Simulation study of LDPC codes for nonuniform sources with side information in Slepian-Wolf coding

The principles of distributed source coding or Slepian-Wolf coding, primarily employed for compression of correlated sources with side information, can also be used for reducing the encoder complexity of a single source. This effectively shifts the encoder complexity to the decoder. This exchange of computational complexities has opened up the door for many applications in wireless video and image communications. Till now, most of the research efforts are concentrated around compression of correlated sources which are uniformly distributed. But nonuniform sources are not uncommon in real world. In this paper, we have studied and demonstrated the suitability and optimality of LDPC syndrome codes for distributed source coding of nonuniform sources using Monte-Carlo simulation. Our study reveals that several capacity approaching LDPC codes, indeed do approach the Slepian-Wolf bound for nonuniform sources as well. The Monte-Carlo simulation results show that, highly biased sources can be compressed to 0.049 bits/sample away from Slepian-Wolf bound for moderate block lengths.