The NIST Communications Technology Laboratory is conducting research in spectrum sharing technologies that increase the availability of radio frequency (RF) spectrum for wireless broadband use. Current research addresses the detection of incumbent signals in the presence of co-channel interference from new entrants, machine-learning classification of signals, modeling of aggregate RF interference, algorithms for efficient deployment of RF sensors, and strategies for dynamic spectrum access. Findings are published in high-quality journals, presented at relevant conferences, and form the basis of contributions to spectrum sharing standards. Applications include, but are not limited to, the 3.5 GHz Citizens Broadband Radio Service, Long Term Evolution (LTE) mobile broadband, radar, and fifth generation (5G) wireless broadband technologies. Both theoretical and hands-on experimental work are of interest. Available laboratory resources include a state-of-the-art vector signal transceiver, vector signal analyzer, RF channel emulator, software-defined radios, and high-performance computing resources including a GPU cluster. Additional information is available at https://www.nist.gov/programs-projects/citizens-broadband-radio-service.
1. W. M. Lees, A. Wunderlich, P. Jeavons, P. D. Hale, and M. R. Souryal, "Deep Learning Classification of 3.5 GHz Band Spectrograms with Applications to Spectrum Sensing," IEEE Transactions on Cognitive Communications and Networking (Early Access), Feb. 2019.
2. T. A. Hall, A. Sahoo, C. Hagwood, and S. Streett, “Dynamic Spectrum Access Algorithms Based on Survival Analysis,” IEEE Transactions on Cognitive Communications and Networking, vol. 3, no. 4, pp. 740-751, Dec. 2017.
3. T. Nguyen, M. Souryal, A. Sahoo, and T. A. Hall, "3.5 GHz Environmental Sensing Capability Detection Thresholds and Deployment," IEEE Transactions on Cognitive Communications and Networking, vol. 3, no. 3, pp. 437-449, Sept. 2017.
Artificial Intelligence; Cognitive Radio; Communication Theory; Detection; Dynamic Spectrum Access; Machine Learning; Radar; Signal Processing; Spectrum Sensing; Spectrum Sharing; Wireless Communications