In most previously reported studies on locally optimum detection of signals, independent observations have been assumed in various noise environments. The use of an independent observation model may cause a considerable performance degradation in detection applications of modern high data-rate communication systems exhibiting dependence among interference components. In this paper, the detection of weak known and random signals is addressed in observations corrupted by multiplicative and first-order Markov additive noises. The asymptotic and finite sample-size performance of several detectors are obtained and compared: it is confirmed that the dependence of noise components need to be taken into account to maintain detection performance appropriately.

The use of an independent sample model may cause a considerable performance degradation in detection applications of modern high data rate communication systems exhibiting dependence among interference components. In this paper, we address the detection of weak known signals in observations corrupted by multiplicative and first-order Markov additive noise. The asymptotic performance of several detectors are obtained and compared, confirming that the dependence among interference components need to be taken into account to maintain performance accordingly.

In this correspondence, we address the derivation of joint distributions and correlation coefficients for seven pairs of statistics used commonly in a number of signal detection schemes. The upper and lower bounds of the correlation coefficients are obtained, and relationships among the correlation coefficients are derived. Explicit values of the correlation coefficients evaluated for some specific noise distributions are shown for easy reference.