ML-empowered RAN
Hosted by and enrolled in the Doctoral Program at the Technical University of Sofia.
More details:
Description of research
This research focuses on developing and implementing intelligent O-RAN intelligent controller (RIC) functionalities for both non-real-time (non-RT) and near-real-time (near-RT) responses. It aims to design and integrate Distributed Applications (dApps) within the non-RT and near-RT RIC, as well as to develop AI-driven solutions for real-time radio access network (RAN) functionalities. The expected outcomes include the implementation of O-RAN functionalities within a testbed platform, incorporating non-RT RIC, near-RT RIC, and dApps. By deploying dApps directly within centralised and distributed units (CUs and DUs), the system will leverage low-level data metrics—such as MAC/PHY-level key performance metrics (KPMs), transmission queues, and beam directionality—to enable AI-driven real-time RAN functions. These functionalities will include beamforming, user scheduling, puncturing, interference management, and modulation management. Additionally, machine learning models will be designed as independent internal applications and deployed within the controllers, allowing their application across various use cases.
Short bio of DC
Taiwo Adewa holds a Bachelor’s Degree in Computer Engineering from the Federal University of Oye-Ekiti, and a Master’s Degree in Computer Science and Network Systems, with specialization in the Internet of Things (IoT) and Wireless Networks, from IMT Atlantique, France.
As part of the ISAC-NEWTON Project, his research is focused on developing artificial intelligence models for automating and optimizing network operations within radio access networks with emphasis on Open-RAN technologies. He is passionate about advancing communication and sensing techniques for IMT-2030 and eager to contribute to innovative research in next-generation wireless networks.
Simplified Summary
This page is about PhD student Taiwo Adewa. He studies at TU Sofia in Bulgaria.