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新论文 · v1arXiv:2611.08970

Adaptive Age of Information Optimization in Rateless Coding-Based Multicast-Enabled Sensor Networks

Hung-Chun Lin, Kuang-Hsun Lin, Hung-Yu Wei

原文摘要

As the demand for real-time information in Internet of Things (IoT) and Wireless Sensor Networks (WSN) scenarios grows with the evolution of bandwidth-intensive 5G applications, multicast transmission becomes increasingly vital. This paper delves into the significant role of multicast in WSN, exploring a novel perspective of using rateless codes over the traditionally employed Hybrid automatic repeat request (HARQ) for eliminating retransmissions in a wireless multicast system. We aim to optimize data freshness, quantified by the Age of Information (AoI), and analyze strategies to minimize time-average AoI in a multicast environment with diverse channel conditions. Specifically, our policy focuses on optimizing the time-average AoI based on sensor devices' feedback. We transform the problem into a Markov Decision Process (MDP) to locate optimal and low-complexity suboptimal policies. We present the first age-minimum scheme for rateless code-based wireless multicast systems. Our numerical simulations reveal that the proposed policies, developed considering unique system structural properties, consistently surpass baseline strategies. We are thus able to preempt updates at the most beneficial time, thereby addressing the issue of the bottleneck device's adverse impact on overall performance.

cs.NI · cs.PF

新论文 · v1arXiv:2611.08949

Age of Information for Power-Saving Devices with DRX Mechanism

Hung-Chun Lin, Kuang-Hsun Lin, Hung-Yu Wei

原文摘要

Reducing the power consumption of User Equipment (UE) has always been a crucial issue in communication systems. As a result, Discontinuous reception (DRX) is introduced in today's New Radio (NR) networks as a power-saving strategy. In addition, the variety of low latency applications in 6G makes the issue of maintaining data freshness increasingly important. This paper aims to study the relationship between freshness and power-saving. To capture the information freshness, we use Age of Information (AoI) as the performance metric. To our best knowledge, unlike previous studies focusing on power saving and delay trade-offs, this is the first study considering the impact of DRX on AoI. We solve the closed-form solution of the peak average AoI with the DRX mechanism. Moreover, we propose a parameters adjustment strategy. Based on the simulation validation, our proposed strategy can enable the UE to keep the AoI close to the theoretical lower bound AoI while achieving high power-saving efficiency.

cs.NI · cs.PF

新论文 · v1arXiv:2611.08772

StarLoc: A Practical Approach to Encoding Geographic Locators in IPv6 for Starlink

Vanya Singla, Mostafa Abdollahi, Jinwei Zhao, Jianping Pan

原文摘要

LEO satellite networks such as Starlink provide Internet connectivity across continents, oceans, and airspace. IP geolocation providers map Starlink's IPv6 prefixes to locations mainly through its self-published GeoFeed. However, users may be far away from their assigned Points of Presence (PoPs), one label covers an entire prefix, and labels and PoP mappings can change while addresses stay the same. We characterize Starlink's IPv6 addressing using 925 historical GeoFeed snapshots (2024--2026), one year of PoP mappings, and over 7.9 million observed IPv6 addresses. Our findings reveal that customer prefixes lack clear geographic structure, prefix labels lie a median of 540km from their PoPs, and relabeling a single /40 can affect the reported locations of up to 65,536 possible /56 delegations. To address these challenges, we propose StarLoc, a geographic Locator--Identifier separation scheme that embeds terminal location into IPv6 addresses using a resolution-adaptive H3 Locator while preserving existing Starlink prefixes, /56 delegations, and stable Endpoint Identifiers. To balance accuracy and privacy, StarLoc reveals only cells with at least a certain number of terminals, and operators can cap resolution in sensitive regions or withhold location entirely. Unlike topology-based schemes, StarLoc encodes physical location and lets Locators follow moving users.

cs.NI

新论文 · v1arXiv:2611.08739

Stochastic AI Model Placement for AI-and-RAN Coexistence in 6G Networks

Alaa M. Altarazi, Mohammad J. Abdel-Rahman

原文摘要

This paper considers AI-service placement over geographically distributed RAN sites equipped with GPU resources partitioned into fixed, isolated multi-instance GPU (MIG) profiles. We propose C$^2$AIR, a chance-constrained optimization model that maximizes expected AI requests served while protecting uncertain RAN workloads and enforcing probabilistic capacity reliability requirements for admitted AI services. C$^2$AIR allows a service to run at multiple sites and accounts for discrete profile, memory, and GPU-slice constraints. We evaluate C$^2$AIR on a four-site deployment calibrated from NVIDIA Aerial RAN capacity figures and MLPerf Inference results, and on seven larger scenarios with up to 15 sites and 40 AI services, computing every violation and overload probability exactly from the demand distributions. C$^2$AIR keeps the RAN violation probability within its target at every hour of the day, whereas reserving for the mean RAN demand violates it in 24\% to 47\% of the intervals. The reliability condition prevents placing AI models that would be overloaded in 85\% of the intervals at an event hour, and multi-site replication serves 2.7 times more AI requests than single-site deployment. The evaluation also shows that coarse MIG instances and a heavy demand tail can make strict RAN protection expensive.

cs.NI

新论文 · v1arXiv:2611.08707

Measuring Bounded Autonomy for an LLM Decomposition Planner on a Live 5G-Core Testbed

