Abstract: The present disclosure includes apparatus, systems and techniques relating to iterative decoder memory arrangement. A described apparatus includes a single R memory component including R banks, a Q memory component including Q banks, a channel detector memory component to store channel extrinsic information associated with current and previous codewords, and an iterative decoder communicatively coupled with the single R memory component, the Q memory component, and the channel detector memory component. The apparatus can be configured to alternate among the R banks for storing R data associated with a current codeword. The apparatus can be configured to alternate among the Q banks for storing Q data associated with a current codeword.

Abstract: Systems and methods for decoding low density parity check (LDPC) codes are provided. An input message, representing a codeword encoded using a parity check matrix, is processed and data associated with each of the layers of the parity check matrix is computed. A first layer of the parity check matrix includes a first circulant configured to be updated using the data associated with a second layer of the parity check matrix. A second circulant in the first layer of the parity check matrix, configured to be updated using the data associated with the second layer of the parity check matrix, is identified. The first and second circulants are updated using the data associated with the first and second layers of the parity check matrix.

Abstract: Systems and methods for adaptively operating a storage device are provided. A level of integrity of storing data in the storage device is determined. A coding scheme is selected based on the determined level of integrity of the storage device. An operation is performed on the storage device using the selected coding scheme.

Abstract: A system includes a first channel detection module configured to receive a first channel estimate including a plurality of correlated data bits from a communication channel. The correlated data bits include statistical relationships between different data bits based on the statistical relationships, group together the plurality of correlated data bits, and generate a first probability vector based on the correlated data bits as grouped together. The first probability vector includes probabilities that each of the correlated data bits has a respective value. A computation module is configured to generate bit estimations based on the first probability vector. A decoding module is configured to, based on the bit estimations, selectively generate an output signal corresponding to the bit estimations, and selectively generate a feedback signal. The computation module is further configured to generate the bit estimations based on the feedback signal.

Abstract: Encoder and decoder apparatus and methods derive a plurality of parity bits from a single codeword. Encoder apparatus may include a receive module receiving a data stream, a parity generation module generating a plurality of parity bits based on the data stream and a word of a tensor-product code, and a parity insertion module combining the plurality of parity bits and the data stream to generate encoded bits. Decoder apparatus may include a detector receiving and outputting encoded data, a first decoder generating first log-likelihood ratios (LLRs) from the encoded data, an error recovery module generating second LLRs from the encoded data, a second decoder that derives syndrome data from the first and second LLRs, a post-processor that combines data from the first decoder with error events from the error recovery module to generate corrected data, the post-processor further identifying a plurality of parity bits in the corrected data.

Abstract: Systems and methods for decoding low density parity check (LDPC) codes are provided. An input message, representing a codeword encoded using a parity check matrix, is processed and data associated with each of the layers of the parity check matrix is computed. A first layer of the parity check matrix includes a first circulant configured to be updated using the data associated with a second layer of the parity check matrix. A second circulant in the first layer of the parity check matrix, configured to be updated using the data associated with the second layer of the parity check matrix, is identified. The first and second circulants are updated using the data associated with the first and second layers of the parity check matrix.

Abstract: A low-density parity check (LDPC) decoder comprises a decoded data stream generator that generates a decoded data stream based on a received data stream and a set of matrix-based codewords. The matrix-based codewords form a LDPC parity check matrix H. A decoder control module at least one of prewrites or replaces a selected portion of at least one of the plurality of codewords with zeros prior to generation of the decoded data stream.

Abstract: A decoder system comprises a tensor-product code (TPC) decoder that decodes a received data stream to generate a decoded signal. A mark module that replaces low-density parity check (LDPC) parity bits of the decoded signal with 0s to generate a reset output signal. A deinterleave module deinterleaves error correction parity bits that are within the reset output signal to generate a deinterleaved signal that comprises a decoded portion and a concatenated portion. The concatenated portion comprises the error correction parity bits. A parity decoder module removes the concatenated portion from the deinterleaved signal.

Abstract: A group of numbers from which the smallest and second-smallest are to be selected are compared in a cascaded tree. Each comparison stage will select the smallest number from two numbers output by the previous stage, into which four numbers are input. The second-smallest number is one of the other three inputs to the previous stage and, as before, all bits of the second-smallest number will not be known until the smallest number is determined. However, because at each stage of the determination, the next stage is reached because the bit values being examined are the same, those bit values of the second-smallest number (and indeed of the smallest number) are known ahead of the final determination of the smallest number. Accordingly, one can begin to output bits of the second-smallest number (as well as of the smallest number) even before that final determination.

Abstract: The present disclosure includes apparatus, systems and techniques relating to iterative decoder memory arrangement. In some implementations, an apparatus includes a memory module to communicate with an iterative code decoder. The memory module includes a single R memory component to store R data associated with a current codeword, and R data associated with a previous codeword. The memory module includes a Q memory component to store Q data associated with the current codeword, and Q data associated with the previous codeword. The memory module includes a channel detector memory component to store channel extrinsic information.

