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 (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.

In high-density recording systems performance is largely determined by noise, n general different noise sources are present, where each source has direct impact on the overall performance. Therefore the characterization of a noise source provides insightful information about the recording system. In general noise characterization involves the choice of an appropriate stochastic model for it and furthermore the estimation of the parameters of the selected stochastic model. The characterization results can be used as diagnostic information to evaluate existing recording systems and they play an important role in state of the art bit-detection techniques. In these techniques the estimated stochastic parameters are used as side information to improve the detection reliability. For this reason an accurate estimation is very important. The estimation algorithm proposed in this paper, achieves a high estimation accuracy for systems where both data-dependent media noise and correlated additive noise are present. This algorithm makes use of the difference in data-dependency between the two noise sources to jointly estimate the stochastic parameters of the two noise sources.

In order to reduce the implementation complexity of maximum likelihood sequence detectors (MLSD), equalized maximum likelihood receivers are often used. This consists of employing an equalizer to transform the channel response to a short target response to which the Viterbi detector is matched. Existing equalizer and target adaptation schemes are often based on the minimum mean-square error (MMSE) criterion which is not always optimal in terms of detection bit-error rate at the Viterbi detector output. In this paper we consider minimum bit-error rate joint adaptation of equalizer and target response and present a practical adaptation algorithm that achieves near minimum bit-error rate performance. Our new equalizer and target adaptation scheme shows significant performance improvements in the presence of channel nonlinearities and media noise when compared to MMSE adaptation schemes. This is very promising for high density recording systems that are mainly hampered by media noise and channel nonlinearities. Moreover, from a complexity standpoint, the proposed algorithm is comparable to the MMSE-based algorithms.

In this paper a channel model is proposed for two-dimensional digital data storage systems. This channel model describes both the intersymbol interference structure and the noise structure. This noise structure is modelled as the output of a two-dimensional data-dependent autoregressive filter. The proposed model is simple, accurate and it is able to track channel variations. An experimental two-dimensional optical system is used to validate the proposed model and to illustrate the characterization process based on the model

Data receivers for storage systems normally operate at a fixed sampling rate 1/TSthat is asynchronous to the baud rate 1/T. A sampling-rate converter (SRC) serves to convert the incoming signal from the asynchronous to the synchronous clock domain. These receivers also contain an equalizer that serves to suppress intersymbol interference and noise. To limit receiver complexity, equalization burden can be shifted towards the SRC. This possibility is not exploited in any existing SRC. This paper presents SRC design methods that combine group delay flatness and out-of-band rejection criteria with the minimum mean square error equalization criterion. Numerical examples for an idealized optical recording channel validate the design methods

In this paper the cancellation of linear Intersymbol Interference (ISI) in two-dimensional systems is discussed. A theory is developed for the error rate of receivers that use tentative decisions to cancel ISI. Furthermore, precise conditions are formulated under which such ISI cancellation can be applied effectively. For many two-dimensional systems these conditions are easily met and therefore the application of ISI cancellation is of significant interest for these systems. The theory and the conditions are validated by simulation results for a two-dimensional channel model. Furthermore, results for an experimental two-dimensional optical storage system show that for a single layer disc with a capacity of 50 GB a substantial performance improvement may be obtained by applying ISI cancellation.

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 algorithms

With storage capacities increasing much faster than data rates, fast read-out of content is becoming a bottleneck for the convenient use of optical storage devices. Two-dimensional optical storage (TwoDOS) is a new concept that solves this data-rate problem by using a multi-spot parallel readout system. In addition, the storage capacity is increased with a factor of at least 2. Using the same read-out physics as in the Blu-ray disc standard, single layered 12 cm discs with capacities up to 50 GB have been read out successfully at bit-rates as high as 560 Mbit/s. Basic pillars of TwoDOS are advanced signal processing and disc mastering techniques, and a proper design of the optical path.

The paper introduces the concept of two-dimensional optical storage (TwoDOS). In this concept, bits are written in a broad spiral consisting of a number of bit-rows stacked together in a hexagonal packing. Bits with a value '1' are represented physically as circular pit-holes on the disc, while bits with a value '0' are characterized by the absence of such a pit-hole. A scalar diffraction model is used to calculate the signal levels for various diameters of the pits. A stripe-wise Viterbi detector is proposed to perform 2D bit-detection with a limited state complexity of the trellis. Simulation results are shown for various diameters of the pits. A 2D modulation code is applied to eliminate patterns that yield a high probability of erroneous detection.