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EURASIP Journal on Wireless Communications and Networking
Fig. 4 | EURASIP Journal on Wireless Communications and Networking

Fig. 4

From: A novel deep learning automatic modulation classifier with fusion of multichannel information using GRU

Fig. 4

Classification accuracy of FGDNN for different sample lengths.presents the classification accuracy of the proposed FGDNN with different sample lengths, N = 128, 256, 512, 1024. After the AMC classifier is trained using signal sequences with length N = 128, 256, 512, 1024, respectively, we evaluate the performance of it using signal sequences with N = 128. Figure 4 shows that the classification accuracy of the proposed AMC classifier is up with increasing in length of signal sequences

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