A Novel UAV-Assisted Multi-Mobility Channel Model for Urban Transportation Emergency Communications

Round 1

Reviewer 1 Report

Authors in this paper proposed  a novel UAV-assisted UAV-to-vehicle (U2V)  geometry-based stochastic model (GBSM) for urban traffic communication scenario is proposed.  The three-dimensional (3D) multi-mobilities of the transmitter (Tx), receiver (Rx), and clusters are  considered by introducing the time-variant acceleration and velocity correspondingly. Moreover,  Markov chain is adopted to analyze the changes of cluster motion states, including survival, death,  dynamic, and static state. By adjusting the parameters, such as vehicle density (ρ) and dynamic-static  ratio (Ω), the model can support various urban traffic scenarios. Based on the proposed model, several  key statistical properties, namely the root mean square (RMS) delay spread, temporal autocorrelation  function (ACF), level-crossing rate (LCR), power delay profile (PDP), and stationary interval (STA)  under different cluster and antenna accelerations are obtained and analyzed. 

the article is of outstanding quality with good results, I accept the article with a minor revision consisting of:

1- Improved the quality of writing in English which is still not comprehensive.

2- in the abstract, the authors do not adequately state their contributions.

3- It is strongly recommended to divide the introduction into several sub-sections (state of the art, related work, motivation and contribution). Please add this paper in related works

Ouamri, M.A., Singh, D., Muthanna, M.A. et al. Performance analysis of UAV multiple antenna-assisted small cell network with clustered users. Wireless Netw 29, 1859–1872 (2023). https://doi.org/10.1007/s11276-023-03240-9

D. Alkama, M. A. Ouamri, M. S. Alzaidi, R. N. Shaw, M. Azni and S. S. M. Ghoneim, "Downlink Performance Analysis in MIMO UAV-Cellular Communication With LOS/NLOS Propagation Under 3D Beamforming," in IEEE Access, vol. 10, pp. 6650-6659, 2022, doi: 10.1109/ACCESS.2022.3142529.

Ouamri, Mohamed Amine, Marius-Emil Oteşteanu, Gordana Barb, and Cedric Gueguen. 2022. "Coverage Analysis and Efficient Placement of Drone-BSs in 5G Networks" Engineering Proceedings 14, no. 1: 18. https://doi.org/10.3390/engproc2022014018

4- please add a table summarising the different notations in your equations to make the article easier to read.

5- Figure 2 is illegible and too complicated to read. Please improve the quality of both figures a and b.

6- What is the difference between equation 2 and equation 3? A better explanation is needed.

7- The proof of equation 39 must be added. How did you derive this formula?

8- your conclusion is poor and needs to be improved. 

English need to be improved carfully 

Author Response

The authors would like to thank the editor and the reviewers for their helpful and insightful comments. We have improved the quality of the manuscript by carefully taking all the comments into account. The modifications in this revised manuscript along with the response to the reviewers’ comments are described below.

Comments and Suggestions for Authors

Authors in this paper proposed a novel UAV-assisted UAV-to-vehicle (U2V) geometry-based stochastic model (GBSM) for urban traffic communication scenario is proposed.  The three-dimensional (3D) multi-mobilities of the transmitter (Tx), receiver (Rx), and clusters are considered by introducing the time-variant acceleration and velocity correspondingly. Moreover, Markov chain is adopted to analyze the changes of cluster motion states, including survival, death, dynamic, and static state. By adjusting the parameters, such as vehicle density (ρ) and dynamic-static ratio (Ω), the model can support various urban traffic scenarios. Based on the proposed model, several key statistical properties, namely the root mean square (RMS) delay spread, temporal autocorrelation function (ACF), level-crossing rate (LCR), power delay profile (PDP), and stationary interval (STA) under different cluster and antenna accelerations are obtained and analyzed. 

the article is of outstanding quality with good results, I accept the article with a minor revision consisting of:

1- Improved the quality of writing in English which is still not comprehensive.

Authors: Following the reviewer’s suggestion, we have carefully checked throughout the whole manuscript and improved the quality of writing in English in the revised manuscript. 

2- in the abstract, the authors do not adequately state their contributions.

