Estimation of read parameters for a read channel of a solid-state storage device using a machine learning apparatus. The machine learning apparatus may be provided with signal count metrics from multiple regions of the memory cell signal space and syndrome weights from an error correction code. Other inputs may also be provided comprising metrics of the memory or read operations. In an example, the read parameters may include one or more reference threshold voltage values for read voltages applied to a memory cell and/or log-likelihood ratio (LLR) values for the memory cell.

The present invention is directed to communication systems and methods. According to a specific embodiment, FEC data streams from multiple FEC data lanes are received. First stage interleaving and inner encoding are performed on the FEC data streams to generate inner encoded data streams. A second stage interleaving process is performed to interleave the inner encoded data streams. There are other embodiments as well.

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 a total number of reads, an aggregation mode for aggregating read results of multiple reads, and whether the read results include soft data. 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.

Methods, systems and devices for forward error correction in orthogonal time frequency space (OTFS) communication systems using non-binary low-density parity-check (NB-LDPC) codes are described. One exemplary method for forward error correction includes receiving data, encoding the data via a non-binary low density parity check (NB-LDPC) code, wherein the NB-LDPC code is characterized by a matrix with binary and non-binary entries, modulating the encoded data to generate a signal, and transmitting the signal. Another exemplary method for forward error correction includes receiving a signal, demodulating the received signal to produce data, decoding the data via a NB-LDPC code, wherein the NB-LDPC code is characterized by a matrix with binary and non-binary entries, and providing the decoded data to a data sink.

A method and an apparatus are provided for decoding a polar code. A simplified successive cancellation list (SSCL) decoding tree for the polar code is generated. The SSCL decoding tree includes a plurality of nodes. One or more nodes of the plurality of nodes are identified as employing Reed-Muller codes for decoding. Decoding of received log-likelihood ratios (LLRs) is performed using Reed-Muller codes at the one or more nodes. Hard decision values are output from the one or more nodes.

A method and apparatus for obtaining data from a memory, estimating a probability of data values of the obtained data based on at least one of a source log-likelihood ratio and a channel log-likelihood ratio, wherein each bit in the obtained data has an associated log-likelihood ratio, determining at least one data pattern parameter for the data and performing a decoding process using the at least one data pattern parameters to determine a decoded data set.

A decoding system and method of a non-volatile memory are provided in which information regarding a characteristic of a non-volatile memory is used to determine an initial log-likelihood-ratio (LLR) table from among a number of LLR tables. The decoding is then performed using the determined initial LLR table.

Hard errors are determined for an unsuccessful decoding of codeword bits read from NAND memory cells via a read channel and input to a low-density parity check (LDPC) decoder. A bit error rate (BER) for the hard errors is estimated and BER for the read channel is estimated. Hard error regions are found using a single level cell (SLC) reading of the NAND memory cells. A log likelihood ratio (LLR) mapping of the codeword bits input to the LDPC decoder is changed based on the hard error regions, the hard error BER, and/or the read channel BER.

A method of soft decoding received signals. The method comprising defining quantisation intervals for a signal value range, determining a number of bits in each quantisation interval that are connected to unsatisfied constraints, providing, the number of bits in each quantisation interval that are connected to unsatisfied constraints, as an input to a trained model, wherein the trained model has been trained to cover an operational range of a device for soft decoding of signals, determining, using the trained model, a log likelihood ratio for each quantisation interval, and performing soft decoding using the log likelihood ratios.

A decoding method, a memory storage device and a memory controlling circuit unit are provided. The method includes: receiving a read command sequence for reading a plurality of bits from the memory cells; calculating a first count value of a first value and a second count value of a second value in the bits; and adjusting a decoding parameter corresponding to the bits to a specific decoding parameter according to the first count value and the second count value, and performing a decoding operation according to the specific decoding parameter, where the adjusted decoding parameter affects a probability that the bits are considered as an error bit in the decoding operation.