{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T02:39:44Z","timestamp":1772159984860,"version":"3.50.1"},"reference-count":35,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2025,12,25]],"date-time":"2025-12-25T00:00:00Z","timestamp":1766620800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62276206"],"award-info":[{"award-number":["62276206"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Sixth-generation (6G) wireless systems aim to integrate terrestrial, aerial, and satellite networks to support large-scale remote sensing and service delivery. In such non-terrestrial networks (NTNs), channels change quickly and the multi-tier architecture is heterogeneous, which makes real-time channel state acquisition and cooperative resource scheduling difficult. This paper proposes an FMA-MADDPG framework that combines a channel prediction module with a constraint-based multi-agent deep deterministic policy gradient scheme. The Fusion of Mamba and Attention (FMA) predictor uses a Mamba state-space backbone and a multi-head self-attention block to learn both long-term channel evolution and short-term fluctuations, and forecasts future CSI. The predicted channel information is added to the agents\u2019 observations so that scheduling decisions can take expected channel variations into account. A constraint-based reward is also designed, with explicit performance thresholds and anti-idle penalties, to encourage fairness, avoid free-riding, and promote cooperation among heterogeneous agents. In a representative NTN uplink scenario, the proposed method achieves higher total reward, efficiency, load balance, and cooperation than several DRL baselines, with relative gains around 10\u201320% on key metrics. These results indicate that prediction-aware cooperative reinforcement learning is a useful approach for resource optimization in future 6G NTN systems.<\/jats:p>","DOI":"10.3390\/s26010148","type":"journal-article","created":{"date-parts":[[2025,12,25]],"date-time":"2025-12-25T23:52:27Z","timestamp":1766706747000},"page":"148","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["FMA-MADDPG: Constrained Multi-Agent Resource Optimization with Channel Prediction in 6G Non-Terrestrial Networks"],"prefix":"10.3390","volume":"26","author":[{"given":"Chunyu","family":"Yang","sequence":"first","affiliation":[{"name":"School of Astronautics, Harbin Institute of Technology, Harbin 150001, China"},{"name":"The 54th Research Institute, China Electronics Technology Group Corporation, Shijiazhuang 050081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-2599-3939","authenticated-orcid":false,"given":"Kejian","family":"Song","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Key Laboratory of Intelligent Perception and Image Understanding, Ministry of Education, Xidian University, Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5412-7793","authenticated-orcid":false,"given":"Jing","family":"Bai","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Key Laboratory of Intelligent Perception and Image Understanding, Ministry of Education, Xidian University, Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cuixing","family":"Li","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Key Laboratory of Intelligent Perception and Image Understanding, Ministry of Education, Xidian University, Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yang","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Astronautics, Harbin Institute of Technology, Harbin 150001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5645-160X","authenticated-orcid":false,"given":"Zhu","family":"Xiao","sequence":"additional","affiliation":[{"name":"College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanhong","family":"Sun","sequence":"additional","affiliation":[{"name":"The 54th Research Institute, China Electronics Technology Group Corporation, Shijiazhuang 050081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,12,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Tang, C., Zhou, X., Zhang, L., Liu, Y., and Dan, Z. 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