TNSM Searchable Index

Author(s)	Title	Year	Publication	Keywords
Mengmeng Sun, Zeyu Tan, Dianlong You, Zhen Chen	PCNet: A Personalized Complementary Network via Tensor Decomposition for Service Recommendation	2026	Early Access	Mashups Tensors Collaborative filtering	Web services are widely utilized across domains such as cloud computing, mobile networks, and Web applications. Due to their single-function nature, these services are often composed into Mashups to achieve more comprehensive functionality. However, the rapid growth in the number and variety of Web services has made it increasingly difficult to identify suitable services for Mashup development. Web service recommendation systems have emerged as a solution to this service overload, supporting innovative practices within the service-oriented development paradigm. While existing methods emphasize recommendation accuracy and relevance, few approaches simultaneously consider the personalized requirements of the Mashup side and the complementary relationships on the service side, both of which are essential for reconstructing the Web service ecosystem’s value chain. To address this gap, we propose PCNet, a Personalized Complementary Network for service recommendation based on tensor decomposition. We conceptualize the interaction dynamics between Mashups and services, as well as coinvocation patterns among services, using a three-dimensional tensor. The RESCAL tensor decomposition technique is then applied to jointly learn these relationships and uncover personalized complementary relationships among services. In addition, we develop a complementary perception module that uses an attention mechanism to dynamically model a Mashup’s focus on different complementary relationships, extending them to higher orders. Experimental results on real-world Web service datasets demonstrate that PCNet significantly outperforms state-of-the-art baselines. The implementation of PCNet is publicly available at: https://github.com/MengMeng3399/PCNet.	10.1109/TNSM.2026.3669613
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
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
Wenxuan Li, Yu Yao, Ni Zhang, Chuan Sheng, Ziyong Ran, Wei Yang	IMADP: Imputation-based Anomaly Detection in SCADA Systems via Adversarial Diffusion Process	2026	Early Access		As the confrontation of the industrial cybersecurity upgrades, multi-dimensional variables measured by the SCADA multi-sensor are critical for assessing security risks in industrial field devices. While Deep Learning (DL) methods based on generative models have demonstrated effectiveness, the impact of missing features in samples and temporal window size on modeling and detection processes has been consistently overlooked. To address these challenges, this work proposes an IMADP framework that integratively solves two tasks of missingness patching and anomaly detection. Firstly, the Window-based Adaptive Selection Strategy (WASS) is also designed to intelligently window samples, reducing reliance on prior settings. Secondly, an imputer is constructed under WASS to restore sample integrity, which is implemented by a fully-connected network centered on Neural Controlled Differential Equations (NCDEs). Thirdly, a adversarial diffusion detection model with the variant Transformer as the inverse solver is proposed. Additionally, the Adaptive Dynamic Mask Mechanism (ADMM) is built upon to bolster the model’s comprehension of inter-dependencies between time and sensor nodes. Simultaneously, adversarial training is introduced to optimize training and detection latency caused by the excessive diffusion step size during the native Conditional Diffusion process. The experimental results validate that the proposed framework has the capability to build detectors using missing training samples, and its overall detection performance, tested across six datasets, is superior to existing methods.	10.1109/TNSM.2026.3670062
Masaki Oda, Akio Kawabata, Eiji Oki	Consistency-Aware Multi-Server Network Design for Delay-Sensitive Applications under Server Failures	2026	Early Access		Real-time applications require low latency and event order guarantees. Distributed server processing is effective for this purpose, and data consistency between servers is crucial. Although existing models in previous work handle data consistency, they do not address server failures. This paper proposes a server allocation model for a consistency-aware multi-server network for delay-sensitive applications with preventive start-time optimization (PSO) under single-server failures. The proposed model considers data consistency between servers and handles single-server failures with PSO. PSO determines the assignment to minimize the worst-case delay over all possible failure scenarios while avoiding service disruption for users connected to non-failed servers. We formulate the proposed model as an integer linear programming (ILP) problem. The decision version of the server allocation problem is proven to be NP-complete, and it becomes difficult to solve in a practical time when the problem size is large. We develop two polynomial-time approximation algorithms with theoretical performance analysis. Numerical results show that the proposed model outperforms start-time optimization in terms of the largest total delay and run-time optimization in terms of avoiding instability. The results also show that the faster of our two developed algorithms achieves a speedup ranging from 2.26×103 to 4.37×106 times compared to the ILP approach, while the maximum delay is, on average, only 1.029 times the optimal value. The results indicate that the speedup effect becomes more significant as the number of users and servers increases.	10.1109/TNSM.2026.3669840
