TNSM Searchable Index

Author(s)	Title	Year	Publication	Keywords
Krati Mittal, Akash Gupta, Parul Garg	Resource Management in Energy Efficient RSMA based Hybrid Satellite Terrestrial Network	2026	Early Access	Antennas Receiving antennas Transmitting antennas	In this paper, we study resource management in terms of energy and spectrum for a cooperative satellite terrestrial network. We investigate a comprehensive analysis of an energy efficient rate splitting multiple access (RSMA) based cooperative satellite terrestrial network. RSMA being more efficient both in terms of energy and spectrum when combined with practical non linear energy harvesting, results in a highly efficient cooperative network. While analyzing the network, we consider that the energy of the signal transmitted by the satellite is non linearly harvested at decode and forward (DF) relay. Further this harvested energy is used to transmit signal from relay to RSMA users. The satellite to relay link is characterised by shadowed Rician fading while the link between relay and RSMA users is characterised by α-μ fading. We derive the closed form expression of outage probability and ergodic capacity considering the impact of key parameters both numerically and asymptotically. Simulation results validate the numerical results obtained.	10.1109/TNSM.2026.3684366
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
Wangqing Luo, Jinbin Hu, Hua Sun, Pradip Kumar Sharma, Jin Wang	SALB: Security-Aware Load Balancing for Large Language Model Training in Datacenter Networks	2026	Early Access	Training Load management Packet loss	To meet the massive compute and high-speed communication demands of Large Language Model (LLM) training, modern datacenters typically adopt multipath topologies such as Fat-Tree and Clos to host parallel jobs across hundreds to thousands of GPUs. However, LLM training exhibits periodic, high-bandwidth communication patterns. Existing load-balancing schemes become misaligned under dynamic congestion and anomalous surges: they struggle to promptly mitigate iteration-peak congestion and lack effective isolation of anomalous traffic. To address this, we propose Security-Aware Load Balancing (SALB) for LLM training. SALB leverages a Deep Reinforcement Learning (DRL) controller with queue and delay signals for packet-level multipath load balancing and employs path binding to confine suspicious flows. By integrating data security into load balancing, SALB simultaneously achieves high throughput and robust traffic isolation. NS-3 simulation results show that, compared with CONGA, Hermes, and ConWeave, SALB reduces the 99th-percentile flow completion time (FCT) of short flows by an average of 65% and increases the throughput of long flows by an average of 54%. It further outperforms the baselines in aggregate throughput, path utilization, and packet loss rate, thereby significantly enhancing system stability, robustness, and data security.	10.1109/TNSM.2026.3678979
Xinyue Zhang, Xuan Zhou, Jie Ma, Zeqi Li, Feng He	Interference-Aware Multi-Metric Delay Evaluation and Optimization for Switched Networks	2026	Early Access	Aerospace electronics Aerospace engineering Radio broadcasting	Switched networks are essential to modern real-time systems, where packet delays must be tightly bounded with minimal variation. Traditional delay analysis often focuses on worst-case bounds, but may overlook delay jitter induced by fine-grained inter-flow interference, which can degrade real-time performance and stability. Existing routing schemes typically rely on proxy indicators such as link load or path length, offering limited explicit control over delay and jitter behavior. To address these limitations, we propose an interference-aware delay evaluation and optimization framework that models the encounter interval and magnitude of flow interference at the packet level. From this, we derive worst-case delay, average delay, and delay jitter, and integrate these metrics into a unified, tunable optimization objective. We design a K-shortest-path genetic algorithm to jointly reduce them. Experimental results over multiple traffic loads demonstrate consistent improvements in delay and jitter performance, indicating that the proposed approach is scalable and practical for delay-sensitive and stability-critical switched networks.	10.1109/TNSM.2026.3680250