Sanjeet Raj Pandey, Axel Küpper

原文摘要

Large language models (LLMs) are entering network-management control loops, yet their plans can fall outside the executable action space. Whether hard deterministic bounds around such a planner have real effect, or merely restate a definition, is an empirical question. We measure it on a live 5G-core testbed, where an LLM planner decides when to decompose control-plane procedures into stateless handlers inside a bounded loop: a JSON schema constrains the output, a projection-repair validator re-checks it, a deterministic fallback guarantees liveness, and every decision is persisted. Three measurements anchor the paper. First, the two tested models, run unbounded with the allowed action sets stated in the prompt, still violated intent-handler rules in 13 of 44 standard calls. Every violation broke an intent-handler combination rule that the deployed flat schema cannot encode (schema enforcement as deployed caught 0/13); a union schema encoding the rule removes the class by construction. Second, with the bounds in place, all 33 decoded live plans were feasible without repair, and a matched-condition probe put the in-distribution unbounded rate at one violation in 20 windows. Third, exploratory: on the design set the planner matched an author-specified selection reference that a threshold policy and three a priori codings each missed. Transfer to held-out scenarios remained partial (5-6 of 10), and the planner's two-clause distillate could replace the runtime planner itself. Together, the results suggest elicitation and distillation, not runtime control, as the LLM's role.

cs.NI

新论文 · v1arXiv:2611.08389

少即是多:空芯光纤在光网络中的新兴全网级用例(特邀)

When Less is More: Emerging Network-Wide Use Cases for Hollow-Core Fibers in Optical Networks [Invited]

Mëmëdhe Ibrahimi, Giovanni Simone Sticca, Angelo Ferrara, Francesco Musumeci, Massimo Tornatore

AI 速读 基于摘要

该文综述了在混合单模光纤/空芯光纤网络中,选择性部署空芯光纤的四类全网级用例,并指出按需局部升级即可获得显著收益。

cs.ET · cs.NI

查看原文摘要

Hollow-Core Fiber (HCF) is emerging as a disruptive technology for next-generation optical networks, offering 30\% lower propagation latency than standard Single-Mode Fiber (SMF), record-low attenuation (0.05-0.1 dB/km), and nonlinearities reduced by three to four orders of magnitude. While the physical-layer benefits of HCF have been extensively demonstrated, recent industrial commitments by hyperscalers (e.g., Microsoft, Amazon) and the rapid maturation of nested antiresonant nodeless fibers (NANF) call for a systematic assessment of the impact of HCF at the network level. Building on a series of prior contributions, we overview four emerging network-wide use cases for the selective deployment of HCF in hybrid SMF/HCF networks: (i) latency-constrained Edge Data Center (edgeDC) consolidation; (ii) energy-efficient high-power amplification; (iii) capacity scaling via single-fiber bidirectional (BiDi) transmission; and (iv) coexistence of classical and quantum (QKD) channels. Consolidated results across realistic metro, national, and continental topologies convey a unifying message: in each use case, a selective HCF upgrade captures a significant share of the achievable benefit compared to a network overhaul. The required HCF budget is use-case-specific, i.e., 6-24\% of the total network fiber length for edgeDC consolidation (up to 50\% fewer edgeDCs), 40\% of the network links for QKD/classical coexistence (up to 38\% fewer quantum modules), and 40-60\% of the network links for energy-efficient capacity scaling via BiDi-HCF (up to 28\% lower network power per Tbps and 70\% more served traffic, at half the HCF strand count of a unidirectional HCF upgrade), whereas for energy-efficient high-power amplification the gain keeps growing with the HCF budget (up to 60\% lower network power per Tbps at the national scale) and what saturates is the benefit of increasing the EDFA output power.

新论文 · v1arXiv:2611.08298

超越量子消息:迈向量子原生网络功能

Beyond Quantum Messages: Toward Quantum-Native Network Functioning

Angela Sara Cacciapuoti, Jessica Illiano, Caterina De Risi, Marcello Caleffi

AI 速读 基于摘要

这篇 Perspective 主张量子现象不仅可作为网络传输的资源,还可直接参与网络功能的实现,并以纠缠生成与操作连通性为例勾勒量子原生功能设计空间。

quant-ph · cs.NI

查看原文摘要

The Quantum Internet is commonly envisioned as a network that transports quantum states and distributes entanglement as the key resource underpinning distributed quantum applications. This view is necessary but incomplete if quantum phenomena are confined to what the network carries or manages, while network functionalities remain grounded predominantly in classical abstractions. This Perspective advances a broader thesis: quantum phenomena can also participate directly in the realization of network functionalities. We first establish the architectural baseline by introducing entanglement as a stateful network resource whose operational condition is non-locally distributed across remote nodes, evolves through manipulation or consumption, and degrades over time through decoherence. Its evolving state must therefore remain visible to the functionalities acting on it. We then move beyond resource visibility and use two mechanisms as proof points of a broader quantum-native functional design space: entanglement generation during propagation through coherent spatial-path superposition, and operational connectivity induced by shared entanglement that can depart from the physical graph. These examples show that paths, neighbors, or participating nodes may become operationally relevant in ways determined by quantum coherence and shared entanglement, rather than by physical connectivity alone. We identify Quantum Addressing as a transversal abstraction capable of representing such relations while complementing, rather than replacing, classical addressing and signaling. The resulting perspective broadens the Quantum Internet from transporting quantum information and distributing entanglement to exploiting quantum phenomena in network functioning, opening a coupled frontier spanning routing, forwarding, control, and functionality placement.

