A XGBoost Based Wireless Interference Relation Mining and Performance Prediction Method

Ultra-dense network (UDN) is considered to be the key technology for the fifth generation (5G) networks to provide high capacity. However, intensive deployment of femtocells bring severe inter-cells interference (ICI), which greatly limits the performance of the network and the capacity gain the system can obtain. Therefore, the key to solve this problem is to obtain accurate interference information through accurate interference modeling. In fact, the wireless big data generated during the operation of the wireless network contains rich wireless interference information. Based on this, this paper proposes an uplink interference identification and signal-to-interference-plus-noise ratio (SINR) prediction algorithm based on XGBoost and interference model. The proposed algorithm uses the wireless big data generated during network operation to train the XGBoost algorithm, mining the signal-to-interference ratio (SIR) and signal-to-noise ratio (SNR) information between links in the wireless network without increasing the overhead of wireless resources, and then combining with the proposed interference model to achieve accurate prediction of the SINR. The simulation results show that when the training data of the target user reaches 5000 pieces, the prediction error of its SINR will be reduced to less than 0.5dB, which effectively reduces the requirement of data quantity and computing power, and can meet the practical application requirements.