kumoh national institute of technology
Networked Systems Lab.

Ade Pitra Hermawan, Rizki Rivai Ginanjar, Dong-Seong Kim, and Jae-Min Lee, "CNN-based Automatic Modulation Classification for 5G Communications", IEEE Communications Letters 2019. (Reject - Resubmission Allowed)
By : Ade Pitra
Date : 2019-10-23
Views : 319

Dear Author(s):

The review of the referenced manuscript, CL2019-2001, entitled CNN-Based Automatic Modulation Classification for 5G Communications, is now complete. I regret to inform you that based on the enclosed reviews and my own reading of your manuscript, I am unable to recommend its publication in IEEE Communications Letters. I agree with the reviewers comments and the reasons for this decision are as follows: Although the proposed work is promising, however, the manuscript lacks details, clarifications, and comparisons with some latest works. In addition, the motivation for reducing the computational complexity of AMC for 5G networks is not very clear. The inputs, output and loss function used for the network are not mentioned. The information on how the dataset is prepared to simulate real-world condition is also missing.

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Dr. Animesh Yadav
Associate Editor
IEEE Communications Letters

Reviewer: 1

Comments to the Author
Some interesting ideas and results have been presented. Some minor suggestions below,
• Since the authors propose the importance of time constraints in 5G applications. The problem is that the wireless devices supporting 5G will not have GPUs (one exception is the AIR-T SDR) in most cases hence the time provided may not be relevant to current 5G devices. Please elaborate on this. Can the computation be provided in terms of a metric that is independent of the processor used?

• The authors state However, most studies measured only the accuracy rate of the system but not the computing time.. There do exist AMC work that discusses computation couple of examples below,

F. Meng et al., "Automatic Modulation Classification: A Deep Learning Enabled Approach," in IEEE Transactions on Vehicular Technology, vol. 67, no. 11, pp. 10760-10772, Nov. 2018.
doi: 10.1109/TVT.2018.2868698

J. Jagannath et al., "Artificial Neural Network Based Automatic Modulation Classification over a Software Defined Radio Testbed," 2018 IEEE International Conference on Communications (ICC), Kansas City, MO, 2018, pp. 1-6.
doi: 10.1109/ICC.2018.8422346

J. Jagannath, D. OConnor, N. Polosky, B. Sheaffer, L.N. Theagarajan, S. Foulke, P.K. Varshney, S.P. Reichhart, Design and evaluation of hierarchical hybrid automatic modulation classifier using software defined radios, in: Proc. of IEEE Annual Computing and Communication Workshop and Conference (CCWC), 2017. Las Vegas, NV, USA

The proposed solution seems to outperform them so it might be worth citing those work and mentioning the improvement achieved. Please make sure the comparison is fair since GPUs may not be the platform used by them.
Other points that were not entirely clear,
• Why are CNN and CLDNN from [12] used rather than the models used in [3] which are more recent?
• Similarly, why wasnt the latest data set from [3] used?

Reviewer: 2

Comments to the Author
In this paper, the author solves a classification problem using CNN architectures. The novelty of the paper needs to be stated. Can the author include more papers that were using deep learning techniques for automatic modulation classification or 5-G communications? Is it the first paper which uses CNN to solve modulation classification problem?

Reject (Resubmission Allowed)