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cs.NI: explore 170 source-linked works published from 2023 to 2026, with original documents and citations.

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Includes records with this source-supplied label or an explicit phrase match in their metadata. Matches indicate a mention, not proof that a paper uses a method or tests a material. Source versions are consolidated by DOI.

Sources: arxiv. Collection updated 2026-09-16. Counts describe this index, not the complete source archives.

arXiv2026

A Wavelength Borrowing Architecture for Optical Data Center Networks - Extended Version

The growth of east-west traffic, along with the cost and power consumption of electronic switching, is motivating the integration of a low-power, high-rate, all-optical layer within the data center network. This paper presents a spine-leaf all-optical architecture in which the default wavelength configuration, one wavelength per source--destination leaf pair, can be reconfigured to accommodate unbalanced traffic demand: wavelengths that are unused or lightly loaded at one leaf are borrowed by another leaf with higher demand. This topology-engineering capability is combined with a traffic-engineering scheme, based on two-hop detouring, enabling the control of wavelength load while limiting the amount of detoured traffic. A key feature of the architecture is that its degree of wavelength reconfigurability is set by a single tunable parameter, the borrowing degree $B$, ranging from none to full; performance evaluation shows that near-optimal performance is achieved well below the maximum $B$, saving the complexity and cost of fully reconfigurable solutions. Furthermore, the optical fabric relies on mature, data-center-grade components, namely AWGs, AWGRs, colorless OXCs, and combiners, whose reconfiguration speed makes the architecture deployable at network tiers where traffic demand persists over seconds or longer, e.g., among groups of racks (pods). The architecture is also TDMA-transparent, a property that future work could exploit to refine the borrowing unit below a whole wavelength without changing the optical fabric.

cs.NI↗
arXiv2026

QUASAR: Quantum Satellite Architecture and Routing Simulator

The deployment of Low Earth Orbit (LEO) satellite constellations is an important step toward global-scale quantum networking. However, evaluating satellite quantum network protocols under spatiotemporal orbital dynamics and quantum physical constraints remains computationally expensive and challenging. In this paper, we propose QUASAR, a lightweight simulator for evaluating entanglement distribution in satellite-based quantum networks. QUASAR provides a decoupled architecture that integrates dynamic orbital topologies, time-varying optical transmittance, and quantum memory decoherence into network- layer attributes. To demonstrate its capabilities, we abstract and implement two representative hardware architectures: Simultaneous Downlink and On-Orbit Stitching. We further introduce an Entanglement Distribution Rate (EDR)-Aware Spatiotemporal Routing (EASR) heuristic as a reference workload. Our case study examines how QUASAR supports different satellite architectures, routing workloads, realistic orbital traces, concurrent requests, and scalable event-driven execution. With over 85% lower network-layer update latency than continuous polling, QUASAR provides a practical and extensible framework for future satellite quantum network protocol evaluation.

cs.NI↗
arXiv2026

Performance Evaluation of HAPS-enabled Coverage Enhancement in Hard-to-Reach Areas

High altitude platform stations (HAPSs) are becoming a key component of future non-terrestrial networks (NTNs). HAPSs can serve a larger area than uncrewed aerial vehicles (UAVs) and offer lower propagation latency, maintenance expense, and energy costs than satellites. A major application of HAPSs is to serve the areas where terrestrial network (TN) deployment is infeasible, especially in hard-to-reach areas and post-disaster areas. For instance, in the Amazon rainforest, the Mediterranean region, and deserts, TN deployment is severely constrained by geographical and environmental conditions. Only areas close to transportation networks or coastlines can be covered, while large areas remain uncovered. Such coverage holes in hard-to-reach areas are typically overlooked in existing literature. Motivated by these realistic cases, in this paper, we use tools from stochastic geometry to mathematically model hard-to-reach areas where cellular terrestrial infrastructure only exists at their perimeter. We propose to deploy a HAPS constellation over this hard-to-reach area to enhance connectivity. For that setup, we derive the downlink (DL) and uplink (UL) coverage performance of the considered user equipment (UE) as a function of the location of the UE inside the coverage hole. Our results show how the number of HAPSs, beamwidth, and HAPS altitude affect the DL and UL coverage probabilities. Finally, we provide multiple useful guidelines for future HAPS deployment.

