As cellular networks evolve towards the 6th generation, machine learning is seen as a key enabling technology to improve the capabilities of the network. Machine learning provides a methodology for predictive systems, which can make networks become proactive. This proactive behavior of the network can be leveraged to sustain, for example, a specific quality of service requirement. With predictive quality of service, a wide variety of new use cases, both safety- and entertainment-related, are emerging, especially in the automotive sector. Therefore, in this work, we consider maximum throughput prediction enhancing, for example, streaming or high-definition mapping applications. We discuss the entire machine learning workflow highlighting less regarded aspects such as the detailed sampling procedures, the in-depth analysis of the dataset characteristics, the effects of splits in the provided results, and the data availability. Reliable machine learning models need to face a lot of challenges during their lifecycle. We highlight how confidence can be built on machine learning technologies by better understanding the underlying characteristics of the collected data. We discuss feature engineering and the effects of different splits for the training processes, showcasing that random splits might overestimate performance by more than twofold. Moreover, we investigate diverse sets of input features, where network information proved to be most effective, cutting the error by half. Part of our contribution is the validation of multiple machine learning models within diverse scenarios. We also use explainable AI to show that machine learning can learn underlying principles of wireless networks without being explicitly programmed. Our data is collected from a deployed network that was under full control of the measurement team and covered different vehicular scenarios and radio environments.

翻译：随着蜂窝网络向第六代演进，机器学习被视为提升网络能力的关键使能技术。机器学习为预测系统提供了方法论，使网络能够具备前瞻性。这种前瞻性网络行为可被用于维持特定的服务质量要求，例如。借助预测性服务质量，大量涉及安全与娱乐的新型应用场景正在涌现，尤其在汽车领域。因此，本文聚焦于最大吞吐量预测的增强，以支持例如流媒体或高清地图应用。我们论述了完整的机器学习工作流，重点关注采样流程细节、数据集特征深度分析、数据划分对结果的影响以及数据可用性等易被忽视的方面。可靠的机器学习模型在其生命周期中需应对诸多挑战。我们阐明了如何通过深入理解采集数据的底层特征来建立对机器学习技术的信心。本文探讨了特征工程与不同训练数据划分方式的影响，证明随机划分可能导致性能被高估超过两倍。此外，我们研究了多样化的输入特征组合，其中网络信息被证实最为有效，可将误差降低一半。本文的贡献之一是在多种场景下验证了多个机器学习模型。我们还利用可解释AI证明，机器学习能够学习无线网络的底层原理而无需显式编程。实验数据采集自测量团队完全控制的已部署网络，覆盖了不同车载场景与无线电环境。