# Cellular Network Capacity and Coverage Enhancement with MDT Data and Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/2202.10968 · HTML: https://mlsanity.com/paper/2202.10968/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 39 references that are in the map.
- First posted on arXiv 22 Feb 2022.

## Builds on (most cited first, 13 of 13)

- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Deep Reinforcement Learning that Matters](https://mlsanity.com/paper/1709.06560/) (2017)
- [Learning to Communicate with Deep Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1605.06676/) (2016)
- [Mean Field Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1802.05438/) (2018)
- [Multiagent Cooperation and Competition with Deep Reinforcement Learning](https://mlsanity.com/paper/1511.08779/) (2015)
- [Deep Reinforcement Learning for Multi-Agent Systems: A Review of Challenges, Solutions and Applications](https://mlsanity.com/paper/1812.11794/) (2018)
- [Multi-Agent Deep Reinforcement Learning for Dynamic Power Allocation in Wireless Networks](https://mlsanity.com/paper/1808.00490/) (2018)
- [Resource Management in Wireless Networks via Multi-Agent Deep Reinforcement Learning](https://mlsanity.com/paper/2002.06215/) (2020)
- [A Very Brief Introduction to Machine Learning With Applications to Communication Systems](https://mlsanity.com/paper/1808.02342/) (2018)
- [Optimizing Coverage and Capacity in Cellular Networks using Machine Learning](https://mlsanity.com/paper/2010.13710/) (2020)
- [Online Antenna Tuning in Heterogeneous Cellular Networks with Deep Reinforcement Learning](https://mlsanity.com/paper/1903.06787/) (2019)
- [Remote Electrical Tilt Optimization via Safe Reinforcement Learning](https://mlsanity.com/paper/2010.05842/) (2020)
- [Competitive Multi-Agent Load Balancing with Adaptive Policies in Wireless Networks](https://mlsanity.com/paper/2110.07050/) (2021)

## Built on by (most cited first, 5 of 5)

- [Coverage and Capacity Optimization in STAR-RISs Assisted Networks: A Machine Learning Approach](https://mlsanity.com/paper/2204.06390/) (2022)
- [DRL Enabled Coverage and Capacity Optimization in STAR-RIS Assisted Networks](https://mlsanity.com/paper/2209.00511/) (2022)
- [Base Station Deployment under EMF constrain by Deep Reinforcement learning](https://mlsanity.com/paper/2601.02385/) (2025)
- [Predicting Drive Test Results in Mobile Networks Using Optimization Techniques](https://mlsanity.com/paper/2502.09305/) (2025)
- [Improving Outdoor Multi-cell Fingerprinting-based Positioning via Mobile Data Augmentation](https://mlsanity.com/paper/2509.19405/) (2025)

## Abstract (arXiv)

Recent years witnessed a remarkable increase in the availability of data and computing resources in communication networks. This contributed to the rise of data-driven over model-driven algorithms for network automation. This paper investigates a Minimization of Drive Tests (MDT)-driven Deep Reinforcement Learning (DRL) algorithm to optimize coverage and capacity by tuning antennas tilts on a cluster of cells from TIM's cellular network. We jointly utilize MDT data, electromagnetic simulations, and network Key Performance indicators (KPIs) to define a simulated network environment for the training of a Deep Q-Network (DQN) agent. Some tweaks have been introduced to the classical DQN formulation to improve the agent's sample efficiency, stability, and performance. In particular, a custom exploration policy is designed to introduce soft constraints at training time. Results show that the proposed algorithm outperforms baseline approaches like DQN and best-fist search in terms of long-term reward and sample efficiency. Our results indicate that MDT-driven approaches constitute a valuable tool for autonomous coverage and capacity optimization of mobile radio networks.