Original scientific paper
https://doi.org/10.24138/jcomss-2025-0205
Hybrid AI-Assisted EM and GIS Framework for Radio Parameter Optimization in Dense Urban Cellular Networks
Mohammed Oussama Benosman
; University of Tlemcen, Algeria
*
Hicham Megnafi
; University of Tlemcen, Algeria
Sidi Mohammed Meriah
; University of Tlemcen, Algeria
* Corresponding author.
Abstract
Optimizing antenna parameters is essential to improve
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 optimization
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, providing
a scalable solution for current and future cellular networks
in large-scale Internet of Things (IoT) scenarios.
Keywords
Cellular Networks; Radio Network Optimization; Hybrid Optimization Approaches; Artificial Intelligence; Metaheuristics; Geographic Information System; Radio Performance; Long Term Evolution-Advanced; Fifth Generation; Internet of Things
Hrčak ID:
348543
URI
Publication date:
31.3.2026.
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