版本更新 · v2arXiv:2604.19427

橄榄园中方向相关路径损耗建模:面向精准农业

Direction-Dependent Path Loss Modeling in Olive Orchards for Precision Agriculture

Mohammad Rowhani Sistani, Katarzyna Kosek-Szott, Pierluigi Gallo

AI 速读 基于摘要

作者提出一种考虑果园行结构布局与设备相对位置的方向相关传播模型,并用868 MHz LoRa实测数据与经典模型对比。

cs.NI · eess.SY

查看原文摘要

Wireless links deployed in orchards often exhibit significant variability in the strength of the received signal that is not adequately captured by classical distance-based propagation models. In row-structured olive groves, signal attenuation differs markedly between along-row and cross-row propagation directions, leading to discrepancies when using omnidirectional propagation assumptions such as those adopted in the Free Space Path Loss (FSPL) model or ITU-R vegetation loss formulations. This paper proposes a topology-based propagation model that explicitly accounts for orchard layout and the relative positions of radio devices within the plantation structure. Experimental validation was conducted using LoRa technology operating at 868 MHz, and the results were compared with established models from the literature and with the proposed two-dimensional model. The proposed approach achieves a closer fit to measured RSSI data than conventional models, providing a more reliable basis for link budgeting and network planning in structured agricultural environments.

新论文 · v1arXiv:2611.08155

中继辅助的AI原生任务导向语义通信:收益与脆弱性

AI-Native Task-Oriented Semantic Communications over a Relay: Benefits and Vulnerabilities

Yalin E. Sagduyu, Tugba Erpek, Aylin Yener

AI 速读 基于摘要

该论文面向中继辅助的语义通信系统,联合设计源编码器、中继变换与目的端解码器,并揭示中继引入的拜占庭攻击脆弱性。

cs.IT · cs.CR · cs.LG · cs.NI · eess.SP

查看原文摘要

Semantic communication (SemCom) using a relay node is considered, where the relay assists in conveying task-relevant information from a source to a destination, with meaning defined by classification labels. A joint end-to-end design of the source encoder, relay transformation, and destination decoders is developed to optimize both semantic inference accuracy and perceptual reconstruction quality for image data. Relay-assisted schemes are shown to outperform direct transmission, with gains that become more pronounced in low signal-to-noise ratio (SNR) regimes across different relaying strategies. Alongside these performance benefits, however, a key security vulnerability introduced by the relay is identified, and a Byzantine attack is formulated in which the relay injects structured, learned perturbations into its transmitted signal. The attack operates on intermediate communication representations subject to channel effects, with perturbations generated through a learned mapping applied at test time. It is shown to significantly degrade semantic inference accuracy at the destination. These results reveal a clear trade-off between SemCom gains and security risks in relay-assisted systems, motivating the need for secure relay designs that account for insider threats.

版本更新 · v2arXiv:2505.07741

BBR's Sharing Behavior with CUBIC and Reno

Fatih Berkay Sarpkaya, Ashutosh Srivastava, Fraida Fund, Shivendra Panwar

原文摘要

TCP BBR's behavior has been explained by various theoretical models, and in particular those that describe how it co-exists with other types of flows. However, as new versions of the BBR protocol have emerged, it remains unclear to what extent the high-level behaviors described by these models apply to the newer versions. In this paper, we systematically evaluate the most influential steady-state and fluid models describing BBR's coexistence with loss-based flows over shared bottleneck links. Our experiments, conducted on a new experimental platform (FABRIC), extend previous evaluations to additional network scenarios, enabling comparisons between the two models and include the recently introduced BBRv3. Our findings confirm that the steady-state model accurately captures BBRv1 behavior, especially against single loss-based flows. The fluid model successfully captures several key behaviors of BBRv1 and BBRv2 but shows limitations, in scenarios involving deep buffers, large numbers of flows, or intra-flow fairness. Importantly, we observe clear discrepancies between existing model predictions and BBRv3 behavior, suggesting the need for an updated or entirely new modeling approach for this latest version. We hope these results validate and strengthen the research community's confidence in these models and identify scenarios where they do not apply.

cs.NI

新论文 · v1arXiv:2611.07982

How Far Can Lightweight LLMs Go in Intent-to-SLO Translation?

Sabbir Ahmed, Hazem Abbas, Salimur Choudhury

原文摘要

Intent-based networking (IBN) promises QoS provisioning in which the network derives the configuration from operator-stated goals. Its first step, translating a natural-language intent into enforceable service-level objectives (SLOs) grounded in a live snapshot of the network state, increasingly relies on large language models (LLMs). These large models add cost and energy to the management loop and typically require multi-GPU, cloud-hosted infrastructure. Can lightweight open LLMs take over this step dependably? We answer this question with a five-dimensional evaluation of ten LLMs with <10B parameters from five families, under four prompting setups, on 716 placement intents. We evaluate translation performance, intent validation, hallucination, energy impact, and robustness to diverse phrasing. The best configurations translate intents with a strict F1 of 0.943 and reject up to 191 of 195 invalid intents. With few-shot reasoning, the strongest models hallucinate unsupported QoS requirements in only about one response in a hundred, whereas the zero-shot setup struggles. A translated intent costs 0.044-0.148 Wh of GPU energy without reasoning, and added energy cost for reasoning pays off for the weaker models. Furthermore, we show that realistic rephrasing lowers the rejection of invalid intents in 15 of 16 configurations for the best-performing models, leaving robust intent validation as a key open challenge for reliable IBN. Our code and data are available: https://github.com/ggck43/intent2slo.

