedback equalizer (DFE), thus reducing the DFE’s computational complexity. This paper proposes sparse predictive DFEs (PDFEs) by using the orthogonal matching pursuit (OMP) and the subspace pursuit (SP), which are two popular greedy algorithms. In contrast to the optimal nonsparse PDFE, which may achieve the minimum mean-squared-error (MSE) performance, the sparse PDFEs yielded significant complexity reductions at an acceptable MSE performance loss. Simulation results demon-strated that the sparse PDFE employing the SP algorithm (SP-PDFE) offers superior symbol error rate (SER) performance as compared with the sparse PDFE employing the OMP algorithm (OMP-PDFE) by using two sparse digital television channels. Moreover, the SER performance of the SP-PDFE tends to be much closer to that of the optimal nonsparse PDFE than the OMP-PDFE does for the same sparsity of tap-weight vector.


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    Title :

    Designs of Sparse Predictive Decision Feedback Equalizers


    Contributors:


    Publication date :

    2019-09-01


    Size :

    378528 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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