TNSM Searchable Index

Author(s)	Title	Year	Publication	Keywords
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
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
Yuya Miyaoka, Masaki Inoue, Kengo Urata, Shigeaki Harada	Chat-Driven Optimal Management for Virtual Network Services	2026	Early Access	Modeling Large language models Central Processing Unit	This paper proposes a chat-driven network management framework that integrates natural language processing (NLP) with optimization-based virtual network allocation, enabling intuitive and reliable reconfiguration of virtual network services. Conventional intent-based networking (IBN) methods depend on statistical language models to interpret user intent, but cannot guarantee the feasibility of generated configurations. To overcome this, we develop a two-stage framework consisting of an Interpreter, which extracts intent from natural language prompts using NLP, and an Optimizer, which computes feasible virtual machine (VM) placement and routing via integer linear programming. In particular, the Interpreter translates user chats into update directions, i.e., whether to increase, decrease, or maintain parameters such as CPU demand and latency bounds, thereby enabling iterative refinement of the network configuration. In this paper, two distinct Interpreter implementations are introduced: a Sentence-BERT model with support vector machine (SVM) classifiers and a large language model (LLM). Experiments in single-user and multi-user settings show that the framework dynamically updates VM placement and routing while preserving feasibility. The LLM-based approach achieves higher accuracy with fewer labeled samples, whereas the Sentence-BERT with SVM classifiers provides significantly lower latency suitable for real-time operation. We also compare our cascade structure method with an end-to-end LLM approach, highlighting our proposed method’s high level of reliability.	10.1109/TNSM.2026.3726950
Liang Liu, Wenbin Zhai, Feng Wang, Youwei Ding, Wanying Lu, Weizhi Meng	Federated Semi-Supervised and Semi-Asynchronous Learning for Anomaly Detection in IoT Networks	2026	Early Access	Labeling Modeling Internet of Things	The expansive attack surfaces and device heterogeneity of Internet of Things (IoT) networks pose significant challenges for anomaly detection. While Federated Learning (FL) enables privacy-preserving detection, existing FL methods typically assume fully labeled client data, which is unrealistic for practical IoT deployments. Resource constraints and network heterogeneity further complicate the trade-off among training efficiency, detection accuracy, and communication overhead. To address these challenges, we propose FedS3A, a Federated Semi-Supervised and Semi-Asynchronous learning framework for IoT anomaly detection. FedS3A operates in a practical disjoint semi-supervised setting where the server holds limited labeled data and clients possess extensive unlabeled data. We apply pseudo-labeling with a dynamically decaying weight to balance server-side supervised training and client-side unsupervised learning. To improve round efficiency, we introduce a semi-asynchronous model update and staleness-tolerant distribution scheme that scales client contributions to the global model based on local model staleness and participation frequency. We also adopt a group-based aggregation function to mitigate the impact of non-IID client data, and utilize sparse difference transmission to reduce communication overhead. We evaluate FedS3A on the CIC-IDS2017 and Edge-IIoTset datasets, and the results demonstrate that FedS3A consistently outperforms representative FL approaches in detection performance and round efficiency. FedS3A achieves over 98% accuracy even under non-IID settings while reducing communication costs by approximately 50%.	10.1109/TNSM.2026.3727228
Lan-jun Jeffrey Chang, Hsuan-Ying Ashely Li, Steven S. W. Lee	Oblivious Routing for Networks with Dual Uncertainties in Demand and Capacity	2026	Early Access	Optimization Routing Joining processes	The Oblivious Routing (OR) problem seeks to determine a static routing strategy that minimizes the worst-case network performance under uncertain traffic demands. Most existing studies assume fixed link capacities and consider uncertainty only in traffic demand. However, in many practical networks, particularly wireless and satellite networks, link capacities are also subject to significant fluctuations. This paper investigates a generalized OR problem that explicitly accounts for uncertainties in both traffic demand and link capacity. We formulate the problem as a nonlinear programming (NLP) model and then transform it into an equivalent linear programming (LP) formulation, enabling efficient computation of the routing solution. Extensive experiments on benchmark networks and randomly generated topologies demonstrate the effectiveness of the proposed approach. Compared with the conventional OR model that assumes fixed link capacities, the proposed capacity-variation-aware routing consistently achieves lower worst-case link utilization under capacity uncertainty while maintaining practical computation times. In the evaluated scenarios, the proposed method achieves an improvement ratio of up to 211% compared to the traditional OR approach.	10.1109/TNSM.2026.3727815
