Provided are a bit flipping device and method and a computer readable program for the same. The bit flipping device for input data having a two-dimensional array pattern includes: a clustering unit configured to generate at least one input data sequence based on the two-dimensional array pattern of the input data and classify the input data sequence into at least one cluster according to a preset method; and a bit flipping unit configured to perform bit flipping on erroneous bits in the input data sequence based on the classified cluster. Therefore, it is possible to further reduce inefficiency while further reducing system complexity compared to the existing error correction code-based bit flipping method by coupling the bit flipping device to an output side of a partial response maximum likelihood (PRML) detector to classify an output value of the PRML detector into at least one cluster and perform bit flipping based on the classified result.

A method comprises: receiving, from a communication channel, non-binary multilevel symbols that represent corresponding multibit labels each including at least a least-significant bit (LSB) and a most-significant bit (MSB), the non-binary multilevel symbols mapped to the multibit labels according to set-partition labeling, which partitions the non-binary multilevel symbols between a first set and a second set according to a first value and a second value of the LSB, respectively; digitizing the non-binary multilevel symbols to produce symbol samples; and performing Soft-Output-Viterbi (SOV) equalization of the non-binary multilevel symbols based on the symbol samples, to produce decoded symbol information corresponding to the non-binary multilevel symbols.

Example systems, read channels, and methods provide bit value detection from an encoded data signal using a neural network soft information detector. The neural network detector determines a set of probabilities for possible states of a data symbol from the encoded data signal. A soft output detector uses the set of probabilities for possible states of the data symbol to determine a set of bit probabilities that are iteratively exchanged as extrinsic information with an iterative decoder for making decoding decisions. The iterative decoder outputs decoded bit values for a data unit that includes the data symbol.

A method of soft-decision decoding including training a machine learning agent with communication signal training data; providing to the trained machine learning agent a signal that has been received via a communications channel; operating the machine learning agent to determine respective probabilities that the received signal corresponds to each of a plurality of symbols; and, based on the determined probabilities, performing soft decision decoding on the received signal.

A cyclo-stationary characteristic of a communications channel and/or storage media is determined. The cyclo-stationary characteristic has K-cycles, K > 1. Markov transition probabilities are determined that depend on a discrete phase ϕ=t mod K, wherein t is a discrete time value. An encoder to optimize the Markov transition probabilities for encoding data sent through the communications channel and/or stored on the storage media. The optimized Markov transition probabilities are used to decode the data from the communication channel and/or read from the storage media.

Example systems, read channel circuits, data storage devices, and methods to provide signal correction based on soft information in a read channel are described. The read channel circuit includes a soft output detector, such as a soft output Viterbi algorithm (SOVA) detector, and a signal correction circuit. The soft output detector passes detected data bits and corresponding soft information to the signal correction circuit. The signal correction circuit uses the soft information to determine a signal correction value, which is combined with input signal to return a corrected signal to the soft output detector for a next iteration. In some configurations, the signal correction value may compensate for DC offset, AC coupling poles, and/or signal asymmetries to reduce baseline wander in the read channel.

The present disclosure includes apparatus, systems, and techniques relating to noise-predictive detector adaptation. A described technique includes operating a decoder system to decode codewords that are based on a received encoded signal by processing the codewords and exchanging information between path and code decoders, operating the path decoder to use estimation parameters to produce first and second paths based on a codeword of the codewords, operating the code decoder to produce a decoded path based on the codeword; determining a winning path of first and second paths based on whether the decoded path matches the first path or the second path; and updating, based on one or more error terms and the winning path, the estimation parameters to favor selection of the winning path by the path decoder and to disfavor selection of a losing path of the first and second paths by the path decoder.

Example systems, read channel circuits, data storage devices, and methods to use inter-symbol interference message passing (ISI-MP) data in a read channel are described. The read channel circuit includes a soft output detector, such as a soft output Viterbi algorithm (SOVA) detector, configured to determine both the first most likely and second most likely sets of symbols and output inter-symbol interference data based on the adjacent symbols and corresponding ISI in each set of symbols. The inter-symbol interference data may be used by an ISI-MP circuit configured to model ISI-MP and provide feedback to an iterative decoder during local iterations.

A soft decision audio decoding system for preserving audio continuity in a digital wireless audio receiver is provided that deduces the likelihood of errors in a received digital signal, based on generated hard bits and soft bits. The soft bits may be utilized by a soft audio decoder to determine whether the digital signal should be decoded or muted. The soft bits may be generated based on a degree of closeness of a detected phase trajectory to known legal phase trajectories determined from the running the phase trajectory through a soft-output Viterbi algorithm. The value of the soft bits may indicate confidence in the strength of the hard bit generation. The soft decision audio decoding system may infer errors and decode perceptually acceptable audio without requiring error detection, as in conventional systems, as well as have low latency and improved granularity.

Methods and apparatus are provided for calculating branch metrics, associated with possible transitions between states of a trellis, in a sequence detector for detecting symbol values corresponding to samples of an analog signal transmitted over a channel. For each sample and each transition, the method calculates a plurality of distance values indicative of distance between that sample and respective hypothesized sample values for that transition. In parallel with calculation of the distance values, the sample is compared with a set of thresholds, each defined between a pair of successive hypothesized symbol values arranged in value order, to produce a comparison result. An optimum distance value is selected as a branch metric for the transition in dependence on the comparison result.