cs.NI · cs.CL

新论文 · v1arXiv:2611.07928

ZeroRAG: Adaptive Semantic Peer Routing for Decentralized Retrieval-Augmented Generation

Petru Neague, Quinten Stokkink, Martijn de Vos, Rafael Pires, Mathis Randl, Anne-Marie Kermarrec, Johan Pouwelse

原文摘要

Retrieval-augmented generation (RAG) is a standard approach to enhance the reliability of large language model (LLM) outputs by retrieving documents relevant to a query and supplying them in the LLM prompt. Today's RAG systems collect, index, and serve these documents from centralized stores. In practice, however, documents are owned by organizations, devices, and individuals who cannot or will not surrender custody of them to a central party, e.g., because of privacy, copyright or regulatory compliance. Existing modern decentralized retrieval techniques either reintroduce a coordinating authority, or rely on a global data structure that requires continuous network-wide coordination (e.g., DHTs). We present ZeroRAG, a decentralized routing layer for document retrieval in RAG that performs semantic query routing across an overlay of peers with- out global data structures or centralized orchestration. Documents remain with the peers that own them, and each peer keeps only lightweight local state: a bounded set of neighbor advertisements and a cache of recently routed queries. Using these, ZeroRAG steers each query through a best-first walk that progressively rewires the overlay toward semantically relevant neighbors, so recall improves over time as the network self-organizes. We evaluate ZeroRAG using three real-world workloads and two decentralized baselines. We find that ZeroRAG outperforms baselines in terms of recall and required network messages, approaches the recall and LLM answer accuracy of a centralized document store, self-heals after catastrophic peer failures, and scales to 100 000 peers.

cs.DC · cs.AI · cs.IR · cs.NI

新论文 · v1arXiv:2611.07827

Instantiating the Knowledge Plane

Francisco Pereira, Justine Sherry, Pratiksha Thaker

原文摘要

In 2003, Clark et al. proposed the "Knowledge Plane", a layer of intelligent agents that share rich information about network state in order to collaboratively manage the Internet across administrative boundaries. The technology of the day lacked the two capabilities it demanded most: reasoning and unstructured communication. Thus, automated network management has remained largely confined to single domains, while interdomain problems are still resolved by human operators on the phone. We argue that agentic large language models can finally unlock the potential of the Knowledge Plane, but that doing so will require solving new research problems that arise particularly in this decentralized, multi-agent, coopetitive setting that have received limited attention in the literature thus far.

cs.NI · cs.AI

新论文 · v1arXiv:2611.07550

SEROA: Stable Encrypted-Traffic Representation and Online Adaptation for Service-Aware Networks

Zongxi Liu, Shuai Mao, Jie Chuai, Hongyang Du

原文摘要

Mobile networks are evolving toward service-aware operation to support heterogeneous applications with diverse quality-ofservice (QoS) and quality-of-experience (QoE) requirements. In practice, however, the widespread use of encryption makes service types difficult to observe directly, which limits the deployment of service-aware optimization in operational networks. Recent raw-byte representation learning methods have improved offline encrypted-traffic classification, but their robustness in realistic deployment remains limited due to temporal drift and the lack of explicit labels for online adaptation. This paper proposes Stable Encrypted-Traffic Representation and Online Adaptation (SEROA), a deployment-oriented framework for encryptedtraffic classification in service-aware mobile networks. In the offline stage, SEROA exploits protocol structure to learn temporally stable representations by applying masked modeling to stable fields, protocolaware transformations and contrastive learning to unstable fields, and time-adversarial learning to suppress time-specific cues. In the online stage, SEROA uses feedback records containing QoE measurements, wireless states, and allocation decisions to update the deployed classifier through contextual-bandit learning. Experiments show that SEROA consistently outperforms representative pretrained baselines under both random and time-based evaluation protocols, and remains effective under QoE-driven online adaptation. These results demonstrate the value of stable representation learning and online adaptation for practical service-aware mobile networks.

cs.NI

新论文 · v1arXiv:2611.07487

When Does Blockwise Additivity Break? Diagnosing and Repairing the Additive Assumption in Distillation-Based Architecture Search

Yuefeng Xu, Jun Yu

原文摘要

Blockwise distillation methods for architecture search, such as DNA, DONNA, LANA and Puzzle, score each candidate replacement block in isolation and assemble a child network by minimizing the sum of per-block scores. We test this additive assumption on DeiT-S and DeiT-B with seven replacement blocks per layer, seven block-latency budgets from 70% to 40%, three fine-tuning seeds and paired bootstrap confidence intervals. (i) At mild compression the additive integer linear program (ILP) is close to a pairwise integer quadratic program (IQP) that models block interactions, but the post-fine-tuning gap widens sharply between the 60% and 55% budgets: at 55%-40% the ILP trails the IQP by 3.3-16.6 top-1 points on ImageNet-100. (ii) We split the KL additivity error into a metric component, caused by the non-linearity of the KL in summed logit changes and computable from cached single-block logits, and a functional component, the score-level effect of non-additive logit changes. The metric component accounts for most of the error on DeiT-S (59-65%), the functional component for most of it on DeiT-B (73-76%). (iii) A hybrid search ranks moves with the logit-sum predictor and measures only the best ones. It needs no dedicated pair measurements, costs 13-27% of the IQP's pair stage, and on ImageNet-100 significantly outperforms the IQP in 7 of 14 settings, with no significant difference in 6 and a significant loss in 1. Further reducing the pre-fine-tuning KL, or scoring after a short fine-tune, gives no consistent gain. On full ImageNet-1k with 1000-class fine-tuning, the sharp widening between 60% and 55% (5.8-17.5 points at 55%-40%), the model-dependent decomposition and the hybrid's advantage (significantly better than the IQP in 13 of 14 settings, no the other) are reproduced.

