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DISCO:用于可扩展物联网共存的分布式频谱合规与编排

DISCO: Distributed Spectrum Compliance and Orchestration for Scalable IoT Coexistence

Lyes Saad Saoud, Moussa Ayyash

arXiv 2607.21387首次发表:更新:

AI 中文总结

研究大规模物联网频谱共存问题,提出DISCO分层架构,将本地频谱学习与其他部分分离,构建可部署合规平面,通过无人机共存案例研究展示其在效率与风险权衡上的优势,平均吞吐量提升,违规率降低。

AI 中文摘要

大规模物联网部署在不确定的流量、衰落、移动性和间歇性协调下,越来越多地与现有、许可和非许可系统共享频谱。现有机制虽解决了共存的重要方面,但缺乏通用控制平面。本文介绍了分布式频谱合规与编排(DISCO),一种分层架构,将本地频谱学习与边缘级合规监管及较慢的云或非地面网络上下文适配分离。它不是新的强化学习优化器或法定频谱规则的替代品,其贡献是一个可部署的合规平面,能监测违规统计、广播紧凑治理信号并调整策略激进程度。通过30个种子的无人机共存案例研究表明,其在效率与风险间有较好权衡,平均吞吐量比固定功率控制高73%,平均违规率为0.053 。最后明确讨论了部署、复杂性、采用边界和开放验证要求等。

英文摘要

Massive Internet of Things (IoT) deployments increasingly share spectrum with incumbent, licensed, and unlicensed systems under uncertain traffic, fading, mobility, and intermittent coordination. Existing mechanisms, including fixed power limits, listen-before-talk procedures, spectrum access databases, and learning-based resource allocation, address important aspects of coexistence, but they do not provide a common control plane to translate a network-wide interference risk budget into lightweight guidance for many autonomous devices. This article introduces Distributed Spectrum Compliance and Orchestration (DISCO), a hierarchical architecture that separates local spectrum learning from edge-level compliance regulation and slower cloud or non-terrestrial-network context adaptation. DISCO is not presented as a new reinforcement-learning optimizer or as a replacement for statutory spectrum rules. Its contribution is a deployable compliance plane that monitors violation statistics, broadcasts a compact governance signal, and adjusts policy aggressiveness without centralizing every transmission decision. A 30-seed UAV coexistence case study illustrates the efficiency--risk trade-off: the reported mean throughput is 81.0~Mbps, 73\% above fixed-power control, while the mean violation rate is 0.053 compared with 0.126 for uncoordinated learning. Because the 95\% confidence interval, [0.030, 0.076], crosses the nominal target of 0.06, the evidence supports statistical regulation near the target, not guaranteed regulatory compliance. Deployment, complexity, adoption boundaries, and open validation requirements are discussed explicitly.

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