IEEE Transactions on Machine Learning in Communications and Networking最新文献

{"title":"Fast Real-World Classification of ECH-Enabled Applications","authors":"Yizhak Kahana;Yaakov J. Stein;Oren Glickman","doi":"10.1109/TMLCN.2026.3655328","DOIUrl":"https://doi.org/10.1109/TMLCN.2026.3655328","url":null,"abstract":"Encrypted Client Hello (ECH) reduces information leakage in encrypted packet connections, thereby complicating traditional Deep Packet Inspection (DPI) methods. While various machine learning approaches have been proposed for classifying ECH network connections, prior research typically requires lengthy observation times and focuses on a limited set of applications in constrained environments. This study demonstrates a highly accurate classification mechanism capable of identifying a large number of applications in real-world traffic cwithin one secondusing only the packets observed during an interval of up to 1 second. To train our models we created a novel dataset derived from operational network traffic, ensuring relevance and diversity in training and evaluation. Furthermore, we propose a novel hierarchical approach that leverages protocol-specific knowledge, enabling scalable and efficient classification in operational settings. We further show that a lightweight Random-Forest classifier attains virtually the same accuracy as deep neural network variants, indicating that complex sequence models are not needed for this task. Our findings highlight the effectiveness of leveraging short Per Packet Information (PPI) sequences and domain-specific features to overcome the challenges posed by ECH, achieving performance superior to that of existing methods.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"4 ","pages":"337-350"},"PeriodicalIF":4.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11358796","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148494641","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Editorial From the Incoming Editor-in-Chief","authors":"Yuguang Fang","doi":"10.1109/TMLCN.2026.3713005","DOIUrl":"https://doi.org/10.1109/TMLCN.2026.3713005","url":null,"abstract":"","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"4 ","pages":"1227-1228"},"PeriodicalIF":4.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11623372","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148627613","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ARCS: Adversarial RF Map Categorization and Synthesis Through Unsupervised Feature Extraction","authors":"Sopan Sarkar;Marwan Krunz;David Manzi;Rajesh Kulkarni","doi":"10.1109/TMLCN.2026.3676377","DOIUrl":"https://doi.org/10.1109/TMLCN.2026.3676377","url":null,"abstract":"In wireless networks, radio-frequency coverage maps (RF maps) are critical for tasks such as capacity planning, coverage estimation, and localization. Traditional methods for obtaining these maps, including site surveys and ray-tracing simulations, are either labor-intensive or computationally expensive, particularly at high frequencies. Generative AI offers a promising alternative for RF map synthesis and data augmentation. However, supervised generative approaches are often infeasible due to the lack of labeled training data, while unsupervised methods typically lack control over the generation process, limiting their practical utility. To overcome these challenges, we propose ARCS (Adversarial RF Map Categorization and Synthesis), a novel generative adversarial network (GAN)-based framework for unsupervised RF map categorization and synthesis. ARCS leverages the principles of information maximizing GAN (InfoGAN) to learn the latent structure of RF maps in an unsupervised manner during training and, through manipulation of the learned and interpretable latent codes, enables controlled generation of RF maps across floor plan regions and transmitter (Tx) locations during inference. To improve training stability and synthesis quality, we integrate a gradient penalty-based Wasserstein GAN objective function along with a customized gradient-based loss function. Extensive experiments on both experimental and simulated datasets show that ARCS generates high-quality RF maps, associates them with discrete regions of the floor plan, and provides fine-grained control of Tx location within each region. Compared with a UNet-based conditional GAN and a conditional diffusion model, ARCS attains the best scores across structural similarity index metric (SSIM), PSNR, MAPE, RMSE, cosine similarity (CS), and Jensen–Shannon divergence (JSD). Moreover, ARCS is extremely fast, requiring a few milliseconds per synthetic map, compared to over 14 seconds with ray-tracing.