Heng-Ru Zhang, Hong Liu	Center-Aware Pairwise Learning for Deep Semi-supervised Anomaly Detection	2026	Early Access	Modeling Anomaly detection Labeling	Anomaly detection (AD) assists in identifying irregularities within data. Existing works have primarily focused on improving detection for seen anomalies. However, most AD methods struggle to effectively detect unseen anomalies, which are novel types absent in training data and deviate from learned patterns. In this paper, we propose the Center-Aware Pairwise Learning (CAPL) network to effectively capture discriminative patterns of anomalies. For the center-aware component, the network extracts a global center vector that encapsulates the representation of all samples. Center loss regularization is then employed to refocus and adjust the sample features, pulling normal samples closer to the center while pushing anomalies further away. The process enhances robustness against distribution shift, improving the detection of unseen anomalies. For pairwise learning, the network captures relational representations by feature embeddings from positive pairs (normal-normal) and negative pairs (normal-anomaly, anomaly-normal and anomaly-anomaly). This strategy enhances generalization by simultaneously learning various normal/anomalous patterns, mitigating overfitting to labeled anomalies. A comprehensive set of experiments is conducted on 7 benchmark datasets using 7 popular algorithms for comparison. The results show that CAPL significantly outperforms competing methods in detecting seen and unseen anomalies. Source codes are available at https://github.com/zhanghrswpu/CAPL.	10.1109/TNSM.2026.3727684
Jian Wu, Xueshuai Sui, Jun Yao, Leyuan Yang, Guohui Li, Jiadong Sun, Lijun Quan	DAO-Migrator: A Dependency-Aware Orchestration Framework for Service Migration in Hybrid SDN	2026	Early Access	Clouds Decentralized autonomous organization Software defined networking	The evolution of Software-Defined Networking (SDN) has produced a widespread hybrid deployment pattern in which enterprise-operated vendor SDN solutions coexist with internally developed SDN platforms. Because each stack manages its own availability zones (AZs) with distinct interfaces and resource models, migrating cloud services across these heterogeneous environments has become a recurring operational challenge. The scale and dependency complexity of modern service topologies make this task difficult to carry out manually, yet mainstream Infrastructure-as-Code (IaC) tools were not designed with migration-aware process control in mind. This paper presents DAO-Migrator, an orchestration framework that treats service migration as a state-trackable, closed-loop process rather than a sequence of independent resource operations. The framework introduces a formal Migration Quadruple to standardize per-resource workflows, a Dynamic Context Passing mechanism to automatically bridge runtime-discovered parameters from investigation to execution, and production-grade assurance primitives including hierarchical concurrency control and instance-granular atomic rollback. Evaluated in a Mobile Cloud production environment over one year of operation, DAO-Migrator achieves an approximately 50-fold reduction in end-to-end delivery time for 100-port migrations (i.e., execution time plus the manual preparation overhead that competing tools offload to operators), reduces Mean Time to Repair (MTTR) by over 90%, and maintains transactional consistency across evaluated failure scenarios, while imposing no measurable overhead on the target cloud’s control plane.	10.1109/TNSM.2026.3727666
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
Martin Trullenque Ortiz, Daniel Camps Mur, Oriol Sallent, Jad Nasreddine	FOM-5G: A Learning-Based Framework for Overload Mitigation in V2X-Enabled 5G Networks	2026	Early Access	5G mobile communication Cells (biology) Vehicle-to-everything	Public networks deploying 5G-Advanced technology are expected to support large-scale connected and cooperative vehicular services. To this end, 3GPP has enabled the deployment of Day 1 V2X safety services over public cellular networks, which provide the global coverage and low latencies these services require. However, high vehicular mobility may result in traffic jams that strain radio resources, leading to cell overload situations that jeopardize the delivery of V2X services. Previous studies have shown that the best action to mitigate cell overloads strongly depends on road topology and the location of the traffic jam with respect to the cell geometry. Moreover, a fundamental insight to address V2X related cell overloads is to realize that the majority of traffic jams are repeatable, hence historical information should be used to take radio resource management (RRM) decisions. In this paper, we present a novel framework for cell overload mitigation in 5G networks (FOM-5G), an RRM solution for ORAN-based public 5G networks that leverages historical information to prevent recurring cell overloads, particularly those affecting V2X services. We validated FOM-5G through comprehensive simulations incorporating three real vehicular mobility patterns from a European city experiencing traffic congestion. The results show that FOM-5G can adapt its congestion control actions to varying traffic conditions, effectively exploit historical data, and achieve superior performance compared to static congestion mitigation approaches.	10.1109/TNSM.2026.3725207
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
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
