Devices and methods described herein decode a sequence of coded symbols by guessing noise. In various embodiments, noise sequences are ordered, either during system initialization or on a periodic basis. Then, determining a codeword includes iteratively guessing a new noise sequence, removing its effect from received data symbols (e.g. by subtracting or using some other method of operational inversion), and checking whether the resulting data are a codeword using a codebook membership function. This process is deterministic, has bounded complexity, asymptotically achieves channel capacity as in convolutional codes, but has the decoding speed of a block code. In some embodiments, the decoder tests a bounded number of noise sequences, abandoning the search and declaring an erasure after these sequences are exhausted. Abandonment decoding nevertheless approximates maximum likelihood decoding within a tolerable bound and achieves channel capacity when the abandonment threshold is chosen appropriately.

A network device includes memory with permanent storage. A request for information is received from a user device. In response to determining that an erasure encoded version of the requested information is not stored in the memory of the network device, the requested information is requested to be transmitted from a storage external to the network device and partially received. In response to determining that an amount of the partially received requested information does not satisfy an erasure encoding threshold, it is determined whether the erasure encoding threshold can be satisfied within a time duration. If so, the partially received requested information is erasure encoded, stored in the permanent storage, and transmitted. If not, the requested information is requested to be retransmitted from the storage external to the network device.

A data processing system includes a storage medium, and a controller including a data processing block, configured to receive data from a host, transmit the received data to the storage medium, read data from the storage medium in response to a read request from the host, and decode the read data by the data processing block according to multiple decoding modes. The data processing block includes a first decoder and a second decoder, and is configured to manage the first decoder and the second decoder to run the decoding for the read data, and activate a fast decoding having shorter latency than a normal decoding after a fast decoding condition is satisfied.

A storage network operates by: generating metadata for a data object; first disperse storage error encoding the metadata to produce a set of metadata slices, wherein the first disperse storage error encoding utilizes first dispersal parameters, the first dispersal parameters including a first decode threshold of 1; generating sets of first data slices via a second disperse storage error encoding of data segments associated with the data object, wherein the second disperse storage error encoding utilizes second dispersal parameters, the second dispersal parameters different from the first dispersal parameters and the second dispersal parameters including a second decode threshold greater than 1; producing an additional data segment associated with the data object wherein the additional data segment is different from the data segments and the metadata; and third disperse storage error encoding the additional data segment to produce a set of second data slices, wherein the third disperse storage error encoding utilizes the first dispersal parameters including the first decode threshold of 1.

An error correction circuit includes a memory that stores at least one decoding parameter, a low density parity check (LDPC) decoder that includes a first variable node storing one bit of the data, receives the at least one decoding parameter from the memory, decides a degree of the first variable node based on the at least one decoding parameter, and decides a decoding rule necessary for decoding of the one bit based on the degree of the first variable node, and an adaptive decoding controller that outputs corrected data based on a decoding result of the LDPC decoder.

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 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 to be transmitted in a current frame; a parity permutator configured to interleave the parity bits and group-wise interleave a plurality of parity bit groups configuring the interleaved parity bits based on a group-wise interleaving pattern including a first pattern and a second pattern to perform parity permutation; a puncturer configured to puncture at least some of the group-wise interleaved parity bit groups; and an additional parity generator configured to select at least some of the punctured parity bit groups to generate additional parity bits to be transmitted in a previous frame of the current frame, based on the first pattern and the second pattern.

Systems and methods are disclosed for a multiple detector data channel and data detection utilizing different cost functions. For example, a digital data channel system can have multiple data detectors where each data detector implements a distinct cost function for detecting data. A cost function analyzer can then selectively choose decisions from the multiple data detectors to generate a data sequence. In some examples, a dual detector system may have one detector implement a Soft-Output Viterbi Algorithm (SOVA) cost function and another detector implement a peak detection algorithm. Further, in some embodiments, the cost function analyzer can implement multiple selection criteria to determine which decisions to include in a data sequence from the multiple data detectors.

A decoding device includes: a hard decision decoder generating a first decoding result by performing hard decision decoding of a polar code using an input signal, and generating a first error detection result by performing first error detection processing on the first decoding result; a soft decision decoder generating a second decoding result by performing successive decoding of a polar code on the input signal, obtained for each decoding step in the successive decoding, and generating a second error detection result by performing second error detection processing on a result obtained by updating the first decoding result using the second decoding result; a data selector selecting and outputting either the first decoding result or the result obtained by updating the first decoding result using the second decoding result; and a controller stopping the soft decision decoder when the data selector outputs the final decoding result.

A method for performance enhancement of polar codes for short frame lengths considering error propagation effects is provided. The method inspects effects of the error propagation on a performance of the polar codes and proposes methods to alleviate degrading effects of the error propagation on a code performance for the short frame lengths.