Kai Huang, Andrea Sordello, Rodolfo Valentim, Luca Vassio, Idilio Drago, Marco Mellia	FedScope – Federated Host Embeddings from Telescope Traffic: Design and Implementation	2026	Early Access	Payloads Military aircraft Space technology	network telescope is a range of IP addresses that host no services. Millions of bots and scanners contact it to look for vulnerable systems, and the traffic it exposes is fundamental to understanding malicious activities. The visibility a telescope offers depends on its size and geolocation, and merging the information from multiple telescopes could help increase visibility and uncover more malicious activities. However, sharing raw telescope data is complicated, calling for solutions that allow one to directly share the knowledge rather than the data obtained from multiple deployments. In this paper, we explore the application of Federated Learning (FL) to create and share such global knowledge from the malicious activities seen in distributed telescopes. For that, we introduce FedScope, an FL-based solution for generating host embeddings in a distributed way. We compare FedScope to local and distributed alternatives in downstream tasks, such as sender classification or coordinated activities detection. We show that FedScope (i) produces embeddings of equal or higher quality than those of a single telescope; (ii) increases coverage, allowing the global model to monitor more malicious actors; (iii) avoids the sharing of the raw data, limiting exchanged data.	10.1109/TNSM.2026.3685756
Cheng Ren, Jinsong Gao, Yu Wang, Yaxin Li, Hongwei Li	GCN-Transformer Assisted Live SFC Migration with Hierarchical Reinforcement Learning in Mobile Edge Computing	2026	Early Access	Feeds Antennas Filtering theory	Empowered by network function virtualization (NFV), mobile edge computing aims to provide low latency and ultra reliable network services to mobile end users, achieved as a service function chain (SFC) consisting of a series of ordered virtual network functions (VNFs). Due to user mobility, live SFC migration is imperative to avoid Quality of Service (QoS) degradation. Recent advances mainly make separate decisions on VNF node remapping and migration path routing in a heuristic manner, or implement both through reinforcement learning within a single agent of ill-defined policy and action space. In this paper, given next access node, we first formulate the live SFC migration problem as an integer linear programming (ILP) model to achieve optimal solutions. Then, we present HRL-QC, a hierarchical reinforcement learning framework that jointly optimizes VNF destination node remapping, migration path and post-migration service path selections for QoS-aware and cost-efficient live SFC migration. A GCN-Transformer block is introduced to capture long-range VNF-to-physical node dependencies, while a two-level actor-critic design couples the decision-makings through inter-level reward passing. Extensive evaluations show that HRL-QC outperforms the state-of-the-art in energy consumption, migration time, end-to-end service delay, and migration success rate, while remaining within a small margin of the optimal ILP solution.	10.1109/TNSM.2026.3681690
Jing Zhang, Chao Luo, Rui Shao	MTG-GAN: A Masked Temporal Graph Generative Adversarial Network for Cross-Domain System Log Anomaly Detection	2026	Early Access	Anomaly detection Adaptation models Generative adversarial networks	Anomaly detection of system logs is crucial for the service management of large-scale information systems. Nowadays, log anomaly detection faces two main challenges: 1) capturing evolving temporal dependencies between log events to adaptively tackle with emerging anomaly patterns, 2) and maintaining high detection capabilities across varies data distributions. Existing methods rely heavily on domain-specific data features, making it challenging to handle the heterogeneity and temporal dynamics of log data. This limitation restricts the deployment of anomaly detection systems in practical environments. In this article, a novel framework, Masked Temporal Graph Generative Adversarial Network (MTG-GAN), is proposed for both conventional and cross-domain log anomaly detection. The model enhances the detection capability for emerging abnormal patterns in system log data by introducing an adaptive masking mechanism that combines generative adversarial networks with graph contrastive learning. Additionally, MTG-GAN reduces dependency on specific data distribution and improves model generalization by using diffused graph adjacency information deriving from temporal relevance of event sequence, which can be conducive to improve cross-domain detection performance. Experimental results demonstrate that MTG-GAN outperforms existing methods on multiple real-world datasets in both conventional and cross-domain log anomaly detection.	10.1109/TNSM.2026.3654642
