{"ID":2848613,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.25562","arxiv_id":"2510.25562","title":"Deep Reinforcement Learning-Based Cooperative Rate Splitting for Satellite-to-Underground Communication Networks","abstract":"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.","short_abstract":"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 dec...","url_abs":"https://arxiv.org/abs/2510.25562","url_pdf":"https://arxiv.org/pdf/2510.25562v2","authors":"[\"Kaiqiang Lin\",\"Kangchun Zhao\",\"Yijie Mao\"]","published":"2025-10-29T14:29:47Z","proceeding":"cs.NI","tasks":"[\"cs.NI\",\"eess.SP\",\"eess.SY\"]","methods":"[\"Reinforcement Learning\"]","has_code":false}