🤖 AI Summary
This work addresses the stringent requirements of 6G for energy efficiency, spectral efficiency, and support of diverse services such as enhanced mobile broadband (eMBB) and ultra-reliable low-latency communication (URLLC) by proposing a cross-layer optimization framework that integrates reconfigurable intelligent surfaces (RIS) with Open Radio Access Network (Open RAN) to enable dynamic, real-time intelligent RAN control. The core contributions include the first system architecture unifying RIS and Open RAN, the PandORA framework for automated deployment of deep reinforcement learning (DRL) applications, novel online reinforcement learning algorithms for network slicing, scheduling, and link adaptation, and intelligent spectrum sharing across terrestrial and non-terrestrial networks. Validated through Colosseum emulation, digital twin models, and a real-world 5G testbed, the proposed approach demonstrates significant improvements in energy efficiency, spectral efficiency, and quality of service, alongside a comprehensive system-level evaluation of RIS-assisted channels integrated into Open RAN.
📝 Abstract
Recent years have seen the evolution of the traditional Radio Access Network (RAN) toward more open, programmable, disaggregated, and intelligent architectures, known as an Open RAN. Future Next Generation (NextG) networks are envisioned to be AI-native, enabling data-driven closed-loop optimization of Base Station resources, while Reconfigurable Intelligent Surfaces (RIS) emerge as key enablers for wireless propagation and spectral efficiency toward 6G and beyond. This dissertation focuses on the design, optimization, and experimental evaluation of NextG RANs integrating Open RAN principles, data-driven control loops, and intelligent resource allocation. The work emphasizes cross-layer optimization, including energy-efficient power control, and explores AI-driven network slicing, scheduling, and link adaptation, demonstrating NextG RANs reconfigurable in real time to meet 6G requirements, first analyzing architectural enablers and modeling frameworks, then prototyping and evaluating solutions on experimental platforms and Digital Twins. Main contributions include: (i) Deep Reinforcement Learning (DRL) solutions for network slicing and scheduling; (ii) PandORA, a framework for automatic design, training, and deployment of DRL-based Open RAN applications on the Colosseum wireless network emulator; (iii) physical-layer RIS channel modeling and optimized resource allocation across spectrum bands; (iv) system-level evaluation of RIS-assisted channels for eMBB and URLLC traffic; (v) integration of RIS within Open RAN; (vi) online RL solutions for link adaptation; and (vii) spectrum sharing between cellular and Non-Terrestrial Network links via power control and beamforming. This work provides algorithmic designs, frameworks, and validation from simulation and hardware-in-the-loop emulation to over-the-air 5G testbed experiments, addressing industry and academic needs for wireless research.