NSF Convergence Accelerator Track G: Combating Vulnerability and Unawareness in 5G Network Security

The project's specific goal is to develop the zero trust chain (ZTC) software that enables military squads to securely share situational awareness in their operations using high-performance, yet often untrusted, 5G networks. The software solution leverages the flexibility of the 5G standard and implements innovative security solutions at different network nodes and layers to empower DoD operators to detect malicious entities in near-real time and establish communication mechanisms to prevent access to or control over DoD traffic. Specifically, through minimal cooperation with 5G network operators, part of the ZTC solution leverages Open-RAN (O-RAN) and 5G core-centric approaches for practical threat monitoring and mitigation. This is complemented by device-centric security enhancements to ensure that DoD devices also implement their own layer of security and do not solely depend on the security protocols of the network provider. Six key features set ZTC apart from other solutions: (i) it builds on the Open Artificial Intelligence Cellular (OAIC) platform for developing O-RAN threat monitoring and mitigation through RAN Intelligent Controllers; (ii) it offers end-to-end secure slicing across the 5G RAN and Core; (iii) it detects threats at user devices in near-real time; (iv) it protects communication through innovation at the application layer rather than modifying existing 5G physical layer protocols and algorithms; (v) it ensures location privacy and resiliency to unknown/unanticipated denial of service (DoS) attacks; and (vi) it does not require modifications to public 5G/O-RAN networks and standards, and only requires installation of low-overhead software modules on 5G user devices and cooperative 5G networks. The Zero Trust X (ZTX) team's work is applicable to commercial and military 5G communication networks and to O-RAN. The ZTX team will implement and experimentally evaluate the proposed ZTC initially on a laboratory-scale integrated 5G/O-RAN testbed, and subsequently on other available testbeds to prepare for commercial transition. The team will apply Convergence Accelerator fundamentals to foster partnerships and to develop a sustainability model with an impact extending well beyond Phase 2 of the program.

NTIA PWSCIF: Testing Methods R&D on Security Metrics, Measures, and Test Procedures for Accessible and Customizable Testing

This project contributes to cellular network security metrics. We leverage software radio technology for designing and developing testing methods and open-source software implementations of a user equipment-based test instrument, Soft Tester UE, which implements test procedures and generates signals enabling accessible and customizable 5G/O-RAN testing.

https://www.rantesterue.org/ 

Collaborative Research: CCRI: New: Open AI Cellular (OAIC): Prototyping Artificial Intelligence-Enabled Control and Testing Systems for Cellular Communications Research

This collaborative project brings together investigators from Mississippi State University (MSU) and Virginia Tech (VT) to develop a software infrastructure that spurs research and development on artificial intelligence (AI)-enabled cellular radio networks. The main contribution is Open AI Cellular (OAIC), the first fully open-source software architecture, library, and toolset that encompass both the AI controllers (OAIC-C) as well as an AI testing framework (OAIC-T) for enabling research on 6G Open Radio Access Networks. This project puts the U.S. in the leading position of research, development, and testing of AI-enhanced cellular networks, initiated and organized by a group of academics and their collaborators and maintained by the wireless research community. The project (1) specifies the methodology along with the technical requirements, a reference architecture and design to facilitate collaborative software development; (2) designs and develops the fundamental OAIC-C software library for common software-defined radio (SDR) platforms; (3) facilitates systematic and exhaustive testing with the OAIC-T framework on community wireless research testbeds that are accessible to researchers; (4) develops verification tools and resources to facilitate rapid prototyping and testing as well as offers a variety of user services, such as mailing lists, code repositories, and learning material; and (5) implements a variety of community engagement and outreach processes, including the organization of workshops to engage with and leverage the community for long-term maintenance and sustainability. 

https://www.openaicellular.org/ 

SWIFT: Large: AI-Enabled Spectrum Coexistence between Active Communications and Passive Radio Services: Fundamentals, Testbed and Data 

