The mobile communication enabled by cellular networks is the one of the main foundations of our modern society. Optimizing the performance of cellular networks and providing massive connectivity with improved coverage and user experience has a considerable social and economic impact on our daily life. This performance relies heavily on the configuration of the network parameters. However, with the massive increase in both the size and complexity of cellular networks, network management, especially parameter configuration, is becoming complicated. The current practice, which relies largely on experts' prior knowledge, is not adequate and will require lots of domain experts and high maintenance costs. In this work, we propose a learning-based framework for handover parameter configuration. The key challenge, in this case, is to tackle the complicated dependencies between neighboring cells and jointly optimize the whole network. Our framework addresses this challenge in two ways. First, we introduce a novel approach to imitate how the network responds to different network states and parameter values, called auto-grouping graph convolutional network (AG-GCN). During the parameter configuration stage, instead of solving the global optimization problem, we design a local multi-objective optimization strategy where each cell considers several local performance metrics to balance its own performance and its neighbors. We evaluate our proposed algorithm via a simulator constructed using real network data. We demonstrate that the handover parameters our model can find, achieve better average network throughput compared to those recommended by experts as well as alternative baselines, which can bring better network quality and stability. It has the potential to massively reduce costs arising from human expert intervention and maintenance.

翻译：蜂窝网络实现的移动通信是现代社会的核心基础之一。优化蜂窝网络性能、提供大规模连接并改善覆盖范围和用户体验，对我们的日常生活具有显著的社会经济影响。这种性能高度依赖网络参数的配置。然而，随着蜂窝网络规模和复杂性的急剧增长，网络管理特别是参数配置正变得日益复杂。当前主要依赖专家先验知识的实践已不充分，需要大量领域专家并承担高昂维护成本。本文提出一种基于学习的切换参数配置框架。该框架的关键挑战在于处理相邻小区间的复杂依赖关系并实现整个网络的联合优化。我们通过两种方式应对这一挑战：首先，引入一种模拟网络对不同状态和参数值响应的新方法——自动分组图卷积网络（AG-GCN）；其次，在参数配置阶段，不直接求解全局优化问题，而是设计局部多目标优化策略，使每个小区通过考虑若干局部性能指标来平衡自身性能与邻居小区性能。我们利用真实网络数据构建的仿真器评估所提算法。实验表明，与专家推荐方案及其他基线方法相比，该模型能够找到实现更高平均网络吞吐量的切换参数，从而带来更优的网络质量与稳定性。该方法有望大幅降低因人工专家干预和维护产生的成本。