AI model lifecycle in RAN: training, validation, deployment, monitoring, retraining

Training: From Data to Model

AI-RAN model training begins with data collection from the live network or digital twin — channel measurements, beam reports, scheduling outcomes, and positioning references. Training typically happens offline on GPU clusters in the cloud or operator data center, not at the RAN edge. Training datasets must represent diverse conditions: urban and rural, day and night, crowded and idle, clear weather and rain. Data preprocessing includes normalization, outlier removal, and augmentation to prevent overfitting. Training produces a candidate model — a neural network with learned weights — that…