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.

A communication device that applies an error in an upper layer in addition to error correction in a physical layer is provided.

The communication device includes an acquisition unit that acquires control information regarding forward error correction (FEC) of an upper layer and control information regarding FEC of a lower layer, an encoding-decoding unit that performs error correction encoding or decoding of an information sequence in the upper layer according to control information regarding the FEC of the upper layer, and a puncturing processing unit that performs puncturing or depuncturing in the upper layer. The information sequence after FEC encoding of the upper layer is divided into blocks, and puncturing and interleaving are performed in units of blocks.

A method of decoding polar codes based on belief propagation includes conventional belief propagation to decode the polar codes first; when a number of iterations exceeds a predefined upper limit and a cyclic redundancy check fails, the method selects log-likelihood ratio vectors of a plurality of R or L messages from a plurality of log-likelihood ratio vectors generated in each of the iterations and generates another set of log-likelihood ratio vectors (referred to as candidate vector group) to be used as initial values of the R or L messages for a subsequent belief propagation to perform belief propagation decoding iterations and cyclic redundancy check again. Whenever a decoding result passes the cyclic redundancy check, the method exits; otherwise, the method iterates the above procedure until a maximum number of candidate vector groups has been reached.

Systems and methods are described for low power error correction coding (ECC) for embedded universal flash storage (eUFS) are described. The systems and methods may include identifying a first element of an algebraic field; generating a plurality of lookup tables for multiplying the first element; multiplying the first element by a plurality of additional elements of the algebraic field, wherein the multiplication for each of the additional elements is performed using an element from each of the lookup tables; and encoding information according to an ECC scheme based on the multiplication.

An accelerated erasure coding system includes a processing core for executing computer instructions and accessing data from a main memory, and a non-volatile storage medium for storing the computer instructions. The processing core, storage medium, and computer instructions are configured to implement an erasure coding system, which includes: a data matrix for holding original data in the main memory; a check matrix for holding check data in the main memory; an encoding matrix for holding first factors in the main memory, the first factors being for encoding the original data into the check data; and a thread for executing on the processing core. The thread includes: a parallel multiplier for concurrently multiplying multiple entries of the data matrix by a single entry of the encoding matrix; and a first sequencer for ordering operations through the data matrix and the encoding matrix using the parallel multiplier to generate the check data.

Methods, systems, and devices for wireless communications are described. A wireless communication system may support techniques for correlation-based hardware sequences for layered decoding. In some cases, a user equipment (UE) may partition layers of a submatrix associated with a parity check decoding procedure into a first set of layers and a second set of layers. The UE may sort each set of layers into a respective set of layer orders (e.g., a first set of layer orders and a second set of layer orders) based on an associated set of correlation values. The UE may combine the first set of layer orders and the second set of layer orders to obtain a set of combined layer orders and may select a decoding schedule from a set of decoding schedules used for decoding each of the combined layer orders based on respective schedule lengths for the set of decoding schedules.

Various embodiments of the present disclosure provide methods and apparatuses for improved belief propagation (BP) decoding. A method performed by a receiver comprises: performing BP decoding on received information; obtaining, in response to the BP decoding being unsuccessful and based on a first table comprising left-to-right messages associated with nodes of a plurality of processing elements (PEs) for the BP decoding and the received information, a second table comprising right-to-left messages associated with the nodes; searching, based on the first table and the second table, a conflict verification processing element (VPE); updating, for the conflict VPE, both signs of the right-to-left messages associated with its right-upper node and right-lower node to be the same and the sign of the right-to-left messages associated with its left-upper node to be positive; updating the second table; and performing the BP decoding based on the updated second table.

According to one embodiment, a learning device includes a noise generation unit, a decoding unit, a generation unit, and a learning unit. The noise generation unit outputs a second code word which corresponds to a first code word to which noise has been added. The decoding unit decodes the second code word and outputs a third code word. The generation unit generates learning data for learning a weight in message passing decoding in which the weight and a message to be transmitted are multiplied, based on whether or not decoding of the second code word into the third code word has been successful. The learning unit determines a value for the weight in the message passing decoding by using the learning data.

An operation method of a receiving node may include performing a decoding operation for calculating first and second output transform values corresponding to first and second unit output nodes in each of a plurality of operation units constituting the polar decoder, based on first and second input transform values corresponding to first and second unit input nodes, and the decoding operation may include setting initial values of first and second variables for calculating the first output transform value; performing an iterative loop operation for updating the first and second variables; and calculating the first output transform value based on values of the first and second variables updated until a time when the iterative loop operation is terminated, wherein the iterative loop operation is terminated without performing iterations in which the first and second variables are determined not to be updated among a plurality of iterations.

Provided herein may be an electronic device using an artificial neural network. The electronic device may include a training data generator configured to determine an input vector corresponding to a trapping set, detected during error correction decoding corresponding to a codeword, and a target vector corresponding to the input vector, and a training component configured to train an artificial neural network based on supervised learning by inputting the input vector to an input layer of the artificial neural network and by inputting the target vector to an output layer of the artificial neural network.