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"4 ","pages":"612-628"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11450453","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147606246","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adversarially Optimized Multi-Space Prototypical Network for Intrusion Detection in 5G-Enabled IoT Systems","authors":"Shahid Latif;Jawad Ahmad;Wadii Boulila;Djamel Djenouri","doi":"10.1109/TMLCN.2026.3673694","DOIUrl":"https://doi.org/10.1109/TMLCN.2026.3673694","url":null,"abstract":"Existing state-of-the-art IDS solutions in 5G-enabled IoT, including deep convolutional architectures, are constrained by data scarcity, limited edge resources, and poor robustness in few-shot learning (FSL) scenarios with scarce attack samples. To address these limitations, we propose MSPL-IDS, a robust FSL-based intrusion detection framework tailored for 5G-enabled IoT environments. Unlike existing methods that rely on a single embedding space and are highly vulnerable to adversarial perturbations, MSPL-IDS employs multi-space prototypical learning to project traffic features into multiple complementary embedding spaces, generating diverse class prototypes that strengthen decision boundaries against evasion attacks. A dual-loss adversarial training strategy jointly improves classification accuracy and robustness, while an attention mechanism adaptively selects the most reliable embedding spaces during inference, enabling low-latency deployment at the network edge. Comprehensive experiments on the 5G-NIDD benchmark demonstrate that MSPL-IDS significantly outperforms state-of-the-art deep learning and FSL-based IDSs under adversarial conditions. While representative models such as ResNet and Inception experience catastrophic accuracy drops to as low as 21.2% under evasion attacks, MSPL-IDS consistently maintains detection accuracy above 87% in identical few-shot settings. On average, the proposed framework achieves accuracy gains of 31.8% under FGSM and 26.4% under PGD attacks, with improvements exceeding 54% at high perturbation levels (<inline-formula> <tex-math>$epsilon ge 0.15$ </tex-math></inline-formula>), while preserving near-optimal clean-data accuracy (88.2–94.9%).","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"4 ","pages":"575-590"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11433812","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147558030","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Equivariant Multi-Agent Reinforcement Learning for Multimodal Vehicle-to-Infrastructure Systems","authors":"Charbel Bou Chaaya;Mehdi Bennis","doi":"10.1109/TMLCN.2026.3683038","DOIUrl":"https://doi.org/10.1109/TMLCN.2026.3683038","url":null,"abstract":"In this article, we study a vehicle-to-infrastructure (V2I) system where distributed base stations (BSs) acting as road-side units (RSUs) collect multimodal (wireless and visual) data from moving vehicles. We consider a decentralized rate maximization problem, where each RSU relies on its local observations to optimize its resources, while all RSUs must collaborate to guarantee favorable network performance. We recast this problem as a distributed multi-agent reinforcement learning (MARL) problem, by incorporating rotation symmetries in terms of vehicles’ locations. To exploit these symmetries, we propose a novel self-supervised learning framework where each BS agent aligns the latent features of its multimodal observation to extract the positions of the vehicles in its local region. Equipped with this sensing data at each RSU, we train an equivariant policy network using a graph neural network (GNN) with message passing layers, such that each agent computes its policy locally, while all agents coordinate their policies via a signaling scheme that overcomes partial observability and guarantees the equivariance of the global policy. We present numerical results carried out in a simulation environment, where ray-tracing and computer graphics are used to collect wireless and visual data. Results show the generalizability of our self-supervised and multimodal sensing approach, achieving more than two-fold accuracy gains over baselines, and the efficiency of our equivariant MARL training, attaining more than 50% performance gains over standard approaches.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"4 ","pages":"706-732"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11480198","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147737180","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"LLM4PG: Adapting Large Language Model for Pathloss Map Generation via Synesthesia of Machines","authors":"Mingran Sun;Lu Bai;Xiang Cheng;Jianjun Wu","doi":"10.1109/TMLCN.2026.3693982","DOIUrl":"https://doi.org/10.1109/TMLCN.2026.3693982","url":null,"abstract":"In this paper, a novel large language model (LLM)-based pathloss map generation model (LLM4PG) is proposed for sixth-generation (6G) artificial intelligence (AI)-native communication systems via Synesthesia of Machines (SoM). To explore the mapping mechanism between sensing images and pathloss maps, a new synthetic intelligent multi-modal sensing-communication dataset for SoM in uncrewed aerial vehicle (UAV)-to-ground (U2G) scenarios, named SynthSoM-U2G, is constructed, including multiple U2G scenarios with multiple frequency bands and multiple flight altitudes. By adapting the LLM to the cross-modal pathloss map generation for the first time, a novel framework that enables effective cross-domain alignment between the multi-modal sensing-communication domain and the natural language domain is introduced. Furthermore, a task-specific adaptation