Distributed compression of binary sources using conventional parallel and serial concatenated convolutional codes

It is shown how conventional parallel (turbo) and serial concatenated convolutional codes can be used to compress close to the Slepian-Wolf limit for the correlated binary sources. Conventional refers to codes already used in channel coding. Focusing on the asymmetric case of compression of an equipolarable memoryless binary source with side information at the decoder, the approach is based on modeling the correlation as a channel and using syndromes. The encoding and decoding procedures are explained in detail. The performance achieved is seen to be better than the recently published results using nonconventional turbo codes and close to the Slepian-Wolf limit.