Hybrid AI-Assisted EM and GIS Framework for Radio Parameter Optimization in Dense Urban Cellular Networks

Abstract

Optimizing antenna parameters is essential to im prove coverage and quality of service in wireless cellular networks. Electrical tilt and azimuth strongly influence the Reference Signal Received Power (RSRP) and the Signal-to Interference-plus-Noise Ratio (SINR), which are key Quality of Service (QoS) indicators. Conventional approaches, based on field measurements and manual tuning, are costly and inefficient in dynamic urban environments. To overcome these limitations, this paper proposes a hybrid framework integrating a high-fidelity electromagnetic simulator and a Geographic Information System to realistically model radio propagation and accurately evaluate performance. The optimization targets electrical tilt—remotely adjustable via the Remote Electrical Tilt (RET) mechanism— as well as azimuth, which requires on-site reconfiguration. The search relies on advanced metaheuristics, namely Genetic Algorithms and Artificial Immune Systems, ensuring efficient exploration and robust convergence. Experiments conducted on the LTE-Advanced network of Algeria Telecom Mobile– Mobilis in Oran demonstrate performance gains of up to 23% in the fitness function, which combines an average RSRP greater than −85 dBm and an average SINR greater than 10 dB, compared to the operator’s configurations obtained through manual optimiza tion based on drive tests. These results confirm the effectiveness of the proposed approach for optimizing antenna parameters in complex urban environments. Beyond performance gains, the proposed framework reduces operational costs and is compatible with Self-Organizing Networks (SON) and RET systems, provid ing a scalable solution for current and future cellular networks in large-scale Internet of Things (IoT) scenarios.

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