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Reinforcement Learning-Based Resource Allocation for Streaming in a Multi-Modal Deep Space Network

Authors
Ha, TaeyunOh, JunsukLee, DonghyunLee, JeonghwaJeon, YonginCho, Sungrae
Issue Date
Dec-2021
Publisher
IEEE Computer Society
Keywords
Channel Coding; Deep Q-learning Network; Deep Space; Scalable Video Coding
Citation
International Conference on ICT Convergence, v.2021, no.October , pp 201 - 206
Pages
6
Journal Title
International Conference on ICT Convergence
Volume
2021
Number
October
Start Page
201
End Page
206
URI
https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/54912
DOI
10.1109/ICTC52510.2021.9621165
ISSN
2162-1233
Abstract
With the development of 5G communication, users' data requirements are increasing rapidly, and the requirements for real-time data communication are increasing. These requirements were embodied as streaming services. This requirement can also be applied in deep space environments, where space communication, which is a relatively constrained environment, places importance on recovering the error rate. Typical channel coding techniques include turbo code, which is used to reduce Bit Error Rate. Taking this into account, this paper constructs deep space virtual environments, and applies various turbo-coding-based modulations for each link. In addition, streaming services use Scalable Video Coding to provide smoother streaming services. In this paper, we propose an algorithm that measures complexity and BER according to various modulation techniques of turbo codes to verify trade-off relationships and to assign data to appropriate links by learning them with Deep Q-learning Network. © 2021 IEEE.
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소프트웨어대학 (소프트웨어학부)
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