Future mobile networks will enable the massive deployment of mobile multimedia applications anytime and anywhere. In this context, mobility management schemes, such as handover and proactive multimedia service migration, will be essential to improve network performance. In this article, we propose a proactive mobility management approach based on group user trajectory prediction. Specifically, we introduce a mobile user trajectory prediction algorithm by combining the Long-Short Term Memory networks (LSTM) with Reinforcement Learning (RL) to automate the model training procedure. We further develop a group user trajectory predictor to reduce prediction calculation overheads of users with similar movement patterns. To validate the impact of the proposed mobility management approach, we present a virtual reality (VR) service migration scheme built on the top of the proactive handover mechanism that benefits from trajectory predictions. Experiment results validate our predictor’s outstanding accuracy and its impacts on enhancing handover and service migration performance to provide quality of service assurance.

中文翻译：

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具有可迁移强化学习轨迹预测的移动性管理

未来的移动网络将能够随时随地大规模部署移动多媒体应用。在这种情况下，移动性管理方案，例如切换和主动多媒体服务迁移，对于提高网络性能至关重要。在本文中，我们提出了一种基于群组用户轨迹预测的主动移动管理方法。具体来说，我们通过将长短期记忆网络 (LSTM) 与强化学习 (RL) 相结合来引入移动用户轨迹预测算法，以实现模型训练过程的自动化。我们进一步开发了一个组用户轨迹预测器，以减少具有相似运动模式的用户的预测计算开销。为了验证提议的移动性管理方法的影响，我们提出了一种虚拟现实 (VR) 服务迁移方案，该方案建立在受益于轨迹预测的主动切换机制之上。实验结果验证了我们的预测器的出色准确性及其对增强切换和服务迁移性能以提供服务质量保证的影响。