NeTS: Medium: Black-box Optimization of White-box Networks: Online Learning for Autonomous Resource Management in NextG Wireless Networks

Synopsis

Next-generation (NextG) wireless networks are anticipated to revolutionize various applications, such as interactive real-time applications like Augmented Reality (AR), while meeting the high Quality-of-Experience (QoE) requirements expected by users. To achieve these goals, NextG networks are undergoing a transformation toward a white-box architecture, characterized by openness, intelligence, and a focus on user needs. Therefore, it is both timely and important to address autonomous resource management within the NextG paradigm. This project aims to facilitate the transition from traditional black-box network designs to a white-box network architecture, which will significantly reduce costs and enhance QoE performance at its core. The findings of this project will be integrated into the curricula of all participating institutions. Furthermore, this project is committed to promoting the engagement of women and underrepresented minority (URM) students through research opportunities and outreach activities at their respective institutions. Mechanisms will be established to foster leadership and participation from URM groups in an annual high-profile research workshop held at OSU.

O-RAN is an operator-driven alliance dedicated to the advancement of radio access networks (RAN) toward an open architecture. This research focuses on harnessing the advanced capabilities of O-RAN, with a specific emphasis on edge-assisted low-latency AR as a key use case, to address autonomous resource management in the NextG paradigm. The research employs a data-driven approach across multiple time scales, using Bayesian optimization (BO) as a sample-efficient online learning and black-box optimization tool. The research develops versatile techniques and building blocks to optimize the QoE performance, structured around three interconnected thrusts: (i) developing a provably efficient multi-time-scale data-driven BO framework integrated with O-RAN, (ii) achieving collaborative BO for multi-RAN learning and optimization, and (iii) applying the developed BO frameworks to edge-assisted low-latency AR applications. The research establishes the analytical foundations and algorithmic frameworks that will be integrated with open-source full-stack O-RAN implementations. The evaluation process involves simulations based on 3GPP standards in ns-3, as well as collaborations with industry partners including AT&T, Qualcomm, and Nokia Bell Labs. Real-world trace data and production-grade O-RAN platforms will be leveraged for evaluation purposes. The outcomes of this research not only contribute to advancing knowledge in machine-learning-enabled NextG systems design but also address critical needs within the broader machine learning and networking research communities.

This project is supported by the National Science Foundation (NSF) under Grants CNS-2312833, CNS-2312834, CNS-2312835, and CNS-2312836 from 10/1/2023 to 9/30/2026.

Personnel

Principal Investigators

• Bo Ji (Virginia Tech, Lead)

• Y. Charlie Hu (Purdue University)

• Xingyu Zhou (Wayne State University)

• Ness B. Shroff (The Ohio State University)

Collaborators

• Nakjung Choi (Nokia Bell Labs, USA)

• Sayak Ray Chowdhury (Microsoft Research, India)

• Arnob Ghosh (New Jersey Institute of Technology, USA)

• Dimitrios Koutsonikolas (Northeastern University, USA)

• Myungjin Lee (Cisco Research, USA)

• Mark Squillante (IBM Research, USA)

Publications

Journal Articles

1. Fengjiao Li, Xingyu Zhou, and Bo Ji, “Distributed Linear Bandits with Differential Privacy,” IEEE Transactions on Network Science and Engineering (TNSE), accepted, January 2024.

Conference Papers

1. Z. Jonny Kong, Qiang Xu, and Y. Charlie Hu, “ARISE: High-Capacity AR Offloading Inference Serving via Proactive Scheduling,” In Proceedings of ACM MobiSys 2024, Tokyo, Japan, June 2024.

2. Omar Basit, Phuc Dinh, Imran Khan, Z. Jonny Kong, Y. Charlie Hu, Dimitrios Koutsonikolas, Myungjin Lee, Chaoyue Liu, “On the Predictability of Fine-grained Cellular Network Throughput using Machine Learning Models,” In Proceedings of IEEE MASS 2024, Seoul, South Korea, September 2024.

3. Xingyu Zhou and Sayak Ray Chowdhury, “On Differentially Private Federated Linear Contextual Bandits,” In Proceedings of ICLR 2024, Vienna, Austria, May 2024.

4. Arnob Ghosh, Xingyu Zhou, and Ness Shroff, “Towards Achieving Sub-linear Regret and Hard Constraint Violation in Model-free RL,” In Proceedings of AISTATS 2024, Valencia, Spain, May 2024.

Broader Impacts

• PI Shroff and PI Ji, along with several other colleagues, co-organized the 2024 Artificial Intelligence Modeling, Analysis, and Control of Complex Systems (AIMACCS) Workshop, which consists of 13 keynotes, an industry roundtable panel for students, a Women in AI meeting, and a student poster session, where students working on this project presented their research results and progress with AI Edge Institute members and other AIMACCS workshop attendees and received useful feedback.

• For Year 2, the PIs have recruited multiple female PhD students and postdoc associates and plan to further the collaboration through co-advising them to work on problems related to this research project.

• PI Ji and PI Zhou have formed a close collaboration with the Mobile Network Systems department at Nokia Bell Labs. The main purpose of the collaboration is to explore novel online learning approaches for adaptive end-to-end network slicing.

• PI Hu has collaborated with Cisco Research, to study cellular network throughput prediction at fine time scale needed to support dynamic adaptation of offloading strategies of edge-assisted AR design.

• PI Ji is currently advising a female postdoc associate and a female PhD student working on edge intelligence and an African American PhD student (co-advised) working on decentralized federated learning for spectrum sensing in O-RAN. In addition, PI Ji has recruited three new female PhD students in Fall 2024.

• PI Ji has supervised three female undergraduate students to conduct research through the Undergraduate Research to PhD (UR2PhD) program organized by Computing Research Association (CRA). Their research was recognized with People’s Choice Award at the 2024 VTURCS Research Symposium.

• PI Ji has actively participated in various educational and outreach activities organized by Center for the Enhancement of Engineering Diversity (CEED) at Virginia Tech, including TechGirls, Black Engineering Excellence at Virginia Tech (BEE VT), Student Transition to Engineering Program (STEP), C-Tech^2, and Galipatia Slush Rush events.

• PI Zhou has initiated a new AI/ML summer camp at WSU for local high school students.