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

This paper presents a likelihood-based method for automatically identifying different quadrature amplitude modulations (QAM) and phase-shift keying (PSK) modulations. This algorithm selects the modulation type that maximizes a log-likelihood function based on the known probability distribution assoc...

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Bibliographic Details
Published in:IEEE transactions on wireless communications 2018-05, Vol.17 (5), p.3417-3430
Main Authors: Daimei Zhu, Mathews, V. John, Detienne, David H.
Format: Article
Language:English
Subjects:
Algorithms
Binary phase shift keying
Blind modulation identification
Codes
Computer simulation
Identification
likelihood function
Modulation
Noise
Phase shift keying
PSK
Pulse shaping methods
QAM
Quadrature amplitude modulation
Random noise
Signal to noise ratio
Simulation
theoretical performance analysis
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Summary:This paper presents a likelihood-based method for automatically identifying different quadrature amplitude modulations (QAM) and phase-shift keying (PSK) modulations. This algorithm selects the modulation type that maximizes a log-likelihood function based on the known probability distribution associated with the phase or amplitude of the received signals for the candidate modulation types. The approach of this paper does not need prior knowledge of carrier frequency or baud rate. Comparisons of theory and simulation demonstrate good agreement in the probability of successful modulation identification under different signal-to-noise ratios (SNRs). The probability of successful identification results in the simulation results show that under additive white Gaussian noise, the system can identify BPSK, QPSK, 8PSK, and QAMs of order 16, 32, 64, 128, and 256 above 99% accuracy at 4-dB SNR when the two other competing methods available in the literatures cannot for an input signal containing 10 000 symbols and 20 samples per symbol. The simulation results also indicate that when the input signal length decreases, the system needs higher SNRs in order to get accurate identification results. Finally, simulations under different noisy environments indicate that the algorithm is robust to variations of noise environments different from the assumed model in the derivations.
ISSN:1536-1276
1558-2248
DOI:10.1109/TWC.2018.2811802