kumoh national institute of technology
Networked Systems Lab.

Cosmas Ifeanyi Nwakanma, Williams-Paul Nwadiugw, Jae-Min Lee and Dong-Seong Kim , "CL2019-2019, entitled Evaluating Massive MIMO Configuration for 5G Network using Exponential Gaussian Process Regression(EGPR)" (Rejected).
By : Cosmas
Date : 2019-10-23
Views : 233

Dear Author(s):

The review of the referenced manuscript, CL2019-2019, entitled Evaluating Massive MIMO Configuration for 5G Network using Exponential Gaussian Process Regression(EGPR), is now complete. I regret to inform you that based on the enclosed reviews and my own reading of your manuscript, I am unable to recommend its publication in IEEE Communications Letters.

Your paper may not be resubmitted for review. The reasons for this are as follows: All of the reviewers have major concerns about the presentation and contribution of this paper. I basically agree with them.
Additional comments include: There are many format errors and presentation problems. The basic idea of this paper has not been clearly presented.
1) It is stated that this work tried to predict the best receive and transmit antenna ratio. However, no corresponding discussions and results have been clearly provided. 2) A lot of notations or equations are given without essential explanation. For example, the detailed expression of ergodic capacity in (6), the utility function V, etc.. Moreover, the algorithm 1 is directly given without any explanation. 3) This work is done for a few number of antennas rather than in the context of massive MIMO.


The reviewers' comments are found at the end of this email.

Thank you for submitting your work to the IEEE Communications Letters.

Regards,

Dr. Weile Zhang
Associate Editor
IEEE Communications Letters

Reviewer: 1

Comments to the Author
See Attached File

Reviewer: 2

Comments to the Author
This paper presents a exponential Gaussian process regression as a better kernel for training and predicting antenna configuration in massive MIMO to meet 5G network requirements for improved capacity utilization. First, the technical contribution is not enough for this journal. In addition, the reviewer may not recommend this paper due to many format errors and presentation problems. Considering above two points, it is suggested as reject.

Reviewer: 3

Comments to the Author
1. This letter presents EGPR as a better kernel for training and predicting antenna configuration iin Massive multiple-input-multiple-output (MIMO) . However, the work has little to do with massive MIMO.

2. The motivaton and the problem of the letter is not clear.

3. Both the transmitting and receiving antennas are equipped on distributed base-stations in Fig. 1, which is not consistent with the fact.

4. Algorithm 1 is confusing.

5. There are some incorrected statements in this letter. For example, they further suggested a smaller or reduced number of transmitters was needed for better performance.

6. In the performance evaluation the 4X4 MIMO is considered, which is not massive MIMO.

7. The references are not in IEEE format.