J. Commun. Inf. Networks最新文献

Resource-Constrained Edge AI with Early Exit Prediction 具有早期退出预测的资源约束边缘AI

J. Commun. Inf. Networks Pub Date : 2022-06-01 DOI: 10.48550/arXiv.2206.07269

Rongkang Dong, Yuyi Mao, Jinchao Zhang

{"title":"Resource-Constrained Edge AI with Early Exit Prediction","authors":"Rongkang Dong, Yuyi Mao, Jinchao Zhang","doi":"10.48550/arXiv.2206.07269","DOIUrl":"https://doi.org/10.48550/arXiv.2206.07269","url":null,"abstract":"By leveraging the data sample diversity, the early-exit network recently emerges as a prominent neural network architecture to accelerate the deep learning inference process. However, intermediate classifiers of the early exits introduce additional computation overhead, which is unfavorable for resource-constrained edge artificial intelligence (AI). In this paper, we propose an early exit prediction mechanism to reduce the on-device computation overhead in a device-edge co-inference system supported by early-exit networks. Specifically, we design a low-complexity module, namely the Exit Predictor, to guide some distinctly\"hard\"samples to bypass the computation of the early exits. Besides, considering the varying communication bandwidth, we extend the early exit prediction mechanism for latency-aware edge inference, which adapts the prediction thresholds of the Exit Predictor and the confidence thresholds of the early-exit network via a few simple regression models. Extensive experiment results demonstrate the effectiveness of the Exit Predictor in achieving a better tradeoff between accuracy and on-device computation overhead for early-exit networks. Besides, compared with the baseline methods, the proposed method for latency-aware edge inference attains higher inference accuracy under different bandwidth conditions.","PeriodicalId":185127,"journal":{"name":"J. Commun. Inf. Networks","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123512667","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: 5

6G-enabled Edge AI for Metaverse: Challenges, Methods, and Future Research Directions 面向元宇宙的支持6g的边缘AI:挑战、方法和未来研究方向

J. Commun. Inf. Networks Pub Date : 2022-04-13 DOI: 10.48550/arXiv.2204.06192

Luyi Chang, Zhe Zhang, Pei Li, Shan Xi, Wei-Xiang Guo, Yukang Shen, Zehui Xiong, Jiawen Kang, D. Niyato, Xiuquan Qiao, Yi Wu

{"title":"6G-enabled Edge AI for Metaverse: Challenges, Methods, and Future Research Directions","authors":"Luyi Chang, Zhe Zhang, Pei Li, Shan Xi, Wei-Xiang Guo, Yukang Shen, Zehui Xiong, Jiawen Kang, D. Niyato, Xiuquan Qiao, Yi Wu","doi":"10.48550/arXiv.2204.06192","DOIUrl":"https://doi.org/10.48550/arXiv.2204.06192","url":null,"abstract":"6G-enabled edge intelligence opens up a new era of Internet of Everything and makes it possible to interconnect people-devices-cloud anytime, anywhere. More and more next-generation wireless network smart service applications are changing our way of life and improving our quality of life. As the hottest new form of next-generation Internet applications, Metaverse is striving to connect billions of users and create a shared world where virtual and reality merge. However, limited by resources, computing power, and sensory devices, Metaverse is still far from realizing its full vision of immersion, materialization, and interoperability. To this end, this survey aims to realize this vision through the organic integration of 6G-enabled edge AI and Metaverse. Specifically, we first introduce three new types of edge-Metaverse architectures that use 6G-enabled edge AI to solve resource and computing constraints in Metaverse. Then we summarize technical challenges that these architectures face in Metaverse and the existing solutions. Furthermore, we explore how the edge-Metaverse architecture technology helps Metaverse to interact and share digital data. Finally, we discuss future research directions to realize the true vision of Metaverse with 6G-enabled edge AI.","PeriodicalId":185127,"journal":{"name":"J. Commun. Inf. Networks","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129458272","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: 56

Hybrid Beamforming Design for Uplink mmWave Systems with a Predefined Low-Resolution Codebook 具有预定义低分辨率码本的上行毫米波系统混合波束形成设计

J. Commun. Inf. Networks Pub Date : 2019-09-01 DOI: 10.11959/J.ISSN.2096-1081.2019.03.01

Chao Han, Jiaxing Wang, Jingchao Wang, Lin Bai

引用次数: 7

User Association in Ultra-Dense Small Cell Dynamic Vehicular Networks: A Reinforcement Learning Approach 超密集小单元动态车辆网络中的用户关联:一种强化学习方法

J. Commun. Inf. Networks Pub Date : 2019-03-01 DOI: 10.1007/J.ISSN.2096-1081.2019.01.01

S. Kapoor, D. Grace, T. Clarke

引用次数: 1

Power Allocation Optimization of Multibeam High-Throughput Satellite Communication Systems 多波束高通量卫星通信系统的功率分配优化

J. Commun. Inf. Networks Pub Date : 2019-03-01 DOI: 10.1007/J.ISSN.2096-1081.2019.01.04

J. Jiao, Shaohua Wu, Yunyu Sun, Ye Wang, Qinyu Zhang

引用次数: 4

Anomalous Power-Usage Behavior Detection from Smart Home Wireless Communications 智能家庭无线通信的异常用电行为检测

J. Commun. Inf. Networks Pub Date : 2019-03-01 DOI: 10.1007/J.ISSN.2096-1081.2019.01.02

Jian Mao, Shishi Zhu, Jingdong Bian, Qixiao Lin, Jianwei Liu

引用次数: 3

Optimized Trajectory Design in UAV Based Cellular Networks for 3D Users: A Double Q-Learning Approach 基于无人机的三维蜂窝网络优化轨迹设计:双q -学习方法

J. Commun. Inf. Networks Pub Date : 2019-02-18 DOI: 10.1007/J.ISSN.2096-1081.2019.01.03

Xuanlin Liu, Mingzhe Chen, Changchuan Yin

引用次数: 22