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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

Temporal Analysis of NetFlow Datasets for Network Intrusion Detection Systems

This paper investigates the temporal analysis of NetFlow datasets for machine learning (ML)-based network intrusion detection systems (NIDS). Although many previous studies have highlighted the critical role of temporal features, such as inter-packet arrival time and flow length/duration, in NIDS, the currently available NetFlow datasets for NIDS lack these temporal features. This study addresses this gap by creating and making publicly available a set of NetFlow datasets that incorporate these temporal features [1]. With these temporal features, we provide a comprehensive temporal analysis of NetFlow datasets by examining the distribution of various features over time and presenting time-series representations of NetFlow features. This temporal analysis has not been previously provided in the existing literature. We also borrowed an idea from signal processing, time frequency analysis, and tested it to see how different the time frequency signal presentations (TFSPs) are for various attacks. The results indicate that many attacks have unique patterns, which could help ML models to identify them more easily.

cs.LG↗
arXiv2026

OpenTwin: Closed-Loop Digital Twins for Trustworthy Policy Deployment in Open RAN

In open radio access networks (O-RAN), the near-real-time RAN Intelligent Controller (RIC) hosts third-party xApps whose training and validation risk disrupting the operational network. Disaggregation amplifies this risk, as no single party can certify a control action end to end. Indeed, our testbed shows an E2 control request reported as successful while the base station never applies the change. Digital twins (DTs) promise safe policy evaluation, yet existing O-RAN DTs largely rely on hand-crafted models, run without feedback from the deployment, and never say how often their predictions can be trusted. To fill this gap, we present OpenTwin, a closed-loop framework that learns the simulator configuration reproducing an operating deployment streamed measurements, certifies the resulting DT by re-simulation, calibrates it online, and evaluates each xApp action before it executes on the physical network. Every trust decision carries an error rate bounded by an operator-prescribed budget, with a drift detector limiting needless resynchronizations and a conformal fidelity gate admitting an action only when its predicted outcome range lies in the safe region. Extensive experiments across simulation and real-world testbeds confirm single-digit percentage error in reproduced measurements, false approvals an order of magnitude below every budget from 0.05 to 0.30, and a gated energy-saving xApp that retains roughly 40% of the saving achievable with perfect foresight.

cs.NI↗
arXiv2026

Network Topologies for QKD Networks

Quantum key distribution (QKD) is a method for distributing cryptographic keys between remote endpoints, enjoying security based on quantum physics. This paper makes a first step towards studying topologies for QKD networks. QKD networks imply several required characteristics for their reliability and efficiency. We express such properties in terms of graph structure. For the comparison of potential graphs, we describe cost functions that allow us to identify families of graphs that are reliable and efficient. To make the discussion realistic, we summarize representative field-reported QKD performance numbers and illustrate the sensitivity of secret-key rate (SKR) to a few dB of additional loss. Last, we present methods to construct large efficient graphs through connecting smaller graphs.

cs.NI↗
arXiv2026

xTRUCE: A Provably Safe Arbiter for Multi-xApp Conflict Mitigation in Agentic O-RAN

The open radio access network (O-RAN) is evolving toward agentic operation, where large language model (LLM)-driven xApps/rApps generate control proposals under operator intents. However, such proposals may be conflicting, infeasible, or hallucinated, and no existing system jointly provides proposal-independent safety, priority-aware reconciliation, and traceable feedback. To this end, we propose a provably safe arbiter, namely xTRUCE, in the near-real-time (Near-RT) RAN intelligent controller for mitigating multi-xApp conflicts in gNB control. We first develop a structured xApp proposal interface and a three-layer constraint hierarchy that places physical limits and operator-defined rules above relaxable performance targets, alongside a dual-timescale control action space. A two-stage arbitration mechanism then minimizes target shortfalls in the operator-priority order to finalize safe E2 actions within the Near-RT latency budget, while returning conflict certificates to xApps and the operator for renegotiation. Finally, we implement xTRUCE in a multi-cell O-RAN use case, and evaluate its multi-process prototype through simulations with live API-backed LLM xApps and over-the-air experiments on OpenAirInterface/FlexRIC-based O-RAN stacks. Results show that xTRUCE ensures gNB control safety with $100\%$ protected services despite severe proposal hallucinations, achieves priority-consistent performance satisfaction under overload, efficiently guides LLM intent renegotiation via certificates, and keeps a delay-safe E2 control loop.

cs.NI↗
arXiv2026

Uncertainty-Aware Multi-Task Learning for Joint Modulation Recognition and SINR Estimation

