A Big Data Enabled Channel Model for 5G Wireless Communication Systems

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A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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Date
2020-06-01
Major/Subject
Mcode
Degree programme
Language
en
Pages
12
211-222
Series
IEEE Transactions on Big Data, Volume 6, issue 2
Abstract
The standardization process of the fifth generation (5G) wireless communications has recently been accelerated and the first commercial 5G services would be provided as early as in 2018. The increasing of enormous smartphones, new complex scenarios, large frequency bands, massive antenna elements, and dense small cells will generate big datasets and bring 5G communications to the era of big data. This paper investigates various applications of big data analytics, especially machine learning algorithms in wireless communications and channel modeling. We propose a big data and machine learning enabled wireless channel model framework. The proposed channel model is based on artificial neural networks (ANNs), including feed-forward neural network (FNN) and radial basis function neural network (RBF-NN). The input parameters are transmitter (Tx) and receiver (Rx) coordinates, Tx-Rx distance, and carrier frequency, while the output parameters are channel statistical properties, including the received power, root mean square (RMS) delay spread (DS), and RMS angle spreads (ASs). Datasets used to train and test the ANNs are collected from both real channel measurements and a geometry based stochastic model (GBSM). Simulation results show good performance and indicate that machine learning algorithms can be powerful analytical tools for future measurement-based wireless channel modeling.
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Keywords
Big data, wireless communications, machine learning, channel modeling, artificial neural network
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Citation
Huang , J , Wang , C-X , Bai , L , Sun , J , Yang , Y , Li , J , Tirkkonen , O & Zhou , M-T 2020 , ' A Big Data Enabled Channel Model for 5G Wireless Communication Systems ' , IEEE Transactions on Big Data , vol. 6 , no. 2 , pp. 211-222 . https://doi.org/10.1109/TBDATA.2018.2884489