TY - JOUR
T1 - Learning-based WiFi traffic load estimation in NR-U systems
AU - Yin, Rui
AU - Zou, Zhiqun
AU - Wu, Celimuge
AU - Yuan, Jiantao
AU - Chen, Xianfu
AU - Yu, Guanding
N1 - Funding Information:
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: yinrui@zju.edu.cn b) E-mail: celimuge@uec.ac.jp (Corresponding author) DOI: 10.1587/transfun.2020EAP1063
Funding Information:
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.
Funding Information:
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.
Publisher Copyright:
Copyright © 2021 The Institute of Electronics, Information and Communication Engineers
Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2021/2
Y1 - 2021/2
N2 - 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.
AB - 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.
KW - Neural network
KW - NR-U
KW - Unsupervised learning
KW - WiFi user numbers
UR - http://www.scopus.com/inward/record.url?scp=85100835793&partnerID=8YFLogxK
U2 - 10.1587/transfun.2020EAP1063
DO - 10.1587/transfun.2020EAP1063
M3 - Article
AN - SCOPUS:85100835793
VL - E104A
SP - 542
EP - 549
JO - IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
JF - IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
SN - 0916-8508
IS - 2
ER -