Distributed Spectrum Compliance and Orchestration (DISCO) is introduced, a hierarchical architecture that separates local spectrum learning from edge-level compliance regulation and slower cloud or non-terrestrial-network context adaptation.
Abstract
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.
A novel Federated Edge Learning (FEL) architecture that integrates software-defined networking principles with gossip-based communication protocols to facilitate collaborative model training while preserving data locality is proposed, offering a scalable, privacy-preserving solution for deploying artificial intelligence at the network edge.
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DCRO, a Distributed Coalition-based Resource Orchestration framework enabling IoT devices to self-organize into dynamic coalitions for cooperative resource management, is presented.
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Uncrewed aerial vehicle (UAV)-enabled federated learning (FL) can provide flexible, on-demand edge intelligence for large-scale IoT deployments, but operating in shared unlicensed bands makes uplink update delivery interference-coupled and unreliable. In this paper, we develop a packet-level transmission framework that captures buffer overflow, delay violations, and transmission errors, and uses the resulting packet delivery ratio (PDR) to represent partial-update reception through a packetized, Bernoulli-masked FL aggregation process. We then formulate a fairness-consensus bilevel (FCB) optimization that jointly controls (i) transmission thresholds to maximize the average PDR while reaching consensus under partial observability and (ii) transmission powers to improve the worst PDR and enforce fairness across IoT learners. To solve this problem, we propose an alternating FCB optimizer composed of a consensus-based threshold controller (CTC), which drives the IoT learners toward a PDR-efficient consensus on transmission thresholds, and a fairness-based power controller (FPC), which updates transmission powers to improve the worst PDR and ensure fairness under the resulting consensus thresholds. Numerical results on CNN-based FL tasks show that the FCB optimizer improves FL aggregation and training performance by enhancing packet-level update delivery, consistently outperforming baseline transmission policies.
The rapid evolution of heterogeneous telecommunication networks has increased the difficulty of maintaining communication performance while operating within spectrum and transmission constraints. Existing network optimization approaches primarily focus on throughput, routing, and interference mitigation, whereas regulatory compliance is commonly treated as a separate post-processing activity. This separation limits the ability of network-control mechanisms to respond proactively to compliance risks. This study develops a Compliance-Aware AI Framework (CA-AIF) that integrates Coalition Formation Games (CFG), variance-aware DUCBQ adaptive routing, and Proximal Policy Optimization (PPO) with a policy-constrained compliance monitoring layer. The framework represents compliance risk through telemetry-derived violation indicators associated with spectrum occupancy, transmission power, and interference conditions and incorporates these indicators into network decision-making. A controlled simulation environment is developed using synthetically generated mobility, interference, channel, and spectrum-occupancy data. The proposed framework is evaluated against Q-learning, Dyna-Q, UCBQ, and DUCBQ using throughput, packet acceptance, routing stability, convergence, compliance violation rate, and computational cost. Across repeated simulations, the proposed framework achieves higher throughput and packet acceptance while reducing compliance violations relative to the evaluated baselines. The results demonstrate that incorporating compliance risk into network optimization can improve the stability of communication decisions without treating compliance as an independent post-processing task. The study contributes a reproducible simulation-based framework for compliance-aware intelligent networking and provides a basis for future validation using operational spectrum-monitoring and regulatory datasets.
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The results demonstrate that QoS-aware distributed work stealing matches the makespan of the evaluated centralised dynamic scheduler without placing it on the critical path, scales without performance degradation across the evaluated range of up to 64 devices, and achieves the highest and most consistent cluster utilisation.
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Simulations across various 5G IoT spectrum environments showed that F-DMRL performed faster adaptation, higher spectral efficiency, and lower interference probability compared to centralized meta-RL, federated DRL, and traditional decentralized RL baselines.
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