Application of Reinforcement Learning for 5G Scheduling Parameter Optimization
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RF Network parametric optimization requires a wealth of experience and knowledge to achieve the optimal balance between coverage, capacity, system efficiency and customer experience from the telecom sites serving the users. With 5G, the complications of Air interface scheduling have increased due to the usage of massive MIMO, beamforming and introduction of higher modulation schemes with varying numerologies. In this work, we tune a machine learning model to "learn" the best combination of parameters for a given traffic profile using Cross Entropy Method Reinforcement Learning and compare these with RF Subject Matter Expert "SME" recommendations. This work is aimed towards automatic parameter tuning and feature optimization by acting as a Self Organizing Network module
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