RL-Loop applies PPO reinforcement learning to dynamically control CPU resources in 5G slices at one-second intervals on a real testbed, cutting average allocation by over 55% with comparable QoS to a fixed reference.
Towards supporting intelligence in 5g/6g core networks: Nwdaf implementation and initial analysis
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RL-Loop: Reinforcement Learning-Driven Real-Time 5G Slice Control for Connected and Autonomous Mobility Services
RL-Loop applies PPO reinforcement learning to dynamically control CPU resources in 5G slices at one-second intervals on a real testbed, cutting average allocation by over 55% with comparable QoS to a fixed reference.