Adaptive repetition scheme with machine learning for 3GPP NB-IoT

Li Sheng Chen; Wei Ho Chung; Ing Yi Chen; Sy Yen Kuo

doi:10.1109/PRDC.2018.00046

Adaptive repetition scheme with machine learning for 3GPP NB-IoT

Li Sheng Chen
, Wei Ho Chung
, Ing Yi Chen
, Sy Yen Kuo

National Taiwan University
National Tsing Hua University
National Taipei University of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

10 Scopus citations

Abstract

In NB-IoT systems, UEs with poor signal quality employ more repetitions to compensate for additional signal attenuation. Excessively high CE levels and repetitions of UEs lead to wastage of valuable wireless resources, whereas inadequate CE levels and repetitions result in data retrieval failure at the receiving end. Therefore, a machine learning-based adaptive repetition scheme for a 3GPP NB-IoT system is proposed in this work to effectively improve overall network transmission efficiency. The results of simulation show the effect of the discount factor? on the convergence behavior of the proposed scheme, with a lower discount factor value denoting the myopic behavior of the proposed scheme, which results from the fact that it places more emphasis on immediate rewards. And the propose scheme is capable of effectively improving the average spectral efficiency.

Original language	English
Title of host publication	Proceedings - 2018 IEEE 23rd Pacific Rim International Symposium on Dependable Computing, PRDC 2018
Publisher	IEEE Computer Society
Pages	252-256
Number of pages	5
ISBN (Electronic)	9781538657003
DOIs	https://doi.org/10.1109/PRDC.2018.00046
State	Published - 02 07 2018
Externally published	Yes
Event	23rd IEEE Pacific Rim International Symposium on Dependable Computing, PRDC 2018 - Taipei, Taiwan Duration: 04 12 2018 → 07 12 2018

Publication series

Name	Proceedings of IEEE Pacific Rim International Symposium on Dependable Computing, PRDC
Volume	2018-December
ISSN (Print)	1541-0110

Conference

Conference	23rd IEEE Pacific Rim International Symposium on Dependable Computing, PRDC 2018
Country/Territory	Taiwan
City	Taipei
Period	04/12/18 → 07/12/18

Bibliographical note

Publisher Copyright:
© 2018 IEEE.

Keywords

Adaptive Repetition
Machine Learning
Narrowband Internet-of-Things (NB-IoT)

Access to Document

10.1109/PRDC.2018.00046

Cite this

Chen, L. S., Chung, W. H., Chen, I. Y., & Kuo, S. Y. (2018). Adaptive repetition scheme with machine learning for 3GPP NB-IoT. In Proceedings - 2018 IEEE 23rd Pacific Rim International Symposium on Dependable Computing, PRDC 2018 (pp. 252-256). Article 8639638 (Proceedings of IEEE Pacific Rim International Symposium on Dependable Computing, PRDC; Vol. 2018-December). IEEE Computer Society. https://doi.org/10.1109/PRDC.2018.00046

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title = "Adaptive repetition scheme with machine learning for 3GPP NB-IoT",

abstract = "In NB-IoT systems, UEs with poor signal quality employ more repetitions to compensate for additional signal attenuation. Excessively high CE levels and repetitions of UEs lead to wastage of valuable wireless resources, whereas inadequate CE levels and repetitions result in data retrieval failure at the receiving end. Therefore, a machine learning-based adaptive repetition scheme for a 3GPP NB-IoT system is proposed in this work to effectively improve overall network transmission efficiency. The results of simulation show the effect of the discount factor? on the convergence behavior of the proposed scheme, with a lower discount factor value denoting the myopic behavior of the proposed scheme, which results from the fact that it places more emphasis on immediate rewards. And the propose scheme is capable of effectively improving the average spectral efficiency.",

keywords = "Adaptive Repetition, Machine Learning, Narrowband Internet-of-Things (NB-IoT)",

author = "Chen, \{Li Sheng\} and Chung, \{Wei Ho\} and Chen, \{Ing Yi\} and Kuo, \{Sy Yen\}",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 23rd IEEE Pacific Rim International Symposium on Dependable Computing, PRDC 2018 ; Conference date: 04-12-2018 Through 07-12-2018",

year = "2018",

month = jul,

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doi = "10.1109/PRDC.2018.00046",

language = "英语",

series = "Proceedings of IEEE Pacific Rim International Symposium on Dependable Computing, PRDC",

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Chen, LS, Chung, WH, Chen, IY & Kuo, SY 2018, Adaptive repetition scheme with machine learning for 3GPP NB-IoT. in Proceedings - 2018 IEEE 23rd Pacific Rim International Symposium on Dependable Computing, PRDC 2018., 8639638, Proceedings of IEEE Pacific Rim International Symposium on Dependable Computing, PRDC, vol. 2018-December, IEEE Computer Society, pp. 252-256, 23rd IEEE Pacific Rim International Symposium on Dependable Computing, PRDC 2018, Taipei, Taiwan, 04/12/18. https://doi.org/10.1109/PRDC.2018.00046

Adaptive repetition scheme with machine learning for 3GPP NB-IoT. / Chen, Li Sheng; Chung, Wei Ho; Chen, Ing Yi et al.
Proceedings - 2018 IEEE 23rd Pacific Rim International Symposium on Dependable Computing, PRDC 2018. IEEE Computer Society, 2018. p. 252-256 8639638 (Proceedings of IEEE Pacific Rim International Symposium on Dependable Computing, PRDC; Vol. 2018-December).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

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AU - Chung, Wei Ho

AU - Chen, Ing Yi

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Y1 - 2018/7/2

N2 - In NB-IoT systems, UEs with poor signal quality employ more repetitions to compensate for additional signal attenuation. Excessively high CE levels and repetitions of UEs lead to wastage of valuable wireless resources, whereas inadequate CE levels and repetitions result in data retrieval failure at the receiving end. Therefore, a machine learning-based adaptive repetition scheme for a 3GPP NB-IoT system is proposed in this work to effectively improve overall network transmission efficiency. The results of simulation show the effect of the discount factor? on the convergence behavior of the proposed scheme, with a lower discount factor value denoting the myopic behavior of the proposed scheme, which results from the fact that it places more emphasis on immediate rewards. And the propose scheme is capable of effectively improving the average spectral efficiency.

AB - In NB-IoT systems, UEs with poor signal quality employ more repetitions to compensate for additional signal attenuation. Excessively high CE levels and repetitions of UEs lead to wastage of valuable wireless resources, whereas inadequate CE levels and repetitions result in data retrieval failure at the receiving end. Therefore, a machine learning-based adaptive repetition scheme for a 3GPP NB-IoT system is proposed in this work to effectively improve overall network transmission efficiency. The results of simulation show the effect of the discount factor? on the convergence behavior of the proposed scheme, with a lower discount factor value denoting the myopic behavior of the proposed scheme, which results from the fact that it places more emphasis on immediate rewards. And the propose scheme is capable of effectively improving the average spectral efficiency.

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Chen LS, Chung WH, Chen IY, Kuo SY. Adaptive repetition scheme with machine learning for 3GPP NB-IoT. In Proceedings - 2018 IEEE 23rd Pacific Rim International Symposium on Dependable Computing, PRDC 2018. IEEE Computer Society. 2018. p. 252-256. 8639638. (Proceedings of IEEE Pacific Rim International Symposium on Dependable Computing, PRDC). doi: 10.1109/PRDC.2018.00046

Adaptive repetition scheme with machine learning for 3GPP NB-IoT

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