cs.NI↗
arXiv2026

On the Delay-Constrained Maximum Concurrent Flow Problem

Real-time services, such as VoIP and large-scale neural network training, require strict transmission delay guarantees. While routing under hop constraints is tractable, real-world delays increase sharply with equipment load, typically modeled using the M/M/1 queuing function where delay is inversely proportional to available bandwidth. We investigate the resulting Delay-Constrained Maximum Concurrent Flow (DCMCF) problem, which seeks to maximize the minimum throughput across all commodities. The problem's complexity stems from the conditional and non-linear nature of the delay constraints, which are active only along the specific paths used by the flow. We prove that DCMCF is strongly NP-hard, even for single-source/single-destination instances. To address the inherent non-convexity of the problem, we introduce a new convex relaxation expressed through second-order cone constraints, obtained from the convex envelope of a function representing the conditional delay associated with a single arc of a given path. The relaxation is shown to outperform existing formulations based on disjunctive programming. Leveraging this result, we develop a polynomial-time approximation algorithm with a provable performance guarantee and present numerical experiments demonstrating the effectiveness of the proposed approach.

cs.NI↗
arXiv2026

Confounding-Valid Conformal Inference for Counterfactual KPIs in Wireless Networks

Conformal counterfactual inference enables network operators to use logged telemetry to reliably answer 'what-if' questions about network operation. These answers typically take the form of prediction sets that contain, with a user-defined probability, the key performance indicators (KPIs) that would have been observed under alternative control actions. A key challenge is that logged telemetry may omit variables used by the controller, resulting in hidden confounding and invalidating the statistical guarantees of counterfactual analysis. In principle, this issue can be addressed using randomized telemetry, collected by assigning control actions independently of the network state. However, because such randomization may disrupt normal operation, randomized telemetry is typically scarce, causing counterfactual analysis based solely on it to produce uninformative prediction sets. To address these challenges, we propose Confounding-Valid Counterfactual Conformal Inference (CV-CCI), which combines abundant, potentially confounded observational telemetry with limited randomized data through the General Synthetic-Powered Inference (GESPI) principle. CV-CCI leverages observational data to improve efficiency while using randomized data to retain finite-sample coverage guarantees under arbitrary hidden confounding. Experiments on two representative radio access network (RAN) control tasks show that CV-CCI remains valid under hidden confounding while producing more efficient prediction sets than state-of-the-art confounding-valid baselines.

cs.LG↗
arXiv2026

Towards Federated, Green, and Resilient 6G Non-Terrestrial Networks

This study focuses on future Non-Terrestrial Networks (NTN) integrated with Terrestrial Networks (TN) for future 5G/6G systems. NTN envisions a 3D architecture, where Low Earth Orbit (LEO) satellite networks will play a key role in bridging the digital divide, complementing the gradual terrestrial 5G/6G rollout concentrated in high-density and high-traffic areas, by ensuring service continuity across broad geographic regions and providing coverage in case of emergencies or in remote areas. In this context, we address networking issues for the integration and federation of Terrestrial and Non-Terrestrial Network (T-NTN) in line with the IMT-2030 vision, focusing on interoperability, spectrum coexistence, unified control and management, and service continuity. Federation is a complementary approach to integration that enables distinct satellite systems to cooperate through agreements, potentially unified satellite terminals, and common resource management. We show that system federation significantly enhances both latency performance and connectivity robustness compared with non-federated LEO architectures. This paper also investigates the challenges and possible solutions for adopting the Open-RAN architecture for T-NTN, including routing options for mega-LEO systems, edge intelligence, and energy efficiency as critical elements for sustainability. Finally, we address the security, privacy, and resilience aspects of federated T-NTN architectures with emphasis on zero-trust, secure routing, trustworthy edge intelligence, Post-Quantum Cryptography (PQC), and Quantum Key Distribution (QKD).

cs.NI↗
arXiv2026

XAI-SDN: An Explainable Entropy-Guided Machine Learning Framework for Real-Time DDoS Detection in Software Defined Networks

