An algebraic approach to network coding
IEEE/ACM Transactions on Networking (TON)
Towards Practical Minimum-Entropy Universal Decoding
DCC '05 Proceedings of the Data Compression Conference
Separating distributed source coding from network coding
IEEE/ACM Transactions on Networking (TON) - Special issue on networking and information theory
Low-complexity coding and source-optimized clustering for large-scale sensor networks
ACM Transactions on Sensor Networks (TOSN)
On practical design for joint distributed source and network coding
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
Factor graphs and the sum-product algorithm
IEEE Transactions on Information Theory
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When correlated sources are to be communicated over a network to more than one sink, joint source-network coding is, in general, required for information theoretically optimal transmission. Whereas on the encoder side simple randomized schemes based on linear codes suffice, the decoder is required to perform joint source-network decoding which is computationally expensive. Focusing on maximum a-posteriori decoders (or, in the case of continuous sources, conditional mean estimators), we show how to exploit (structural) knowledge about the network topology as well as the source correlations giving rise to an efficient decoder implementation (in some cases even with linear dependency on the number of nodes). In particular, we show how to statistically represent the overall system (including the packets) by a factor-graph on which the sum-product algorithm can be run. A proof-of-concept is provided in the form of a working decoder for the case of three sources and two sinks.