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Applied Optics

Applied Optics


  • Vol. 43, Iss. 6 — Feb. 20, 2004
  • pp: 1368–1378

Discrete Magnitude-Squared Channel Modeling, Equalization, and Detection for Volume Holographic Storage Channels

Mehmet Keskinoz and B. V. K. Vijaya Kumar  »View Author Affiliations

Applied Optics, Vol. 43, Issue 6, pp. 1368-1378 (2004)

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As storage density increases, the performance of volume holographic storage channels is degraded, because intersymbol interference and noise also increase. Equalization and detection methods must be employed to mitigate the effects of intersignal interference and noise. However, the output detector array in a holographic storage system detects the intensity of the incident light’s wave front, leading to loss of sign information. This sign loss precludes the applicability of conventional equalization and detection schemes. We first address channel modeling under quadratic nonlinearity and develop an efficient model named the discrete magnitude-squared channel model. We next introduce an advanced equalization method called the iterative magnitude-squared decision feedback equalization (IMSDFE), which takes the channel nonlinearity into account. The performance of IMSDFE is quantified for optical-noise-dominated channels as well as for electronic-noise-dominated channels. Results indicate that IMSDFE is a good candidate for a high-density, high-intersignal-interference volume holographic storage channel.

© 2004 Optical Society of America

OCIS Codes
(210.2860) Optical data storage : Holographic and volume memories

Mehmet Keskinoz and B. V. K. Vijaya Kumar, "Discrete Magnitude-Squared Channel Modeling, Equalization, and Detection for Volume Holographic Storage Channels," Appl. Opt. 43, 1368-1378 (2004)

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  1. F. H. Mok, “Angle-multiplexed storage of 5000 holograms in lithium niobate,” Opt. Lett. 18, 915–917(1993).
  2. R. M. Shelby, J. A. Hoffnagle, G. W. Burr, C. M. Jefferson, M.-P. Bernal, H. Coufal, R. K. Grygier, H. Gunther, R. M. Macfarlane, and G. T. Sincerbox, “Pixel-matched holographic data storage with megabit pages,” Opt. Lett. 22, 1509–1511(1997).
  3. M. Keskinoz, “Modeling, equalization and detection for two-dimensional quadratic storage channels,” Ph.D. dissertation (Carnegie Mellon University, Pittsburgh, Pa., 2001).
  4. M. Keskinoz and B. V. K. V. Kumar, “Application of linear minimum mean-squared-error equalization for volume holographic data storage,” Appl. Opt. 38, 4387–4393(1999).
  5. M. Keskinoz and B. V. K. V. Kumar, “Linear minimum mean squared error (LMMSE) equalization for holographic data storage,” in Proceedings of IEEE International Conference on Communications (Institute of Electrical and Electronics Engineers, New York, 1999), pp. 1957–1961.
  6. J. F. Heanue, M. C. Bashaw, and L. Hesselink, “Channel codes for digital holographic storage,” J. Opt. Soc. Am. A 12, 2432–2439(1995).
  7. J. F. Heanue, K. Gurkan, and L. Hesselink, “Signal detection for page-access optical memories with intersymbol interference,” Appl. Opt. 35, 2431–2438(1996).
  8. B. M. King and M. A. Neifeld, “Parallel detection algorithms for page-oriented optical memories,” Appl. Opt. 37, 6275–6298(1998).
  9. K. M. Chugg, X. Chen, and M. A. Neifeld, “Two-dimensional equalization in coherent and incoherent page-oriented optical memory,” J. Opt. Soc. Am. A 16, 549–562(1999).
  10. V. Vadde and B. V. K. V. Kumar, “Channel modeling and estimation for intrapage equalization in pixel-matched volume holographic data storage,” Appl. Opt. 38, 4374–4386(1999).
  11. V. Vadde and B. V. K. V. Kumar, “Channel estimation and intra-page equalization for digital volume holographic storage,” in Optical Data Storage 1997 Topical Meeting, H. Birecki and J. Z. Kwiecien, eds., Proc. SPIE 3109, 250–255(1997).
  12. A. K. Jain, Fundamentals of Digital Image Processing (Prentice-Hall, Englewood Cliffs, N.J., 1989).
  13. M. P. Bernal, G. W. Burr, H. Coufal, and M. Quintanilla, “Balancing interpixel cross talk and detector noise to optimize areal density in holographic storage systems,” Appl. Opt. 37, 5377–5385(1998).
  14. J. Hong, C. Gu, and G. Sornat, “Bit-error rate and statistics of complex amplitude noise in holographic data storage,” Opt. Lett. 21, 1070–1072(1996).
  15. A. Papoulis, Probability, Random Variables and Stochastic Processes, 3rd ed.(McGraw-Hill, New York, 1991).
  16. F. H. Mok, G. W. Burr, and D. Psaltis, “System metric for holographic memory systems,” Opt. Lett. 21, 896–898(1996).
  17. J. G. Proakis, Digital Communications, 3rd ed.(McGraw-Hill, New York, 1995).
  18. G. W. Burr, J. Ashley, H. Coufal, R. K. Grygier, J. A. Hoffnagle, C. M. Jefferson, and B. Marcus, “Modulation coding for pixel-matched holographic data storage,” Opt. Lett. 22, 639–641(1997).

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