Joint modulation recognition and signal-to-interference-plus-noise ratio (SINR) estimation can reduce duplicated processing in intelligent receivers, but the two tasks have different uncertainty characteristics. This letter proposes an uncertainty-aware multi-task model that transforms each short normalized in-phase/quadrature window into 36 deterministic, label-free statistics, learns a shared representation, and uses task-specific adapters for modulation classification and heteroscedastic SINR regression. A joint uncertainty score combines classification entropy and predicted regression variance to support selective inference. Simulations cover QPSK, 8PSK, 16QAM, and 64QAM under matched additive white Gaussian noise/Rayleigh channels and an unseen frequency-selective Rician channel. Over five independent seeds, the proposed model improves matched and unseen-channel accuracy over conventional multi-task learning by 14.86 and 8.61 percentage points, respectively, while reducing SINR mean absolute error by 1.60 and 1.61 dB. Confidence-based rejection further lowers modulation error under channel mismatch.

cs.NI↗
arXiv2026

Adaptive RIS-aided Communications through ML-based Generation of Phase Masks

Reconfigurable Intelligent Surfaces (RISs) are an attractive technology for Millimeter Wave (mmWave) communications due to their ability to passively reflect incident signals. However, current implementations of RIS rely on performing computationally-intensive algorithms offline to generate phase masks, which are stored as a codebook on the embedded microcontroller on the RIS. The codebook size is restricted by the embedded microcontroller's storage capacity, which limits the ability of the RIS to adapt to evolving channel conditions and deployment scenarios. In this demo, we showcase an Machine Learning (ML)-based solution for dynamically generating new phase masks during runtime. Our approach leverages a ML model deployed on the microcontroller for approximating the output of a phase mask generation algorithm, responding to new inputs while remaining smaller than a codebook.

eess.SY↗
arXiv2026

Enhancing Network Resilience via Graph-Based Anomaly Detection in Sovereign Functions

Sovereign network functions, e.g., routing protocols, are becoming increasingly complex and susceptible to failures arising from protocol configuration anomalies and anomalous configurations. This paper interprets the protocol configuration anomaly detection problem as detection of structural inconsistencies of connected nodes and edges in a bipartite graph that captures both physical network entities and logical protocol states. This graph structural inconsistency detector (GSID) model is proposed to solve the problem efficiently. To handle the heterogeneous nature of protocol configuration parameters, GSID employs an adaptive configuration encoder (ACE) that dynamically selects encoding strategies per parameter to preserve fine-grained numerical discrepancies. To expose the subtle inconsistencies of connected nodes and edges in the bipartite graph, GSID uses an inconsistency dynamic attention (IDA) mechanism that scores edges by drawing asymmetric attentions from both ends, rule compliance from one end and route connectivity from the other. It is demonstrated experimentally that GSID outperforms state-of-the-art baselines by threefold in F1 score and by 23.2% in accuracy. Ablation studies validate the effectiveness of both the ACE and IDA modules. Tests on unseen network scales and real-world network topologies show the superior adaptability of our GSID, compared to the baselines.

cs.NI↗
arXiv2026

An LLM-Based Framework for Intent-Driven Network Topology Design

Designing deployable and resilient network topologies from natural language requirements remains a challenging problem in network automation. This work investigates the ability of Large Language Models (LLMs) to generate structurally valid and constraint-compliant network topologies through a constraint-driven pipeline combining hierarchical modeling and systematic validation. The framework is evaluated via a multimodel comparison of proprietary and open-weight LLMs across four realistic network scenarios released as a public dataset. We assess structural correctness using node and edge F1-scores against reference topologies, and evaluate resilience through server and content connectivity metrics. In addition, we analyze common failure modes, including interface mismatches and directional inconsistencies in generated topologies. Overall, this work provides a systematic benchmark for understanding how LLMs handle structural and resilience constraints in topology synthesis, and supports informed model selection for AI-driven network design.

cs.NI↗
arXiv2026

Deep Learning-Assisted UAV Localization Framework for Post-Disaster Search and Rescue Missions

The precise locating of trapped victims is arguably the most challenging issue in SAR operations, particularly when infrastructure is destroyed and SAR teams only have low-power beacon signals from smartphones to search with. This paper presents a framework for centralized and cooperative UAV-based localization with deep learning-based channel classification and 3D environment-adaptive target estimation. A CNN-LSTM classifier is employed based on a dataset generated at 867.5 MHz with NYUSIM. This classifier labels the individual links of a UAV to a target. Then, these labels activate specific localization solvers: a first-order Taylor-expanded WLS method for LOS settings, an LSRE-SOCP method with iterative refinement for NLOS, and a hybrid projection-based scheme for mixed environments. Through exhaustive simulations, this paper shows that the proposed framework significantly reduces runtime and yields high localization accuracy even when the transmit power and path-loss conditions are unknown. The method can be used in diverse propagation scenarios, making it easy to deploy in reality. The whole NYUSIM-generated dataset is made publicly available to support future research on disaster-aware wireless localization.

