On Deep Reinforcement Learning for Traffic Steering Intelligent ORAN

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%.