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2022 | OriginalPaper | Chapter

42. Channel State Information Spectrum Gap Filling Using Shallow Neural Networks

Authors : Avishek Mukherjee, Beata Hejno, Manish Osti

Published in: ITNG 2022 19th International Conference on Information Technology-New Generations

Publisher: Springer International Publishing

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Abstract

The chapter delves into the challenge of predicting Channel State Information (CSI) in Wi-Fi networks without continuous probing, leveraging existing CSI measurements from nearby channels. It introduces CSIFill, a shallow neural network-based approach, and demonstrates its high accuracy in predicting CSI coefficients, even in varying channel conditions. The study includes real-world experiments and evaluates the performance of CSIFill against actual measured CSI, showcasing its potential to improve network efficiency and speed. The chapter also discusses related work, theoretical background, and the details of the CSIFill algorithm, making it a comprehensive resource for professionals in the field.
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Metadata
Title
Channel State Information Spectrum Gap Filling Using Shallow Neural Networks
Authors
Avishek Mukherjee
Beata Hejno
Manish Osti
Copyright Year
2022
Book
ITNG 2022 19th International Conference on Information Technology-New Generations

Print ISBN: 978-3-030-97651-4

Electronic ISBN: 978-3-030-97652-1

Copyright Year: 2022

DOI
https://doi.org/10.1007/978-3-030-97652-1_42

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