Authors: In the revised manuscript (see the sentence in red font in Abstract on Page 1), we have updated the related descriptions of abstract to further state the contribution.

3- It is strongly recommended to divide the introduction into several sub-sections (state of the art, related work, motivation and contribution). Please add this paper in related works

Ouamri, M.A., Singh, D., Muthanna, M.A. et al. Performance analysis of UAV multiple antenna-assisted small cell network with clustered users. Wireless Netw 29, 1859–1872 (2023). https://doi.org/10.1007/s11276-023-03240-9

10. Alkama, M. A. Ouamri, M. S. Alzaidi, R. N. Shaw, M. Azni and S. S. M. Ghoneim, "Downlink Performance Analysis in MIMO UAV-Cellular Communication With LOS/NLOS Propagation Under 3D Beamforming," in IEEE Access, vol. 10, pp. 6650-6659, 2022, doi: 10.1109/ACCESS.2022.3142529.

Ouamri, Mohamed Amine, Marius-Emil Oteşteanu, Gordana Barb, and Cedric Gueguen. 2022. "Coverage Analysis and Efficient Placement of Drone-BSs in 5G Networks" Engineering Proceedings 14, no. 1: 18. https://doi.org/10.3390/engproc2022014018

Authors: As the reviewer suggested, we have divided the introduction into several sub-sections, such as related works and motivation, and contributions (see the sentence in red font in Section 1 on Pages 2 and 3). Moreover, we have added the related works in the revised manuscript and cited the mentioned three paper as references (see the references 6, 8, and 9 on Page 1).

4- please add a table summarising the different notations in your equations to make the article easier to read.

Authors: Thank you for your suggestion. In the revised manuscript, we have added a detailed table to summary the different notations in equations (see table I in Section 2 on Page 11).

5- Figure 2 is illegible and too complicated to read. Please improve the quality of both figures a and b.

Authors: In the revised manuscript, we have improved the quality of both figures a and b (see Figure 2 on Page 4).

6- What is the difference between equation 2 and equation 3? A better explanation is needed.

Authors: The difference between equation 2 and equation 3 is explained as below. During the modeling process, the propagation environments can be described as LoS and NLoS components. For the NLoS component, it contains two parts. One part is from the fixed obstacles and irregular moving objects, which can be abstracted as multi-bounced (MB) component, and the other part is the mobile obstacles around the Rx with regular moving trajectories, which can be abstracted as single-bounced (SB) component. The MB and SB components can be established as twin-cluster and single-cluster models, respectively. For the twin-cluster model, it mainly considers the influence of some fixed obstacles and irregular moving objects, and can be expressed as equation 2. The phase shift is caused by relative movement of Tx and cluster A, as well Rx and cluster Z. Moreover, the time delay between Tx and Rx is calculated by the propagation delay and the virtual delay between the cluster A and Z. For the single-cluster model, it focuses on the regular mobile obstacles around the Rx and can be expressed as equation 3. Both of them have different propagation parameters, such as cluster number, ray number inside each cluster, powers, delays and so on.

In the revised manuscript, we have added some descriptions (see the sentence in red font in Section 2.2.1 on Page 5) about the difference between equation 2 and equation 3 as below.

“…The MB and SB components can be established as twin-cluster and single-cluster models, respectively. For the MB component, it mainly considers the influence of some fixed obstacles and irregular moving objects, and can be expressed as

…. equation (2) …

For the SB component, it focuses on the influence of regular mobile obstacles around Rx, and can be expressed as

…. equation (3) …”

7- The proof of equation 39 must be added. How did you derive this formula?

Authors: Thank you for your suggestion. In this manuscript, the local region of stationarity was used to characterize channel non-stationarity from the point of view of channel power variation. It is described as the longest time interval within which the correlation coefficient of two averaged power delay profiles (APDPs) is over a given threshold. The correlation coefficient of two averaged power delay profiles (APDPs) is expressed as equation 9. It is a correlation calculation of two APDPs at different time instants, which can be further used to measure the stationary interval. To provide the source of formula, we have added the related reference [36] (see Section 3.4 on Page 12).

8- your conclusion is poor and needs to be improved. 

