Path Loss Models for Cellular Mobile Networks Using Artificial Intelligence Technologies in Different Environments

Round 1

Reviewer 1 Report

The paper uses neural network technology to build Path Loss Models, which is an interesting topic. The paper also needs to be improved as follows:

1.Note the structure of the paper.

(1)In some sections, there is only Section 1, such as sections 6.1.1,6.21.

(2)Some sections are too short, such as Section 7, which can be merged into other sections.

(3)It is preferable to combine sections 6 and 7.

(4)Figures 5, 7 and 9 are derived from Figure 2. I think they can be omitted. The author just needs to list Figure 2, and describe it when using 1-layer, 2-layer, and 3-layer models.

2. In the second section, it is better to summarize these modes by using charts and tables, to explain the characteristics of various models, such as empirical and semi-empirical, the scope of application, parameters, etc., instead of describing and listing individual models independently in each section.

3.In section 3, I suggest using a table to summarize these AI methods. Moreover, the references should be placed at the beginning of each section, not at the end.

4.lines 410-422 and lines134-143 can be expressed in table form.

5.There are some formatting errors in the paper

(1) For example, in the summary: (e.g., Okumura Model Hata Model), hower),

(2) 335-338 lines, space between symbols

6.When MPL appears for the first time, it must have a full name. The full text is not seen.

7. In this paper, there is only one reference in 2021, one in 2019, and three in 2018. It is suggested that there should be more references in recent three years.

Author Response

Dear Respected Reviewer,

Responses are enclosed in the attached Word file.

 

Best Regards,

Author Response File: Author Response.docx

Reviewer 2 Report

1.  There is an unnecessary closing bracket in line no. 16. Remove it.

2. The abstract should be rewritten for a better understanding of the work.

3. The author(s) claimed that they have merged the accuracy and computational speed. What you have achieved out of this? Comment.

4. Although you have stated your contribution but please also discuss how your work is similar and different from the existing work.

5. In figure 2, you have considered only 2 layers ( a shallow network). What benefit did you get out of this? Why you have not considered a deep network?

6. Give a flowchart/algorithm of the proposed work.

7. Results were presented but a rigorous discussion of the results is required.

 

Author Response

Dear Respected Reviewer,

Responses are enclosed in the attached Word file.

 

Best Regards,

Author Response File: Author Response.docx

Round 2

Reviewer 1 Report

1. The suggested problems are revised.

2.Some figures should be redrawn.

(1)Figure 2, Figure 4, Figure 5 should be redrawn like the style as Figure 1 or Figure 3.

(2)Figure 6-9 is not clear, they should be redrawn.

(3)Figure 10-13, and Figure 16 should be redrawn like Figure 14 or Figure 19. 

Author Response

A description of the modifications are enclosed in the attached file. 

Author Response File: Author Response.docx

Reviewer 2 Report

I am satisfied with the response submitted.

Author Response

Thank you for your previous review. It was very helpful to enhance our paper.