Support vector machines based detection for holographic data storage systems

Nonlinear nature of Holographic Data Storage Systems (HDSS) suggests that nonlinear equalization and detection techniques may be beneficial. 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 2-D data pages is about 17% better than the simple threshold detection on unequalized pages.