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Robust initial LLRs for iterative decoders in presence of non-Gaussian noise
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Cooperative diversity in the presence of impulsive noise
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Statistical modeling of co-channel interference
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Robust nonparametric cyclic correlation-based spectrum sensing for cognitive radio
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Fast and robust modulation classification via Kolmogorov-Smirnov test
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IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Fast and robust spectrum sensing via Kolmogorov-Smirnov test
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Robust multi-user detection based on quantum bee colony optimisation
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Robust multiuser detection method based on neural-net preprocessing in impulsive noise environment
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III
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Hi-index | 754.85 |
The subject here is generalized (i.e., non-Gaussian) noise models, and specifically their first-order probability density functions (PDFs). Attention is focused primarily on the author's canonical statistical-physical Class A and Class B models. In particular, Class A noise describes the type of electromagnetic interference (EMI) often encountered in telecommunication applications, where this ambient noise is largely due to other, “intelligent” telecommunication operations. On the other hand, ambient Class B noise usually represents man-made or natural “nonintelligent”-i.e., nonmessage-bearing noise-and is highly impulsive. Class A noise is not an α-stable process, nor is it reducible to such, except in the limiting Gaussian cases of high-density noise (by the central limit theorem). Class B noise is also asymptotically normal (before model approximation). Under rather broad conditions, principally governed by the source propagation and distribution scenarios, the PDF of Class B noise alone (no Gaussian component) can usually be approximated by (1) a symmetric Gaussian α-stable (SαS) model in the case of narrowband reception, or when the PDF ω1(α) of the amplitude is symmetric; and (2) a nonsymmetric α-stable (NSαS) model (no Gaussian component) can be constructed in broadband regimes. New results here include: (i) counting functional methods for constructing the general qth-order characteristic functions (CFs) of Class A and Class B noise, from which (all) moments and (in principle), the PDFs follow; (ii) the first-order CFs, PDFs, and cumulative probabilities (APDs) of nonsymmetric broadband Class B noise, extended to include additive Gauss noise (AGN); (iii) proof of the existence of all moments in the basic Class A and Class B models; (iv) the key physical role of AGN and the fact that AGN removes α-stability; (v) the explicit roles of the propagation and distribution scenarios; and (vi) extension to noise fields. Although telecommunication applications are emphasized, Class A and Class B noise models apply selectively, but equally well, to other physical regimes, e.g., underwater acoustics and EM (radar, optics, etc.). Supportive empirical data are included