IEEE Networking Letters最新文献_第8页

On the Uplink Repetitions in LoRaWAN LoRaWAN中上行链路重复的研究

IF 3.3

IEEE Networking Letters Pub Date : 2025-12-01 Epub Date: 2025-10-15 DOI: 10.1109/LNET.2025.3622036

Dimitrios Zorbas;Aruzhan Sabyrbek

引用次数: 0

NOMA-ISAC Empowered UAV Networks: Joint Clustering and Power Allocation Optimization NOMA-ISAC授权无人机网络：联合聚类与功率分配优化

IF 3.3

IEEE Networking Letters Pub Date : 2025-12-01 Epub Date: 2025-08-12 DOI: 10.1109/LNET.2025.3598036

Jiajie Zhou;Chao Yang;Junlin Huang;Suidan Yuan;Xin Chen;Shengli Xie

引用次数: 0

Latency-Reliability-Accuracy Optimization for Wireless Transmission of Sensory Data 传感数据无线传输的延迟-可靠性-精度优化

IF 3.3

IEEE Networking Letters Pub Date : 2025-12-01 Epub Date: 2025-08-29 DOI: 10.1109/LNET.2025.3603996

Suraj Suman;Anjali Shaw;Petar Popovski

引用次数: 0

XR Offloading Across Multiple Time Scales: The Roles of Power, Temperature, and Energy 跨多个时间尺度的XR卸载：功率、温度和能量的作用

IF 3.3

IEEE Networking Letters Pub Date : 2025-12-01 Epub Date: 2025-07-29 DOI: 10.1109/LNET.2025.3593665

Francesco Malandrino;Olga Chukhno;Alessandro Catania;Antonella Molinaro;Carla Fabiana Chiasserini

{"title":"XR Offloading Across Multiple Time Scales: The Roles of Power, Temperature, and Energy","authors":"Francesco Malandrino;Olga Chukhno;Alessandro Catania;Antonella Molinaro;Carla Fabiana Chiasserini","doi":"10.1109/LNET.2025.3593665","DOIUrl":"https://doi.org/10.1109/LNET.2025.3593665","url":null,"abstract":"Extended reality (XR) devices, commonly known as wearables, must handle significant computational loads under tight latency constraints. To meet these demands, they rely on a combination of on-device processing and edge offloading. This letter focuses on offloading strategies for wearables and assesses the impact of offloading decisions over three distinct time scales: instantaneous power consumption, short-term temperature fluctuations, and long-term battery duration. We introduce a comprehensive system model that captures these temporal dynamics, and propose a stochastic and stationary offloading strategy, called TAO (for temperature-aware offloading), designed to minimize the offloading cost while adhering to power, thermal, and energy constraints. Our performance evaluation, leveraging COMSOL models of real-world wearables, confirms that TAO successfully avoids exceeding temperature limits while keeping additional edge offloading to a minimum. These results also highlight how properly accounting for all features of wearables allows fully exploiting edge offloading opportunities.","PeriodicalId":100628,"journal":{"name":"IEEE Networking Letters","volume":"7 4","pages":"352-356"},"PeriodicalIF":3.3,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148514418","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

TopoFreer: Towards Topology-Free Cellular Traffic Prediction Through Self-Adaptive Local Spatio-Temporal Graph Convolution TopoFreer：基于自适应局部时空图卷积的无拓扑蜂窝交通预测

IF 3.3

IEEE Networking Letters Pub Date : 2025-12-01 Epub Date: 2025-10-15 DOI: 10.1109/LNET.2025.3622332

Citong Que;Faisal Nadeem Khan

引用次数: 0

Multipath TCP With Single Radio Access Technologies: A Paradox or an Opportunity? 多路径TCP与单一无线接入技术：悖论还是机遇？

IF 3.3

IEEE Networking Letters Pub Date : 2025-12-01 Epub Date: 2025-07-22 DOI: 10.1109/LNET.2025.3590887

João Torrinhas;Miguel Luís;Duarte Dias;Pedro Rito;Susana Sargento

引用次数: 0

Distributed Constrained Real-Time Control of Multiple Batteries in Green Networks 绿色网络中多电池分布式约束实时控制

IF 3.3

IEEE Networking Letters Pub Date : 2025-12-01 Epub Date: 2025-06-23 DOI: 10.1109/LNET.2025.3582414