of the LLM is achieved through fine-tuning, with a properly designed layer selection and activation scheme tailored to the unique demands of massive-scale and high-quality pathloss map generation. Compared with the conventional artificial intelligence generated content (AIGC) models, the proposed LLM4PG enables accurate pathloss map generation and demonstrates strong generalization across various scenarios, frequency bands, and flight altitudes under three-dimensional (3D) high-mobility U2G scenarios. The accuracy and generality of the proposed LLM4PG are validated by comparing simulation results and ray-tracing (RT)-based results. Simulation results demonstrate that the proposed LLM4PG can achieve accurate pathloss map generation with a normalized mean squared error (NMSE) of 0.0428, outperforming the conventional AIGC model by more than 3.16 dB. The generality of the proposed LLM4PG across different conditions achieves an NMSE of 0.0492, outperforming the conventional AIGC model by more than 4.52 dB.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"4 ","pages":"864-881"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11520928","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148171864","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Throughput Estimation of Data Transport Networks From Digital Twin Measurements","authors":"Nageswara S. V. Rao;Anees Al-Najjar;Rajkumar Kettimuthu;Ian Foster;Kyle Chard;Ryan Chard;Amal Gueroudji;Matthieu Dorier;Valérie Hayot-Sasson;Hai Duc Nguyen;Maxime Gonthier;Tekin Bicer;Haochen Pan;Eliu Huerta;Bogdan Nicolae;Parth Patel;Justin M. Wozniak","doi":"10.1109/TMLCN.2026.3692126","DOIUrl":"https://doi.org/10.1109/TMLCN.2026.3692126","url":null,"abstract":"Digital twins of networked infrastructures, known as Virtual Infrastructure Twins (VITs), are increasingly used for software development, pre-deployment testing, and design space exploration. While VITs avoid the costs and potential disruptions associated with experiments on operational networks, their throughput measurements are typically not sufficiently accurate for performance profiling of wide-area networks that they emulate. Here, machine learning (ML) methods are developed to transform these inaccurate VIT network throughput measurements to closely match in peak and overall profile of those from a physical testbed or production network. First, a micro kernel network reflecting a physical network is utilized to collect one-time measurements on a host to support this ML transformation. Then, a generic multi-modal ML method is developed to learn a map that transforms measurements from subsequent VITs on the same host to match past, current and follow-on testbed and cloud networks. ML generalization equations are derived to establish its correctness and probabilistically guarantee its generalization accuracy. Experimental results are presented for a variety of VIT hosts with target testbed and cloud networks; they include a case study of a four-site science ecosystem wherein inaccurate convex VIT measurement profiles are transformed into accurate concave profiles of target networks.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"4 ","pages":"846-863"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11514121","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148171867","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Distributed Agentic AI Framework for Autonomous Edge-Cloud Service Orchestration","authors":"Berend J. D. Gort;Godfrey M. Kibalya;Angelos Antonopoulos","doi":"10.1109/TMLCN.2026.3705714","DOIUrl":"https://doi.org/10.1109/TMLCN.2026.3705714","url":null,"abstract":"Orchestrating services across heterogeneous 6G edge-cloud infrastructures requires autonomous coordination systems managing distributed computational resources while satisfying Quality-of-Service (QoS) requirements. Recent advances in Large Language Models (LLMs) enable development of autonomous agents capable of complex reasoning and decision-making for such orchestration tasks. However, applying generic agentic AI frameworks from the machine learning literature to orchestration domains introduces reliability limitations, as trial-and-error decision patterns are unsuitable for environments where errors disrupt services. This work presents AgentEdge, a novel distributed intelligence framework that implements specialized autonomous agents in four orchestration roles: intent processing, infrastructure monitoring, strategic planning, and action execution. AgentEdge introduces the PARES (Perceive, Act, Reason, Evaluate, Sustain) framework establishing minimum capabilities required for autonomous agent qualification. Central to AgentEdge is the ActSimCrit (Action-Simulation-Critic) planning methodology, which validates orchestration plans through digital twin simulation before execution, eliminating direct infrastructure experimentation risks. Agents coordinate multi-step operations and adapt strategies based on constraint feedback. Structured outputs constrain agent decision spaces to feasible orchestration actions while preserving optimization flexibility. Experimental evaluation in six orchestration scenarios validates AgentEdge through comparison with baseline agentic frameworks and ablation studies. AgentEdge achieves <inline-formula> <tex-math>$2.76times $ </tex-math></inline-formula> higher success rate compared to generic agentic frameworks (ReAct, LATS) and <inline-formula> <tex-math>$10times $ </tex-math></inline-formula> reduction in API call variability. The core ActSimCrit digital twin component alone contributes <inline-formula> <tex-math>$1.47times $ </tex-math></inline-formula> success improvement when compared to direct planning without simulation. AgentEdge achieves significant power savings across infrastructure scales from 8 to 35 nodes.