Research on mobility prediction in 5G and beyond for vertical industries

Wenhui Wang, Xiaoyan Duan, Wanfei Sun, Ming Ai
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引用次数: 1

Abstract

Intelligent mobility management and service control based on UE mobility prediction is one hot topic in current AI-assisted 5G/B5G research, which can also be applied to vertical industries communication. We analyzed the standard progress in 3GPP and IMT-2020 on AI based mobility prediction, as well as the challenges and possible solutions. We selected three different models, which are Long Short-Term Memory (LSTM) model, Attention Bidirectional Long Short-Term Memory (BiLSTM-attention) model and Artificial Neural Network (ANN) model for AI based mobility prediction, using the data from Geolife project of Microsoft Research Asia as input. By model retraining and optimization, we achieved higher accuracies of trajectory prediction (around 90%) with shorter training time. In addition, we presented examples of applying UE mobility prediction in various verticals, for which our optimized models may be applied.
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垂直行业5G及以后移动预测研究
基于UE移动预测的智能移动管理和业务控制是当前ai辅助5G/B5G研究的热点之一,也可应用于垂直行业通信。我们分析了3GPP和IMT-2020在基于人工智能的移动性预测方面的标准进展,以及面临的挑战和可能的解决方案。以微软亚洲研究院Geolife项目数据为输入,选择长短期记忆(LSTM)模型、注意力双向长短期记忆(BiLSTM-attention)模型和人工神经网络(ANN)模型进行基于人工智能的移动预测。通过对模型的再训练和优化,以更短的训练时间实现了更高的轨迹预测精度(90%左右)。此外,我们还介绍了在不同垂直领域应用UE迁移预测的示例,我们的优化模型可以应用于这些垂直领域。
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