IEEE INFOCOM 2023 - IEEE Conference on Computer Communications最新文献_第10页

Impact of International Submarine Cable on Internet Routing 国际海底电缆对互联网路由的影响

IEEE INFOCOM 2023 - IEEE Conference on Computer Communications Pub Date : 2023-05-17 DOI: 10.1109/infocom53939.2023.10229024

Honglin Ye, Shuai Wang, Dan Li

{"title":"Impact of International Submarine Cable on Internet Routing","authors":"Honglin Ye, Shuai Wang, Dan Li","doi":"10.1109/infocom53939.2023.10229024","DOIUrl":"https://doi.org/10.1109/infocom53939.2023.10229024","url":null,"abstract":"International submarine cables (ISCs) connect various countries/regions worldwide, and serve as the foundation of Internet routing. However, little attention has been paid to studying the impact of ISCs on Internet routing. This study addresses two questions to bridge the gap between ISCs and Internet routing: (1) For a given ISC, which Autonomous Systems (ASes) are using it, and (2) How dependent is Internet routing on ISCs. To tackle the first question, we propose Topology to Topology (or T2T), a framework for the large-scale measurement of static mapping between ASes and ISCs, and apply T2T to the Internet to reveal the status, trends, and preferences of ASes using ISCs. We find that ISCs used by Tier-1 ASes are more than 30× of stub ASes. For the second question, we design an Internet routing simulator, and evaluate the behavior change of Internet routing when an ISC fails based on the mapping between ASes and ISCs. The results show that benefited from the complex mesh of ISCs, the failures of most ISCs have limited impact on Internet routing, while a few ISCs can have a significant impact. Finally, we analyze severely affected ASes and recommend how to improve the resilience of the Internet.","PeriodicalId":387707,"journal":{"name":"IEEE INFOCOM 2023 - IEEE Conference on Computer Communications","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125104260","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

A Close Look at 5G in the Wild: Unrealized Potentials and Implications 近距离观察5G:未实现的潜力和影响

IEEE INFOCOM 2023 - IEEE Conference on Computer Communications Pub Date : 2023-05-17 DOI: 10.1109/INFOCOM53939.2023.10229016

Yanbing Liu, Chunyi Peng

引用次数: 6

An End-to-end Learning Framework for Joint Compensation of Impairments in Coherent Optical Communication Systems 相干光通信系统损伤联合补偿的端到端学习框架

IEEE INFOCOM 2023 - IEEE Conference on Computer Communications Pub Date : 2023-05-17 DOI: 10.1109/INFOCOM53939.2023.10228854

Rui Zhang, Min Liao, Jun Chen, Xusong Ning, Lin Li, Qinli Yang, Yongsheng Xu, Junming Shao

{"title":"An End-to-end Learning Framework for Joint Compensation of Impairments in Coherent Optical Communication Systems","authors":"Rui Zhang, Min Liao, Jun Chen, Xusong Ning, Lin Li, Qinli Yang, Yongsheng Xu, Junming Shao","doi":"10.1109/INFOCOM53939.2023.10228854","DOIUrl":"https://doi.org/10.1109/INFOCOM53939.2023.10228854","url":null,"abstract":"The application of machine learning techniques in Coherent Optical Communication (COC) systems has gained increasing attention in recent years. One representative and successful application is to employ neural networks to compensate the signal impairments of devices in the COC system. However, existing studies usually concentrate on each individual device or one impairment, the various impairments sourced from multiple devices (e.g., non-linear distortion, memory and crosstalk effects) are not well investigated. More importantly, due to the impairment isolation caused by frequency offset, traditional studies only compensate the impairments of transmitter or receiver individually. In this paper, we consider a more practical and challenging experimental setup environment: joint compensation of multiple impairments associated with all devices of transmitter and receiver simultaneously. To this end, we propose an end-to-end compensation framework from the transmitter to the receiver in COC systems with three associated modules: an auxiliary channel neural network for impairment modeling, a pre-compensation neural network deployed in the transmitter, and a post-compensation neural network deployed in the receiver. Different from previous works, the proposed framework not only allows modeling all impairments of multiple devices, but also provides a new venue for joint compensation of the transmitter and receiver simultaneously. The solution has been successfully verified by the high baud rate (120Gbaud) coherent optical professional test platform and shows impressive optical Signalto-Noise Ratio (SNR) gains.","PeriodicalId":387707,"journal":{"name":"IEEE INFOCOM 2023 - IEEE Conference on Computer Communications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125870252","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

QueuePilot: Reviving Small Buffers With a Learned AQM Policy QueuePilot:使用学习到的AQM策略恢复小缓冲区

IEEE INFOCOM 2023 - IEEE Conference on Computer Communications Pub Date : 2023-05-17 DOI: 10.1109/INFOCOM53939.2023.10228975

Micha Dery, Orr Krupnik, I. Keslassy

引用次数: 0

SVDFed: Enabling Communication-Efficient Federated Learning via Singular-Value-Decomposition SVDFed:通过奇异值分解实现高效通信的联邦学习

IEEE INFOCOM 2023 - IEEE Conference on Computer Communications Pub Date : 2023-05-17 DOI: 10.1109/INFOCOM53939.2023.10229042

