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.
翻译:近些年来,通信网络的数据和计算资源显著增加,这促使数据驱动高于网络自动化的模型驱动算法。本文调查了驱动器测试(MDT)驱动的深强化学习(DRL)算法,以优化覆盖范围和能力,调整天线倾斜于TIM细胞网络的一组细胞。我们共同利用MDT数据、电磁模拟和网络关键业绩指标(KPIs)来定义培训深QNetwork(DQN)代理的模拟网络环境。一些Tweaks已被引入传统DQN配方,以提高该代理器的样本效率、稳定性和性能。特别是,定制探索政策旨在在培训时间引入软约束。结果显示,拟议的算法在长期奖励和抽样效率方面优于DQN和最佳搜索等基线方法。我们的结果表明,MDT驱动方法是移动无线电网络自主覆盖和能力优化的宝贵工具。