Imran Ahmed, Bijoy Chand Chatterjee, Eiji Oki	AnalyticalSAR: Analytical Modeling for Blocking Performance with Security-Aware Reconfiguration in Spectrally-Spatially Elastic Optical Networks	2026	Early Access		Spectrally-spatially elastic optical networks (SS-EONs) enable ultra-high data rate transmission, which raises critical concerns about physical-layer security vulnerabilities, particularly against eavesdropping and unauthorized network access. Dynamic resource allocation through lightpath reconfiguration presents an effective approach to improving security by reducing request exposure windows. However, implementing secure reconfiguration in SS-EONs introduces significant complexity due to the complex relationships between spectral allocation and spatial resource management constraints. This paper proposes an analytical model for blocking performance with security-aware reconfiguration (AnalyticalSAR) in SS-EONs based on continuous-time Markov chain analysis to tackle these security challenges. The AnalyticalSAR provides analytical assessment of how spectrum reconfiguration affects both network security and blocking performance while accounting for inter-core and intermode crosstalks. The model generates all viable states accounting for spectrum reconfiguration processes and their corresponding transitions to establish state probabilities. Our analysis incorporates two distinct spectrum allocation policies: core-modespectrum random fit (CMS-RF) and core-mode-spectrum first fit (CMS-FF) policies. Our model supports diverse traffic scenarios, including single-class requests with uniform slot requirements and multi-class requests including heterogeneous bandwidth demands. To overcome computational complexity limitations in single-hop analyses, we develop an heuristic iterative approach and subsequently extend this approach to multi-hop network scenarios. We compare AnalyticalSAR, the heuristic iterative approach, and Monte Carlo simulation studies for a single-hop link. Analytical evaluation reveals that random spectrum reconfiguration substantially improves security metrics while introducing minimal blocking probability increases. These performance trade-offs depend critically on number of spectrum reconfiguration, link and network load conditions, and available link capacity. The results validate that AnalyticalSAR achieves an effective compromise between security enhancement and operational performance, providing a practical framework for secure resource management in SS-EON deployments.	10.1109/TNSM.2026.3669870
Ghofran Khalaf, May Itani, Sanaa Sharafeddine	A UAV-Aided Digital Twin Framework for IoT Networks with High Accuracy and Synchronization	2026	Early Access		Digital Twin (DT) technology has emerged as a promising link between the physical and virtual worlds, enabling simulation, prediction, and real-time performance optimization in different domains. In this work we develop a high-fidelity digital twin framework, focusing on synchronization and accuracy between physical and digital systems to enhance data-driven decision making. To achieve this, we deploy several stationary UAVs in optimized locations to collect data from IoT devices, which were used to monitor multiple physical entities and perform computations to evaluate their status. We formulate a mixed-integer non-convex program to maximize the total amount of data collected from all IoT devices while ensuring a constrained age of digital twin threshold and solve it using successive convex approximation (SCA). To cope with realistic scenarios involving unpredictable environments and large network sizes, we model our problem as a Markov Decision Process (MDP), and propose a deep reinforcement learning-based approach using a Twin Delayed Deep Deterministic Policy Gradient (TD3) to optimize the unmanned aerial vehicle positions and the sum rate. Finally, we present different simulation results of the SCA and TD3 based solutions together with two baseline approaches and evaluated the sum rate in terms of IoT device count, AoDT threshold, task arrival rate and UAVs’ computational capacity. In all simulation results, the proposed TD3-based approach consistently proved to be superior as compared to the baseline solutions.	10.1109/TNSM.2026.3670040