cs.NI · cs.NE

新论文 · v1arXiv:2611.07452

Understanding Self-Supervised Representation Learning for Encrypted Traffic

Zongxi Liu, Shuai Mao, Jie Chuai, Hongyang Du

原文摘要

SSL has been adopted for encrypted traffic classification to learn representations from unlabeled traces and reduce reliance on manual feature engineering. However, recent studies show that data leakage can inflate reported performance and that pretrained models can underperform shallow classifiers once leakage is controlled. These findings raise questions about what information SSL can learn from encrypted traffic and how different pretraining mechanisms exploit this information. In this paper, we systematically investigate these questions by examining the sources of learnable information, the effectiveness of contextual pretraining, alternative self-supervision mechanisms, and the limits of representation learning. We first find that visible protocol fields retain contextual dependencies that are strongest between adjacent bytes, while payload interventions establish the classification value of retained plaintext structure. Guided by these insights, we investigate byte-level contextual learning through ByteBERT and TrafficDCNet, showing that aligning prediction granularity with local dependencies produces more discriminative representations. We further examine cross-view contrastive learning through AlignTraffic and find that complementary supervision improves representation quality beyond contextual prediction alone. Across four public classification tasks, AlignTraffic outperforms shallow learning by 5.97% to 30.70%, with additional evaluations on industrial traffic demonstrating its effectiveness in broader settings. Finally, evaluations on Tor traffic and individual packets reveal limitations when application information is concealed or unevenly distributed. These findings provide a systematic understanding of how observable information and self-supervision mechanisms shape the effectiveness and scope of representation learning for encrypted traffic.

cs.NI

新论文 · v1arXiv:2611.07390

Deep reinforcement learning-based optimization of multi-RAT handovers

Pavan Kumar Mangipudi, Satya Krishna Pothapragada, Alina Zare, Janise McNair

原文摘要

A major evolutionary goal and challenge for next-generation wireless systems is seamless multiple radio access technology (multi-RAT) mobility. In a multi-RAT environment, handover decisions must account for both user quality of service and network priorities, including signal quality, resource availability, energy consumption, latency, and security. Although WiFi/cellular mobility is well explored in research and standardization, many conventional handover mechanisms continue to rely primarily on received signal strength. Such decisions may overlook link delay, congestion, and the distinct operating characteristics of heterogeneous RATs. In this work, we present the Reinforced Flexible Multi-RAT Selection framework (ReFlexMRS), a configurable Deep Reinforcement Learning (DRL)-based approach for multi-criteria handover target selection. ReFlexMRS is evaluated using an SDN-enabled multi-RAT testbed. The decision problem is formulated as a Markov Decision Process with a finite action space and is addressed using a Deep Q-Network (DQN) with convolutional layers that extract relationships among network measurement features. The agent is trained offline from collected network observations using reward guided Q-value updates, then used to select handover targets during inference. A 5G-WiFi case study demonstrates that ReFlexMRS can trade a small amount of throughput for substantial improvements in link delay, handover stability, and WiFi offloading. Compared with RSSI-based selection, the proposed formulations retain approximately 90-95% of the baseline throughput while reducing total handovers by up to 18% and WiFi-to-5G handovers by up to 13%, while maintaining the required RSSI levels.

cs.NI

版本更新 · v2arXiv:2607.22944

Invariant Discovery for Networked Systems

Hongyu Hè, Alexander Krentsel, Sylvia Ratnasamy, Maria Apostolaki

原文摘要

Invariants, the relations expected to hold among measured signals of a network, underpin applications from verification to traffic generation, telemetry imputation, and input validation. Yet, writing them by hand demands rare expertise in both formal logic and networking. Testing all possible combinations of telemetry variables statistically is intractable at network scale, while existing automatic tools still require a human-authored grammar of admissible invariants and learn only exact, "hard" rules that break under real-world noise. Our key idea is to use an LLM to inspect variable metadata, classify counter data types, and predict candidate syntactic relationships that form a compact grammar, followed by a deterministic statistical search within that grammar. If the initial grammar fails to recover trusted known invariants on held-out telemetry, Autogram instructs the LLM to iteratively widen the grammar until recovery succeeds. By confining the LLM to grammar induction and re-induction while delegating candidate verification to logical and statistical checks, Autogram turns non-deterministic AI proposals into auditable invariants with formal guarantees within the induced grammar. We design and prototype Autogram and evaluate it on both public and production WAN telemetry, recovering expert-derived invariants with full recall and low false positives. We close with a discussion of open problems on the path toward fully open-ended invariant discovery.

