# D2S

Published articles for D2S.

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## IRIS: Intelligent Random Access with Integrated Shared Learning for Decentralized LEO Direct-to-Satellite Uplinks

DevFeed: [IRIS: Intelligent Random Access with Integrated Shared Learning for Decentralized LEO Direct-to-Satellite Uplinks](<https://devfeed.tech/articles/iris-intelligent-random-access-with-integrated-shared-learning-for-decentralized-leo-direct-to-satellite-uplinks-121321.md>)

Original publisher: [Read original article](<https://arxiv.org/abs/2611.07201>)

Author: Abdullah Al Noman, Gaurav Singh, Rameez Ahmed, Debashri Roy

Published: 2026-11-06T05:00:00Z

Content type: article

Language: en

Sources: [arXiv: Networking](<https://devfeed.tech/sources/arxiv-networking.md>)

Topics: [AI Networking](<https://devfeed.tech/topics/ai-networking.md>), [openenv](<https://devfeed.tech/topics/openenv.md>)

Tags: [a2a](<https://devfeed.tech/tags/a2a.md>), [ablation](<https://devfeed.tech/tags/ablation.md>), [agent](<https://devfeed.tech/tags/agent.md>), [arxiv](<https://devfeed.tech/tags/arxiv.md>), [channel](<https://devfeed.tech/tags/channel.md>), [context](<https://devfeed.tech/tags/context.md>), [coverage](<https://devfeed.tech/tags/coverage.md>), [cs-ni](<https://devfeed.tech/tags/cs-ni.md>), [d2s](<https://devfeed.tech/tags/d2s.md>), [decentralized](<https://devfeed.tech/tags/decentralized.md>), [deep](<https://devfeed.tech/tags/deep.md>), [delay](<https://devfeed.tech/tags/delay.md>), [delta](<https://devfeed.tech/tags/delta.md>), [deployments](<https://devfeed.tech/tags/deployments.md>), [efficiency](<https://devfeed.tech/tags/efficiency.md>), [evaluations](<https://devfeed.tech/tags/evaluations.md>), [fairness](<https://devfeed.tech/tags/fairness.md>), [learning](<https://devfeed.tech/tags/learning.md>), [leo](<https://devfeed.tech/tags/leo.md>), [random](<https://devfeed.tech/tags/random.md>), [satellite](<https://devfeed.tech/tags/satellite.md>)

### AI overview

IRIS addresses grant-free LEO satellite uplink contention without real-time centralized scheduling. In A2A-IRIS, each UE uses a shared binary transmission policy informed by local traffic and channel data plus compact peer states exchanged over sidelink; Vanilla-IRIS uses local information only. In the evaluated rural and suburban deployments, A2A-IRIS reached 2.1579 and 5.4478 Mbps (99.9% and 96.1% of scenario-specific upper bounds), improving throughput over Vanilla-IRIS by 40.8% and 325.0%. It also outperformed centralized schedulers with one- or two-epoch delay by about 51-130%. The abstract reports improved fairness, coverage, and transmission efficiency, moderate DQN inference overhead, and sustained performance across the evaluated active-UE load range. Results are from the described simulations using TLE/SGP4 trajectories, with a Walker-Delta constellation as fallback.

### Source excerpt

arXiv:2611.07201v1 Announce Type: new Abstract: 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.