Statistical spectral analysis: a nonprobabilistic theory
Statistical spectral analysis: a nonprobabilistic theory
Bounded Power Signal Spaces for Robust Control and Modeling
SIAM Journal on Control and Optimization
Classification of co-channel communication signals using cyclic cumulants
ASILOMAR '95 Proceedings of the 29th Asilomar Conference on Signals, Systems and Computers (2-Volume Set)
Cyclostationarity: half a century of research
Signal Processing
LTI approximation of nonlinear systems via signal distribution theory
Automatica (Journal of IFAC)
Foundations of the functional approach for signal analysis
Signal Processing - Special section: Multimodal human-computer interfaces
Higher-order cyclic cumulants for high order modulation classification
MILCOM'03 Proceedings of the 2003 IEEE conference on Military communications - Volume I
IEEE Transactions on Signal Processing
The higher order theory of generalized almost-cyclostationary timeseries
IEEE Transactions on Signal Processing
Least-squares LTI approximation of nonlinear systems and quasistationarity analysis
Automatica (Journal of IFAC)
IEEE Journal on Selected Areas in Communications
On the ideal convergence of sequences of fuzzy numbers
Information Sciences: an International Journal
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In this paper, a new technique to design signals for secure communications is proposed, which provides data securing at the physical layer in noncooperative environments. Specifically, the proposed technique does not allow to an unauthorized third party the discovery of the modulation format and, hence, the demodulation of the signal. The technique consists in generating signals whose time-averaged statistical functions such as autocorrelation, moments, and cumulants, do not converge as the data-record length approaches infinity. Therefore, all modulation classification methods based on estimates of these functions, as well as their spectral counterparts, fail to identify the characteristics of the modulation format. The proposed technique is based on the concept of nonrelatively measurable functions and sequences. Nonrelatively measurable functions are such that the empirical distribution function does not converge as the data-record length approaches infinity. Thus, none of their statistical functions defined in terms of infinite-time averages is convergent. Guidelines to construct nonrelatively measurable signals are given. Moreover, examples of such signals are provided and the receiver performance for the authorized party is briefly analyzed. Simulation experiments on the estimation of second-order cyclic statistical functions are performed to show the lack of convergence of the cyclic estimators as the data record is increased.