TNSM Searchable Index

Author(s)	Title	Year	Publication	Keywords
Eiji Oki, Masaki Maeda, Ryuta Shiraki	Design of Survivable Entanglement Path Provisioning under Node Failures in Quantum Networks	2026	Early Access	Modeling Joining processes Probability	Quantum networks enable end-to-end quantum communication by leveraging inherently probabilistic processes, such as distributing entanglement between distant nodes and performing entanglement swapping at intermediate repeater nodes. Existing work has focused on maximizing the entanglement path success probability under link failures. However, the occurrence of physical node failures poses a significantly greater challenge to maintaining stable entanglement connections. To tackle this problem, it is necessary to develop a path provisioning design model that can preserve a high end-to-end entanglement success probability despite the occurrence of node failures. This paper proposes a survivable path provisioning design model to determine node- disjoint entanglement paths in quantum networks, which takes into account both the link-level transmission success probabilities and the node-level entanglement swapping success probabilities at nodes. The proposed model aims to maximize the entanglement path success probability for survivability under node failure. We express the proposed design model in the form of an integer linear programming problem. Numerical results show that the proposed model attains greater survivability compared with the baseline algorithms.	10.1109/TNSM.2026.3728093
Yuanming Huang, Xiaojuan Wang, Mingshu He	HyTMTC: A Pre-Training Method for Multi-Scenario Network Traffic Classification with Hybrid Transformer-Mamba	2026	Early Access	Modeling Training Telecommunication traffic	Network traffic classification is central to security monitoring and network management in heterogeneous environments. Existing deep learning approaches are often trained for a single scenario and require large amounts of labeled data, making them difficult to reuse when applications, traffic types, or encryption settings change. We present HyTMTC, a pre-training and fine-tuning framework for multi-scenario network traffic classification. HyTMTC encodes each flow as a unified multimodal token sequence that combines raw bytes, packet length, and direction, allowing protocol traces and communication behavior to be modeled together. To match the one-dimensional nature of traffic data, HyTMTC adopts a hybrid Transformer-Mamba backbone. Mamba captures contiguous byte- and packet-level patterns, while Transformer attention strengthens interactions across non-adjacent fields and packets. A self-attention fusion module further integrates the multimodal representations during fine-tuning. This design improves traffic representation without relying on scenario-specific feature engineering. Experiments on seven public datasets show that HyTMTC achieves an average F1-score of 95.03% and outperforms nine representative baselines. It also remains effective in encrypted, VPN, and few-shot settings, and maintains the ability to detect unknown attacks. These results demonstrate its effectiveness and stability for multi-scenario network traffic classification.	10.1109/TNSM.2026.3725912
Martine S. Lenders, Carsten Bormann, Thomas C. Schmidt, Matthias Wählisch	A Leaner and Faster Web: How CBOR Can Improve Dynamic Content Encoding in JSON and DNS over HTTPS	2026	Early Access	Internet of Things Encoding Internet	The Internet community has taken major efforts to decrease latency on the World Wide Web with significant improvements in accelerating content transport and in compressing static content. Less attention, however, has been dedicated to compression of dynamic content. Such content is commonly provided by JSON and DNS over HTTPS. Dynamic content objects continue to grow in size, which increases latency and fosters the digital inequality. In this paper, we propose to mitigate this increase by utilizing Concise Binary Object Representation (CBOR), a standard originally designed for the constrained Internet of Things (IoT) to restrict packet sizes and enable efficient encoding of data objects. We provide protocol design and three new data sets for the evaluation of dynamic content, DNS, and the loading of websites. Our key findings are the following: (i) Switching the data representation from JSON to CBOR reduces data by up to 80%. This size reduction can decrease loading times by up to 13.8% when downloading large objects—even in local setups. (ii) Enabling CBOR for DNS over HTTPS (DoH) and DNS over CoAP (DoC) reduces packet sizes significantly. Compressing only names combined with unpacked CBOR achieves maximum gain of 52.2%, using more complex but still lightweight Packed CBOR allows minimizing packets by up to 95.5%. Our lean decoder for name compression can fit into as little as 314 bytes of build size. Our results clearly show the potential of CBOR outside of IoT scenarios. Parts of this research have already influenced work within the IETF.	10.1109/TNSM.2026.3722114
