Emulation of the Rician K-Factor of 5G Propagation in a Source Stirred Reverberation Chamber

Round 1

Reviewer 1 Report

Authors have proposed a novel algorithm to emulate the Rician K-factor for the testing of 5G communication. They have introduced a novel algorithm to control the propagation environment and increase the Rician factor without introducing the Lossy objects in the chamber. I have the following observations which the authors should incorporate in the revised version of the manuscript.

1) Author should start the introductory paragraph in the introduction section with a brief discussion about 5G and then should outline the K-factor discussion

2) Authors are suggested to provide the flowgraph of the main algorithm.

3) It shall be great if the authors can present an inside view of the reverberation chamber, especially how Tx and Rx antennas are positioned with respect to each other. Only one scenario shall be sufficient.

4) Authors should mention the complexity of increasing the Rician factor using traditional methods versus using the proposed algorithm

5) Please make a correction at line 246 i.e. 'sake of demonstrate'

 

Author Response

Please see attachmment.

 

Author Response File: Author Response.pdf

Reviewer 2 Report

Dear Authors,

The content of your article fits perfectly within the scope of Electronics journal's "EMC Analysis in Wireless Communication" Special Issue. One reason is that a key research topic involves the issue of the fifth generation of mobile technologies (5G).

This is very important due to the tuning of the Rician K-factor as one of the main indicators to characterise the statistical properties of a wireless channel.

The authors focused their research on scenario analysis inside reverberation chambers (RCs) with the intention of simulating an urban environment. To achieve this goal, a method and a corresponding novel algorithm for emulating the Rician K-factor of an RC-based wireless communication environment are proposed. It is relevant and interesting.

The above-mentioned goal was based on the 14 publications analysed in the initial two sections of the article and representing the most relevant recent advances in the field by the authors. The references are relevant to the topic and cover both historical literature and more recent developments.

The methodology presented and its principles in Section 3 “The Proposed Method” are reasonable and sound.

Analytical and experimental results are presented and explained. The authors have successfully proved the reliability and robustness of their proposed method.

The manuscript contains some new data.

The paper is presented in logical way and overall written well.

The content of the Discussion and Conclusions is consistent with the evidences and arguments presented and addresses the main question asked.

 Comments and Suggestions for Authors

1.      The study focused on the Rician ?-factor as one of the main indicators to describe the statistical properties of a wireless channel. What were the other alternatives?

2.      To what extent are the obtained results scalable, i.e. transferable to the sector of other neural networks? To what extent are the results obtained scalable, i.e. transferable to other bands reserved for 5G data transmission?

3.      Typos. The following typos were noted in the article: e.g., i) no indication in the text on reference No. 13; ii) redundant full stop before [16] (line 222); iii) please to improve the readability of the contents in Figures 1 and 2.

4.      According to the recommendations of the International Advisory Board, based for example on the experience of the Web of Science: i) percentage of own publications must be less than 12% in the Reference List; ii) percentage of local publications (from your country) must be less than 20% in the Reference List. For example, you have 19% of your own publications. So please follow recommendations i) and ii) above and update your References list.

Author Response

Please see the attachment

Author Response File: Author Response.pdf