Performance Analysis of Multi-Hop Flying Mesh Network Using Directional Antenna Based on β-GPP
Round 1
Reviewer 1 Report
The paper describes a multi-hop Flymesh based on a β- GPP distribution. Flymesh was simulated in different environments by modified specific parameter. Also, an approximate expression of the coverage probability was obtained, adopting an approximation of the diagonal core matrix to analyzed the ability of FlyMesh to provide services.
The current paper is interesting and covers a challenging issue. Authors presented a detailed related work, however it would be interesting to introduce a comparison between their work and the refereed works. They present their proposed model thoroughly and the numerical results are interesting and explanatory.
The number of references is sufficient and oriented towards the scope of paper.
Authors can also discuss managerial and research implications.
Author Response
Response to Reviewer 1 Comments
Thank you very much for your valuable comments and suggestions, which are very helpful to the improvement of paper quality. All revisions made in the new version are labelled yellow. Please check the manuscript for details. Thanks.
Point 1: Authors can also discuss managerial and research implications.
Response 1: Thank you for affirming our work. This paper mainly studies the performance of UAV multi-hop relay network. Based on this, we can conduct more detailed management and application of UAV network. Meanwhile, in the following work, we will analyze and discuss the research and management of UAV network in detail.
Author Response File: 
Author Response.pdf
Reviewer 2 Report
I found the paper to be well-written, and the authors have provided a detailed analysis of their proposed system.
The authors have clearly stated the problem that their paper is attempting to address, which is the improvement of the performance of multi-hop flying mesh networks. They have also provided a clear explanation of the beta-Ginibre point process algorithm, which they have used to enhance the performance of the directional antenna in their system.
The paper's methodology is sound, and the authors have conducted extensive simulations to evaluate the proposed system's performance. They have used several performance metrics such as packet delivery ratio, end-to-end delay, and throughput to evaluate the system's performance.
In terms of the paper's organization, I recommend the authors to improve the flow of the introduction section by adding more background information about multi-hop flying mesh networks and directional antenna systems. This information will enable readers who may not be familiar with the subject to understand the paper's context better.
Overall, I recommend the paper for publication in the journal, and I believe it will be a valuable contribution to the field of Drones as well as wireless communication networks.
NA
Author Response
Response to Reviewer 2 Comments
Thank you very much for your valuable comments and suggestions, which are very helpful to the improvement of paper quality. All revisions made in the new version are labelled yellow. Please check the manuscript for details. Thanks.
Point 1: In terms of the paper's organization, I recommend the authors to improve the flow of the introduction section by adding more background information about multi-hop flying mesh networks and directional antenna systems. This information will enable readers who may not be familiar with the subject to understand the paper's context better.
Response 1: Thank you for your recognition of our work and your suggestions on the revision of the paper. We add references [3] [4] and [5] to the introduction of background to provide more background information for facilitating the understanding of our work.
Author Response File: Author Response.pdf
Reviewer 3 Report
The authors proposed:
"Performance Analysis of Multi-hop Flying Mesh Network Using Directional Antenna Based on β-GPP"
. The work seems interesting , however there are few concerns that authors should address :
1. The authors should provide clearer background of the study.
2. Some more problem definition will help
3. there are also some typos and few grammatical errors and clarity as follows:
Lines 177-181 check error and gramma
Equation 171 needs clarification
What is rejection parameter
What is DF?
equation
See comments in authors section
Author Response
Response to Reviewer 3 Comments
Thank you very much for your valuable comments and suggestions, which are very helpful to the improvement of paper quality. All revisions made in the new version are labelled yellow. Please check the manuscript for details. Thanks.
Point 1: The authors should provide clearer background of the study.
Response 1: Thank you for your recognition of our work and your suggestions on the revision of the paper. We add references [3] [4] and [5] to the introduction section, to provide more information about the research background and motivation.
Point 2: Some more problem definition will help
Response 2: We re-analyzed the problem and provided more details, including descriptions and definitions.
Point 2: There are also some typos and few grammatical errors and clarity as follows:
1.Lines 177-181 check error and gramma
Response: Sorry for the typos and grammar errors. We thoroughly checked for grammar errors and fixed them.
2.Equation 171 needs clarification
Response: It is the signal-to-interference-to-noise ratio. It is adopted to describe the ratio of the effective signal to the ambient interference. It can to some extent reflects the possibility that the transmitted information being successfully received.
3.What is rejection parameter
Response: It is a parameter β (0<β<1) that describes the environment. In other words, β is different under different transmission environments. In the city with many obstacles, dense and evenly distributed communication nodes, 0.5<β<1. In rural areas, communication nodes are sparsely distributed, 0<β<0.5. To make the description more accurate, we replace the rejection parameter with the repulsive parameter.
4.What is DF?
Response: DF is the acronym for decode-and-forward. The decode-and-forward refers to the processing behavior that after receiving a signal, the relay node decodes the signal, then re-encodes the decoding result, and finally forwards it to the destination node.
Author Response File: Author Response.pdf
Reviewer 4 Report
The paper needs some major improvements before publication:
1. The manuscript needs to be carefully checked because there are many grammatical errors and typos.
2. There are quantities which are not properly defined such as equations 9,10,13,19 and 24 and the subscript need to be carefully defined.
3. What is the criteria author used to choose the value of the parameter beta in the model suggested?
4. Why the author did not consider the latency and network throughput as a performance metric as well?
5. The author did not discuss the network resource and allocation optimization such as what is target coverage required and what is the optimal number of drones required.
