Biomedical subjects

Mehmet Keskinoz

Publications and source records attributed to Mehmet Keskinoz.

2 recordsLinked to original sources

PubMed2006

Iterative soft-minimum mean-square error equalization for digital nonlinear page-oriented memories.

Digital page-oriented volume holographic memory (POVHM) is a promising candidate for next-generation ultrahigh capacity optical data storage technology. As the capacity of the POVHMs increases, the bit error rate performance of the system is degraded due to increased interpixel interference (IPI) and noise. To improve the system performance under these adverse effects and to increase the capacity, joint iterative soft equalization-detection and error correction decoding might be attractive. To address that, by considering the nonlinearity inherent in the channel, an iterative soft equalization method that is optimized in the minimum mean-square error (MMSE) sense, called the iterative soft-MMSE (ISMMSE) equalization, is devised. The performance of the ISMMSE is evaluated by use of numerical experiments under different amounts of IPI and optical noise. Simulation results suggest that the ISMMSE is a good candidate for an ultrahigh capacity POVHM, which employs joint iterative equalization-detection and decoding.

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PubMed2004

Discrete magnitude-squared channel modeling, equalization, and detection for volume holographic storage channels.

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.

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