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Distributions and expectations of singular random variables

Published online by Cambridge University Press:  14 July 2016

L. L. Campbell*
Affiliation:
Queen's University
A. L. McKellips*
Affiliation:
Queen's University
P. H. Wittke*
Affiliation:
Queen's University
*
Postal addresses: Department of Mathematics and Statistics and
Postal addresses: Department of Mathematics and Statistics and
∗∗Department of Electrical Engineering, Queen's University, Kingston, Ontario, Canada K7L 3N6.

Abstract

Intersymbol and cochannel interference in a communications channel can often be modelled as the sum of an infinite series of random variables with weights which decay fairly rapidly. Frequently, this yields a random variable which is singular but non-atomic. The Hausdorff dimension of the distribution is estimated and methods for calculating expectations are studied. A connection is observed between the dimension and the complexity of the calculation of an expectation.

Type
Research Papers
Copyright
Copyright © Applied Probability Trust 1995 

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Footnotes

This research was supported by the Natural Sciences and Engineering Research Council of Canada through Grants OGP0002151 and OGP0003391 to Campbell and Wittke, and through a scholarship to McKellips.

References

Beaulieu, N. C. (1991) The evaluation of error probabilities for intersymbol and cochannel interference. IEEE Trans. Commun. 39, 17401749.CrossRefGoogle Scholar
Billingsley, P. (1965) Ergodic Theory and Information. Wiley, New York.Google Scholar
Cover, T. M. and Thomas, J. A. (1991) Elements of Information Theory. Wiley, New York.Google Scholar
Eggleston, H. G. (1949) The fractional dimension of a set defined by decimal properties. Quart. J. Math. Oxford Series 20, 3136.CrossRefGoogle Scholar
Feller, W. (1966) An Introduction to Probability Theory and its Applications, Vol. II. Wiley, New York.Google Scholar
Falconer, K. (1990) Fractal Geometry: Mathematical Foundations and Applications. Wiley, Toronto.Google Scholar
Garsia, A. M. (1963) Entropy and singularity of infinite convolutions. Pacific J. Math. 13, 11591169.CrossRefGoogle Scholar
Helstrom, C. W. (1986) Calculating error probabilities for intersymbol and cochannel interference. IEEE Trans. Commun. 34, 430435.CrossRefGoogle Scholar
Jessen, B. and Wintner, A. (1935) Distribution functions and the Riemann zeta function. Trans. Amer. Math. Soc. 38, 4888.CrossRefGoogle Scholar
Kahane, J. P. and Salem, R. (1958) Sur la convolution d'une infinité de distributions de Bernoulli. Colloq. Math. 6, 193202.CrossRefGoogle Scholar
McLane, P. J. (1974) Lower bounds for finite intersymbol error rates. IEEE Trans. Commun. 22, 853857.CrossRefGoogle Scholar
Smith, W. S., Wittke, P. H. and Campbell, L. L. (1993) Error probabilities on fading channels with intersymbol interference and noise. IEEE Trans. Inf. Theory 39, 15981607.CrossRefGoogle Scholar
Wittke, P. H., Smith, W. S. and Campbell, L. L. (1988) Infinite series of interference variables with Cantor-type distributions. IEEE Trans. Inf. Theory 34, 14281436.CrossRefGoogle Scholar