Correlation-sensitive adaptive sequence detection

Kavcic, A.; Moura, J.M.F.

doi:10.1109/20.668084

Cited by 41 publications

(25 citation statements)

References 26 publications

(38 reference statements)

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“…The maximum likelihood sequence detector (MLSD) for a signal-dependent Gaussian noise has been first derived in [10] under the assumption that the noise can be modeled as an autoregressive (AR) process: the resulting structure is a Viterbi algorithm which incorporates signal-dependent noise prediction into the branch metric computation [11]. Finally, in [12], linear prediction extended to signal-dependent transition noise was applied to near-MLSD and other low-complexity sequence detection structures.…”

Section: Introductionmentioning

confidence: 99%

Multidimensional signal processing and detection for storage systems with data-dependent transition noise

Pighi

Raheli

Amadei³

2006

IEEE Trans. Magn.

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In the last decade, significant research on detection algorithms capable of mitigating the effects of colored Gaussian thermal noise and transition noise in storage systems, has been performed. In this paper, we present a new detection scheme based on a multidimensional detector front end and multidimensional linear prediction, applied to maximum a posteriori probability (MAP) sequence detection. This method improves the bit-error-rate (BER) performance with respect to previous approaches and makes the detector quite insensitive to transition noise. We show that the gain in terms of BER versus signal-to-noise ratio with our detector increases with the user density. The results obtained for a magnetic storage channel are extendable to optical storage systems as well.Index Terms-Longitudinal and perpendicular recording, magnetic storage systems, multidimensional linear prediction, multidimensional signal processing, optical storage systems, transition noise.

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Section: Introductionmentioning

confidence: 99%

Multidimensional signal processing and detection for storage systems with data-dependent transition noise

Pighi

Raheli

Amadei³

2006

IEEE Trans. Magn.

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“…The data dependent noise arises due to the statistics of percolation and nonlinear effects between transitions [1]. In [2], an experimental evidence is provided confirming the presence of a data-dependent noise in highdensity magnetic recording.…”

Section: Introductionmentioning

confidence: 94%

Lower bound for ML sequence detection in ISI channels with Gauss Markov noise

Kumar

2013

2013 IEEE International Conference on Communications (ICC)

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In past, inter-symbol interference (ISI) channels with data dependent Gauss Markov noise have been prevalently used to model read channels for magnetic recording and other data storage systems. The maximum likelihood sequence detection in such channels is solved using the Viterbi algorithm. However, the problem of finding an analytical lower bound on the bit error rate of the Viterbi detector in this case has not been fully investigated. Current techniques do not give the tight lower bound as the bound computation involves only minimum-length error events. In this work, we consider a subset of the class of ISI channels with data dependent Gauss-Markov noise. We derive a lower bound on the pairwise error probability (PEP) between the correct bit sequence and the estimated bit sequence that can be expressed as a product of functions depending on current and previous states of the correct and the estimated sequence. In this case, the PEP is asymmetric which precludes the usage of error state diagram method for the bound computation. We present a method of finding a lower bound on the BER through constructing a product trellis. Simulations results corroborate the analysis of the lower bound and demonstrate that the analytic lower bound on BER is tight in a high SNR regime.

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“…The received samples were also post-processed with a CS-MLSE based on [9]. This algorithm considers the correlation of the samples by assuming that they follow a multivariate Gaussian distribution.…”

Section: Correlation Sensitive Mlsementioning

confidence: 99%