Li-Chin Siang, Wen-Hsing Kuo, Pei-Chieh Lin, Chih-Wei Huang, De-Nian Yang	FoV Prediction-Based Adaptive Streaming Mechanism for 6DoF Volumetric MR Applications in Multi-Base-Station Networks	2026	Early Access	Payloads Antennas Feeds	The emergence of mixed reality (MR) as a significant application in mobile networks has garnered significant attention. Wireless headsets enable unrestricted user movement within femtocell networks comprising numerous small base stations, offering a promising solution for MR applications. However, the complexity of these systems poses challenges in optimizing resource allocation across base stations. This paper proposes a novel resource allocation method for volumetric MR streaming in multi-base-station environments. The method consists of two phases. Firstly, the method uses neural networks to model and forecast users’ viewing directions. Leveraging these predictions, their confidence levels, and layer characteristics, the algorithm adjusts video quality for each user and allocates transmission resources across base stations to optimize overall performance. Through comprehensive analysis, we prove that this novel problem is NP-hard and show that our approach achieves a performance within a bounded gap from the optimal solution. Simulation results reveal that our proposed algorithm outperforms existing techniques, enhancing aggregate performance across diverse scenarios.	10.1109/TNSM.2026.3685670
Vibha Jain, Prabal Verma, Mohit Kumar, Aryan Kaushik	Blockchain-enabled Incentive Mechanism for Federated Learning: A Multi-Agent Deep Deterministic Policy Gradient Approach	2026	Early Access	Broadcasting Broadcast technology Central Processing Unit	The expeditious growth of the Internet of Things (IoT) generates massive data, which allows advanced machine learning. However, the traditional approach of centralized model training raises the issue of high bandwidth consumption and privacy. Federated learning (FL) mitigates this by enabling local training on raw data with centralized aggregation to generate the global model. The effectiveness of FL depends upon the active participation of resource-constrained local devices. This article presents a blockchain-enabled incentive mechanism for FL leveraging the Multi-Agent Deep Deterministic Policy Gradient (MA-DDPG) algorithm. Specifically, an incentive scheme is formulated with the MEC (Mobile Edge Computing) server as the leader agent and local devices as learning agents in a cooperative environment. We formalize a two-stage Stackelberg game to establish a Nash equilibrium, which ensures fair and utility-optimized reward distribution for MEC and devices. A Markov Decision Process (MDP) is utilized to solve the equilibrium with incomplete knowledge, and utilities are optimized using the MA-DDPG algorithm. The proposed model considers data quality and device contribution to obtain optimal reward distribution and participation strategies dynamically. The experimental results show an approximate 38% improvement in MEC utility and approx 17% in device utility, with rapid convergence (approximately 300-500 episodes) at a learning rate of 0.0001.	10.1109/TNSM.2026.3682129
Arad Kotzer, Tom Azoulay, Yoad Abels, Aviv Yaish, Ori Rottenstreich	SoK: DeFi Lending and Yield Aggregation Protocol Taxonomy, Empirical Measurements, and Security Challenges	2026	Early Access	Filtering Application specific integrated circuits Filters	Decentralized Finance (DeFi) lending protocols implement programmable credit markets without intermediaries. This paper systematizes the DeFi lending ecosystem, spanning collateralized lending (including over- and under- collateralized designs, and zero-liquidation loans), uncollateralized primitives (e.g., flashloans), and yield aggregation protocols which allocate capital across underlying lending platforms. Beyond a taxonomy of mechanisms and comparing protocols, we provide empirical on-chain measurements of lending activity and user behavior, using Compound V2 and AAVE V2 as case studies, and connect empirical observations to protocol design choices (e.g., interestrate models and liquidation incentives). We then characterize vulnerabilities that arise due to notable designs, focusing on interestrate setting mechanisms and time-measurement approaches. Finally, we outline open questions at the intersection of mechanism design, empirical measurement and security for future research.	10.1109/TNSM.2026.3682174
