Intelligent Approaches to Managing Communication Channels in Hybrid Terrestrial-Satellite Networks with LEO Segment

Dilmurod Davronbekov; Yuriy Pisetskiy; Alisher Khayrullaev; Khasan Khamidov; Sherzod Pulatov; Jamshid Isroilov; Kirill Votinov

doi:10.5614/itbj.ict.res.appl.2026.20.1.1

Authors

Dilmurod Davronbekov Department of Mobile communication technologies, TUIT named after Muhammad al-Khwarizmi, Tashkent 100084, Amir Temur Avenue 108
Yuriy Pisetskiy Department of Mobile communication technologies, TUIT named after Muhammad al-Khwarizmi, Tashkent 100084, Amir Temur Avenue 108,
Alisher Khayrullaev Department of Mobile communication technologies, TUIT named after Muhammad al-Khwarizmi, Tashkent 100084, Amir Temur Avenue 108,
Khasan Khamidov ?UNICON.UZ? LLC, Tashkent 100187, Karasuv-3, Mingbulok-38,
Sherzod Pulatov Department of Mobile communication technologies, TUIT named after Muhammad al-Khwarizmi, Tashkent 100084, Amir Temur Avenue 108,
Jamshid Isroilov Department of Mobile communication technologies, TUIT named after Muhammad al-Khwarizmi, Tashkent 100084, Amir Temur Avenue 108,
Kirill Votinov Department of Mobile communication technologies, TUIT named after Muhammad al-Khwarizmi, Tashkent 100084, Amir Temur Avenue 108

DOI:

https://doi.org/10.5614/itbj.ict.res.appl.2026.20.1.1

Keywords:

blocking probability, channel management, deep learning, Erlang B, LEO networks, M/M/c/c, NTN, multi-agent DRL, radio resource management, spectral efficiency, SDN coordinator

Abstract

The integration of terrestrial 5G/6G networks with Low Earth Orbit (LEO) satellite systems is creating a class of hybrid Satellite?Terrestrial Integrated Networks/Non-Terrestrial Networks (STIN/NTN) for which channel management in the face of highly dynamic topologies and traffic heterogeneity is becoming a key challenge. This paper examines intelligent approaches to radio resource allocation in such networks based on deep reinforcement learning (DRL) and multi-agent algorithms. A conceptual architecture for a channel management system in a hybrid network with a LEO segment is proposed, including a centralized Software-Defined Networking/Artificial Intelligence (SDN/AI) coordinator and distributed DRL agents at the satellite and ground base station levels. To analytically interpret the gains from intelligent management, an aggregated M/M/c/c teletraffic model is introduced, using the concept of an ?equivalent number of channels,? which allows for linking DRL resource allocation with classical assessments of blocking probability and throughput. The obtained numerical results show that increasing the effective system capacity by 20% due to intelligent inter-segment load redistribution leads to a 30?40% reduction in blocking and a 10?18% increase in throughput in the high-load region, which is consistent with the results of detailed DRL studies for LEO networks presented in the modern literature.

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References

Kuang, L., Chen, X., Jiang, C., Zhang, H. & Wu, S., Radio Resource Management in Future Terrestrial-Satellite Communication Networks, IEEE Wireless Communications., 24(5), pp. 81-87, 2017. doi:10.1109/MWC.2017.1700043.

Geraci, G., Lez-Pez, D., Benzahta, M. &Chatzinotas, S., Integrating Terrestrial and Non-Terrestrial Networks: 3D Opportunities and Challenges, IEEE Communications Magazine, 61(4), 42-48, 2023. doi:10.1109/MCOM.002.2200366.

Vquez, M., Henarejos, P., Pez-Neira, E.G., Voight, A., Gil, J.C., Pappalardo, I., Di Credico, F. & Lancellotti, R.M., On the Use of AI for Satellite Communications, 2020, arXiv:2007.10110. doi:10.48550/arXiv.2007.10110.

Fontanesi, G., Ort, F., Lagunas, E., Monzon Baeza, V., Vquez, M., Vquez-Peralvo, J.A., Minardi, M., Vu, H.N., Honnaiah, P.J., Lacoste, C., Drif, Y., Abdu, T.S., Eappen, G., Rehman, J., Garc-Socorr, L.M., Martins, W.A., Henarejos, P., Al-Hraishawi, H., Merlano Duncan, J.C., Vu, T.X. & Chatzinotas, S., Artificial Intelligence for Satellite Communication and Non-Terrestrial Networks: A Survey. 25 April 2023. arXiv:2304.13008. doi:10.48550/arXiv.2304.13008.

Hu, Y., Qiu, F., Zheng, F. &Zhao, J., Multi-Dimensional Resource Allocation Strategy for LEO Satellite Communication Uplinks Based on Deep Reinforcement Learning, Journal of Cloud Computing, 13, 56, 2024. doi:10.1186/s13677-024-00621-z.

Nauman, A., Alshahrani, H.M., Nemri, N., Othman, K.M., Aljehane, N.O., Maashi, M., Dutta, A.K., Assiri, M. & Khan, W.U., Dynamic Resource Management in Integrated NOMA Terrestrial-Satellite Networks Using Multi-Agent Reinforcement Learning, Journal of Network and Computer Applications, 116,103770, 2024. doi:10.1016/j.jnca.2023.103770.

Mansur, M.M.I, Webber, J.L., Sathishkumar, K., Bostani, A., Mehbodniya, A. & Madhan, K., Integrated Terrestrial-Satellite 6G Networks for Ubiquitous Global Connectivity and Low-Latency Services. NJAP, 7(1), pp.307-317, 2025. doi:10.31838/NJAP/07.01.34.