Depeng Xu, Guozhen Cheng, Hongchao Hu, Quan Ren, Xiaohan Yang, Kangxu Wang	STNet: Multi-Scale Spatiotemporal Learning and Adaptive Fusion for Few-Shot Tor Traffic Classification	2026	Early Access	Modules (abstract algebra) Modeling Accuracy	The Tor network’s anonymity is increasingly exploited for cybercrime, creating a demand for accurate traffic classification under strict few-shot constraints. While recent efforts like WF-Transformer demonstrate strong temporal modeling capabilities, they still require abundant labeled data and struggle to generalize under defense-induced distortions and open-world unknown traffic. To address these gaps, we propose STNet (SpatioTemporal Multi-scale Augmentation and fusion Network), an episode-based few-shot learning architecture for Tor traffic classification. Unlike simple module stacking, STNet adopts a modular decoupling design: (1) a Multi-Scale Spatiotemporal Feature Fusion (MSMF) module captures packet-level and flow-level patterns to resist obfuscation; (2) scenario-adaptive modules tackle domain shifts in closed-world settings and feature scarcity in open-world settings; and (3) a Hierarchical Layer Attention (HLA) mechanism dynamically fuses heterogeneous features from different deployment positions. Extensive experiments on real-world Tor traffic show that STNet consistently outperforms representative baselines including WF-Transformer. In closed-world settings, it limits the accuracy drop under WalkieTalkie obfuscation to 13.6 percentage points. In open-world 10-shot evaluation, it achieves 92.1% AU-COVR and 79.1% unknown-class F1-score, surpassing the best baseline by 4.9 and 6.0 percentage points, respectively. These results demonstrate the effectiveness of decoupling universal feature extraction from scenario-specific adaptation in few-shot Tor traffic analysis.	10.1109/TNSM.2026.3722541
Shuang Zheng, Xing Zhang, Michael Sheng, Haixu Wang, Wenbo Wang	Beam Hopping Low Earth Orbit Satellite Resource Allocation for Differentiated Services and Robustness Analysis under Model Attacks	2026	Early Access	Beams Satellites Resource management	Beam hopping (BH)-enabled Low Earth Orbit (LEO) satellites play a pivotal role in next-generation communication networks, providing global coverage, improving spectrum efficiency, and supporting flexible adaptation to heterogeneous service demands. To fully exploit these capabilities, artificial intelligence (AI) techniques are increasingly employed for dynamic resource allocation and power management. However, limited onboard resources and potential adversarial perturbations pose challenges to both efficiency and robustness. To address these issues, we leverage digital twin technology to accurately capture the spatio-temporal dynamics of user–satellite visibility, providing precise state information for decision-making. Building on this, we formulate a joint optimization framework for BH scheduling and power allocation as a Markov Decision Process and propose the BRIDGE—BH with Reinforcement learning incorporating Integrated Dirichlet and Gumbel-TopK Exploration—which integrates a quality of service (QoS)-driven subchannel scheduling mechanism to ensure efficient and differentiated resource allocation. The model’s robustness is systematically evaluated under three classical adversarial attacks. Simulation results demonstrate that our approach achieves superior energy efficiency, service throughput, and fairness, while the robustness analysis shows stable performance under the considered bounded adversarial perturbations.	10.1109/TNSM.2026.3710750