One of the biggest risks faced by Software Defined Networks (SDN) is the Distributed Denial of Service (DDoS) attack in which a compromised controller can make an entire network unusable. To address these challenges, we suggest an entropy-guided machine learning framework, called XAI-SDN, for real-time DDoS detection in SDN environments which is lightweight and explainable. The framework extends the flow features extracted by CICFlowMeter with eight Shannon entropy metrics obtained by an $\mathcal{O}(1)$ rolling algorithm and uses a Random Forest classifier with SHAP TreeExplainer for providing transparency at the prediction level. On a fixed temporal split, XAI-SDN achieves an accuracy of 99.9987\%, a macro F1-score of 99.9621\%, and an AUC-ROC of 1.0000 on the full 3.59 million flows of the CIC-DDoS2019 SYN benchmark. The pipeline sustains 0.0165~ms per flow (60{,}606 flows/s) without the use of SHAP and 0.5122~ms per flow (1{,}953 flows/s) with full support of SHAP under the 99.14\% prevalence of DDoS traffic, which is a step towards achieving a balance between the detection performance and operational transparency in next-generation SDN security.

cs.CR↗
arXiv2026

WIP: Energy-Efficient LLM-Based Serving Cluster Formulation in Cell-Free Massive MIMO

One way to increase the Energy Efficiency (EE) of 6G wireless networks is to utilize existing network infrastructure more efficiently. This can be achieved by introducing User-Centric Cell-Free Massive Multiple-Input-Multiple-Output (UCCF MMMIMO), which allows for simultaneously serving a single user by multiple Base Stations (BSs). From this perspective, the key challenge is to decide which BSs should serve a given user, known as the Serving Cluster Formulation (SCF). In this paper, we propose to deal with this problem by using an Artificial Intelligence (AI) agent based on a Large Language Model (LLM), targeting improvement of EE. We evaluated the proposed AI agent using a complex, 3D Ray Tracer-based, cellular network simulator, comparing a few GPT models and state-of-the-art algorithms. The results show up to 32% gain in EE of the proposed AI agent compared to the baseline.

cs.NI↗
arXiv2026

SDN-Orchestrated Dual-Path 5G/SATCOM Maritime Communications for Carrier Strike Groups

Carrier Strike Group (CSG) communications must sustain mission traffic across heterogeneous links whose quality varies with distance, weather, fading, and intermittent outages. We present an integrated Software-Defined Networking (SDN)/Software-Defined Radio (SDR) framework that unifies 5G and satellite communications (SATCOM) under a single control plane for dual-path failover in CSG communications. The framework is evaluated via stochastic simulation across four operational regions: Norfolk, the Norwegian Sea, the Philippine Sea, and the North Pacific storm track. The framework couples a centralized SDN controller with a Rician-faded 5G channel under Adaptive Modulation and Coding (AMC), a Ku-band SATCOM channel with ITU-R P.618/P.838 rain attenuation, and Gilbert-Elliott burst-availability processes on both links. The controller routes each packet by traffic class and current link state. Experiments use 60 independent trials of 50,000 packets per parameter setting with common random numbers, paired confidence intervals, and convergence diagnostics across fault, fading, and region sweeps. For the Norfolk weather baseline, SDN routing reaches 93.09% reliability versus 36.22% (5G-only) and 89.87% (SATCOM-only), with paired gains of +56.87 and +3.22 percentage points. The advantage grows under joint low-Rician-K/high-burst-fault stress, and AMC improves 5G robustness as Signal-to-Interference-plus-Noise Ratio degrades. SDN remains the highest-reliability mode across all four regions, indicating that cross-layer SDN-controlled failover improves maritime resilience without relying on deterministic link abstractions.

