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IEEE Networking Letters Publication Information IEEE网络通讯出版信息
IEEE Networking Letters Pub Date : 2025-10-17 DOI: 10.1109/LNET.2025.3615346
{"title":"IEEE Networking Letters Publication Information","authors":"","doi":"10.1109/LNET.2025.3615346","DOIUrl":"https://doi.org/10.1109/LNET.2025.3615346","url":null,"abstract":"","PeriodicalId":100628,"journal":{"name":"IEEE Networking Letters","volume":"7 3","pages":"C2-C2"},"PeriodicalIF":0.0,"publicationDate":"2025-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11206549","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145352078","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
IEEE Networking Letters Author Guidelines IEEE网络通讯作者指南
IEEE Networking Letters Pub Date : 2025-10-17 DOI: 10.1109/LNET.2025.3615348
{"title":"IEEE Networking Letters Author Guidelines","authors":"","doi":"10.1109/LNET.2025.3615348","DOIUrl":"https://doi.org/10.1109/LNET.2025.3615348","url":null,"abstract":"","PeriodicalId":100628,"journal":{"name":"IEEE Networking Letters","volume":"7 3","pages":"244-245"},"PeriodicalIF":0.0,"publicationDate":"2025-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11206548","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145352099","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
Guest Editorial Special Issue on Generative AI and Large Language Models-Enabled Edge Intelligence 客座编辑特刊：生成人工智能和大语言模型支持的边缘智能
IEEE Networking Letters Pub Date : 2025-10-17 DOI: 10.1109/LNET.2025.3613807
Geng Sun;Octavia A. Dobre;Adlen Ksentini;Dusit Niyato;Wen Wu;Jiacheng Wang
{"title":"Guest Editorial Special Issue on Generative AI and Large Language Models-Enabled Edge Intelligence","authors":"Geng Sun;Octavia A. Dobre;Adlen Ksentini;Dusit Niyato;Wen Wu;Jiacheng Wang","doi":"10.1109/LNET.2025.3613807","DOIUrl":"https://doi.org/10.1109/LNET.2025.3613807","url":null,"abstract":"The IEEE Networking Letters special issue on Generative AI (GAI) and Large Language Models (LLMs)-enabled Edge Intelligence explores the latest advancements and applications of GAI and LLMs at the network edge, which aim to address the growing demand for intelligence and adaptability driven by the rapid evolution of modern communication systems. Unlike traditional artificial intelligence (AI) technologies, GAI and LLMs possess powerful self-learning, data-modeling, and context-aware capabilities. Specifically, GAI can extract deep features and semantic information from large amounts of data, which allows it to make intelligent predictions and optimize processes for a flexible response to changes in complex environments. Moreover, by simulating network behaviors under different scenarios and generating new data points, GAI can optimize network architecture and expand training datasets to improve model robustness and generalization. Additionally, LLMs can rapidly adapt to diverse tasks through a few-shot learning. Furthermore, they can incorporate techniques such as retrieval-augmented generation (RAG), thereby strengthening their capacity to handle complex text-based tasks and achieve precise information generation and intelligent interaction.","PeriodicalId":100628,"journal":{"name":"IEEE Networking Letters","volume":"7 3","pages":"157-160"},"PeriodicalIF":0.0,"publicationDate":"2025-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11206727","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145352045","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
IEEE Networking Letters Society Information IEEE网络通讯协会信息
IEEE Networking Letters Pub Date : 2025-10-17 DOI: 10.1109/LNET.2025.3615350
{"title":"IEEE Networking Letters Society Information","authors":"","doi":"10.1109/LNET.2025.3615350","DOIUrl":"https://doi.org/10.1109/LNET.2025.3615350","url":null,"abstract":"","PeriodicalId":100628,"journal":{"name":"IEEE Networking Letters","volume":"7 3","pages":"C3-C3"},"PeriodicalIF":0.0,"publicationDate":"2025-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11206546","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145352105","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
Microservice-Based Architecture for Situation Awareness in Network Digital Twin 基于微服务的网络数字孪生态势感知体系结构
IEEE Networking Letters Pub Date : 2025-10-15 DOI: 10.1109/LNET.2025.3621696
Carlos A. Gómez-Vega;Alessandro Vaccari;Francesco Ferrari;Riccardo Marini;Moe Z. Win;Andrea Conti
