论文标题
使用机器学习优化蜂窝网络中的覆盖范围和容量
Optimizing Coverage and Capacity in Cellular Networks using Machine Learning
论文作者
论文摘要
无线蜂窝网络具有许多参数，这些参数通常会在部署后调整并随着网络的变化而重新调节。许多操作参数会影响收到的参考信号（RSRP），参考信号接收质量（RSRQ），信噪比与互联 - 加上噪声（SINR）（SINR）以及最终吞吐量。在本文中，我们开发并比较了两种方法，以通过共同优化跨部门的发射功率和下部（高程倾斜）设置来最大化覆盖范围并最大程度地减少干扰。为了离线评估不同的参数配置，我们构建了一个捕获地理相关性的现实模拟模型。使用此模型，我们评估了两种优化方法：深层确定性策略梯度（DDPG），增强学习（RL）算法和多目标贝叶斯优化（BO）。我们的模拟表明，两种方法都显着胜过随机搜索并收敛到可比的帕累托前沿，但是BO收敛的情况下，比DDPG少了两个数量级。我们的结果表明，数据驱动的技术可以有效地自动化蜂窝网络中的覆盖范围和能力。
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.
