From the course: AI in RAN (Radio Access Network): Transforming Mobile Networks

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Integrating machine learning in Open RAN

Integrating machine learning in Open RAN

From the course: AI in RAN (Radio Access Network): Transforming Mobile Networks

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Integrating machine learning in Open RAN

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Building a self-driving network isn't magic. It is like assembling a high-tech car. First engineers design the parts, which are the machine learning models, then test and refine them before deploying them to handle everything from split-second decisions to long-term upgrades. Let's get into more details of machine learning deployment in Open RAN step-by-step. It all starts with design. Engineers use toolkits like tensorflow, keras or scikit-learn to build machine learning models. Imagine creating a model that predicts user throughput under different signal strength. At this stage, the model is like an untrained brain. It has potential but no knowledge yet. Next comes the training part where the model is sent to the training hosts, where it learns patterns from real world network data such as signal strength and user mobility logs. Once trained, engineers fine tune it like calibrating a race car's engine, making sure it performs well under different scenarios before deployment. After…

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