Franck Messaoudi, Luhan Wang, Abdelkader Mekrache, Adlen Ksentini, Bingxuan Li, Jialei Su, Sofiane Messaoudi, Salim El Ghalbzouri	The Brewing Storm in 5G’s Data Plane: Design and Evaluation of a High-Performance eBPF/XDP-Based User Plane Function	2026	Early Access	Quality of service Fluid flow Kernel	This paper presents the design and implementation of a novel 5G UPF leveraging eBPF technology to meet the stringent performance and programmability requirements of emerging 6G systems. Traditional UPF implementations often struggle to balance performance, flexibility, and resource efficiency-challenges particularly critical in CPU- and I/O-constrained edge environments. The proposed eBPF-based UPF architecture mitigates these limitations by embedding core functionalities, such as packet classification, forwarding, and QoS enforcement, directly within the Linux kernel via eBPF programs attached through XDP and tc hook points. Performance evaluation using TRex demonstrates that the proposed solution achieves competitive throughput, low packet loss, and efficient CPU utilization across traffic profiles. Moreover, it maintains full compliance with 5G Core Network standards. Comparative analysis with well-established open-source UPF implementations further underscores its advantages. This work highlights the potential of eBPF as a foundational technology for building next-generation, programmable UPFs optimized for edge cloud deployments in the 6G era.	10.1109/TNSM.2026.3720812
Abderrahmane Boulahdour, Miloud Bagaa, Adlen Ksentini, Ahmed Ouameur Messaoud, Daniel Massicotte	Towards Software-Defined TSN Scheduling: An eBPF Approach for Stream Processing and Delay Analysis in Industry 5.0	2026	Early Access	Streams Timing Bridges	This paper presents a software-defined Time-Sensitive Networking (TSN) architecture that implements the IEEE 802.1Qcr Asynchronous Traffic Shaper (ATS) using Extended Berkeley Packet Filter (eBPF) technology within Linux-based TSN bridges. By moving traffic shaping logic to the kernel level, our solution eliminates the need for dedicated hardware and enables dynamic, programmable control of frame filtering, metering, and queuing. A Software-Defined Networking (SDN) controller complements the design, providing centralized orchestration of TSN behavior through standardized interfaces and a unified network view. We implement the ATS scheduling model to compute and enforce per-stream eligibility times, supporting time-aware scheduling of concurrent streams within the same priority class. This enables deterministic traffic delivery, which is critical for industrial automation and control. Our approach allows seamless integration into existing infrastructures and aligns with the flexibility objectives of Industry 5.0. Performance evaluations demonstrate accurate scheduling behavior under heterogeneous traffic conditions and quantify the delay introduced by a TSN bridge for multiple coexisting streams.	10.1109/TNSM.2026.3729563
Mustafa Türk, Müge Sayıt, Ali C. Begen, Andreas J. Kassler	FROG: Fast Response to Optimization Goals for HTTP Adaptive Streaming over SDN	2026	Early Access	Optimization Videos Servers	In modern video streaming systems, clients typically make independent bitrate decisions without full knowledge of network conditions, often leading to inefficient resource usage and unstable quality. Network-assisted adaptive streaming is rapidly gaining importance in Network and Service Management (NSM), as operators strive to deliver consistently high Quality of Experience (QoE) under dynamic traffic conditions. This paper introduces FROG, a novel, real-time Software-Defined Networking (SDN)-assisted framework designed to coordinate multi-server and multipath HTTP Adaptive Streaming (HAS) using standardized metrics carriage: Common Media Client Data (CMCD) and Common Media Server Data (CMSD). FROG employs an innovative two-stage optimization workflow in which an initial Linear Programming (LP) model rapidly determines feasible bandwidth bounds, server selection, and path capacities, thereby transforming the remaining optimization into a sequential layer-selection process for coordinated quality selection and flow allocation. This decomposition enables sub-second optimization at the scale of thousands of users, while buffer-aware client feedback is integrated to proactively prevent stalls and maintain system stability. Experiments on an emulated SDN testbed demonstrate that FROG achieves QoE comparable to that of the optimal MILP solution on tractable instances while virtually eliminating video stalls, and significantly reduces quality oscillations, outperforming state-of-the-art network-assisted approaches by up to 2.32× under playback-driven evaluation scenarios. Scalability experiments with up to 4,000 clients further demonstrate sub-second optimization runtimes, confirming the practicality of FROG for large-scale deployments.	10.1109/TNSM.2026.3729597
