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NIST Smart Connected Systems Newsletter

February 2025

• NIST Researchers Publish Comprehensive Survey on Entanglement Routing in Quantum Networks
• NIST Researchers Showcase Successful mmWave Industrial IoT Measurement Capability
• NIST-Led Industrial Wireless Standard IEEE 3388 Passes Important Review Steps
• NIST Publishes Electromagnetic Interference Dataset from TIG Arc Welding
• NIST Researcher Describes Data Considerations for Industrial Artificial Intelligence

NIST Researchers Publish Comprehensive Survey on Entanglement Routing in Quantum Networks

In a recent paper titled "Entanglement Routing in Quantum Networks: A Comprehensive Survey," published in IEEE Transactions on Quantum Engineering, researchers from the National Institute of Standards and Technology (NIST) and Aliro Technologies provide an extensive analysis of entanglement routing strategies in near-term quantum networks. Authored by Amar Abane, Michael Cubeddu, Van Sy Mai, and Abdella Battou, the study offers a structured overview of the challenges, methodologies, and future directions in quantum network routing.

A key objective of the survey is to make quantum networking concepts more accessible to classical network engineers by using familiar terminology and references. By framing the discussion in a way that aligns with classical networking principles, the paper serves as a bridge between the classical and quantum networking communities, helping engineers navigate the complexities of quantum communication.

Entanglement routing is a fundamental process in quantum networks, determining how short-range entanglements are combined through swapping operations to establish reliable long-distance entanglement between remote nodes. Unlike classical routing, quantum routing must account for unique constraints, including entanglement fidelity, the probabilistic nature of swapping operations, and the short coherence time of quantum states. The paper addresses both theoretical challenges and practical implementation aspects of developing efficient routing protocols to enable scalable and reliable quantum communication.

For readers already familiar with quantum networking, the survey provides a structured classification of quantum routing approaches, drawing parallels to classical networking strategies while addressing quantum-specific challenges. It introduces a taxonomy that distinguishes between the routing and forwarding phases, offering a modular framework to define and categorize quantum routing strategies. Covering more than a decade of research, the study classifies existing routing protocols into reactive, proactive, and hybrid strategies, analyzing their trade-offs in efficiency, resource utilization, and adaptability. Additionally, it explores critical limitations of current entanglement routing methods, such as decoherence, fidelity degradation, and synchronization requirements in distributed quantum networks, while outlining potential advancements like software-defined networking (SDN) principles and hybrid centralized-decentralized control models.

The insights presented in this survey have significant implications for the development of the Quantum Internet, where reliable long-distance entanglement will be essential for secure quantum communication, distributed quantum computing, and advanced metrology. By bridging theoretical models with practical implementation challenges, the research lays the foundation for next-generation quantum routing protocols. The proposed taxonomy and insights will play a crucial role in shaping future research and technological advancements in quantum networking.

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NIST Researchers Showcase Successful mmWave Industrial IoT Measurement Capability

On February 5, 2025, NIST researchers presented the results of their highly successful Innovation in Measurement Science (IMS) project, “The World’s Best IIoT Testbed,” to NIST senior management including the Associate Director for Laboratory Programs (ADLP), Dr. Chuck Romine. NIST’s internal competitive IMS program provides up to 5 years of funding for proposals from NIST staff that have potential for high, transformative impact and for significant advancement of NIST’s capabilities and mission. As a result of this IIoT testbed project, NIST has produced a new hybrid reflective/anechoic chamber capability for assessing the radio performance of industrial wireless devices and networks operating in millimeter-wave bands between 28 GHz and 63 GHz. This new chamber provides a calibrated and traceable environment for evaluating industrial wireless devices with beamforming antennas in realistic, reflective environments. These bands are supported by the 3GPP 5G standard for devices operating in the FR2 band and also by IEEE 802.11 devices operating at 60 GHz.

This project supported a coordinated effort among several NIST Laboratories, including the Communications Technology Laboratory (CTL), Information Technology Laboratory (ITL), Engineering Technology Laboratory (EL), and Physical Measurement Laboratory (PML). Rick Candell of CTL’s Smart Connected Systems Division and Kate Remley of CTL’s RF Technology Division were two principal proposers and champions.  

