Systems and methods are disclosed for a multiple detector data channel and data detection utilizing different cost functions. For example, a digital data channel system can have multiple data detectors where each data detector implements a distinct cost function for detecting data. A cost function analyzer can then selectively choose decisions from the multiple data detectors to generate a data sequence. In some examples, a dual detector system may have one detector implement a Soft-Output Viterbi Algorithm (SOVA) cost function and another detector implement a peak detection algorithm. Further, in some embodiments, the cost function analyzer can implement multiple selection criteria to determine which decisions to include in a data sequence from the multiple data detectors.

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 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 the detected point and a detected noise power, or by using 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.

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 deep neural network (DNN) media noise predictor configured for one-dimensional-magnetic (1DMR) recording or two-dimensional-magnetic (TDMR) is introduced. Such architectures are often combined with a trellis-based intersymbol interference (ISI) detection component in a turbo architecture to avoid the state explosion problem by separating the inter-symbol interference (ISI) detection and media noise estimation into two separate detectors and uses the turbo-principle to exchange information between them so as to address the modeling problem by way of training a DNN-based media noise estimators. Thus, beneficial aspects include a reduced bit-error rate (BER), an increased areal density, and a reduction in computational complexity and computational time.

A decoding method performed by a receive end device is disclosed. The decoding method includes: receiving a first bit signal; performing level-M forward error correction (FEC) decoding on the first bit signal to obtain a second bit signal, where M is a positive integer greater than zero; checking the second bit signal to obtain a first check result; performing level-(M+1) FEC decoding on the second bit signal based on the first check result to obtain a third bit signal; and, upon determining that M+1 reaches a first preset threshold, performing data processing on the third bit signal to obtain a fourth bit signal, where the fourth bit signal is used by the receive end device to obtain service data transmitted by a transmit end device.

A Viterbi Equalizer having a limited number of stages is disclosed. In some embodiments, the Viterbi Equalizer may have only four stages. The Viterbi Equalizer produces soft decisions, which comprise a final decision and reliability information related to that final decision. The Viterbi Equalizer is able to provide reliability information even if all paths do not converge on the final decision at the last stage. The reliability information is calculated based on if and when the paths in the trellis converge on a final decision. This reliability information can be used downstream, such as by another Viterbi Algorithm block to perform forward error correction. The use of soft decision provides gains of up to several dB in performance. Additionally, the Viterbi Equalizer is low cost and readily implemented in hardware or software.

A method and apparatus for decoding are disclosed. The method includes receiving a first Forward Error Correction (FEC) block of read values, starting a hard-decode process in which a number of check node failures is identified and, during the hard-decode process comparing the identified number of check node failures to a decode threshold. When the identified number of check node failures is not greater than the decode threshold the hard-decode process is continued. When the identified number of check node failures is greater than the decode threshold, the method includes: stopping the hard-decode process prior to completion of the hard-decode process; generating output indicating that additional reads are required; receiving one or more additional FEC blocks of read values, mapping the first FEC block of read values and the additional FEC blocks of read values into soft-input values; and performing a soft-decode process on the soft-input values.

The invention discloses a method and a receiving device of the Viterbi algorithm. The method is applicable for a Viterbi decoder that receives an output signal generated by a convolution code encoder processing an original signal. The convolution code encoder includes M registers and M is a positive integer greater than or equal to 2. The method includes the following steps. First, for the first to the Mth data of the output signal, the Viterbi decoder performs the add-compare-select operation based on the known M initial values of the M registers. Then, for the Mth-last to the last data of the output signal, the Viterbi decoder performs the add-compare-select operation based on the known last M bits values of the original signal, thereby reducing the computational complexity of the add-compare-select unit.

A decoding method performed by a receive end device is disclosed. The decoding method includes: receiving a first bit signal; performing level-M forward error correction (FEC) decoding on the first bit signal to obtain a second bit signal, where M is a positive integer greater than zero; checking the second bit signal to obtain a first check result; performing level-(M+1) FEC decoding on the second bit signal based on the first check result to obtain a third bit signal; and, upon determining that M+1 reaches a first preset threshold, performing data processing on the third bit signal to obtain a fourth bit signal, where the fourth bit signal is used by the receive end device to obtain service data transmitted by a transmit end device.