Ali Alfatemi, Ahmed Alfaqeer, Mohamed Rahouti, Zakirul Alam Bhuiyan, Abdellah Chehri	Sentinel: A Neuro-Symbolic Co-Evolutionary Framework for Trustworthy Network and Service Management Against DDoS Attacks	2026	Early Access	Modeling Distributed denial-of-service attack Management	Distributed Denial of Service (DDoS) attacks threaten service continuity in next-generation networks, where autonomous mitigation must suppress attack traffic without unnecessarily degrading legitimate service. This paper presents Sentinel, a neuro-symbolic co-evolutionary framework for DDoS-aware network and service management. Sentinel combines a Proximal Policy Optimization (PPO) defender with a symbolic safety layer that repairs unsafe actions, enforces domain-specific constraints, and supports interpretable rule crystallization. Training further uses a Hall-of-Fame archive of historical attackers and benchmark checkpoint selection to mitigate late-stage co-evolutionary degradation. We evaluate Sentinel over 2,000 generations and 10 independent seeds across benign, mild-attack, strong-attack, chaos/flash-crowd, and held-out ICMP flood scenarios. Results show that Sentinel Pareto-dominates the ShieldOnly ablation on mild, strong, and chaos scenarios under a joint service-quality, leakage, and outage criterion, and Pareto-dominates unshielded PPO on zero-shot ICMP. In the ICMP setting, Sentinel eliminates severe outages in the evaluated scenario (0 vs. 145.7 for PPO) and reduces leakage by 8.6 percentage points through symbolic ICMP overrides. Benchmark checkpointing reduces strong-attack leakage by 8.1 percentage points and severe outages by 98.3 steps relative to the final checkpoint. Rule crystallization achieves 97.97% held-out accuracy using a depth-4 decision tree over four interpretable traffic features. Strict SLA compliance is not achieved under attack conditions, and all results are simulation-based within the modeled traffic and attack distributions.	10.1109/TNSM.2026.3729146
Lin Cong, Junru Cai, Ying Wang, Peng Yu, Xuesong Qiu, Shaoyong Guo, Ao Xiong	Dynamic E2E Channel Orchestration in Metro Transport Network	2026	Early Access	Algorithms Optimization Timing	In the era of 5G and beyond, the hard-isolated channels enabled by time slot cross-connects in metro transport network (MTN) effectively meet the demands of emerging network services for low latency, low jitter, flexible bandwidth, and secure isolation. However, the dynamic arrival and departure of tenant virtual network request (VNRs) lead to resource fragmentation within the MTN transport network, resulting in inefficient resource utilization. To mitigate network resource fragmentation, we formulate the MTN dynamic channel orchestration problem and propose a fragmentation-aware MTN dynamic channel orchestration method. This method comprises two key components: a greedy graph-reconstruction-based channel mapping algorithm and a fragmentation-aware channel reconfiguration algorithm. The former optimizes MTN channel resource allocation to achieve static channel orchestration, while the latter, leveraging a simulated annealing-based channel reconfiguration strategy, dynamically adjusts channel allocations based on the static orchestration results, thereby reducing fragmentation levels. Compared to existing approaches, under varying network load conditions, the proposed channel mapping algorithm reduces the consumption of network time slot resources by 16.3% -20.6%, while the channel reconfiguration algorithm significantly lowers the levels of network fragmentation by 48.7% -79.5%, and reduces the running time by 91.3%–94.2% compared with the baseline.	10.1109/TNSM.2026.3723823
