Due to an ever-increasing number of participants and new areas of application, the demands on mobile communications systems are continually increasing. In order to deliver higher data rates, enable mobility and guarantee QoS requirements of subscribers, these systems and the protocols used are becoming more complex. By using higher frequency spectrums, cells become smaller and more base stations have to be deployed. This leads to an increased number of handovers of user equipments between base stations in order to enable mobility, resulting in potentially more frequent radio link failures and rate reduction. The persistent switching between the same base stations, commonly referred to as "ping-pong", leads to a consistent reduction of data rates. In this work, we propose a method for handover optimization by using proximal policy optimization in mobile communications to learn an adaptive handover protocol. The resulting agent is highly flexible regarding different travelling speeds of user equipments, while outperforming the standard 5G NR handover protocol by 3GPP in terms of average data rate and number of radio link failures. Furthermore, the design of the proposed environment demonstrates remarkable accuracy, ensuring a fair comparison with the standard 3GPP protocol.

翻译：随着参与用户数量的持续增长以及新型应用领域的不断涌现，移动通信系统的要求也在不断提高。为了提供更高的数据速率、支持移动性并保障用户的服务质量（QoS）需求，这些系统及其所使用的协议正变得越来越复杂。通过使用更高频率的频谱，小区尺寸变得更小，需要部署更多的基站。这导致用户设备在各基站之间的切换次数增加（以实现移动性），进而可能引发更频繁的无线链路失败和速率下降。在相同基站之间持续切换（通常称为"乒乓效应"）会导致数据速率持续降低。本研究提出了一种基于近端策略优化（PPO）的切换优化方法，通过学习自适应切换协议来优化移动通信中的切换过程。所获得的智能体对不同用户设备移动速度具有高度灵活性，同时在平均数据速率和无线链路失败次数方面优于3GPP标准5G NR切换协议。此外，所提环境的设计展现出显著的准确性，确保了与标准3GPP协议进行公平比较。