A Neural Systems Formulation for AI-Native Radio Networks
Michael Chiaramonte · 2026
This paper introduces the RA-NN — a neural systems formulation that treats the radio access network itself as a learnable system. Rather than applying AI models on top of legacy RAN abstractions, the RA-NN defines a new architectural foundation where the network learns its own structure from per-UE observations, builds a shared latent substrate, and derives control policies directly from that substrate.
