A Survey of NOMA-Aided Cell-Free Massive MIMO Systems

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

This tutorial addresses techniques that currently are important and highly relevant: NOMA techniques, cell-free systems and massive MIMO systems. The authors study multiple theoretical and practical aspects associated to those techniques separately and their combination. Due to the wide range aspects, most of them are studied in a shallow way, but a deeper study of them is probably not feasible in a single paper. I recommend the publication of this paper, but the authors should first address the folllowing issues:

1) The writing is in general acceptable, but a careful revision is recommendable.

2) It would be important to include some additional discussion on signal processing complexity issues. The same applies to implementation aspects like the impact of nonlinear effects on NOMA and massive MIMO signals (especially with pre-coding) and the optimization problemas associated to spacial resource allocation (multi-objective optimization including max-min fairness, capacity, power, meta-heuristics approaches, etc.).

3) I am not sure if the classification of the different learning techniques is correct. Deep reinforcement learning should not be a subset of deep learning, with overlapping with supervising learning?

 

Comments on the Quality of English Language

This tutorial addresses techniques that currently are important and highly relevant: NOMA techniques, cell-free systems and massive MIMO systems. The authors study multiple theoretical and practical aspects associated to those techniques separately and their combination. Due to the wide range aspects, most of them are studied in a shallow way, but a deeper study of them is probably not feasible in a single paper. I recommend the publication of this paper, but the authors should first address the folllowing issues:

1) The writing is in general acceptable, but a careful revision is recommendable.

2) It would be important to include some additional discussion on signal processing complexity issues. The same applies to implementation aspects like the impact of nonlinear effects on NOMA and massive MIMO signals (especially with pre-coding) and the optimization problemas associated to spacial resource allocation (multi-objective optimization including max-min fairness, capacity, power, meta-heuristics approaches, etc.).

3) I am not sure if the classification of the different learning techniques is correct. Deep reinforcement learning should not be a subset of deep learning, with overlapping with supervising learning?

 

Author Response

Please see the attachment

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

Comment to electronics 2777720
This paper provides a comprehensive review and survey of NOMA
aided cell free massive MIMO and non orthogonal multiple access
( (CF mMIMO NOMA). Specifically, this paper present s a
comprehensive review of massive MIMO, CF mMIMO, and NOMA. This
paper then present s a state of art research review of CF mMIMO NOMA.
Finally, this paper discuss es the challenges and potential of combining CF
mMIMO NOMA with other enabling technologies to enhance
performance.
Over
all, this is an excellent paper. The paper is well organized, and
very well written. It is a pleasure to read. Hence, I believe that the
manuscript deserves publication.

Comments for author File: Comments.pdf

Author Response

Thank you very much for taking the time to review this manuscript and we appreciate your positive feedback.

Reviewer 3 Report

Comments and Suggestions for Authors

Perfect! 

I have completed the review of the article, and it appears well-structured and ready for publication in its current form. The organization of the topics is clear and coherent, making the reading smooth and understandable. The evidence and support used are robust, contributing to reinforcing your statements. If there are no further changes to be made for the authors, I believe it is ready to be published in this form.

Author Response

Thank you very much for taking the time to review this manuscript and we appreciate your positive feedback.