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Weighted Distance-Based Quantization for Distributed Estimation
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  • Weighted Distance-Based Quantization for Distributed Estimation
  • Weighted Distance-Based Quantization for Distributed Estimation
저자명
Kim. Yoon Hak
간행물명
Journal of information and communication convergence engineering
권/호정보
2014년|12권 4호|pp.215-220 (6 pages)
발행정보
한국정보통신학회
파일정보
정기간행물|ENG|
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이 논문은 한국과학기술정보연구원과 논문 연계를 통해 무료로 제공되는 원문입니다.
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기타언어초록

We consider quantization optimized for distributed estimation, where a set of sensors at different sites collect measurements on the parameter of interest, quantize them, and transmit the quantized data to a fusion node, which then estimates the parameter. Here, we propose an iterative quantizer design algorithm with a weighted distance rule that allows us to reduce a system-wide metric such as the estimation error by constructing quantization partitions with their optimal weights. We show that the search for the weights, the most expensive computational step in the algorithm, can be conducted in a sequential manner without deviating from convergence, leading to a significant reduction in design complexity. Our experments demonstrate that the proposed algorithm achieves improved performance over traditional quantizer designs. The benefit of the proposed technique is further illustrated by the experiments providing similar estimation performance with much lower complexity as compared to the recently published novel algorithms.