Zuodong Wu, Dawei Zhang, Mianxiong Dong, Kaoru Ota	PDRAA: An Efficient Privacy Data Retrieval Protocol with Anonymous Authorization Based on Verifiable Credential	2026	Early Access	Payloads Broadcasting Broadcast technology	In the data-driven era, the unchecked collection and processing of personal data has given rise to serious privacy concerns. In response, the General Data Protection Regulation (GDPR) was introduced to grant individuals stronger control over the use of their data. Privacy data retrieval methods show considerable promise in this context, but further improvements are required to balance the principles of lawfulness and data minimization. To address this problem, we propose PDRAA, an efficient privacy data retrieval protocol with anonymous authorization based on the verifiable credential (VC). Specifically, our designed VC achieves anonymous identification of data subjects and facilitates fine-grained access control by supporting selective disclosure of attributes. By combining VC with non-interactive zero-knowledge (NIZK) proofs, PDRAA enables data subjects to anonymously authenticate via VC presentation. This allows the data controller to verify the legitimacy of retrieval requests while ensuring compliance with the principle of data minimization. Besides, PDRAA introduces a re-randomization mechanism to prevent linkability attacks during the authorization process and provides lightweight, flexible authorization revocation. Moreover, we utilize Labeled Private Set Intersection (Labeled PSI) technology to meet the privacy requirements of participants and support batch retrieval. Our protocol takes a comprehensive security analysis within the Universal Composability framework. Experimental results demonstrate that PDRAA outperforms existing methods in terms of performance, which is significant for promoting compliance with GDPR.	10.1109/TNSM.2026.3681957
Shuai Liu, Xiangxu Meng, Yun Zhong, Wenfeng Li, Kanglian Zhao	Dual-timescale Joint Optimization for Dynamic Edge Service Deployment and Task Scheduling in Space-air-ground Integrated Networks	2026	Early Access	Low earth orbit satellites Artificial satellites Propagation losses	This paper investigates dual-timescale joint optimization for dynamic edge service deployment and task scheduling in space-air-ground integrated networks (SAGINs) with mobile edge computing (MEC). We decompose the original problem into a long-term (LT) service deployment subproblem and a short-term (ST) task scheduling subproblem to address the timescale disparity and coupling constraints. For the LT subproblem, an improved whale optimization algorithm (IWOA) is developed with a cosine-based nonlinear convergence factor, vertical-horizontal crossover, and elite opposition-based learning to enhance exploration and convergence. For the ST subproblem, we model task scheduling as a decentralized partially observable Markov decision process (Dec-POMDP) and adopt multi-agent proximal policy optimization (MAPPO) with KL regularization and clipping for stable policy updates. A hierarchical alternating framework (with a self-attention module for traffic-feature extraction) is designed to coordinate the two timescales iteratively. Simulation results demonstrate that, compared with the baseline WOA-PPO algorithm, the proposed method increases service deployment benefits by 27.2% and reduces task scheduling costs by 38.5%.	10.1109/TNSM.2026.3685292
Yingpu Nian, Xiang Jia, Baishun Zhou, Zhi Wang, Bo Yi, Xinhao Zhou, Haodong Li, Yuan Yang, Xingwei Wang, Geyong Min, Keqin Li	XAForward: Accelerating Distributed Large-scale Language Model Training Through Fast eXpress Data Path	2026	Early Access	Payloads Military aircraft Space technology	With the rapid development of Artificial Intelligence Generated Content (AIGC), single data centers are increasingly unable to meet the growing demands for data and computational resources in distributed large-scale language model (LLM) training. In this context, distributed training across heterogeneous data centers has become a necessary choice to enhance computational power and flexibility. However, the networks in heterogeneous data centers are polymorphic, with diverse communication protocols and network architectures. This heterogeneity renders traditional routing devices ineffective