Fundamentals of Convolutional Coding
Fundamentals of Convolutional Coding
A practical joint network-channel coding scheme for reliable communication in wireless networks
Proceedings of the tenth ACM international symposium on Mobile ad hoc networking and computing
Design of joint network-low density parity check codes based on the EXIT charts
IEEE Communications Letters
MIMO-Assisted hard versus soft decoding-and-forwarding for network coding aided relaying systems
IEEE Transactions on Wireless Communications
On unequal error protection of convolutional codes from an algebraic perspective
IEEE Transactions on Information Theory
Joint channel and network coding for cooperative diversity in a shared-relay environment
IEEE Transactions on Wireless Communications
Mitigating error propagation in two-way relay channels with network coding
IEEE Transactions on Wireless Communications
IEEE Transactions on Information Theory
Unequal error protection for convolutional codes
IEEE Transactions on Information Theory
Coding for Errors and Erasures in Random Network Coding
IEEE Transactions on Information Theory
A Rank-Metric Approach to Error Control in Random Network Coding
IEEE Transactions on Information Theory
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In this paper, we study joint network/channel decoding for multi--source multi--relay heterogeneous wireless networks. When convolutional and network codes are used at the physical and network layers, respectively, we show that error correction and diversity properties of the whole network can be characterized by an equivalent and distributed convolutional network/channel code. In particular, it is shown that, by properly choosing the network code, the equivalent code can show Unequal Error Protection (UEP) properties, which might be useful for heterogeneous wireless networks in which each source might ask for a different quality--of--service requirement or error probability. Using this representation, we show that Maximum--Likelihood (ML) joint network/channel decoding can be performed by using the trellis representation of the distributed convolutional network/channel code. Furthermore, to deal with decoding errors at the relays, a ML--optimum receiver which exploits side information on the source--to--relay links is proposed.