This application discloses example decoding methods, example decoder, and example decoding apparatuses. One example decodine method includes performing soft decision decoding on a first sub-codeword in a plurality of sub-codewords to obtain a hard decision result. It is determined whether to skip a decoding iteration. In response to determining not to skip the decoding iteration, a first turn-off identifier corresponding to the first sub-codeword is set to a first value based on the hard decision result. The first turn-off identifier indicates whether to perform soft decision decoding on the first sub-codeword in a next decoding iteration. The soft decision decoding is not performed on the first sub-codeword in the next decoding iteration when a value indicated by the first turn-off identifier is the first value. The hard decision result is stored.

An error correction method includes performing a first error correction operation, the first error correction operation including performing a syndrome check operation by calculating a syndrome matrix corresponding to a codeword based on a parity check matrix, performing a decoding operation for the codeword according to a result of the syndrome check operation, and iterating the decoding operation until the syndrome check operation is passed for a codeword acquired as the decoding operation is performed or an iteration count of the decoding operation reaches a threshold count; accumulating syndrome matrixes, which are calculated as the decoding operation is iterated, to an accumulation matrix; and performing a second error correction operation for a last codeword acquired through the iterating of the decoding operation for the codeword, based on the accumulation matrix, when the iteration count reaches the threshold count.

Systems and methods are disclosed for full utilization of a data path's dynamic range. In certain embodiments, an apparatus may comprise a circuit including a first filter to digitally filter and output a first signal, a second filter to digitally filter and output a second signal, a summing node, and a first adaptation circuit. The summing node combine the first signal and the second signal to generate a combined signal at a summing node output. The first adaptation circuit may be configured to receive the combined signal, and filter the first signal and the second signal to set a dynamic amplitude range of the combined signal at the summing node output by modifying a first coefficient of the first filter and a second coefficient of the second filter based on the combined signal.

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.

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.

This application discloses example decoding methods, example decoders, and example decoding apparatuses. One example decoding method includes performing soft decision decoding on a first sub-codeword in a plurality of sub-codewords to obtain a hard decision result. It is determined whether to skip a decoding iteration. In response to determining not to skip the decoding iteration, a first turn-off identifier corresponding to the first sub-codeword is set to a first value based on the hard decision result. The first turn-off identifier indicates whether to perform soft decision decoding on the first sub-codeword in a next decoding iteration. The soft decision decoding is not performed on the first sub-codeword in the next decoding iteration when a value indicated by the first turn-off identifier is the first value. The hard decision result is stored.

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.

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.

Provided is an optical transmission/reception device including an error correction decoding unit (36) for decoding a received sequence encoded with an LDPC code, in which the error correction decoding unit (36) is configured to perform decoding processing using a parity check matrix (70) of a spatially-coupled LDPC code, which includes a plurality of parity check sub-matrices (71) combined with each other, in which the decoding processing is windowed decoding processing that uses a window (80) over one or more parity check sub-matrices (71), and in which a window size of the window (80) and a decoding iteration count due to throughput and requested correction performance are variable and input from a control circuit (12) connected to the error correction decoding device (36).

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.