Examples relate to a decoding apparatus, a decoding device, a decoding method, a decoding computer program, and a communication device, a memory device and a storage device comprising such a decoding apparatus or decoding method. A decoding apparatus for performing iterative decoding on a codeword comprises processing circuitry comprising a plurality of processing units, and control circuitry configured to control the iterative decoding of the codeword. The iterative decoding is based on a parity-check matrix. The matrix is sub-divided into two or more partitions. The control circuitry is configured to operate in a first mode of operation to process a codeword having a first length, and to operate in a second mode of operation to process a codeword having a second length. The control circuitry is configured to multiplex the utilization of the plurality of processing units across the two or more partitions of the matrix at least in the second mode of operation.

A forward error correction decoder and method of decoding a codeword is provided. The decoder comprises a convergence processor for estimating an expectation of codeword convergence. The convergence processor is configured to calculate a first value of a figure of merit; calculate a second value of the figure of merit; combine the second value of the figure of merit and the first value of the figure of merit to produce a progress value; compare the progress value of the decoding to a progress threshold; and increase a maximum number of iterations of the decoder if the progress value is greater than the progress threshold. The maximum number of iterations may be initially set to a low number beneficial for power consumption and raw throughput. Increasing the maximum number of iterations devotes additional resources to a particular codeword and is beneficial for error rate performance.

System and methods described herein includes a method for iterative decoding. The method includes instantiating an iterative decoding procedure to decode a codeword. At each iteration of the iterative decoding procedure, the method further includes retrieving information relating to a plurality of current decoding variables at a current iteration, determining a first current decoding variable to be skipped for the current iteration based on the information, and processing a second decoding variable without processing the first decoding variable to update related decoding variables from the plurality of current decoding variables.

Methods and systems are disclosed for decoding codewords, wherein codewords comprise at least one circulant and are stored in a first dimension of a matrix, and wherein each circulant in a codeword is associated with a location in a second dimension in the matrix. The method includes determining whether a first location in a second dimension of a first circulant of a first codeword corresponds to a second location in the second dimension of a second circulant of a second codeword. The method includes, in response to determining that the first location does not correspond to the second location, decoding the first and second circulant with a first decoding process. The method includes, in response to determining that the first location corresponds to the second location, decoding the first and second circulant with a second decoding process.

In one embodiment, an electronic system includes a decoder configured to decode an encoded data unit using multiple variable nodes and multiple check nodes to perform a low-density parity check (LDPC) decoding process. The encoded data unit can be received from a solid-state memory array. As part of performing the LDPC decoding process, the decoder can (i) convert reliability information representing first non-binary values to reliability information representing first binary values, (ii) determine reliability information representing second binary values using the reliability information representing first binary values, and (iii) convert the reliability information representing the second binary values to reliability information representing second non-binary values.

The present disclosure relates to a pre-5th-generation (5G) or 5G communication system to be provided for supporting higher data rates beyond 4th-generation (4G) communication system such as a long term evolution (LTE). A method of a receiving apparatus in a communication system supporting a low density parity check (LDPC) code is provided. The method includes deactivating variable nodes of which absolute values of log likelihood ratio (LLR) values are greater than or equal to a first threshold value; changing LLR values of variable nodes of which absolute values of LLR values are less than a second threshold value among variable nodes other than the deactivated variable nodes to a preset value, and detecting LLR values of check nodes based on LLR values of the variable nodes other than the deactivated variable nodes.

Provided is a decoder that is at least temporarily implemented by a processor of a computing device. The decoder includes a calculator configured to repeatedly perform a calculation of a bit node and a calculation of a check node for an input frame, a processor configured to determine whether to input the bit node to a next calculation of the check node based on a code of the bit node, and an outputter configured to output a decoded code based on the bit node determined to be input.

Methods and devices are disclosed for receiving and detecting sparse data sequences using a message passing algorithm (MPA) with early propagation of belief messages. Such data sequences may be used in wireless communications systems supporting multiple access, such as sparse code multiple access (SCMA) systems. The determination and passing of one or more messages for an edge between a function node and a variable node in a factor graph representation of the system may be performed in serial with determined values available early for subsequent computations. The serial computations may be scheduled based on various factors.

A decoder performs: computing (S501) a value (i,j) of a performance-improvement metric for each kernel K.sub.i,j; and sorting (S502) the kernels in a list in decreasing order of the values (i,j). The decoder then performs a beliefs propagation iterative process as follows: updating (S503) output beliefs for the W top kernels of the list , and propagating said output beliefs as input beliefs of the neighbour kernels of said W top kernels; updating (S504) output beliefs for each neighbour kernel of said W top kernels following update of their input beliefs, and re-computing (S505) the performance-improvement metric value (i,j) for each said neighbour kernel; setting (S505) the performance-improvement metric for said W top kernels to a null value; and re-ordering (S506) the kernels in the list . Then, the decoder repeats the beliefs propagation iterative process until a stop condition is met.

The present disclosure provides a method for optimizing a protograph-based LDPC code over an underwater acoustic (UAW) channel. The traditional protograph-based LDPC code over an UAW channel does not consider performance in an error floor region. The method first determines parameters such as a protograph-based LDPC code length, a basic protograph, a target decoding threshold, a threshold adjustment factor, and an ACE check parameter. The protograph is optimized, and the method constructs a parity check matrix by using a UAW channel-based PEG/ACE hybrid algorithm, performs ACE check on the parity check matrix, and calculates a decoding threshold for the matrix passing the check. If the decoding threshold is within a range of an iterative decoding threshold, the parity check matrix is a final optimized matrix. Otherwise, the method continues to optimize the protograph until a parity check matrix passing the check is obtained.