sdn超密集网络中基于虚拟蜂窝的主动移动管理

Qiang Liu, Gang Chuai, Jingrong Wang, Jianping Pan, Weidong Gao, Xuewen Liu
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引用次数: 3

摘要

在下一代移动通信系统中,超密集网络(UDN)是一种很有前途的提高网络容量的技术。然而,由于频繁的切换和繁重的信令开销,给移动性管理带来了一些新的挑战。由于车辆的快速行驶速度,使得车辆在响应式切换决策下对切换延迟更加敏感,从而使问题变得更加严重。本文以实际车辆移动数据集为基础,提出了一种基于虚拟单元技术的车辆主动移动管理解决方案。在基于长短期记忆神经网络的轨迹预测框架的辅助下,在集中式软件定义网络控制器中设计了四个功能模块来支持主动解决方案。然后仔细设计相应的信令程序,与虚拟单元一起工作以降低信令成本。该预测框架能够达到令人满意的下一个位置的预测效果。与被动方案相比,该方案消除了切换延迟,并将切换信令成本降低了35%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Proactive Mobility Management Based on Virtual Cells in SDN-Enabled Ultra-Dense Networks
Ultra-dense networking (UDN) is a promising technology to improve the network capacity in the next-generation mobile communication system. However, it brings in some new challenges to mobility management due to the frequent handovers and heavy signaling overhead. The problem becomes severe for vehicles owing to their fast moving speed, making it more sensitive to the handover delay with reactive handover decision. In this paper, driven by a real-world vehicle mobility dataset, we propose a proactive mobility management solution based on the virtual cell technique for vehicles. Assisted by a trajectory prediction framework based on the long short-term memory neural network, four function modules are designed in the centralized Software-Defined Networking controller to support the proactive solution. The corresponding signaling procedure is then carefully designed, working with virtual cells to reduce the signaling cost. The prediction framework can achieve satisfactory performance of predicting the next location. The proposed proactive solution eliminates the handover delay and reduces the handover signaling cost by 35% compared with the reactive approach.
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