eess.SP↗
arXiv2026

Predictive Lightweight MARL for Resilient Coverage in Sparse-Signaling Aerial Networks

This letter proposes the Predictive Lightweight Multi-Agent Reinforcement Learning (PL-MARL) framework to ensure resilient coverage in bandwidth-constrained UAV swarms. To counter coordination collapse caused by sparse signaling and information aging, we introduce a Kinematic-Aware Inference Engine that proactively reconstructs neighbor trajectories via physical priors. This approach enables an efficient computation-for-communication trade-off, decoupling structural resilience from signaling frequency. Simulations confirm that PL-MARL maintains superior coverage and mission continuity under extreme signaling scarcity and node failure. Our results validate proactive inference as a scalable, low-latency solution for robust aerial coordination, effectively minimizing control overhead to preserve spectrum for payload services while ensuring resilience against interference.

cs.NI↗
arXiv2026

Enhanced Dynamic Beamwidth Selection-based THz MAC Protocol for Wireless Data Center Networks

Terahertz (THz) wireless communication offers a promising alternative to traditional wired links in data centers (DCs), enabling ultra high data rates, low latency, and greater scalability. However, THz signals suffer from high path loss, necessitating the use of directional antennas (DAs). While DAs enhance signal strength, they introduce challenges such as deafness and synchronisation, typically addressed through receiver initiated MAC protocols. Most existing THz MAC protocols use fixed beamwidths, which results in a key performance trade off: narrow beams improve gain for long range links but reduce throughput for short distances due to increased alignment overhead, while wide beams benefit short links but degrade performance over longer distances. To overcome this limitation, we propose DBS-ADAPT, a dynamic beamwidth selection based MAC protocol that adjusts the antenna beamwidth according to the distance between nodes, maximising throughput without compromising link range. We also introduce an enhanced version, EDBS-ADAPT, which further reduces beamwidth switching and control overhead while preserving throughput gains. Both protocols are evaluated using the NS-3 THz module. Simulation results show that DBS-ADAPT improves average throughput by up to 22% and reduces delay up to 10% compared to the baseline ADAPT-3 protocol. EDBS-ADAPT further cuts beamwidth switching overhead by 95%, making it more efficient for scalable and high performance wireless DC environments.

cs.NI↗
arXiv2026

Temporally Consistent Graph Q-Networks for Intelligent Network Control

Mobile networks continue to grow in complexity and next generation networks are expected to support both increasing traffic loads and more diverse services. As network complexity rises, optimizing antenna parameters under dynamic or changing objectives becomes increasingly challenging. We propose a novel multi-agent reinforcement learning (MARL) algorithm for high-level control and orchestration of mobile networks. The Temporally Consistent Graph Q-Network (TC-GQN) algorithm learns a self-predicting representation of the whole network that is task-independent and aggregates information from all base-stations. A graph neural network is trained using a global reward function to assign coordinated local actions based on the learned encoding of the global network state. We evaluate the algorithm in a simulated environment to orchestrate an energy-saving feature across multiple sectors and multiple carriers under different quality of service (QoS) constraints. The proposed algorithm outperforms state-of-the-art graph-based baselines and a competitive rule-based controller by improving hardware sleep time while maintaining QoS. Moreover, the learned representation enables rapid adaptation to changing intents.

cs.NI↗
arXiv2026

GreenFLag: A Green Agentic Approach for Energy-Efficient Federated Learning

Progressing toward a new generation of mobile networks, a clear focus on integrating distributed intelligence across the system is observed to drive performance, autonomy, and real-time adaptability. Federated learning (FL) stands out as a key emerging technique, enabling on-device model training while preserving data locality. However, its operation introduces substantial energy and resource demands. Energy needs are mostly met by grid power sources, while FL resource orchestration strategies remain limited. This work introduces GreenFLag, an agentic resource orchestration framework designed to minimize the energy consumption from the grid power to complete FL workflows, guarantee FL model performance, and reduce grid power reliance by incorporating renewable sources into the system. GreenFLag leverages a Soft-Actor Critic reinforcement learning approach to jointly optimize computational and communication resources, while accounting for communication contention and the dynamic availability of renewable energy. Evaluations using a real-world open dataset from Copernicus, demonstrate that GreenFLag significantly reduces grid energy consumption by 94.8% on average, compared to three state-of-the-art baselines, while primarily relying on green power.

