Near optimal Viterbi decoders for convolutional codes in symmetric alpha-stable noise
The design of Viterbi decoders for signals in noise modeled using the symmetric α-stable distribution is considered. The traditional Viterbi decoder, which has a branch metric optimized for Gaussian noise, performs poorly in symmetric α-stable noise. Since the optimal maximum likelihood branch metric is impractically complex, many suboptimal metrics have been proposed, such as the hard decision, p-norm and absolute (1-norm) metric. A Viterbi decoder that uses the absolute branch metric has better performance and lower complexity, however, its performance degrades when α decreases. In this paper, the effects of the suboptimal metrics on the performance of the Viterbi decoder are analyzed, and a clear justification for the performance of the decoder that uses the Gaussian and absolute metrics is provided. Moreover, this analysis is used to design a low complexity suboptimal branch metric that improves the performance of the Viterbi decoder by about 0.75 to 2 dB compared to the absolute branch metric for different values of α, at almost no additional complexity.
|Conference||2010 IEEE 72nd Vehicular Technology Conference Fall, VTC2010-Fall|
Shehata, T.S. (Tarik S.), Marsland, I, & El-Tanany, M. (2010). Near optimal Viterbi decoders for convolutional codes in symmetric alpha-stable noise. Presented at the 2010 IEEE 72nd Vehicular Technology Conference Fall, VTC2010-Fall. doi:10.1109/VETECF.2010.5594452