← 研踪 PaperTrailarXiv:2611.07201 · v1
IRIS: Intelligent Random Access with Integrated Shared Learning for Decentralized LEO Direct-to-Satellite Uplinks
首次提交 2026-11-05 06:42
版本更新 2026-11-05 06:42
雷达观测 2026-11-06 10:04
摘要
Grant-free uplink access in low-Earth-orbit (LEO) non-terrestrial networks (NTNs) poses a coordination challenge when multiple UEs share a satellite channel without real-time centralized scheduling under long propagation delays and time-varying channels. Conventional random access does not exploit peer context, leading to collisions and inefficient channel use as contention increases. To address this problem, we propose IRIS, a multi-agent reinforcement learning framework for decentralized grant-free direct-to-satellite (D2S) uplink access, with A2A-IRIS as its sidelink-assisted Agent-to-Agent (A2A) realization. Each UE executes a shared binary transmission policy using local traffic/channel information augmented by compact peer states exchanged over UE-to-UE sidelink, while Vanilla-IRIS serves as a local-only ablation. Evaluations in rural and suburban LEO D2S deployments use TLE-based trajectories propagated using the Simplified General Perturbations 4 (SGP4), with a Walker-Delta constellation as fallback. A2A-IRIS achieves 2.1579 and 5.4478 Mbps, corresponding to 99.9% and 96.1% of the respective scenario-specific upper bounds. Relative to Vanilla-IRIS, throughput improves by 40.8% and 325.0%, with collision rates of 0.0000 and 0.0433, while one- and two-epoch delayed centralized schedulers are outperformed by approximately 51--130%. A2A-IRIS also improves fairness, service coverage, and transmission efficiency with moderate Deep Q-Network (DQN) inference overhead. Active-UE load scalability results further show sustained performance over the evaluated load range. These results demonstrate the effectiveness of sidelink-assisted A2A coordination for decentralized LEO D2S uplink access under propagation delay and delayed channel feedback.
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v1 · 2026-11-05 06:42 · 最新观测版本
IRIS: Intelligent Random Access with Integrated Shared Learning for Decentralized LEO Direct-to-Satellite Uplinks
Grant-free uplink access in low-Earth-orbit (LEO) non-terrestrial networks (NTNs) poses a coordination challenge when multiple UEs share a satellite channel without real-time centralized scheduling under long propagation delays and time-varying channels. Conventional random access does not exploit peer context, leading to collisions and inefficient channel use as contention increases. To address this problem, we propose IRIS, a multi-agent reinforcement learning framework for decentralized grant-free direct-to-satellite (D2S) uplink access, with A2A-IRIS as its sidelink-assisted Agent-to-Agent (A2A) realization. Each UE executes a shared binary transmission policy using local traffic/channel information augmented by compact peer states exchanged over UE-to-UE sidelink, while Vanilla-IRIS serves as a local-only ablation. Evaluations in rural and suburban LEO D2S deployments use TLE-based trajectories propagated using the Simplified General Perturbations 4 (SGP4), with a Walker-Delta constellation as fallback. A2A-IRIS achieves 2.1579 and 5.4478 Mbps, corresponding to 99.9% and 96.1% of the respective scenario-specific upper bounds. Relative to Vanilla-IRIS, throughput improves by 40.8% and 325.0%, with collision rates of 0.0000 and 0.0433, while one- and two-epoch delayed centralized schedulers are outperformed by approximately 51--130%. A2A-IRIS also improves fairness, service coverage, and transmission efficiency with moderate Deep Q-Network (DQN) inference overhead. Active-UE load scalability results further show sustained performance over the evaluated load range. These results demonstrate the effectiveness of sidelink-assisted A2A coordination for decentralized LEO D2S uplink access under propagation delay and delayed channel feedback.
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