Hao Wang, Xuefeng Liu, Jianwei Niu, Shaojie Tang

{"title":"SVDFed: Enabling Communication-Efficient Federated Learning via Singular-Value-Decomposition","authors":"Hao Wang, Xuefeng Liu, Jianwei Niu, Shaojie Tang","doi":"10.1109/INFOCOM53939.2023.10229042","DOIUrl":"https://doi.org/10.1109/INFOCOM53939.2023.10229042","url":null,"abstract":"Federated learning (FL) is an emerging paradigm of distributed machine learning. However, when applied to wireless network scenarios, FL usually suffers from high communication cost because clients need to transmit their updated gradients to a server in every training round. Although many gradient compression techniques like sparsification and quantization are proposed, they compress clients’ gradients independently, without considering the correlations among gradients. In this paper, we propose SVDFed, a collaborative gradient compression framework for FL. SVDFed utilizes Singular Value Decomposition (SVD) to find a few basis vectors, whose linear combination can well represent clients’ gradients at a certain round. Due to the correlations among gradients, these basis vectors can still well approximate new gradients in many subsequent rounds. With the help of basis vectors, clients only need to upload the coefficients of the linear combination to the server, which greatly reduces communication cost. In addition, SVDFed leverages the classical PID (Proportional, Integral, Derivative) control to determine the proper time to update basis vectors to maintain their representation ability. Through experiments, we demonstrate that SVDFed outperforms existing gradient compression methods in FL. For example, compared to a popular gradient quantization method QSGD, SVDFed can reduce the communication overhead by 66 % and pending time by 99 %.","PeriodicalId":387707,"journal":{"name":"IEEE INFOCOM 2023 - IEEE Conference on Computer Communications","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129419009","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

Utilizing the Neglected Back Lobe for Mobile Charging 利用被忽略的后瓣进行移动充电

IEEE INFOCOM 2023 - IEEE Conference on Computer Communications Pub Date : 2023-05-17 DOI: 10.1109/INFOCOM53939.2023.10228855

Meixuan Ren, Dié Wu, Jing Xue, Wenzheng Xu, J. Peng, Tang Liu

{"title":"Utilizing the Neglected Back Lobe for Mobile Charging","authors":"Meixuan Ren, Dié Wu, Jing Xue, Wenzheng Xu, J. Peng, Tang Liu","doi":"10.1109/INFOCOM53939.2023.10228855","DOIUrl":"https://doi.org/10.1109/INFOCOM53939.2023.10228855","url":null,"abstract":"Benefitting from the breakthrough of wireless power transfer technology, the lifetime of Wireless Sensor Networks (WSNs) can be significantly prolonged by scheduling a mobile charger (MC) to charge sensors. Compared with omnidirectional charging, the MC equipped with directional antenna can concentrate energy in the intended direction, making charging more efficient. However, all prior arts ignore the considerable energy leakage behind the directional antenna (i.e., back lobe), resulting in energy wasted in vain. To address this issue, we study a fundamental problem of how to utilize the neglected back lobe and schedule the directional MC efficiently. Towards this end, we first build and verify a directional charging model considering both main and back lobes. Then, we focus on jointly optimizing the number of dead sensors and energy usage effectiveness. We achieve these by introducing a scheduling scheme that utilizes both main and back lobes to charge multiple sensors simultaneously. Finally, extensive simulations and field experiments demonstrate that our scheme reduces the number of dead sensors by 49.5% and increases the energy usage effectiveness by 10.2% on average as compared with existing algorithms.","PeriodicalId":387707,"journal":{"name":"IEEE INFOCOM 2023 - IEEE Conference on Computer Communications","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121155266","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: 1

CSI-StripeFormer: Exploiting Stripe Features for CSI Compression in Massive MIMO System CSI- stripeformer:利用条纹特征的CSI压缩在大规模MIMO系统

IEEE INFOCOM 2023 - IEEE Conference on Computer Communications Pub Date : 2023-05-17 DOI: 10.1109/INFOCOM53939.2023.10229094

Qingyong Hu, Hua Kang, Huangxun Chen, Qianyi Huang, Qian Zhang, Min Cheng

{"title":"CSI-StripeFormer: Exploiting Stripe Features for CSI Compression in Massive MIMO System","authors":"Qingyong Hu, Hua Kang, Huangxun Chen, Qianyi Huang, Qian Zhang, Min Cheng","doi":"10.1109/INFOCOM53939.2023.10229094","DOIUrl":"https://doi.org/10.1109/INFOCOM53939.2023.10229094","url":null,"abstract":"The massive MIMO gain for wireless communication has been greatly hindered by the feedback overhead of channel state information (CSI) growing linearly with the number of antennas. Recent efforts leverage the DNN-based encoder-decoder framework to exploit correlations within the CSI matrix for better CSI compression. However, existing works have not fully exploited the unique features of CSI, resulting in an unsatisfactory performance under high compression ratios and sensitivity to multipath effects. Instead of treating CSI as common 2D matrices like images, we reveal the intrinsic stripe-based correlation across the CSI matrix. Driven by this insight, we propose CSI-StripeFormer, a stripe-aware encoder-decoder framework to exploit the unique stripe feature for better CSI compression. We design a lightweight encoder with asymmetric convolution kernels to capture various shape features. We further incorporate novel designs tailored for stripe features, including a novel hierarchical Transformer backbone in the decoder and a hybrid attention mechanism to extract and fuse correlations in angular and delay domains. Our evaluation results show that our system achieves an over 7dB channel reconstruction gain under a high compression ratio of 64 in multipath-rich scenarios, significantly superior to current state-of-the-art approaches. This gain can be further improved to 17dB given the extended embedded dimension of our backbone.","PeriodicalId":387707,"journal":{"name":"IEEE INFOCOM 2023 - IEEE Conference on Computer Communications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131692921","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

