Soft-decision metrics for coded orthogonal signaling in symmetric alpha-stable noise

This paper derives new soft decision metrics for coded orthogonal signaling in symmetric α-stable noise, which has been used to model impulsive noise. In addition to the optimum metrics for Gaussian (α=2) noise and Cauchy (α=1) noise, a class of generalized likelihood ratio (GLR) metrics with lower side information requirements is derived. Through numerical results for a turbo code example, the Cauchy decoder is found to be robust for a wide range of α, and GLR metrics are found which provide performance gains relative to the Gaussian metric, but with lower complexity and less a priori information.