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The standardization process of the fifth generation (5G) wirelesscommunications has recently been accelerated and the first commercial 5Gservices would be provided as early as in 2018. The increasing of enormoussmartphones, new complex scenarios, large frequency bands, massive antennaelements, and dense small cells will generate big datasets and bring 5Gcommunications to the era of big data. This paper investigates variousapplications of big data analytics, especially machine learning algorithms inwireless communications and channel modeling. We propose a big data and machinelearning enabled wireless channel model framework. The proposed channel modelis based on artificial neural networks (ANNs), including feed-forward neuralnetwork (FNN) and radial basis function neural network (RBF-NN). The inputparameters are transmitter (Tx) and receiver (Rx) coordinates, Tx-Rx distance,and carrier frequency, while the output parameters are channel statisticalproperties, including the received power, root mean square (RMS) delay spread(DS), and RMS angle spreads (ASs). Datasets used to train and test the ANNs arecollected from both real channel measurements and a geometry based stochasticmodel (GBSM). Simulation results show good performance and indicate thatmachine learning algorithms can be powerful analytical tools for futuremeasurement-based wireless channel modeling.

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