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Comments to the Author:
Please prepare a revision based on reviewers' comments.
Comments to the Author
There are minor grammatical errors throughout the paper. I recommend that the authors ask a native-English speaker to proofread and correct those errors.
The "research opportunities" section (VI) should be reduced and incorporated into the Introduction. Placing this section at the end of the paper interrupts the flow and distracts the reader from the results & conclusions of the present work.
Otherwise I believe that the paper is technically sound and sufficiently novel. Good work.
Comments to the Author
In Fig. 1 ¡°where deep learning is promisingly discovered for radio signal processing at the device and network levels¡±. Deep learning methods are widely used for signal processing not only at device and network levels, but also at the application level for tasks in Fig. 1, such as emotion recognition, object detection, speech recognition, and etc..
The idea of ¡°CNN model with a branch¡± is similar with ¡°identity mapping¡± in ResNet model (for one branch) and DensNet model (for multi-branch). What¡¯s the different or special about AMR-Net.
In Section ¥³, ¡°For example, compared with a regular CNN which adopts 3 x 3 kernels, our network gains approximately 44 percent and 67 percent of fewer parameters with 1 x 5 and 1 x 3 kernels, respectively.¡±. The calculation of network complexity seems not correct which using the size of a single convolution kernel.
For one thing, the ¡°44 percent¡± and ¡°67 percent is¡± can only reflect changes in the number of parameters of the convolution kernel which is not the whole network complexity.
For another thing, in fact, the effect of one 5x5 kernel is equal to that of two 3 x 3 kernels in deep learning, where both them can convert a 15 by 15 image into a 6 by 6 image with no padding and step = 1. So, simply comparing one 3 x 3 kernel and one 1 x 5 or 1 x 3 kernel is not rigorous.
In order to further demonstrate the superiority of the network, more classic DL model should be consider in Method comparison, such as VGG, ResNet, DensNet and etc. Besides, the comparsion should be conducted in some open datasets.
Comments to the Author
Authors consider the problem of automatic waveform recognition, and aim to bypass feature engineering based classification techniques via deep learning. To this end an architecture is proposed that directly processes the received I/Q samples, which is tested on classification of 4 principle pulsed radar waveforms: rectangular, linear frequency modulation (FM), Barker phase-coded, and stepped FM.
The paper has considerable tutorial content on the state-of-the art waveform recognition. Overview of CNN's lack a diagram or a supporting figure to accompany the descriptions provided in Section 3. Authors provide sufficient discussion on their numerical findings, which study the performance of their architecture with respect to the hyper-parameters of learning. The paper is well organized and well written. My comments on the manuscript are as follows:
In Section 4A, authors when forming their training/test sets specify sampling their parameters through the uniform distribution. It is confusing that at a fixed sampling rate, where sampling N (number of signal samples) determines the pulse-width, the number of cycles and the central frequency of the signal would also be uniformly sampled. Yet these are altogether stated as parameters that were sampled from the uniform distribution for all waveforms under consideration. Authors should elaborate on the nature of the training set, perhaps by providing a table of parameters for each waveform class.
Authors among their contributions mention less computational expense than the baseline CNN methods. To this end, it would be beneficial to provide the total training-time and test-time with respect to the baseline deep models in comparison. Furthermore, authors stress the significance of their global averaging and their branch structure within their model, however do not test the effectiveness of this component in their numerical assessments. An effective way to address this would be to modify the concatenation step by leaving out certain global averaging branches during training to compare with the current fully branched model.
Finally, authors for their numerical evaluations only consider 4 classes of waveforms, which is less than those encountered in the literature [*], where several polyphase codes are also considered (Frank, P1-P4). To this end, authors could assess the effectiveness of their model with a wider-range of pulsed radar waveforms.
[*] Zhang, Ming, Lutao Liu, and Ming Diao. "LPI radar waveform recognition based on time-frequency distribution." Sensors 16.10 (2016): 1682.