$X_2$ BI-CRITERIA OPTIMIZATION OF ENODEB IN AN EUTRAN NETWORK USING LTE TECHNOLOGY

The optimal deployment of eNodeB in an eUTRAN LTE network is a key challenge to ensure efficient coverage and minimize inter-cell interference, while meeting quality of service (QoS) requirements. This paper proposes a dual criteria dimensioning approach based on a genetic algorithm, aimed at simultaneously optimizing network coverage and interference management, two aspects essential for the performance of LTE networks in dense urban environments. The developed model is based on a multi-objective mathematical formulation, integrating two optimization functions: maximization of coverage by optimizing the spatial distribution and transmitting power of eNodeB, and minimization of inter-cell interference by adjusting the frequency configuration and spacing of base stations. The genetic algorithm used enables efficient exploration of the solution space by applying evolutionary operators (selection, crossover and mutation) to seek an optimal balance between these two objectives. Simulations carried out on scenarios show that the proposed genetic approach delivers an 18% improvement in coverage and a 22% reduction in interference compared with the fixed hexagonal planning method. The results obtained demonstrate that the bi-criteria genetic approach enables optimized management of the LTE network, improving QoS while guaranteeing efficient use of spectrum resources, but remains highly dependent on the genetic operators’ parameterization. Conversely, due to the Pareto front, dynamic optimization of the LTE network is achieved, maximizing coverage by 90% while minimizing interference by 22.7% in high-density urban environments. This study thus provides a robust and scalable solution for the strategic deployment of eNodeB in next-generation networks.