基于智能RAN控制的下一代无线网络跨层优化与系统级设计
Cross-Layer Optimization and System-Level Design of Next-Generation Wireless Networks via Intelligent RAN Control
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中文总结 AI 辅助
本研究针对下一代无线网络，结合开放RAN与AI技术，提出DRL方案、PandORA框架等多项核心成果，完成RIS相关设计与系统级验证，满足6G无线研究的产业与学术需求。
中文摘要 AI 辅助
近年来，传统无线接入网（Radio Access Network，RAN）正朝着更开放、可编程、分离式和智能化的架构演进，即开放RAN（Open RAN）。未来下一代（Next Generation，NextG）网络被设想为原生AI驱动，可实现基站资源的数据驱动闭环优化，而可重构智能表面（Reconfigurable Intelligent Surfaces，RIS）作为支撑6G及以后无线传播与频谱效率的关键技术。本论文聚焦集成开放RAN原则、数据驱动控制环路和智能资源分配的NextG RAN的设计、优化与实验评估，重点研究跨层优化，包括节能功率控制，探索AI驱动的网络切片、调度和链路自适应，展示NextG RAN可实时重构以满足6G需求。论文首先分析架构支撑技术和建模框架，然后在实验平台和数字孪生（Digital Twins）上对方案进行原型设计与评估。主要贡献包括：（i）面向网络切片与调度的深度强化学习（Deep Reinforcement Learning，DRL）方案；（ii）PandORA框架，用于在Colosseum无线网络仿真器上自动设计、训练和部署基于DRL的开放RAN应用；（iii）物理层RIS信道建模与跨频段优化资源分配；（iv）面向增强移动宽带（enhanced Mobile Broadband，eMBB）和超可靠低延迟通信（Ultra-Reliable Low-Latency Communications，URLLC）业务的RIS辅助信道的系统级评估；（v）RIS与开放RAN的集成；（vi）面向链路自适应的在线强化学习（RL）方案；（vii）通过功率控制与波束成形实现蜂窝与非地面网络（Non-Terrestrial Network）链路间的频谱共享。本研究提供了算法设计、框架，以及从仿真、硬件在环仿真到空中5G测试床实验的验证，满足了无线领域的产业与学术研究需求。
英文摘要
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.
