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Support vector machines based data detection for holographic data storage systems

The nonlinear nature of holographic data storage systems (HDSS) suggests that nonlinear equalization and detection techniques may be beneficial. The complexity involved in nonlinear methods does not often make them practical solutions. Support vector machines (SVMs) are recently being studied for pattern recognition applications. We investigated linear SVM detection and observed that the bit error rate (BER) using SVM for data detection on linear minimum mean squared error (LMMSE) equalized holographically recorded and retrieved 2D data pages is about 17% better than the simple threshold detection on unequalized pages.