Chengwei Liao, Guofeng Yan, Hengliang Tan, Jiao Du, Xia Deng, Heng Wu	jTOLP-MADRL: A MADRL-based Joint Optimization Algorithm of Task Offloading Location and Proportion for Latency-sensitive Tasks in Vehicle Edge Computing Network	2026	Early Access		In Vehicle Edge Computing Network (VECN), task offloading is a key technique to provide the satisfactory quality of service (QoS) for latency-sensitive tasks. However, the diversity of computational resources in edge nodes (i.e., RSU and idle vehicles) and the mobility of vehicles present significant challenges to task offloading. Hence, to address these challenges, we propose an offloading scheme that jointly allocates RSU nodes (including MEC servers) and idle service vehicle resources in this paper. We first prioritize these tasks based on their maximum tolerable latency and design a utility function to capture the executing cost for latency-sensitive tasks. Then, we propose a joint optimization algorithm of task offloading location and proportion based on Multi-agent Deep Reinforcement Learning (jTOLP-MADRL algorithm) for latency-sensitive tasks in VECN, which consists of two sub-algorithms: the Offloading Location Selection (OLS) algorithm and the Offloading Proportion Allocation (OPA) algorithm. Additionally, we design a Convolutional Recurrent Actor-Critic Network (CRACN) to enhance the learning efficiency of the OLS algorithm. Finally, we indicate our algorithm is effective based on simulation results. Compared with the other benchmark algorithms, jTOLP-MADRL can significantly reduce latency and enhance system utility.	10.1109/TNSM.2026.3669913
Muhammad Fahimullah, Michel Kieffer, Sylvaine Kerboeuf, Shohreh Ahvar, Maria Trocan	Decentralized Coalition Formation of Infrastructure Providers for Resource Provisioning in Coverage Constrained Virtualized Mobile Networks	2026	Early Access	Indium phosphide III-V semiconductor materials Resource management	The concept of wireless virtualized networks enables Mobile Virtual Network Operators (MVNOs) to utilize resources made available by multiple Infrastructure Providers (InPs) to set up a service. Nevertheless, existing centralized resource provisioning approaches fail to address such a scenario due to conflicting objectives among InPs and their reluctance to share private information. This paper addresses the problem of resource provisioning from several InPs for services with geographic coverage constraints. When complete information is available, an Integer Linear Program (ILP) formulation is provided, along with a greedy solution. An alternative coalition formation approach is then proposed to build coalitions of InPs that satisfy the constraints imposed by an MVNO, while requiring only limited information sharing. The proposed solution adopts a hedonic game-theoretic approach to coalition formation. For each InP, the decision to join or leave a coalition is made in a decentralized manner, relying on the satisfaction of service requirements and on individual profit. Simulation results demonstrate the applicability and performance of the proposed solution.	10.1109/TNSM.2026.3663437
Qichen Luo, Zhiyun Zhou, Ruisheng Shi, Lina Lan, Qingling Feng, Qifeng Luo, Di Ao	Revisit Fast Event Matching-Routing for High Volume Subscriptions	2026	Early Access	Real-time systems Vectors Search problems	Although many scalable event matching algorithms have been proposed to achieve scalability for publish/subscribe services, the content-based pub/sub system still suffer from performance deterioration when the system has large numbers of subscriptions, and cannot support the requirements of real-time pub/sub data services. In this paper, we model the event matching problem as an existence problem which only care about whether there is at least one matching subscription in the given subscription set, differing from existing works that try to speed up the time-consuming search operation to find all matching subscriptions. To solve this existence problem efficiently, we propose DLS (Discrete Label Set), a novel subscription and event representation model. Based on the DLS model, we propose an event matching algorithm with O(Nd) time complexity to support real-time event matching for a large volume of subscriptions and high event arrival speed, where Nd is the node degree in overlay network. Experimental results show that the event matching performance can be improved by several orders of magnitude compared with traditional algorithms.	10.1109/TNSM.2026.3664517
Jing Huang, Yabo Wang, Honggui Han	SCFusionLocator: A Statement-Level Smart Contract Vulnerability Localization Framework Based on Code Slicing and Multi-Modal Feature Fusion	2026	Early Access	Smart contracts Feature extraction Location awareness	Smart contract vulnerabilities have led to over $20 billion in losses, but existing methods suffer from coarse-grained detection, two-stage “detection-then-localization” pipelines, and insufficient feature extraction. This paper proposes SCFusionLocator, a statement-level vulnerability localization framework for smart contracts. It adopts a novel code-slicing mechanism (via function call graphs and data-flow graphs) to decompose contracts into single-function subcontracts and filter low-saliency statements, paired with source code normalization to reduce noise. A dual-branch architecture captures complementary features: the code-sequence branch uses GraphCodeBERT (with data-flow-aware masking) for semantic extraction, while the graph branch fuses call/control-flow/data-flow graphs into a heterogeneous graph and applies HGAT for structural modeling. SCFusionLocator enables end-to-end statement-level localization by framing tasks as statement classification.We release BJUT_SC02, a large dataset of over 240,000 contracts with line-level labels for 58 vulnerability types. Experiments on BJUT_SC02, SCD, and MANDO datasets show SCFusionLocator outperforms 8 conventional tools and nearly 20 ML baselines, achieving over 90% average F1 at the statement level, with better generalization to similar unknown vulnerabilities, and remains competitive in contract-level detection.	10.1109/TNSM.2026.3664599
