Exponential sums and faster than Nyquist signaling

  • D. Hajela
Conference paper
Part of the Lecture Notes in Mathematics book series (LNM, volume 1383)


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. [1]
    G. Forney, “Lower Bounds on Error Probability in the Presence of Large Intersymbol Interference”, IEEE Trans. Com., COM-20, No. 1 (1972), pp. 76–77.Google Scholar
  2. [2]
    G. Foschini, “Performance Bound for Maximum Likelihood Reception of Digital Data”, IEEE Trans. Information Theory, IT-21 (1975), pp. 47–50.MathSciNetCrossRefzbMATHGoogle Scholar
  3. [3]
    D. Hajela, “On Computing the Minimum Distance for Faster than Nyquist Signaling”, The 1987 Symposium on Information Theory and its Applications (SITA ′87), November 1987, Enoshima Island, Japan.Google Scholar
  4. [4]
    A. Ingham, “Some Trigonometrical Inequalities With Applications to The Theory of Series”, Mathematische Zeitschrift, Vol. 41 (1936), pp. 367–379.MathSciNetCrossRefzbMATHGoogle Scholar
  5. [5]
    J. Mazo, “Faster Than Nyquist Signaling,” Bell System Technical Journal, Vol. 54, No. 8, (1975), pp. 1451–1462.MathSciNetCrossRefzbMATHGoogle Scholar
  6. [6]
    A. Wyner, “Upper Bound on Error Probability For Detection With Unbounded Intersymbol Interference”, Bell System Technical Journal, Vol. 54, No. 7, (1975), pp. 1341–1351.MathSciNetCrossRefzbMATHGoogle Scholar
  7. [7]
    A. Zygmund, “Trigonometric Series”, Vol. 2, Cambridge University Press, 1977.Google Scholar
  8. [8]
    G. Foschini, “Contrasting Performance of Faster Binary Signaling with QAM”, AT&T Bell Labs. Tech. J. 63 (1984), pp. 1419–1445.CrossRefGoogle Scholar
  9. [9]
    D. Hajela, “Some New Results on Faster Than Nyquist Signaling”, Proceedings of the Twenty-first Annual Conference on Information Sciences and Systems, John Hopkins University, March 1987, pp. 399–403.Google Scholar

Copyright information

© Springer-Verlag 1989

Authors and Affiliations

  • D. Hajela
    • 1
  1. 1.BellcoreMorristown

Personalised recommendations