Alex Piccioni, Andrea Marotta, Claudia Rinaldi, Dajana Cassioli, Fabio Graziosi	From Ground to Air: Making Mobile Networks Ready for Urban Air Mobility	2026	Early Access	Urban air mobility Modeling Optimization	Urban Air Mobility (UAM) is expected to be an innovative component of future transportation systems. It introduces great flexibility (additional degrees of freedom) to mobility in 3D space and paves the way to novel design methodologies of wireless communication networks to support both critical and non-critical communications. Main requirements for these networks are high reliability, low latency, and high throughput, barely addressable with existing terrestrial networks unless these are upgraded on the basis of a thorough assessment of coverage capabilities and network planning methodologies. This work investigates the suitability of modern mobile networks, such as 5G and forthcoming 6G, to enable the delicate UAM operations and identifies the gaps to be filled in. The study introduces an enhanced evaluation framework based on a system-level simulator tailored to UAM-specific characteristics in realistic scenarios. Our methodology allows us to determine the minimum number of enhanced Base Stations (eBSs) that must be upgraded to satisfy the considered throughput and BLock Error Rate (BLER) requirements under the adopted system assumptions. The framework integrates a channel-aware selection strategy that prioritizes the upgrades in the subset of base stations that allow the provisioning of the most favorable aggregated link conditions. We explore a variety of realistic deployment scenarios, analyzing the impact of parameters such as UAM Vehicle (UV) population, performance requirements, transmit power, and target BLER on achievable network performance. The results highlight the trade-offs between infrastructure density and service quality, offering practical guidelines for network operators to design UAM-ready deployments along the evolution path toward 6G systems.	10.1109/TNSM.2026.3728202
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
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
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
Deemah H. Tashman, Soumaya Cherkaoui	Trustworthy AI-Driven Dynamic Hybrid RIS: Joint Optimization and Reward Poisoning-Resilient Control in Cognitive MISO Networks	2026	Early Access	Reconfigurable intelligent surfaces Reliability Optimization	Cognitive radio networks (CRNs) are a key mechanism for alleviating spectrum scarcity by enabling secondary users (SUs) to opportunistically access licensed frequency bands without harmful interference to primary users (PUs). To address unreliable direct SU links and energy constraints common in next-generation wireless networks, this work introduces an adaptive, energy-aware hybrid reconfigurable intelligent surface (RIS) for underlay multiple-input single-output (MISO) CRNs. Distinct from prior approaches relying on static RIS architectures, our proposed RIS dynamically alternates between passive and active operation modes in real time according to harvested energy availability. We also model our scenario under practical hardware impairments and cascaded fading channels. We formulate and solve a joint transmit beamforming and RIS phase optimization problem via the soft actor-critic (SAC) deep reinforcement learning (DRL) method, leveraging its robustness in continuous and highly dynamic environments. Notably, we conduct the first systematic study of reward poisoning attacks on DRL agents in RIS-enhanced CRNs, and propose a lightweight, real-time defense based on reward clipping and statistical anomaly filtering. Numerical results demonstrate that the SAC-based approach consistently outperforms established DRL base-lines, and that the dynamic hybrid RIS strikes a superior trade-off between throughput and energy consumption compared to fully passive and fully active alternatives. We further show the effectiveness of our defense in maintaining SU performance even under adversarial conditions. Our results advance the practical and secure deployment of RIS-assisted CRNs, and highlight crucial design insights for energy-constrained wireless systems.	10.1109/TNSM.2026.3660728
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
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
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
Ryota Nakamura, Kengo Urata, Shigeaki Harada	A Virtual Network Embedding Method Enhancing Self-consumption of Renewable Energy	2026	Early Access	Timing Joining processes Real-time systems	To achieve carbon neutrality in telecommunications networks, renewable energy (RE) must be utilized more efficiently despite its inherent fluctuations. These fluctuations often cause mismatches between power generation and demand, leading to surplus power in some sites and shortages in others. To address this issue, we formulate the problem of maximizing RE use in the allocation of virtualized ICT workloads, which can be realized through techniques such as VM live migration across sites and time periods. To efficiently solve the formulated problem, we propose a heuristic search method that leverages virtualization to enable VM workload migration across sites and time periods, thereby optimizing resource allocation according to RE availability. The proposed method is designed to increase RE consumption while managing trade-offs with communication quality and equipment efficiency. Simulation results demonstrate that our approach can improve RE utilization by up to 30% while maintaining acceptable performance, even under prediction errors. These findings highlight the potential of virtualization-based control frameworks for building more sustainable and environmentally conscious telecommunications networks.	10.1109/TNSM.2026.3726977