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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 : 2024-03-05 DOI: 10.1109/LNET.2024.3365245
{"title":"IEEE Communications Society","authors":"","doi":"10.1109/LNET.2024.3365245","DOIUrl":"https://doi.org/10.1109/LNET.2024.3365245","url":null,"abstract":"","PeriodicalId":100628,"journal":{"name":"IEEE Networking Letters","volume":"6 1","pages":"C3-C3"},"PeriodicalIF":0.0,"publicationDate":"2024-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10460369","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140042859","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 Networking Letters 作者指南
IEEE Networking Letters Pub Date : 2024-03-05 DOI: 10.1109/LNET.2024.3365243
{"title":"IEEE Networking Letters Author Guidelines","authors":"","doi":"10.1109/LNET.2024.3365243","DOIUrl":"https://doi.org/10.1109/LNET.2024.3365243","url":null,"abstract":"","PeriodicalId":100628,"journal":{"name":"IEEE Networking Letters","volume":"6 1","pages":"75-76"},"PeriodicalIF":0.0,"publicationDate":"2024-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10460361","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140042913","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
Admission Shaping With Network Calculus 利用网络微积分进行接纳整形
IEEE Networking Letters Pub Date : 2024-03-01 DOI: 10.1109/LNET.2024.3372407
Anne Bouillard
{"title":"Admission Shaping With Network Calculus","authors":"Anne Bouillard","doi":"10.1109/LNET.2024.3372407","DOIUrl":"https://doi.org/10.1109/LNET.2024.3372407","url":null,"abstract":"Several techniques can be used for computing deterministic performance bounds in FIFO networks. The most popular one, as far as Network Calculus is concerned, is Total Flow Analysis (TFA). Its advantages are its algorithmic efficiency, acceptable accuracy and adapted to general topologies. However, handling cyclic dependencies is mostly solved for token-bucket arrival curves. Moreover, in many situations, flows are shaped at their admission in a network, and the network analysis does not fully take advantage of it. In this letter, we generalize the approach to piece-wise linear concave arrival curves and to shaping several flows together at their admission into the network. We show through numerical evaluation that the performance bounds are drastically improved.","PeriodicalId":100628,"journal":{"name":"IEEE Networking Letters","volume":"6 2","pages":"115-118"},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141286615","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
Near-Perfect Coverage Manifold Estimation in Cellular Networks via Conditional GAN 通过条件 GAN 在蜂窝网络中实现近乎完美的覆盖态势估计
IEEE Networking Letters Pub Date : 2024-02-13 DOI: 10.1109/LNET.2024.3365717
Washim Uddin Mondal;Veni Goyal;Satish V. Ukkusuri;Goutam Das;Di Wang;Mohamed-Slim Alouini;Vaneet Aggarwal
{"title":"Near-Perfect Coverage Manifold Estimation in Cellular Networks via Conditional GAN","authors":"Washim Uddin Mondal;Veni Goyal;Satish V. Ukkusuri;Goutam Das;Di Wang;Mohamed-Slim Alouini;Vaneet Aggarwal","doi":"10.1109/LNET.2024.3365717","DOIUrl":"10.1109/LNET.2024.3365717","url":null,"abstract":"This letter presents a conditional generative adversarial network (cGAN) that translates base station location (BSL) information of any Region-of-Interest (RoI) to location-dependent coverage probability values within a subset of that region, called the region-of-evaluation (RoE). We train our network utilizing the BSL data of India, the USA, Germany, and Brazil. In comparison to the state-of-the-art convolutional neural networks (CNNs), our model improves the prediction error (\u0000<inline-formula> <tex-math>$L_{1}$ </tex-math></inline-formula>\u0000 difference between the coverage manifold generated by the network under consideration and that generated via simulation) by two orders of magnitude. Moreover, the cGAN-generated coverage manifolds appear to be almost visually indistinguishable from the ground truth.","PeriodicalId":100628,"journal":{"name":"IEEE Networking Letters","volume":"6 2","pages":"97-100"},"PeriodicalIF":0.0,"publicationDate":"2024-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139786554","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
Greedy Shapley Client Selection for Communication-Efficient Federated Learning 为提高通信效率的联合学习选择贪婪的沙普利客户端
IEEE Networking Letters Pub Date : 2024-02-08 DOI: 10.1109/LNET.2024.3363620
Pranava Singhal;Shashi Raj Pandey;Petar Popovski