{"title":"Microservice-Based Architecture for Situation Awareness in Network Digital Twin","authors":"Carlos A. Gómez-Vega;Alessandro Vaccari;Francesco Ferrari;Riccardo Marini;Moe Z. Win;Andrea Conti","doi":"10.1109/LNET.2025.3621696","DOIUrl":"https://doi.org/10.1109/LNET.2025.3621696","url":null,"abstract":"Real-time digital twin of network functionalities and behaviors at different levels, namely network digital twin (NDT), is essential for efficient next-generation (xG) network and service management. To accurately represent and emulate the network behavior, NDT requires real-time situation awareness. This letter introduces a microservice-based architecture (MSA) for exposing situation awareness in NDT. Multimodal localization and sensing provide situational information of connected devices and unconnected targets employing heterogeneous technologies, namely xG, ultra-wideband, and radar. We propose a data model to describe generic assets incorporating situational information for its integration within NDT. A case study shows the functionalities of the proposed architecture in an industrial network considering a use case of mobile robots.","PeriodicalId":100628,"journal":{"name":"IEEE Networking Letters","volume":"8 ","pages":"24-28"},"PeriodicalIF":0.0,"publicationDate":"2025-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147280526","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
AI-Driven Dynamic Network Slicing Optimization Leveraging Temporal Graph Networks 利用时间图网络的人工智能驱动的动态网络切片优化
IEEE Networking Letters Pub Date : 2025-10-06 DOI: 10.1109/LNET.2025.3617577
George Makropoulos;Harilaos Koumaras;Nancy Alonistioti
{"title":"AI-Driven Dynamic Network Slicing Optimization Leveraging Temporal Graph Networks","authors":"George Makropoulos;Harilaos Koumaras;Nancy Alonistioti","doi":"10.1109/LNET.2025.3617577","DOIUrl":"https://doi.org/10.1109/LNET.2025.3617577","url":null,"abstract":"As 5th Generation (5G) and Beyond 5G (B5G) networks evolve, dynamic resource allocation and management is crucial for supporting the diversity of devices and the mixed data traffic types. Network slicing enables the logical segmentation of an infrastructure to meet specific Quality of Service (QoS) requirements posed by applications, but factors such as fluctuating traffic, user mobility, and cross-slice interference, pose challenges towards proactive resource allocation. Traditional methods struggle with these factors, leading to inefficiencies. Therefore, this letter explores the concept of an AI-driven network performance prediction and resource allocation framework using Temporal Graph Networks (TGNs). By integrating TGN with the NS-3 simulator, the work in this letter demonstrates an efficient approach to predict network throughput. The proposed solution advances spatiotemporal Artificial Intelligence (AI) techniques enabling more accurate prediction of network performance and adaptive resource optimization, supporting dynamic network slicing.","PeriodicalId":100628,"journal":{"name":"IEEE Networking Letters","volume":"7 4","pages":"362-366"},"PeriodicalIF":0.0,"publicationDate":"2025-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145982365","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
Coverage Prediction for mmWave Mobile Communication Systems at the Edge: A Vision Transformer Approach 边缘毫米波移动通信系统的覆盖预测：一种视觉变压器方法
IEEE Networking Letters Pub Date : 2025-10-01 DOI: 10.1109/LNET.2025.3616319
Leran Qi;Shaowei Wang
{"title":"Coverage Prediction for mmWave Mobile Communication Systems at the Edge: A Vision Transformer Approach","authors":"Leran Qi;Shaowei Wang","doi":"10.1109/LNET.2025.3616319","DOIUrl":"https://doi.org/10.1109/LNET.2025.3616319","url":null,"abstract":"Coverage prediction is the pivotal quality-of-service indicator in cellular systems, which typically relies on propagation models for path loss estimation, leading to substantial computational overhead. In this letter, we present an efficient vision transformer (ViT)-based framework to directly capture correlations among terrain, interference, and coverage characteristics, bypassing the conventional path loss modeling. Experiments on urban scenarios demonstrate that ViT achieves the lowest prediction error against the ray tracing benchmark, outperforming COST 231 by more than 10% and exhibiting lower error than convolutional neural network (CNN)-based models, including AlexNet, ResNet, and a recent CNN for propagation prediction. Moreover, it also reduces computation time by up to two orders of magnitude compared to propagation models. This highlights the potential of our proposed method for low-latency coverage prediction with minimal prediction error in resource-constrained edge environments.","PeriodicalId":100628,"journal":{"name":"IEEE Networking Letters","volume":"8 ","pages":"74-77"},"PeriodicalIF":0.0,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147280525","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
AI-Based Estimation of Bandwidth Availability for Data Offloading in Edge-Cloud Computing 边缘云计算中基于人工智能的数据卸载带宽可用性估计
IEEE Networking Letters Pub Date : 2025-09-26 DOI: 10.1109/LNET.2025.3614770
