论文标题
5G及以后的移动性管理：一种新颖的智能移交，具有自适应时间触发时间和磁滞余量
Mobility Management in 5G and Beyond: A Novel Smart Handover with Adaptive Time-to-Trigger and Hysteresis Margin
论文作者
论文摘要
第五代（5G）新广播（NR）和超越技术将支持增强的移动宽带，非常低的延迟通信以及大量移动设备。因此，对于非常高速的用户，在交换过程中从一个单元向另一个单元的迁移过程中需要保持无缝的移动性。由于存在大量移动设备，因此密集网络的高移动性的管理变得至关重要。此外，要触发时间（TTT）和磁滞边缘需要动态适应性，这显着影响了移交延迟和整体吞吐量。因此，在本文中，我们提出了一种基于在线学习的机制，称为基于学习的智能移动管理（LIM2），用于5G及以后的移动性管理，并具有智能的TTT和磁滞价值。 LIM2使用Kalman滤波器来预测服务和邻居细胞的未来信号质量，使用基于状态的奖励 - 状态（SARSA）基于基于状态的钢筋进行切换的目标细胞，并使用Epsilon-Greedy政策适应了TTT和滞后。我们在NS-3中实现了LIM2的原型，并广泛分析其性能，在此观察到，LIM2算法可以在非常高速的移动性场景中显着改善移交操作。
The 5th Generation (5G) New Radio (NR) and beyond technologies will support enhanced mobile broadband, very low latency communications, and huge numbers of mobile devices. Therefore, for very high speed users, seamless mobility needs to be maintained during the migration from one cell to another in the handover. Due to the presence of a massive number of mobile devices, the management of the high mobility of a dense network becomes crucial. Moreover, a dynamic adaptation is required for the Time-to-Trigger (TTT) and hysteresis margin, which significantly impact the handover latency and overall throughput. Therefore, in this paper, we propose an online learning-based mechanism, known as Learning-based Intelligent Mobility Management (LIM2), for mobility management in 5G and beyond, with an intelligent adaptation of the TTT and hysteresis values. LIM2 uses a Kalman filter to predict the future signal quality of the serving and neighbor cells, selects the target cell for the handover using state-action-reward-state-action (SARSA)-based reinforcement learning, and adapts the TTT and hysteresis using the epsilon-greedy policy. We implement a prototype of the LIM2 in NS-3 and extensively analyze its performance, where it is observed that the LIM2 algorithm can significantly improve the handover operation in very high speed mobility scenarios.
