TY - GEN
T1 - Deep Q Networks with Centralized Learning over LEO Satellite Networks in a 6G Cloud Environment
AU - Rodrigues, Tiago Koketsu
AU - Kato, Nei
N1 - Funding Information:
ACKNOWLEDGMENT This work was conducted under the national project, Research and Development of Ka-Band Satellite Control for Various Use Cases as part of the Research and Development for Expansion of Radio Wave Resources (JPJ000254), supported by the Ministry of Internal Affairs and Communications (MIC), Japan.
Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - With 6G networks, we can expect more devices to start operating in remote areas, away from the conventional network infrastructure. With an increase in the number of devices, we should also see more data that needs to be processed and analyzed. An adequate response to this scenario is using satellite networks to reach remote devices and transfer the big data generated by them to be analyzed in cloud servers through Machine Learning models. However, while this is a good solution for data analysis, it can run into bottlenecks caused by long transmission in the limited channels of satellite networks. In this paper, we will model and analyze this service model, allowing us to identify the limitations of centralized learning over satellite networks. This study manages to determine when centralized learning with remote devices is a viable solution and when it needs to be complemented by other techniques due to poor performance caused by long transmission and overloaded communication channels.
AB - With 6G networks, we can expect more devices to start operating in remote areas, away from the conventional network infrastructure. With an increase in the number of devices, we should also see more data that needs to be processed and analyzed. An adequate response to this scenario is using satellite networks to reach remote devices and transfer the big data generated by them to be analyzed in cloud servers through Machine Learning models. However, while this is a good solution for data analysis, it can run into bottlenecks caused by long transmission in the limited channels of satellite networks. In this paper, we will model and analyze this service model, allowing us to identify the limitations of centralized learning over satellite networks. This study manages to determine when centralized learning with remote devices is a viable solution and when it needs to be complemented by other techniques due to poor performance caused by long transmission and overloaded communication channels.
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U2 - 10.1109/GLOBECOM48099.2022.10000709
DO - 10.1109/GLOBECOM48099.2022.10000709
M3 - Conference contribution
AN - SCOPUS:85146916771
T3 - 2022 IEEE Global Communications Conference, GLOBECOM 2022 - Proceedings
SP - 5905
EP - 5910
BT - 2022 IEEE Global Communications Conference, GLOBECOM 2022 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 IEEE Global Communications Conference, GLOBECOM 2022
Y2 - 4 December 2022 through 8 December 2022
ER -