cs.NI↗
arXiv2026

Idea and Theory of Particle Access

Aiming at some problems existing in the current quality of service (QoS) mechanism of large-scale networks (i.e. poor scalability, coarse granularity for provided service levels, poor fairness between different service levels, and improving delay performance at the expense of sacrificing some resource utilization), the paper puts forward the idea and thoery of particle access. In the proposed particle access mechanism, the network first granulates the information flow (that is, the information flow is subdivided into information particles, each of which is given its corresponding attributes), and allocates access resources to the information particle group which is composed of all the information particles to be transmitted, so as to ensure that the occupied bandwidth resources is minimized on the premise of meeting the delay requirements of each information particle. Moreover, in the paper, the concepts of both information particle and information particle group are defined; Basic properties of the minimum reachable access bandwidth of an information particle group are analyzed; The influences of time attribute and attribute of bearing capacity of an information particle group on the minimum reachable access bandwidth are analyzed; Finally, an effective method for the calculation of the minimum reachable access bandwidth of an information particle group is given, and a particle access algorithm based on dynamically adjusting the minimum reachable access bandwidth is proposed. The research of the paper pave a new way for further improving QoS mechanisms of large-scale networks, and lay the corresponding theoretical foundation.

cs.NI↗
arXiv2026

Joint Network-and-Server Congestion in Multi-Source Traffic Allocation: A Convex Formulation and Price-Based Decentralization (Extended Version)

This paper studies an important rate allocation problem that arises in many networked and distributed systems: steady-state traffic rate allocation from multiple sources to multiple service nodes when both (i) the access-path delay on each source-node route is rate-dependent (capacity-constrained) and convex, and (ii) each service node (also capacity-constrained) experiences a load-dependent queueing delay driven by aggregate load from all sources. We show that the resulting flow-weighted end-to-end delay minimization is a convex program, yielding a global system-optimal solution characterized by KKT conditions that equalize total marginal costs (a path marginal access term plus a node congestion price) across all utilized routes. This condition admits a Wardrop-type interpretation: for each source, all utilized options equalize total marginal cost, while any option with strictly larger total marginal cost receives no flow. Building on this structure, we develop a lightweight distributed pricing-based algorithm in which each service node locally computes and broadcasts a scalar congestion price from its observed aggregate load, while each source updates its traffic split by solving a small separable convex allocation problem under the advertised prices. Numerical illustrations demonstrate convergence of the distributed iteration to the centralized optimum and highlight the trade-offs induced by jointly modeling access and service congestion.

cs.DC↗
arXiv2026

Initialization and Rate-Quality Functions for Generative Network Layer Protocols

Generative AI (GenAI) creates full content based on compact encodings. While GenAI has been used for applications where the generated content is returned to the encoding sender, it can also extend the capacity of communication networks by transmitting compact encodings through capacity-limited links, then generating and forwarding approximations from the GenAI node to the destination. This poses the challenge of evaluating approximation quality as a function of the rate between the source and GenAI node, while accounting for the communication overhead of learning. We present a method- and modality-agnostic initialization protocol for learning rate-quality functions in GenAI-aided networks, defining three variants: source-, node-, and destination-oriented, each with different messaging flows based on where quality is measured. The protocol augments node discovery protocols (e.g., MCP, A2A) when sources lack confidence in advertised model performance. We illustrate operation via a minimum estimation budget calculated using a distribution-free tolerance limit , and validate using a case study on image transmission under quality constraints. Results confirm the calculated budget meets the target quality requirement, with positive gains over JPEG after around 20 post-learning transmissions for a perceptual metric and more than 100 for a goal-oriented metric, providing a practical foundation for GenAI-based network compression.

cs.NI↗
arXiv2026

AEGIS: Risk-Budgeted Online Scheduling for Resilient Continuous Edge Inference

Continuous edge inference requires sustained wireless and computing support across successive service instances. Under recurring channel degradation, transient edge overload, and multi-user contention, isolated deadline misses may accumulate into persistent service degradation. Existing schedulers mainly optimize instantaneous latency or per-timeslot utility and provide limited control over such cross-time effects. To address this issue, we propose AEGIS (Adaptive Exposure-Governed Inference Scheduling), a risk-budgeted online framework for service-level operational resilience. AEGIS regulates predicted deadline-risk exposure through dynamically replenished per-user risk budgets and establishes an explicit finite-horizon bound on cumulative admitted-risk exposure. One-step state estimation supports anticipatory delay and risk assessment, while the centralized bandwidth-computing allocation is transformed into an exact-potential formulation and solved through asynchronous coordinate updates. Simulation results under varying system scales and stress conditions show that adaptive replenishment improves service continuity and recovery relative to fixed replenishment, while risk budgeting reduces predicted-risk exposure compared with more aggressive scheduling alternatives.