Liu, H., Wang, Y., Li, P. & Cheng, J., A Multi-Agent Deep Reinforcement Learning-Based Handover Scheme for Mega-Constellation under Dynamic Propagation Conditions, IEEE Transactions on Wireless Communications, 23(10), pp. 13579-13596, 2024. doi:10.1109/TWC.2024.3407358.

Lee, C., Bang, I., Kim, T., Lee, H., Jung, B.C. & Chae, S.H., Multi-Agent Deep Reinforcement Learning Based Handover Strategy for LEO Satellite Networks, IEEE Communications Letters, 29(5), pp. 1117-1121, 2025. doi:10.1109/LCOMM.2025.3554818.

Kumar, A., Gaur, N., Chakravarthy, S. & Nanthaamornphong A., Enhancing Satellite Networks with Deep Reinforcement Learning: A Focus on IoT Connectivity And Dynamic Resource Management, Results in Optics, 18,100765, 2025. doi:10.1016/j.rio.2024.100765.

Xu, S., Liu, R., Zhang, H., Han, Z. & Zhao, L., Heterogeneous Resource Allocation in LEO Networks: A Federated Multi-Agent Deep Reinforcement Learning Method, in Proceedings of the 2024 International Conference on Communication and Information Processing (ICCIP), pp. 211-215, 2024. doi:10.1145/3708657.3708691.

Ayofe, O.A., Okafor, K.C., Longe, O.M., Alabi, C.A., Tekanyi, A.M.S., Usman, A.D., Musa, M.J., Abdullahi, Z.M., Agbon, E.E., Adikpe, A.O., Anoh, K., Adebisi, B., Imoize, A.L. & Idris, H., SDN-Based Integrated Satellite Terrestrial Cyber-Physical Networks with 5G Resilience Infrastructure: Future Trends and Challenges, Technologies, 12, 263, 2024. doi:10.3390/technologies12120263.

ESA Connectivity and Secure Communications. Converging Earth and Space: AI-Driven TN/NTN Connectivity. ESA White Paper, 2025.

Fang, X., Feng, W., Wei, T., Chen, Y., Ge, N. & Wang, C.X., 5G Embraces Satellites for 6G Ubiquitous IoT: Basic Models for Integrated Satellite Terrestrial Networks, IEEE Internet of Things Journal, 8(18), pp.14399-14417, 2021. doi:10.1109/JIOT.2021.3068596.

Tirmizi, S.B.R., Chen, Y., Lakshminarayana, S., Feng, W. & Khuwaja, A.A., Hybrid Satellite-Terrestrial Networks Toward 6G: Key Technologies and Open Issues, Sensors, 22, 8544, 2022. doi:10.3390/s22218544.

Cover, T.M. & Thomas J.A., Elements of Information Theory. 2nd ed. Hoboken (NJ): Wiley-Interscience,2006.

Liu, Y., Wang, L., Lu, Z. & Shou, G., A QoS Guaranteed Efficient Integration of UPF and LEO Satellite Networks, IEEE Transactions on Network and Service Management, 21(4), pp. 3727-3739, 2024. doi:10.1109/TNSM.2024.3400255.

Li, L., Chen W. & Letaief K.B., Ultra-Reliable and Low Latency Wireless Communications with Burst Traffics: A Large Deviation Method, IEEE Global Communications Conference (GLOBECOM), pp. 1-6, 2021. doi:10.1109/GLOBECOM46510.2021.9685307.

Ibraimov, R., Davronbekov, D. & Sultonova, M., Evaluation of the Possibility of Use of Atmospheric Optical Systems in Transport Networks of Mobile Communication on the Criterion of Reliability, 2022 International Conference on Information Science and Communications Technologies (ICISCT), 2022. doi:10.1109/ICISCT55600.2022.10146880.

Navarro, T. & Da Costa Dinis D., Future Trends in AI For Satellite Communications, Proceedings of ICMLT, Oslo, Norway, 2024. doi:10.1145/3674029.3674040.

Thangavel, K., Sabatini, R., Gardi, A., Ranasinghe,, K., Hilton, S., Servidia, P. & Spiller, D., Artificial Intelligence for Trusted Autonomous Satellite Operations, Progress in Aerospace Sciences, 144,100960, 2024. doi:10.1016/j.paerosci.2023.100960.

Khujamatov, H., Davronbekov, D., Khayrullaev, A., Abdullaev, M., Mukhiddinov, M. & Cho, J., ERIRMS Evaluation of the Reliability of IoT-Aided Remote Monitoring Systems of Low-Voltage Overhead Transmission Lines, Sensors, 24, 5970, 2024. doi:10.3390/s24185970.

Davronbekov, D., Stability analysis of hybrid intersatellite communication links in low-orbit LEO systems, 2025 Optical Communication, Photonics, Telecommunications, and Intelligent Machine Applications (OPTIMA); Tashkent, Uzbekistan, pp. 449-455, 2025. doi:10.1109/OPTIMA67660.2025.11380359.

Davronbekov, D., Khayrullaev, A., Isroilov, J., Sharopov, A., Hybrid FSO-Satellite Architecture for Remote Monitoring Networks: Analysis and Evaluation, 2025 Optical Communication, Photonics, Telecommunications, and Intelligent Machine Applications (OPTIMA); Tashkent, Uzbekistan, pp. 392-397, 2025. doi:10.1109/OPTIMA67660.2025.11380386.