Robust Channel Estimation Scheme for Multi-UAV MmWave MIMO Communication with Jittering

Round 1

Reviewer 1 Report

Thank you for your submission.

 

Your paper well describes robust channel estimation scheme for UAV communication system with certain formula.

There are some comments and typos in your description.

 

1.

I could not catch an example of mm-wave wireless front-end structure.

Considering that the word of "mm-wave Beamforming" in your paper's title, it is better to describe the actual physical composition of mm-wave-based MIMO system including the link budget between UAVs.

 

2.

Your paper try to make clear the optimized direction of mm-wave signal as minimizing the jittering effect.

The controllable theta is naturally determined from the composition of a phased array antenna of Tx and Rx.

It seems that it is unclear the applicable range in terms of these controllable parameter which strongly depends on the specification of the wireless front-end.

 

3.

Your results is based on the numerical simulation.

I guess it is better to show the feasibility or possibility in terms of actual implementation to system for showing a value of your work.

 

4.

There is typo in "unknown" in L121.

 

5. (Opinion)

I wonder that the velocity and location of UAVs should be taken into account for accurate description for controlling UAVs.

It seems that these parameters are not included in your paper. Is it accurate formulation?

Author Response

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Reviewer 2 Report

1. No information on mmWave beamforming in the introduction section and why mmWave was considered has not been captured.

2. The physical meanings of the figures are not well articulated in relation to the proposed scheme.

Author Response

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Reviewer 3 Report

This paper deals with the problem of robust super-resolution channel estimation for the jitter multi-UAV system with hybrid beamforming. The paper should be improved as follows.   1) UAVs can be also used in smart cities applications, see, e.g.,   Detection and blind channel estimation for UAV-aided wireless sensor networks in smart cities under mobile jamming attack, IEEE Internet of Things Journal 9 (14), 11932-11950 9 2021   2) It is not clear how beam alignment issues have been accounted for, see, e.g.,   Anti-jamming beam alignment in millimeter-wave MIMO systems IEEE Transactions on Communications 70 (8), 5417-5433   3) The estimation of AoA information via compressed sensing techniques can be classified into three main categories: on–grid, off–grid, and gridless estimation. Relationships of the proposed method with all such alternatives should be pointed out.  

Author Response

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Reviewer 4 Report

This paper develops a novel robust super-resolution channel estimation scheme for jitter multi-UAV communication system. The partially adaptive momentum estimation method is improved to optimize the objective function of the jitter UAV communication system. The results from theoretical analysis and simulation show that the jitter channel estimation accuracy of the proposed scheme is superior to that of existing schemes.

The paper can be considered for publishing, but some suggestions could improve the quality of the article.

1.AOA/AOD should be replace with AOAs/AODs.

2.The range of jittering value mu (line 98) should be given for the UAV model.  

3.There is no description for æ in line 8 of Algorithm 1. 

Author Response

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Round 2

Reviewer 2 Report

1. In Fig 3 and 4, the performance of AMP [29] was omitted for comparison. In [29], the authors adopted deep learning approach. The reviewer thinks that this may not be fair comparison. Clarification is needed. 

2. The authors stated "Thus, perfect CSI knowledge of the UAVs cannot be a guarantee, and the system performance is degraded because of the imperfect CSI [31,32]. However, the authors assumed perfect CSI in the analysis of the numerical simulation. Note that in practical scenarios perfect CSI cannot be achieved. 

Author Response

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Round 3

Reviewer 2 Report

The reviewer is satisfied with the current version of the manuscript.