Intelligent deep reinforcement learning-based scheduling in relay-based HetNets
Abstract We consider a fundamental file dissemination problem in a two-hop relay-based heterogeneous network consisting of a macro base station, a half-duplex relay station, and multiple users. To minimize the dissemination delay, rateless code is employed at the base station. Our goal is to find an...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2023-11-01
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| Series: | EURASIP Journal on Wireless Communications and Networking |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13638-023-02325-5 |
| _version_ | 1797416389972066304 |
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| author | Chao Chen Zhengyang Wu Xiaohan Yu Bo Ma Chuanhuang Li |
| author_facet | Chao Chen Zhengyang Wu Xiaohan Yu Bo Ma Chuanhuang Li |
| author_sort | Chao Chen |
| collection | DOAJ |
| description | Abstract We consider a fundamental file dissemination problem in a two-hop relay-based heterogeneous network consisting of a macro base station, a half-duplex relay station, and multiple users. To minimize the dissemination delay, rateless code is employed at the base station. Our goal is to find an efficient channel-aware scheduling policy at the half-duplex relay station, i.e., either fetch a packet from the base station or broadcast a packet to the users at each time slot, such that the file dissemination delay is minimized. We formulate the scheduling problem as a Markov decision process and propose an intelligent deep reinforcement learning-based scheduling algorithm. We also extend the proposed algorithm to adapt to dynamic network conditions. Simulation results demonstrate that the proposed algorithm performs very close to a lower bound on the dissemination delay and significantly outperforms baseline schemes. |
| first_indexed | 2024-03-09T06:02:31Z |
| format | Article |
| id | doaj.art-9ef05331763b4334bcad053a1d9baf24 |
| institution | Directory Open Access Journal |
| issn | 1687-1499 |
| language | English |
| last_indexed | 2024-03-09T06:02:31Z |
| publishDate | 2023-11-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | EURASIP Journal on Wireless Communications and Networking |
| spelling | doaj.art-9ef05331763b4334bcad053a1d9baf242023-12-03T12:07:29ZengSpringerOpenEURASIP Journal on Wireless Communications and Networking1687-14992023-11-012023111810.1186/s13638-023-02325-5Intelligent deep reinforcement learning-based scheduling in relay-based HetNetsChao Chen0Zhengyang Wu1Xiaohan Yu2Bo Ma3Chuanhuang Li4School of Information and Electronic Engineering (Sussex Artificial Intelligence Institute), Zhejiang Gongshang UniversitySchool of Information and Electronic Engineering (Sussex Artificial Intelligence Institute), Zhejiang Gongshang UniversitySchool of Information and Electronic Engineering (Sussex Artificial Intelligence Institute), Zhejiang Gongshang UniversitySchool of Information and Electronic Engineering (Sussex Artificial Intelligence Institute), Zhejiang Gongshang UniversitySchool of Information and Electronic Engineering (Sussex Artificial Intelligence Institute), Zhejiang Gongshang UniversityAbstract We consider a fundamental file dissemination problem in a two-hop relay-based heterogeneous network consisting of a macro base station, a half-duplex relay station, and multiple users. To minimize the dissemination delay, rateless code is employed at the base station. Our goal is to find an efficient channel-aware scheduling policy at the half-duplex relay station, i.e., either fetch a packet from the base station or broadcast a packet to the users at each time slot, such that the file dissemination delay is minimized. We formulate the scheduling problem as a Markov decision process and propose an intelligent deep reinforcement learning-based scheduling algorithm. We also extend the proposed algorithm to adapt to dynamic network conditions. Simulation results demonstrate that the proposed algorithm performs very close to a lower bound on the dissemination delay and significantly outperforms baseline schemes.https://doi.org/10.1186/s13638-023-02325-5Channel-aware schedulingHeterogeneous networkRateless codeDeep reinforcement learning |
| spellingShingle | Chao Chen Zhengyang Wu Xiaohan Yu Bo Ma Chuanhuang Li Intelligent deep reinforcement learning-based scheduling in relay-based HetNets EURASIP Journal on Wireless Communications and Networking Channel-aware scheduling Heterogeneous network Rateless code Deep reinforcement learning |
| title | Intelligent deep reinforcement learning-based scheduling in relay-based HetNets |
| title_full | Intelligent deep reinforcement learning-based scheduling in relay-based HetNets |
| title_fullStr | Intelligent deep reinforcement learning-based scheduling in relay-based HetNets |
| title_full_unstemmed | Intelligent deep reinforcement learning-based scheduling in relay-based HetNets |
| title_short | Intelligent deep reinforcement learning-based scheduling in relay-based HetNets |
| title_sort | intelligent deep reinforcement learning based scheduling in relay based hetnets |
| topic | Channel-aware scheduling Heterogeneous network Rateless code Deep reinforcement learning |
| url | https://doi.org/10.1186/s13638-023-02325-5 |
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