Eleni Stai;Giorgos Gotzias;Marianna Papadostefanaki;Symeon Papavassiliou

{"title":"Distributed Constrained Real-Time Control of Multiple Batteries in Green Networks","authors":"Eleni Stai;Giorgos Gotzias;Marianna Papadostefanaki;Symeon Papavassiliou","doi":"10.1109/LNET.2025.3582414","DOIUrl":"https://doi.org/10.1109/LNET.2025.3582414","url":null,"abstract":"Energy sustainability is endeavored in all networked systems and renewable energy sources (RESs) will play a fundamental role towards its achievement. Due to their uncertainties, RESs integration for powering any type of network requires support for handling energy surplus or shortage due to, e.g., unexpected weather conditions or generation-demand lags. Such support can be offered by batteries that have become crucial for a large scale integration of RESs to networked systems and when multiple batteries serve one network, their coordinated control will allow for maximizing the benefits of their operation. In this letter, we propose a real-time control scheme for multiple batteries based on a multi-agent reinforcement learning scheme that is distributed in execution but centralized in training so as to achieve a high level of coordination but in a scalable way. Most importantly, it is safe in the sense that it accounts for binding lookahead constraints on the batteries state-of-energy, which express long-term feasibility and cost efficiency goals in the network operation. We have performed a case study in smart grids with the network goal of following a dispatch plan in real-time. Numerical results showcase the effectiveness of the proposed scheme both in terms of cost performance and of constraints violation avoidance versus baseline approaches.","PeriodicalId":100628,"journal":{"name":"IEEE Networking Letters","volume":"7 4","pages":"332-336"},"PeriodicalIF":3.3,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148514413","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

Hybrid Granular Controller Provisioning in Software Defined Satellite Internet 软件定义卫星互联网中的混合粒度控制器配置

IF 3.3

IEEE Networking Letters Pub Date : 2025-12-01 Epub Date: 2025-09-12 DOI: 10.1109/LNET.2025.3609310

Wanying Huang;Shimao Chen

引用次数: 0

FLARE: Flying Learning Agents for Resource Efficiency in Next-Gen UAV Networks 下一代无人机网络资源效率的飞行学习代理

IF 3.3

IEEE Networking Letters Pub Date : 2025-12-01 Epub Date: 2025-09-17 DOI: 10.1109/LNET.2025.3611236

Xuli Cai;Poonam Lohan;Burak Kantarci

{"title":"FLARE: Flying Learning Agents for Resource Efficiency in Next-Gen UAV Networks","authors":"Xuli Cai;Poonam Lohan;Burak Kantarci","doi":"10.1109/LNET.2025.3611236","DOIUrl":"https://doi.org/10.1109/LNET.2025.3611236","url":null,"abstract":"This letter addresses a critical challenge in the context of 6G and beyond wireless networks, the joint optimization of power and bandwidth resource allocation for aerial intelligent platforms, specifically uncrewed aerial vehicles (UAVs), operating in highly dynamic environments with mobile ground user equipment (UEs). We introduce FLARE (Flying Learning Agents for Resource Efficiency), a learning-enabled aerial intelligence framework that jointly optimizes UAV positioning, altitude, transmit power, and bandwidth allocation in real-time. To adapt to UE mobility, we employ Silhouette-based K-Means clustering, enabling dynamic grouping of users and UAVs’ deployment at cluster centroids for efficient service delivery. The problem is modeled as a multi-agent control task, with bandwidth discretized into resource blocks and power treated as a continuous variable. To solve this, our proposed framework, FLARE, employs a hybrid reinforcement learning strategy that combines Multi-Agent Deep Deterministic Policy Gradient (MADDPG) and Deep Q-Network (DQN) to enhance learning efficiency. Simulation results demonstrate that our method significantly enhances user coverage, achieving a 73.45% improvement in the number of served users under a 5 Mbps data rate constraint, outperforming MADDPG baseline.","PeriodicalId":100628,"journal":{"name":"IEEE Networking Letters","volume":"7 4","pages":"313-317"},"PeriodicalIF":3.3,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148514852","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

Time-Constrained Federated Learning (FL) in Push-Pull IoT Wireless Access 推拉式物联网无线接入中的时间约束联邦学习(FL

IF 3.3

IEEE Networking Letters Pub Date : 2025-12-01 Epub Date: 2025-10-22 DOI: 10.1109/LNET.2025.3624066

Van Phuc Bui;Junya Shiraishi;Petar Popovski;Shashi Raj Pandey

引用次数: 0