cs.NI↗
arXiv2026

Identifying AI Web Scrapers Using Canary Tokens

From pre-training to query-time augmentation, web-scraped data helps to improve the quality and contextual relevancy of content generated by large language models (LLMs). However, large-scale web scraping to feed LLMs can affect site stability and raise legal, privacy, or ethics concerns. If website owners wish to limit LLM-related web scraping on their site, due to these or other concerns, they may turn to scraper access control mechanisms like the Robots Exclusion Protocol. To be most effective, such mechanisms require site owners to first identify the scrapers that they wish to restrict (e.g., via User-Agent strings). Existing mechanisms to identify LLM-related scrapers rely on voluntary disclosure by companies, one-off experiments by researchers, or crowd-sourced reports -- methods that are neither reliable nor scalable. This paper proposes a novel technique for accurately and automatically inferring LLM-related scrapers. We host dynamic websites that serve unique canary tokens to each visiting scraper, then prompt LLMs for information about our sites. If an LLM consistently generates outputs containing tokens unique to a scraper, it provides evidence of exposure to that scraper. Via experiments across 22 production LLM systems, we demonstrate that our approach can reliably identify which scrapers feed which LLM, including several that are not publicly known or disclosed by the companies. Our approach provides a promising avenue for unprivileged third parties to infer which scrapers serve data to which LLMs, potentially enabling better control over unwanted scraping.

cs.CR↗
arXiv2026

Route Based Map Matching via a Structured Codebook and Token Sequence Decoding

This study proposes an efficient and computationally light route based map matching method for GPS track data on urban expressway networks. The key idea is to exploit a symbolic structure of named lines and named junctions that link level map matching leaves unused. We represent each candidate route as a sequence of line and junction names, take the set of such sequences as a route codebook, and formulate map matching as scored alignment of a probe trajectory against members of the codebook. Probes become token sequences via a mesh quantizer, a precomputed grid mapping each coordinate to a line or junction token, and the decoder returns a member of the codebook by construction. The codebook is indexed by a DAFSA $\times$ Levenshtein automaton, a fuzzy lookup technique from approximate string matching and speech recognition; the per query decoding cost is orders of magnitude lower than a brute force scan. We evaluate the method on a deformed replica of the Tokyo Metropolitan Expressway topology. The method recovers the exact route at moderate GPS noise and continues to identify the line and junction sequence under heavy noise; a sensitivity analysis maps the mesh resolution operating range. Real probe evaluation, channel model calibration, and a head to head HMM comparison are left to a forthcoming version.

math.OC↗
arXiv2026

Learning-Based Collaborative MEC for LLM Inference with Soft-Deadline Awareness via Transformer-Enhanced PPO

This paper investigates collaborative mobile edge computing (MEC) servers for large language model (LLM) inference under soft deadline constraints. In this system, to improve the quality of service, computations are expected to be completed within their deadlines. However, due to dependencies among tasks or subtasks, any missed deadline can lead to catastrophic consequences for the entire request. In this context, this work proposes an extended deadline mechanism with constrained flexibility. The main challenges lie in handling large-scale computations under strict latency constraints while limiting the number of allowable deadline extensions, especially in the presence of task dependencies within each request. To tackle these challenges, we develop a transformer-enhanced proximal policy optimization (PPO) framework that enables efficient collaboration among MEC servers. The proposed approach aims to maximize the number of tasks completed within their deadlines while minimizing the use of deadline extensions. By capturing temporal dependencies and cross-server interactions, the transformer improves decision-making for task migration. Simulation results demonstrate that the proposed method significantly outperforms conventional PPO and heuristic-based approaches in terms of task completion rate and overall system efficiency.

