FOCS '02 Proceedings of the 43rd Symposium on Foundations of Computer Science
Compression with Side Information Using Turbo Codes
DCC '02 Proceedings of the Data Compression Conference
EURASIP Journal on Wireless Communications and Networking
IEEE/ACM Transactions on Networking (TON) - Special issue on networking and information theory
Signal Processing - Special section: Distributed source coding
Rate-adaptive codes for distributed source coding
Signal Processing - Special section: Distributed source coding
An efficient SF-ISF approach for the Slepian-wolf source coding problem
EURASIP Journal on Applied Signal Processing
Factor graphs and the sum-product algorithm
IEEE Transactions on Information Theory
Distributed source coding using syndromes (DISCUS): design and construction
IEEE Transactions on Information Theory
Raptor codes on binary memoryless symmetric channels
IEEE Transactions on Information Theory
Joint turbo decoding and estimation of hidden Markov sources
IEEE Journal on Selected Areas in Communications
Distributed Joint Source-Channel Coding of Video Using Raptor Codes
IEEE Journal on Selected Areas in Communications
Hi-index | 35.68 |
In this correspondence, the problem of distributed source coding (DSC) of binary sources with side information at the decoder is addressed. A scheme is proposed based on raptor codes which are a new class of rateless codes. The decoding scheme is adapted to this problem by implementing a message passing strategy between the constituent decoders of raptor codes at each decoding iteration. The case in which the sources are modeled as independent and identically distributed (i.i.d) processes as well as the more general case in which the sources are modeled as hidden Markov processes (HMPs) are considered. The proposed approach achieves better performance than those achieved by the solutions based on turbo codes, and by the solutions based on regular low density parity check (LDPC) codes when i.i.d. sources are considered. On the other hand, when modeling sources as HMPs, an additional module to exploit the underlying Markovian nature is necessary to achieve good performance.