A 5G Testbed for AI-and-RAN on AI Edge: Design and Performance Evaluation

Abstract

The AI-RAN Alliance has proposed the coexistence of Artificial Intelligence (AI) workloads and Radio Access Network (RAN) functions on shared edge infrastructure, referred to as AI-and-RAN. However, practical understanding of resource contention between compute-intensive AI tasks and timing-constrained RAN operations remains limited, largely due to the lack of end-to-end platforms that enable controlled co-location and measurement across system and RAN layers. To fill this gap, this paper presents an end-to-end AI-and-RAN testbed for controlled co-location experiments on an unified CPU–GPU platform. We deploy a containerized 5G RAN stack with an Open Radio Access Network (O-RAN) compliant E2 interface for monitoring alongside edge Large Language Model (LLM) inference workloads. To expose resource sensitivity, we intentionally constrain the CPU budget and vary AI workload intensity. Through measurements of Medium Access Control (MAC) layer key performance indicators (KPIs) and edge-level resource utilization, we demonstrate that LLM inference reduces downlink (DL) throughput and increases Block Error Rate (BLER), providing empirical insights to inform future AI-and-RAN orchestration strategies.