Infinera Corporation

Stanley H Blakey

Han Henry Sun

Yuejian Wu

Alexander Kaganov

Jeffrey Thomas Rahn

Sandy Thomson

A system is to receive a word on which to perform error correction; obtain segments, from the word, each segment including a respective subset of samples; update, on a per segment basis, the word based on extrinsic information associated with a previous word; identify sets of least reliable positions (LRPs) associated with the segments; create a subset of LRPs based on a subset of samples within the sets of LRPs; generate candidate words based on the subset of LRPs; identify errors within the word or the candidate words; update, using the extrinsic information, a segment of the word that includes an error; determine distances between the candidate words and the updated word that includes the updated segment; identify best words associated with shortest distances; and perform error correction, on a next word, using other extrinsic information that is based on the best words.

Iterative forward error correction (FEC) on segmented words using a soft-metric arithmetic scheme

A system is configured to receive a word on which to perform forward error correction; identify least reliable positions that correspond to encoded bits, within the word, associated with a lowest level of reliability; generate candidate words based on different combinations of inverted encoded bits; identify a pair of candidate words that includes a candidate word and another candidate word, the candidate word includes an inverted most reliable bit of the encoded bits within the candidate word; identify a quantity of errors within the candidate word; determine whether the quantity of errors corresponds to an odd value; invert a parity bit associated with the candidate word when the quantity of errors corresponds to the odd value; select the other candidate word when the parity bit is inverted; and perform forward error correction, on the word, using the other candidate word based on selection of the other candidate word.

Reducing processing complexity and cost associated with a soft forward error correction (FEC) operation

A system receives a first word on which to perform error correction; identifies combinations in which encoded bits, within the first word, can be inverted; generates candidate words based on the first word and the combinations; decodes the candidate words; determines distances between the decoded words and the first word; selects, as a second word, one of the decoded words associated with a shortest distance; compares the second word to the first word to identify errors within the first word; generates a value to cause a reliability level of the first word to increase when a quantity of the errors is less than a threshold; generates another value to cause a reliability level of the first word to decrease when the quantity of the errors is not less than the threshold; and outputs a third word based on the first word, and the value or the other value.

Forward error correction (FEC) convergence by controlling reliability levels of decoded words in a soft FEC decoder

A system is configured to receive a word that includes a group of samples; randomly select a subset of the samples; identify first samples, from the subset, with a lowest level of reliability; select another subset of the samples; identify second samples, from the other subset, with a lowest level of reliability; and create a merged subset based on selected first samples and selected second samples. The system is also configured to select a further subset of the samples; identify third samples, from the further subset, with a lowest level of reliability; identify fourth samples, from the merged subset, associated with a lowest level of reliability; create another merged subset based on a greater probability that fourth samples than third samples are included in the other merged subset; and generate another word based a sample from the other merged subset; and process the word using the other word.

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Kanata, Canada

Stanley H Blakey