Networking
Understanding Conflict and Compatibility Conditions for Agents in AI-RAN
Arshia Zolghadr, Joao F. Santos, Imtiaz Nasim et al. (+3 more)
Disaggregated mobile networks expose open interfaces for independent
agents to control the
Radio Access Network (RAN). The interactions between
agents with contrasting objectives can result in conflicts. However, existing conflict mitigation methods treat
agents that control the same parameter as being in a conflict that requires arbitration of their actions, without assessing whether their objectives can be satisfied simultaneously. In this paper, we introduce a control-theoretic approach to determine when
agents can coexist and share control of the same parameters. We model threshold-based
agents often encountered in the literature as sampled deadband feedback controllers and represent their objectives through Key Performance Indicator (KPI) targets and tolerances that define acceptable deadband bounds. We derive compatibility conditions from these bounds and show that compatibility for any number of
agents and monitored KPIs can be established through pairwise tests. We propose a conflict mitigation policy that allows compatible
agents to share control of the same network parameters while restricting arbitration to cases where their objectives cannot be satisfied simultaneously. We evaluate our policy through simulations using NIST's ns3-oran extension of ns-3, and our results show that it enables
agents to share control with near-zero Service Level Agreement (SLA) drift when their objectives are compatible and the shared acceptable range is attainable, while matching Priority Mitigation when their objectives are incompatible.
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All authors Arshia Zolghadr, Joao F. Santos, Imtiaz Nasim, Deniz Aytemiz, Nicholas J. Kaminski, Jacek Kibilda
Networking · Cross-listed from cs.CR
NetAgent: Multi-Task Agentic Network Traffic Analysis Made Practical
Hao Fu, Dawn Song and Peng Gao
Network traffic analysis is central to network security, spanning tasks from intrusion detection to encrypted traffic classification. Existing approaches either train task-specific models that generalize poorly or rely on costly traffic foundation models that still struggle under distribution shift. We present NetAgent, the first agentic framework for multi-task traffic analysis. Through a carefully designed
agent loop, NetAgent supports complex task understanding, on-the-fly decomposition and orchestration, dynamic replanning, and long-horizon analysis, without task-specific training. It introduces five key designs: (1) knowledge-augmented workflow planning that maps attack knowledge to traffic features to bridge the semantic gap; (2) a comprehensive tool action space with 150+ verified tools extracted from 50+ published systems; (3) a unified code execution space for flexible action composition; (4) a three-tier
memory for long-term knowledge consolidation; and (5) sandboxing and runtime repair for reliable execution. Across 9 major
benchmarks, NetAgent outperforms all baselines (23 single-task and 5 multi-task) on nearly all tasks and generalizes substantially better to unseen traffic distribution (90.04% F1 vs. 2.74% and 3.04% for the best single-task and multi-task baselines) and under realistic background shift (4.85-point F1 drop vs. 74.88-point and 74.80-point drop for the best single-task and multi-task baselines). These results reveal that existing methods owe much of their reported success to overfitting
dataset-specific patterns and degrade sharply in realistic network environments, while NetAgent's agentic design remains accurate, generalizable, and robust.
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Networking
High Speed Network Processing for Scientific Computing in the Open Cloud Testbed
Miriam Leeser, Michael Zink, Suranga Handagala et al. (+3 more)
This paper presents an FPGA-based SmartNIC architecture designed to address critical network challenges in data-intensive scientific computing. As scientific workflows increasingly demand high-bandwidth data movement and real-time monitoring across wide-area networks, the Open Cloud Testbed (OCT) provides an ideal platform for investigating in-network processing solutions at line rate. Our approach focuses on implementing network visibility, optimization, and orchestration capabilities through FPGA acceleration. The proposed system leverages P4-programmable data-plane functions to support packet parsing, marking, telemetry extraction, traffic steering, and machine learning inference, all without requiring host CPU intervention or modifications to application servers. As a concrete example, our framework integrates the SciTags standard, which enables associating network traffic with scientific context through packet marking. We implement a high-performance network bridge that counts packets by their SciTag values, as identified in the IPv6 Flow Label. Experimental evaluation on AMD U280 FPGAs in OCT demonstrates the feasibility of this approach, showing imperceptible processing latency and line-rate packet handling, while accurately monitoring scientific data flows.
