Automatic digital modulation recognition based on euclidean distance in hyperspace

The recognition vector of the decision-theoretic approach and that of cumulant-based classification are combined to compose a higher dimension hyperspace to get the benefits of both methods. The method proposed in this paper can cover more kinds of signals including signals with order higher than 4 in the AWGN channel even under low SNR values, i.e. those down to -5 dB. The composed vector is input into an RBF neural network to get more reasonable reference points. Eleven kinds of signals, say 2ASK, 4ASK, 8ASK, 2PSK, 4PSK, 8PSK, 2FSK 4FSK, 8FSK, 16QAM and 64QAM, are involved in the discussion.