Abstract:
The invention proposes an approach, where on the recording side, after conventional error correction encoding (201), parity-check bits allowing to identify dominant error events are inserted first (202), after which the resulting bitstream is modulation encoded (203). Correspondingly, at the readback side, the parity-based post processing (208) takes place after modulation decoding (207).
Abstract:
L'invention concerne un procédé de décodage de données reçues représentatives de données source et de redondance, obtenues par un code de base systématique de rendement ½, délivrant des mots de code, mettant en œuvre un traitement des données reçues, délivrant des vecteurs reçus, dont chaque composante comprend une estimation de la donnée source ou de redondance correspondante. Selon l'invention, un tel procédé comprend, pour au moins un desdits vecteurs reçus, les étapes : identification d'au moins une estimation de données source considérée douteuse; construction d'au moins un vecteur de données alternatif; codage de chacun desdits vecteurs de données alternatifs délivrant un mot de code alternatif; -calcul d'une distance entre ledit vecteur de données reçues et chacun desdits mots de code alternatifs; sélection du mot de code alternatif pour lequel la distance est la plus faible.
Abstract:
Die Erfindung betrifft ein Verfahren zum Senden eines Datenblocks (102) mit folgenden Schritten: -Kanalkodierung des Datenblocks durch Einfügung der Bits eines vordefinierten Bitstroms (108) in den Datenblock nach einem vordefinierten Schema zur Hinzufügung von Redundanz, sodass ein Übertragungsblock erzeugt wird, wobei die Einfügung der Bits in den Datenblock unabhängig von dem Inhalt des Datenblocks ist, -Senden des Übertragungsblocks über einen Nachrichtenkanal (116).
Abstract:
The invention relates to a method of decoding a matrix built from concatenated elementary codes with uniform interleaving having nl lines, n2 columns and nl*n2 symbols, wherein the method comprises a process of all the lines- and columns-vectors of the matrix by symbols groups, this process comprises a first decoding for simultaneously processing all the symbols of a group of symbols according to their lines and then a second decoding for simultaneously processing all the symbols of the said group of symbols according to their columns while using extrinsic information produced in the line processing step, the symbols groups being thus successively processed in lines and in columns .
Abstract:
A communications system for reducing bit errors in a received data sequence provides a method for generating candidate code-word sequences for evaluation by a CRC decoder. The system may determine a most-likely received sequence using the probable code-word list of candidate sequences. The number of candidate sequences may be reduced using computational complexity reduction methods. A communications device also provides a candidate sequence generator for use with a CRC decoder to determine a most-likely received sequence and to reduce bit errors in a received sequence.
Abstract:
A soft decision maximum likelihood detection method and apparatus including forward error correcting code. The decoding technique utilizes soft decision maximum likelihood decoding especially suited for codes that operate on symbols rather than bits. The decoding technique utilizes a maximum likelihood function in combination with an erasure correcting coding scheme to correct b+1 errors in a block wherein the erasure correcting code itself can correct up to b erasures. The decoding method uses likelihood values for all possible symbol values for each received symbol in addition to hard decisions. Two example embodiments disclosed include a Soft Diagonal Parity code and an Even Odd Parity (EOP) code.
Abstract:
A soft decision maximum likelihood detection method and apparatus including forward error correcting code. The decoding technique utilizes soft decision maximum likelihood decoding especially suited for codes that operate on symbols rather than bits. The decoding technique utilizes a maximum likelihood function in combination with an erasure correcting coding scheme to correct b+1 errors in a block wherein the erasure correcting code itself can correct up to b erasures. The decoding method uses likelihood values for all possible symbol values for each received symbol in addition to hard decisions. Two example embodiments disclosed include a Soft Diagonal Parity code and an Even Odd Parity (EOP) code.