Framework: Clustering-Driven Approach for Base Station Parameter Optimization and Automation (CeDA-BatOp)

Abstract

The growing demand for fast and reliable wireless services has led to the deployment of more base stations, which has made manual optimization of base station parameters more complex and time-consuming. This can lead to suboptimal network performance and a poor user experience. To address this challenge, we propose a Clustering-Driven Approach for Base Station Parameter Optimization and Automation (CeDA-BatOp), an automated framework for predicting optimized base station parameters. Our framework first compares three clustering algorithms: K-means, DBSCAN, and Agglomerative Clustering, selecting the most suitable one for specific scenarios based on their unique attributes. Simultaneously, our framework leverages machine learning (ML) algorithms to predict the optimal parameters for each base station with an evaluation of multiple ML models to identify the best fit for our data. It also incorporates data drift monitoring to track gradual changes in data distribution over time, ensuring ML model accuracy through periodic retraining. In the simulated scenario, our framework achieved an average of 76% reduction in memory overhead and simplified training by utilizing fewer models, effectively minimizing computational resources. The drift detection system demonstrated an exceptional accuracy of 98.87%, outperforming other cases. These results highlight the potential of our framework to significantly benefit network operators by automating base station parameter tuning, reducing human involvement, and substantially improving network performance and cost savings.