A unifying framework for detecting cyclostationarity with applications to interweave cognitive radio
Final Report Abstract
Cyclostationary signals can model periodic phenomena occurring in a wide range of areas in science and technology, including communications, meteorology, oceanography, climatology, astronomy, and economics. The detection of CS signals is a particularly important problem. First of all, if signals are CS, then this fact should be exploited in their processing for the best performance, but on the other hand, attempting to use properties of CS signals, when in fact they are not CS, leads to decreased performance. Secondly, the presence or absence of CS signals can be used to trigger other actions. For instance, detection of CS signals is a key ingredient in the dynamic spectrum management of interweave cognitive radio, where cognitive users are allowed to access unused licensed bands. This requires testing for the presence of licensed users, which transmit CS signals. Additionally, in a passive radar system, cyclostationarity detection can be used to detect and localize targets. Because detection of cyclostationarity is such an important problem, many detectors have been proposed for it. However, a close analysis of these detectors reveals that most of the proposed techniques are not based on sound statistical theory. While they may be sensible ad-hoc detectors, they do not offer any kind of optimality. Moreover, most of these detectors make unrealistic assumptions such as known cycle period or not accounting for the fact that sampled CS signals are generally only almost CS. Thus, the main objective of our project was the development of detectors for cyclostationarity based on solid statistical arguments, without the need for unrealistic simplifying assumptions.
Publications
- “Detection of cylostationarity in the presence of temporal or spatial structure with applications to cognitive radio,” in Proc. Intl. Conf. Acoustics, Speech, Signal Processing (ICASSP), Shanghai, China, 2016
A. Pries, D. Ramírez, and P. J. Schreier
(See online at https://doi.org/10.1109/ICASSP.2016.7472478) - “Detection of almost-cyclostationarity: An approach based on a multiple hypothesis test,” in Proc. Asilomar Conf. on Signals, Systems, Computers, Pacific Grove, CA, Nov. 2017
S. Horstmann, D. Ramírez, and P. J. Schreier
(See online at https://doi.org/10.1109/ACSSC.2017.8335636) - “Joint detection of almost-cyclostationary signals and estimation of their cycle period,” IEEE Signal Processing Letters, vol. 25, no. 11, pp. 1695-1699, November 2018
S. Horstmann, D. Ramírez, and P. J. Schreier
(See online at https://doi.org/10.1109/LSP.2018.2871961) - “LMPIT-inspired tests for detecting a cyclostationary signal in noise with spatio-temporal structure,” IEEE Transactions on Wireless Communications, vol. 17, no. 9, pp. 6321–6334, September 2018
A. Pries, D. Ramírez, and P. J. Schreier
(See online at https://doi.org/10.1109/TWC.2018.2859314) - “Two-channel passive detection exploiting cyclostationarity,” in Proc. European Signal Processing Conf. (Eusipco), A Coruna, Spain, Sep. 2019
S. Horstmann, D. Ramírez, and P. J. Schreier
(See online at https://doi.org/10.23919/EUSIPCO.2019.8902989) - “Two-channel passive detection of cyclostationary signals in noise with spatio-temporal structure,” in Proc. Asilomar Conf. on Signals, Systems, Computers, Pacific Grove, CA, Nov. 2019
S. Horstmann, D. Ramírez, P. J. Schreier, and A. Pries
(See online at https://doi.org/10.1109/IEEECONF44664.2019.9048746) - “Two-channel passive detection of cyclostationary signals,” IEEE Transactions on Signal Processing
S. Horstmann, D. Ramírez, and P. J. Schreier
(See online at https://doi.org/10.1109/TSP.2020.2981767)