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All authors Miriam Leeser, Michael Zink, Suranga Handagala, Carlos Ruben Dell'Aquila, Verena Martinez Outschoorn, Rafael Coelho Lopes de Sa
Networking · Cross-listed from cs.CR
Evaluating Behavioral Context for Interpretable IAM Policy Risk Scoring in Cloud Environments
Yassin Elsharkawy
IAM policy analysis typically emphasizes the authorization capabilities encoded in a policy, but security analyst review priority may also depend on the behavioral and environmental context surrounding a policy event. This paper evaluates whether contextual information provides measurable incremental value for interpretable IAM policy risk prioritization beyond policy and effective-authorization information. AWS is used as the experimental cloud provider because its IAM and audit-telemetry ecosystem enables controlled evaluation using AWS IAM Context Bench, a
benchmark containing 534 real AWS experimental observations across policy, environment, and behavioral scenarios, including matched cases where policy and environment remain fixed while behavioral context changes. Three Explainable Boosting Machine models are evaluated under the same leakage-controlled grouped cross-validation protocol: a policy-centric baseline, a policy-plus-environment model, and a full-context model incorporating CloudTrail telemetry. The full-context model substantially reduces analyst-priority prediction error relative to the policy-centric baseline and closely tracks the reference priority ordering. In matched same-policy context pairs, the policy-centric model remains invariant, whereas the full-context model separates benign and suspicious behavioral conditions with high directional accuracy. The results also show improved concentration of high-priority cases at the top of simulated analyst review queues. These findings indicate that behavioral and environmental context can provide useful incremental information for analyst-oriented IAM risk prioritization while preserving an interpretable additive model structure. The formulation is applicable beyond AWS conceptually, although cross-provider validation remains future work.
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Networking · Cross-listed from cs.DC
Memory-Efficient Expert Routing for Distributed MoE Training
Arnab Kanti Tarafder, Jaume Guasch-Martí, Gokcen Kestor et al. (+1 more)
As Mixture-of-Experts (MoE) models scale toward hundreds of experts and higher top-$k$ routing,
memory efficiency in distributed training becomes a critical bottleneck. Peak
memory is dominated by the MoE block, not
attention: every intermediate buffer in the MoE dispatch pipeline is individually scaled by top-k routing. The standard all-to-all dispatcher sends all routed
tokens in a single collective step, requiring the full top-$k$-expanded buffer to be constructed at once. In this work, we propose RelayMoE, a ring-based MoE execution model that computes locally as expert weights or
tokens circulate, avoiding full top-$k$-expanded dispatch buffers. RelayMoE selects between expert and
token routing according to communication volume and overlaps transfers with computation. The ring structure naturally supports
memory-efficient MoE recomputation during backward: each hop reconstructs expert intermediates, uses them to compute gradients, and releases them before the next hop. The saved
memory supports longer sequences and larger batches, or retains more
attention activations to reduce
attention recomputation and improve training throughput. We evaluate RelayMoE on 30B$-$57B production MoE models and varied expert configurations. In single-layer MoE experiments, RelayMoE achieves a $2\times$ average speedup over Megatron-LM. In full-model training under the same GPU
memory budget, it improves throughput by up to $2.02\times$ and extends the largest tested trainable sequence length by up to $2.85\times$.
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All authors Arnab Kanti Tarafder, Jaume Guasch-Martí, Gokcen Kestor, Jie Ren
Networking · Cross-listed from quant-ph
Pauli Error Composition Determines the Multipartite Advantage in Conference Key Agreement
Ryder Carlson, Lochlan Fitzgerald, Kidus Peter et al. (+1 more)
GHZ-based conference key agreement distributes a shared key to $N$ parties in one network use, whereas pairwise BB84 needs $N-1$. The survival of that advantage has been studied against party number, loss, and
topology, but almost always with the channel fixed at depolarizing. Simulating both protocols exactly for $N = 3, 4, 5$ under dephasing, amplitude damping, and depolarizing noise, we find the crossover is set by the channel's Pauli error composition, not its identity: normalized to average infidelity, amplitude damping and depolarizing agree to within about 1% at each group size, while dephasing can tolerate roughly $2.4\times$ more at $N = 3$. We derive a closed-form rate for arbitrary per-party Pauli noise and use it to prove that concentrating a fixed noise budget on one arm is strictly worse than spreading it, and that giving the dealer role to the cleanest arm improves the rate by roughly 18%. One structural fact drives every result: the phase-error term is a product over all $N$ arms, while the bit-error term stays local to a pair.