Wei Sai, Yihui Lu, Xin Guo	A Privacy-Preserving Security Framework for Multi-Party Data Fusion Computing Based on Homomorphic Encryption	2026	Early Access	Security Protocols Information rates	To prevent plaintext exposure in multi-party collaborative computing, this paper proposes a distributed secure multi-party computation protocol based on the Cheon-Kim-Kim-Song (CKKS) homomorphic encryption scheme. Data is encoded and encrypted at the source into CKKS complex polynomial ciphertext, enabling vectorized fusion under shared evaluation keys and threshold decryption in a decentralized architecture without a trusted central authority. Experiments on heterogeneous multi-institution datasets demonstrate low numerical error (9.0×10⁻⁷ at polynomial order 2¹⁶ and depth 12), effective scalability (throughput increasing from 1.12×10⁵ to 1.32×10⁵ ops/s and latency decreasing from 56 ms to 38 ms as nodes scale from 4 to 16), and strong robustness (70% decryption success at a 60% threshold and 95% recovery under malicious interference), showing that the framework achieves efficient computation with strict privacy protection for cross-party data fusion.	10.1109/TNSM.2026.3717343
Aida Meftah, Tri Nhu Do, Georges Kaddoum, Chamseddine Talhi	Federated Learning-Empowered Jamming Detection for Stochastic Uplink and Downlink Integrated Terrestrial and Non-Terrestrial Networks	2026	Early Access	Jamming Modeling Signal detection	In this paper, we present a novel approach that integrates the reliability and fast data transfer capabilities of terrestrial-based infrastructure with the extensive coverage of non-terrestrial networks, thereby creating a seamless and resilient communication network. The proposed integration addresses the issue of adaptive jamming in uplink (UL) and downlink (DL) transmissions, which threatens communication efficiency and reliability. To this end, we first introduce a 3D simulation design that accurately models UL and DL transmissions within stochastic integrated terrestrial and non-terrestrial networks (SI-TNTNs), incorporating adaptive jamming strategies and employing a stochastic node distribution based on the Homogeneous Poisson Point Process (HPPP). Next, in order to train encoder networks deployed on clients for both UL and DL transmissions, we propose a distributed learning framework called the federated partial model aggregation (FedPMA) algorithm that uses the spectral correlation function (SCF) for feature representation. The proposed approach combines encoder parameter aggregation with a multivariate normal (MVN)-based probabilistic reliability estimation derived locally from encoder outputs, which guides the aggregation process. Only encoder parameters and scalar reliability scores are exchanged between clients and the parameter server (PS), while raw data and latent representations remain local. Our experimental results reveal that our approach not only enhances data privacy, but also significantly improves model performance and convergence, thus paving the way for the development of robust, interference-sensitive communications strategies suitable for next-generation non-terrestrial network (NTN)-enabled wireless systems.	10.1109/TNSM.2026.3727347
Raeed Al-Sabri, Abdullatif Albaseer, Mohamed Abdallah, Ala Al-Fuqaha	DMGCRL: Dynamic Multi-Scale Graph Contrastive Representation Learning For Network Intrusion Detection	2026	Early Access	Modeling Intrusion detection Labeling	Graph neural networks (GNNs) have recently attracted significant attention in network intrusion detection systems (NIDS) due to their ability to model network traffic as graphs and capture complex relationships within network flows. However, existing GNN-based methods face critical limitations: they rely on limited or noisy labeled data and struggle to detect threats at various scales, ranging from local anomalies (e.g., port scanning) to coordinated subnetwork attacks (e.g., botnets) and global network-wide campaigns (e.g., DDoS attacks). To bridge this gap, we propose Dynamic Multiscale Graph Contrastive Representation Learning (DMGCRL), a self-supervised framework that hierarchically models network intrusions at different levels. At the node level, DMGCRL constructs structure-aware subnetworks around individual traffic flows to capture fine-grained behavioral deviations. For subnetwork-level threats, it employs substructure-aware pooling to identify coordinated anomalies among clustered malicious nodes. Finally, at the global level, DMGCRL derives representations that reflect the holistic state of the network, enabling detection of large-scale threats, such as distributed malware propagation. DMGCRL designs a shared GNN encoder with a multi-level contrastive loss to align multiscale representations while largely eliminating label dependence. It learns discriminative features from unlabeled traffic, refines decision boundaries without supervision, and reveals anomalies by contrasting related and unrelated nodes across scales. Performance evaluation was conducted on five publicly available network traffic datasets for binary and multiclass detection. Results show that DMGCRL consistently outperforms SOTA methods, achieving an F1 score of 99.86% on NF-CSECIC-IDS2018-V2 and 96.11% on NF-UNSW-NB15-V2 under binary detection and the lowest mean false positive rates, 1.28% and 2.33% under multiclass detection on the two datasets.	10.1109/TNSM.2026.3726282
