Resilient Radio Access Networks: AI and the Unknown Unknowns

TL;DR

This paper discusses the importance of AI in enhancing the resilience of 5G radio access networks, especially against unforeseen disruptions, highlighting current limitations of statistical learning methods.

Contribution

It analyzes the challenges of designing AI systems for resilient networks and connects these challenges to online learning and causal inference.

Findings

Current statistical learning methods have significant limitations for resilience.

Theoretical results link resilience challenges to online learning and causal inference.

Highlights the need for new AI approaches for unpredictable network disruptions.

Abstract

5G networks offer exceptional reliability and availability, ensuring consistent performance and user satisfaction. Yet they might still fail when confronted with the unexpected. A resilient system is able to adapt to real-world complexity, including operating conditions completely unanticipated during system design. This makes resilience a vital attribute for communication systems that must sustain service in scenarios where models are absent or too intricate to provide statistical guarantees. Such considerations indicate that artifical intelligence (AI) will play a major role in delivering resilience. In this paper, we examine the challenges of designing AIs for resilient radio access networks, especially with respect to unanticipated and rare disruptions. Our theoretical results indicate strong limitations of current statistical learning methods for resilience and suggest connections to online learning and causal inference.