The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.
Optimizing Space-Air-Ground Integrated Networks by Artificial Intelligence
Open accessAbstract
It is widely acknowledged that the development of traditional terrestrial communication technologies cannot provide all users with fair and high quality services due to scarce network resources and limited coverage areas. To complement the terrestrial connection, especially for users in rural, disaster-stricken, or other difficult-to-serve areas, satellites, UAVs, and balloons have been utilized to relay communication signals. On this basis, SAGINs have been proposed to improve the users' QoE. However, compared with existing networks such as ad hoc networks and cellular networks, SAGINs are much more complex due to the various characteristics of three network segments. To improve the performance of SAGINs, researchers are facing many unprecedented challenges. In this article, we propose the AI technique to optimize SAGINs, as the AI technique has shown its predominant advantages in many applications. We first analyze several main challenges of SAGINs and explain how these problems can be solved by AI. Then, we consider the satellite traffic balance as an example and propose a deep learning based method to improve traffic control performance. Simulation results evaluate that the deep learning technique can be an efficient tool to improve the performance of SAGINs.
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Cites
Energy-Efficient UAV Communication With Trajectory Optimization
Zeng, Y., & Zhang, R. (2017) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2017.2688328 · Cited by 2,164
Space-Air-Ground Integrated Network: A Survey
Liu, J., Shi, Y., Fadlullah, Z. M., & Kato, N. (2018) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2018.2841996 · Cited by 1,281
Self-citationFadlullah, Z. M., Tang, F., Mao, B., Kato, N., Akashi, O., Inoue, T., & Mizutani, K. (2017) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2017.2707140 · Cited by 810
Self-citationKato, N., Fadlullah, Z. M., Mao, B., Tang, F., Akashi, O., Inoue, T., & Mizutani, K. (2017) · IEEE Wireless Communications · doi:10.1109/mwc.2016.1600317wc · Cited by 400
Self-citationMao, B., Fadlullah, Z. M., Tang, F., Kato, N., Akashi, O., Inoue, T., & Mizutani, K. (2017) · IEEE Transactions on Computers · doi:10.1109/tc.2017.2709742 · Cited by 350
Self-citationTang, F., Fadlullah, Z. M., Mao, B., & Kato, N. (2018) · IEEE Internet of Things Journal · doi:10.1109/jiot.2018.2838574 · Cited by 274
Self-citationTang, F., Mao, B., Fadlullah, Z. M., Kato, N., Akashi, O., Inoue, T., & Mizutani, K. (2018) · IEEE Wireless Communications · doi:10.1109/mwc.2017.1700244 · Cited by 247
Self-citationSynergy of Big Data and 5G Wireless Networks: Opportunities, Approaches, and Challenges
Zhang, N., Yang, P., Ren, J., Chen, D., Yu, L., & Shen, X. (2018) · IEEE Wireless Communications · doi:10.1109/mwc.2018.1700193 · Cited by 229
Towards Energy-Efficient Routing in Satellite Networks
Yang, Y., Xu, M., Wang, D., & Wang, Y. (2016) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2016.2611860 · Cited by 209
Green Resource Allocation Based on Deep Reinforcement Learning in Content-Centric IoT
He, X., Wang, K., Huang, H., Miyazaki, T., Wang, Y., & Guo, S. (2020) · IEEE Transactions on Emerging Topics in Computing · doi:10.1109/tetc.2018.2805718 · Cited by 208
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Letaief, K. B., Chen, W., Shi, Y., Zhang, J., & Zhang, Y.-J. A. (2019) · IEEE Communications Magazine · doi:10.1109/mcom.2019.1900271 · Cited by 2,207
Evolution of Non-Terrestrial Networks From 5G to 6G: A Survey
Azari, M. M., Solanki, S., Chatzinotas, S., Kodheli, O., Sallouha, H., Colpaert, A., Mendoza Montoya, J. F., Pollin, S., Haqiqatnejad, A., Mostaani, A., et al. (2022) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2022.3199901 · Cited by 668
Future Intelligent and Secure Vehicular Network Toward 6G: Machine-Learning Approaches
Tang, F., Kawamoto, Y., Kato, N., & Liu, J. (2020) · Proceedings of the IEEE · doi:10.1109/jproc.2019.2954595 · Cited by 620
Self-citationWhen Machine Learning Meets Privacy in 6G: A Survey
Sun, Y., Liu, J., Wang, J., Cao, Y., & Kato, N. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2020.3011561 · Cited by 211
Self-citationExplainable AI for 6G Use Cases: Technical Aspects and Research Challenges
Wang, S., Qureshi, M. A., Miralles-Pechuán, L., Huynh-The, T., Gadekallu, T. R., & Liyanage, M. (2024) · IEEE Open Journal of the Communications Society · doi:10.1109/ojcoms.2024.3386872 · Cited by 170
Metadata
- Authors
- Kato, N., Fadlullah, Z. M., Tang, F., Mao, B., Tani, S., Okamura, A., & Liu, J.
- Venue
- IEEE Wireless Communications
- Published
- 2019-08
- Volume
- 26
- Issue
- 4
- Pages
- 140-147
- ISSN
- 1536-1284 (ISSN-L)
- 1558-0687
- OpenAlex
- W2886485362
- Cited by
- 404
- References
- 15
- FWCI
- 40.08
- Type
- journal article
- Language
- en
- Open access
- green
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- Kato, N., Fadlullah, Z. M., Tang, F., Mao, B., Tani, S., Okamura, A., & Liu, J. (2019). Optimizing Space-Air-Ground Integrated Networks by Artificial Intelligence. IEEE Wireless Communications. https://doi.org/10.1109/mwc.2018.1800365
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