Holographic data storage (HDS) has attracted much attention as a next-generation storage medium. Because HDS suffers from two-dimensional (2D) inter-symbol interference (ISI), the partial-response maximum-likelihood (PRML) method has been studied to reduce 2D ISI. However, the PRML method has various drawbacks. To solve the problems, we propose a modified decision feedback equalizer (DFE) for HDS. To prevent the error propagation problem, which is a typical problem in DFEs, we also propose a reliability factor for HDS. Various simulations were executed to analyze the performance of the proposed methods. The proposed methods showed fast processing speed after training, superior bit error rate performance, and consistency.

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We propose a combination method for a diffusion affine projection algorithm. There are two steps to estimate unknown optimal weight in Adapt-then-combine strategy of diffusion adaptive filtering. In an adaptation step, variable step-size affine projection algorithm works to get an improvement of convergence rate and small steady-state error. In a diffusion step, the proposed combination method is applied to achieve small steady-state errors without additional communicational complexity. The cooperative strategy using information across the network achieves fast convergence rate, small steady-state error and robustness against outliers compared to a non-cooperative way. The proposed algorithm is motivated from the optimal combination rule and it has a physical meaning of weighting on how close the estimates reach in steady state.