A transmitter is provided. The transmitter includes: a low density parity check (LDPC) encoder configured to encode input bits to generate an LDPC codeword including the input bits and parity bits; a repeater configured to select at least a part of bits constituting the LDPC codeword and add the selected bits after the input bits; and a puncturer configured to puncture at least a part of the parity bits.

A method for channel-coding information bits using a code generation matrix including 32 rows and A columns corresponding to length of the information bits includes, channel-coding the information bits having “A” length using basis sequences having 32-bit length corresponding to columns of the code generation matrix, and outputting the channel-coded result as an output sequence. If “A” is higher than 10, the code generation matrix is generated when (A-10) additional basis sequences were added as column-directional sequences to a first or second matrix. The first matrix is a TFCI code generation matrix composed of 32 rows and 10 columns used for TFCI coding. The second matrix is made when at least one of an inter-row location or an inter-column location of the first matrix was changed. The additional basis sequences satisfy a value 10 of a minimum Hamming distance.

In general, according to an embodiment, a memory system includes a memory device including a memory cell; and a controller. The controller is configured to: receive first data from the memory cell in a first data reading; receive second data from the memory cell in a second data reading that is different from the first data reading; convert a first value that is based on the first data and the second data, to a second value in accordance with a first relationship; and convert the first value to a third value in accordance with a second relationship that is different from the first relationship.

A method includes receiving a request for host data, receiving a codeword that is associated with the host data, performing a decoding operation for a first portion of the codeword to generate a segment of decoded data, determining whether the segment of the decoded data satisfies the request for the host data, and in response to determining that the segment of the decoded data satisfies the request for the host data, terminating the decoding operation for remaining portions of the codeword.

Computer implemented systems and methods for migrating datacenter data include providing a quantity of carriers having a data storage capacity, receiving, by the quantity of carriers, a quantity of data stored in a first data storage system having a first location and including erasure coded data blocks. The quantity of carriers migrates to a second data storage system having a second location; and transmits the quantity of data to the second data storage system.

A polar code decoding apparatus according to an embodiment includes a divider configured to generate a decoding tree in which a plurality of nodes including one or more critical sets for a polar-encoded codeword are formed in a hierarchical structure, and divide the decoding tree into one or more partitions, each partition equally including lowest nodes of the decoding tree, a determiner configured to determine a memory size for storing a primary decoding result based on a specific partition, the specific partition being selected from among the one or more partitions based on the number of critical sets included in each partition, and a decoder configured to decode the codeword primarily by using a successive cancellation (SC) decoding technique.

A memory system includes an encoder and a decoder. The encoder is configured to generate multi-dimensionally-coded data to be written into the non-volatile memory. Data bits of the multi-dimensionally-coded data are grouped into first and second dimensional codes with respect to first and second dimensions, respectively. The decoder is configured to, with respect to each of the first and second dimensional codes included in read multi-dimensionally-coded data, generate a syndrome value of the dimensional code, generate low-reliability location information, generate a soft-input value based on the syndrome value and the low-reliability location information, decode the dimensional code through correction of the dimensional code using the soft-input value, and store modification information indicating a bit of the dimensional code corrected through the correction and reliability information indicating reliability of the correction. The decoder generates the soft-input value also based on the modification information and the reliability information in the memory.

Methods and systems for analyzing data are disclosed. An example method can comprise receiving a first data signal, decoding the first data signal, determining a second data signal based on the decoded first data signal, and determining a modulation error ratio based on a difference between the first data signal and the second data signal.

Examples described herein utilize multi-layer neural networks, such as multi-layer recurrent neural networks to estimate an error-reduced version of encoded data based on a retrieved version of encoded data (e.g., data encoded using one or more encoding techniques) from a memory. The neural networks and/or recurrent neural networks may have nonlinear mapping and distributed processing capabilities which may be advantageous in many systems employing a neural network or recurrent neural network to estimate an error-reduced version of encoded data for an error correction coding (ECC) decoder, e.g., to facilitate decoding of the error-reduced version of encoded data at the decoder. In this manner, neural networks or recurrent neural networks described herein may be used to improve or facilitate aspects of decoding at ECC decoders, e.g., by reducing errors present in encoded data due to storage or transmission.

Examples described herein utilize multi-layer neural networks, such as multi-layer recurrent neural networks to estimate an error-reduced version of encoded data based on a retrieved version of encoded data (e.g., data encoded using one or more encoding techniques) from a memory. The neural networks and/or recurrent neural networks may have nonlinear mapping and distributed processing capabilities which may be advantageous in many systems employing a neural network or recurrent neural network to estimate an error-reduced version of encoded data for an error correction coding (ECC) decoder, e.g., to facilitate decoding of the error-reduced version of encoded data at the decoder. In this manner, neural networks or recurrent neural networks described herein may be used to improve or facilitate aspects of decoding at ECC decoders, e.g., by reducing errors present in encoded data due to storage or transmission.