Jiazhong Lu, Jimin Peng, Jian Shu, Jiali Yin, Xiaolei Liu	Adversarial Sample Based on Structured Fusion Noise for Botnet Detection in Industrial Control Systems	2026	Early Access	Botnet Industrial control Feature extraction	The industrial control system’s artificial intelligence-based botnet intrusion detection system has a high detection performance and efficiency in an environment without interference. However, these systems are not immune to evasion through adversarial samples. In this study, we introduce a feature extraction technique tailored for ICS botnet detection. This approach classifies traffic packets based on network traffic attributes and ICS-specific identification codes, encompassing the statuses of ICS devices, enhancing detection precision. Meanwhile, this strategy addresses challenges in ICS data collection and bolsters experimental efficacy. To build a comprehensive botnet intrusion dataset within an ICS, we concurrently utilized existing ICS devices to collect both standard ICS and botnet traffic. Additionally, we present an innovative adversarial sample generation method for botnet detection models, integrating both time-domain and frequency-domain noise. Testing under three real-world ICS attack scenarios revealed our technique can markedly degrade the classification performance of eight leading AI-based detection models, emphasizing its potential for evading AI-based ICS intrusion detectors.	10.1109/TNSM.2026.3665504
Tuan-Vu Truong, Van-Dinh Nguyen, Quang-Trung Luu, Phi-Son Vo, Xuan-Phu Nguyen, Fatemeh Kavehmadavani, Symeon Chatzinotas	Accelerating Resource Allocation in Open RAN Slicing via Deep Reinforcement Learning	2026	Early Access	Resource management Open RAN Ultra reliable low latency communication	The transition to beyond-fifth-generation (B5G) wireless systems has revolutionized cellular networks, driving unprecedented demand for high-bandwidth, ultra low-latency, and massive connectivity services. The open radio access network (Open RAN) and network slicing provide B5G with greater flexibility and efficiency by enabling tailored virtual networks on shared infrastructure. However, managing resource allocation in these frameworks has become increasingly complex. This paper addresses the challenge of optimizing resource allocation across virtual network functions (VNFs) and network slices, aiming to maximize the total reward for admitted slices while minimizing associated costs. By adhering to the Open RAN architecture, we decompose the formulated problem into two subproblems solved at different timescales. Initially, the successive convex approximation (SCA) method is employed to achieve at least a locally optimal solution. To handle the high complexity of binary variables and adapt to time-varying network conditions, traffic patterns, and service demands, we propose a deep reinforcement learning (DRL) approach for real-time and autonomous optimization of resource allocation. Extensive simulations demonstrate that the DRL framework quickly adapts to evolving network environments, significantly improving slicing performance. The results highlight DRL’s potential to enhance resource allocation in future wireless networks, paving the way for smarter, self-optimizing systems capable of meeting the diverse requirements of modern communication services.	10.1109/TNSM.2026.3665553
Adel Chehade, Edoardo Ragusa, Paolo Gastaldo, Rodolfo Zunino	Hardware-Aware Neural Architecture Search for Encrypted Traffic Classification on Resource-Constrained Devices	2026	Early Access	Accuracy Computational modeling Cryptography	This paper presents a hardware-efficient deep neural network (DNN), optimized through hardware-aware neural architecture search (HW-NAS); the DNN supports the classification of session-level encrypted traffic on resource-constrained Internet of Things (IoT) and edge devices. Thanks to HW-NAS, a 1D convolutional neural network (CNN) is tailored on the ISCX VPN-nonVPN dataset to meet strict memory and computational limits while achieving robust performance. The optimized model attains an accuracy of 96.60% with just 88.26K parameters, 10.08M floating-point operations (FLOPs), and a maximum tensor size of 20.12K. Compared to state-of-the-art (SOTA) models, it achieves reductions of up to 444-fold, 312-fold, and 15-fold in these metrics, respectively, significantly minimizing memory footprint and runtime requirements. The model also demonstrates