IEEE Networking Letters最新文献

2025 Index IEEE Networking Letters Vol. 7 2025索引IEEE网络通讯第7卷

IEEE Networking Letters Pub Date : 2026-02-09 DOI: 10.1109/LNET.2026.3662547

引用次数: 0

AI/ML-Based QoS Recommendations for Energy Optimization in 6G Networks 基于AI/ ml的6G网络能量优化QoS建议

IEEE Networking Letters Pub Date : 2026-02-02 DOI: 10.1109/LNET.2026.3660985

Sara Ghasvarianjahromi;Abbas Kiani;Amanda Xiang;John Kaippallimalil;Tony Saboorian;Nirwan Ansari

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IEEE Networking Letters Society Information IEEE网络通讯协会信息

IEEE Networking Letters Pub Date : 2026-01-16 DOI: 10.1109/LNET.2025.3636374

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IEEE Networking Letters Author Guidelines IEEE网络通讯作者指南

IEEE Networking Letters Pub Date : 2026-01-16 DOI: 10.1109/LNET.2025.3636372

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QoS-Aware Network Selection in Hybrid RF-VLC Wireless Systems: A Stackelberg Game Approach 混合RF-VLC无线系统中qos感知网络选择：一个Stackelberg博弈方法

IEEE Networking Letters Pub Date : 2026-01-13 DOI: 10.1109/LNET.2026.3653993

Muthiah Sivavelan S.;Astitva Kamble;Mahendra K. Shukla;Om Jee Pandey

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End-to-End AI Lifecycle Management for 6G: Bridging DataOps and MLOps 6G的端到端人工智能生命周期管理：桥接数据操作和mlop

IEEE Networking Letters Pub Date : 2026-01-01 Epub Date: 2026-02-03 DOI: 10.1109/LNET.2026.3661251

Menuka Perera Jayasuriya Kuranage;Ameur Mazene;Abdelkader Mekrache;Adlen Ksentini

{"title":"End-to-End AI Lifecycle Management for 6G: Bridging DataOps and MLOps","authors":"Menuka Perera Jayasuriya Kuranage;Ameur Mazene;Abdelkader Mekrache;Adlen Ksentini","doi":"10.1109/LNET.2026.3661251","DOIUrl":"https://doi.org/10.1109/LNET.2026.3661251","url":null,"abstract":"As 6G networks evolve toward a data-centric architecture with full automation capabilities, the softwarization of network functions enables access to a wide spectrum of data across technical, management, and operational domains. This rich data provides a strong foundation for building efficient, dynamic, and scalable network environments, where Artificial Intelligence (AI) and Machine Learning (ML) play a vital role in enabling data-driven intelligence within 6G networks. Consequently, streamlining AI/ML workflows becomes essential to fully harness the potential of 6G. Given the massive volume of data generated and the extensive applicability of AI/ML across various layers of the 6G architecture, including the Radio Access Network (RAN), Core, and Edge, managing AI/ML workflows at scale presents significant challenges. To address these challenges, we propose a novel microservice-oriented DataOps-MLOps framework designed to streamline AI/ML workflows across all domains. The proposed framework natively supports model training, model versioning and incorporates an automated data pipelining approach that facilitates seamless data collection and real-time inference. To validate the framework’s effectiveness, we implemented a traffic anomaly detection use case involving end-to-end (E2E) ML model training, deployment, and real-time inference. Experimental results demonstrate that the proposed framework significantly streamlines AI/ML operations, unlocking the full potential of AI/ML integration in future 6G networks.","PeriodicalId":100628,"journal":{"name":"IEEE Networking Letters","volume":"8 ","pages":"169-173"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147665431","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Accelerating Inter-App Communications for Time-Critical O-RAN Control Loops 加速时间关键O-RAN控制回路的应用程序间通信

IEEE Networking Letters Pub Date : 2026-01-01 Epub Date: 2026-03-04 DOI: 10.1109/LNET.2026.3670470

Lorenzo Rosa;Andrea Garbugli;Domenico Scotece;Luca Foschini

{"title":"Accelerating Inter-App Communications for Time-Critical O-RAN Control Loops","authors":"Lorenzo Rosa;Andrea Garbugli;Domenico Scotece;Luca Foschini","doi":"10.1109/LNET.2026.3670470","DOIUrl":"https://doi.org/10.1109/LNET.2026.3670470","url":null,"abstract":"The Open Radio Access Network (O-RAN) is reshaping cellular architectures through disaggregation, openness, and programmability, enabling intelligent control across modular RAN components. A growing ecosystem of user-defined control-plane applications, namely rApps, xApps, and the emerging dApps, operates at different timescales and unlocks advanced control loop capabilities, but introduces diverse and stringent Quality of Service (QoS) requirements for communication. Current implementations typically rely on cloud-based messaging systems, which privilege transparency and ease of use for developers and limit support for time-sensitive workloads. In this letter, we propose that O-RAN control loops base their internal communication on INSANE, a cloud-native, data-centric middleware that supports multiple networking stacks, including kernel-bypass option such as eBPF XDP, DPDK, and RDMA. Our preliminary evaluation shows that INSANE achieves nearly <inline-formula> <tex-math>$2times $ </tex-math></inline-formula> higher throughput than widely used alternatives, while reducing 99.9th-percentile latency of over an order of magnitude for small messages. At the same time, INSANE preserves a uniform and easy-to-use programming interface. These results highlight INSANE as a promising foundation for faster, more predictable control loops, a significant step toward the ultimate goal of enabling AI-driven RAN optimizations in O-RAN systems.","PeriodicalId":100628,"journal":{"name":"IEEE Networking Letters","volume":"8 ","pages":"140-143"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11421355","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147665507","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}