Authors: Thank you for your suggestion. In the revised manuscript, we have improved the conclusion part (see Section 5 on Page 16).

Author Response File: Author Response.pdf

Reviewer 2 Report

Since the results rely on simulations, are there are experimental results that can be used to validate simulation results?

Well written with minor edits

Author Response

The authors would like to thank the editor and the reviewers for their helpful and insightful comments. We have improved the quality of the manuscript by carefully taking all the comments into account. The modifications in this revised manuscript along with the response to the reviewers’ comments are described below.

Comments and Suggestions for Authors：

Since the results rely on simulations, are there are experimental results that can be used to validate simulation results?

Authors: Thank you for your suggestion. In this paper, a novel UAV-assisted UAV-to-vehicle (U2V) geometry-based stochastic model (GBSM) is proposed for urban traffic communication scenario. The proposed model considers the three-dimensional (3D) multi-mobilities of the transmitter (Tx), receiver (Rx), and clusters, and the state changes of moving clusters around Rx. Meanwhile, the scenario-specific parameters, such as vehicle density (ρ) and dynamic-static ratio (Ω) are introduced.

To validate the proposed channel model, the related channel measurement was carried out on the Xingzhong Road in Jinan. The Rx and Tx are 50 m apart, both at a speed of 5 m/s, moving in opposite directions. There were a large number of trees on both sides of the road and a large number of vehicles moving around Rx. During the channel measurement, the multi-mobility of Tx, Rx, and clusters around Rx was considered, which is in accordance with modeling scenario.

Based on the measurement data, the channel impulse response is obtained and the CDF of delay spread is then calculated. As shown in Figure 9, the simulation result of our proposed model can match well with the measurement data, which shows the accuracy and usability of proposed model. Therefore, as the reviewer suggested, we have acquired some experimental results. Using these experimental results, the proposed model was better validated (Please see the sentence in red font in Section 4 on Page 16, and the figure 9).

Author Response File: Author Response.pdf

Reviewer 3 Report

The manuscript presents a UAV-assisted multi-mobileity channel model for urban trasportation. The idea is of interest, however, the authors have to improve the manuscript.

1. Initially they have to clarify the difference of their work with the following GBSM models:

Ma, Z.; Ai, B.; He, R.; Wang, G.; Niu, Y.; Zhong, Z. A wideband non-stationary air-to-air channel Model for UAV communications. IEEE Trans. Veh. Technol. 2020, 69, 1214–1226.

Ma, Z.; Ai, B.; He, R.; Zhong, Z. A 3D air-to-air wideband non-stationary channel model of UAV communications. In Proceedings 90th Veh. Technol. Conf. (VTC-2019 Fall), Honolulu, HI, USA, Sep. 2019; pp. 1–5.

Mao, X.; Wang, C.-X.; Chang, H. A 3D non-stationary geometry-based stochastic model for 6G UAV air-to-air channels. In Proceedings 13th Int. Conf. Wireless Commun. Signal Process. (WCSP), Changsha, China, Oct. 2021; pp. 1–5.

2. Fig. 1 must be improved and changed. It looks like taken from a video-game.

3. In contribution no. 3, all the abbreviated definitions have to be defined.

4. In the caption of Fig. 2 correct to "channel".

5. The power delay profile in (38) should be defined as P not Lambda. It is commonly written as P.

6. The Level Crossing Rate is known and there is no need to write (41).

7. The most significant of all is the Results Section. Figures 5, 6, 8 and 9 must be enlarged. Furthermore the authors have to elaborate more on their results and present more qualitative and intutitive observations derived from the simulations.

Good qualtity of English, probably minor editing will be required.

Author Response

The authors would like to thank the editor and the reviewers for their helpful and insightful comments. We have improved the quality of the manuscript by carefully taking all the comments into account. The modifications in this revised manuscript along with the response to the reviewers’ comments are described below.

Comments and Suggestions for Authors

The manuscript presents a UAV-assisted multi-mobility channel model for urban transportation. The idea is of interest, however, the authors have to improve the manuscript.

