论文标题
使用MDT数据和深入增强学习的蜂窝网络能力和覆盖范围增强
Cellular Network Capacity and Coverage Enhancement with MDT Data and Deep Reinforcement Learning
论文作者
论文摘要
近年来，通信网络中的数据和计算资源的可用性显着增加。这导致数据驱动的网络自动化算法的增加。本文研究了驱动器测试（MDT）驱动的深钢筋学习（DRL）算法的最小化，以通过从Tim的蜂窝网络中调整天线倾斜度来优化覆盖范围和容量。我们共同利用MDT数据，电磁模拟和网络关键性能指标（KPI）来定义模拟网络环境，以训练深Q-Network（DQN）代理。一些调整已被引入经典的DQN公式，以提高代理的样本效率，稳定性和性能。特别是，定制勘探政策旨在在培训时引入软限制。结果表明，就长期奖励和样本效率而言，所提出的算法的表现优于DQN和最佳拳手搜索等基线方法。我们的结果表明，MDT驱动的方法构成了自动覆盖和移动无线网络容量优化的宝贵工具。
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.
