||Kirtland Air Force Base, NM 871175776
Recent advances in spectrum sensing technologies for jointly detecting and classifying signals of interest have produced significant gains in terms of improving spectrum efficiency and utilization for commercial terrain radio systems. The ability of spectrum sensing adopting cognitive radios to make real-time autonomous decisions and dynamically spectrum access allows them to intelligently share spectrum and extract more bandwidth, which improves overall spectrum efficiency. However, compared with terrain communications, satellite communications (SATCOM) often deal with large radio propagation distances with uncertain traffic requests and deliveries. Moreover, radio communications, including noise and interference, are highly dynamic and comprehensive in time, frequency, waveform and transmission patterns, which likely may not contain distinctive features. Therefore, SATCOM needs faster, more energy efficient, accurate wideband spectrum sensing schemes other than traditional counterparts. Multiple-dimension spectrum sensing is a key enabler of spectrum exploration through which situational awareness about incumbent spectrum will be developed and behaviors of other interference traffics can be learned as a function of time, frequency, waveform, and transmission profiles.
Scientific foundations and design principles pertaining to cooperative spectrum sensing tailored towards SATCOM terminals and satellite ground hubs include spectrum awareness and understanding, sensing efficiency, and accuracy for assured SATCOM applications. Given the concept of operations involving multiple SATCOM terminals and ground hubs, specific R&D activities include, but are not limited to (1) cooperative spectrum sensing with information exchange and adaptive channel allocation schemes, (2) machine learning to study underlying dynamics of spectrum, (3) alternatives for reduction of sensing overhead and energy cost, and (4) uncertainty analysis of sensing results in various channel conditions and interference types.
Poisel RA: Modern Communications Jamming Principles and Techniques, Second edition: Artech House, 2011
Sun H, et al: IEEE Wireless Communications (20)2: 74-81, 2013
Murphy KP: Machine Learning: A Probabilistic Perspective. The MIT Press, 2012
Tian X, Tian Z, Blasch E, et.al: Sliding Window Energy Detection for Spectrum Sensing under Low SNR Conditions. Wiley's Journal on Wireless Communications and Mobile Computing, 2015
SATCOM; Multi-dimension direction spectrum sensing; Cognitive radio; Cooperative spectrum sensing; Radio frequency classification; Comprehensive situation awareness; Interferences; Dynamic spectrum access; Spectrum efficiency; Energy efficiency; Sensing efficiency; Sensing accuracy; Game theory; Machine learning; Uncertainty;