| Autor: |
Cheng ZHANG, Jiaye ZHU, Zening LIU, Yongming HUANG |
| Jazyk: |
čínština |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
Tongxin xuebao, Vol 44, Pp 86-98 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
1000-436X |
| DOI: |
10.11959/j.issn.1000-436x.2023235 |
| Popis: |
To cope with the high throughput demand caused by the proliferation of wireless network users, a multi-agent reinforcement learning based dynamic optimization algorithm of cell range expansion (CRE) offset was proposed for interference scenarios in macro-pico heterogeneous networks.Based on the value decomposition network framework of collaborative multi-agent reinforcement learning, a personalized online local decision of CRE offset for all pico-base stations was achieved by reasonably utilizing and interacting the intra-system user distribution and their interference levels among pico-base stations.Simulation results show that the proposed algorithm has significant advantages in increasing system throughput, balancing the throughput of each base station and improving edge-user throughput, compared to CRE=5 dB and distributed Q-learning algorithms. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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