Joint Beamforming and Trajectory Design for Aerial Intelligent Reflecting Surface-Aided Secure Transmission

Round 1

Reviewer 1 Report

Abstract

line 4: change the word guarantee. You cannot guarantee security but you can improve security.

The research finding(s) is presented as a generic statement. It should be quantified to demonstrate validity of the study. 

Introduction

Line 30: wireless relays require an additional power )changed requires to require)

line 59: the acronym LoS stands for ............

Contribution to knowledge

1. what is novel in this study? 

a similar work has been done by:  Pang, X., Zhao, N., Tang, J., Wu, C., Niyato, D., & Wong, K. K. (2021). IRS-assisted secure UAV transmission via joint trajectory and beamforming design. IEEE Transactions on Communications, 70(2), 1140-1152.

4. the Minorization-Maximization (MM) algorithm-based method has been applied a similar works for the same purpose. How can it be stated as a contribution to knowledge? Peng, Z., Zhang, Z., Pan, C., Li, L., & Swindlehurst, A. L. (2021). Multiuser full-duplex two-way communications via intelligent reflecting surface. IEEE Transactions on Signal Processing, 69, 837-851.

Review the section-contribution to knowledge. All that have been stated are already in the public domain. 

 

 Although there is elaborate analysis from the simulation, the results were not clearly stated. 

Author Response

First of all, we would like to thank all the anonymous reviewers for their very constructive and detailed comments that help improve the quality of our paper. All of their comments and suggestions have been carefully addressed in the revised letter. The corresponding changes and refinements we have made are all summarized in the attached revision summary. It is noted that only those comments that suggest clarifications, improvements or corrections are quoted and responded below. Other positive comments are not repeated here but kindly thanked. For easier cross-reference, we mark the reviewers’ comments in black and our responses in red. For the point-by-point response to the reviewers’ comments, please see the attachment. Thank you again!

Author Response File: Author Response.pdf

Reviewer 2 Report

1. I suggest author to do a comparison study for this article, how the proposed method is different/better/unique compared to state of the art. For example: 

a) L. Ge, P. Dong, H. Zhang, J. -B. Wang and X. You, "Joint Beamforming and Trajectory Optimization for Intelligent Reflecting Surfaces-Assisted UAV Communications," in IEEE Access, vol. 8, pp. 78702-78712, 2020, doi: 10.1109/ACCESS.2020.2990166.

2. Need more information on dataset - how is it collected, what dataset looks like. As Figure 2 represents the DLNN model. It's not clear how input data are fed into the model, is it numerical, integer, and units to each input. Also, I'm not entirely clear if it's a classification problem with '2' outputs.

3. Figure 4 - 'convergence' is not explained/defined anywhere, except outside figure.

Author Response

First of all, we would like to thank all the anonymous reviewers for their very constructive and detailed comments that help improve the quality of our paper. All of their comments and suggestions have been carefully addressed in the revised letter. The corresponding changes and refinements we have made are all summarized in the attached revision summary. It is noted that only those comments that suggest clarifications, improvements or corrections are quoted and responded below. Other positive comments are not repeated here but kindly thanked. For easier cross-reference, we mark the reviewers’ comments in black and our responses in red. For the point-by-point response to the reviewers’ comments, please see the attachment. Thank you again!

Author Response File: Author Response.pdf

Reviewer 3 Report

This paper studies the secure transmission challenge confronted with future communication systems. In our considered model, the confidential communication between legitimate users is strengthened by an aerial intelligent reflecting surface (AIRS) deployed on aerial platforms such as an unmanned aerial vehicle (UAV). The average secrecy rate for all time slots is first investigated to guarantee information security during AIRS flights. Then, the transmit beamforming, phase-shifting matrix, and trajectory of AIRS are jointly designed, aiming to maximize the average secrecy rate performance between legitimate users. On account of the non-convexity of the formulated objective function and the coupled three key variables, we resort to an alternative strategy that converts the original objective into three sub-problems and solves them recursively. In particular, the transmit beamforming is designed based on the generalized eigenvalue optimization method, and the closed-form solution is derived. For the AIRS-related optimization, a Minimization-Maximization (MM)-based algorithm and a deep deterministic policy gradient (DDPG)-based method are proposed to derive the solutions of phase shift matrix and trajectory, respectively. Simulation results verify that our proposed AIRS-empowered secure transmission scheme considerably boosts the average secrecy rate compared to traditional terrestrial IRS-assisted systems. 

I have the following major comments and concerns:

1) Typos and Grammar Errors: a) Abstract line 11, please change Minorization to Minimization; b) Introduction, line 19, is revolutionizing to revolutionizes; c) System Model, line 98, As shown Fig. 1, to As shown in Fig. 1. Please carefully proofread all the paper and correct all typos and grammar errors.

2) It is recommended to add some essential tables to this paper for readers; add a table of notations/symbols used in this work; add a table of comparison including all related work, and show how this work is different from state of the art.

3) Some related works are missing. For instance, Energy Efficient Transmission Design for NOMA Backscatter-Aided UAV Networks with Imperfect CSI; Opportunities for physical layer security in UAV communication enhanced with intelligent reflective surfaces. Please read all the related works to this topic and revise your introduction section.

 

 4) The proposed system model is very simple; just consider one user and one Eve. How to extend your model to a large-scale network. Also, explain the effect of a large network on the proposed optimization scheme in terms of solution, complexity, and connection density. 

5) Please explain the mathematical complexity of the proposed optimization scheme in terms of big O.

6) It is better to directly compare your proposed scheme with literature/ or exhaustive search approach.

7) The authors have written a joint solution in the title of this paper. However, the problem is decoupled into subproblems and solved by alternating optimization, and I think it is not joint; perhaps different variables are simultaneously optimized. Please justify this.

Author Response

First of all, we would like to thank all the anonymous reviewers for their very constructive and detailed comments that help improve the quality of our paper. All of their comments and suggestions have been carefully addressed in the revised letter. The corresponding changes and refinements we have made are all summarized in the attached revision summary. It is noted that only those comments that suggest clarifications, improvements or corrections are quoted and responded below. Other positive comments are not repeated here but kindly thanked. For easier cross-reference, we mark the reviewers’ comments in black and our responses in red. For the point-by-point response to the reviewers’ comments, please see the attachment. Thank you again!

Author Response File: Author Response.pdf

Round 2

Reviewer 3 Report

Thank you so much for addressing my comments. Before accepting this paper, I have one more comment.  As the IRS is equipped with UAV and it is moving. How to achieve channel information in such a mobile scenario? Assuming perfect channel information is not practical. How it can affect the security of the end user if the channel information has errors. I would suggest to study papers on wireless optimization scenarios that consider imperfect channel information. Energy efficiency optimization for backscatter enhanced NOMA cooperative V2X communications under imperfect CSI and Energy-efficient backscatter aided uplink NOMA roadside sensor communications under channel estimation errors are highly recommended to study and report.

Author Response

First of all, we would like to thank all the anonymous reviewers for their very constructive and detailed comments that help improve the quality of our paper. All of their comments and suggestions have been carefully addressed in the revised letter. The corresponding changes and refinements we have made are all summarized in the attached revision summary. It is noted that only those comments that suggest clarifications, improvements or corrections are quoted and responded below. Other positive comments are not repeated here but kindly thanked. For easier cross-reference, we mark the reviewers’ comments in black and our responses in red. For the point-by-point response to the reviewers’ comments, please see the attachment. Thank you again!

Author Response File: Author Response.pdf