| Autor: |
Feng YAN, Xiaowei LIN, Zhenghao LI, Xia XU, Weiwei XIA, Lianfeng SHEN |
| Jazyk: |
čínština |
| Rok vydání: |
2023 |
| Předmět: |
|
| Zdroj: |
Tongxin xuebao, Vol 44, Pp 12-24 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
1000-436X |
| DOI: |
10.11959/j.issn.1000-436x.2023179 |
| Popis: |
In view of the fact that 5G networks are used to meet the service requirements of various power terminals in smart grid, a spectrum allocation algorithm based on multi-agent reinforcement learning was proposed.Firstly, for the integrated access backhaul system deployed in smart grid, considering the different communication requirements of services in lightweight and non-lightweight terminal, the spectrum allocation problem was formulated as a non-convex mixed-integer programming aiming to maximize the overall energy efficiency.Secondly, the above problem was modeled as a partially observable Markov decision process and transformed into a fully cooperative multi-agent problem, then a spectrum allocation algorithm was proposed which was based on multi-agent proximal policy optimization under the framework of centralized training and distributed execution.Finally, the performance of the proposed algorithm was verified by simulation.The results show that the proposed algorithm has a faster convergence speed and can increase the overall transmission rate by 25.2% through effectively reducing intra-layer and inter-layer interference and balancing the access and backhaul link rates. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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