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Variable partial-update NLMS algorithms with data-selective updating

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  • Special Focus on Convergence Communications
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Abstract

We note that some existing algorithms are based on the normalized least-mean square (NLMS) algorithm and aim to reduce the computational complexity of NLMS all inherited from the solution of the same optimization problem, but with different constraints. A new constraint is analyzed to substitute an extra searching technique in the set-membership partial-update NLMS algorithm (SM-PU-NLMS) which aims to get a variable number of updating coefficients for a further reduction of computational complexity. We get a closed form expression of the new constraint without extra searching technique to generate a novel set-membership variable-partial-update NLMS (SM-VPU-NLMS) algorithm. Note that the SM-VPU-NLMS algorithm obtains a faster convergence and a smaller mean-squared error (MSE) than the existing SM-PU-NLMS. It is pointed out that the closed form expression can also be applied to the conventional variable-step-size partial-update NLMS (VSS-PU-NLMS) algorithm. The novel variable-step-size variable-partial-update NLMS (VSS-VPU-NLMS) algorithm is also verified to get a further computational complexity reduction. Simulation results verify that our analysis is reasonable and effective.

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Authors and Affiliations

  1. Tsinghua National Laboratory for Information Science and Technology, Department of Automation, Tsinghua University, Beijing, 100084, China

    FengChao Zhu, FeiFei Gao & HongXing Zou

  2. High-Tech Institute of Xi’an, Xi’an, 710025, China

    FengChao Zhu & MinLi Yao

Authors
  1. FengChao Zhu
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  2. FeiFei Gao
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  3. MinLi Yao
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  4. HongXing Zou
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Correspondence to FeiFei Gao.

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Zhu, F., Gao, F., Yao, M. et al. Variable partial-update NLMS algorithms with data-selective updating. Sci. China Inf. Sci. 57, 1–11 (2014). https://doi.org/10.1007/s11432-014-5078-8

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  • DOI: https://doi.org/10.1007/s11432-014-5078-8

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