Modulation classification for QAM signals based on log-likelihood estimation in NCA environments

In this paper, we focus on the automatic modulation for unsynchronized quadrature amplitude modulation (QAM) signals in both time and frequency in the practical communication systems. In practice, the receiver has little prior knowledge about the transmitted signal such as the timing error and frequency offset, directly applying the log-likelihood estimation to recognize the QAM signal would cause the decline in the recognition performance. To resolve this challenging problem, the paper presents the improved recognition scheme: before recognition, apply the Gardner loop timing synchronization and differential approach to the received signal. Simulation result shows that the proposed scheme is robust to timing and frequency offset and when SNR≥12dB and the number of symbols N is more than 1000, the successful classification rate of the 16QAM signal reach 98%.