In 5G cellular networks, frequency range 2 (FR2) introduces higher frequencies that cause rapid signal degradation and challenge user mobility. In recent studies, a conditional handover procedure has been adopted as an enhancement to baseline handover to enhance user mobility robustness. In this article, the mobility performance of conditional handover is analyzed for a 5G mm-wave network in FR2 that employs beamforming. In addition, a resource-efficient random access procedure is proposed that increases the probability of contention-free random access during a handover. Moreover, a simple yet effective decision tree-based supervised learning method is proposed to minimize the handover failures that are caused by the beam preparation phase of the random access procedure. Results have shown that a tradeoff exists between contention-free random access and handover failures. It is also seen that the optimum operation point of random access is achievable with the proposed learning algorithm for conditional handover. Moreover, a mobility performance comparison of conditional handover with baseline handover is also carried out. Results have shown that while baseline handover causes fewer handover failures than conditional handover, the total number of mobility failures in the latter is less due to the decoupling of the handover preparation and execution phases.

翻译：在5G蜂窝网络中，频率范围2（FR2）引入较高频段导致信号快速衰减并带来用户移动性挑战。近期研究采用条件切换流程作为基线切换的增强方案以提升用户移动鲁棒性。本文针对采用波束赋形的FR2频段5G毫米波网络，分析了条件切换的移动性能。此外，提出了一种资源高效的随机接入流程，提高了切换过程中无竞争随机接入的成功概率。同时，提出了一种简单有效的基于决策树的监督学习方法，以最小化由随机接入流程波束准备阶段引起的切换失败。结果表明，无竞争随机接入与切换失败之间存在权衡关系。通过所提出的条件切换学习算法，可实现随机接入的最优工作点。此外，还对条件切换与基线切换进行了移动性能对比。结果表明，尽管基线切换的切换失败次数少于条件切换，但由于条件切换的切换准备与执行阶段解耦，后者的总移动性失败次数更少。