Project Grant 2312875
- This $900,000 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program supports research to enhance the performance, intelligence, and security of Open Radio Access Network (O-RAN) technology. The project, led by Virginia Polytechnic Institute & State University (Virginia Tech), aims to address key challenges in O-RAN across three interconnected research thrusts: Developing a data-driven approach for real-time multi-user...
- This Project Grant award of $285,694, provided by the National Science Foundation (NSF) under the Integrative Activities program (CFDA 47.083), aims to develop a safe online hierarchical learning framework for Open Radio Access Network (O-RAN) mobile networks. The key goals are to enable online resource allocation at near-real-time RICs using safe deep reinforcement learning, and online service orchestration at non-real-time RICs through robust Bayesian learning. Additionally, the project will...
- This $900,000 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program will fund the REPAIRT project at Northeastern University. The REPAIRT project aims to establish the scientific and engineering foundations required to detect, neutralize, and prevent adversarial AI in Open Radio Access Network (O-RAN) systems. The project will develop algorithms to flag malicious AI behavior before and after deployment, and introduce...
- This Project Grant award, funded by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CFDA 47.070) program, supports the development of new diagnostic testing methods for Open Radio Access Network (O-RAN) systems, which are critical components of 5G and future mobile network infrastructure. The $200,000 award, effective October 1, 2024 through September 30, 2027, aims to create a systematic approach to mobile network diagnostic testing that...
- This Project Grant award from the National Science Foundation's Division of Computer and Network Systems, under the CFDA Program 47.070 "Computer and Information Science and Engineering", provides $300,000 in funding to Wayne State University from October 1, 2023 to September 30, 2026. The 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 quality-of-experience...
- This $300,000 federal Project Grant award, funded by the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) program, supports collaborative research at Michigan State University to enhance the performance, intelligence, and security of O-RAN (Open Radio Access Network) technologies. The project aims to address key challenges in O-RAN by developing data-driven beamforming techniques, machine learning algorithms for multi-user MIMO control, and safeguard...
- This Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) provides $300,000 to Purdue University from October 1, 2023 to September 30, 2026. The project aims to develop advanced machine learning and optimization techniques to enable autonomous resource management in next-generation wireless networks, with a focus on improving quality-of-experience for augmented reality applications. Key objectives...
- This $200,000 Project Grant from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) supports research to develop new diagnostic testing methods for 5G and beyond mobile network infrastructure, particularly the Radio Access Network (RAN) subsystem. The project aims to address limitations in current RAN testing by: (1) enhancing test coverage through exploiting dependencies across RAN software procedures and interfaces, (2)...
- This Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) provides $670,000 to the University of Arizona to investigate the security and verifiability of resource allocation for next-generation wireless networks. The key objectives are to: 1) establish a comprehensive threat model against reinforcement learning-based resource allocation methods, 2) design robust resource allocation approaches for...
- This $200,000 Project Grant award, funded by the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070), supports a collaborative research project to develop new diagnostic testing methods for the Radio Access Network (RAN) subsystem of 5G and future mobile network technologies. The project aims to enhance RAN testing in three key areas: (1) improving test coverage by identifying dependencies and root causes, (2) enabling non-intrusive,...
NETS: MEDIUM: RESILIENT-BY-DESIGN DATA-DRIVEN NEXTG OPEN RADIO ACCESS NETWORKS -SOCIETY INCREASINGLY DEPENDS ON CELLULAR NETWORKS, MAKING IT CRITICAL TO ASSURE THAT THE NETWORKS ARE SECURE AGAINST CYBER ATTACKS. NEXT-GENERATION CELLULAR NETWORKS ARE EXPECTED TO RELY ON MACHINE LEARNING (ML) ALGORITHMS TO ACHIEVE REAL-TIME RESOURCE OPTIMIZATION ACROSS SPACE, TIME, FREQUENCY AND DEVICES. THIS PROJECT STUDIES SECURITY THREATS TO THOSE ML ALGORITHMS AND DEVELOPS SOLUTIONS TO PROTECT THEM, FOCUSING ON THE OPEN RADIO ACCESS NETWORKS (OPEN RAN) ARCHITECTURE WHICH IS RAPIDLY BECOMING WIDESPREAD. ALL PROJECT OUTPUTS (ALGORITHMS, HARDWARE/SOFTWARE DESIGNS, AND DATASETS) WILL BE MADE PUBLICLY AVAILABLE THROUGH THE NSF RFDATAFACTORY WEBSITE, HELPING TO ADDRESS THE CURRENT LACK OF LARGE-SCALE DATASETS FOR DATA-DRIVEN WIRELESS RESEARCH. AS PART OF THE PROJECT, SEVERAL GRADUATE STUDENTS WILL DEVELOP UNIQUE EXPERTISE AT THE CROSSROADS OF ML, SECURITY, EMBEDDED SYSTEMS AND WIRELESS NETWORKS. THE PROJECT?S KEY FINDINGS WILL BE INTEGRATED INTO NEW GRADUATE COURSES IN WIRELESS ML SECURITY, AND WILL ENRICH ONGOING INITIATIVES AT NORTHEASTERN UNIVERSITY FOR UNDERGRADUATE AND K-12 STUDENTS COMING FROM UNDERREPRESENTED MINORITY GROUPS. NOVEL OPTIMIZATION FRAMEWORKS ARE INVESTIGATED TO MODEL ADVERSARIAL ML ATTACKS IN OPEN RANS. THESE FINDINGS ARE USED TO DESIGN ML ARCHITECTURE SEARCH ALGORITHMS TO FIND ML MODELS FOR OPEN RANS THAT ARE RESILIENT TO ATTACK WHILE STILL SATISFYING CONSTRAINTS SUCH AS END-TO-END LATENCY AND ENERGY CONSUMPTION. THE PROJECT DESIGNS ANOMALY DETECTION TECHNIQUES TO ENHANCE RESILIENCE IN DYNAMIC SETTINGS, AND DYNAMIC DEFENSE STRATEGIES AGAINST REAL-TIME DATASET POISONING ATTACKS. THE PROPOSED TECHNIQUES ARE EVALUATED USING ONE OR MORE OF THE FOLLOWING TESTBEDS: THE COLOSSEUM NETWORK EMULATOR, THE OPENRANGYM FRAMEWORK, AND THE NSF PAWR POWDER PLATFORM. 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.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $55.0k | 8/21/25 | ||
| Not listed | $900.0k | 8/8/23 |