Joint Trajectory and Scheduling Optimization for The Mobile UAV Aerial Base Station: A Fairness Version

Round 1

Reviewer 1 Report

In this article an approach for optimization UAV aerial base station with ant colony algorithm and genetic algorithm is proposed, in order to maximize network capacity, considering fairness, with UAV in hover in a circular trajectory.  Authors, show some results in simulation.

Recommendation to improve this article:

Authors does not explain, how consideration of fairness is formulated?

In lines 224-225, 263-264 and 287-288, comparatives should be reformulated.

In equation 6, C matrix size do not correspond to each element.

In line 201, authors does not explain, how Genetic Algorithms work with complex problem and parallelism.

Equation 17 is not clear, it should be reformulated.

In algorithm 2 , MGA is not defined.

In simulation section, some authors show parameters values to compare performance, but in this part, there is not explained what is the performance using others values. 

In this part, figures are not explained and some values like ro are not defined.

Objective function is not defined in figure 6 and in figure 5.

In line 305, authors does not describe the performance of the network, which kind of information is used?

Authors should explain the network capacity and convergence used to compare GA and ACA-GA algorithms.

Author Response

Please see the attachment.

Reviewer 2 Report

The paper presents an approach to schedule transmissions between ground devices and an aerial base station operated by a UAV that circles around an urban area. The optimal strategy is search for by applying ant-based algorithms and genetic algorithms. The concept is evaluated by means of simulation.

While the principal idea is interesting to investigate, the following weaknesses should be addressed:

1) Why should a UAV be operated in an urban area to support ground vehicles? Operating a UAV is very costly in terms of energy/replacement of the UAV (in case of battery powered UAVs) - the reasons should be elaborated by comparing the different options (ground BS versus aerial BS). It may be useful to target scenarios without good cellular infrastructure (thus, not urban areas).

2) The core mechanisms are described in general and well-known examples are used to elaborate them. I recommend to explain the algorithms briefly in general and then to describe how the ant-based approach and the GA -based approach are applied to the specific UAV use case.

3) As the mechanisms are not sufficiently explained and detailed information about the simulation setup is missing, the results described in Section 4 cannot be fully evaluated. In Figure 6, the improvement in network capacity should be visible according to the figure caption. As this is not directly clear by giving the number returned by the objective function, I suggest to improve the representation as it is indeed necessary to show the improvement in terms of e.g. throughput.

4) The following details should be improved:

- English grammar is not fully correct (plural vs. singular, use of articles).

- Section 1, last sentence: simulation cannot verify algorithms as it is no proof that the algorithms are ok - I suggest to use "validate" or "evaluate".

- Equation 2: non LOS links are introduced by just adding a noise component. As non LOS is difficult to model due to the different signal degradation options depending on the concrete object(s) between sender and receiver. It should be explained how non LOS is modeled and, thus, how the noise component is calculated.

- Line101:the angles are surprising -why are they exactly taking these values for suburban etc. environments? Can these values really be generalized?

-The figures in the results section miss units (e.g. Figure 4, y-axis: should be meter).

Author Response

Please see the attachment.

Author Response File: Author Response.docx

Round 2

Reviewer 1 Report

In this second version of the article, there are some suggestions, in order to improve it.

A comparison with mobile BS could be do, in order to prove effectiveness of UAV-BS. 

In section 2, P variable of probability and  Power variable P, confuse reader. A new variable sould be defined.

Los in ligne 97, is not defined.

SNR is not defined before use in equation 10.

In ligne 170, how authors infer that equation 14 is non-convex optimization problem? An explanation or reference will be add.

In section 4 and in figure 2, ro is not well typed, ligne 302 and 312.

Comparative phrase in ligne 328, should be rewrite.

Figure 4, should be modified and explained.

Author Response

Please see the attachment.

Author Response File: Author Response.docx