cs.NI↗
arXiv2026

Toward Mobile and Converged Backhaul: The Promise of Wireless Access and Backhaul

Wireless Access and Backhaul (WAB) is emerging as a key enabler for flexible and cost-efficient 5G deployments, offering a modular architecture that decouples access and backhaul while supporting multi-technology and mobile backhaul links. This article introduces the WAB framework standardized in 3GPP Release 19, outlining its architecture and operational principles. A practical implementation built with commercial hardware and open-source software demonstrates the feasibility and efficiency of WAB systems. We further explore four representative application scenarios - ranging from on-demand coverage to mobile Software-Defined Wide Area Network (SD-WAN) connectivity - and discuss the technical challenges that must be addressed for large-scale adoption. These insights highlight WAB as a promising foundation for 5G-Advanced and a stepping stone toward future 6G networks.

cs.NI↗
arXiv2026

SMCC-Empowered Digital Twins for Sensorless Monitoring in Large-Scale AI-Driven IoT Systems

The deployment of AI-driven Digital Twins (DTs) in large-scale Internet-of-Things (IoT) ecosystems demands continuous, high-fidelity synchronization between the physical environment and its virtual replica. Conventional approaches rely on dense sensor deployments, which introduce prohibitive costs in terms of hardware, energy, and network bandwidth. In this paper, we propose SMCC-DT, an integrated Sensing-Memory-Communication-Computation (SMCC) framework that enables sensorless monitoring of physical assets by exploiting Integrated Sensing and Communication (ISAC) waveforms at the 6G Edge. Under the SMCC-DT paradigm, a single radio signal simultaneously extracts environmental telemetry (Sensing) and delivers it to an Edge server (Communication), where a large-scale AI model is loaded into constrained memory (Memory) and executed (Computation) to update the DT state. We formulate the DT synchronization problem as a cross-layer optimization that jointly allocates transmit power, beamforming vectors, memory partitions, and CPU frequency to minimize the end-to-end synchronization latency subject to sensing accuracy, throughput, memory capacity, and computational budget constraints. Because the resulting mixed-integer nonlinear program is NP-hard, we design a Proximal Policy Optimization (PPO)-based Deep Reinforcement Learning (DRL) agent, termed SMCCAGENT, that learns near-optimal resource allocation policies online. Extensive simulations over a 500-node industrial IoT testbed demonstrate that SMCC-DT reduces DT synchronization latency by 38.7% and total energy consumption by 27.4% compared to state-of-the-art orthogonal and compute-only baselines, while sustaining sensing accuracy above 95% and model inference throughput above 30 frames per second.

cs.DC↗
arXiv2026

How Helpful is LLM Assistance in Network Operations? A Case Study at a Large Demonstration Network

This paper reports on a real-world case study in which over 100 network engineers assessed how a Large Language Model (LLM) can assist in building and operating a network. The versatility of LLMs has accelerated their adoption across a wide range of domains, and assisting network operations is one such promising application. LLMs are probabilistic models, unlike deterministic protocols and configurations; therefore, clarifying their capabilities -- how and to what extent LLMs can help in network operations -- is a crucial step toward adopting LLMs. To offer practical insights into this issue, we conducted an extensive experiment on a large demonstration network built for a public exhibition, consisting of 21 racks with heterogeneous network devices. In the experiment, a total of 105 network engineers used an LLM-based chatbot while building and operating the network. The chatbot was equipped with three external functions: retrieval-augmented generation for domain-specific knowledge, CLI control of network devices running on the network, and access to a ticket system. The participants gave evaluations for the chatbot's responses on a best-effort basis. Analysis of the chat histories shows that 68.1% of the evaluations were positive, indicating a quantitative baseline of the LLM's helpfulness in network operations. Our results also demonstrate that understanding the capabilities of the chatbot is important for eliciting better responses. Moreover, we provide detailed use case analyses while sharing actual user--chatbot interactions.

cs.NI↗
arXiv2026

Enabling Mobile Base Stations in 5G via Wireless Access Backhaul (WAB): A Multi-Band Experimental Study

