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arXiv 2101.07032eess.SP
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基于联邦学习的毫米波车联网主动切换

Federated Learning Based Proactive Handover in Millimeter-wave Vehicular Networks

Kaiqiang Qi, Tingting Liu, Chenyang Yang

摘要:

主动切换可以避免频繁的切换,减少切换时延,对维持毫米波车载网络中移动用户的服务质量(QoS)具有重要作用。为了减少训练主动切换学习模型的通信成本,我们提出了一个联邦学习(FL)框架。提出的FL框架可以适应每个用户的有限存储容量,增加参与FL的用户数量,并适应动态移动模式。仿真结果验证了所提出的FL框架的有效性。与反应式切换方案相比,该切换方案可以减少不必要的切换,同时提高用户的QoS。

英文摘要:

Proactive handover can avoid frequent handovers and reduce handover delay, which plays an important role in maintaining the quality of service (QoS) for mobile users in millimeter-wave vehicular networks. To reduce the communication cost of training the learning model for proactive handover, we propose a federated learning (FL) framework. The proposed FL framework can accommodate the limited storage capacity of each user, increase the number of users who participate in the FL, and adapt to the dynamic mobility pattern. Simulation results validate the effectiveness of the proposed FL framework. Compared to reactive handover schemes, the proposed handover scheme can reduce unnecessary handovers and improve the QoS of users simultaneously.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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