Performance Evaluation of Joint Network-Channel Coding under a Real Network Topology Model

Pang, Kun; Lin, Zihuai; Li, Yonghui; Vucetic, Branka

doi:10.1109/vetecs.2010.5493691

Cited by 4 publications

(2 citation statements)

References 9 publications

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“…Similar to [28], we can observe a UEP behavior in Figure 4. Furthermore, the decoder in (23) with side information about the source-to-relay links, i.e., it knows the error probability in (23), provides better performance. Finally, we notice that decoding errors at the relays can seriously degrade the BER.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…where er j is an error vector that accounts for the errors at relay R j . By assuming that the decoding errors at the relay are independent and identically distributed, we can write (conditioning on the channel is avoided for ease of notation): By using (22) and (23), the Viterbi algorithm can be performed on the equivalent trellis associated to G(D).…”

Section: Channel-aware Receiver Designmentioning

confidence: 99%

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Joint Network/Channel Decoding for Heterogeneous Multi-Source/Multi-Relay Cooperative Networks

Poulliat¹,

Renzo²

2011

Proceedings of the 5th International ICST Conference on Performance Evaluation Methodologies and Tools

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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-ofservice 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.

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Section: Simulation Resultsmentioning

confidence: 99%

Section: Channel-aware Receiver Designmentioning

confidence: 99%