AI-Driven Optimization Framework for Ultra-Reliable Low-Latency Communication in Open RAN Architectures

• Grant number: 25/01009-9
• Support Opportunities: Scholarships in Brazil - Post-Doctoral
• Start date: February 01, 2026
• End date: January 31, 2028
• Field of knowledge: Physical Sciences and Mathematics - Computer Science - Computer Systems
• Principal Investigator: Fabio Luciano Verdi
• Grantee: Husam Rajab
• Host Institution: Centro de Ciências em Gestão e Tecnologia (CCGT). Universidade Federal de São Carlos (UFSCAR). Campus de Sorocaba. Sorocaba , SP, Brazil
• Company: Universidade Estadual de Campinas (UNICAMP). Faculdade de Engenharia Elétrica e de Computação (FEEC)
• Associated research grant: 21/00199-8 - SMART NEtworks and ServiceS for 2030 (SMARTNESS), AP.PCPE

Abstract

This research addresses the critical challenge of enabling Ultra-Reliable Low-Latency Communication (URLLC) in disaggregated mobile network architectures, focusing on Open RAN. The primary objective is to develop robust optimization algorithms for adaptive network slicing by leveraging mathematical optimization, reinforcement learning (RL), and neural-based adaptive control mechanisms. The proposed framework will integrate real-time decision-making, contributing theoretical advancements to 6G systems that converge communication, control, and computation. To achieve these goals, the study will employ simulation using NS-3, emulation using programmable switches such as Tofino, and real-world testbeds using OpenAirInterface (OAI). This structured approach ensures comprehensive validation across diverse environments. This research will highlight practical applications of URLLC, particularly in Industry 4.0 for factory automation, as well as immersive technologies such as Augmented Reality (AR), Virtual Reality (VR), and Extended Reality (XR). The study aims to advance operational standards and foster innovation in mission-critical industries by tailoring solutions to these latency-sensitive use cases. (AU)