A Big Data Enabled Channel Model for 5G Wireless Communication Systems
This work addresses the need for efficient channel modeling in 5G communications, leveraging big data and machine learning, but it is incremental as it applies existing ANN methods to this domain.
The paper tackles the challenge of modeling wireless channels for 5G systems by proposing a framework that uses artificial neural networks (ANNs) to predict channel properties like received power and delay spread, based on input parameters such as transmitter-receiver coordinates and distance, with simulation results showing good performance.
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.