Robust distributed MKC graph signal estimation algorithm with reduced communication burden

Meiyu Yuan; Wei Li

doi:10.1117/12.3012315

21 December 2023 Robust distributed MKC graph signal estimation algorithm with reduced communication burden

Meiyu Yuan, Wei Li

Proceedings Volume 12970, Fourth International Conference on Signal Processing and Computer Science (SPCS 2023); 129701W (2023) https://doi.org/10.1117/12.3012315
Event: Fourth International Conference on Signal Processing and Computer Science (SPCS 2023), 2023, Guilin, China

Abstract

As a nascent framework in the field of signal processing, graph signal processing (GSP) presents a potent strategy for handling unstructured data. By infusing the least mean squares algorithm into graph signals, the GSP-LMS algorithm takes form. Nevertheless, the effectiveness of GSP-LMS wanes when confronted with non-Gaussian noise. To address the issue, the presentstudy introduces the maximum multi-kernel correntropy (MKC) criterion, presenting a robust distributed MKCbased graph signal estimation algorithm named GSP-DMKC. Benefiting from the adaptability of loss function, this algorithm demonstrates the capability to tackle intricate noise scenarios. Recognizing the constraints of communication resources in real-world scenarios, a partial transmission strategy is introduced to design the GSP-DMKC-RC algorithm, thereby mitigating communication overhead. GSP-DMKC-RC applies information compression during both adaptive and combinatorial steps. Furthermore, when opting for an appropriate compression rate, the GSP-DMKC-RC can attain estimation performance comparable to that of GSP-DMKC, concurrently easing the communication load. Ultimately, simulations are set up to affirm the excellence of algorithms.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Meiyu Yuan and Wei Li "Robust distributed MKC graph signal estimation algorithm with reduced communication burden", Proc. SPIE 12970, Fourth International Conference on Signal Processing and Computer Science (SPCS 2023), 129701W (21 December 2023); https://doi.org/10.1117/12.3012315

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