Blind Modulation Classification over Fading Channels Using Expectation-Maximization

We propose a blind modulation classification algorithm when the channel coefficient, the noise power and the energy of the transmitted signal are unknown at the receiver. First, under each candidate modulation scheme, we evaluate the unknown parameters using the iterative expectation maximization algorithm. Modulation classification is then accomplished by minimizing the distance between the log-likelihood of the received data and the expected log-likelihood under each candidate modulation scheme. Results are presented from simulations in terms of detection probability vs. SNR for the class of BPSK, QPSK, 16QAM and 64QAM modulation schemes. The results show a significant improvement over QHLRT and are very close to the upper bound ALRT-UB [1].