An apparatus for decoding data includes a data decoding circuit configured to decode data encoded with an irregular low density parity check code based on a parity check matrix with non-uniform column weights, and at least one scaling circuit configured to scale values in the data decoding circuit with a scaling value that is dependent at least in part on a column weight of the likelihood values being scaled.

A bit interleaver, a bit-interleaved coded modulation (BICM) device and a bit interleaving method are disclosed herein. The bit interleaver includes a first memory, a processor, and a second memory. The first memory stores a low-density parity check (LDPC) codeword having a length of 64800 and a code rate of 2/15. The processor generates an interleaved codeword by interleaving the LDPC codeword on a bit group basis. The size of the bit group corresponds to a parallel factor of the LDPC codeword. The second memory provides the interleaved codeword to a modulator for 1024-symbol mapping.

A bit interleaver, a bit-interleaved coded modulation (BICM) device and a bit interleaving method are disclosed herein. The bit interleaver includes a first memory, a processor, and a second memory. The first memory stores a low-density parity check (LDPC) codeword having a length of 64800 and a code rate of 3/15. The processor generates an interleaved codeword by interleaving the LDPC codeword on a bit group basis. The size of the bit group corresponds to a parallel factor of the LDPC codeword. The second memory provides the interleaved codeword to a modulator for 4096-symbol mapping.

A bit interleaver, a bit-interleaved coded modulation (BICM) device and a bit interleaving method are disclosed herein. The bit interleaver includes a first memory, a processor, and a second memory. The first memory stores a low-density parity check (LDPC) codeword having a length of 64800 and a code rate of 3/15. The processor generates an interleaved codeword by interleaving the LDPC codeword on a bit group basis. The size of the bit group corresponds to a parallel factor of the LDPC codeword. The second memory provides the interleaved codeword to a modulator for 4096-symbol mapping.

A bit interleaver, a bit-interleaved coded modulation (BICM) device and a bit interleaving method are disclosed herein. The bit interleaver includes a first memory, a processor, and a second memory. The first memory stores a low-density parity check (LDPC) codeword having a length of 64800 and a code rate of 2/15. The processor generates an interleaved codeword by interleaving the LDPC codeword on a bit group basis. The size of the bit group corresponds to a parallel factor of the LDPC codeword. The second memory provides the interleaved codeword to a modulator for 1024-symbol mapping.

A multi-gear ECC decoder includes a high power decoder and a low power decoder. In order to significantly reduce the decoding time for high-BER codewords using a slow high power decoder, rather than decoding codewords in either slow high power or fast low power, a controller switches between slow high power decoding and fast low power decoding during the decoding process. The controller first will determine, based on a predetermined factor, whether to start decoding in slow high power or fast low power. Once a decoding power is determined, then the decoding will begin. During the decoding process the decoding transitions from a first power lever decoder to a second power level decoder. The decoding will continue in the second decoding power level after the transition, until the decoding is completed or if another switch needs to occur for insufficient decoding.

An apparatus includes an interface, main and secondary processing modules, and circuitry. The interface is configured to receive input data to be processed in accordance with a GLDPC code defined by a parity-check-matrix including multiple sub-matrices, each sub-matrix including a main diagonal and one or more secondary diagonals, and each of the main and secondary diagonals includes N respective block matrices. The main processing module is configured to calculate N first partial syndromes based on the input data and on the block matrices of the main diagonals. The secondary processing module is configured to calculate N second partial syndromes based on the input data and on the block matrices of the secondary diagonals. The circuitry is configured to produce N syndromes by respectively combining the N first partial syndromes with the N second partial syndromes, and to encode or decode the input data, based on the N syndromes.

Example systems, data storage devices, testers, and methods for storage device configuration using mutual information are described. A data storage device may include channel circuit configuration settings for the encoding and decoding of data written to a non-volatile storage medium. Mutual information metrics may be calculated based on a multi-bit symbol size to compensate for inter-symbol interference and compared to mutual information thresholds to determine the configuration settings, such as bit and track densities, error correction codes, and modulation codes. Mutual information metrics may be used to characterize heads and media independent of the configuration settings.

Example systems, data storage devices, testers, and methods for storage device configuration using symbol context mutual information are described. A data storage device may include channel circuit configuration settings for the encoding and decoding of data written to a non-volatile storage medium. The configuration settings may be determined by determining a known pattern for a sector, determining a series of symbol contexts, determining mutual information for each symbol context, and using the symbol context mutual information to determine relationships among configuration settings, such as bit size, error correction code rate, and modulation code. Once determined, the configuration settings may be used to configure the modulation code and ECC rate for the channel circuit of the data storage device.

Technology is described herein for learning parameters for a parameterized iterative message passing decoder, and to a corresponding parameterized iterative message passing decoder. Learning the parameters may adapt the decoder to statistical dependencies introduced by the specific code's graph. Taking into account the statistical dependencies may allow the code to be shorter and/or denser. Note that the statistical dependencies in the graph may be extremely complex. Machine learning may be used to learn the parameters. The parameters may be learned when decoding data stored in the memory device. Learning the parameters may adapt the decoder to properties of data stored in the memory device, physical properties of the memory device, and/or patterns in host data.