An interference management algorithm using big data analytics in LTE cellular networks

Abstract

Recently, a series of approaches have been developed to mitigate the interference and improve the system capacity in LTE networks. However, due to the diversity of data source, existing approaches have their own limitations in supervised and unsupervised learning. To deal with the downlink inter-cell interference which is caused by the frequency reuse and the characteristics of the OFDMA, an interference management algorithm using big data is proposed. Since big data analytics has become more and more popular in wireless optimization, it is a very effective approach to mitigate the interference and the outage probability by analyzing the huge amount of wireless network measurements and diagnosis data. By obtaining and analyzing the measurement report (MR) and counters (records of the performance indicators of networks) from existing networks, an interference management algorithm (IMA) is proposed based on big data analytics. The numerical results show that the interference management process is low cost and high-efficiency for telecom operators.