A unifying framework for detecting cyclostationarity with applications to interweave cognitive radio

• Applicant: Professor Peter Schreier, Ph.D., since 2/2016
• Subject Area: Electronic Semiconductors, Components and Circuits, Integrated Systems, Sensor Technology, Theoretical Electrical Engineering
• Term: from 2015 to 2020
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 280558790

Cyclostationary (CS) 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 (CR), where cognitive users are allowed to access unused licensed bands. This requires testing for the presence of licensed users, which transmit CS signals. 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. Finally, they only test for the presence of CS signals versus the alternative of wide-sense stationary signals, but do not consider nonstationary signals as a second alternative. Thus, the main objective of our proposed project is the development of detectors for cyclostationarity based on solid statistical arguments, without the need for unrealistic simplifying assumptions. More specifically, we will develop detectors for cyclostationarity versus nonstationarity and wide-sense stationarity. These detectors will be based on well-established statistical techniques, such as the generalized likelihood ratio test, the locally most powerful invariant test, and -- if it exists -- the uniformly most powerful invariant test. All these detectors will be extended to multivariate time series. We will also consider the case of unknown cycle period. Rather than decoupling this problem from the detection problem by first estimating the cycle period, our approach will be to estimate it jointly with the detection problem. Since the sampling of a cyclostationary signal generally results in an almost cyclostationary signal this will involve the development of tests for almost-cyclostationarity. We will then apply our techniques to selected problems in interweave CR and passive radar. In order to evaluate the performance of the estimators in a realistic CR environment, we implement them on a hardware testbed, which is made available to us by our Spanish collaboration partner GTAS at the University of Cantabria.

• DFG Programme: Research Grants
• Ehemaliger Antragsteller: Dr. David Ramírez García, until 1/2016