Maximum Log-Likelihood Function-Based QAM Signal Classification over Fading Channels

In this paper, we propose a modulation classification algorithm for M-ary QAM signals in Rician and Rayleigh fading channels. The developed algorithms are based on the maximum log-likelihood functions, which are derived from received signals. First of all, we derived the amplitude PDF of M-ary QAM signal over flat and slowly Rayleigh and Rician fading channel, then we developed the log-likelihood functions and then the decision functions for classification. To demonstrate the performance of the proposed classifier, we give an example to classify the 16/32 QAM signals. Results indicate that the performance of classifier is heavily dependent on the severity of channel fading. When channel is AWGN, which means that there exists only one path (may be specular path) between transmitter and receiver, and the Rician factor k, approaches infinity in this case, henceforth, the performance is the best. The performance, however, is degraded with the decrease of k, and finally the classifier performs worst when channel becomes Rayleigh. Further performance improvement can be achieved by increasing the length of record.