6. In section 4, please add a table that represents the simulation parameters.
7. What is the complexity of using β-GPP compared to PPP?
8. In section 4, the author did not define the deployment area by considering the physical constraints, obstacles, or regions of interest within the deployment area.
9. The author did not explain what is the path loss model used in the simulation results section?
10. The author did not discuss the effectiveness of this model in the presence of interference.
The manuscript needs to be carefully checked because there are many grammatical errors and typos.
Author Response
Response to Reviewer 4 Comments
Thank you very much for your valuable comments and suggestions, which are very helpful to the improvement of paper quality. All revisions made in the new version are labelled yellow. Please check the manuscript for details. Thanks.
Point 1: The authors should provide clearer background of the study.
Response 1: Thank you for your recognition of our work and your suggestions on the revision of the paper. We add references [3] [4] and [5] to the introduction section, to provide more information about the research background and motivation.
Point 2: There are quantities which are not properly defined such as equations 9,10,13,19 and 24 and the subscript need to be carefully defined.
Response 2: We did a thorough check about all the equations, and defined the subscripts.
Point 3: What is the criteria author used to choose the value of the parameter beta in the model suggested?
Response 3: It is a parameter β (0<β<1) that describes the environment. In other words, β is different under different transmission environments. In the city with many obstacles, dense and evenly distributed communication nodes, 0.5<β<1. In rural areas, communication nodes are sparsely distributed, 0<β<0.5. To make the description more accurate, we replace the rejection parameter with the repulsive parameter.
Point 4: Why the author did not consider the latency and network throughput as a performance metric as well?
Response 4: This paper mainly analyzes the performance of UAVs network. Due to the limitation of the length and content of this paper, delay and throughput are not considered, which are also important indicators to analyze network performance. We will take them into account in the next step. Please refer to references for other parameters that are not considered in this paper.
[1] Z. M. Fadlullah, D. Takaishi, H. Nishiyama, N. Kato and R. Miura, "A dynamic trajectory control algorithm for improving the communication throughput and delay in UAV-aided networks," in IEEE Network, vol. 30, no. 1, pp. 100-105, January-February 2016, doi: 10.1109/MNET.2016.7389838.
[2] J. -H. Lee, K. -H. Park, Y. -C. Ko and M. -S. Alouini, "Throughput Maximization of Mixed FSO/RF UAV-Aided Mobile Relaying With a Buffer," in IEEE Transactions on Wireless Communications, vol. 20, no. 1, pp. 683-694, Jan. 2021, doi: 10.1109/TWC.2020.3028068.
Point 5: The author did not discuss the network resource and allocation optimization such as what is target coverage required and what is the optimal number of drones required.
Response 5: Thank you for your good advice. We mainly analyze the network performance of UAVs distributed according to GPP model. Our results can provide theoretical support for network resource and allocation optimization. Next, we will conduct in-depth research on this basis.
Point 6 :In section 4, please add a table that represents the simulation parameters.
Response 6: We have modified it in the paper, please see the simulation part of the paper. Please look at lines 217-218.
Point 7: What is the complexity of using β-GPP compared to PPP?
Response 7: PPP is a homogeneous Poisson process that mimics the randomness of network nodes. The GPP class uses non-homogeneous Poisson process, and the simulated network nodes have certain regular distribution in different environments. Since GPP can simulate the distribution of network nodes in different environments, we can know that the complexity of GPP is greater than that of PPP. However, it has not been analyzed from a quantitative perspective. Please refer to references for other parameters that are not considered in this paper.
[1] R. W. Heath, Jr, M. Kountouris, and T. Bai, “Modeling heterogeneous network interference using Poisson point processes,” IEEE Trans. Signal Process. , vol. 61, no. 16, pp. 4114–4126, Aug. 2013.
[2] N. Miyoshi and T. Shirai, “A cellular network model with Ginibre confifigurated base stations,” Adv. Appl. Probab., vol. 46, no. 3, pp. 1–12, 2014. [Online]. vailable: http://www.is.titech.ac.jp/research/research-report/ B/B-467.pdf
[3] F. Baccelli and B. Blaszczyszyn, Stochastic Geometry and Wireless Networks, Volume I: Theory/Volume II: Applications, vol. 1. Delft, The Netherlands: Now Publishers, 2010.
Point 8: In section 4, the author did not define the deployment area by considering the physical constraints, obstacles, or regions of interest within the deployment area.
Response 8: Thank you for your suggest. This paper mainly considers the effect of the location distribution of network nodes on network performance, so an infinite domain is simulated. Regional limits, barriers and boundary nodes also affect the performance of the network, which can be the direction of our next research.
Point 9:The author did not explain what is the path loss model used in the simulation results section?
Response 9: Because the path fading model has been introduced in the modeling section, it is omitted in the simulation section. We ignore the small-scale fading, and the signal fading conforms to the exponential distribution with a mean of 1.
Point 10: The author did not discuss the effectiveness of this model in the presence of interference.
Response 10: Thank you for your valuable advice. This paper mainly considers the network performance in the case of self-interference. We also consider the presence of external interference. First, the interference node conforms to the GPP model, so the analysis results in this paper can be continued. Second, the interference node does not conform to the GPP model, so external interference and self-interference need to be regarded as two models for analysis. For the above two cases, we will further analyze and discuss in the future work.
Author Response File: Author Response.pdf
