Stochastic geometry-based models for cellular network deployment
(2023)
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- Abstract
- The growing demand for network capacity due to mobile devices and data-hungry applications has prompted engineers to explore advanced solutions. Network densification, especially in urban areas, where small cells complement the existing cellular network, has emerged as the most promising approach. It improves coverage in underserved areas and provides faster data rates in high-traffic locations. Deployment of this technology requires a network-level analysis to provide network planners with valuable guidelines regarding network design parameters like base station (BS) density. Managing inter-tier interference and exposure, subject to legal thresholds, is crucial, requiring efficient power level characterization. The choice between stochastic and deterministic channel models then becomes a priority, with a trade-off between accuracy and computational complexity. This study employs stochastic geometry (SG) theory to derive analytical expressions for network-level metrics averaged over multiple realizations of a network generated by doubly stochastic processes. SG however requires simplified assumptions regarding the network topology and the channel model. To validate these assumptions and demonstrate limitations, deployment trends from the model are compared with Monte Carlo simulations that uses ray tracing and real street patterns. Finally, losses arising from simpler channel models in an actual deployment optimization scenario are quantified.
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Citations
Demey, S. (2023). Stochastic geometry-based models for cellular network deployment. https://hdl.handle.net/2078.5/233708