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Detection in alpha‐stable noise environments based on prediction
Author(s) -
Ilow Jacek,
Hatzinakos Dimitrios
Publication year - 1997
Publication title -
international journal of adaptive control and signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.73
H-Index - 66
eISSN - 1099-1115
pISSN - 0890-6327
DOI - 10.1002/(sici)1099-1115(199711)11:7<555::aid-acs452>3.0.co;2-i
Subject(s) - noise (video) , gaussian noise , detector , computer science , baseband , monte carlo method , bandwidth (computing) , algorithm , limit (mathematics) , mathematics , control theory (sociology) , statistics , telecommunications , artificial intelligence , mathematical analysis , control (management) , image (mathematics)
In this paper we consider detection of baseband signals in partial response signalling (PRS) systems in the presence of additive, coloured noise. The additive noise in the system is a mixture of Gaussian noise and impulsive noise modelled as an alpha‐stable process. The dependence in observation samples results from the excess bandwidth in the matched filters of the receivers. The detectors proposed are based on a noise estimation–cancellation technique. In particular, by exploiting past decisions as well as past received samples, we estimate the noise and subsequently cancel it. We adopt two approaches for designing predictors: in the first we use a minimum mean square error (MMSE) criterion and we employ Volterra filters as predictors; in the second we use the minimum dispersion (MD) criterion and we limit our attention to linear predictors. The effects of the predictor order, the number of exploited samples and the filtering allocation on the system performance are examined through Monte Carlo simulations. It is demonstrated that the proposed detectors, while having simple structures, offer substantial performance improvements over conventional detectors. © 1997 John Wiley & Sons, Ltd.