The present disclosure relates to a 5.sup.th generation (5G) or pre-5G communication system for supporting a data transmission rate higher than that of a post-4.sup.th generation (4G) communication system such as Long Term Evolution (LTE). The present disclosure is for decoding a polar code in a communication system. An operation method of a reception device comprises the steps of: receiving data encoded by means of a polar code and comprising a plurality of bits; confirming one or more bits which do not require a decoding operation among the plurality of bits; and decoding at least some of the bits remaining after excluding the one or more bits.

Disclosed are devices, systems and methods for precoding and decoding polar codes using local feedback are described. One example method for improving an error correction capability of a decoder includes receiving a noisy codeword vector of length n, the codeword having been generated based on a concatenation of a convolutional encoding operation and a polar encoding operation and provided to a communication channel prior to reception by the decoder, performing a successive-cancellation decoding operation on the noisy codeword vector to generate a plurality of polar decoded symbols (n), generating a plurality of information symbols (k) by performing a convolutional decoding operation on the plurality of polar decoded symbols, wherein k/n is a rate of the concatenation of the convolutional encoding operation and the polar encoding operation, and performing a bidirectional communication between the successive-cancellation decoding operation and the convolutional decoding operation.

Systems and methods are provided for performing error recovery using LLRs generated from multi-read operations. A method may comprise selecting a set of decoding factors for a multi-read operation to read a non-volatile storage device multiple times. The set of decoding factors may include an aggregation mode for aggregating read results of multiple reads. The method may further comprise issuing a command to the non-volatile storage device to read user data according to the set of decoding factors, generating a plurality of Log-Likelihood Ratio (LLR) values using a mapping engine from a pre-selected set of LLR value magnitudes based on the set of decoding factors, obtaining an aggregated read result in accordance with the aggregation mode and obtaining an LLR value from the plurality of LLR values using the aggregated read result as an index.

A multidimensional multilevel coding (MLC) encoder comprises a soft forward error correction (FEC) encoder receiving first bits for generating soft FEC encoded bits, a redundancy generator receiving a subset of the soft FEC encoded bits for generating redundant bits, and a hard FEC encoder receiving second bits for generating hard FEC encoded bits. Combinations of the soft FEC encoded bits, the redundant bits, and the hard FEC encoded bits form labels for mapping to a plurality of constellation points. A MLC decoder comprises a redundancy decoder, a soft FEC decoder and a hard FEC decoder. The redundancy decoder combines log-likelihood-ratios (LLR) of soft FEC encoded bits received from the MLC encoder to allow the soft FEC decoder to produce decoded bits. Decoding of hard FEC encoded bits by the hard FEC decoder is conditioned on values of the bits decoded by the soft FEC decoder.

A multidimensional multilevel coding (MLC) encoder comprises a soft forward error correction (FEC) encoder receiving first bits for generating soft FEC encoded bits, a redundancy generator receiving a subset of the soft FEC encoded bits for generating redundant bits, and a hard FEC encoder receiving second bits for generating hard FEC encoded bits. Combinations of the soft FEC encoded bits, the redundant bits, and the hard FEC encoded bits form labels for mapping to a plurality of constellation points. A MLC decoder comprises a redundancy decoder, a soft FEC decoder and a hard FEC decoder. The redundancy decoder combines log-likelihood-ratios (LLR) of soft FEC encoded bits received from the MLC encoder to allow the soft FEC decoder to produce decoded bits. Decoding of hard FEC encoded bits by the hard FEC decoder is conditioned on values of the bits decoded by the soft FEC decoder.

A decoding method of low-density parity-check (LDPC) codes based on partial average residual belief propagation includes the following steps: S1: calculating a size of a cluster π in a protograph based on a code length m and a code rate of a target codeword; S2: pre-computing an edge residual r.sub.c.sub.i.sub..fwdarw.v.sub.j corresponding to each edge from a variable node to a check node in a check matrix H; S3: calculating, based on π, a partial average residual (PAR) value corresponding to each cluster in the check matrix H; S4: sorting m/π clusters in descending order of corresponding PAR values, and updating an edge with a largest edge residual in each cluster; S5: updating edge information m.sub.c.sub.i.sub..fwdarw.v.sub.i from a check node c.sub.i to a variable node v.sub.j, and then updating a log-likelihood ratio (LLR) value L(v.sub.j) of the variable node v.sub.j; and S6: after the updating, making a decoding decision.

The method includes: reading a memory cell having a encoded information bit, so as to obtain an LLR value of a current memory cell with reference to a pre-established LLR table according to a storage time, a threshold voltage partition and a comprehensive distribution corresponding to the current memory cell during reading; and performing a soft decoding operation on a codeword in the memory cell having the encoded information bit according to the read LLR value of the current memory cell, wherein the comprehensive distribution of the current memory cell is determined according to an influence of a memory cell adjacent to the current memory cell on a distribution of the current memory cell; an input of the pre-established LLR table comprises a storage time, a threshold voltage partition and a comprehensive distribution, and an output of the pre-established LLR table comprises an LLR value.

A decoding method of low-density parity-check (LDPC) codes based on partial average residual belief propagation includes the following steps: S1: calculating a size of a cluster π in a protograph based on a code length m and a code rate of a target codeword; S2: pre-computing an edge residual r.sub.c.sub.i.sub..fwdarw.v.sub.j corresponding to each edge from a variable node to a check node in a check matrix H; S3: calculating, based on π, a partial average residual (PAR) value corresponding to each cluster in the check matrix H; S4: sorting m/π clusters in descending order of corresponding PAR values, and updating an edge with a largest edge residual in each cluster; S5: updating edge information m.sub.c.sub..fwdarw.v.sub.i from a check node c.sub.i to a variable node v.sub.j, and then updating a log-likelihood ratio (LLR) value L(v.sub.j) of the variable node v.sub.j; and S6: after the updating, making a decoding decision.

The present disclosure discloses a new coding scheme, which is constructed by superimposing together a pair of basic codes in a twisted manner. A SCL decoding algorithm is proposed for the TPST codes, which may be early terminated by a preset threshold on the empirical divergence functions (EDF) to trade off performance with decoding complexity. The SCL decoding of TPST is based on the efficient list decoding of the basic codes, where the correct candidate codeword in the decoding list is distinguished by employing a typicality-based statistical learning aided decoding algorithm. Lower bounds for the two layers of TPST are derived, which may be used to predict the decoding performance and to show the near-ML performance of the proposed SCL decoding algorithm. The construction of TPST codes may be generalised by allowing different basic codes for the two layers.

A method of soft-decision decoding including training a machine learning agent with communication signal training data; providing to the trained machine learning agent a signal that has been received via a communications channel; operating the machine learning agent to determine respective probabilities that the received signal corresponds to each of a plurality of symbols; and, based on the determined probabilities, performing soft decision decoding on the received signal.