Learning-based WiFi traffic load estimation in NR-U systems

Rui Yin, Zhiqun Zou, Celimuge Wu*, Jiantao Yuan, Xianfu Chen, Guanding Yu

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

7 Citations (Scopus)

Abstract

The unlicensed spectrum has been utilized to make up the shortage on frequency spectrum in new radio (NR) systems. To fully exploit the advantages brought by the unlicensed bands, one of the key issues is to guarantee the fair coexistence with WiFi systems. To reach this goal, timely and accurate estimation on the WiFi traffic loads is an important prerequisite. In this paper, a machine learning (ML) based method is proposed to detect the number of WiFi users on the unlicensed bands. An unsupervised Neural Network (NN) structure is applied to filter the detected transmission collision probability on the unlicensed spectrum, which enables the NR users to precisely rectify the measurement error and estimate the number of active WiFi users. Moreover, NN is trained online and the related parameters and learning rate of NN are jointly optimized to estimate the number of WiFi users adaptively with high accuracy. Simulation results demonstrate that compared with the conventional Kalman Filter based detection mechanism, the proposed approach has lower complexity and can achieve a more stable and accurate estimation.
Original languageEnglish
Pages (from-to)542-549
JournalIEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
VolumeE104A
Issue number2
DOIs
Publication statusPublished - Feb 2021
MoE publication typeA1 Journal article-refereed

Funding

Manuscript received May 12, 2020. Manuscript revised July 18, 2020. Manuscript publicized August 20, 2020. †The authors are with the School of Information and Electrical Engineering, Zhejiang University City College, Hangzhou, China. ††The author is with Graduate School of Informatics and Engineering, The University of Electro-Communications, Chofu-shi, 182-8585 Japan. †††The author is with the Institute of Ocean Sensing and Networking of the Ocean College, Zhejiang University, Zhoushan, China. ††††The author is with the VTT Technical Research Centre of Finland, Finland. ∗This work was supported in part by the the National Natural Science Foundation of China under Grant No. 61771429, No. 61703368, and in part by Zhejiang University City College Scientific Research Foundation under Grant No. JZD18002. Zhejiang Provincial Key Laboratory of Information Processing, Communication and Networking, Zhejiang, China. Zhejiang Lab’s International Talent Fund for Young Professionals and Aviation Key Laboratory of Science and Technology on Fault Diagnosis and Health Management 20183333001 also provided support for this work. a) E-mail: [email protected] b) E-mail: [email protected] (Corresponding author) DOI: 10.1587/transfun.2020EAP1063 This work was supported in part by the the National Natural Science Foundation of China under Grant No. 61771429, No. 61703368, and in part by Zhejiang University City College Scientific Research Foundation under Grant No. JZD18002. Zhejiang Provincial Key Laboratory of Information Processing, Communication and Networking, Zhe-jiang, China. Zhejiang Lab’s International Talent Fund for Young Professionals and Aviation Key Laboratory of Science and Technology on Fault Diagnosis and Health Management 20183333001 also provided support for this work.

Keywords

  • Neural network
  • NR-U
  • Unsupervised learning
  • WiFi user numbers

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