Comtech Telecommunications Corp.

Eric John Hewitt

Alan Robert Danielson

Peter Sean Ladow

Tom Hansen

Bradley William Lindstrom

A method and apparatus for decoding a linear block encoded string of information bits comprising: converting the string into a plurality of codewords. Performing hard and soft decisions on each codeword to generate a hard and soft decision vector. Computing the syndrome and finding the location of the two minimum values by Galois Field Arithmetic. Designating these values LOW1 and LOW2 and xoring with a Nc1, thus generating Nc2. Swapping Nc1 with Nc2 and determining the lowest soft decision value, Min1 and a next lowest value, Min2. The two bit locations creating Min1 are designated as MinA and MinB. MinA being replaced with Min2 minus the value MinA. MinB being replaced with Min2 minus the value at MinB. Generating an output codeword by subtracting Min1 from all other bit locations values and 2's complementing all soft values with 0 in their location. Creating the new soft value vector.

Enhanced turbo product code decoder system

A hyper encoder module encodes a block of data having a plurality of sub-blocks. Each sub-block includes a plurality of systematic block code codewords. A parity sub-block is added to the block. The parity sub-block is a first sub-block rotated by a predetermined number of bits. Each subsequent sub-block in the n-dimensional block is rotated by an appropriate number of bits and bit-wise XORed. An encoder method and apparatus which includes the hyper encoder module receives the block of data. A row of the block is immediately output and encoded by a first module according to a first encoding scheme. A column is encoded by a second module according to a second encoding scheme. A second set of encoded data is generated, iteratively updated and output by the second module. The hyper encoder module hyper-diagonally encodes the information bits as described above and then output.

Enhanced turbo product codes

A method and apparatus for decoding a linear block encoded string of information bits comprising: converting the string into a plurality of codewords. Performing hard and soft decisions on each codeword to generate a hard and soft decision vector. Computing the syndrome and finding the location of the two minimum values by Galois Field Arithmetic. Designating these values LOWand LOWand xoring with a Nc, thus generating Nc. Swapping Ncwith Ncand determining the lowest soft decision value, Minand a next lowest value, Min. The two bit locations creating Minare designated as MinA and MinB. MinA being replaced with Minminus the value MinA. MinB being replaced with Minminus the value at MinB. Generating an output codeword by subtracting Minfrom all other bit locations values and 2's complementing all soft values with 0 in their location. Creating the new soft value vector.

A method and apparatus for decoding a linear block encoded string of information bits comprising: converting the string into a plurality of codewords. Performing hard and soft decisions on each codeword to generate a hard and soft decision vector. Computing the syndrome and finding the location of the two minimum values by Galois Field Arithmetic. Designating these values LOWand LOWand xoring with a Ncthus generating NcSwapping Ncwith Ncand determining the lowest soft decision value, Minand a next lowest value, MinThe two bit locations creating Minare designated as MinA and MinB. MinA being replaced with Minminus the value MinA. MinB being replaced with Minminus the value at MinB. Generating an output codeword by subtracting Minfrom all other bit locations values and 2's complementing all soft values with 0 in their location. Creating the new soft value vector. Some embodiments include a system and method that organizes an encoded codeword. The encoded codeword has several codeword bits. The method receives the encoded codeword, assigns multiple codeword bits to at least one memory address in a plurality of memory addresses, and iteratively decodes the received codeword by utilizing the plurality of memory addresses in a predetermined order. The predetermined order is based on a dimension of the received codeword.

Enhanced turbo product code decoder system utilizing a codeword organization method

The present invention is a turbo product code decoder decoding multi-dimensional coding schemes. The decoder may be implemented in any digital communication system receiving an encoded stream of data. The decoder is configured for receiving soft decision values. The decoder iteratively decodes the data by generating new soft difference values for each axis-iteration of decoding. These soft difference values represent the change in soft decision values after each axis-iteration. The soft difference values from each axis-iteration are then summed with the original soft decision values in decoding each of the other axis. After any full iteration&mdash;i.e. after all axis dimensions have been decoded one full time, the previous difference values for any axis are discarded when that axis is decoded in subsequent iterations. Accordingly, the same information is not continuously fed into the decoder during each subsequent iteration, thereby decreasing the likelihood of error and offering improved decoding. Moreover, using unique nearest neighbor computation logic, the decoder generates valid nearest neighbors more efficiently without requiring the use of a look-up table, thereby reducing the amount of time required to decode. Finally, the decoder utilizes four decoders arranged in parallel along with a unique memory array accessing scheme such that multiple rows or columns may be decoded at the same time, thereby increasing the data throughput time of the decoder.

Turbo product code decoder

The present invention is a turbo product code decoder capable of decoding multi-dimensional coding schemes. The decoder may be implemented in any digital communication system capable of receiving an encoded stream of data. The decoder is configured for receiving soft decision values. The decoder iteratively decodes the data by generating new soft difference values for each axis-iteration of decoding. These soft difference values represent the change in soft decision values after each axis-iteration. The soft difference values from each axis-iteration are then summed with the original soft decision values in decoding each of the other axis. After any full iteration&mdash;i.e. after all axis dimensions have been decoded one full time, the previous difference values for any axis are discarded when that axis is decoded in subsequent iterations. Accordingly, the same information is not continuously fed into the decoder during each subsequent iteration, thereby decreasing the likelihood of error and offering an improvement over prior decoders. Moreover, using unique nearest neighbor computation logic, the decoder of the present invention is able to generate valid nearest neighbors more efficiently without requiring the use of a look-up table, thereby reducing the amount of time required to decode. Finally, the decoder of the present invention utilizes four decoders arranged in parallel along with a unique memory array accessing scheme such that multiple rows or columns may be decoded at the same time, thereby increasing the data throughput time of the decoder over prior turbo product code decoders.

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Pullman, WA, United States of America

Eric John Hewitt