Maria L. L. Brito;Maria C. M. Ferreira;Ariel L. C. Portela;Rafael L. Gomes
{"title":"AI-Based Estimation of Bandwidth Availability for Data Offloading in Edge-Cloud Computing","authors":"Maria L. L. Brito;Maria C. M. Ferreira;Ariel L. C. Portela;Rafael L. Gomes","doi":"10.1109/LNET.2025.3614770","DOIUrl":"https://doi.org/10.1109/LNET.2025.3614770","url":null,"abstract":"Edge-cloud computing requires dynamic and accurate bandwidth estimation for intelligent data offloading decisions. Within this context, this letter proposes an AI-based mechanism to estimate bandwidth availability using lightweight network measurements for data offloading, called Ensemble Learning for Bandwidth Estimation (ELBE). The proposal employs a stacking ensemble of regression models, as well as defines a Bandwidth Availability Index (BAI) based on network measurements. The experiments performed, using data from Brazil’s national research network, indicate a suitable performance of ELBE, achieving low error (around 3%) and high accuracy (up to 99%), while keeping computational efficiency (execution around 1ms).","PeriodicalId":100628,"journal":{"name":"IEEE Networking Letters","volume":"8 ","pages":"69-73"},"PeriodicalIF":0.0,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11182171","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147280494","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
Edge-Based Platform for Large Language Models Deployment in Internet of Things Applications 面向物联网应用中大型语言模型部署的边缘平台
IEEE Networking Letters Pub Date : 2025-09-26 DOI: 10.1109/LNET.2025.3614653
Marc Jayson Baucas;Petros Spachos
{"title":"Edge-Based Platform for Large Language Models Deployment in Internet of Things Applications","authors":"Marc Jayson Baucas;Petros Spachos","doi":"10.1109/LNET.2025.3614653","DOIUrl":"https://doi.org/10.1109/LNET.2025.3614653","url":null,"abstract":"Decision-making support systems are a growing trend in Internet of Things (IoT)-based applications. It is due to the introduction of Large Language Model (LLM) deployment platforms. However, cloud-centric approaches struggle with their implementation due to scalability issues. This letter proposes an edge-based alternative to improve the scalability of LLM deployment in IoT-based applications. It also presents the incorporation of Raspberry Pis with low-cost, compact, and trending Artificial Intelligence (AI)-based applications, making it an ideal choice as an IoT device for deploying the LLM locally. Experiments evaluate the responsiveness of the LLM under the proposed edge-based platform. The results show its potential in minimizing the likelihood of network scalability issues, while the edge-based approach manages the LLM locally through the Raspberry Pi.","PeriodicalId":100628,"journal":{"name":"IEEE Networking Letters","volume":"8 ","pages":"78-81"},"PeriodicalIF":0.0,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147280529","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
DRL-Driven Edge-Aware Utility Optimization for Multi-Slice 6G Networks 多片6G网络drl驱动的边缘感知效用优化
IEEE Networking Letters Pub Date : 2025-09-24 DOI: 10.1109/LNET.2025.3614549
Khaled M. Naguib;Soumaya Cherkaoui;Mahmoud M. Elmessalawy;Ahmed M. Abd El-Haleem;Ibrahim I. Ibrahim
{"title":"DRL-Driven Edge-Aware Utility Optimization for Multi-Slice 6G Networks","authors":"Khaled M. Naguib;Soumaya Cherkaoui;Mahmoud M. Elmessalawy;Ahmed M. Abd El-Haleem;Ibrahim I. Ibrahim","doi":"10.1109/LNET.2025.3614549","DOIUrl":"https://doi.org/10.1109/LNET.2025.3614549","url":null,"abstract":"Virtual Reality (VR) services delivered over 6G networks demand ultra-low latency and high bandwidth to ensure seamless user experiences. This letter presents an intelligent resource allocation and edge caching framework for 6G O-RAN networks, leveraging Deep Q-Network (DQN) learning for optimizing edge caching and dynamic resource provisioning across multiple network slices within an O-RAN-compliant architecture.By incorporating DRL agents into the network control plane, the proposed system enables proactive and adaptive content distribution as well as real-time computational resource allocation that meets the quality-of-service demands of eMBB, URLLC, and especially the emerging MBRLLC slices essential for VR. Simulation results demonstrate that the DQN-based framework consistently outperforms traditional methods in reducing latency and improving throughput, leading to more reliable and responsive support for immersive VR applications in 6G environments.","PeriodicalId":100628,"journal":{"name":"IEEE Networking Letters","volume":"8 ","pages":"14-18"},"PeriodicalIF":0.0,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147280888","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
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