| Paper ID | C-2-2.3 |
| Paper Title |
OPTIMAL COMBINATION WEIGHT FOR SPARSE DIFFUSION LEAST-MEAN-SQUARE BASED ON CONSENSUS PROPAGATION |
| Authors |
Ayano Nakai-Kasai, Kazunori Hayashi, Kyoto University, Japan |
| Session |
C-2-2: Advanced Topics in Signal Processing & Machine Learning - Acoustic & Biomedical Applications |
| Time | Wednesday, 09 December, 15:30 - 17:00 |
| Presentation Time: | Wednesday, 09 December, 16:00 - 16:15 Check your Time Zone |
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All times are in New Zealand Time (UTC +13) |
| Topic |
Signal and Information Processing Theory and Methods (SIPTM): Special Session: Advanced Topics in Signal Processing & Machine Learning - Acoustic & Biomedical Applications |
| Abstract |
This paper considers distributed adaptive signal processing for tracking an unknown sparse parameter vector in large-scale networks. We propose a sparsity-promoting diffusion least-mean-square algorithm based on consensus propagation, which is an average consensus algorithm using message passing techniques. The main contributions of the paper are optimizing coefficients in the algorithm in terms of the steady-state error to achieve better convergence and robustness, and presenting the adaptive implementation. |