Cross-Layer Optimization and System-Level Design of Next-Generation Wireless Networks via Intelligent RAN Control
2026-08-03 • Networking and Internet Architecture
Networking and Internet Architecture
AI summaryⓘ
The authors studied how to improve future wireless networks by making them smarter and more flexible using Open RAN technology and AI. They developed and tested new methods for controlling network resources like power and data flow, including special surface materials called RIS that help signal quality. They created tools to automatically design AI solutions for managing the network and tested everything using simulations and real hardware setups. Their work shows ways to optimize wireless communication for faster and more reliable service, including sharing spectrum between ground and satellite links.
Open RANNext Generation NetworksDeep Reinforcement Learning (DRL)Reconfigurable Intelligent Surfaces (RIS)Network SlicingLink AdaptationSpectrum SharingEnergy-Efficient Power ControlDigital TwinsWireless Network Emulation
Authors
Maria Tsampazi
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.