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All authors Ryder Carlson, Lochlan Fitzgerald, Kidus Peter, Wilson Smith
Networking
Ofan: Optimal Load Balancing for AI Training
Sarah McClure, Evyatar Cohen, Jakob Krebs et al. (+5 more)
The extreme collective completion time (CCT) demands of AI workloads challenge existing packet spraying
algorithms, which can have trouble efficiently load-balancing workloads that are sent at full line rates. We trace this to a structural cause: on a fat tree, once a packet picks its upward path, the downward path to its destination is unique, so destination-oblivious schemes cannot undo the imbalance it creates. We prove that such schemes can suffer from $\Theta(\sqrt{m})$ queueing for messages of size $m$, thus eventually triggering rate reductions by the congestion control. Instead, we suggest Ofan, a switch-based destination-aware LB scheme that can reach $O(1)$ queueing. We also present its pOfan variant that fits the pipe-based architecture of current switches. Our P4 implementation shows that it consumes modest resources. An end-to-end FSDP2 evaluation with Llama-3 405B-parameter models shows that Ofan cuts CCT inflation by $16$–$39\times$ when compared to existing
algorithms.
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All authors Sarah McClure, Evyatar Cohen, Jakob Krebs, Alexander Shpiner, Mark Silberstein, Scott Shenker, Sylvia Ratnasamy, Isaac Keslassy
Networking
Who Can Safely Wait? Risk-Aware 5G Scheduling for Low-Altitude UAV Operation
André Melo, Yueqi Chen, Lourenço Pereira Jr. et al. (+1 more)
Cellular-connected
UAV operations already fly in many countries. However, cellular networks were designed for terrestrial users and remain blind to the operational consequences of delaying information from low-altitude aircraft. We present Risk-Aware (RA), a framework that translates information freshness and operational context into aircraft-specific operational risk, enabling uplink scheduling based on operational consequence rather than network metrics alone. RA balances the consequence of further delay against the service cost of heterogeneous single- and multi-slot updates. It combines a regulation-inspired risk proxy, risk-directed prioritization based on marginal risk growth, and a Max-Weight scheduler with Lyapunov-based performance guarantees. Although RA is derived from an analytically tractable model, we evaluate it under moving
UAV trajectories, dynamic operational context, geographically varying population exposure, and realistic
UAV video traffic. Across 48 operating points and fleets of up to 120
UAVs in São Paulo delivery operations and the Interlagos Formula 1 Grand Prix setting, RA reduces 99th-percentile per-
UAV risk by up to 96% over proportional fair and 86% over round robin, while supporting fleets up to 1.5$\times$ and 3$\times$ larger, respectively, within the same risk envelope.
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All authors André Melo, Yueqi Chen, Lourenço Pereira Jr., Igor Kadota
Networking
SoK: Semantic Decision Engines in Network Control Loops
Delong Li, Chen Li, Xu Wang et al. (+3 more)
A semantic decision engine such as Jev can return a valid answer and still miss a network deadline, select an infeasible action or leave the service unverified. We systematize 139 paper families by decision interface, execution path and check ownership. Fifty families claim that their engine fits a control loop or time budget, but only four support the claim with matched measurement. Across all 139, four report deadline attainment. The gap concentrates where the decision has no deterministic computation step. Those 72 families make 22 of the claims, none supported, and name a coverage owner in only two. Bounded tests under one event model show that each gap can reverse an admission verdict. A decision that meets a 10 s budget for every isolated request meets it for none once decisions queue ahead of replayed execution times. The same engine passes one coverage check and fails another. We derive a minimum reporting record, design rules and a research agenda for admitting decision engines to control loops.
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All authors Delong Li, Chen Li, Xu Wang, Haochen Gong, Rui Lang, Guangsheng Yu
Networking
Graph Neural Network-Driven Deep Reinforcement Learning for Scalable RIS Allocation
Martin Mark Zan and Stefan Schwarz
Reconfigurable Intelligent Surfaces (RISs) offer a promising paradigm to mitigate blockage and extend millimeter-wave coverage in 6G multi-
cell networks. However, dynamically allocating shared RIS infrastructure across competing base stations is an NP-hard problem posing severe scalability bottlenecks. In this paper, we propose a scalable framework combining Graph Neural Networks (GNNs) with Deep Reinforcement Learning (DRL) for dynamic shared RIS orchestration. By formulating allocation as a Markov Decision Process, we introduce a physical
topology sparsification strategy that prunes dense channel
matrices into a sparse tripartite graph. This
pruning reduces edge density by 77% and removes representation noise, thereby improving global coverage probability while reducing computational complexity. Our relational message-passing architecture naturally generalizes to arbitrary network dimensions without model retraining. Furthermore, structural ablation studies reveal that physical path loss localizes surface dependencies, enabling a highly efficient localized graph design with linear computational scaling. Extensive simulations in dense urban environments demonstrate that under strict infrastructure budget constraints, the proposed GNN-DRL framework consistently outperforms greedy heuristic baselines by up to ~12% in coverage while delivering faster inference speed via GPU acceleration.
