I am exploring the use of reinforcement learning (RL) to control aspects of 5G gNB behaviour, such as handover optimization, power control, or scheduling.
Can NetSim support such RL-based control? Specifically:
- Does it allow real-time interaction with an external RL agent?
- Can gNB parameters be modified dynamically during simulation?
Yes, NetSim supports RL-based control of 5G gNBs. It allows real-time interaction with external RL agents via APIs in Python or MATLAB. NetSim sends network states and rewards to the agent, which computes actions and sends them back to control the simulation loop.
gNB parameters can be modified dynamically. This enables RL agents to adaptively optimize functions like scheduling, power control, and handover behavior. Instead of triggering handovers directly, RL can tune parameters like Time-To-Trigger (TTT) to influence handover decisions.
There are working examples for RL-based delay-aware scheduling and downlink power control. These use frameworks like OpenAI Gymnasium, TensorFlow, Keras, and PyTorch.
See: https://tetcos.com/machine-learning-netsim.html for details.
You might find the following ResearchGate discussions helpful:
- Interfacing AI/ML models with NetSim https://www.researchgate.net/post/Interfacing_AI_ML_models_with_NetSim
- How to implement RL algorithms for 5G https://www.researchgate.net/post/How_to_implement_RL_algorithms_for_5G
- How to Implement Deep Reinforcement Learning in 5G with NetSim https://www.researchgate.net/post/How_to_Implement_Deep_Reinforcement_Learning_in_5G_with_NetSim/1
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