Improved SPMA Protocol Based on the BiLSTM Prediction Model for the Space–Air–Ground Information Network
The space–air–ground information network (SAGIN) has been widely used due to its excellent performances including wide coverage and high flexibility. However, the dynamic network topology of SAGIN presents challenges for traditional protocols. The statistical priority-based multiple access (SPMA) co...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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American Association for the Advancement of Science (AAAS)
2025-01-01
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| Series: | Space: Science & Technology |
| Online Access: | https://spj.science.org/doi/10.34133/space.0265 |
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| _version_ | 1849723225939902464 |
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| author | Jinyue Liu Peng Gong Weidong Wang Siqi Li Zhixuan Feng Yu Liu Guangwei Zhang Jihao Zhang |
| author_facet | Jinyue Liu Peng Gong Weidong Wang Siqi Li Zhixuan Feng Yu Liu Guangwei Zhang Jihao Zhang |
| author_sort | Jinyue Liu |
| collection | DOAJ |
| description | The space–air–ground information network (SAGIN) has been widely used due to its excellent performances including wide coverage and high flexibility. However, the dynamic network topology of SAGIN presents challenges for traditional protocols. The statistical priority-based multiple access (SPMA) control protocol has received widespread attention because it effectively allocates resources in networks with heterogeneous terminals and dynamic topology. However, the existing SPMA protocols suffer from issues like large errors and low prediction accuracy in channel load statistics. Therefore, this paper proposes an improved SPMA based on the bi-directional long short-term memory (BiLSTM) neural network. First, we analyze and correct errors in channel load statistics at the physical layer, then develop a BiLSTM-based channel load prediction model, and finally simulated the improved SPMA using Matlab. Experimental results show that the proposed channel load prediction model achieves good prediction accuracy, and the improved SPMA protocol markedly improves channel utilization, providing differentiated services for multi-priority businesses. |
| format | Article |
| id | doaj-art-273748ad9f41489faabc116a63de65c2 |
| institution | DOAJ |
| issn | 2692-7659 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | American Association for the Advancement of Science (AAAS) |
| record_format | Article |
| series | Space: Science & Technology |
| spelling | doaj-art-273748ad9f41489faabc116a63de65c22025-08-20T03:11:05ZengAmerican Association for the Advancement of Science (AAAS)Space: Science & Technology2692-76592025-01-01510.34133/space.0265Improved SPMA Protocol Based on the BiLSTM Prediction Model for the Space–Air–Ground Information NetworkJinyue Liu0Peng Gong1Weidong Wang2Siqi Li3Zhixuan Feng4Yu Liu5Guangwei Zhang6Jihao Zhang7National Key Laboratory of Mechatronic Engineering and Control, School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 10086, China.National Key Laboratory of Mechatronic Engineering and Control, School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 10086, China.National Key Laboratory of Mechatronic Engineering and Control, School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 10086, China.National Key Laboratory of Mechatronic Engineering and Control, School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 10086, China.National Key Laboratory of Mechatronic Engineering and Control, School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 10086, China.National Key Laboratory of Mechatronic Engineering and Control, School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 10086, China.National Key Laboratory of Mechatronic Engineering and Control, School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 10086, China.National Key Laboratory of Mechatronic Engineering and Control, School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 10086, China.The space–air–ground information network (SAGIN) has been widely used due to its excellent performances including wide coverage and high flexibility. However, the dynamic network topology of SAGIN presents challenges for traditional protocols. The statistical priority-based multiple access (SPMA) control protocol has received widespread attention because it effectively allocates resources in networks with heterogeneous terminals and dynamic topology. However, the existing SPMA protocols suffer from issues like large errors and low prediction accuracy in channel load statistics. Therefore, this paper proposes an improved SPMA based on the bi-directional long short-term memory (BiLSTM) neural network. First, we analyze and correct errors in channel load statistics at the physical layer, then develop a BiLSTM-based channel load prediction model, and finally simulated the improved SPMA using Matlab. Experimental results show that the proposed channel load prediction model achieves good prediction accuracy, and the improved SPMA protocol markedly improves channel utilization, providing differentiated services for multi-priority businesses.https://spj.science.org/doi/10.34133/space.0265 |
| spellingShingle | Jinyue Liu Peng Gong Weidong Wang Siqi Li Zhixuan Feng Yu Liu Guangwei Zhang Jihao Zhang Improved SPMA Protocol Based on the BiLSTM Prediction Model for the Space–Air–Ground Information Network Space: Science & Technology |
| title | Improved SPMA Protocol Based on the BiLSTM Prediction Model for the Space–Air–Ground Information Network |
| title_full | Improved SPMA Protocol Based on the BiLSTM Prediction Model for the Space–Air–Ground Information Network |
| title_fullStr | Improved SPMA Protocol Based on the BiLSTM Prediction Model for the Space–Air–Ground Information Network |
| title_full_unstemmed | Improved SPMA Protocol Based on the BiLSTM Prediction Model for the Space–Air–Ground Information Network |
| title_short | Improved SPMA Protocol Based on the BiLSTM Prediction Model for the Space–Air–Ground Information Network |
| title_sort | improved spma protocol based on the bilstm prediction model for the space air ground information network |
| url | https://spj.science.org/doi/10.34133/space.0265 |
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