A two-dimensional partial response maximum likelihood detection scheme for misalignment compensation in holographic data storage systems

Abstract

A two-dimensional (2D) partial response maximum likelihood (PRML) detection scheme for misalignment compensation for holographic data storage systems is proposed. In the proposed detection scheme, a misalignment estimator and a predetermined table are added to the conventional 2D PRML scheme to estimate and reflect the effect of misalignment in the vertical and horizontal directions, respectively. In the vertical detection, the misalignment estimator located behind the vertical 2D equalizer and one-dimensional PRML detector estimates the misalignment by the adaptive least-mean-square algorithm (LMS). To improve the estimation accuracy, the misalignment is estimated iteratively by modifying the vertical equalizer coefficients reflecting the estimated misalignment. By considering the estimated misalignment, the vertical 2D equalizer coefficients, horizontal 2D equalizer coefficients, and 2D partial response (PR) target are selected among the predetermined table. In the predetermined table, the vertical and horizontal 2D equalizer coefficients for various misalignments were previously computed on the basis of the misalignment and 2D PR target by the LMS. By employing the horizontal 2D equalizer coefficients and 2D PR target reflecting the misalignment in the horizontal detection, the effect of misalignment is effectively mitigated. Simulation results show that the proposed scheme has more than 3 dB gains than the conventional scheme in the 10% misalignment in the horizontal and vertical directions.

An ant colony optimization aided two-dimensional soft output viterbi algorithm detection scheme for holographic data storage systems

“…Therefore, the number of candidate vectors for the exhaustive search is 2 15 = 32 768, which is too complex. To achieve high reliability with low complexity, we employ the low-complexity ACO algorithm 20,21) for the detection of the unreliable bits, where ants search iteratively for the better solution utilizing pheromone-based communication. In the multiple-input multiple-output detection scheme 28) for wireless communication systems, the ACO detector can achieve the near-optimal BER performances by only searching less than 1% candidate vectors compared to the exhaustive search.…”

“…5,6) Therefore, more powerful signal processing techniques are necessarily required to achieve high-density recording in HDS systems. [7][8][9][10][11] Various detection schemes [12][13][14][15][16][17][18][19][20][21][22] have been introduced for HDS systems. A one-dimensional (1D) partial response maximum likelihood (PRML) detection scheme using a 2D equalizer with a 1D partial response (PR) target was proposed in Ref.…”

Equalization scheme with misalignment estimation based on multi-layer perceptrons for holographic data storage systems

“…4) Therefore, many techniques have been studied to mitigate 2D ISI and achieve high-density recording such as partial response maximum likelihood (PRML) detection. [5][6][7][8][9][10][11][12][13][14][15] In addition, the BER performances are seriously degraded when the misalignment arises in the HDS systems. 14) In the conventional schemes, the misalignment is compensated by taking the estimated misalignment into account in design of a equalizer and a detector.…”

“…[5][6][7][8][9][10][11][12][13][14][15] In addition, the BER performances are seriously degraded when the misalignment arises in the HDS systems. 14) In the conventional schemes, the misalignment is compensated by taking the estimated misalignment into account in design of a equalizer and a detector. 14) In this paper, we propose a neural network (NN) based equalization scheme with misalignment estimation based on the multi-layer perceptron (MLP), which is robust to the channel with misalignment.…”

“…14) In the conventional schemes, the misalignment is compensated by taking the estimated misalignment into account in design of a equalizer and a detector. 14) In this paper, we propose a neural network (NN) based equalization scheme with misalignment estimation based on the multi-layer perceptron (MLP), which is robust to the channel with misalignment. MLP is a class of artificial NNs and well known as a useful techniques for modeling of nonlinear function.…”