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2006
DOI: 10.1007/s00034-004-1204-4
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Improved Robust Techniques for Multiuser Detection in Non-Gaussian Channels

Abstract: In many physical channels where multiuser detection techniques are to be applied, the ambient channel noise is known through experimental measurements to be decidedly non-Gaussian, due largely to impulsive phenomena. This is due to the impulsive nature of man-made electromagnetic interference and a great deal of natural noise. This paper presents a robust multiuser detector for combating multiple access interference and impulsive noise in code division multiple access (CDMA) communication systems. A new M-esti… Show more

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Cited by 25 publications
(23 citation statements)
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References 27 publications
(28 reference statements)
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“…In this section, the influence functions of M-estimators proposed in the literature [12,14] are listed (see Fig. 1).…”
Section: A Influence Functionsmentioning
confidence: 99%
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“…In this section, the influence functions of M-estimators proposed in the literature [12,14] are listed (see Fig. 1).…”
Section: A Influence Functionsmentioning
confidence: 99%
“…The Huber's M-estimator is determined by the Huber penalty function kν depends on the robustness measures derived from the influence function of [14], where k is a constant. …”
Section: A Influence Functionsmentioning
confidence: 99%
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“…However, the batch algorithm [2] requires singular value decomposition (SVD), which is inconvenient for adaptive implementation. Further, it requires accurate rank estimation of the correlation matrix, which is not easy in an inherently noisy environment [4]- [9].…”
Section: Introductionmentioning
confidence: 99%
“…Recently, the problem of robust multiuser detection in nonGaussian channels has been addressed in the literature [1] & [2], which were developed based on the Huber and Hampel estimators. The Huber's estimator [3] can limit the effect of gross errors; however, the effect may still have to be large enough to reach an unacceptable level.…”
Section: Introductionmentioning
confidence: 99%