IEEE Networking Letters最新文献_第4页

IEEE Networking Letters Author Guidelines IEEE Networking Letters 作者指南

IEEE Networking Letters Pub Date : 2024-10-25 DOI: 10.1109/LNET.2024.3482831

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IEEE Communications Society 电气和电子工程师学会通信协会

IEEE Networking Letters Pub Date : 2024-10-25 DOI: 10.1109/LNET.2024.3482829

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Latency-Aware Resource Allocation for Mobile Edge Generation and Computing via Deep Reinforcement Learning 基于深度强化学习的移动边缘生成和计算的延迟感知资源分配

IEEE Networking Letters Pub Date : 2024-10-24 DOI: 10.1109/LNET.2024.3486194

Yinyu Wu;Xuhui Zhang;Jinke Ren;Huijun Xing;Yanyan Shen;Shuguang Cui

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Progressive Growth-Based Momentum Contrast for Unsupervised Representative Learning in Classification Tasks 分类任务中无监督代表性学习的渐进式增长动量对比

IEEE Networking Letters Pub Date : 2024-10-17 DOI: 10.1109/LNET.2024.3482295

Yantao Li;Shijun Ling;Hongyu Huang;Feno H. Rabevohitra

{"title":"Progressive Growth-Based Momentum Contrast for Unsupervised Representative Learning in Classification Tasks","authors":"Yantao Li;Shijun Ling;Hongyu Huang;Feno H. Rabevohitra","doi":"10.1109/LNET.2024.3482295","DOIUrl":"https://doi.org/10.1109/LNET.2024.3482295","url":null,"abstract":"Contrastive unsupervised learning has made significant progress, but there is still potential for improvement by capturing finer details in input data. In this letter, we present PGMoCo, a Progressive Growth-based Momentum Contrast framework for unsupervised representative learning in classification tasks. PGMoCo begins by learning the overall distribution of samples at a coarse scale and progressively refines the representation by incorporating increasingly finer details. PGMoCo consists of data augmentation, progressive growth, an alternative multilayer perceptron (MLP) head, and a loss function. First, PGMoCo applies transformation-based data augmentation to the input samples. Then, it progressively learns features at multiple scales, uses an alternative MLP head to project latent representations into a contrastive loss space, and finally employs a specialized loss function to classify the samples. We evaluate PGMoCo on three datasets: CIFAR-10 and PolyU Palmprint (image classification) and H-MOG (person identification). PGMoCo achieves classification accuracies of 86.76% on CIFAR-10, 95.94% on PolyU Palmprint, and 80.10% on H-MOG, outperforming existing state-of-the-art methods.","PeriodicalId":100628,"journal":{"name":"IEEE Networking Letters","volume":"7 1","pages":"31-35"},"PeriodicalIF":0.0,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143645189","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

Viewport Prediction via Adaptive Edge Offloading 通过自适应边缘卸载的视口预测

IEEE Networking Letters Pub Date : 2024-10-14 DOI: 10.1109/LNET.2024.3480149

Ahmet Gunhan Aydin;Haris Vikalo

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Decentralized Coded Caching With Distributed Storage Across Data and Parity Servers 分散式编码缓存，跨数据和奇偶校验服务器分布式存储

IEEE Networking Letters Pub Date : 2024-10-14 DOI: 10.1109/LNET.2024.3479914

Monolina Dutta;Anoop Thomas;Frank Y. Li

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Adaptive Compression of Massive MIMO Channel State Information With Deep Learning 基于深度学习的海量MIMO信道状态信息自适应压缩

IEEE Networking Letters Pub Date : 2024-10-07 DOI: 10.1109/LNET.2024.3475269

Faris B. Mismar;Aliye Özge Kaya

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Optimal Classifier for an ML-Assisted Resource Allocation in Wireless Communications 无线通信中 ML 辅助资源分配的最佳分类器

IEEE Networking Letters Pub Date : 2024-10-04 DOI: 10.1109/LNET.2024.3474253

Rashika Raina;David E. Simmons;Nidhi Simmons;Michel Daoud Yacoub

{"title":"Optimal Classifier for an ML-Assisted Resource Allocation in Wireless Communications","authors":"Rashika Raina;David E. Simmons;Nidhi Simmons;Michel Daoud Yacoub","doi":"10.1109/LNET.2024.3474253","DOIUrl":"https://doi.org/10.1109/LNET.2024.3474253","url":null,"abstract":"This letter advances on the outage probability (OP) performance of a machine learning (ML)-assisted single-user multi-resource system. We focus on OP optimality and the trade-off between outage improvement and the mean number of resources scanned until a suitable resource is captured. We first present expressions for the OP of this system, complemented by an outage loss function (OLF) for its minimization. We then derive: (i) the necessary and sufficient properties of an optimal model (OpM) and (ii) expressions for the average number of resources scanned by both OpM and non-OpMs. Here, non-OpMs refer to those trained with the OLF and binary cross entropy (BCE) loss functions. We establish that optimal performance requires a channel that exhibits no time decorrelation properties. For very high decorrelation values, we find that models trained using the OLF and BCE perform similarly. For intermediate (practical) decorrelation values, OLF outperforms BCE, and both approach the OpM as decorrelation tends to zero. Our analysis further reveals that, to be able to capture a suitable resource, models trained with the OLF scan a slightly higher number of resources than the OpM and those trained with BCE. This increase in the mean number of scanned resources is offset by a significant enhancement in the OP as compared to BCE.","PeriodicalId":100628,"journal":{"name":"IEEE Networking Letters","volume":"6 3","pages":"158-162"},"PeriodicalIF":0.0,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10705105","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142517769","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

An eBPF-Based Trace-Driven Emulation Method for Satellite Networks 基于 eBPF 的卫星网络轨迹驱动仿真方法

IEEE Networking Letters Pub Date : 2024-10-03 DOI: 10.1109/LNET.2024.3472034

Weibiao Tian;Ye Li;Jinwei Zhao;Sheng Wu;Jianping Pan

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Bayesian Optimization Framework for Channel Simulation-Based Base Station Placement and Transmission Power Design 基于信道仿真的基站布局与发射功率设计的贝叶斯优化框架

IEEE Networking Letters Pub Date : 2024-09-27 DOI: 10.1109/LNET.2024.3469175

Koya Sato;Katsuya Suto

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