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

合作研究：NeTS：Medium：基于 NextG 的高性能、智能和安全 O-RAN 的集成多时间尺度方法

• 批准号: 2312448
• 负责人: Huacheng Zeng
• 金额: $ 30万
• 依托单位: Michigan State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-10-01 至 2026-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2312448&HistoricalAwards=false
• 关键词: Collaborative; Research; NeTS; Medium; Integrated

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.

最近由O-RAN联盟领导的开放无线电接入网络（RAN）接口的运动已经为未来的无线网络引入了新的范例。O-RAN以其开放性和智能性为主要特征，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供应商中选择最好的硬件和软件。这种开放性还促进了基于机器学习（ML）的智能集成到RAN中，并有望进一步提高性能。 该项目旨在解决O-RAN中的几个主要挑战，以提高其性能，智能和可信度。该项目通过在无线算法和协议设计、机器学习和网络安全方面的创新，加速了O-RAN生态系统的演进。该项目的成果为无线行业和学术研究界提供了关于O-RAN技术的新潜力、挑战和创新解决方案的宝贵见解。此外，该项目还通过新的教材加强教学活动，促进不同背景的妇女和学生参与无线通信和计算机科学研究。该项目旨在通过解决三种不同时间尺度的控制回路中的几个基本挑战，提高O-RAN的性能、智能和可信度。该项目包括三个相互关联的研究重点。第一个推力集中在O-RAN的分布式单元（O-DU）中的实时多用户多输入多输出（MU-MIMO）波束成形。它开发了一种数据驱动的波束成形方法，该方法考虑了信道的不确定性。第二个重点是在近RT RAN智能控制器（RIC）内设计用于MU-MIMO控制的ML算法。它建立了一个基于优化的框架，以生成用于训练ML模型的高质量标记数据集。第三个目标是进一步了解O-RAN的非RT RIC中ML模型的漏洞，并开发针对数据操纵攻击的保护解决方案。这三个研究重点在O-RAN架构中紧密垂直整合，其成果为设计O-RAN的综合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知识价值和更广泛的影响审查标准进行评估，被认为值得支持。