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Collaborate Q-learning Aided Load Balance in Satellites Communications | IEEE Conference Publication | IEEE Xplore

Collaborate Q-learning Aided Load Balance in Satellites Communications


Abstract:

In recent years, satellite communications have played an increasingly important role in daily life. With the explosive growth of new businesses, the expectations for the ...Show More

Abstract:

In recent years, satellite communications have played an increasingly important role in daily life. With the explosive growth of new businesses, the expectations for the performance and reliability of satellite communications are greater than ever. However, due to the unique characteristics of satellite node (e.g., fast transmission speed, saturation of resources), it brings unprecedented challenges for load balance in multiple satellite paths. In this paper, to overcome this issue, we proposed a multi-agent reinforcement learning aided load balance architecture. We formulate the load balance in satellites communications as a partially observable Markov decision process (POMDP). Besides, we adopt a multi-agent reinforcement algorithm named Collaborate Q-learning (CollaQ) in our architecture. In addition, some stimulation are performed to evaluate the correctness of our architecture and algorithm.
Date of Conference: 28 June 2021 - 02 July 2021
Date Added to IEEE Xplore: 09 August 2021
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Conference Location: Harbin City, China

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