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Shuffled Linear Regression

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Abstract

In this chapter, we shall introduce a shuffled linear regression model for joint data decoding and device identification in IoT networks. It is first formulated as a maximum likelihood estimation (MLE) problem. To solve this MLE problem, two algorithms are presented: one is based on sorting, and the other algorithm returns an approximate solution to the MLE problem. Next, theoretical analysis on the shuffled linear regression based on the algebraic-geometric theory is presented. Based on the analysis, an algebraically initialized expectation-maximization algorithm is introduced to solve the problem.

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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.School of Information Science and TechnologyShanghai Tech UniversityShanghaiChina
  2. 2.School of Information Science and TechnologyShanghaiTech UniversityShanghaiChina
  3. 3.Department of Electronic & Information EngineeringHong Kong Polytechnic UniversityKowloonHong Kong

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