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

• URL: http://arxiv.org/abs/2202.10968v1
• Date: Tue, 22 Feb 2022 15:16:53 GMT
• Title: Cellular Network Capacity and Coverage Enhancement with MDT Data and Deep Reinforcement Learning
• Authors: Marco Skocaj, Lorenzo Mario Amorosa, Giorgio Ghinamo, Giuliano Muratore, Davide Micheli, Flavio Zabini, Roberto Verdone
• Abstract summary: 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.
• Score: 2.2412873466757297
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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