Efficient distributed estimation based on non-regular quantized data
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We consider parameter estimation in distributed systems in which measurements at local nodes are quantized ina non-regular manner, where multiple codewords are mapped into a single local measurement. For the systemwith non-regular quantization, to ensure a perfect independent encoding at local nodes, a local measurement canbe encoded into a set of a great number of codewords which are transmitted to a fusion node where estimationis conducted with enormous computational cost due to the large cardinality of the sets. In this paper, we proposean efficient estimation technique that can handle the non-regular quantized data by efficiently finding the feasiblecombination of codewords without searching all of the possible combinations. We conduct experiments to showthat the proposed estimation performs well with respect to previous novel techniques with a reasonablecomplexity.
Abstract
We consider parameter estimation in distributed systems in which measurements at local nodes are quantized ina non-regular manner, where multiple codewords are mapped into a single local measurement. For the systemwith non-regular quantization, to ensure a perfect independent encoding at local nodes, a local measurement canbe encoded into a set of a great number of codewords which are transmitted to a fusion node where estimationis conducted with enormous computational cost due to the large cardinality of the sets. In this paper, we proposean efficient estimation technique that can handle the non-regular quantized data by efficiently finding the feasiblecombination of codewords without searching all of the possible combinations. We conduct experiments to showthat the proposed estimation performs well with respect to previous novel techniques with a reasonablecomplexity.
참고문헌 (12)
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