The Performance Analysis of Diffusion LMS Algorithm in Sensor Networks Based on Quantized Data and Random Topology
We study the performance of diffusion LMS (Least-Mean-Square) algorithm for distributed parameter estimation problem over sensor networks with quantized data and random topology, where the data are quantized before transmission and the links are interrupted at random times. To achieve unbiased estim...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2016-08-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1177/155014779685385 |
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| _version_ | 1850212323017359360 |
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| author | Junlong Zhu Mingchuan Zhang Changqiao Xu Jianfeng Guan Hongke Zhang |
| author_facet | Junlong Zhu Mingchuan Zhang Changqiao Xu Jianfeng Guan Hongke Zhang |
| author_sort | Junlong Zhu |
| collection | DOAJ |
| description | We study the performance of diffusion LMS (Least-Mean-Square) algorithm for distributed parameter estimation problem over sensor networks with quantized data and random topology, where the data are quantized before transmission and the links are interrupted at random times. To achieve unbiased estimation of the unknown parameter, we add dither (small noise) to the sensor states before quantization. We first propose a diffusion LMS algorithm with quantized data and random link failures. We further analyze the stability and convergence of the proposed algorithm and derive the closed-form expressions of the MSD (Mean-Square Deviation) and EMSE (Excess Mean-Square Errors), which characterize the steady-state performance of the proposed algorithm. We show that the convergence of the proposed algorithm is independent of quantized data and random topology. Moreover, the analytical results reveal which factors influence the network performance, and we show that the effect of quantization is the main factor in performance degradation of the proposed algorithm. We finally provide computer simulation results that illustrate the performance of the proposed algorithm and verify the results of the theoretical analysis. |
| format | Article |
| id | doaj-art-8e5f9cde8e55486c97c29e2ce9236a33 |
| institution | OA Journals |
| issn | 1550-1477 |
| language | English |
| publishDate | 2016-08-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-8e5f9cde8e55486c97c29e2ce9236a332025-08-20T02:09:22ZengWileyInternational Journal of Distributed Sensor Networks1550-14772016-08-011210.1177/155014779685385The Performance Analysis of Diffusion LMS Algorithm in Sensor Networks Based on Quantized Data and Random TopologyJunlong Zhu0Mingchuan Zhang1Changqiao Xu2Jianfeng Guan3Hongke Zhang4 State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China National Engineering Laboratory for Next-Generation Internet Interconnection Devices, Beijing Jiaotong University, Beijing 100876, China State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China National Engineering Laboratory for Next-Generation Internet Interconnection Devices, Beijing Jiaotong University, Beijing 100876, ChinaWe study the performance of diffusion LMS (Least-Mean-Square) algorithm for distributed parameter estimation problem over sensor networks with quantized data and random topology, where the data are quantized before transmission and the links are interrupted at random times. To achieve unbiased estimation of the unknown parameter, we add dither (small noise) to the sensor states before quantization. We first propose a diffusion LMS algorithm with quantized data and random link failures. We further analyze the stability and convergence of the proposed algorithm and derive the closed-form expressions of the MSD (Mean-Square Deviation) and EMSE (Excess Mean-Square Errors), which characterize the steady-state performance of the proposed algorithm. We show that the convergence of the proposed algorithm is independent of quantized data and random topology. Moreover, the analytical results reveal which factors influence the network performance, and we show that the effect of quantization is the main factor in performance degradation of the proposed algorithm. We finally provide computer simulation results that illustrate the performance of the proposed algorithm and verify the results of the theoretical analysis.https://doi.org/10.1177/155014779685385 |
| spellingShingle | Junlong Zhu Mingchuan Zhang Changqiao Xu Jianfeng Guan Hongke Zhang The Performance Analysis of Diffusion LMS Algorithm in Sensor Networks Based on Quantized Data and Random Topology International Journal of Distributed Sensor Networks |
| title | The Performance Analysis of Diffusion LMS Algorithm in Sensor Networks Based on Quantized Data and Random Topology |
| title_full | The Performance Analysis of Diffusion LMS Algorithm in Sensor Networks Based on Quantized Data and Random Topology |
| title_fullStr | The Performance Analysis of Diffusion LMS Algorithm in Sensor Networks Based on Quantized Data and Random Topology |
| title_full_unstemmed | The Performance Analysis of Diffusion LMS Algorithm in Sensor Networks Based on Quantized Data and Random Topology |
| title_short | The Performance Analysis of Diffusion LMS Algorithm in Sensor Networks Based on Quantized Data and Random Topology |
| title_sort | performance analysis of diffusion lms algorithm in sensor networks based on quantized data and random topology |
| url | https://doi.org/10.1177/155014779685385 |
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