Highly dynamic and mobile applications, such as vehicular networks, require stable connectivity, which is often challenging to achieve. Network densification is a key approach to address this issue and can be achieved cost-effectively through mobile base stations and wireless relaying. However, existing solutions rely on rigid and complex architectures that hinder deployment in dynamic scenarios. The recently standardized Wireless Access Backhaul (WAB) architecture represents a key evolution, enabling flexible and modular wireless relay networks with native support for mobility and multi-technology wireless backhaul. This paper presents the first experimental realization of a multi-band WAB testbed, combining an FR2 backhaul and an FR1 access link using open-source software and commercial off-the-shelf components. The proposed framework validates end-to-end WAB operation under mobility and demonstrates the extension of FR2 coverage while maintaining compatibility with legacy FR1 user equipment. Experimental campaigns in vehicular and outdoor-to-indoor scenarios confirm that WAB effectively mitigates FR2 limitations, particularly in uplink and Non-Line-of-Sight conditions. These results highlight WAB as a practical and scalable approach for vehicular and next-generation wireless networks.

cs.NI↗
arXiv2026

Trust-Aware Routing for Distributed Generative AI Inference at the Edge

Emerging deployments of Generative AI increasingly execute inference across decentralized and heterogeneous edge devices rather than on a single trusted server. In such environments, a single device failure or misbehavior can disrupt the entire inference process, making traditional best-effort peer-to-peer routing insufficient. Coordinating distributed generative inference therefore requires mechanisms that explicitly account for reliability, performance variability, and trust among participating peers. In this paper, we present G-TRAC, a trust-aware coordination framework that integrates algorithmic path selection with system-level protocol design to ensure robust distributed inference. First, we formulate the routing problem as a \textit{Risk-Bounded Shortest Path} computation and introduce a polynomial-time solution that combines trust-floor pruning with Dijkstra's search, achieving sub-millisecond median routing latency at practical edge scales, and remaining below 10 ms at larger scales. Second, to operationally support the routing logic in dynamic environments, the framework employs a \textit{Hybrid Trust Architecture} that maintains global reputation state at stable anchors while disseminating lightweight updates to edge peers via background synchronization. Experimental evaluation on a heterogeneous testbed of commodity devices demonstrates that G-TRAC significantly improves inference completion rates, effectively isolates unreliable peers, and sustains robust execution even under node failures and network partitions.

cs.DC↗
Compare source metadata on this page
WorkPublishedSource identifierSource
Temporal Analysis of NetFlow Datasets for Network Intrusion Detection Systems2026-08-282503.04404arxiv
OpenTwin: Closed-Loop Digital Twins for Trustworthy Policy Deployment in Open RAN2026-08-282605.24662arxiv
Network Topologies for QKD Networks2026-08-282608.28036arxiv
xTRUCE: A Provably Safe Arbiter for Multi-xApp Conflict Mitigation in Agentic O-RAN2026-09-252608.28532arxiv
Uncertainty-Aware Multi-Task Learning for Joint Modulation Recognition and SINR Estimation2026-08-282608.28865arxiv
Adaptive RIS-aided Communications through ML-based Generation of Phase Masks2026-08-282608.28890arxiv
Enhancing Network Resilience via Graph-Based Anomaly Detection in Sovereign Functions2026-08-272605.17716arxiv
An LLM-Based Framework for Intent-Driven Network Topology Design2026-08-272607.00292arxiv
Deep Learning-Assisted UAV Localization Framework for Post-Disaster Search and Rescue Missions2026-08-062609.02916arxiv
Predictive Lightweight MARL for Resilient Coverage in Sparse-Signaling Aerial Networks2026-07-242607.22109arxiv
Enhanced Dynamic Beamwidth Selection-based THz MAC Protocol for Wireless Data Center Networks2026-09-272607.17996arxiv
Temporally Consistent Graph Q-Networks for Intelligent Network Control2026-06-112606.13848arxiv
GreenFLag: A Green Agentic Approach for Energy-Efficient Federated Learning2026-06-092603.29933arxiv
Toward Mobile and Converged Backhaul: The Promise of Wireless Access and Backhaul2026-06-042606.06075arxiv
SMCC-Empowered Digital Twins for Sensorless Monitoring in Large-Scale AI-Driven IoT Systems2026-06-042609.09161arxiv
How Helpful is LLM Assistance in Network Operations? A Case Study at a Large Demonstration Network2026-05-192605.19627arxiv
Enabling Mobile Base Stations in 5G via Wireless Access Backhaul (WAB): A Multi-Band Experimental Study2026-04-092511.16259arxiv
Trust-Aware Routing for Distributed Generative AI Inference at the Edge2026-03-302603.28622arxiv

These are bibliographic comparisons, not experimental rankings. Follow the original document for methods and conditions.