A Likelihood-Based Algorithm for Blind Identification of QAM and PSK Signals - 2018


This Project presents a chance-based mostly methodology for automatically identifying totally different quadrature amplitude modulations (QAM) and part-shift keying (PSK) modulations. This algorithm selects the modulation kind that maximizes a log-likelihood function based on the known likelihood distribution related to the phase or amplitude of the received signals for the candidate modulation types. The approach of this Project does not need prior knowledge of carrier frequency or baud rate. Comparisons of theory and simulation demonstrate smart agreement in the likelihood of successful modulation identification beneath different signal-to-noise ratios (SNRs). The probability of successful identification leads to the simulation results show that underneath additive white Gaussian noise, the system can establish BPSK, QPSK, 8PSK, and QAMs of order sixteen, 32, sixty four, 128, and 256 higher than ninety nine% accuracy at 4-dB SNR when the two alternative competing methods accessible in the literatures cannot for an input signal containing ten 00zero symbols and 20 samples per symbol. The simulation results also indicate that when the input signal length decreases, the system desires higher SNRs so as to get accurate identification results. Finally, simulations underneath different noisy environments indicate that the algorithm is robust to variations of noise environments totally different from the assumed model within the derivations.

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