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"4 ","pages":"1138-1157"},"PeriodicalIF":4.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11573150","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148494638","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Learning Efficient Communication Protocols for Multi-Agent Reinforcement Learning","authors":"Xinren Zhang;Jiadong Yu;Zixin Zhong","doi":"10.1109/TMLCN.2026.3719222","DOIUrl":"https://doi.org/10.1109/TMLCN.2026.3719222","url":null,"abstract":"Multi-Agent Systems (MAS) have emerged as a powerful paradigm for modeling complex interactions among autonomous entities in distributed environments. In Multi-Agent Reinforcement Learning (MARL), communication enables coordination but can lead to inefficient information exchange, since agents may generate redundant or non-essential messages. While prior work has focused on boosting task performance with information exchange, the existing research lacks a thorough investigation of both the appropriate definition and the optimization of communication protocols (communication topology and message). To fill this gap, we introduce a unified framework for learning multi-round communication protocols that are both effective and efficient. Within this framework, we propose three novel Communication Efficiency Metrics (CEMs) to guide and evaluate the learning process: the Information Entropy Efficiency Index (IEI) and Specialization Efficiency Index (SEI) for efficiency-augmented optimization, and the Topology Efficiency Index (TEI) for explicit evaluation. We integrate IEI and SEI as the adjusted loss functions to promote informative messaging and role specialization, while using TEI to quantify the trade-off between communication volume and task performance. Through comprehensive experiments, we demonstrate that our learned communication protocols can significantly enhance communication efficiency and achieves better cooperation performance with improved success rates.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"4 ","pages":"1335-1352"},"PeriodicalIF":4.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11638242","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148749137","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Semantic Video Transmission Over LEO Satellites With Graph Neural Networks","authors":"Mohamed Karaa;Hakim Ghazzai;Gianluca Setti;Lokman Sboui","doi":"10.1109/TMLCN.2026.3725994","DOIUrl":"https://doi.org/10.1109/TMLCN.2026.3725994","url":null,"abstract":"Providing large-scale video surveillance in remote areas is challenging due to limited terrestrial communication infrastructure and the high data rates required by raw video transmission. Low Earth Orbit (LEO) satellites offer a viable connectivity solution, but their uplink capacity remains constrained, particularly for low-power devices. Semantic communication addresses this limitation by transmitting task-relevant information rather than raw video streams. In this work, we address the semantic information transmission problem in camera-to-satellite uplink communication. The task consists of selecting semantic representations from all cameras to maximize the total transmitted semantic information under strict resource constraints. We formulate the problem as an NP-hard constrained optimization task and address it using a low-complexity and scalable graph node classification approach that recasts representation selection. We propose a lightweight graph neural network that exploits the camera–satellite topology to predict the optimal semantic representation for each camera by learning context-aware node embeddings through message passing. A search-based post-processing algorithm is introduced to enforce the feasibility of the predicted solutions within transmission time requirements. Our extensive evaluations show that the proposed approach outperforms learning-based and search-based baselines in terms of accuracy, optimality, and constraint satisfaction, while maintaining low inference latency. The proposed system scales to large camera networks and enables semantic information transmission with minimal information loss and an average gap of 0.67% compared to the solver-based reference solution.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"4 ","pages":"1406-1420"},"PeriodicalIF":4.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11662122","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148928330","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}