Optimizing cellular base station placement with data driven models
Stochastic Geometry Meets Optimization: Data-Driven Cellular Planning
Information TheoryNetworking and Internet Architecture
Summary
Cellular network performance depends on placing base stations in the right spots. Traditional methods pick exact locations but don't show broader patterns or alternatives. This paper develops a new approach that uses real user location data and mathematical models to find the best density of stations across an area, instead of exact points. This helps highlight where to focus deployments and allows flexibility for multi-layer networks and gradual expansion. The authors tested their method with both simulated and real-world user data to show how it performs.
What this means in practice
- •For cellular network planners: Design base station density maps that balance coverage and capacity using real user location patterns for efficient network upgrades.
- •For urban infrastructure planners: Integrate spatial user demand data in planning wireless infrastructure to better prioritize areas with high service needs.
Authors
Abhishek K. Shukla, Abhishek K. Gupta, Ketan Rajawat
Abstract
Deployment optimization of base stations (BSs) is critical for improving cellular network performance metrics such as coverage and capacity, impacting user experience, scalability, and infrastructure cost-effectiveness. Traditional methods that optimize precise BS locations do not generally provide broader insights into spatial deployment priorities or alternative viable deployments. Stochastic geometry approaches, on the other hand, offer a more interpretable intensity-based framework, but rarely utilize real-world user location data or investigate optimal BS deployment strategies. In this work, we propose a data-driven stochastic geometry-based approach that optimizes BS deployment intensity to maximize a network performance metric using samples from an unknown user-location distribution. We specialize the framework to an inhomogeneous Poisson point process (PPP) for cellular coverage analysis. The resulting infinite-dimensional problem is discretized over the deployment area and solved using sampled user locations. We characterize the discretization and sampling errors, apply stochastic mirror ascent to solve the empirical problem, and analyze the selection of concrete BS deployments using independent validation samples. Unlike traditional methods, our approach yields an optimized spatial intensity that reveals deployment priorities across regions and accommodates practical deployment constraints, multi-tier networks, and incremental deployment scenarios. Numerical experiments with synthetic and real-world spatial demand data demonstrate the proposed approach under different network configurations.