A decoding method for a rewritable non-volatile memory module is provided according to an exemplary embodiment of the disclosure. The decoding method includes: reading first data from memory cells of the rewritable non-volatile memory module, wherein the first data includes a first bit stored in a first memory cell; obtaining a storage state of at least one second memory cell which is different from the first memory cell; obtaining first reliability information corresponding to the first bit according to the storage state of the second memory cell, wherein the first reliability information is different from default reliability information corresponding to the first bit; and decoding the first data according to the first reliability information. Therefore, a decoding efficiency can be improved.

This application relates to a decoding method and a decoder. The decoding method includes: calculating, based on a path selection result of a second hit group in a current code block to be decoded, LLRs (log-likelihood ratios) of a first bit group in the code block, where the path selection result includes L paths; calculating BMs (branch metrics) of the first bit group based on the LLRs; selecting at least L BMs for each of the L paths; determining PMs (path metrics) of the first bit group based on the at least L BMs and a path selection result of a previous hit group of the first bit group; and determining a path selection result of the first bit group based on the PMs. In an entire decoding process, other phases before the PM calculation phase can be performed in parallel, thereby reducing a decoding delay, and improving decoding efficiency.

Systems and methods are disclosed for decoding data. A first block of data may be obtained from a storage medium or received from a computing device. The first block of data includes a first codeword generated based on an error correction code. A first set of likelihood values is obtained from a neural network. The first set of likelihood values indicates probabilities that the first codeword will be decoded into one of a plurality of decoded values. A second set of likelihood values is obtained from a decoder based on the first block of data. The second set of likelihood values indicates probabilities that the first codeword will be decoded into one of the plurality of decoded values. The first codeword is decoded to obtain a decoded value based on the first set of likelihood values and the second set of likelihood values.

Systems and methods for decoding block and concatenated codes are provided. These include advanced iterative decoding techniques based on belief propagation algorithms, with particular advantages when applied to codes having higher density parity check matrices such as iterative soft-input soft-output and list decoding of convolutional codes, Reed-Solomon codes and BCH codes. Improvements are also provided for performing channel state information estimation including the use of optimum filter lengths based on channel selectivity and adaptive decision-directed channel estimation. These improvements enhance the performance of various communication systems and consumer electronics. Particular improvements are also provided for decoding HD radio signals, satellite radio signals, digital audio broadcasting (DAB) signals, digital audio broadcasting plus (DAB+) signals, digital video broadcasting-handheld (DVB-H) signals, digital video broadcasting-terrestrial (DVB-T) signals, world space system signals, terrestrial-digital multimedia broadcasting (T-DMB) signals, and China mobile multimedia broadcasting (CMMB) signals. These and other improvements enhance the decoding of different digital signals.

An error management system for a data storage device can generate soft-decision log-likelihood ratios (LLRs) using multiple reads of memory locations. 0-to-1 and 1-to-0 bit flip count data provided by multiple reads of reference memory locations can be used to generate probability data that is used to generate possible LLR values for decoding target pages. Possible LLR values are stored in one or more look-up tables.

A node (110, 115) receives (804) transmissions associated with a given set of information bits, wherein each of the transmissions use a different polar code and share one or more information bits of the given set of information bits. The node determines (808), at each of a plurality of polar decoders (505, 605) of the node, soft information for each information bit included in an associated one of the transmissions, wherein each of the plurality of polar decoders is associated with a different transmission of the transmissions. The node provides (812), from each polar decoder of the plurality to one or more other polar decoders of the plurality, the determined soft information for any information bits shared by their respective associated transmissions, and uses (816) the provided soft information in an iterative decoding process to decode one or more of the received transmissions.

A method includes: generating a trellis; generating one or more predicted symbols using a first non-linear model; computing and saving two or more branch metrics using a priori log-likelihood ratio (LLR) information, a channel observation, and the one or more predicted symbols; if alpha forward recursion has not yet completed, generating alpha forward recursion state metrics using a second non-linear model; if beta backward recursion has not yet completed, generating beta backward recursion state metrics using a third non-linear model; if sigma forward recursion has not yet completed, generating sigma forward recursion state metrics using the branch metrics, the alpha state metrics, and the beta backward recursion state metrics; generating extrinsic information comprising a difference of a posteriori LLR information and the a priori LLR information; computing and feeding back the a priori LLR information; and calculating the a posteriori LLR information.

A method includes: generating a trellis; generating one or more predicted symbols using a first non-linear model; computing and saving two or more branch metrics using a priori log-likelihood ratio (LLR) information, a channel observation, and the one or more predicted symbols; if alpha forward recursion has not yet completed, generating alpha forward recursion state metrics using a second non-linear model; if beta backward recursion has not yet completed, generating beta backward recursion state metrics using a third non-linear model; if sigma forward recursion has not yet completed, generating sigma forward recursion state metrics using the branch metrics, the alpha state metrics, and the beta backward recursion state metrics; generating extrinsic information comprising a difference of a posteriori LLR information and the a priori LLR information; computing and feeding back the a priori LLR information; and calculating the a posteriori LLR information.

A node (110, 115) receives (804) transmissions associated with a given set of information bits, wherein each of the transmissions use a different polar code and share one or more information bits of the given set of information bits. The node determines (808), at each of a plurality of polar decoders (505, 605) of the node, soft information for each information bit included in an associated one of the transmissions, wherein each of the plurality of polar decoders is associated with a different transmission of the transmissions. The node provides (812), from each polar decoder of the plurality to one or more other polar decoders of the plurality, the determined soft information for any information bits shared by their respective associated transmissions, and uses (816) the provided soft information in an iterative decoding process to decode one or more of the received transmissions.

A receiver includes a polar decoder for decoding an encoded codeword transmitted over a communication channel. The receiver includes a front end to receive over a communication channel a codeword including a sequence of bits modified with noise of the communication channel and a soft decoder operated by a processor to produce a soft output of the decoding. The codeword is encoded by at least one polar encoder with a polar code. The processor is configured to estimate possible values of the bits of the received codeword using a successive cancelation list (SCL) decoding to produce a set of candidate codewords, determine a distance between each candidate codeword and a soft input to the soft decoder, and determine a likelihood of a value of a bit in the sequence of bits using a difference of distances of the candidate codewords closest to the received codeword and having opposite values at the position of the bit.