A Novel Machine Learning Approach to Estimating KPI and PoC for LTE-LAA-Based Spectrum Sharing

Abstract

Machine learning (ML) approaches have been extensively exploited to model and to improve wireless communication networks in the past few years. Nonetheless, the estimation of key performance indicators (KPIs) and their uncertainties in Long Term Evolution License Assisted Access (LTE-LAA) based coexistence systems is not adequately addressed. For example, it is not clear if an ML method can accurately predict achievable KPIs (e.g. throughput) and the probability of coexistence (PoC) of LTE-LAA coexistence systems based on partial or no information of MAC and physical layer protocols and parameters. In this paper, we develop a novel ML method by combining a neural network with a logistic regression algorithm to track and estimate KPIs and PoC of coexisting LTE-LAA and wireless local area network (WLAN) links. This ML method can be applied when KPI samples at the base stations (BSs) and access points (APs) are available, without using knowledge of MAC and physical layer parameters. Comparison between the ML and simulation results indicate that the proposed ML method can track the system KPIs and predict the system PoC with good accuracy.