Shannon Entropy for Load-Balanced Cellular Network Planning: Data-Driven Voronoi Optimization of Base-Station Locations

In this paper, we introduce a stochastic shape optimization technique for base-station placement in cellular wireless communication networks. We formulate the data-driven facility location problem in a gradient-based framework and propose an algorithm that computes stochastic gradients efficiently via nearest-neighbor evaluations on Voronoi diagrams. This enables the use of Shannon-entropy objectives that promote balanced coverage and yield more than two orders of magnitude reduction in per-iteration runtime compared to a conventional integral-based optimization that assumes full knowledge of the under-lying density, making the proposed approach practical for real deployments. We highlight the requirements of facility location balancing problems with the introduction of the Adjusted Entropy Ratio and show a significant improvement in load balancing compared to the baseline algorithms, particularly in scenarios where baseline algorithms fall short in subdividing crowded areas for more equitable coverage. A downlink telecom evaluation with realistic propagation and interference models further shows that the proposed method configuration substantially improves user-rate fairness and load balance. Our results also show that Self-Organizing Maps (SOMs) provide an effective initialization by capturing the structure of the users’ location data.