{"title":"Greedy Shapley Client Selection for Communication-Efficient Federated Learning","authors":"Pranava Singhal;Shashi Raj Pandey;Petar Popovski","doi":"10.1109/LNET.2024.3363620","DOIUrl":"https://doi.org/10.1109/LNET.2024.3363620","url":null,"abstract":"The standard client selection algorithms for Federated Learning (FL) are often unbiased and involve uniform random sampling of clients. This has been proven sub-optimal for fast convergence under practical settings characterized by significant heterogeneity in data distribution, computing, and communication resources across clients. For applications having timing constraints due to limited communication opportunities with the parameter server (PS), the client selection strategy is critical to complete model training within the fixed budget of communication rounds. To address this, we develop a biased client selection strategy, GreedyFed, that identifies and greedily selects the most contributing clients in each communication round. This method builds on a fast approximation algorithm for the Shapley Value at the PS, making the computation tractable for real-world applications with many clients. Compared to various client selection strategies on several real-world datasets, GreedyFed demonstrates fast and stable convergence with high accuracy under timing constraints and when imposing a higher degree of heterogeneity in data distribution, systems constraints, and privacy requirements.","PeriodicalId":100628,"journal":{"name":"IEEE Networking Letters","volume":"6 2","pages":"134-138"},"PeriodicalIF":0.0,"publicationDate":"2024-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141286637","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
Interval Hyperbolic Localization Based on Iterative Contraction for Cellular Networks 基于迭代收缩的蜂窝网络区间双曲定位
IEEE Networking Letters Pub Date : 2024-02-05 DOI: 10.1109/LNET.2024.3362708
Biao Zhou;Xuan Su;Min Pang;Le Yang
{"title":"Interval Hyperbolic Localization Based on Iterative Contraction for Cellular Networks","authors":"Biao Zhou;Xuan Su;Min Pang;Le Yang","doi":"10.1109/LNET.2024.3362708","DOIUrl":"https://doi.org/10.1109/LNET.2024.3362708","url":null,"abstract":"Time difference of arrival (TDOA) positioning results obtained using commonly adopted algebraic methods lack uncertainty information. In this letter, we propose to incorporate interval computation into TDOA-based hyperbolic localization and employ an iterative contraction strategy to generate interval positioning results that guarantee to enclose the true solution. With the newly developed algorithm, interval TDOA measurements are considered as interval hyperbolas and partitioned into non-overlapping sets of rectangles using the dichotomy method. The intersection of these rectangles is determined and applied to update the target location interval through an iterative contraction process to shrink the location interval until convergence. Simulations are conducted to evaluate the accuracy, uncertainty and validity of the proposed interval hyperbolic localization algorithm. It is shown that the new algorithm can attain the Cramér-Rao lower bound under high level Gaussian noise and produce, with a probability close to one, positioning intervals enclosing the true target location.","PeriodicalId":100628,"journal":{"name":"IEEE Networking Letters","volume":"6 2","pages":"87-91"},"PeriodicalIF":0.0,"publicationDate":"2024-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141286744","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
FractionalLB: Controller Load Balancing Using Fractional Switch Migration in Software-Defined Networks FractionalLB：在软件定义网络中使用分数交换机迁移实现控制器负载平衡
IEEE Networking Letters Pub Date : 2024-01-22 DOI: 10.1109/LNET.2024.3357089
Upendra Prajapati;Bijoy Chand Chatterjee;Amit Banerjee
{"title":"FractionalLB: Controller Load Balancing Using Fractional Switch Migration in Software-Defined Networks","authors":"Upendra Prajapati;Bijoy Chand Chatterjee;Amit Banerjee","doi":"10.1109/LNET.2024.3357089","DOIUrl":"https://doi.org/10.1109/LNET.2024.3357089","url":null,"abstract":"In the context of multi-controller software-defined networks (SDNs), the efficacy of load-balancing can be improved through fractional switch migration. This letter proposes a controller load balancing approach using fractional switch migration, named FractionalLB. FractionalLB aims to reduce the difference between the load of each controller and its corresponding predefined threshold, which is used to identify overloaded and underloaded controllers. The load balancing is achieved by redistributing the load fractionally from the heavily loaded controller to other controllers. We formulate FractionalLB as an optimization problem using a mixed-integer linear program (MILP). When MILP is not tractable, we introduce a heuristic approach based on MILP. Numerical results indicate that FractionalLB outperforms a conventional scheme that follows time-sharing switch migration in terms of synchronization cost and the min-max controller’s load ratio.","PeriodicalId":100628,"journal":{"name":"IEEE Networking Letters","volume":"6 2","pages":"129-133"},"PeriodicalIF":0.0,"publicationDate":"2024-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141286617","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
