Error Exponents for Neyman–Pearson Detection of Markov Chains in Noise
Alex S. Leong, Subhrakanti Dey, Jamie Evans · IEEE Transactions on Signal Processing · 2007
A numerical method for computing the error exponent for Neyman-Pearson detection of two-state Markov chains in noise is presented, for both time-invariant and fading channels. We give numerical studies showing the behavior of the error exponent as the transition parameters of the Markov chain and the signal-to-noise ratio (SNR) are varied. Comparisons between the high-SNR asymptotics in Gaussian noise for the time-invariant and fading situations will also be made.