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- W2104009246 abstract "We consider the problem of distributed estimation of a Gauss-Markov random field using a wireless sensor network (WSN), where due to the stringent power and communication constraints, each sensor has to quantize its data before transmission. In this case, the convergence of conventional iterative matrix-splitting algorithms is hindered by the quantization errors. To address this issue, we propose a one-bit adaptive quantization approach which leads to decaying quantization errors. Numerical results show that even with one bit quantization, the proposed approach achieves a superior mean square deviation performance (with respect to the global linear minimum mean-square error estimate) within a moderate number of iterations." @default.
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- W2104009246 date "2010-05-01" @default.
- W2104009246 modified "2023-09-26" @default.
- W2104009246 title "Distributed Estimation of Gauss - Markov Random Fields With One-Bit Quantized Data" @default.
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- W2104009246 doi "https://doi.org/10.1109/lsp.2010.2043157" @default.
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