in recognizing and processing gradient data. Moreover, frequent copying and excessive parsing of gradient data by routing devices across heterogeneous data centers significantly increase model training time. To address these challenges, we propose XAForward, a method for accelerating distributed LLM in heterogeneous data centers using eXpress Data Path (XDP). Specifically, XAForward introduces a polymorphic-compatible protocol that reconstructs the header of gradient data packets to enable efficient data forwarding across different communication protocols in heterogeneous data centers. Additionally, to accelerate distributed LLM computing and reduce gradient data copying and excessive parsing during training, XAForward leverages kernel-bypass techniques based on XDP for packet processing and kernel-level data forwarding using network index identifiers. Experimental results show that, compared to state-of-the-art methods, XAForward reduces the distributed LLM training time by approximately 35% to 40%.	10.1109/TNSM.2026.3684024
Sifeddine Salmi, Messaoud Ahmed Ouameur, Miloud Bagaa, George C. Alexandropoulos, Abdellah Tahenni, Daniel Massicotte, Adlen Ksentini	AI-Native O-RAN Architectures for 6G: Towards Real-Time Adaptation, Conflict Resolution, and Efficient Resource Management	2026	Early Access	Telemetry Aerospace and electronic systems Antennas	Open Radio Access Network (O-RAN) enables modular and intelligent control of radio resources through open interfaces and programmable RAN components. As networks evolve toward sixth-generation (6G) systems, the proliferation of autonomous xApps and rApps introduces a critical challenge: Coordinating concurrent AI-driven control actions under tight near-real-time constraints while avoiding instability and conflicting decisions. This paper focuses on two tightly coupled enablers for AI-native O-RAN orchestration: Conflict-aware control and intent-driven automation. We propose an AI-native orchestration framework centered on a CME integrated into the Near-RT RIC, and a complementary LLM-based intent orchestration module deployed in the Non-RT RIC. The CME is designed to autonomously arbitrate conflicting xApp actions by learning adaptive mitigation policies from structured conflict signals, system context, and performance feedback, rather than relying on static priorities or predefined conflict classes. The LLM module translates high-level operator intents into policy constraints and control objectives that guide conflict resolution and xApp behavior. Overall, this work advances AI-native O-RAN orchestration by grounding conflict-aware control and LLM-assisted intent translation in practical measurements, and by outlining a clear path toward scalable, adaptive, and resilient control mechanisms required for future 6G RIC deployments.	10.1109/TNSM.2026.3684880
Md Arif Hassan, Bui Duc Manh, Cong T. Nguyen, Chi-Hieu Nguyen, Dinh Thai Hoang, Diep N. Nguyen, Nguyen Van Huynh, Dusit Niyato	SBW 3.0: A Blockchain-Enabled Framework for Secure and Efficient Information Management in Web 3.0	2026	Early Access	Jamming Protocols Semantic Web	In this paper, we propose an effective blockchain-enabled information management framework, named Smart Blockchain-based Web 3.0 (SBW 3.0). Our framework aims to handle information within Web 3.0 efficiently, enhance data security and privacy, create new revenue streams, and encourage users to contribute valuable information to websites. To this end, SBW 3.0 employs blockchain technology and smart contracts to manage the decentralized data collection in Web 3.0. Moreover, we introduce a robust consensus mechanism grounded in Delegated Proof-of-Stake (DPoS) to reward user contributions. Furthermore, we develop a non-cooperative game model to examine user behavior in this context and conduct thorough analysis to prove the uniqueness of the Nash equilibrium in our proposed system. Through simulations, we evaluate the performance of SBW 3.0 and analyze the effects of various critical parameters on information contribution. Our results validate the theoretical analysis, showing that the proposed consensus mechanism successfully encourages nodes and users to provide more information, thus overcoming the current limitations of Web 3.0 regarding data decentralization and management.	10.1109/TNSM.2026.3683881