This project combines emerging technologies to address research challenges across multiple layers of the network protocol stack and across active and passive RF systems to tackle the critical problem of active-passive RF spectrum coexistence. It develops novel sparsity and AI-based RFI detection and mitigation techniques at the physical and application layers of passive sensing systems. It introduces a wireless channel virtualization and waveform optimization framework at the physical layer of active transceivers--applicable to current and next generation wireless systems--to enable AI-based sparse time-frequency scheduling at the active transmitter's physical and medium access control layers. The proposed algorithms and waveforms will be co-optimized with the passive sensing system's RFI detection and mitigation strategy using offline training to further improve spectrum coexistence. To this end, the project is designing and developing a one-of-a-kind testbed in collaboration with NASA for collecting, processing and sharing remote sensing datasets in conjunction with ground and drone-based active communication systems with ground truth data.

https://sites.google.com/view/swift-ai-spectrum 

Collaborative Research: SWIFT-SAT: INtegrated Testbed Ensuring Resilient Active/Passive CoexisTence (INTERACT): End-to-End Learning-Based Interference Mitigation for Radiometers

The project INTERACT proposes three key innovations: 1) A new Unmanned Aerial System (UAS) based passive radiometer system will be developed. This system together with the experimental development of various active transmission scenarios covering different geometries, transmitter parameters and waveforms at non-restricted bands will result in the first-ever large experimental RF dataset with ground truth information for passive/active RF coexistence. A digital twin for passive radiometry in the emulation environment of AERPAW will be developed to enable experimenters to facilitate extensive, yet realistic RF mitigation experiments in a Cloud environment. 2) Novel data-driven end-to-end learning-based RFI detection and mitigation approaches will be developed. The proposed solutions will focus on approaches that can achieve high-resolution RFI detection in the time-frequency domains, learning based radiometer calibration, and joint mitigation to estimate the scientific observation of radiometers under RFI. These solutions do not require centralized servers and are designed to work on passive radiometer systems in order to detect and mitigate RFI without any information exchange between coexisting systems. 3) The research will produce new deep reinforcement learning and subspace-based RFI mitigation approaches using the feedback from active and passive systems.

DURIP: O‐RAN Security and Spectrum Access Research Testbed

The open radio access network (O-RAN) describes an industry-driven open architecture for building next generation RANs. Instead of legacy interfaces that are vendor-specific, it defines open interfaces for enabling innovation across all layers. It supports RAN virtualization and data driven cellular network management, establishing logical entities and well-specified interfaces and procedures for data collection, distribution, and processing. O-RAN based 5G and future 6G networks will incorporate artificial intelligence (AI) into the deployment, operation, and maintenance of the network. We recognize end-to-end security, deterministic latency, physical layer real-time control, and testing of AI-based RAN control applications as the critical research and development directions and propose developing an O-RAN security and spectrum access research testbed. The goal of the testbed is enabling research and education on the O-RAN architecture, interfaces, services, security, and spectrum access and sharing technologies. The software-defined radio testbed uses commercial off-the-shelf radio, computing, and radio frequency (RF) testing hardware and widely available software that can be customized and upgraded. We will leverage open-source software provided by industry and research centers, including srsran, OpenAirInterface, and the O-RAN Software Community, as well as the open specifications provided by the O-RAN Alliance and the Third Generation Partnership Project. The testbed facilitates development, network configuration, automation, and experimentation in different RF environments—over-the-air and in controlled RF environment—enabling reproducible research, education, and training.

5G Landscape for Industrial loT

(Project 21-18-01 5G System Configuration Research for Industrial Building Application)

This project surveys 5G architectures, configurations, and requirements for supporting widespread industrial automation and IIoT through modern cellular networks. We will explore the existing products, solutions, and costs as well as the emerging technologies, including O-RAN and 6G and 7G technologies. The technical survey will be complemented with experiences and needs expressed by small, medium and large enterprises. The outcome will be a comprehensive survey of 5G and Beyond technologies, architectures, configurations, and recommendations for industry with the rigor of an academic research report.