Ping He, Yu Yao, Xu Li, Yao Hu, Wei Yang	FIGAN: Diversity-Oriented Traffic Generation for Industrial Protocol Format Inference	2026	Early Access	Protocols Modeling Fuzzing	Protocol Format Inference is a pivotal step in the reverse engineering of proprietary protocols, yet its effectiveness is constrained by the scarcity of high-quality training data. In industrial control systems, the rigid and cyclical nature of traffic results in a "long-tail" distribution, where diverse functional scenarios are severely underrepresented. Existing generative approaches, primarily designed for fuzzing or intrusion detection, fail to resolve the intrinsic conflict between syntactic validity and semantic diversity required for protocol format inference. To bridge this gap, we propose FIGAN, a stage-wise decoupled generative framework tailored to synthesize high-fidelity traffic for protocol format inference. By isolating flexible distribution learning from rigid syntax enforcement, FIGAN liberates the generative process to extrapolate novel payload variations from a continuous latent space, effectively surmounting the limitations of sparse seed data. Specifically, the framework integrates three synergistic modules: first, heuristic pre-processing that constructs semantic templates as a prior knowledge base; second, a generative adversarial architecture optimized via discrete relaxation to explore high-dimensional payload patterns independently of syntax rules; and finally, a closed-loop verification mechanism that performs syntactic calibration and functional validation against simulated device responses. Evaluations on four real-world protocols (Modbus TCP, S7Comm, Omron FINS, and DNP3) demonstrate that FIGAN significantly outperforms state-of-the-art baselines. The source code has been open-sourced https://github.com/MissHP111/FIGAN.	10.1109/TNSM.2026.3717268
Soonbeom Kwon, Yusu Noh, Youngwoo Jang, Illyoung Choi, Byungchul Tak, In-geol Chun, Young-Kyoon Suh	Scalable and Robust Resource Provisioning via Adaptive Task Scheduling for Edge Devices	2026	Early Access	Schedules Scheduling Cloning	Edge devices, such as wearables, drones, and CCTV systems, are vital for real-time data collection in urban intelligence. However, their limited computational and storage capacities pose significant challenges. While offloading to public clouds offers scalability, it often incurs high latency and operational costs. Conversely, centralizing workloads on edge servers may result in the underutilization of high-performance edge devices. To address these limitations, we introduce ERPF, a Kubernetes-based Edge Resource Provisioning Framework that augments the capabilities of heterogeneous edge environments. ERPF orchestrates dynamic volume provisioning, GPU-aware resource allocation, execution context migration, and adaptive task distribution to improve system flexibility and efficiency. Building on this, we propose a novel adaptive task scheduling technique, termed eATS, composed of three key mechanisms: (i) Partition Smoothing Scheme for stable task granularity control, (ii) Resilient Edge Reintegration for failure detection and task reassignment, and (iii) Competitive Task Cloning for speculative execution with fastest-result commitment. The proposed eATS scheme reduces task execution time by up to 27.6%, lowers partition size variability by 8.7×, and improves scheduling robustness across heterogeneous edge devices over the baseline.	10.1109/TNSM.2026.3694238
