arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2002.12561eess.SPcs.LGcs.NI

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

Southeast University(东南大学);Purple Mountain Laboratories(紫金山实验室);Beihang University(北京航空航天大学);Shandong University(山东大学);ShanghaiTech University(上海科技大学);Shanghai Jiaotong University(上海交通大学);Aalto University(阿尔托大学);Shanghai Institute of Microsystem and Information Technology (SIMIT), Chinese Academy of Sciences (CAS)(中国科学院上海微系统与信息技术研究所)

Jie Huang, Cheng-Xiang Wang, Lu Bai, Jian Sun, Yang Yang, Jie Li, Olav Tirkkonen, Ming-Tuo Zhou

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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