kumoh national institute of technology
Networked Systems Lab.

Sanjay Bhardwaj, Rizki Rivai Ginanjar, Dong-Seong Kim,"Deep Q-learning Based Resource Allocation in Industrial Wireless Networks for URLLC", IET Communication, 2020
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Date : 2019-12-05
Views : 277

Associate Editor Comments:

Comments to the author

The work can publish subject to minor improvements.



Reviewer Comments:

Comments to the Author

#1 Submitted by: Reviewer 1
The paper proposes deep Q-learning (DQL) based resource allocation strategies in IWN is proposed. The method is tested with existing approaches showing that this algorithm is able to find the best performing measures to improve the allocation of resources. The theory is experimentally verified and shows that the suggested technique leads to the distribution of URLLC resources in an efficient manner. I am not an expert in this field, but the results seem to be promising, the overall manuscript organization is good and I think that the paper should be published. The legends in the figures should be bigger.

#2 Submitted by: Reviewer 2
In the paper, deep Q-learning based resource allocation strategies in industrial wireless node is proposed.
More simulation parameters are required such as data rate, frequency, etc.
Fig. 2 should be larger to be read easily.
A figure demonstrating network environment should be added.
Simulation environment should be presented in detail.
Recent papers in the field should be examined: "(2019) Cooperative communication based access technique for sensor networks, International Journal of Electronics, DOI: 10.1080/00207217.2019.1636313"