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Graph Signal Reconstruction from Low-Resolution Multi-Bit Observations
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Low hardware cost and power consumption in information transmission,processing and storage is an urgent demand for many big data problems,in which the high-dimensional data often be modelled as graph signals.This paper considers the problem of recovering a smooth graph signal by using its low-resolution multi-bit quantized observations.The underlying problem is formulated as a regularized maximum-likelihood optimization and is solved via an expectation maximization scheme.With this scheme,the multi-bit graph signal recovery(MB-GSR)is effi-ciently implemented by using the quantized observations collected from random subsets of graph nodes.The simula-tion results show that increasing the sampling resolution to 2 or 3 bits per sample leads to a considerable perfor-mance improvement,while the energy consumption and implementation costs remain much lower compared to the implementation of high resolution sampling.
Graph signal reconstructionMaximum-likelihoodExpectation maximizationLow-bit quantiza-tion
Zhaoting LIU、Chen YU、Yafeng WANG、Shuchen LIU
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School of Communication Engineering,Hangzhou Dianzi University,Hangzhou 310018,China
National Natural Science Foundation of ChinaOpening Foundation of Zhejiang Engineering Research Center of MEMS
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