The Singapore University of Technology and Design (SUTD) has joined forces with the AI-RAN Alliance to establish an AI-RAN Alliance-endorsed lab, marking a significant step in advancing AI-RAN (Artificial Intelligence-Radio Access Networks) research and development. This collaboration aims to accelerate innovation in AI-driven wireless communications, creating new opportunities for academic and industry partnerships.
A Hub for AI-Native RAN Innovation
As the telecommunications industry transitions toward AI-native architectures, the demand for cutting-edge research and real-world testing environments has never been greater.
The AI-RAN Alliance-endorsed lab at SUTD will serve as a centre of excellence, providing a platform for:
- Developing and testing of AI-RAN solutions that enhance network efficiency, adaptability, and performance.
- Bridging academia and industry, fostering collaboration between researchers, students, and AI-RAN Alliance member organisations.
- Exploring next-generation AI algorithms that optimise network orchestration, energy efficiency, and spectrum utilisation.
What This Means for the Industry
The AI-RAN Alliance-endorsed lab at SUTD will provide a collaborative environment for its members where industry leaders, start-ups, and researchers can experiment with AI-driven network architectures, automation, and optimisation. With access to state-of-the-art facilities and expertise, pushing the boundaries of the next-generation AI-native RAN deployments is now possible.
Looking Ahead
As AI continues to redefine network infrastructure, initiatives like the AI-RAN Alliance-endorsed lab at SUTD will be crucial in shaping the future of intelligent, self-optimising radio access networks. With the AI-RAN Alliance’s support, this collaboration is set to drive groundbreaking advancements in AI-powered telecommunications.
Onsite Access and Allocate Resource
- Resources (equipment and tools) must be booked in advance and based on devices availability and priority of the project. In general, it is first-come first-serve basis.
- With special hardware setup (e.g., with test equipment), member companies can request FCCLab members’ supports.
Equipment

The AI-RAN Alliance-Endorsed Lab is equipped with the following list of test and measurement equipment.
- Multi-UEs emulator (Keysight UeSIM, VIAVI TM500 UESim)
- O-RUs emulator with embedded multi-UEs emulation (Keysight RuSIM)
- Mobile Core network emulator (Keysight CoreSIM, VIAVI TeraVM Core Emulator)
- 3GPP Security Assurance Specifications (SCAS) test modules (Keysight O-RAN Security Test Suite)
- DDoS attacks emulator (Keysight O-RAN Security Test Suite)
- Cloud attacks emulator (Keysight O-RAN Security Test Suite)
- Vulnerability and Port Scanning solution (Keysight O-RAN Security Test Suite)
- Test Automation (Keysight Test Automation Framework)
- RAN simulator for RIC and xApp/rApp test (from VIAVI)
- Test devices (handsets, CPEs, dongles, drones, robots, etc.)
- Network switches
- Network Emulator NE3 (from Keysight)
- Paragon Neo (from Calnex) for S-plane testing
- Time Synch Analyzer TSA and DU Emulator (from Keysight)
- Fronthaul, Midhaul Packet Capture Appliance
- Optical transceiver modules and breakout cables up to 100G
- Fiber optical network tap
- Timing and sync equipment GPS, PTP grandmaster etc
- Mobile testing tool (TEMS Pocket and TEMS Discovery from Infovista)
- Shield room and Faraday cage for testing FR1
- O-RU conformance test equipment
- Energy efficiency test equipment: AC Power Analyzer, DC Advance Power System, and Keysight E-Plane Test Suite
- NR/IoT NTN simulation for UE test (From Alifecom NE7500, NE6000)
- NR/IoT NTN Digital Twin for NTN testing (R&S CMX500, CMW500 and VIAVI TM500)
- TN and NTN Channel Emulator (Keysight PROPSIM)
- Vector Signal Generator, Spectrum Analyzer (from multiple vendors)
- MIMO testing tools: MTRX (from Keysight)
- Adjustable RF Attenuators, up/down RF converters
Remote Access and Datasets
- VPN connection: for certified members or partners.
- Willing to shared the collected datasets for training AI/ML models; and trained ML models to AI-RAN Alliance members.
Contact Us
Dr Ngo Van Mao (vanmao_ngo@sutd.edu.sg)
