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Radio Access and Propagation Metrology Group


  • Works on novel methodologies to characterize and exploit radio-frequency channel propagation for innovative radio design and improved spectrum access.
  • Designs channel sounder architectures, algorithms to extract relevant properties, and methods to develop channel propagation models.
  • Uses machine learning methods where applicable to advance autonomous channel model development, channel estimation, antenna beamforming, and to optimize other radio functions.
  • Utilizes channel characteristics to support radio resource allocation and optimization at the physical and media access layers.
  • Disseminates results in the form of publicly available measurement and modeling data, top-tier journal and conference publications, contributions to the standard developing organization, and collaboration with industry, academia, and other government agencies.


NextG Channel Measurement and Modeling

60 GHz channel sounder

The exponential increase in wireless data transmission from smartphones has led to the saturation of the sub-6 GHz bands forcing providers to explore the use of millimeter-wave (mmWave) regime and eventually to the sub-Terahertz (sub-THz) regime, what we refer to collectively as NextG. To compensate the high propagation loss at mmWave and sub-THz, phased array antennas with high gain and narrow beamwidth will be employed. The high directionality of NextG systems will fundamentally change channel propagation models and channel sounding systems and techniques used to measure the model properties. The National Institute of Standards and Technology is on the forefront of defining that change, supporting the need currently facing the wireless industry to accurately characterize wireless propagation in support of next generation wireless communications systems. (learn more)

Physical Layer Design and Evaluation


Exploring mmWave in next-generation communications has created exciting opportunities to support high throughput and ultra low latency applications. However, it also raised new challenges, such as high propagation loss, prone to link blockage, and more sparse channels compared to sub-6GHz systems. The physical layer is the lowest layer in the wireless communication protocol stack, which directly handles bit-level transmission and reception over wireless channels. In this project, NIST uses measurement-based channel models in the design, development, and evaluation of next-generation wireless communications systems. (learn more)

Machine Learning for Internet of Things (IoT)

Transfer learning with convolutional neural networks
Transfer learning with convolutional neural networks

Machine Learning (ML) has many applications, which include monitoring and control of automated systems such as factories and warehouses. These Industrial Internet of Things (IIoT) systems generate massive amounts of data that must be processed and used to adaptively control system operations, while making the most efficient use of the system's available communications, computing, and energy resources. NIST is investigating how ML systems interact with IIoT systems and is developing tools for measuring the performance of these systems, which will support the continued evolution of the manufacturing, energy, and transportation sectors of the U.S. economy. (learn more)

Citizens Broadband Radio Service

 Citizens Broadband Radio Service

The Citizens Broadband Radio Service (CBRS) band is 150 MHz of spectrum made available for commercial broadband use on a shared basis with the federal government. NIST is creating models, tools, and datasets to assist in the development and enhancement of commercial systems that protect mission-critical federal systems from harmful radio frequency interference. (learn more)



Radio Access and Propagation Metrology Group