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Network softwarization has revolutionized the architecture of cellularwireless networks. State-of-the-art container based virtual radio accessnetworks (vRAN) provide enormous flexibility and reduced life cycle managementcosts, but they also come with prohibitive energy consumption. We argue thatfor future AI-native wireless networks to be flexible and energy efficient,there is a need for a new abstraction in network softwarization that caters forneural network type of workloads and allows a large degree of servicecomposability. In this paper we present the NeuroRAN architecture, whichleverages stateful function as a user facing execution model, and iscomplemented with virtualized resources and decentralized resource management.We show that neural network based implementations of common transceiverfunctional blocks fit the proposed architecture, and we discuss key researchchallenges related to compilation and code generation, resource management,reliability and security.

NeuroRAN: Rethinking Virtualization for AI-native Radio Access Networks in 6G