cs.DC↗
arXiv2026

Indirect Estimation of SINR via SSB and CSI-RS RSRP in 5G NR

Predicting user equipment (UE) performance is essential for proactive network control, resource management, and digital twin sandboxes. However, the inherent flexibility and complexity of beam-based 5G new radio (NR) networks make accurate performance forecasting highly challenging. This paper proposes a data-driven approach to predict the average downlink signal-to-interference-plus-noise ratio (SINR) relying exclusively on standardized reference-signal measurements, namely synchronization signal block (SSB) and channel state information-reference signal (CSI-RS) reference signal received power (RSRP). We formulate this prediction as a supervised learning problem and evaluate various input feature representations using a third generation partnership project (3GPP)-compliant synthetic dataset. Our analysis reveals that filtering measurements based on active CSI-RS beams significantly enhances prediction accuracy while reducing input dimensionality. This activity-aware strategy demonstrates the strong viability of machine learning models for proactive network optimization.

cs.NI↗
arXiv2026

An Adversarial Zero-Shot Learning Approach for Anomaly Detection in Multivariate IoT Traffic Data

Anomaly detection in Internet of Things (IoT) networks presents unique challenges due to the diversity of devices, lack of labeled data, and domain variability across environments. In this paper, we propose a novel framework for multivariate time-series anomaly detection that leverages adversarial learning and contrastive loss within a sequence-based Variational Autoencoder (VAE) architecture. Our method enables zero-shot domain adaptation by jointly optimizing domain-invariant latent representations and semantically structured embedding spaces, without requiring labeled data or raw feature transfer. To address the heterogeneity of IoT deployments, we introduce encoder and decoder adaptor layers that align feature distributions across domains while preserving contextual semantics. Additionally, we propose a destination-based segmentation strategy to better model real-world communication structures in IoT traffic. Our framework is comprehensively evaluated on six distinct datasets spanning industrial, enterprise, general-purpose, smart home, and military automation domains across 44 transfer scenarios. Experimental results demonstrate strong zero-shot generalization in several cross-domain settings and competitive performance against a contrastive domain-adaptation baseline under realistic, heterogeneous, and privacy-constrained IoT conditions.

cs.LG↗
Compare source metadata on this page
WorkPublishedSource identifierSource
A Wavelength Borrowing Architecture for Optical Data Center Networks - Extended Version2026-09-042609.04874arxiv
QUASAR: Quantum Satellite Architecture and Routing Simulator2026-09-042609.04920arxiv
Performance Evaluation of HAPS-enabled Coverage Enhancement in Hard-to-Reach Areas2026-09-042609.05067arxiv
On the Delay-Constrained Maximum Concurrent Flow Problem2026-09-042609.05068arxiv
Confounding-Valid Conformal Inference for Counterfactual KPIs in Wireless Networks2026-09-042609.05073arxiv
Towards Federated, Green, and Resilient 6G Non-Terrestrial Networks2026-09-042609.05184arxiv
XAI-SDN: An Explainable Entropy-Guided Machine Learning Framework for Real-Time DDoS Detection in Software Defined Networks2026-09-042609.05701arxiv
WIP: Energy-Efficient LLM-Based Serving Cluster Formulation in Cell-Free Massive MIMO2026-09-042609.05725arxiv
SDN-Orchestrated Dual-Path 5G/SATCOM Maritime Communications for Carrier Strike Groups2026-09-042609.05729arxiv
Idea and Theory of Particle Access2026-09-032203.15191arxiv
Joint Network-and-Server Congestion in Multi-Source Traffic Allocation: A Convex Formulation and Price-Based Decentralization (Extended Version)2026-09-032602.03246arxiv
Initialization and Rate-Quality Functions for Generative Network Layer Protocols2026-09-032603.11122arxiv
AEGIS: Risk-Budgeted Online Scheduling for Resilient Continuous Edge Inference2026-10-122605.02179arxiv
Identifying AI Web Scrapers Using Canary Tokens2026-09-032605.13706arxiv
Route Based Map Matching via a Structured Codebook and Token Sequence Decoding2026-09-032607.22543arxiv
Learning-Based Collaborative MEC for LLM Inference with Soft-Deadline Awareness via Transformer-Enhanced PPO2026-09-032608.02031arxiv
Indirect Estimation of SINR via SSB and CSI-RS RSRP in 5G NR2026-09-032609.03488arxiv
An Adversarial Zero-Shot Learning Approach for Anomaly Detection in Multivariate IoT Traffic Data2026-09-032609.03505arxiv

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