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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Shi, Y., Dong, J., Zhang, J. (2020). Shuffled Linear Regression. In: Low-overhead Communications in IoT Networks. Springer, Singapore. https://doi.org/10.1007/978-981-15-3870-4_5
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DOI: https://doi.org/10.1007/978-981-15-3870-4_5
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