A processing device in a memory system receives a request to read data from a memory device. In response to receiving the request, the processing device performs an iterative error correction process on the data, wherein at least one iteration after a first iteration in the error correction process uses a criterion that is based at least partially on a previous iteration or partial iteration, and wherein performing the iterative error correction process comprises flipping any bits in the data having an associated number of unsatisfied parity check equations that satisfies a threshold criterion associated with the previous iteration.

A memory system includes a plurality of memory cells each storing multiple bits and a memory controller having a processor. The memory controller is configured to read outputs from the memory cells in response to a read command from a host to generate first raw data of a first page and second raw data of a second page adjacent to the first page. The memory controller is further configured to perform a hard decision (HD) decoding on the first raw data to generate first decoded data. The processor is configured to apply the first decoded data and the second raw data as input features to a machine learning algorithm to generate reliability information. The memory controller is further configured to perform a HD decoding on the second raw data using the reliability information to generate second decoded data.

A memory system includes a non-volatile memory and a controller. The controller is configured to perform iterative correction on a plurality of frames of data read from the non-volatile memory. The iterative correction includes performing a first error correction on each of the frames including a first frame having errors not correctable by the first error correction, generating a syndrome on a set of second frames that include the first frame, performing a second error correction on the second frames using the syndrome, and performing a third error correction on the first frame. Each of the frames includes user data and first parity data used in the first error correction, the first parity data of the first frame also being used in the third error correction.

Provided are a bit flipping device and method and a computer readable program for the same. The bit flipping device for input data having a two-dimensional array pattern includes: a clustering unit configured to generate at least one input data sequence based on the two-dimensional array pattern of the input data and classify the input data sequence into at least one cluster according to a preset method; and a bit flipping unit configured to perform bit flipping on erroneous bits in the input data sequence based on the classified cluster. Therefore, it is possible to further reduce inefficiency while further reducing system complexity compared to the existing error correction code-based bit flipping method by coupling the bit flipping device to an output side of a partial response maximum likelihood (PRML) detector to classify an output value of the PRML detector into at least one cluster and perform bit flipping based on the classified result.

A memory system includes a non-volatile memory and a controller. The controller is configured to perform iterative correction on a plurality of frames of data read from the non-volatile memory. The iterative correction includes performing a first error correction on each of the frames including a first frame having errors not correctable by the first error correction, generating a syndrome on a set of second frames that include the first frame, performing a second error correction on the second frames using the syndrome, and performing a third error correction on the first frame. Each of the frames includes user data and first parity data used in the first error correction, the first parity data of the first frame also being used in the third error correction.

A syndrome-based decoding method and apparatus for a block turbo code are disclosed. An embodiment of the present invention provides a syndrome-based decoding method for a block turbo code that includes an extended Hamming code as a component code, where the decoding method includes: (a) generating an input information value for a next half iteration by using channel passage information and the extrinsic information and reliability factor of a previous half iteration; (b) generating a hard decision word by way of a hard decision of the input information value; (c) calculating an n number of 1-bit syndromes, which corresponds to the number of columns or rows of the block turbo code, by using the hard decision word; and (d) determining whether or not to proceed with the next half iteration by using the calculated n number of 1-bit syndromes.

A memory system includes a plurality of memory cells each storing multiple bits and a memory controller having a processor. The memory controller is configured to read outputs from the memory cells in response to a read command from a host to generate first raw data of a first page and second raw data of a second page adjacent to the first page. The memory controller is further configured to perform a hard decision (HD) decoding on the first raw data to generate first decoded data. The processor is configured to apply the first decoded data and the second raw data as input features to a machine learning algorithm to generate reliability information. The memory controller is further configured to perform a HD decoding on the second raw data using the reliability information to generate second decoded data.

A method of correcting data stored in a memory device includes: applying an iterative decoder to the data; determining a total number of rows in first data the decoder attempted to correct; estimating first visible error rows among the total number that continue to have an error after the attempt; estimating residual error rows among the total number that no longer have an error after the attempt; determining second visible error rows in second data of the decoder that continue to have an error by permuting indices of the residual error rows according to a permutation; and correcting the first data using the first visible error rows.

This disclosure provides systems, methods and apparatus, including computer storage media, for retransmission of sidelink transmissions using network coding with binned feedback. A transmitting device transmits a transport block and a request for a network coding (NC) encoding device to retransmit the transport block to a plurality of user equipment (UEs). The UEs decode the transport block and report an acknowledgment (ACK) or negative acknowledgment (NACK) on a physical sidelink feedback channel (PSFCH) resource associated with a bin for the UEs. The NC encoding device decodes the PSFCH resource for each bin to determine an ACK or NACK status for each bin, and determines whether to encode the transport block in a NC combination packet. The UEs receive the NC combination packet including an encoding of a subset of transport blocks. The receiving devices transmit an ACK or NACK on a PSFCH resource for a bin for each transport block.

Methods, systems, and apparatuses include receiving a codeword stored in a memory device. Syndrome information and energy function values are determined for bits of the codeword. A bit flipping criterion is selected using the syndrome information from a plurality of values. A bit of the codeword is flipped when the energy function values for a bit of the codeword satisfies the bit flipping criterion. A corrected codeword that results from the flipping of the bits is returned.