Maximal-likelihood decoding and performance analysis of a noisy channel network with network coding
Paper i proceeding, 2007
We investigate sink decoding methods and performance analysis
approaches for a network with intermediate node encoding (coded
network). The network consists of statistically independent noisy
channels. The sink bit error probability (BEP) is the performance
measure. We first discuss soft-decision decoding without
statistical information on the upstream channels (the channels not
directly connected to the sink). The example shows that the
decoder cannot significantly improve the BEP from the
hard-decision decoder. We develop the union bound to analyze the
decoding approach. The bound can show the asymptotic (regarding
SNR: signal-to-noise ratio) performance. Using statistical
information of the upstream channels, we then show the method of
maximum-likelihood (ML) decoding. With the decoder, a significant
improvement in the BEP is obtained. To evaluate the union bound
for the ML decoder, we use an equivalent signal point procedure.
It can be reduced to a least-squares problem with linear
constraints for medium-to-high SNR.
Network coding
Union Bound
Noisy channel
Bit error probability
Maximum-Likelihood Decoding