In this effort, Mohamed Hany and Rick Candell provided communications systems expertise for industrial environments, connection to industry through standardization and the NIST Industrial Wireless Systems Technical Interest Group, and delivered sustained technical contributions using machine learning for the analysis of measurements, channel exemplar extraction, and chamber automation using reinforcement learning approaches. Michael Frey and Lucas Koepke of ITL developed a framework for applying confidence bounds to wireless-device performance metrics such as error vector magnitude as a function of channel conditions. Kate Remley, Rob Horansky, Josh Kast and others in CTL focused on the hybrid-chamber development and calibration, enabling users to physically create myriad spatially varying, reflective channels as might be found on a factory floor. Calibrations allow separately identifying non-idealities of the testbed environment from non-ideal performance of the device under test. Vladimir Aksyuk of PML and Aly Artusio-Glimpse, Matt Simons, and Chris Holloway of CTL developed and patented a wafer-scale atomic sensor array with a dielectric photonic integrated circuit for non-invasive RF channel measurements.  

Follow-on work utilizing this new capability is planned to include the development of dynamic channel replication for the evaluation of rapid-response phased-array antenna designs and, given the highly variable behavior of hybrid anechoic/reflective chambers, an AI-assisted configuration of the chamber to accurately produce desired channel characteristics.

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NIST-Led Industrial Wireless Standard IEEE 3388 Passes Important Review Steps

For several years, NIST’s Industrial Wireless Systems Project Leader Rick Candell and his team have led the working group developing the draft IEEE 3388 Standard for the Performance Assessment of Industrial Wireless Systems. This draft standard has reached a major milestone by passing its IEEE Standards Association RevCom (or Standards Review Committee) review and receiving its subsequent IEEE Standards Association Standards Board approval. This standard establishes a functional model for radio frequency (RF) industrial wireless performance degradation factors (referred to as “aggressors”), and it provides a reference test architecture for performance evaluation processes and methods for industrial wireless networks used in mission-critical applications, such as manufacturing, power generation, precision time-sensitive sensing, and closed-loop control where wireless is a primary communications mode. Standardized testing prior to deployment will help make wireless systems more reliable for mission-critical applications where appropriate. The next steps include taking a deep dive through the specification of interference and propagation aggressors as well as establishing profiles for specific industry verticals. This milestone demonstrates the team’s commitment to advancing industry standards and fostering innovation in these critical areas.

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NIST Publishes Electromagnetic Interference Dataset from TIG Arc Welding

The NIST Industrial Wireless System team has published a new dataset titled "Electromagnetic Interference Measurements from Tungsten Inert Gas (TIG) Arc Welding." This dataset contains comprehensive electromagnetic interference (EMI) measurements from TIG welding processes conducted in the NIST fabrication shop. The measurements were obtained using a typical arc welding power source and recorded at three distinct frequencies: 900 MHz, 2.4 GHz, and 5.3 GHz. These three bands are currently the primary RF bands utilized by industrial wireless networks; higher frequency bands are being considered for future uses.  The data collection was performed with a bandwidth of 160 MHz and a sample rate of 625 MHz, providing high-resolution insights into the EMI characteristics during the welding operations. This dataset may be useful for understanding the EMI behavior in TIG welding and can be instrumental in developing interference mitigation strategies, aiding in RF band selection and frequency planning, and improving welding technology and regulations. A NIST report, Industrial Wireless non-Communications Aggressor Reproduction: A Playback Approach for TIG Welding Measurements, is also available, and includes helpful guidance on reproducing the TIG welding interference signal for wireless performance assessment.

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NIST Researcher Describes Data Considerations for Industrial Artificial Intelligence

In February 2025, NIST researcher Dr. M. Sharp published the second installment of a 4-part Manufacturing Extension Partnership (MEP) manufacturing innovation blog series that provides a beginner’s guide to Industrial Artificial Intelligence (IAI) applications. The current blog posting focuses on the data characteristics needed for AI to add measurable value to manufacturing operations. The author notes that it is important to understand what data, assumptions, rules, and shortcuts feed into an IAI system during its training, testing, development, and deployment stages. Various AI data topics are covered, including the need for available data to match real-world conditions and represent the full scope of the desired use cases. Common data pitfalls are also identified, including incomplete data, inadequate data variation, and large gaps in data. For additional background and context, the first blog in the series described IAI and provided simple questions to ask when investing in and using AI-enhanced systems and tools in an industrial application.

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