kumoh national institute of technology
Networked Systems Lab.

Selecting Gaussian Process Regression Kernels for Evaluating MIMO Antenna Configuration Journal: ICT Express( Revision)
By : Cosmas
Date : 2020-06-30
Views : 174

Ref: ICTE_2020_114_R1
Title: Selecting Gaussian Process Regression Kernels for Evaluating MIMO Antenna Configuration
Journal: ICT Express

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Comments from the editors and reviewers:
-Reviewer 1

-

In this paper, the exponential Gaussian process regression (EGPR) is shown to be the best kernel for the performance evaluation of MIMO antenna configuration. However, based on the revised manuscript, I have some important questions.



1) In general, as shown in the equation (7.10) of [Tse07], the capacity of the MIMO channel is an increasing function of min(Tx,Rx). It is required to clarify the reason why the 5X20 antenna configuration is optimal.



2) In Fig. 2 and Fig. 3, the capacity is not an increasing function of min(Tx,Rx), which is totally different from the the equation (7.10) of [Tse07]. Why the capacity in Fig. 2 and Fig. 3 is not an increasing function of min(Tx,Rx)?



3) In the equation (6), $\delta$ is suggested be more specific form as in the equation (7.10) of [Tse07].



4) In Fig. 2, it seems that there are two pair of true-predicted points at the 2 Tx antennas. Is this a mistake?



[Tse07] D. Tse and P. Viswanath, Fundamentals of Wireless Communication. Cambridge, U.K.: Cambridge Univ. Press, 2007.


-Reviewer 2

-
The authors have addressed all of my previous comments.
The acceptance of this paper is recommended.


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