Killian Cressant, Federico Larroca, Stefano Secci, Pedro B. Velloso	On Graph Design for GNN-based Network Anomaly Detection	2026	Early Access	Telemetry Aerospace and electronic systems Radio broadcasting	Graph Neural Networks (GNNs) have gained significant attention for multivariate time series analysis in recent years. However, applying them to real-world networking data introduces several key challenges, particularly in designing meaningful and effective graph structures. In this paper, we propose a novel method for constructing initial graphs tailored for time series anomaly detection in complex network environments. Our approach, called COSI (COrrelation SImilarity), leverages two fundamental properties of real-world data: feature name semantics and statistical correlation. By combining natural language processing (NLP) with correlation analysis, COSI produces graph structures that significantly enhance GNN model performance for anomaly detection tasks, outperforming conventional graph construction methods in almost all evaluation scores across all datasets. We extensively evaluate COSI on three datasets, including two real-world networking datasets and one widely used benchmark dataset, and we open source the implementation to encourage reproducibility, further research, and practical adoption by the community.	10.1109/TNSM.2026.3684653
Abdeltif Azzizi, Mohamad Al Adraa, Chadi Assi, Michael Y. Frankel, Vladimir Pelekhaty	Experimental Topological Analysis in Next-Generation Data Center Networks: STRAT and Clos Topologies	2026	Early Access	Telemetry Aerospace and electronic systems Payloads	This paper presents an experimental and simulationbased evaluation of two data center network (DCN) topologies: the widely adopted hierarchical Clos architecture and STRAT, a flat, expander-based topology designed around passive optical interconnects. While Clos offers proven scalability and performance, it incurs hardware complexity and suffers from congestion in oversubscribed scenarios. STRAT eliminates aggregation and spine layers entirely—using only Top-of-Rack (ToR) switches interconnected via static optical patch panels—to reduce cost, simplify deployment, and enhance path diversity. Our goal is to assess these topologies based on their inherent architectural properties—namely throughput, congestion resilience, scalability, and cost—without relying on congestion control protocols or centralized traffic engineering. To this end, we adopt simple forwarding schemes based purely on local information: ECMP for Clos, and ECMP with Dynamic Group Multipath (DGM) for STRAT. We evaluate both topologies on a physical testbed built from commercial Ethernet switches and further validate scalability through packet-level simulations of networks with up to 256 switches and 1,024 hosts using OMNeT++. We also introduce DEALER, a lightweight routing algorithm tailored to STRAT’s topology, and evaluate its effectiveness in dynamic conditions. Our results show that STRAT achieves up to 43% higher throughput and requires approximately 40% fewer switches than a comparable Clos topology. These gains are further supported by Load Area Under Curve (LAUC) analysis and congestion hotspot visualizations. Overall, our study highlights STRAT as a compelling and practical alternative to conventional DCN architectures, offering deployable scalability, improved performance under load, and reduced infrastructure cost.	10.1109/TNSM.2026.3685175
Guisong Yang, Yechao Huang, Panxing Huang, Xingyu He	A Distributed SDN Controller-Based Computing Framework for Effective in-orbit Computing	2026	Early Access	Low earth orbit satellites Artificial satellites Aerospace and electronic systems	The rapid development of Low Earth Orbit (LEO) satellite networks has made in-orbit computing more feasible, offering a solution for processing real-time, diverse user tasks. Compared with traditional cloud computing in ground cloud computing center, directly computing on the LEO satellite can significantly reduce task-processing delay. However, challenges remain, including the limited sensing and computing capabilities of satellites, high delays in processing task requests, and frequent switching of control domains due to the relative movement between LEO satellites and nodes in other orbits. To address these challenges and improve task management, computing is treated as a Virtual Network Function (VNF), managed by Software-Defined Networking (SDN) controllers. This paper proposes a distributed SDN controller-based computing framework, where task information is forwarded to SDN controllers, which then use a task scheduling strategy to allocate tasks to suitable