1. Initially they have to clarify the difference of their work with the following GBSM models:

[1] Ma, Z.; Ai, B.; He, R.; Wang, G.; Niu, Y.; Zhong, Z. A wideband non-stationary air-to-air channel Model for UAV communications. IEEE Trans. Veh. Technol. 2020, 69, 1214–1226.

[2] Ma, Z.; Ai, B.; He, R.; Zhong, Z. A 3D air-to-air wideband non-stationary channel model of UAV communications. In Proceedings 90th Veh. Technol. Conf. (VTC-2019 Fall), Honolulu, HI, USA, Sep. 2019; pp. 1–5.

[3] Mao, X.; Wang, C.-X.; Chang, H. A 3D non-stationary geometry-based stochastic model for 6G UAV air-to-air channels. In Proceedings 13th Int. Conf. Wireless Commun. Signal Process. (WCSP), Changsha, China, Oct. 2021; pp. 1–5.

Authors: Thank you for your suggestion. To clarify the related studies, we have introduced and cited the above three references (see the sentence in red font in Section 1.1 on Page 2). Moreover, the difference of the above models and our work is summarized as below:

• The channel models are different. In Ref [1], a 3D non-stationary A2A GBSM considering a Markov mobility model is proposed. It mainly focuses on the study of UAV movement in 3D space. In Ref [2], a 3D non-stationary wideband A2A GBSM is proposed, which introducing the velocities and trajectories changes of Tx and Rx. In Ref [3], a 3D non-stationary wideband A2A GBSM considering 3D arbitrary movement and time-domain non-stationary is proposed. In our manuscript, a novel UAV-assisted U2V GBSM is presented. Firstly, it considers the multi-mobilities of transmitter (Tx), receiver (Rx), and clusters by introducing the time-variant acceleration and velocity correspondingly. Then, Markov chain is adopted to analyze the changes of cluster motion states, including survival, death, dynamic, and static state, which can mimic the propagation channel practically. Finally, the scenario-specific parameters, such as vehicle density (ρ) and dynamic-static ratio (Ω), are introduced. By adjusting the parameters, the model can support various urban traffic scenarios. Therefore, there is a big difference of the proposed channel models in above references and in our paper.
• The application scenarios are different. Different propagation scenario will bring their unique channel characteristics. The above channel models are proposed for UAV air-to-air communication environments. The proposed model in our manuscript is presented for UAV-assisted UAV-to-vehicle (U2V) scenarios. Therefore, different channel models focus on describing their unique channel characteristics, respectively.

2. Fig. 1 must be improved and changed. It looks like taken from a video-game.

Authors: Thank you for your suggestion. In the revised manuscript, we have improved and changed the Fig. 1 (see Figure 1 on Page 4).

3. In contribution no. 3, all the abbreviated definitions have to be defined.

Authors: As the reviewer suggested, we have defined all the abbreviations (see the sentence in red font in Section 1.2 on Page 3).

4. In the caption of Fig. 2 correct to "channel".

Authors: As the reviewer suggested, we have corrected the caption of Fig. 2 in the revised manuscript (see Figure 2 on Page 4).

5. The power delay profile in (38) should be defined as P not Lambda. It is commonly written as P.

Authors: As the reviewer suggested, we have changed the definition of power delay profile as P in equation (38) in the revised manuscript (see Section 3.3 on Page 12).

6. The Level Crossing Rate is known and there is no need to write (41).

Authors: Follow the reviewer’s suggestion, we have deleted the equation (41) and its related derivations in the revised manuscript (see Section 3.5 on Page 13).

7. The most significant of all is the Results Section. Figures 5, 6, 8 and 9 must be enlarged. Furthermore the authors have to elaborate more on their results and present more qualitative and intutitive observations derived from the simulations.

Authors: As the reviewer suggested, we have enlarged the Figure 5, 6, 8, and 9 to make the results clearer. Furthermore, we have added some descriptions and analysis of the related results (see the sentence in red font in Section 4 on Pages 14, 15, and 16).

Author Response File: Author Response.pdf

Round 2

Reviewer 3 Report

The authors improved the manuscript and addressed all the raised questions.

I believe that the manuscript can be published.

The authors improved the manuscript and addressed all the raised questions.

I believe that the manuscript can be published.