NeuroRAN Rethinking Virtualization for AI‐native Radio Access Networks in 6G. Issue 4 (9th February 2023)

• Record Type: Journal Article
• Authors: Carbone, Paris; Dán, Gyorgy; Gross, James; Göransson, Bo; Petrova, Marina
• Is Part Of: Insight. Volume 25:Issue 4(2022)
• Page Start: 74
• Page End: 79
• Publication Date: 2023-02-09
• Subjects: function as a service -- serverless computing -- network softwarization -- neural networks -- energy efficiency -- radio access network -- AI‐native wireless; Systems engineering -- Periodicals; Systems engineering; Periodicals; 620.0011
• DOI: 10.1002/inst.12416
• Languages: English
• ISSNs: 2156-485X

Abstract

ABSTRACT: Network softwarization has revolutionized the architecture of cellular wireless networks. State‐of‐the‐art container based virtual radio access networks (vRAN) provide enormous flexibility and reduced life‐cycle management costs, but they also come with prohibitive energy consumption. We argue that for future AI‐native wireless networks to be flexible and energy efficient, there is a need for a new abstraction in network softwarization that caters for neural network type of workloads and allows a large degree of service composability. In this paper we present the NeuroRAN architecture, which leverages stateful function as a user facing execution model, and is complemented with virtualized resources and decentralized resource management. We show that neural network based implementations of common transceiver functional blocks fit the proposed architecture, and we discuss key research challenges related to compilation and code generation, resource management, reliability and security.