In future 6G networks, dependable networks will enable telecommunication services such as remote control of robots or vehicles with strict requirements on end-to-end network performance in terms of delay, delay variation, tail distributions, and throughput. With respect to such networks, it is paramount to be able to determine what performance level the network segment can guarantee at a given point in time. One promising approach is to use predictive models trained using machine learning (ML). Predicting performance metrics such as one-way delay (OWD), in a timely manner, provides valuable insights for the network, user equipments (UEs), and applications to address performance trends, deviations, and violations. Over the course of time, a dynamic network environment results in distributional shifts, which causes catastrophic forgetting and drop of ML model performance. In continual learning (CL), the model aims to achieve a balance between stability and plasticity, enabling new information to be learned while preserving previously learned knowledge. In this paper, we target on the challenges of catastrophic forgetting of OWD prediction model. We propose a novel approach which introducing the concept of multi-generator for the state-of-the-art CL generative replay framework, along with tabular variational autoencoders (TVAE) as generators. The domain knowledge of UE capabilities is incorporated into the learning process for determining generator setup and relevance. The proposed approach is evaluated across a diverse set of scenarios with data that is collected in a realistic 5G testbed, demonstrating its outstanding performance in comparison to baselines.

翻译：在未来6G网络中，可靠网络将支持远程控制机器人或车辆等电信服务，这些服务对端到端网络性能在延迟、延迟变化、尾部分布和吞吐量方面有严格要求。对于此类网络，关键要能够确定网络段在给定时间点能够保证的性能水平。一种有前景的方法是使用通过机器学习（ML）训练的预测模型。及时预测单向延迟（OWD）等性能指标，能为网络、用户设备（UE）和应用程序提供有价值的见解，以应对性能趋势、偏差和违规。随着时间的推移，动态网络环境会导致分布偏移，从而引发灾难性遗忘和ML模型性能下降。在持续学习（CL）中，模型旨在实现稳定性与可塑性之间的平衡，使其能够学习新信息的同时保留先前习得的知识。本文针对OWD预测模型的灾难性遗忘挑战，提出一种新颖方法，为最先进的CL生成回放框架引入多生成器概念，并以表格变分自编码器（TVAE）作为生成器。该方法将UE能力的领域知识融入学习过程，以确定生成器设置及相关性。通过在现实5G测试床收集的数据，在多样化场景中对所提方法进行评估，结果表明其相较于基线方法具有卓越性能。