Graph Signal Reconstruction from Low-Resolution Multi-Bit Observations  

作　　者：Zhaoting LIU Chen YU Yafeng WANG Shuchen LIU 

机构地区：[1]School of Communication Engineering,Hangzhou Dianzi University,Hangzhou 310018,China

出　　处：《Chinese Journal of Electronics》2024年第1期153-160,共8页电子学报（英文版）

基　　金：supported by the National Natural Science Foundation of China(Grant No.61671192);the Opening Foundation of Zhejiang Engineering Research Center of MEMS(Grant No.MEMSZJERC2204)。

摘　　要：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 efficiently implemented by using the quantized observations collected from random subsets of graph nodes.The simulation results show that increasing the sampling resolution to 2 or 3 bits per sample leads to a considerable performance improvement,while the energy consumption and implementation costs remain much lower compared to the implementation of high resolution sampling.

关 键 词：Graph signal reconstruction MAXIMUM-LIKELIHOOD Expectation maximization Low-bit quantization 

分 类 号：TN911.7[电子电信—通信与信息系统]