Quality, Reliability, Security and Robustness in Heterogeneous Networks. 9th International Conference, QShine 2013, Greader Noida, India, January 11-12, 2013, Revised Selected Papers

Research Article

Reduced Complexity Pseudo-fractional Adaptive Algorithm with Variable Tap-Length Selection

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  • @INPROCEEDINGS{10.1007/978-3-642-37949-9_39,
        author={Asutosh Kar and Mahesh Chandra},
        title={Reduced Complexity Pseudo-fractional Adaptive Algorithm with Variable Tap-Length Selection},
        proceedings={Quality, Reliability, Security and Robustness in Heterogeneous Networks. 9th International Conference, QShine 2013, Greader Noida, India, January 11-12, 2013, Revised Selected Papers},
        proceedings_a={QSHINE},
        year={2013},
        month={7},
        keywords={Adaptive filter tap-length structure adaptation least mean square (LMS) system identification mean square error (MSE)},
        doi={10.1007/978-3-642-37949-9_39}
    }
    
  • Asutosh Kar
    Mahesh Chandra
    Year: 2013
    Reduced Complexity Pseudo-fractional Adaptive Algorithm with Variable Tap-Length Selection
    QSHINE
    Springer
    DOI: 10.1007/978-3-642-37949-9_39
Asutosh Kar1,*, Mahesh Chandra2,*
  • 1: IIIT
  • 2: BIT
*Contact email: asutosh@iiit-bh.ac.in, shrotriya69@rediffmail.com

Abstract

The structural complexity and overall performance of the adaptive filter depend on its structure. The number of taps is one of the most important structural parameters of the liner adaptive filter. In practice the system length is not known a-priori and has to be estimated from the knowledge of the input and output signals. In a system identification framework the tap length estimation algorithm automatically adapts the filter order to the desired optimum value which makes the variable order adaptive filter a best identifier of the unknown plant. In this paper an improved pseudo-fractional tap-length selection algorithm has been proposed to find out the optimum tap-length which best balances the complexity and steady state performance. Simulation results reveal that the proposed algorithm results in reduced complexity and faster convergence in comparison to existing tap-length learning methods.