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- W2793670130 abstract "Here, we are interested in the case where a randomly chosen subset of agents share deliberately with their neighbors local information corrupted with gross errors, which is a common phenomenon in strategic communication. Within this framework, we propose a twofold extension of the state-of-the-art diffusion adaptation techniques over mobile networks. First, the agents apply the block least mean squares algorithm exploiting a limited memory of prior regression vectors and prior estimates of distance to the target in the time domain. Second, the agents estimate the combination weights based on negative exponentials of the scaled distance of intermediate location estimates within their neighborhood from the marginal median of these estimates in the spatial domain. The experimental learning curves demonstrate that, at the expense of some additional computational requirements, the enhanced adapt-then-combine (ATC) diffusion strategy can cope with the outliers and yield better results than the standard ATC strategy." @default.
- W2793670130 created "2018-03-29" @default.
- W2793670130 creator A5051404366 @default.
- W2793670130 creator A5065184406 @default.
- W2793670130 date "2017-11-01" @default.
- W2793670130 modified "2023-09-26" @default.
- W2793670130 title "Using marginal median and block least mean squares in mobile adaptive networks" @default.
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- W2793670130 doi "https://doi.org/10.1109/globalsip.2017.8308634" @default.
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