Open RAN LSTM Traffic Prediction and Slice Management using Deep Reinforcement Learning
This addresses the challenge of maintaining QoS in dynamic network slicing for 5G applications like autonomous driving, though it is incremental by combining existing methods.
The paper tackles dynamic network slice management in 5G/ORAN systems by proposing a distributed deep reinforcement learning approach with LSTM-based traffic prediction, resulting in significant reductions in QoS violations.
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.