With emerging applications such as autonomous driving, smart cities, and smart factories, network slicing has become an essential component of 5G and beyond networks as a means of catering to a service-aware network. However, managing different network slices while maintaining quality of services (QoS) is a challenge in a dynamic environment. To address this issue, this paper leverages the heterogeneous experiences of distributed units (DUs) in ORAN systems and introduces a novel approach to ORAN slicing xApp using distributed deep reinforcement learning (DDRL). Additionally, to enhance the decision-making performance of the RL agent, a prediction rApp based on long short-term memory (LSTM) is incorporated to provide additional information from the dynamic environment to the xApp. Simulation results demonstrate significant improvements in network performance, particularly in reducing QoS violations. This emphasizes the importance of using the prediction rApp and distributed actors' information jointly as part of a dynamic xApp.

翻译：随着自动驾驶、智慧城市和智能工厂等新兴应用的涌现，网络切片已成为5G及未来网络中满足服务感知需求的关键组成部分。然而，在动态环境中管理不同网络切片的同时维持服务质量（QoS）仍面临挑战。为解决该问题，本文利用ORAN系统中分布式单元（DU）的异构经验，提出了一种基于分布式深度强化学习（DDRL）的ORAN切片xApp新方法。此外，为提升强化学习（RL）智能体的决策性能，本文融合了基于长短期记忆（LSTM）网络的预测rApp，以向xApp提供动态环境的附加信息。仿真结果表明，该方法显著提升了网络性能，尤其在减少QoS违规方面效果突出。这凸显了联合利用预测rApp与分布式智能体信息作为动态xApp组成部分的重要性。