Qiru Chen, Xinping Guan, Lei Xu, Yanzhou Zhang, Qimin Xu, Cailian Chen	Knowledge-Aware Schedulability Analysis for Time-Sensitive Networking: A GNN-Based Method	2026	Early Access	Modeling Fluid flow Schedules	Industrial automation is rapidly evolving toward flexible production. This transition requires networks to ensure the deterministic transmission of varying traffic sets across different production stages. Consequently, the system must be capable of rapidly analyzing whether fixed network resources can accommodate all service requirements prior to actual scheduling. While Time-Sensitive Networking (TSN) provides the deterministic transmission for such environments, existing schedulability assessments rely on exhaustive scheduling tests. However, the scheduling process is inherently an NP-hard constraint satisfaction problem, whose heavy computational overhead severely limits deployment agility. Therefore, it is critical to develop a method that can rapidly predict the constraint satisfiability of diverse traffic sets without repetitive and time-consuming scheduling. In this work, given the inherent graph-structured nature of network infrastructure and traffic patterns, we design a graph neural network model to explicitly capture complex spatial dependencies. As node attributes, sparse basic traffic features are distilled as expert knowledge and integrated, thereby enhancing prediction accuracy. When a traffic set is deemed unschedulable, we explore the traffic features and links with the greatest impact. Based on this, a feature-driven rerouting strategy is proposed to find a more schedulable traffic behavior. The evaluation results show that the model demonstrates the capability to process thousands of datasets within hundreds of microseconds, while guaranteeing a prediction accuracy of over 90% and an increase in the count in schedulable flows by about 25% compared to the standard Dijkstra’s shortest path algorithm baseline.	10.1109/TNSM.2026.3724835
Francisco Muro, Eduardo Baena, Tomaso De Cola, Sergio Fortes, Raquel Barco	AI-Driven Optimization of Virtual Network Function Allocation in 6G Non-Terrestrial Networks	2026	Early Access	Resource management Optimization Satellites	The integration of 6G technologies into Non-Terrestrial Networks (NTNs) raises a fundamental orchestration problem: how to allocate Virtual Network Functions (VNFs) across satellite and terrestrial domains under tight onboard resource constraints and a continuously changing topology. The virtualized 6G Open Radio Access Network (O-RAN) paradigm makes it possible to run 5G software stacks on Software-Defined Radios (SDRs) based on General Purpose Processors (GPPs), but it also turns VNF placement into a high-dimensional, multi-objective decision that static heuristics and model-based formulations struggle to capture. This paper addresses that gap by introducing an AI-driven VNF allocation framework for 6G-NTN environments built on an O-RAN-based distributed architecture and orchestrated on top of Kubernetes. The VNF allocation problem is formalized for a multi-domain 6G-NTN scenario with constrained satellite resources, and a measurement-based test campaign is designed to characterize the emulated platform in terms of virtual resource utilization and end-to-end performance. The framework couples tree-based machine learning predictors with a gradient-free optimizer to reach the optimal feasible allocation, outperforming two heuristic baselines drawn from the VNF placement literature by reducing the service RTT by up to 39% and delivering up to 3× higher YouTube DL throughput with respect to the best feasible heuristic. Beyond these gains, the proposed framework establishes a measurement-driven, reproducible methodology for VNF allocation in 6GNTN scenarios, demonstrating that AI-driven orchestration can systematically uncover non-obvious resource configurations that purely analytical or static approaches consistently miss.	10.1109/TNSM.2026.3724474
Pingping Dong, Liying Chen, Xuan Yao, Kai Wang, Lianming Zhang, Jiawei Huang	Fumer: Proactive Time-Shifting for Synchronized Periodic Traffic in Distributed Training	2026	Early Access	Training Timing Modeling	The growth of distributed training models, with parameters now reaching the billion-scale, has shifted the system bottleneck from computation to communication. While Remote Direct Memory Access (RDMA) is widely deployed to improve network performance by circumventing the kernel mechanism, the synchronization-computation cycles under the synchronous parallel mode introduce a highly synchronized and periodic “on-off” bursty traffic pattern, which poses significant challenges to data center networking. Consequently, distributed training suffers from two critical bottlenecks: instantaneous congestion during communication and persistent link idleness during computation. These issues lead to severe bandwidth contention and resource underutilization, ultimately hindering overall training