Collaborative Research: NeTS: Medium: An Integrated Multi-Time Scale Approach to High-Performance, Intelligent, and Secure O-RAN based NextG

最近由O-RAN联盟领导的开放无线接入网络(RAN)接口的运动为未来的无线网络引入了一种新的范例。凭借其开放性和智能化的关键特征，O-RAN实现了RAN开发和部署的“混合匹配”方法，允许电信运营商从不同的供应商选择最佳的硬件和软件。这种开放性还促进了基于机器学习(ML)的智能集成到RAN中，并承诺进一步提高性能。该项目旨在解决O-RAN中的几个主要挑战，目的是提高其性能、智能性和可信性。通过在无线算法和协议设计、ML和网络安全方面的创新，该项目加快了O-RAN生态系统的发展。该项目的成果为无线行业和学术研究团体提供了有关O-RAN技术的新潜力、挑战和创新解决方案的宝贵见解。此外，该项目促进不同背景的女性和学生参与无线通信和计算机科学研究，同时通过新的课程材料加强教学活动。该项目旨在通过解决三个不同时间尺度的控制回路中的几个基本挑战来提高O-RAN的性能、智商和可信度。该项目由三个相互关联的研究项目组成。第一个重点是O-RAN分布式单元(O-DU)中的实时多用户多输入多输出(MU-MIMO)波束形成。它开发了一种数据驱动的波束形成方法，该方法考虑了信道的不确定性。第二个重点是针对近实时RAN智能控制器(RIC)中的MU-MIMO控制的ML算法的设计。它建立了一个基于优化的框架来生成用于训练ML模型的高质量标签数据集。第三个推力旨在提高对O-RAN非RT RIC中ML模型漏洞的认识，并开发针对数据操纵攻击的安全解决方案。这三项研究工作紧密结合在O-RAN架构中，它们的结果为设计O-RAN的全面解决方案奠定了基础。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Recent movement to open up radio access network (RAN) interfaces, led by the O-RAN Alliance, has introduced a new paradigm for future wireless networks. With its key features of openness and intelligence, O-RAN enables a ``mix-and-match” approach to RAN development and deployment, allowing telecom carriers to select the best hardware and software from different vendors. Such openness also catalyzes the integration of machine learning (ML) based intelligence into the RAN and promises further performance improvement. This project aims to address several major challenges in O-RAN, with the objective of enhancing its performance, intelligence, and trustworthiness. Through innovation in wireless algorithm and protocol design, ML, and network security, this project expedites the evolution of O-RAN ecosystem. The outcomes of this project provide valuable insights to the wireless industry and academic research community regarding new potentials, challenges, and innovative solutions surrounding O-RAN technologies. Moreover, the project promotes the participation of women and students with diverse backgrounds in wireless communications and computer science research while enhancing pedagogical activities through new course materials.This project aims to enhance the performance, intelligence, and trustworthiness of O-RAN by tackling several fundamental challenges across its control loops of three different time scales. The project consists of three interconnected research thrusts. The first thrust focuses on real-time multi-user multi-input and multi-output (MU-MIMO) beamforming in O-RAN’s distributed unit (O-DU). It develops a data-driven approach for beamforming that accounts for channel uncertainty. The second thrust focuses on the design of ML algorithms for MU-MIMO control within the near-RT RAN Intelligent Controller (RIC). It establishes an optimization-based framework to generate high-quality labeled datasets for training ML models. The third thrust aims to advance knowledge of the vulnerabilities of ML models in the non-RT RIC of O-RAN and develop safeguard solutions against data manipulation attacks. The three research thrusts are tightly integrated vertically within the O-RAN architecture and their outcome lays the foundation for designing a comprehensive solution for O-RAN.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.