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Networking
Explicit QUIC Proxies for Server-Side Geo-blocking Bypass
Aurélien Buchet, Soyong Kim, Tom Barbette et al. (+1 more)
Geo-restricted content is increasingly common on the Internet, forcing users to rely on circumvention techniques, such as VPNs, to access the web from a seemingly different location. However, these often come with a financial cost and can degrade performance. The rise in popularity of the QUIC protocol, which allows connections to migrate between paths, opens opportunities to circumvent such restrictions. We scan web servers and find that a large portion of geo- blocked content is enforced on the server, at the application layer, rather than on-path. This check is performed once, when the request arrives, and is not repeated as the connection continues. This allows a client to issue its request from a whitelisted IP address and, once the server has accepted it, migrate the connection to an otherwise unauthorized address for the rest of the transfer (post-header migration). It bypasses the block while maximizing direct traffic, thereby reducing eventual circumvention-related costs. Building on this insight, we introduce Stork, an HTTP/2-to-HTTP/3 web proxy that bypasses geo-blocking while introducing negligible additional latency. We demonstrate that our solution is compatible with popular clients and servers. In controlled experiments with 2 MB requests, our proxy migrates 99% of the transferred data onto the unauthorized path. Across real-world targets that support QUIC migration, it migrates at least 75% of the data for more than 52% of them. On real geo-blocked content, post-header migration bypasses the block for 90% of domains, whereas post-handshake migration, as used by prior work, succeeds for only 60%.
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All authors Aurélien Buchet, Soyong Kim, Tom Barbette, Cristel Pelsser
Networking
Identification Codes for Ad Hoc Networks: Use Cases and Challenges
Athin Mohan, Roberto Ferrara and Christian Deppe
In this study, we focus on large-scale ad hoc networks characterized by a high node density. To achieve energy efficiency, the devices must minimize their data transmission, thereby reducing the total number of bits sent and the associated energy consumption. We consider using ID codes for the address field in the MAC layer and reduce the header size. The work explores a range of use cases and challenges that demonstrate the benefits of incorporating these ID codes into packet routing mechanisms for ad hoc networks, thereby providing a foundation for understanding their applicability and the necessary design adaptations.
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Networking
Generative-AI for XR Content Transmission in the Metaverse: Potential Approaches, Challenges, and a Generation-Driven Transmission Framework
Zhe Zhang, Yili Jiang, Xin Wei et al. (+3 more)
How to efficiently transmit large volumes of Extended Reality (XR) content through current networks has been a major bottleneck in realizing the Metaverse. The recently emerging Generative Artificial Intelligence (GAI) has already revolutionized various technological fields and provides promising solutions to this challenge. In this article, we first demonstrate current networks' bottlenecks for supporting XR content transmission in the Metaverse. Then, we explore the potential approaches and challenges of utilizing GAI to overcome these bottlenecks. To address these challenges, we propose a GAI-based XR content transmission framework which leverages a cloud-edge collaboration architecture. The cloud servers are responsible for storing and rendering the original XR content, while edge servers utilize GAI models to generate essential parts of XR content (e.g., subsequent frames, selected objects, etc.) when network resources are insufficient to transmit them. A Deep Reinforcement Learning (DRL)-based decision module is proposed to solve the decision-making problems. Our case study demonstrates that the proposed GAI-based transmission framework achieves a 2.8-fold increase in normal frame ratio (percentage of frames that meet the quality and latency requirements for XR content transmission) over baseline approaches, underscoring the potential of GAI models to facilitate XR content transmission in the Metaverse.
