This paper aims to develop the intelligent traffic steering (TS) framework, which has recently been considered as one of the key developments of 3GPP for advanced 5G. Since achieving key performance indicators (KPIs) for heterogeneous services may not be possible in the monolithic architecture, a novel deep reinforcement learning (DRL)-based TS algorithm is proposed at the non-real-time (non-RT) RAN intelligent controller (RIC) within the open radio access network (ORAN) architecture. To enable ORAN's intelligence, we distribute traffic load onto appropriate paths, which helps efficiently allocate resources to end users in a downlink multi-service scenario. Our proposed approach employs a three-step hierarchical process that involves heuristics, machine learning, and convex optimization to steer traffic flows. Through system-level simulations, we show the superior performance of the proposed intelligent TS scheme, surpassing established benchmark systems by 45.50%.

翻译：本文旨在构建智能流量导向（TS）框架，该框架近期已被3GPP视为高级5G的关键发展方向之一。由于在单块架构中，异构服务的关键性能指标（KPI）难以同时实现，本文在开放无线接入网（ORAN）架构的非实时（non-RT）RAN智能控制器（RIC）中，提出了一种基于深度强化学习（DRL）的新型TS算法。为赋能ORAN的智能特性，我们将流量负载分配至合适路径，从而在下行多业务场景中高效地将资源分配给终端用户。所提方法采用包含启发式算法、机器学习与凸优化的三步分层流程来引导流量流向。通过系统级仿真，我们展示了所提智能TS方案的卓越性能，其相较于基准系统提升了45.50%的性能。