Bayesian Network Prediction of Mobile User Throughput in 5G Wireless Networks

Abstract

By combining artificial intelligence and machine learning, next-generation cellular systems will enable advanced data analysis techniques to achieve efficient service quality management and network automation. In this paper, the Bayesian Network (BN) is used for the reliability prediction of throughput. The forecast is to predict future test results through parameter estimation. In the studied Bayesian network learning stage, the load of the base station, user location and moving speed all affect the user’s received signal-to noise ratio (SNR) and signal interference plus noise ratio (SINR). The test result is the probability or number of times that throughput of a low-speed mobile user satisfies the threshold. Computer simulation results show that the model can well infer the user’s throughput under low speed movement conditions.