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All authors Zhe Zhang, Yili Jiang, Xin Wei, Mingkai Chen, Haiwei Dong, Shui Yu
Networking · Cross-listed from cs.CR
Adaptive-Shot Hybrid Quantum Anomaly Detection for Tactile Internet Security: Reliability-Aware Measurement Allocation Under Resource Constraints
Mubassir Serneabat Sudipto, Shakil Ahmed, Ashfaq Khokhar et al. (+1 more)
Tactile Internet (TI) security analytics must balance reliable thresholded decisions with constrained computational and measurement resources. We study this tension for finite-shot hybrid
quantum anomaly inference and introduce the Adaptive-Shot Variational
Quantum Circuit (AS-VQC) policy. This validation-calibrated policy begins each record at 128 shots and cumulatively escalates through 256, 512, and 1024 shots only when the finite-shot anomaly score remains close to a validation-selected security threshold. The
quantum scorer is evaluated as an off-path security analytics component rather than part of the haptic critical path. Using a 4,875-record CESNET-TimeSeries24-derived aggregate-flow
benchmark, leakage-safe random, entity-group-disjoint, and temporal holdouts, and five trained
quantum neural network (QNN) checkpoints per holdout, the primary AS-VQC-95 (beta = 0.95) policy averages 129.2, 276.9, and 131.2 shots per record, saving 87.4%, 73.0%, and 87.2% of the uniform 1024-shot baseline (Fixed-1024), respectively. The decision disagreement with analytic (exact-expectation) inference is 0.771%, 0.409%, and 0.635%, lower than both the uniform 128-shot baseline (Fixed-128) and a matched-budget shuffled-allocation control. Fixed-1024 remains more decision-stable, establishing a measurable reliability-resource trade-off rather than cost-free equivalence. A more conservative AS-VQC-99 (beta = 0.99) further reduces disagreement while using fewer than 512 average shots across all holdouts. These results show that finite
quantum measurements can be treated as an inference resource and concentrated on boundary-sensitive TI-security decisions while exposing checkpoint-dependent escalation under unseen-entity conditions.
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All authors Mubassir Serneabat Sudipto, Shakil Ahmed, Ashfaq Khokhar, Samir M. Iqbal
Networking
Implementation of Zero-shot Semantic Communication on Software Defined Radio
Thanh Le, Arif Dataesatu, Homare Murakami et al. (+1 more)
Semantic communication has recently gained traction for its ability to reduce the amount of data transmitted over a communication link by transmitting a task-oriented representation instead of the raw source. Zero-shot semantic communication sends a general
embedding from a
vision-
language model (VLM), so the same transmitter can serve new classification tasks without retraining. Most evidence for this advantage, however, comes from numerical simulation. We implement zero-shot semantic communication on a software-defined
radio platform: a Raspberry Pi drives a pair of Analog Devices Active Learning Module (ADALM)-Pluto transceivers, with an image encoder at the transmitter and a text encoder at the receiver, and determines the zero-shot classification results via cosine similarity. We compare two VLMs, CLIP and MobileCLIP, across various channel conditions, i.e., different signal-to-noise ratios (SNRs). We validate that the semantic link spends 9x fewer channel uses per image than a JPEG plus 16-ary quadrature amplitude modulation baseline and still reaches 82% accuracy on CIFAR-10 at 22.3 dB, where the baseline scores 0%. On the traffic sign recognition
dataset (TSRD), MobileCLIP correctly classifies 98.3% of unseen images at the same SNR. Offloading the image encoder to a neural processing unit reduces encoding to 13.4 ms per image, 49x faster than a Raspberry Pi 4 CPU, placing the transmitter within a real-time budget. Our implementation is publicly available at https://github.com/thanhlexyz/zsscsdr.
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All authors Thanh Le, Arif Dataesatu, Homare Murakami, Takeshi Matsumura
Networking
6G as It Is Actually Being Built: Insights from Early 3GPP Standardization
Ngo Hoang Tu, Huy T. Nguyen, Mao V. Ngo et al. (+3 more)
As mobile communications cross the threshold from 5G to 6G, the 3GPP has entered a decisive phase. Following the Technical Specification Group (TSG) plenary meetings of June 2026 and the associated 6G workshop in Singapore, the Release-20 study phase is now well underway across all three TSGs:
Radio Access Network (RAN), Service and System Aspects (SA), and Core Network and Terminals (CT). Concurrently, the timeline for the first normative 6G specifications in Release 21 has been established. Rather than offering another technology
vision, or a chronological account of committee progress, this article asks what the first body of agreed 6G material tells us. We review service requirements, system architecture, the emerging
radio interface, native AI,
ISAC, non-terrestrial networks (NTN), and core-network protocols, tracing how each moves from study item toward specification. Read across the three TSGs, this material also supports seven cross-cutting lessons: AI has moved from a feature to a primitive spanning every stage, non-terrestrial access has crossed from overlay to substrate, and connectivity is now one objective among three alongside computing and sensing; the industry favors deployability over redesign, with spectrum access and site reuse, rather than waveform innovation, appearing to be the tighter constraint; and standardization targets AI workflows rather than AI models, with testability across vendors recurring as a decisive criterion for which techniques survive. Because the material spans very different stages of maturity, every item cited is tagged with its status at a single snapshot date (approved, draft, individually proposed, or vendor-demonstrated), and the lessons are offered as the authors' synthesis rather than as 3GPP positions. We close with the road toward trials and commercial launch around 2030, and with the open questions these lessons help prioritize.