versatility, achieving up to 99.86% across multiple VPN and traffic classification (TC) tasks; it further generalizes to external benchmarks with up to 99.98% accuracy on USTC-TFC and QUIC NetFlow. In addition, an in-depth approach to header-level preprocessing strategies confirms that the optimized model can provide notable performance across a wide range of configurations, even in scenarios with stricter privacy considerations. Likewise, a reduction in the length of sessions of up to 75% yields significant improvements in efficiency, while maintaining high accuracy with only a negligible drop of 1-2%. However, the importance of careful preprocessing and session length selection in the classification of raw traffic data is still present, as improper settings or aggressive reductions can bring about a 7% reduction in overall accuracy. The quantized architecture was deployed on STM32 microcontrollers and evaluated across input sizes; results confirm that the efficiency gains from shorter sessions translate to practical, low-latency embedded inference. These findings demonstrate the method’s practicality for encrypted traffic analysis in constrained IoT networks.	10.1109/TNSM.2026.3666676
Pengcheng Guo, Zhi Lin, Haotong Cao, Yifu Sun, Kuljeet Kaur, Sherif Moussa	GAN-Empowered Parasitic Covert Communication: Data Privacy in Next-Generation Networks	2026	Early Access	Interference Generators Generative adversarial networks	The widespread integration of artificial intelligence (AI) in next-generation communication networks poses a serious threat to data privacy while achieving advanced signal processing. Eavesdroppers can use AI-based analysis to detect and reconstruct transmitted signals, leading to serious leakage of confidential information. In order to protect data privacy at the physical layer, we redefine covert communication as an active data protection mechanism. We propose a new parasitic covert communication framework in which communication signals are embedded into dynamically generated interference by generative adversarial networks (GANs). This method is implemented by our CDGUBSS (complex double generator unsupervised blind source separation) system. The system is explicitly designed to prevent unauthorized AI-based strategies from analyzing and compromising signals. For the intended recipient, the pretrained generator acts as a trusted key and can perfectly recover the original data. Extensive experiments have shown that our framework achieves powerful covert communication, and more importantly, it provides strong defense against data reconstruction attacks, ensuring excellent data privacy in next-generation wireless systems.	10.1109/TNSM.2026.3666669
Fernando Martinez-Lopez, Lesther Santana, Mohamed Rahouti, Abdellah Chehri, Shawqi Al-Maliki, Gwanggil Jeon	Learning in Multiple Spaces: Prototypical Few-Shot Learning with Metric Fusion for Next-Generation Network Security	2026	Early Access	Measurement Prototypes Extraterrestrial measurements	As next-generation communication networks increasingly rely on AI-driven automation, ensuring robust and secure intrusion detection becomes critical, especially under limited labeled data. In this context, we introduce Multi-Space Prototypical Learning (MSPL), a few-shot intrusion detection framework that improves prototype-based classification by fusing complementary metric-induced spaces (Euclidean, Cosine, Chebyshev, and Wasserstein) via a constrained weighting mechanism. MSPL further enhances stability through Polyak-averaged prototype generation and balanced episodic training to mitigate class imbalance across diverse attack categories. In a few-shot setting with as few as 200 training samples, MSPL consistently outperforms single-metric baselines across three benchmarks: on CICEVSE Network2024, AUPRC improves from 0.3719 to 0.7324 and F1 increases from 0.4194 to 0.8502; on CICIDS2017, AUPRC improves from 0.4319 to 0.4799; and on CICIoV2024, AUPRC improves from 0.5881 to 0.6144. These results demonstrate that multi-space metric fusion yields more discriminative and robust representations for detecting rare and emerging attacks in intelligent network environments.	10.1109/TNSM.2026.3665647
Zewei Han, Go Hasegawa	BBR-ES: An Extended-State Optimization for BBR Congestion Control	2026	Early Access	Delays Bandwidth Internet	In recent years, many optimization proposals for TCP BBR have been introduced, but most rely mainly on delay variations and do not fully resolve BBR’s limitations in RTT fairness, link utilization, and delay control in networks. This paper proposes BBR with Extended State (BBR-ES), which extends BBR’s state machine with a short stabilization state and a trend-based transition mechanism that react to per-flow bandwidth and RTT evolution instead of global delay alone. BBR-ES uses lightweight bandwidth and RTT trend tracking to adjust its sending rate while preserving BBR’s model-based design. Experiments on both emulated (Mininet) and real-world Internet paths (Amazon EC2) show that BBR-ES consistently improves RTT fairness and link utilization over BBRv1, BBRv3, and CUBIC while keeping queuing delay moderate and bounded; in most settings, it achieves Jain’s fairness index above 0.9 and link utilization above 98%. These results indicate that BBR-ES is a practical candidate for deployment in large-scale content delivery and a useful design reference for future model-based congestion control schemes.	10.1109/TNSM.2026.3668966
