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- W2132543952 abstract "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 640AM. are involved in the discussion." @default.
- W2132543952 created "2016-06-24" @default.
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- W2132543952 date "2006-08-01" @default.
- W2132543952 modified "2023-09-26" @default.
- W2132543952 title "Automatic Digital Modulation Recognition Based on Euclidean Distance in Hyperspace" @default.
- W2132543952 doi "https://doi.org/10.1093/ietcom/e89-b.8.2245" @default.
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