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All authors Ngo Hoang Tu, Huy T. Nguyen, Mao V. Ngo, Tran Thien Thanh, Vo Nguyen Quoc Bao, Tony Q. S. Quek
Networking
LocAttMamba: A Low-Complexity Mamba Framework with Attention-Based Multi-AP Fusion for Indoor Localization
MohammadMahdi Ghadaksaz, Mohammad Farzanullah and Melike Erol-Kantarci
Accurate and low-complexity indoor localization is important for location-based services in fifth generation (5G) and sixth generation (6G) networks, where positioning devices operate under limited computational budgets and non conditions. Indoor localization has been studied widely using traditional signal-level localization approaches. However, these techniques often show degraded performance in on-line-of-sight (NLoS) scenarios. Recently, artificial intelligence (AI)-based techniques, including
transformers, have been applied to address these challenges. While
transformer-based architectures can capture the dependencies within the measurements collected from distributed access points (APs), their high computational complexity results in a large number of multiply-accumulate operations and long inference time. In contrast, lightweight recurrent and convolutional models trade this cost for degraded accuracy. In this paper, we propose LocAttMamba, a low-complexity localization framework in which the channel impulse response (CIR) and time-based features of each AP are processed by a separate Mamba encoder with near-linear complexity, and the resulting per-AP
embeddings are fused through a multi-head
attention layer that weights each AP according to its importance at every time step. The framework jointly predicts the user location and its per-axis uncertainty, which is refined through a post-hoc calibration step. We evaluate the proposed framework using two real-world 5G and ultra-wideband (UWB) measurement
datasets. Our numerical results reveal that LocAttMamba obtains a mean two-dimensional (2-D) positioning error of 1.004 m and 0.599 m, respectively, on 5G and UWB
datasets, outperforming the second-best
benchmark by 9.79% and 5.82%, while requiring the fewest multiply-accumulate operations among all evaluated models and being 4.4-16 times faster than the
transformer-based
benchmarks.
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Networking
Distributed Quantum-Assisted Robust AoII Minimization in Satellite-Ground Integrated Edge Networks
Mohammad Arif Hossain, Tanzimul Alam Fahim, Weiqi Liu et al. (+1 more)
Mission-critical edge applications in 6G-and-beyond networks, such as autonomous systems, disaster response, and infrastructure monitoring, require that the edge decision-maker's estimate of a monitored process remain correct, not merely up to date. Satellite-ground integrated networks (SAGIN) often provide the only connectivity in infrastructure-limited or disaster-affected regions, yet satellite handover and shadowing interrupt links, during which the process may change state several times, leaving the edge node's estimate substantially wrong. Age of information (AoI) tracks only elapsed time and cannot distinguish a harmless delay from a dangerous error. We instead adopt the age of incorrect information (AoII), which penalizes both the duration and magnitude of estimation error, and formulate, to our knowledge, the first network-level, multi-node AoII minimization problem over SAGIN under
stochastic handover and shadowing. We propose SENTINEL, a distributed hybrid
quantum-classical framework that jointly schedules update rates, satellite-to-base-station associations, and bandwidth allocation to minimize the worst-case time-average AoII. Because AoII is history dependent, it resists per-slot optimization; a renewal-interval decomposition that separates source dynamics from channel disruption yields a closed-form AoII cost per inter-delivery interval. The resulting robust scheduling problem, VANGUARD, is formulated as a QUBO, mapped to an Ising
Hamiltonian, and solved via distributed QAOA with ADMM-based coordination across satellite and ground domains. Every returned schedule carries a certified worst-case AoII over all disruption scenarios. Simulations show that SENTINEL outperforms learning-based and random baselines, matches a state-aware threshold policy in small networks while additionally providing a worst-case guarantee, and remains close to an exact minimax reference.
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All authors Mohammad Arif Hossain, Tanzimul Alam Fahim, Weiqi Liu, Nirwan Ansari