Machine-Learning-Based Uplink Throughput Prediction from Physical Layer Measurements

被引:10
|
作者
Eyceyurt, Engin [1 ]
Egi, Yunus [2 ,3 ]
Zec, Josko [4 ]
机构
[1] Nevsehir Haci Bektas Veli Univ, Fac Engn & Arts, Elect & Elect Engn, TR-50300 Nevsehir, Turkey
[2] Amer Univ Middle East, Coll Engn & Technol, Egaila 54200, Kuwait
[3] Sirnak Univ, Fac Engn, Elect & Elect Engn, TR-73000 Sirnak, Turkey
[4] Florida Inst Technol, Comp Engn & Sci, Melbourne, FL 32901 USA
关键词
machine learning; uplink throughput prediction; LTE & 5G radio metrics; PERFORMANCE;
D O I
10.3390/electronics11081227
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The uplink (UL) throughput prediction is indispensable for a sustainable and reliable cellular network due to the enormous amounts of mobile data used by interconnecting devices, cloud services, and social media. Therefore, network service providers implement highly complex mobile network systems with a large number of parameters and feature add-ons. In addition to the increased complexity, old-fashioned methods have become insufficient for network management, requiring an autonomous calibration to minimize utilization of the system parameter and the processing time. Many machine learning algorithms utilize the Long-Term Evolution (LTE) parameters for channel throughput prediction, mainly in favor of downlink (DL). However, these algorithms have not achieved the desired results because UL traffic prediction has become more critical due to the channel asymmetry in favor of DL throughput closing rapidly. The environment (urban, suburban, rural areas) affect should also be taken into account to improve the accuracy of the machine learning algorithm. Thus, in this research, we propose a machine learning-based UL data rate prediction solution by comparing several machine learning algorithms for three locations (Houston, Texas, Melbourne, Florida, and Batman, Turkey) and determine the best accuracy among all. We first performed an extensive LTE data collection in proposed locations and determined the LTE lower layer parameters correlated with UL throughput. The selected LTE parameters, which are highly correlated with UL throughput (RSRP, RSRQ, and SNR), are trained in five different learning algorithms for estimating UL data rates. The results show that decision tree and k-nearest neighbor algorithms outperform the other algorithms at throughput estimation. The prediction accuracy with the R2 determination coefficient of 92%, 85%, and 69% is obtained from Melbourne, Florida, Batman, Turkey, and Houston, Texas, respectively.
引用
收藏
页数:15
相关论文
共 50 条
  • [31] Machine-learning-based calving prediction from activity, lying, and ruminating behaviors in dairy cattle
    Borchers, M. R.
    Chang, Y. M.
    Proudfoot, K. L.
    Wadsworth, B. A.
    Stone, A. E.
    Bewley, J. M.
    JOURNAL OF DAIRY SCIENCE, 2017, 100 (07) : 5664 - 5674
  • [32] Machine-Learning-Based phase diagram construction for high-throughput batch experiments
    Tamura, Ryo
    Deffrennes, Guillaume
    Han, Kwangsik
    Abe, Taichi
    Morito, Haruhiko
    Nakamura, Yasuyuki
    Naito, Masanobu
    Katsube, Ryoji
    Nose, Yoshitaro
    Terayama, Kei
    SCIENCE AND TECHNOLOGY OF ADVANCED MATERIALS-METHODS, 2022, 2 (01): : 153 - 161
  • [33] High-Throughput Test Paves the Way for Machine-Learning-Based Optimization of Adhesives
    Roberts, Paul
    Frechette, Joelle
    ACS CENTRAL SCIENCE, 2021, 7 (07) : 1102 - 1104
  • [34] Machine-learning-based image categorization
    Han, YT
    Qi, XJ
    IMAGE ANALYSIS AND RECOGNITION, 2005, 3656 : 585 - 592
  • [35] Machine-Learning-Based Accessibility System
    Banerjee K.
    Singh A.
    Akhtar N.
    Vats I.
    SN Computer Science, 5 (3)
  • [36] Machine Learning Based Optimal Modulation Format Prediction for Physical Layer Network Planning
    Rafique, Danish
    2018 20TH ANNIVERSARY INTERNATIONAL CONFERENCE ON TRANSPARENT OPTICAL NETWORKS (ICTON), 2018,
  • [37] Machine-learning-based asymptotic homogenisation and localisation considering boundary layer effects
    Pan, Xiwei
    Zhou, Zhengcheng
    Ma, Chuang
    Li, Shaoshuai
    Zhu, Yichao
    INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN ENGINEERING, 2024, 125 (01)
  • [38] Machine-Learning-Based Detection of Aerodynamic Disturbances Using Surface Pressure Measurements
    Hou, Wei
    Darakananda, Darwin
    Eldredge, Jeff D.
    AIAA JOURNAL, 2019, 57 (12) : 5079 - 5093
  • [39] Machine-Learning-Based Predictive Handover
    Masri, Ahmed
    Veijalainen, Teemu
    Martikainen, Henrik
    Mwanje, Stephen
    Ali-Tolppa, Janne
    Kajo, Marton
    2021 IFIP/IEEE INTERNATIONAL SYMPOSIUM ON INTEGRATED NETWORK MANAGEMENT (IM 2021), 2021, : 648 - 652
  • [40] Machine-learning-based approach for nonunion prediction following osteoporotic vertebral fractures
    Takahashi, Shinji
    Terai, Hidetomi
    Hoshino, Masatoshi
    Tsujio, Tadao
    Kato, Minori
    Toyoda, Hiromitsu
    Suzuki, Akinobu
    Tamai, Koji
    Yabu, Akito
    Nakamura, Hiroaki
    EUROPEAN SPINE JOURNAL, 2023, 32 (11) : 3788 - 3796