computing nodes for processing. To support the implementation of this framework, we first propose a heuristic SDN controller placement strategy that uses a tiling method to divide the LEO satellite network into SDN control domains and places the controller at the midpoint of each domain Then, we propose a Double Deep Q-Network (DDQN) algorithm for in-orbit task scheduling, which adaptively optimizes task scheduling strategy to minimize task-processing delay and ensure a high task completion rate. Finally, Simulations are conducted in two parts to evaluate the framework. The first part validates the DDQN-based task scheduling strategy, achieving significant reductions in task-processing delay and improved task completion rates compared to conventional strategies. The second part assesses the impact of SDN control domain shape and size on task-processing delay, confirming domain size as the dominant factor influencing delay.	10.1109/TNSM.2026.3685308
Zhenzhen Yan, Lizhi Peng, Peiqiang Liu, Yingshuo Bao, Bo Yang	NT-Transformer: A Non-Pretrained Encrypted Network Traffic Classification Model	2026	Early Access	Payloads Military aircraft Space technology	Network traffic classification plays an indispensable role in network management, Quality of Service (QoS), and cybersecurity. With the widespread encryption techniques applied to network traffic, it has become increasingly challenging to classify network traffic into different management groups accurately. In recent years, pre-training Transformer-based models have been successfully applied to Natural Language Processing (NLP), and researchers have also introduced such models into encrypted network traffic analysis. However, besides the similarities of words in NLP and byte codes in network traffic, there exist essential differences between them, which may cause inefficacy of the pretrained model when being applied to new traffic data. In this paper, we propose a non-pretrained encrypted network traffic classification model based on Transformer called NT-Transformer, which can directly learn labeled network traffic features at two levels of granularity, namely, byte level (uni-gram or bi-gram) and flow level (packet size and packet inter-arrival time), without the relatively expensive pre-training procedure of unlabeled data. This method is validated on three public datasets and three sets of recently collected network traffic data. Experimental results indicate that in some scenarios, pretrained models offer limited performance gains when applied to new encrypted network traffic data not encountered during pretraining, and NT-Transformer with uni-gram byte representation outperforms the state-of-the-art models in terms of pushing the F1 score up by 0.25% - 2.24%.	10.1109/TNSM.2026.3683410
Alba Jano, Serkut Ayvaşik, Yash Deshpande, Wolfgang Kellerer	QUEST: User-Based Quality of Service Aware Uplink Resource Scheduling	2026	Early Access	Payloads Military aircraft Space technology	Efficient radio resource management (RRM) in 5G networks is increasingly challenged by the diverse quality of service (QoS) requirements of emerging applications and the growing uplink (UL) traffic from resource-constrained devices. Existing scheduling approaches often lack user and service-specific context, limiting their ability to guarantee timely and energy-efficient data transmission, particularly critical for the internet of things (IoT) and mission-critical services. In this work, we introduce QUEST, a QoS-aware UL scheduling framework that exploits the 5G QoS model alongside network and device context to efficiently allocate radio resources. Designed and evaluated in an indoor factory environment, QUEST supports users with various heterogeneous 5QI services under dynamic multi-user conditions. Evaluation results, validated through both real-world measurements and 3GPP-compliant simulations, show that QUEST consistently outperforms traditional channel- and QoS-aware schedulers. It improves QoS compliance, reduces packet drops and serving time, and enhances energy efficiency. For users with stringent QoS demands, measurements show a 13% increase in successfully transmitted packets and a 6.2% reduction in delay for 50% of transmissions, compared to the best-performing baseline. Benchmarking against an optimal scheduler shows that QUEST achieves the closest performance among baselines, while maintaining low complexity, making it a practical and scalable solution for 5G and beyond UL RRM.	10.1109/TNSM.2026.3685537