efficiency. To address these challenges, this paper proposes Fumer, a proactive periodic traffic optimization framework that shifts the congestion control paradigm from reactive rate adjustment to proactive time-shifting. Specifically, Fumer leverages In-band Network Telemetry (INT) and Fast Fourier Transform (FFT) with signal-wave separation to decompose interleaved traffic signals, aiming to overcome the lack of periodic awareness. Furthermore, Fumer employs an off-peak transmission optimization algorithm to calculate optimal time-shift values, thereby tackling synchronized congestion and link idleness. By executing proactive off-peak scheduling, Fumer shifts overlapping communication windows into idle periods to smooth traffic peaks in the time domain. Experimental results show that Fumer boosts average path throughput across all workloads to 86.3 Gbps, improving upon DCQCN (42.6 Gbps) by 102.6% and RECC by 22.1%. Furthermore, it reduces the average and 99.9th-percentile iteration times by up to 25.0%-45.4% and 25.5%-49.3%, respectively, demonstrating its efficacy and robustness across diverse large-scale training workloads.	10.1109/TNSM.2026.3728016
Mubashir Murshed, Glaucio H. S. Carvalho, Robson E. De Grande	Holistic Intelligent Traffic Steering Management in Multi-RAT Vehicular Networks	2026	Early Access	Radio access technologies Rats Vehicles	Multiple Radio Access Technology (multi-RAT) environments provide a promising foundation for service-aware communication in intelligent transportation systems (ITS) and smart cities. However, traffic steering (TS) in highly mobile and ultra-dense vehicular networks remains challenging due to dynamic network conditions, heterogeneous RAT capabilities, varying vehicle requirements, packet loss, latency, and frequent ping-pong RAT switching. In this context, we propose Holistic Intelligent Traffic Steering (HITS), a proactive bi-level TS management framework for multi-RAT vehicular networks. HITS integrates centralized network-wide guidance with local vehicleside decision-making. At the central level, a Graph Convolutional Network–Long Short-Term Memory (GCN–LSTM) model captures holistic spatio-temporal network dynamics and evaluates RAT optimality. At the local level, a State-Action-Reward- State-Action (SARSA) reinforcement learning agent performs adaptive, vehicle-specific RAT selection using local observations and central-level optimality guidance. Results show that HITS achieves up to 6.5% higher average throughput, reduces packet loss ratio by more than 30.2%, lowers latency by nearly 12.2%, and reduces the ping-pong RAT switching rate by over 24% compared with baseline and state-of-the-art (SoTA) TS approaches.	10.1109/TNSM.2026.3729840
Yali Yuan, Yu Huang, Xingjian Zeng, Hantao Mei, Guang Cheng	M3S-UPD: Efficient Multi-Stage Self-Supervised Learning for Fine-Grained Encrypted Traffic Classification with Unknown Pattern Discovery	2026	Early Access	Labeling Modeling Electronic mail	The growing complexity of encrypted network traffic presents dual challenges for modern network management: accurate multiclass classification of known applications and reliable discovery of unknown traffic patterns. Although deep learning models show promise in controlled environments, their real-world deployment is hindered by data scarcity, concept drift, and operational constraints. This paper proposes M3S-UPD, a novel Multi-Stage Self-Supervised learning framework for encrypted traffic classification and unknown pattern discovery that synergistically integrates semi-supervised learning with representation analysis. Our approach provides a unified framework for known-class classification and unknown pattern discovery through a four-phase iterative process: 1) probabilistic embedding generation, 2) clustering-based structure discovery, 3) distribution-aligned outlier identification, and 4) confidence-aware model updating. Key innovations include a self-supervised mechanism for unknown pattern discovery that requires neither synthetic samples nor prior knowledge, and a continuous learning framework designed for reliable model updating. Experimental results show that M3S-UPD not only outperforms existing methods on the few-shot encrypted traffic classification task, but also simultaneously achieves competitive performance on the zero-shot unknown pattern discovery task. The code is available at https://github.com/fatmo666/M3S-UPD/.	10.1109/TNSM.2026.3729337