Machine Learning-Based Online Coverage Estimator (MLOE): Advancing Mobile Network Planning and Optimization

Abstract

Nowadays, the dependency on high-performance digital mobile connectivity is not limited to human usage but also the intelligent objects increasingly deployed to serve the needs of Internet of Things (IoT) applications. However, the current network planning technique limitation has constrained the real potential of mobile digital connectivity development. This situation has hindered sustainable Internet-oriented economic and technological development. The 3rd generation partnership project (3GPP), through its specification release 18 (Rel.18), has included and leveraged the potential capabilities of machine learning (ML) technologies in advanced mobile network planning. The main objective is to enhance mobile network planning performance and reduce complexity. To materialize this aim, we propose a novel ML-based Online coverage Estimator (MLOE) tool developed based on Random Forest (RF) ML algorithm. It uses seven unique features to predict the mobile network performance through reference signal received power (RSRP). Accordingly, the results showed that MLOE outperformed traditional empirical techniques and previous works. The final trained RF algorithm has achieved an outstanding root mean square error (RMSE) of 2.65 dB and a coefficient of determination ( $R^{2}$ ) of 0.93. With the dynamic and fast-growing mobile technology, MLOE has been deployed on an online platform using MATLAB® Web App Server, which offers a modular and scalable architecture.

Keywords

• Machine learning
• MATLAB
• mobile networks
• path loss
• received signal strength indicator
• RSRP