引用次数: 0

Optimized Deployment of Core Network Slices for Satellite-Terrestrial Integrated Networks 星地融合网核心网片的优化部署

IEEE Networking Letters Pub Date : 2026-01-01 Epub Date: 2026-02-23 DOI: 10.1109/LNET.2026.3667202

Lianglin Pan;Xiumei Yang;Yu Zhao

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Network Slice Embedding With Flexible Configurations in 5G Networks and Beyond 5G及以后网络中灵活配置的网络切片嵌入

IEEE Networking Letters Pub Date : 2026-01-01 Epub Date: 2026-01-13 DOI: 10.1109/LNET.2026.3653831

Quang-Trung Luu;Do-Minh Tran;Minh-Thanh Nguyen;Michel Kieffer;Dinh Thai Hoang;Tai-Hung Nguyen;Huu-Thanh Nguyen;Van-Dinh Nguyen

{"title":"Network Slice Embedding With Flexible Configurations in 5G Networks and Beyond","authors":"Quang-Trung Luu;Do-Minh Tran;Minh-Thanh Nguyen;Michel Kieffer;Dinh Thai Hoang;Tai-Hung Nguyen;Huu-Thanh Nguyen;Van-Dinh Nguyen","doi":"10.1109/LNET.2026.3653831","DOIUrl":"https://doi.org/10.1109/LNET.2026.3653831","url":null,"abstract":"Network slicing enables the creation of multiple virtual networks (i.e., slices) over a shared network infrastructure, each tailored to a specific service. A key challenge lies in network slice embedding, which maps virtual network functions (VNFs) and links onto the physical network. Unlike prior works that assumed fixed configurations, we design a flexible system that allows for selecting the best configuration for each slice based on current physical resource availability during embedding. This leads to a joint optimization of (<inline-formula> <tex-math>$i$ </tex-math></inline-formula>) slice configuration selection (SCS) and <inline-formula> <tex-math>$(ii)$ </tex-math></inline-formula> slice admission control and embedding (SACE). To solve this, we propose two approaches: an exact method that formulates the joint SCS-SACE problem as an integer linear program (ILP), and a scalable alternative that decouples the problem, solving SCS via reinforcement learning and SACE via either ILP or a heuristic. Simulation results show that allowing flexible configuration selection improves slice acceptance by up to 10%, enabling more efficient slice deployment in resource-constrained networks.","PeriodicalId":100628,"journal":{"name":"IEEE Networking Letters","volume":"8 ","pages":"135-139"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147665312","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Hierarchical Decision Mamba Meets Agentic AI: A Novel Approach for RAN Slicing in 6G 分层决策曼巴与代理人工智能：6G无线局域网切片的新方法

IEEE Networking Letters Pub Date : 2026-01-01 Epub Date: 2026-02-02 DOI: 10.1109/LNET.2026.3660628

Md Arafat Habib;Medhat Elsayed;Majid Bavand;Pedro Enrique Iturria Rivera;Yigit Ozcan;Melike Erol-Kantarci

{"title":"Hierarchical Decision Mamba Meets Agentic AI: A Novel Approach for RAN Slicing in 6G","authors":"Md Arafat Habib;Medhat Elsayed;Majid Bavand;Pedro Enrique Iturria Rivera;Yigit Ozcan;Melike Erol-Kantarci","doi":"10.1109/LNET.2026.3660628","DOIUrl":"https://doi.org/10.1109/LNET.2026.3660628","url":null,"abstract":"Radio Access Network (RAN) slicing enables multiple logical networks to exist on top of the same physical infrastructure by allocating resources to distinct service groups, where radio resource scheduling plays a key role in ensuring compliance with slice-specific Service-Level Agreements (SLAs). Existing configuration-based or intent-driven Reinforcement Learning (RL) approaches usually rely on static mappings and SLA conversions. The current literature does not integrate natural language understanding with coordinated decision-making. To address these limitations, we propose an Agentic AI framework for 6G RAN slicing, driven by a super agent built using Hierarchical Decision Mamba (HDM) controllers and a Large Language Model (LLM). The super agent interprets operator intents and translates them into actionable goals using the LLM, which are used by HDM to coordinate inter-slice, intra-slice, and self-healing agents. Compared to transformer-based and reward-driven baselines, the proposed Agentic AI framework demonstrates consistent improvements across key performance indicators, including higher throughput, improved cell-edge performance, and reduced latency across different slices.","PeriodicalId":100628,"journal":{"name":"IEEE Networking Letters","volume":"8 ","pages":"120-124"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147665368","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0