Detection of Markov Random Fields on Two-Dimensional Intersymbol Interference Channels

TL;DR

This paper introduces an iterative detection algorithm combining two soft-input/soft-output detectors for binary Markov random fields affected by 2D intersymbol interference and noise, improving detection accuracy in optical storage channels.

Contribution

A novel concatenated iterative detection algorithm for 2D MRFs with ISI, demonstrating significant SNR savings and robustness over existing methods.

Findings

Achieves 0.5 to 2.0 dB SNR improvement at BER 10^{-5}.

Performance improves with increased MRF correlation and lower SNR.

Robust to parameter mismatches in MRF detection.

Abstract

We present a novel iterative algorithm for detection of binary Markov random fields (MRFs) corrupted by two-dimensional (2D) intersymbol interference (ISI) and additive white Gaussian noise (AWGN). We assume a first-order binary MRF as a simple model for correlated images. We assume a 2D digital storage channel, where the MRF is interleaved before being written and then read by a 2D transducer; such channels occur in recently proposed optical disk storage systems. The detection algorithm is a concatenation of two soft-input/soft-output (SISO) detectors: an iterative row-column soft-decision feedback (IRCSDF) ISI detector, and a MRF detector. The MRF detector is a SISO version of the stochastic relaxation algorithm by Geman and Geman in IEEE Trans. Pattern Anal. and Mach. Intell., Nov. 1984. On the 2 x 2 averaging-mask ISI channel, at a bit error rate (BER) of 10^{-5}, the concatenated algorithm achieves SNR savings of between 0.5 and 2.0 dB over the IRCSDF detector alone; the savings increase as the MRFs become more correlated, or as the SNR decreases. The algorithm is also fairly robust to mismatches between the assumed and actual MRF parameters.