# Optimizing Coverage and Capacity in Cellular Networks using Machine Learning

arXiv: https://arxiv.org/abs/2010.13710 · HTML: https://mlsanity.com/paper/2010.13710/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Oct 2020.

## Built on by (most cited first, 21 of 21)

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- [Cellular Network Capacity and Coverage Enhancement with MDT Data and Deep Reinforcement Learning](https://mlsanity.com/paper/2202.10968/) (2022)
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- [Coverage and Capacity Optimization in STAR-RISs Assisted Networks: A Machine Learning Approach](https://mlsanity.com/paper/2204.06390/) (2022)
- [Scalable Bayesian optimization with high-dimensional outputs using randomized prior networks](https://mlsanity.com/paper/2302.07260/) (2023)
- [DRL Enabled Coverage and Capacity Optimization in STAR-RIS Assisted Networks](https://mlsanity.com/paper/2209.00511/) (2022)
- [Model Based Residual Policy Learning with Applications to Antenna Control](https://mlsanity.com/paper/2211.08796/) (2022)
- [Bayes-Split-Edge: Bayesian Optimization for Constrained Collaborative Inference in Wireless Edge Systems](https://mlsanity.com/paper/2510.23503/) (2025)
- [Learning Optimal Antenna Tilt Control Policies: A Contextual Linear Bandit Approach](https://mlsanity.com/paper/2201.02169/) (2022)
- [Cell-Free Data Power Control Via Scalable Multi-Objective Bayesian Optimisation](https://mlsanity.com/paper/2212.10299/) (2022)
- [Learning Cellular Coverage from Real Network Configurations using GNNs](https://mlsanity.com/paper/2304.10328/) (2023)
- [MOCA-HESP: Meta High-dimensional Bayesian Optimization for Combinatorial and Mixed Spaces via Hyper-ellipsoid Partitioning](https://mlsanity.com/paper/2508.06847/) (2025)
- [BONSAI: Bayesian Optimization with Natural Simplicity and Interpretability](https://mlsanity.com/paper/2602.07144/) (2026)

## Abstract (arXiv)

Wireless cellular networks have many parameters that are normally tuned upon deployment and re-tuned as the network changes. Many operational parameters affect reference signal received power (RSRP), reference signal received quality (RSRQ), signal-to-interference-plus-noise-ratio (SINR), and, ultimately, throughput. In this paper, we develop and compare two approaches for maximizing coverage and minimizing interference by jointly optimizing the transmit power and downtilt (elevation tilt) settings across sectors. To evaluate different parameter configurations offline, we construct a realistic simulation model that captures geographic correlations. Using this model, we evaluate two optimization methods: deep deterministic policy gradient (DDPG), a reinforcement learning (RL) algorithm, and multi-objective Bayesian optimization (BO). Our simulations show that both approaches significantly outperform random search and converge to comparable Pareto frontiers, but that BO converges with two orders of magnitude fewer evaluations than DDPG. Our results suggest that data-driven techniques can effectively self-optimize coverage and capacity in cellular networks.