Correcting Dependent Errors in Sequences Generated by Finite-State Processes

IEEE Transactions on Information Theory

Volumn 39, Issue 4, 1993, Pages 1249-1260

  Bouloutas, Anastasios T ^b  

^a Och Spine at New York Presbyterian Hospitals   (United States)

^b IBM T J WATSON RESEARCH CENTER   (United States)

Author keywords

channel inversion; correcting data; detection and estimation; Viterbi decoder

Indexed keywords

ALGORITHMS; BINARY SEQUENCES; CODES (SYMBOLS); CODING ERRORS; COMMUNICATION CHANNELS (INFORMATION THEORY); COMPUTATIONAL COMPLEXITY; DECODING; FINITE AUTOMATA; MATHEMATICAL MODELS; RECURSIVE FUNCTIONS;

CHANNEL INVERSION ALGORITHM; CHANNEL INVERTER; CHANNEL MODEL; DATA CORRECTING FRAMEWORK; FINITE STATE PROCESSES; VITERBI ALGORITHMS;

ERROR CORRECTION;

EID: 0027845874     PISSN: 00189448     EISSN: 15579654     Source Type: Journal    
DOI: 10.1109/18.243442     Document Type: Article

References (20)

1. J. B. Anderson, “Sequential decoding based on an error criterion,” IEEE Trans. Inform. Theory, vol. 38, pp. 987–1001, May 1987.
2. D. Bertsekas, Dynamic Programming. Englewood Cliffs, NJ: Prentice Hall, 1987, ch. 1.
3. A. Bouloutas, G. W. Hart, and M. Schwartz, “Two extensions of the Viterbi algorithm,” IEEE Trans. Inform. Theory, vol. 37, pp. 430–436, Mar. 1991.
4. N. Chomsky, “Three models for the description of language,” IRE Trans. Inform. Theory, vol. IT-2, pp. 113–124, 1956, reprinted with corrections in Readings in Mathematical Psychology, vol. II, R. Luce, R. Bush, and E. Galanter, Eds. New York: Wiley.
5. M. V. Eyuboglu and S. U. H. Qureshi, “Reduced-state sequence estimation for coded modulation on intersymbol interference channels,” IEEE J. Select. Areas Commun., vol. 7, pp. 989–995, 1989.
6. G. Forney, “The Viterbi algorithm,” IEEE Proc., vol. 61, pp. 268–278, 1973.
7. R. Gallager, Information Theory and Reliable Communication. New York: Wiley, 1968, ch. 4.
8. G. W. Hart, “Nonintrusive appliance load monitoring,” IEEE Proc., pp. 1870–1892, Dec. 1992.
9. G. W. Hart, “Residential energy monitoring and computerized surveillance via utility power flows,” IEEE Technol. and Soc., pp. 12–16, June 1989.
10. G. W. Hart, “Minimum information estimation of structure,” M.I.T. Dept. EECS Ph.D. dissertation, and Laboratory for Information and Decision Systems Tech. Rept. #1664, 1987.
11. J. Hoperoft and J. Ullman, Introduction to Automata Theory, Languages, and Computation. Reading, MA: Addison-Wesley, 1979, ch. 2–3.
12. F. Pollara, R. McEliece, and K. Abdel-Ghaffar, “Finite-state codes,” IEEE Trans. Inform. Theory, vol. 34, pt. I, pp. 1083–1089, Sept. 1988.
13. L. Rabiner, S. Levinson, and M. Sondhi, “On the application of vector quantization and hidden Markov models to speaker-independent, isolated word recognition,” BSTJ-62„ p. 1075, 1983.
14. F. Schweppe, Uncertain Dynamic Systems. Englewood Cliffs, NJ: Prentice-Hall, 1973, ch. 3.
15. R. Sedgewick, Algorithms. Reading, MA: Addison-Wesley, 1984, chs. 19, 20, 32.
16. C. Shannon, “A mathematical theory of communication,” Bell Syst. Techn., vol. 27, pp. 379-423+623-656, 1948.
17. G. Ungerboeck, “Channel coding with multilevel/phase signals,” IEEE Trans. Inform. Theory, vol. IT-28, pp. 55–67, 1982.
18. A. Viterbi, “Error bounds for convolutional codes and an asymptotically optimum decoding algorithm,” IEEE Trans. Inform. Theory, vol. IT-13, pp. 260–269, 1967.
19. A. Viterbi, “Convolutional codes and their performance in communication systems,” IEEE Trans. Commun., vol. com-19, pp. 751–772, 1971.
20. A. Viterbi and J. Omura, Principles of Digital Communication and Coding. New York: McGraw-Hill, 1979, chs. 4, 5.