Network optimization: Concepts, Processes, drive testing for enhanced mobile communication systems

as mobile communication networks evolve to meet rising user demands, optimizing network performance has become critical to ensure seamless connectivity. This study proposes a comprehensive, AI-enhanced network optimization framework that integrates drive testing, advanced analytics, and real-time decision-making. Emphasis is placed on addressing the shortcomings of traditional parameter-based methods by incorporating user-centric, empirical data. The methodology involves the design of a drive testing system, development of machine learning-based optimization strategies, and validation through field trials. Results demonstrate improvements in key performance indicators (KPIs) such as RSRP, SINR, throughput, and call drop rates. This work contributes both practically and academically, enabling operators to enhance Quality of Service (QoS) and Quality of Experience (QoE) while reducing operational costs.