Behnam Ojaghi, Ricard Vilalta, Raül Muñoz	IBNS: Optimizing Intent-Based 6G Network Slicing for Conflict Detection and Mitigation	2026	Early Access	Quality of service 6G mobile communication Complexity theory	The Sixth-Generation (6G) mobile networks aim to automate network resource allocation and support both new and existing vertical services, each with diverse Quality of Service (QoS) intent requirements. Digital Service Providers (DSPs) must consider specific intent expectations, and targets set by different services, and re-configure and prioritize the most critical intents when resources are insufficient. This paper presents an optimization model for a flexible network paradigm using an Intent-Based Network Slicing (IBNS) framework that can manage the complexity of QoS intents and identify slice intent conflicts through closed-loop evaluation and monitoring. It dynamically adjusts the slice configurations to handle and mitigate detected conflicts by executing the agreed-upon Service Level Agreement (SLA) objectives (%) for higher-priority slices to ensure that critical intents are addressed. According to the results, this approach successfully meets the SLA target we set for slice but it negatively impacts the performance of other slices and degrades their slice capacity.	10.1109/TNSM.2026.3668027
Can Wang, Run-Hua Shi, Jiang-Yuan Lian, Pei-Xuan Wang, Ze-Hui Jiang	Quantum-Enhanced Matching Mechanism for Secure and Efficient Power IoT Data Trading	2026	Early Access	Protocols Photonics Databases	The exponential growth of power IoT data has created immense potential for intelligent energy management, but it also presents critical challenges in achieving secure and efficient data trading. In particular, emerging data trading scenarios demand support for range nearest neighbor matching, which current schemes fail to address. This paper proposes a novel quantum trading matching scheme tailored for power data markets, which, for the first time, supports range nearest neighbor matching while balancing accuracy, efficiency, and privacy. To improve matching efficiency, we design a quantum private query (QPQ) mechanism based on a bidirectional sliding window (BSW), which replaces traditional linear search with dynamic range expansion. Furthermore, to ensure strong privacy and security in real-world scenarios, we develop a two-layer QPQ framework that performs data feature matching and identity retrieval separately, supported by a customized key distribution strategy. Our solution resists quantum attacks and significantly reduces computational overhead. At the same time, by utilizing non-ideal photon sources, it offers a practical and privacy-preserving solution for large-scale power data trading and matching.	10.1109/TNSM.2026.3667701
Yanxu Lin, Renzhong Zhong, Jingnan Xie, Yueting Zhu, Byung-Gyu Kim, Saru Kumari, Shakila Basheer, Fatimah Alhayan	Privacy-Preserving Digital Publishing Framework for Next-Generation Communication Networks: A Verifiable Homomorphic Federated Learning Approach	2026	Early Access	Electronic publishing Federated learning Cryptography	Next-generation communication networks are revolutionizing digital publishing through intelligent content distribution and collaborative optimization capabilities. However, existing federated learning approaches face fundamental limitations, including trusted third-party dependencies, excessive communication overhead, and vulnerability to collusion attacks between servers and participants. This paper introduces VHFL-DP, a verifiable homomorphic federated learning framework for digital publishing environments operating within 6G network infrastructures. The framework addresses critical privacy and scalability challenges through four key innovations: a distributed cryptographic key generation protocol that eliminates trusted third-party requirements, Chinese remainder theorem-based dimensionality reduction, auxiliary validation nodes that enable independent verification with constant-time complexity, and an intelligent incentive mechanism that rewards digital publishing platforms based on objective contribution quality metrics. Experimental evaluation on MNIST and Amazon reviews datasets across six baseline methods demonstrates that VHFL-DP achieves superior performance with accuracy improvements of 4.2% over the best baseline method. The framework maintains constant verification time ranging from 2.73 to 2.91 seconds regardless of platform count, increasing from ten to fifty, or dropout rates reaching thirty percent. Security evaluation reveals strong resilience with only 2.4 percentage point accuracy degradation under poisoning attacks compared to 6.7-7.0 points for baseline method, inference attack success near random guessing at 51.3%, and 92.4% successful aggregation under Byzantine adversaries.	10.1109/TNSM.2026.3667167