cs.NI · cs.AI · cs.LG · cs.SC

版本更新 · v2arXiv:2605.04373

Worst-Case Discovery and Runtime Protection for RL-Based Network Controllers

Hongyu Hè, Minhao Jin, Maria Apostolaki

原文摘要

RL-based controllers achieve strong average-case performance in networking tasks such as congestion control and adaptive bitrate streaming. Yet their performance can degrade severely under network conditions where strong performance is still achievable. Identifying such conditions and quantifying the resulting performance gap is intractable by enumeration, while the sequential and closed-loop nature of RL controllers makes formal verification methods impractical. We present ReGuard, a framework that discovers worst-case scenarios for a given RL controller and protects it against them at inference time without retraining. ReGuard formulates scenario discovery as a bilevel regret-maximization problem, certifying a lower bound on the controller's worst-case performance gap. ReGuard then analyzes the discovered counterfactual trajectories and compiles them into lightweight logic rules that intervene only when a risky state is detected, leaving the controller's behavior unchanged otherwise. We evaluate ReGuard across three RL-based network controllers: Pensieve, Sage, and Park. ReGuard discovers scenarios in which each controller suffers a 43-64% performance loss relative to what is achievable under the same conditions. ReGuard exposes performance gaps 57% to 6 times larger than those found by the strongest baselines and shrinks those gaps by 79-85% in its first iteration via lightweight rule-based protection, iteratively driving the remaining gap toward nominal levels. ReGuard's protection extends beyond the scenarios it discovers, improving performance across a wide range of network conditions.

cs.NI · cs.AI · eess.SY

新论文 · v1arXiv:2611.07250

Characterizing AI Service Traffic for 6G: An Open Testbed and Initial Results from 3GPP SA4

Imed Bouazizi

原文摘要

The design of sixth generation (6G) mobile systems requires measurements of artificial intelligence (AI) service traffic under controlled network conditions. The 3rd Generation Partnership Project (3GPP) Service and System Aspects Working Group 4 (SA4) documents such measurements in draft Technical Report 26.870. In this paper, we describe the open testbed used for the study and analyze the results recorded in version 0.6.2 and its companion workbook. The testbed combines network emulation, packet capture, application measurements, and scenario execution against hosted and self-hosted models. The reported uplink to downlink volume ratios span more than six orders of magnitude. A personal assistant agent has a ratio of 0.007 at its local control interface and 0.50 on its wide-area egress. Deterministic agent signaling follows the configured round-trip delay. In a tokenized video experiment with 125 questions per profile, the number of correct answers changes by at most three through 10% independent uplink loss. Larger losses reduce accuracy, but the available repetitions do not establish a tolerance threshold. We also analyze emulator behavior that affects the interpretation of the study. Per-packet jitter introduces reordering, correlated random-loss settings suppress rare loss events, and a Gilbert-Elliott parameter mapping changes the stationary loss rate. The results support characterization by scenario, measurement layer, and versioned network configuration.

cs.NI

新论文 · v1arXiv:2611.07225

PRB Starvation at Slot Time via Rank Indicator Inflation: Attacking Proportional Fair Memory in the Scheduler

Ryan Barker, Julia Boone, Tolunay Seyfi, Alirez Ebrahimi, Ethan Farquhar, Brian Davis, Joseph Boccuzzi, Fatemeh Afghah

原文摘要

Proportional fair (PF) scheduling in fifth-generation (5G) New Radio (NR) ranks user equipment (UE) by instantaneous rate over an exponentially weighted moving average (EWMA) of delivered service. We identify a served-but-undelivered denial-of-service (DoS) attack in which rank indicator (RI) inflation induces persistent physical resource block (PRB) starvation. Unsupported spatial layers cause first-attempt delivery failure, keeping the attacker's service EWMA low; the evaluated PF allocator repeatedly grants it the full PRB pool, denying honest UEs transmission opportunities. We formalize this recurrence and evaluate it in 3,300 paired, 200 slot multi-user shards spanning rank 1 and rank 2 Sionna Ray Tracing geometries, three link adaptation controllers, PF and round robin (RR), four target identities, and five seeds. On the rank 2 geometry, selective RI inflation yields 94--95\% target capture, reduces PRB Jain fairness from 0.997 to 0.28, and produces mean honest zero-grant runs of 41--56\,ms; RR preserves equal allocation. Exact offline replay and a Monte Carlo recurrence isolate the delivered-service denominator as the causal path. Cross-field and controller ablations show a weaker, controller-dependent channel-quality trigger, while activity-gated EWMA defenses remain structurally blind. The results motivate RI-consistency checks before feedback acquires scheduling authority and slot-time PRB evidence inside the O-RAN Distributed Unit.

cs.NI

新论文 · v1arXiv:2611.07201

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

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

原文摘要

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.

cs.NI

新论文 · v1arXiv:2611.07191

A First Look at Starlink Maritime on Cruise Ships

Jinwei Zhao, Kousar Malekinasab, Jianping Pan

原文摘要

Low-Earth-Orbit (LEO) satellite networks are reshaping maritime connectivity, offering much lower latency and higher capacity than traditional geostationary (GEO) links. As a single user terminal (UT) cannot serve hundreds to thousands of passengers, a cruise ship often deploys several UTs behind an onboard multi-WAN router for aggregated capacity and load balancing. Consequently, passenger connectivity depends not only on the satellite links but also on how the router distributes sessions among UTs. In this paper, we measure Starlink maritime connectivity on two cruise ships of different scales, Utopia of the Seas near the Bahamas in December 2025 and Silver Muse in the Adriatic Sea in July 2026, combining UT telemetry, inside-out measurements from a passenger device, and outside-in measurements from cloud servers near Starlink Points of Presence (PoPs). We find that the onboard network, rather than the satellite links, largely determines the passenger experience, as cruise lines rate limit each passenger device. Moreover, the public IP address of a passenger device changes not only at PoP handovers but also within the same PoP. Furthermore, while the country code reported by the UT reflects the maritime jurisdiction of the ship, the serving PoP follows neither this jurisdiction nor geographic proximity alone, suggesting factors beyond the distance-based PoP boundaries observed in Starlink aviation. To the best of our knowledge, this is the first systematic study of Starlink on commercial cruise ships, complementing prior land mobile and in-flight measurements.

