Frequently Asked Questions
Why treat the RAN like a neural network?
Because neural networks are trainable systems — they learn by adjusting internal parameters to improve a global objective using gradients. A RAN, like a neural net, is a network of interconnected components with tunable parameters and observable performance. A RA-NN applies this same principle: it uses KPI feedback as a loss signal, computes gradient-aligned attributions, and learns which actions lead to improvement. If this works for deep learning AI — why not in telecom?
Is a RA-NN constantly "training" the live network?
Yes and no. A RA-NN is not running continuous backpropagation on the live system like a deep neural network in training mode. But it does optimize the live network over time using a policy that was trained using structured, gradient-aligned feedback. In that sense, the network is trainable, and its behavior improves — just like how neural networks optimize weights during training.
What is a RA-NN actually being trained to do?
A RA-NN is trained to learn a policy that optimizes the RAN intelligently over time. The training signal comes from KPI gradients — directional feedback that tells the system how changes in parameters affect performance. These gradients are computed based on deviations from target KPIs, structural credit attribution, or pre/post comparisons. They're not limited to simple deltas — they can reflect state vs. ideal state, weighted preferences, or more complex, goal-conditioned objectives.
Just like neural networks use gradients to optimize their internal weights, RA-NN uses KPI gradients to optimize external system behavior. The result is an explainable, structure-aware policy that acts like a trained optimizer — one that knows not just what to change, but why.
How is a RA-NN different from other AI-RAN solutions?
Most AI-RAN approaches treat AI as a bolt-on — a helper that tunes or analyzes parts of the system from the outside.
A RA-NN redefines the system itself. It treats the RAN as a neural network: cells as neurons, parameters as weights, and KPIs as the loss.
This isn’t AI for the RAN.
This is the true AI-RAN.
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