Data-Driven Telecommunication Infrastructure: AI Clustering and Geodesic Measurement for Strategic Tower Optimization

The optimization of Base Transceiver Station (BTS) location is a major challenge in current urban areas, owing to fast population increase and rising need for high-performance communications networks. This paper describes a revolutionary strategy to BTS deployment that employs advanced clustering algorithms to improve network performance and coverage in densely populated urban locations. Four clustering algorithms are assessed, including K-Means, DBSCAN, Hierarchical Clustering, and K-Medoids, while taking into account urban variables such as housing density, land use, and geographic distribution. The paper makes two major contributions: dynamic change of the K-Means algorithm's cluster count and efficient centroid initialization using real-world urban data. Geodesic distance measures are used to examine the spatial relationships between BTS locations, resulting in more accurate and efficient tower de-ployment. Experimental results show that the modified K-Means algorithm beats the other techniques, with a Calinski-Harabasz index of 1662.46 and a Davies-Bouldin index of 0.868, showing improved cluster cohesiveness and separation. This technique lowers deployment costs while improving network coverage, resulting in more precise BTS placement and better resource use. These findings fill a gap in the literature by providing vital insights into data-driven urban optimization methodologies. They also have substantial implications for the planning and development of smart city infrastructure, furthering the future of wireless network architecture in urban contexts.