Sequential modulation classification of dependent samples
We classify the modulation scheme of a received signal waveform modeled by a finite state Markov chain. We compare the likelihood ratio test (LRT) known as a fixed-sample-size classifier, which uses a fixed amount of data, and the sequential probability ratio test (SPRT) known as a fixed-error-rate classifier, which uses a variable amount of data just enough to achieve a certain correct rate. The SPRT approach has several advantages, including reduced computational complexity, less decision delay, controllable classification error rate, etc. The performance of MPSK and trellis-coded modulation (TCM) classifiers are demonstrated.