Maximum-Likelihood Sequence Detector for Dynamic Mode High Density Probe Storage

There is an increasing need for high density data storage devices driven by the increased demand of consumer electronics. In this work, we consider a data storage system that operates by encoding information as topographic profiles on a polymer medium. A cantilever probe with a sharp tip (few nm radius) is used to create and sense the presence of topographic profiles, resulting in a density of few Tb per in.2. The prevalent mode of using the cantilever probe is the static mode that is harsh on the probe and the media. In this article, the high quality factor dynamic mode operation, that is less harsh on the media and the probe, is analyzed. The read operation is modeled as a communication channel which incorporates system memory due to inter-symbol interference and the cantilever state. We demonstrate an appropriate level of abstraction of this complex nanoscale system that obviates the need for an involved physical model. Next, a solution to the maximum likelihood sequence detection problem based on the Viterbi algorithm is devised. Experimental and simulation results demonstrate that the performance of this detector is several orders of magnitude better than the performance of other existing schemes.Comment: This paper is published in IEEE Trans. on communicatio

Non-Binary Message-Passing Algorithms for Magnetic Channels with Data-Dependent Noise

The paper proposes an implementation of the message passing algorithm adapted to iterative channel detection. The algorithm uses soft messages associated to non binary symbols in order to cancel cycles in the equivalent Tanner graphs, achieving optimal performance after a low number of iterations. This architecture, suited to very fast channel detectors, is applied to magnetic recording channels and adapted to the non stationary nature of the magnetic media noise

Near minimum bit-error rate equalizer adaptation for PRML systems

Receivers for partial response maximum-likelihood systems typically use a linear equalizer followed by a Viterbi detector. The equalizer tries to confine the channel intersymbol interference to a short span in order to limit the implementation complexity of the Viterbi detector. Equalization is usually made adaptive in order to compensate for channel variations. Conventional adaptation techniques, e.g. LMS, are in general suboptimal in terms of bit-error rate. In this paper we present a new equalizer adaptation algorithm that seeks to minimize bit-error rate at the Viterbi detector output. The algorithm extracts information from the sequenced amplitude margin (SAM) histogram and incorporates a selection mechanism that focuses adaptation on particular data and noise realizations. From a complexity standpoint, the algorithm is as simple as the conventional LMS algorithm. Simulation results, for an idealized optical storage channel, confirm a substantial performance improvement relative to existing adaptation algorithm

Near minimum bit-error rate equalizer adaptation for PRML systems

Abstract-Receivers for partial response maximum-likelihood systems typically use a linear equalizer followed by a Viterbi detector. The equalizer tries to confine the channel intersymbol interferenceto a short span in order to limit the implementation complexity of the Viterbi detector. Equalization is usually made adaptive in order to compensate for channel variations. Conventional adaptation techniques, e.g., LMS, are, in general, suboptimal in terms of bit-error rate (BER). In this paper, we present a new equalizer adaptation algorithm that seeks to minimize the BER at the Viterbi detector output. The algorithm extracts information from the sequenced amplitude margin (SAM) histogram and incorporates a selection mechanism that focuses adaptation on particular data and noise realizations. The selection mechanism is based on the reliability of the add compare select (ACS) operations in the Viterbi detector. From a complexity standpoint, the algorithm is essentially as simple as the conventional LMS algorithm. Moreover, we present a further simplified version of the algorithm that does not require any hardware multiplications. Simulation results, for an idealized optical storage channel, confirm a substantial performance improvement relative to existing adaptation algorithms. Index Terms-Adaptive equalizers, intersymbol interference, partial response signaling, sequenced amplitude margin (SAM), Viterbi detection