kumoh national institute of technology
Networked Systems Lab.

Selecting Gaussian Process Regression Kernels for Evaluating MIMO Antenna Configuration Journal: ICT Express
By : Cosmas
Date : 2020-06-04
Views : 194

Ref: ICTE_2020_114
Title: Selecting Gaussian Process Regression Kernels for Evaluating MIMO Antenna Configuration
Journal: ICT Express

Dear Professor. Kim,

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Comments from the editors and reviewers:
-Reviewer 1


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

-Reviewer 2

This paper suggests to use the exponential Gaussian process regression (EGPR) as a kernel for training of the neural network in MIMO antenna configuration.
However, the authors need to improve this paper as follows:

(1) The structure of the used neural network should be described in detail.

(2) It is hard to find a new idea contributed by the authors.

(3) In (6), since delta is also a function of Tx and Rx, delta needs to be changed into delta(Tx, Rx) and (6) needs to be expressed in detail. Moreover, there is no explicit explanation on how (6) is used to obtain simulation results.

(4) In Figs. 2 and 3, the performance difference between the true values and the predicted values is too large. Therefore, it is difficult to see the accuracy of the used neural network.

(5) In Figs. 2 and 3, The authors need to explain why the capacity decreases when the number of the transmit or receive antennas increases.

(6) The authors need to explain why the 5x20 configuration gives the best channel capacity.

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