Deep Reinforcement Learning-Based Cooperative Rate Splitting for Satellite-to-Underground Communication Networks
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866908915330973696 |
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| author | Lin, Kaiqiang Zhao, Kangchun Mao, Yijie |
| author_facet | Lin, Kaiqiang Zhao, Kangchun Mao, Yijie |
| contents | Reliable downlink communication in satellite-to-underground networks remains challenging due to severe signal attenuation caused by underground soil and refraction in the air-soil interface. To address this, we propose a novel cooperative rate-splitting (CRS)-aided transmission framework, where an aboveground relay decodes and forwards the common stream to underground devices (UDs). Based on this framework, we formulate a max-min fairness optimization problem that jointly optimizes power allocation, message splitting, and time slot scheduling to maximize the minimum achievable rate across UDs. To solve this high-dimensional non-convex problem under uncertain channels, we develop a deep reinforcement learning solution framework based on the proximal policy optimization (PPO) algorithm that integrates distribution-aware action modeling and a multi-branch actor network. Simulation results under a realistic underground pipeline monitoring scenario demonstrate that the proposed approach achieves average max-min rate gains exceeding $167\%$ over conventional benchmark strategies across various numbers of UDs and underground conditions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_25562 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Deep Reinforcement Learning-Based Cooperative Rate Splitting for Satellite-to-Underground Communication Networks Lin, Kaiqiang Zhao, Kangchun Mao, Yijie Networking and Internet Architecture Systems and Control Signal Processing Reliable downlink communication in satellite-to-underground networks remains challenging due to severe signal attenuation caused by underground soil and refraction in the air-soil interface. To address this, we propose a novel cooperative rate-splitting (CRS)-aided transmission framework, where an aboveground relay decodes and forwards the common stream to underground devices (UDs). Based on this framework, we formulate a max-min fairness optimization problem that jointly optimizes power allocation, message splitting, and time slot scheduling to maximize the minimum achievable rate across UDs. To solve this high-dimensional non-convex problem under uncertain channels, we develop a deep reinforcement learning solution framework based on the proximal policy optimization (PPO) algorithm that integrates distribution-aware action modeling and a multi-branch actor network. Simulation results under a realistic underground pipeline monitoring scenario demonstrate that the proposed approach achieves average max-min rate gains exceeding $167\%$ over conventional benchmark strategies across various numbers of UDs and underground conditions. |
| title | Deep Reinforcement Learning-Based Cooperative Rate Splitting for Satellite-to-Underground Communication Networks |
| topic | Networking and Internet Architecture Systems and Control Signal Processing |
| url | https://arxiv.org/abs/2510.25562 |