The diffusion least mean square algorithm with variable q-gradient
•We propose a novel v-q-DLMS based on the q-gradient method.•The analysis for v-q-DLMS is conducted in the mean and mean square sense.•The variable q-version ARV is proposed.•The superiorities of the proposed algorithm are validated by simulations. To avoid the local minima in optimization for distr...
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| Published in | Digital signal processing Vol. 127; p. 103531 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier Inc
01.07.2022
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1051-2004 1095-4333 |
| DOI | 10.1016/j.dsp.2022.103531 |
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| Abstract | •We propose a novel v-q-DLMS based on the q-gradient method.•The analysis for v-q-DLMS is conducted in the mean and mean square sense.•The variable q-version ARV is proposed.•The superiorities of the proposed algorithm are validated by simulations.
To avoid the local minima in optimization for distributed adaptive filters, the q-gradient diffusion least mean square (q-DLMS) algorithm uses the q-gradient vector to estimate parameters of interest in distributed networks. However, the q value in q-DLMS is fixed and required to be determined beforehand. To this end, a novel variable q-DLMS (v-q-DLMS) algorithm is proposed in this paper to improve the performance of q-DLMS and avoid the selection issue of q, simultaneously. The theoretical results of the proposed v-q-DLMS algorithm regarding accuracy and convergence are provided for performance analysis. In addition, the variable q-version of combination rule is derived by minimizing mean square derivation. Simulation results on distributed networks validate the correctness of obtained theoretical results and illustrate the superiorities of the proposed v-q-DLMS algorithm from the aspects of accuracy, convergence rate, and robustness. |
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| AbstractList | •We propose a novel v-q-DLMS based on the q-gradient method.•The analysis for v-q-DLMS is conducted in the mean and mean square sense.•The variable q-version ARV is proposed.•The superiorities of the proposed algorithm are validated by simulations.
To avoid the local minima in optimization for distributed adaptive filters, the q-gradient diffusion least mean square (q-DLMS) algorithm uses the q-gradient vector to estimate parameters of interest in distributed networks. However, the q value in q-DLMS is fixed and required to be determined beforehand. To this end, a novel variable q-DLMS (v-q-DLMS) algorithm is proposed in this paper to improve the performance of q-DLMS and avoid the selection issue of q, simultaneously. The theoretical results of the proposed v-q-DLMS algorithm regarding accuracy and convergence are provided for performance analysis. In addition, the variable q-version of combination rule is derived by minimizing mean square derivation. Simulation results on distributed networks validate the correctness of obtained theoretical results and illustrate the superiorities of the proposed v-q-DLMS algorithm from the aspects of accuracy, convergence rate, and robustness. |
| ArticleNumber | 103531 |
| Author | Wang, Shiyuan Cai, Peng Huang, Gangyi Qian, Junhui Zhang, Tao |
| Author_xml | – sequence: 1 givenname: Peng surname: Cai fullname: Cai, Peng organization: College of Electronic and Information Engineering, Southwest University, Chongqing 400715, China – sequence: 2 givenname: Shiyuan orcidid: 0000-0002-5028-5839 surname: Wang fullname: Wang, Shiyuan email: wsy@swu.edu.cn organization: College of Electronic and Information Engineering, Southwest University, Chongqing 400715, China – sequence: 3 givenname: Junhui surname: Qian fullname: Qian, Junhui organization: School of Microelectronic and Communication Engineering, Chongqing University, Chongqing 400044, China – sequence: 4 givenname: Tao surname: Zhang fullname: Zhang, Tao organization: College of Electronic and Information Engineering, Southwest University, Chongqing 400715, China – sequence: 5 givenname: Gangyi surname: Huang fullname: Huang, Gangyi organization: College of Electronic and Information Engineering, Southwest University, Chongqing 400715, China |
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| Keywords | Distributed estimation Diffusion adaptive filter q-gradient Optimization Diffusion LMS |
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| SubjectTerms | Diffusion adaptive filter Diffusion LMS Distributed estimation Optimization q-gradient |
| Title | The diffusion least mean square algorithm with variable q-gradient |
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