cs.NI

版本更新 · v4arXiv:2503.13726

Towards Energy- and QoS-aware Load Balancing for 5G Advanced: Leveraging O-RAN to Achieve Sustainability and Energy Efficiency

Gustavo Z. Bruno, Gabriel M. Almeida, Aloizio Da Silva, Luiz A. DaSilva, Joao F. Santos, Alexandre Huff, Kleber V. Cardoso, Cristiano B. Both

原文摘要

The increasing energy consumption of next-generation mobile networks necessitates the adoption of autonomous and energy-aware management strategies. This article proposes a novel adaptive solution leveraging the Open Radio Access Network (O-RAN) architecture to optimize energy efficiency while maintaining Quality of Service (QoS). The proposed approach introduces a hierarchical O-RAN-aligned control framework in which a Non-Real-Time RAN Intelligent Controller (Non-RT RIC) periodically computes energy-aware policies from long-term historical data, while a Near-Real-Time RIC enforces those policies through per-User Equipment (UE) handover actions on a sub-second timescale. We formulate a joint energy- and QoS-aware load balancing problem as a Mixed-Integer Non-Linear Programming (MINLP) model. This model optimizes UE association across E2 Nodes to minimize transmission power and autonomously deactivate underutilized cells, while satisfying strict QoS constraints. To validate the proposed solution, we deploy it in an experimental environment that simulates massive sports events. Experimental results demonstrate 72% energy savings over a 24-hour trace and sub-second handover delays (0.123-0.204 s/UE), confirming the solution's feasibility for autonomous and sustainable 5G Advanced networks. This work provides guidelines for deploying energy-efficient strategies in O-RAN environments and underscores the potential of adaptive solutions for sustainable mobile communications.

cs.NI

版本更新 · v2arXiv:2605.14171

CSI-JEPA: Towards Foundation Representations for Ubiquitous Wi-Fi Sensing with Minimal Supervision

Xuanhao Luo, Zhizhen Li, Yuchen Liu

原文摘要

Channel state information (CSI) provides a widely available sensing modality for human and environment perception, but existing CSI sensing models usually rely on task-specific supervised training and require substantial labeled data for each task, device, user, or environment. This limits their scalability in practical deployments where unlabeled CSI is abundant but labeled data is costly to collect. In this paper, we present CSI-JEPA, a self-supervised predictive representation learning framework for label-efficient and transferable Wi-Fi sensing. CSI-JEPA learns reusable temporal-spectral representations from unlabeled CSI samples by predicting latent features of masked channel regions from visible context. To better match the physical structure of CSI, it jointly tokenizes CSI along the time and subcarrier dimensions and introduces a channel variation-aware masking strategy that prioritizes regions with stronger local channel dynamics. After pretraining, the encoder is frozen and adapted to downstream sensing tasks using lightweight task-specific adapters. We evaluate CSI-JEPA on seven real-world Wi-Fi sensing tasks spanning diverse objectives and deployment settings. The results show that CSI-JEPA improves downstream sensing performance over competitive baselines, achieving up to 10.64 percentage points (pp) mean accuracy gain over the supervised Transformer and matched-budget label savings of up to 95.0%. Across unseen tasks, environments, hardware platforms, and CSI organizations, frozen CSI-JEPA improves downstream adaptation by up to 22.92 pp over non-pretrained baselines, demonstrating strong representation transferability.

cs.LG · cs.NI

新论文 · v1arXiv:2611.07068

ReDrone5G: Field Trial Evaluation of Remote UAV Media and Control over Public and Private 5G Networks

Kashif Mehmood, Tu Dac Ho, Katina Kralevska, Hacı Mehmet Güzey

原文摘要

Drone-assisted live media production stresses cellular networks by combining high-rate uplink video, continuous telemetry, and latency-sensitive command/control on a mobile aerial platform. This paper presents ReDrone5G, an end-to-end field-trial platform evaluated across two practical fifth-generation (5G) deployment paths: a commercial public non-standalone (NSA) network in Sivas, Turkiye, and a private OpenAirInterface (OAI)-based standalone (SA) testbed in Trondheim, Norway. The same drone, media, telemetry/control, and monitoring stack is used in both trials, which are treated as complementary, non- equivalent field deployments rather than a controlled A/B comparison. Results show that the commercial deployment sustains remote drone media operation, while the evaluated private deployment exhibits lower control RTT and video latency and higher video throughput. The private setup also exposes signal-to-noise ratio (SNR), reference signal received power (RSRP), modulation and coding scheme (MCS), physical resource block (PRB), and block error rate (BLER), providing cross-layer radio/network context. ReDrone5G shows that public 5G is useful for feasibility validation, while private 5G enables repeatable experimentation, tuning, and future service assurance for drone-media workflows.

cs.NI

新论文 · v1arXiv:2611.06832

蜂窝连接无人机走廊的共存感知天线倾角与资源分配

Coexistence-Aware Antenna Tilt and Resource Allocation for Cellular-Connected UAV Corridors

