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Smart city article details
| Title | Cooperative Multi-Type Multi-Agent Deep Reinforcement Learning For Resource Management In Space-Air-Ground Integrated Networks |
|---|---|
| ID_Doc | 16151 |
| Authors | Zhang H.; Tang H.; Ding W.; Zhang X.-P. |
| Year | 2023 |
| Published | UbiComp/ISWC 2023 Adjunct - Adjunct Proceedings of the 2023 ACM International Joint Conference on Pervasive and Ubiquitous Computing and the 2023 ACM International Symposium on Wearable Computing |
| DOI | http://dx.doi.org/10.1145/3594739.3612912 |
| Abstract | The Space-Air-Ground Integrated Network (SAGIN), integrating heterogeneous devices including low earth orbit (LEO) satellites, unmanned aerial vehicles (UAVs), and ground users (GUs), holds significant promise for the advancing smart city applications. However, resource management of the SAGIN is a challenge requiring urgent study in that inappropriate resource management will cause poor data transmission, and hence affect the services in smart cities. In this paper, we develop a comprehensive SAGIN system that encompasses five distinct communication links and propose an efficient cooperative multi-type multi-agent deep reinforcement learning (CMT-MARL) method to address the resource management issue. The experimental results highlight the efficacy of proposed CMT-MARL, as evidenced by key performance indicators such as the overall transmission rate and transmission success rate. These results underscore the potential value and feasibility of future implementation of the SAGIN. © 2023 Owner/Author. |
| Author Keywords | multi-agent reinforcement learning; resource management; SAGIN |
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