Generative Network Layer for Communication Systems With Artificial Intelligence 人工智能通信系统的生成网络层
IEEE Networking Letters Pub Date : 2024-01-15 DOI: 10.1109/LNET.2024.3354114
Mathias Thorsager;Israel Leyva-Mayorga;Beatriz Soret;Petar Popovski
{"title":"Generative Network Layer for Communication Systems With Artificial Intelligence","authors":"Mathias Thorsager;Israel Leyva-Mayorga;Beatriz Soret;Petar Popovski","doi":"10.1109/LNET.2024.3354114","DOIUrl":"https://doi.org/10.1109/LNET.2024.3354114","url":null,"abstract":"The traditional role of the network layer is the transfer of packet replicas from source to destination through intermediate network nodes. We present a generative network layer that uses Generative AI (GenAI) at intermediate or edge network nodes and analyze its impact on the required data rates in the network. We conduct a case study where the GenAI-aided nodes generate images from prompts that consist of substantially compressed latent representations. The results from network flow analyses under image quality constraints show that the generative network layer can achieve an improvement of more than 100% in terms of the required data rate.","PeriodicalId":100628,"journal":{"name":"IEEE Networking Letters","volume":"6 2","pages":"82-86"},"PeriodicalIF":0.0,"publicationDate":"2024-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141286745","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
Shared Spectrum Cooperative Networks: A Linear Programming-Based Approach 共享频谱合作网络：基于线性规划的方法
IEEE Networking Letters Pub Date : 2024-01-05 DOI: 10.1109/LNET.2024.3350434
Z. Mohammadi;M. Soleimanpour Moghadam;S. Talebi;H. Ahmadi
{"title":"Shared Spectrum Cooperative Networks: A Linear Programming-Based Approach","authors":"Z. Mohammadi;M. Soleimanpour Moghadam;S. Talebi;H. Ahmadi","doi":"10.1109/LNET.2024.3350434","DOIUrl":"https://doi.org/10.1109/LNET.2024.3350434","url":null,"abstract":"This letter investigates a desirable power allocation scheme for shared spectrum networks and formulate it as a constrained optimization model that falls into the nonlinear class fractional programming problems. The investigation focuses on different solutions for the problem under consideration. The first investigated approach utilizes the convex optimization problem (COP) equivalent from of the problem by employing the Charnes-Cooper transformation. This approach finds the optimal solution and thus is the most appropriate from a viewpoint of optimality. The second approach employs the approximate linear programming (LP). From the complexity point of view, the LP problem can be cast as the benchmark. Our proposed linear model has lower complexity with an acceptable accuracy leading to decreasing the time delay to make the final decision on the availability. Simulation results indicate a higher coherence between the two latter approaches and the superior performance than the state-of-the-art literature in different system parameter.","PeriodicalId":100628,"journal":{"name":"IEEE Networking Letters","volume":"6 2","pages":"77-81"},"PeriodicalIF":0.0,"publicationDate":"2024-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141286616","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
Malicious Traffic Detection With Noise Labels Based on Cross-Modal Consistency 基于跨模态一致性的噪声标签恶意流量检测
IEEE Networking Letters Pub Date : 2024-01-02 DOI: 10.1109/LNET.2023.3349301
Qingjun Yuan;Weina Niu;Yongjuan Wang;Gaopeng Gou;Bin Lu
{"title":"Malicious Traffic Detection With Noise Labels Based on Cross-Modal Consistency","authors":"Qingjun Yuan;Weina Niu;Yongjuan Wang;Gaopeng Gou;Bin Lu","doi":"10.1109/LNET.2023.3349301","DOIUrl":"https://doi.org/10.1109/LNET.2023.3349301","url":null,"abstract":"To train robust malicious traffic identification models under noisy labeled datasets, a number of learning with noise labels approaches have been introduced, among which parallel training methods have been proved to be effective. Parallel training methods tend to select samples with disagreement to mitigate the risk of self-control degradation. However, it also introduces noisy knowledge into training. In this letter, we try to avoid introducing noisy knowledge by enhancing the consistency of the representations of parallel networks. Meanwhile, the two networks are heterogeneous and introduce information from different modalities, thus mitigating the risk of self-control degradation from multiple perspectives.","PeriodicalId":100628,"journal":{"name":"IEEE Networking Letters","volume":"6 2","pages":"148-151"},"PeriodicalIF":0.0,"publicationDate":"2024-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141286584","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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