Open RAN LSTM Traffic Prediction and Slice Management using Deep
  Reinforcement Learning
Fatemeh Lotfi Fatemeh Afghah
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.