Mixup Training for Generative Models to Defend Membership Inference Attacks 防范隶属推理攻击的生成模型混合训练

IEEE INFOCOM 2023 - IEEE Conference on Computer Communications Pub Date : 2023-05-17 DOI: 10.1109/INFOCOM53939.2023.10229036

Zhe Ji, Qiansiqi Hu, Liyao Xiang, Chenghu Zhou

引用次数: 0

WakeUp: Fine-Grained Fatigue Detection Based on Multi-Information Fusion on Smart Speakers 唤醒:基于多信息融合的智能音箱细粒度疲劳检测

IEEE INFOCOM 2023 - IEEE Conference on Computer Communications Pub Date : 2023-05-17 DOI: 10.1109/INFOCOM53939.2023.10229021

Zhiyuan Zhao, Fan Li, Yadong Xie, Yu Wang

{"title":"WakeUp: Fine-Grained Fatigue Detection Based on Multi-Information Fusion on Smart Speakers","authors":"Zhiyuan Zhao, Fan Li, Yadong Xie, Yu Wang","doi":"10.1109/INFOCOM53939.2023.10229021","DOIUrl":"https://doi.org/10.1109/INFOCOM53939.2023.10229021","url":null,"abstract":"With the development of society and the gradual increase of life pressure, the number of people engaged in mental work and working hours have increased significantly, resulting in more and more people in a state of fatigue. It not only reduces people’s work efficiency, but also causes health and safety related problems. The existing fatigue detection systems either have different shortcomings in diverse scenarios or are limited by proprietary equipment, which is difficult to be applied in real life. Motivated by this, we propose a multi-information fatigue detection system named WakeUp based on commercial smart speakers, which is the first to fuse physiological and behavioral information for fine-grained fatigue detection in a non-contact manner. We carefully design a method to simultaneously extract users’ physiological and behavioral information based on the MobileViT network and VMD decomposition algorithm respectively. Then, we design a multi-information fusion method based on the statistical features of these two kinds of information. In addition, we adopt an SVM classifier to achieve fine-grained fatigue level. Extensive experiments with 20 volunteers show that WakeUp can detect fatigue with an accuracy of 97.28%. Meanwhile, WakeUp can maintain stability and robustness under different experimental settings.","PeriodicalId":387707,"journal":{"name":"IEEE INFOCOM 2023 - IEEE Conference on Computer Communications","volume":"118 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131225888","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

LightNestle: Quick and Accurate Neural Sequential Tensor Completion via Meta Learning LightNestle:通过元学习快速准确的神经序列张量补全

IEEE INFOCOM 2023 - IEEE Conference on Computer Communications Pub Date : 2023-05-17 DOI: 10.1109/INFOCOM53939.2023.10228967

Yuhui Li, Wei Liang, Kun Xie, Dafang Zhang, Songyou Xie, Kuan Li

{"title":"LightNestle: Quick and Accurate Neural Sequential Tensor Completion via Meta Learning","authors":"Yuhui Li, Wei Liang, Kun Xie, Dafang Zhang, Songyou Xie, Kuan Li","doi":"10.1109/INFOCOM53939.2023.10228967","DOIUrl":"https://doi.org/10.1109/INFOCOM53939.2023.10228967","url":null,"abstract":"Network operation and maintenance rely heavily on network traffic monitoring. Due to the measurement overhead reduction, lack of measurement infrastructure, and unexpected transmission error, network traffic monitoring systems suffer from incomplete observed data and high data sparsity problems. Recent studies model missing data recovery as a tensor completion task and show good performance. Although promising, the current tensor completion models adopted in network traffic data recovery lack an effective and efficient retraining scheme to adapt to newly arrived data while retaining historical information. To solve the problem, we propose LightNestle, a novel sequential tensor completion scheme based on meta-learning, which designs (1) an expressive neural network to transfer spatial knowledge from previous embeddings to current embeddings; (2) an attention-based module to transfer temporal patterns into current embeddings in linear complexity; and (3) meta-learning-based algorithms to iteratively recover missing data and update transfer modules to catch up with learned knowledge. We conduct extensive experiments on two real-world network traffic datasets to assess our performance. Results show that our proposed methods achieve both fast retraining and high recovery accuracy.","PeriodicalId":387707,"journal":{"name":"IEEE INFOCOM 2023 - IEEE Conference on Computer Communications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131272853","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: 1