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首页> 外文会议>IEEE International symposium on Personal, Indoor, and Mobile Radio Communications >User mobility prediction based on Lagrange's interpolation in ultra-dense networks
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User mobility prediction based on Lagrange's interpolation in ultra-dense networks

机译：超密集网络中基于拉格朗日插值的用户移动性预测

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The concept of Ultra-Dense Networks (UDNs) was first introduced by Mobile and wireless communications Enablers for the Twenty-twenty Information Society (METIS) and it is considered as a promising technology in the future 5G. In UDNs, due to the dense deployment of femtocells, building User-Centric Networks (UCNs) is a clear trend and Virtual Cells (VCs) are the core function of UCNs. As user mobility prediction can enhance the mobility of UCNs and improve the handover performance of VCs, it is a critical issue in UDNs. Unfortunately, to the best of our knowledge, no literatures about user mobility prediction in UDNs are available. At present, most existing mobility prediction works are carried out in LTE networks and they only consider the cells which are uniformly distributed. What's more, as femtocells are densely deployed, the existing prediction works cannot be applied to UDNs directly. Therefore, we are trying to find a practical scheme with lower algorithmic complexity which can be applied to UDNs. In this paper, we explore a realistic block scenario with femtocells deployed according to Poisson Point Process (PPP). Exploiting context information, we propose a novel approach which fitting users' moving path based on Lagrange's interpolation. We evaluate users' transition probability to neighboring femtocells according to the slope of the trajectory polynomial and the distance between users and neighboring femtocells. Simulation results show that when choosing synchronous transmission points (TPs) based on the distance between users and neighboring TPs, five TPs with the highest transition probability can achieve the highest prediction accuracy. Besides, the combination of distance and direction can reduce the number of synchronous TPs without decreasing the prediction accuracy and it would perform better at a higher speed.
机译：超密集网络（UDN）的概念最初是由二十二十信息协会（METIS）的移动和无线通信使能者提出的，并且被认为是未来5G的有希望的技术。在UDN中，由于毫微微小区的密集部署，建立以用户为中心的网络（UCN）是明显的趋势，而虚拟小区（VC）是UCN的核心功能。由于用户移动性预测可以增强UCN的移动性并改善VC的切换性能，因此这是UDN中的关键问题。不幸的是，据我们所知，尚无有关UDN中用户移动性预测的文献。当前，大多数现有的移动性预测工作是在LTE网络中进行的，并且它们仅考虑均匀分布的小区。而且，随着毫微微小区的密集部署，现有的预测工作无法直接应用于UDN。因此，我们试图找到一种算法复杂度较低的实用方案，该方案可以应用于UDN。在本文中，我们探索了根据Poisson Point Process（PPP）部署毫微微小区的现实块方案。利用上下文信息，我们提出了一种基于拉格朗日插值拟合用户移动路径的新颖方法。我们根据轨迹多项式的斜率和用户与相邻毫微微小区之间的距离，评估用户向相邻毫微微小区的过渡概率。仿真结果表明，根据用户与相邻TP的距离选择同步传输点（TP）时，具有最高转移概率的5个TP可以获得最高的预测精度。此外，距离和方向的组合可以减少同步TP的数量，而不会降低预测精度，并且在更高的速度下性能会更好。

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