kumoh national institute of technology
Networked Systems Lab.

Godwin Brown Tunze, Thien Huynh-The, Jae-Min Lee, and Dong-Seong Kim, Sparsely Connected CNN for Efficient Automatic Modulation Recognition, IEEE Transaction on Vehicular Technology, 2020.(Major Revision)
By : Godwin
Date : 2020-09-08
Views : 103

Dear Prof. Kim:

The review of your paper

VT-2020-02150: Sparsely Connected CNN for Efficient Automatic Modulation Recognition

has been completed. Below please find comments from the reviewers.

Editor's Comments: Three independent reviewers evaluated the paper and pointed out some issues that need to be carefully addressed and/or clarified. In a possible resubmission, the authors must make sure that all the raised concerns have been addressed satisfactorily.

Based on the reviewers' comments, publication of the paper in its present form is not recommended. It is recommended that you resubmit your manuscript, WITHIN TWO MONTHS from the date of this email, as revised in accordance with the comments. If you fail to do so, your paper will be considered withdrawn from the review process.

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Sincerely yours,

Daniel Benevides da Costa
Editor, IEEE Transactions on Vehicular Technology


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Reviews for VT-2020-02150

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Reviewer: 1

ADDITIONAL COMMENTS TO THE AUTHOR:
This paper proposed a convolutional neural network (CNN) called SCGNet for low-complexity and robust modulation recognition in intelligent communication receivers. However, it still needs major modifications before acceptance.


1. There are a lot of language mistakes that need to be addressed. Please proofread the paper carefully.

2. Compare and cite the proposed algorithm against the latest work in the same domain, such as CSFL: A novel unsupervised convolution neural network approach for visual pattern classification , Face recognition: A novel un-supervised convolutional neural network method , A Benchmark Dataset and Learning High-level Semantic Embeddings of Multimedia for Cross-media Retrieval , Optimization of CNN through novel training strategy for visual classification problems, Unsupervised pre-trained filter learning approach for efficient convolution neural network.

3. The training of CNN based algorithm is very computational expensive. Justify, the reason behind author choice of CNN based algorithm for automatic modulation recognition.

4. Explain why the author choose multiple convolution layers simultaneously in their architecture. However, we are aware that convolution operator extracts low-level features in the initial layers and high-level features in higher layers. The benchmark CNN architecture follows alternatively convolution layers. Justify?

5. The experimental results in this paper are too simple and there is a lack of specific comparison of different methods. It is difficult to explain the superiority of the proposed SCGNet model. The experimental results give too few examples, and more comparisons and examples can be added.

6. Figure 1 about convolution is not very expressive. It is recommended to improve it to illustrate better the concept of convolution.

7. In my opinion, it will help a lot showing a simple example for every convolution type to illustrate it. The description of some convolution types is superficial (e.g., grouped convolution).


Reviewer: 2

ADDITIONAL COMMENTS TO THE AUTHOR:
This paper proposes a new convolutional neural network (CNN) called SCGNet for low-complexity and robust modulation recognition in intelligent communication receivers. The idea seems interesting. I have some suggestions:

1.- An initial table with all the acronyms could help to understand the manuscript.
2.- As the model is a sparse network, I recommend the authors to provide a state of art disscusion about Deep Efficient Models as: EfficientNet, ShiftResNet, TresNet, SEResNeXt50, MixNet, MobileNet, etc.
3.- Please, include a link to the code in the paper
4.- Please, include a deeper discussion about the FLOPs.

Reviewer: 3

ADDITIONAL COMMENTS TO THE AUTHOR:
Writing:
1. If support vector machines (SVMs), then neural networks (NNs) not NN.
2. (i.e.., CNN with few connections) -> N (i.e., CNN with few connections).
3. (*) represents the convolution operation -> * represents the convolution operation

Methodology:
1. Fig. 1. the grouped convolutional layer has a similar feature as in Video Foreground Extraction Using Multi-View Receptive Field and Encoder–Decoder DCNN for Traffic and Surveillance Applications, IEEE Transactions on Vehicular Technology, 2019. It is recommended to discuss this work in the literature review.
2. Can you justify why the proposed SCGNet has lesser inference speed than DrCNN, although it has lower trainable parameters than of DrCNN?