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Generalized Variable Step-Size Diffusion Continuous Mixed p-Norm Algorithm

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Abstract

The generalized variable step-size diffusion continuous mixed p-norm (GVSS-DCMPN) algorithm is proposed in this paper, which is derived based on the improved continuous mixed p-norm (CMPN) strategy. In detail, a linear function is designed for the CMPN strategy. The proposed GVSS-DCMPN algorithm capable of exploiting various error norms to obtain performance improvement in non-Gaussian noise environment can be viewed as the generalization of the traditional p-norm-based algorithms in the sense of continuous errors. In particular, the GVSS-DCMPN algorithm transforms into the VSS-DCMPN algorithm when the slope of the linear function is set to 0. The computational complexity as well as the mean convergence is analyzed in the paper. Simulation results over the diffusion network show that the proposed algorithm achieves performance gain over some existing diffusion algorithms.

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Data Availability Statement

The data that support the findings of this study are available from the corresponding author on request.

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Acknowledgements

This work was partially supported by National Science Foundation of PR China (Grants: 61871461, 61571374, and 61433011), Sichuan Science and Technology Program (Grants: 19YYJC0681, 2020JDTD0009), National Rail Transportation Electrification and Automation Engineering Technology Research Center (Grant: NEEC-2019-A02).

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Authors and Affiliations

  1. Key Laboratory of Magnetic Suspension Technology and Maglev Vehicle, Ministry of Education, Southwest Jiaotong University, Chengdu, 610031, China

    Long Shi & Haiquan Zhao

  2. School of Electrical Engineering, Southwest Jiaotong University, Chengdu, 610031, China

    Long Shi & Haiquan Zhao

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  1. Long Shi
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  2. Haiquan Zhao
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Correspondence to Haiquan Zhao.

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Shi, L., Zhao, H. Generalized Variable Step-Size Diffusion Continuous Mixed p-Norm Algorithm. Circuits Syst Signal Process 40, 3609–3620 (2021). https://doi.org/10.1007/s00034-020-01640-2

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