Abstract
Automatic modulation recognition (AMR) and demodulation are two essential components in cognitive radio receivers. This paper proposes a novel method based on MSOM neural networks to automatically recognize the modulation type and demodulate the radio signal at the same time. This efficient method is directly applied to the normalized radio signal samples and has relatively low computation complexity. A dynamic AMR method is also introduced, which further can reduce the computation without obvious loss in recognition. In this paper, four modulation types, i.e. BPSK, MSK, 2FSK and QPSK, are investigated. Our simulation results show that, compared with the traditional cyclic feature-based methods, the proposed MSOM classifier has better performance while requiring less number of signal samples, and it can also perform demodulation at good accuracy.
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