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

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.

Multi-Frequency Multi-Scenario Millimeter Wave MIMO Channel Measurements and Modeling for B5G Wireless Communication Systems

“…Meanwhile, as the frequency range, signal bandwidth, an-tenna numbers, and application scenarios increase rapidly, advanced technologies such as artificial intelligence and machine learning will be fully applied to and integrated with B5G communication systems. Potential machine learning algorithms, such as artificial neural network, convolutional neural network, generative adversarial network, and clustering and classification algorithms may be used to channel modeling, such as channel characteristics prediction, MPCs clustering, and scenarios classification, based on the massive channel measurement datasets [54]. Other new massive MIMO technologies, such as cell-free massive MIMO [55]- [57], ultramassive MIMO [58], [59], and large intelligent surface [60], [61] should also be evaluated by real channel measurements.…”

Towards 6G wireless communication networks: vision, enabling technologies, and new paradigm shifts

“…• AI enabled channel measurements and models. Some preliminary studies have shown the potential of AI and ML based wireless channel measurements and models, for example, MPCs clustering, scenario classification, and channel prediction, by using clustering, classification, and regression algorithms [335]. Different ML algorithms, such as artificial neural network (ANN), convolutional neural network (CNN), and generative adversarial network can be applied to wireless channel modeling [336].…”

Cooperative Spectrum Sensing using DQN in CRN

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