Tarun Singh Rawat, Pradyumna Kumar Bishoyi

AI 速读 基于摘要

针对蜂窝连接无人机走廊与地面用户共存场景,联合优化天线倾角与PRB分配,在走廊速率和地面用户时延约束下最大化地面用户和速率。

cs.NI

查看原文摘要

Cellular-connected UAV corridors reuse terrestrial infrastructure to provide predictable aerial connectivity, but coexistence with ground users (GUEs) creates competing demands on antenna tilt and physical resource blocks (PRBs). Prior work optimizes tilt for signal quality alone and does not address joint tilt-PRB planning under corridor rate and GUE delay constraints, nor the allocation policy needed when combined resource claims exceed the PRB budget. We address both gaps by formulating a joint antenna-tilt and PRB-allocation problem that maximizes GUE sum-log-rate subject to a minimum UAV corridor rate and an $ M/G/1$-based GUE mean-delay bound. We decompose the problem into two subproblems: antenna tilts are adapted via projected gradient ascent, while PRB allocation follows a techno-economic approach. Under resource scarcity, we cast the PRB scarcity as a bankruptcy problem and apply the weighted Talmud rule to balance GUE and UAV claims. Simulation results demonstrate that relaxing the GUE delay bound consistently reduces UAV rate dissatisfaction under the proposed allocation.

版本更新 · v2arXiv:2611.03771

ISAC-Assisted Proactive Handover in mmWave Networks: Value of Foresight and Localization Requirements

Seyed Bagher Hashemi Natanzi, Bo Tang

原文摘要

Millimeter-wave (mmWave) links can lose tens of decibels within milliseconds when a person or vehicle crosses the line of sight, and reactive handover recovers only after the outage has begun. Integrated sensing and communication (ISAC) promises to let the network see blockers coming and move users to another cell in advance. This paper asks how much such foresight is worth relative to the best reactive handover, and what sensing accuracy is needed to harvest it. We formulate cost-aware cell selection with knowledge of the future signal-to- noise ratio as a sequential decision problem, define the value of foresight through a policy-independent bound, and develop an ISAC-assisted handover framework in which a radio unit senses blockers with its own communication waveform and a simulated Open RAN xApp plans handovers. Ray-traced experiments in a 28 GHz street canyon, reported on held-out scenarios, show that, with a short look-ahead, perfect foresight closes most of the gap between service-aware (Layer-3) reactive handover and an instantaneous oracle. Harvesting this value, however, requires the user and nearby blockers to be localized to about a decimeter, well beyond what our radar tracker and meter- level user positioning provide; with realistic errors, even an uncertainty-aware planner largely defers to reactive handover.

eess.SY · cs.NI · eess.SP

版本更新 · v3arXiv:2606.04838

From Network Experience to Subscriber Retention: An Explainable AI Framework for Mobile Operators

Faris B. Mismar, Abdol Saleh, Ivan Maxmillian Putra Pasaribu, Suhelmy Syaifuddin

原文摘要

This paper presents an explainable AI-driven framework for predicting net churn among prepaid mobile subscribers. The framework formulates churn prediction as a machine learning task mapped onto an industry reference architecture. Moreover, it introduces a quantile-based feature densification transform that reconciles learning features of unequal length and granularity into a homogeneous design matrix, evaluates a label-aware truncation diagnostic that sets aside low-confidence subscribers, and generates automated English-language insights describing the impact of each feature on churn propensity. We implement and validate the framework on real production data from a globally leading telco with tens of millions of prepaid subscribers spanning approximately $800$ learning features and over $250$ TB of commercial, experience, and network data. As a diagnostic, setting aside roughly $3$% of the subscribers, selected using the true labels, lifts $F_1$ on the retained subscribers to the target. This is not an achievable operating performance on all subscribers. In our data and feature set, aggregated radio access network counters carried less predictive signal than commercial and experience indicators, despite their greater representation. The quality of experience index, which is derived from network measurements, ranked second in importance. Our results also suggest that churn risk rises as subscriber value declines, with high-value segments disproportionately exposed to gradual value leakage rather than outright attrition.

cs.NI

新论文 · v1arXiv:2611.06307

When Does Risk-Controlled Throughput Forecasting Actually Control Risk? A Reproduction Study on LEO Satellite Link

Mayan Sharma, Kriti Saini, Devansh Behl, Suganya R, S. Vijayaragavan

原文摘要

Risk-budgeted throughput forecasting converts a point forecast of LEO satellite capacity into a conservative bound whose overestimation rate is held at a user-specified budget, so that admission control can act on it safely. We reproduce the state of the art, BG-CFQS, on the three Starlink traces its authors used and on an independent fourth dataset, and report four findings. First, its risk guarantee is reproducible but conditional on the calibration split: under a random split we recover every published quantity, including risk-pass on 3 of 3 datasets, but under a contiguous temporal split, which is what a deployed terminal faces, it holds on 1 of 3. Second, independent of any split, the method cannot serve a risk budget below 0.15, the low end of its candidate quantile set. On all four datasets the achieved rate is identical at budgets of 0.05, 0.10 and 0.15, overshooting by 1.4x to 3.7x at the tightest. Third, static conformal calibration cannot improve a ranking, only shift an operating point; we confirm this exactly on latency spike detection, where online calibration improves AUPRC by up to 51%. Fourth, group-conditional online recalibration splits into two mechanisms. The online part replicates: it tracks the budget within 1% from 0.05 to 0.35, where static calibration is 16 to 21% off. The group-conditional part does not: on three of four datasets, conditioning on any available covariate, including measured serving-satellite geometry, is worse than not conditioning, and our attempt to predict which datasets benefit failed. What is new here is empirical, not algorithmic: we claim the findings, not the methods.

cs.LG · cs.AI · cs.NI