Abstract: Systems, methods, and other embodiments associated with iterative decoders are described. According to one embodiment, an apparatus includes a set of decoders that are configured to receive data to be decoded. The apparatus may also include a controller configured to separately control each decoder to initiate a decoding sequence based on an occurrence of a transition point. The transition point is a global transition that occurs iteratively for the set of decoders and is based on iterations in a decoding sequence.

Abstract: A decoding system for a communication channel includes N parallel channel detection modules that generate N first probability vectors based on sequences of X correlated bits in each of N groups of correlated bits, respectively. N parallel updating modules generate M second probability vectors based on the N first probability vectors and N feedback signals. N parallel estimation modules generate estimates of the X correlated bits in each of the N groups of correlated bits based on the M second probability vectors. N parallel decoding modules generate the N feedback signals and N output signals based on the estimates of the X correlated bits in each of the N groups of correlated bits. X is an integer greater than one, M is an integer greater than or equal to one, and N is an integer greater than or equal to M.

Abstract: A decoder memory system comprises a first memory comprising at least a portion of a parity check matrix H. A second memory receives the portion from the first memory and that is associated with a previous decoding iteration. A third memory communicates with the first memory, receives the parity check matrix H and is associated with a current decoding iteration. A fourth memory comprises likelihood ratios. A control module generates a LDPC decoded signal based on the parity check matrix H, the previous decoded iteration and the likelihood ratios.

Abstract: In one or more embodiments, a method, computer-readable media, and/or computational unit acts or is capable of receiving, from a single, integrated memory, current and previous iterations of Log Likelihood Ratio (“LLR”) parameters for a current iteration of a Low-Density Parity-Check code (“LDPC code”) error-correcting process. These may then perform an LDPC code error-correcting algorithm using the current and previous iterations of LLR parameters. Following this, these embodiment(s) may overwrite the previous iteration of LLR parameters with a now-current iteration of LLR parameters and treat the current iteration of LLR parameters as a now-previous iteration of LLR parameters. Both of these iterations of LLR parameters for the now-current iteration may then be received following overwrite of the previous iteration of LLR parameters with the now-current iteration of LLR parameters.

Abstract: Systems, methods, and other embodiments associated with decoder based data recovery are described. According to one embodiment, an apparatus includes a decoder configured to perform a decoding process on codewords to verify that the codewords meet coding constraints. The decoder includes a recovery unit configured to store recovery instructions for performing a modified decoding process. The recovery unit is further configured to execute the stored recovery instructions when a decoded codeword fails to meet the coding constraints.

Abstract: An encoder system includes a receive module that receives a data stream. A parity generation module generates parity bits based on the data stream and a tensor-product code. A parity insertion module combines the parity bits and the data stream to generate encoded bits.

Abstract: A decoder for error correction an encoded message, such as one encoded by a turbo encoder, with reduced iterations due to an improved stopping criterion. The decoder includes an error correction loop that iteratively processes a message that is encoded prior to transmittal over a communication channel. The error correction loop generates, such as with a Reed-Solomon decoder, an error location polynomial in each iterative process. A stopping mechanism in the decoder allows an additional iteration of the message decoding based on the error location polynomial, such as by obtaining the degree of the error location polynomial and comparing it to a threshold. In one example, the threshold is the maximum number of symbol errors correctable by the Reed-Solomon code embodied in the decoder. The stopping mechanism allows additional iterations when the stopping criterion (or polynomial degree) is greater than the maximum number of symbol errors correctable by the Reed-Solomon code.

Abstract: Low density parity check (LDPC) codes (LDPCCs) have an identical code blocklength and different code rates. At least one of the rows of a higher-rate LDPC matrix is obtained by combining a plurality of rows of a lower-rate LDPC matrix with the identical code blocklength as the higher-rate LDPC matrix.

Abstract: A decoder for error correction an encoded message, such as one encoded by a turbo encoder, with reduced iterations due to an improved stopping criterion. The decoder includes an error correction loop that iteratively processes a message that is encoded prior to transmittal over a communication channel. The error correction loop generates, such as with a Reed-Solomon decoder, an error location polynomial in each iterative process. A stopping mechanism in the decoder allows an additional iteration of the message decoding based on the error location polynomial, such as by obtaining the degree of the error location polynomial and comparing it to a threshold. In one example, the threshold is the maximum number of symbol errors correctable by the Reed-Solomon code embodied in the decoder. The stopping mechanism allows additional iterations when the stopping criterion (or polynomial degree) is greater than the maximum number of symbol errors correctable by the Reed-Solomon code.

Abstract: A decoder for error correction an encoded message, such as one encoded by a turbo encoder, with reduced iterations due to an improved stopping criterion. The decoder includes an error correction loop that iteratively processes a message that is encoded prior to transmittal over a communication channel. The error correction loop generates, such as with a Reed-Solomon decoder, an error location polynomial in each iterative process. A stopping mechanism in the decoder allows an additional iteration of the message decoding based on the error location polynomial, such as by obtaining the degree of the error location polynomial and comparing it to a threshold. In one example, the threshold is the maximum number of symbol errors correctable by the Reed-Solomon code embodied in the decoder. The stopping mechanism allows additional iterations when the stopping criterion (or polynomial degree) is greater than the maximum number of symbol errors correctable by the Reed-Solomon code.