Title :
Error Exponents for Neyman–Pearson Detection of Markov Chains in Noise
Author :
Leong, Alex S. ; Dey, Subhrakanti ; Evans, Jamie S.
Author_Institution :
Melbourne Univ., Parkville
Abstract :
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.
Keywords :
Gaussian noise; Markov processes; exponential distribution; fading channels; signal detection; Gaussian noise; Markov chains; Neyman-Pearson detection; error exponents; fading channels; time-invariant channels; Closed-form solution; Detectors; Fading; Gaussian noise; Hidden Markov models; Probability; Radar detection; Signal detection; Signal processing; Signal to noise ratio; Error exponent; Neyman–Pearson detection; fading channel; hidden Markov model (HMM);
Journal_Title :
Signal Processing, IEEE Transactions on
DOI :
10.1109/TSP.2007.897863
