kumoh national institute of technology
Networked Systems Lab.

Seung-Hwan Kim, Jae-Woo Kim, Williams-Paul Nwadiugwu and Dong-Seong Kim, "Deep Learning-based Robust Automatic Modulation Classification for Cognitive Radio Networks", IEEE Access ( Early Access ), June 2021. (IF: 3.745)
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Date : 2021-05-06
Views : 53

DOI: 10.1109/ACCESS.2021.3091421

Abstract
In this paper, a novel deep learning-based robust automatic modulation classification (AMC) method is proposed for cognitive radio networks. Generally, as network input of AMC convolutional neural networks (CNNs) images or complex signals are utilized in time domain or frequency domain. In terms of the image that contains RGB(Red, Green, Blue) levels the input size may be larger than the complex signal, which represents the increase of computational complexity. In terms of the complex signal it is normally used as $2 \times N$ size for the input, which is divided into in-phase and quadrature-phase (IQ) components. In this paper, the input size is extended as $4 \times N$ size by copying IQ components and concatenating in reverse order to improve the classification accuracy. Since the increase in the amount of computation complexity due to the extended input size, the proposed CNN archiecture is designed to reduce the size from $4 \times N$ to $2 \times N$ by an average pooling layer, which can enhence the classification accuracy as well. The simulation results show that the classification accuracy of the proposed model is higher than the conventional models in the almost signal-to-noise ratio (SNR) range.

Reviewer: 1

Recommendation: Accept (minor edits)

Comments:
The authors have adequately addressed all the comments that raised in a previous round of review.

Additional Questions:
1) Does the paper contribute to the body of knowledge?: Yes

2) Is the paper technically sound?: Yes

3) Is the subject matter presented in a comprehensive manner?: Yes

4) Are the references provided applicable and sufficient?: Yes

5) Are there references that are not appropriate for the topic being discussed?: No

5a) If yes, then please indicate which references should be removed.:


Reviewer: 2

Recommendation: Accept (minor edits)

Comments:
It can be accepted in its current form.

Additional Questions:
1) Does the paper contribute to the body of knowledge?: Yes

2) Is the paper technically sound?: Yes

3) Is the subject matter presented in a comprehensive manner?: Yes

4) Are the references provided applicable and sufficient?: Yes

5) Are there references that are not appropriate for the topic being discussed?: No

5a) If yes, then please indicate which references should be removed.:


Reviewer: 3

Recommendation: Accept (minor edits)

Comments:
Can you try to find out whether the network has a better recognition effect on signals of a certain modulation mode, so as to facilitate the development of targeted practical applications.

Additional Questions:
1) Does the paper contribute to the body of knowledge?: It has a certain application value.

2) Is the paper technically sound?: Yes

3) Is the subject matter presented in a comprehensive manner?: Yes

4) Are the references provided applicable and sufficient?: Yes

5) Are there references that are not appropriate for the topic being discussed?: No

5a) If yes, then please indicate which references should be removed.: