The Use of Genetic Algorithms in Atoll Software for Enhanced Optimization of Radio Site Locations in Cellular Networks

The optimization of the placement of radio sites is crucial for improving the performance of telecommunications networks. This paper explores the application of genetic algorithms (GA) for the optimal placement of radio antennas to enhance network coverage and performance. By utilizing real-world data from existing sites, the study employs advanced machine learning techniques to evaluate and optimize key physical parameters of antennas, such as antenna height, GPS coordinates, azimuth, and tilt. In this paper, we aim to plan and optimize the radio component of cellular networks by integrating the optimized results into Atoll, a network planning software. This ensures seamless data integration and allows for precise simulation of network scenarios. The proposed methodology validates its effectiveness by comparing predicted coverage with actual measurements, highlighting improvements in network capacity and interference reduction. This approach not only optimizes existing infrastructure but also provides a scalable solution for future network expansions. The results underscore the potential of genetic algorithms to revolutionize network design, leading to enhanced service quality and increased customer satisfaction.