Advanced Predictive Modelling for Radio Resource Control (RRC) Sessions in Long-Term Evolution (LTE)

Abstract

This research investigates radio resource control (RRC) session management in long-term evolution (LTE) networks, focusing on the distinctive issues posed by high-density urban settings, heterogeneous device ecosystems, and data-intensive applications. The (RRC) protocol, which works at Layer-3, controls important tasks including setting up connections, changing them, and ending them. It also switches user equipment (UE) between the (RRC_IDLE) and (RRC_CONNECTED) states. This research utilizes (3GPP) standards (TS 36.331) to analyze how telecom operators, such as China Mobile and Reliance Jio, optimize (RRC) parameters to manage network load, enhance battery efficiency, and maintain quality of service (QoS) in areas with (TDD-LTE) bands (e.g., Band 40, 41) and significant traffic from various applications. To improve (RRC) session management, an innovative predictive modelling framework is suggested that uses weighted ensemble approaches that include artificial neural networks (ANN), recurrent neural networks with long short-term memory (RNN-LSTM), and convolutional neural networks (CNN). These models use synthetic (LTE) data that is specific to different network properties, such as (RSRP), (RSRQ), traffic load, (UE) type, and mobility, to forecast important metrics like session length. The ensemble technique, which is based on inverse (RMSE), makes predictions more accurate than individual models. This is because it takes into account differences between urban and rural areas.