2019 IEEE Globecom Workshops (GC Wkshps)最新文献_第4页

Low Complexity Coordination Strategies at Multi-Lane Intersections 多车道交叉口低复杂度协调策略研究

2019 IEEE Globecom Workshops (GC Wkshps) Pub Date : 2019-12-01 DOI: 10.1109/GCWkshps45667.2019.9024524

Changhao Liu, Yangan Mo, B. Gao, Tingting Zhang

引用次数: 3

Retrieving Quantum Backscattered Signals in the Presence of Noise 噪声存在下量子反向散射信号的检索

2019 IEEE Globecom Workshops (GC Wkshps) Pub Date : 2019-12-01 DOI: 10.1109/GCWkshps45667.2019.9024525

Hany Khalifa, R. Jäntti

引用次数: 0

Lightweight Secure Communication Mechanism Towards UAV Networks 面向无人机网络的轻量级安全通信机制

2019 IEEE Globecom Workshops (GC Wkshps) Pub Date : 2019-12-01 DOI: 10.1109/GCWkshps45667.2019.9024530

Teng Li, Jianfeng Ma, Xindi Ma, Chenyang Gao, He Wang, Chengyan Ma, Jing Yu, D. Lu, Jiawei Zhang

引用次数: 5

LSTM for Mobility Based Content Popularity Prediction in Wireless Caching Networks 无线缓存网络中基于移动性的内容流行度预测的LSTM

2019 IEEE Globecom Workshops (GC Wkshps) Pub Date : 2019-12-01 DOI: 10.1109/GCWkshps45667.2019.9024419

Hanlin Mou, Yuhong Liu, Li Wang

引用次数: 14

Adaptive Multi-Attention Convolutional Neural Network for Fine-Grained Image Recognition 用于细粒度图像识别的自适应多注意卷积神经网络

2019 IEEE Globecom Workshops (GC Wkshps) Pub Date : 2019-12-01 DOI: 10.1109/GCWkshps45667.2019.9024585

Ang Li, Jianxin Chen, B. Kang, Wenqin Zhuang, Xuguang Zhang

{"title":"Adaptive Multi-Attention Convolutional Neural Network for Fine-Grained Image Recognition","authors":"Ang Li, Jianxin Chen, B. Kang, Wenqin Zhuang, Xuguang Zhang","doi":"10.1109/GCWkshps45667.2019.9024585","DOIUrl":"https://doi.org/10.1109/GCWkshps45667.2019.9024585","url":null,"abstract":"Fine-grained recognition is still a difﬁcult task in pattern recognition applications due to the challenge of accurate localization of discriminative parts. Recent CNN-based methods generally utilize attention mechanism to produce attention masks without part labels/annotations and extract corresponding image parts from them. However, these methods extract the attention parts by using ﬁxed-size rectangles to crop images regardless of the size of objects to be recognized, which will hinder the feature expression of the following Part-CNNs. In this paper, we propose an adaptive cropping module based on the information of attention masks to adjust size of cropping rectangles. The trainingprocessofadaptivecroppingmoduleandPart-CNNscan reinforce each other with the proposed rank loss and the classic softmax loss. To further balance and fuse all attention parts, we propose a part weighting module to evaluate part contributions. Under the optimization of sort loss, the part weighting module will produce part weights in the same order as prediction scores learned by attention parts. The backbone of our network is MA-CNN. Different from MA-CNN, the new proposed adaptive cropping module and part weighting module can jointly guide the framework to produce a more discriminative ﬁne-grained feature. Experiments show that the AMA-CNN outperforms MA-CNN by 1.1% on CUB200-2011 bird dataset.","PeriodicalId":210825,"journal":{"name":"2019 IEEE Globecom Workshops (GC Wkshps)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124478326","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: 3

A Routing Optimization Method for Software-Defined SGIN Based on Deep Reinforcement Learning 基于深度强化学习的软件自定义SGIN路由优化方法

2019 IEEE Globecom Workshops (GC Wkshps) Pub Date : 2019-12-01 DOI: 10.1109/GCWkshps45667.2019.9024680

Zhe Tu, Huachun Zhou, Kun Li, Guanglei Li, Qihui Shen

{"title":"A Routing Optimization Method for Software-Defined SGIN Based on Deep Reinforcement Learning","authors":"Zhe Tu, Huachun Zhou, Kun Li, Guanglei Li, Qihui Shen","doi":"10.1109/GCWkshps45667.2019.9024680","DOIUrl":"https://doi.org/10.1109/GCWkshps45667.2019.9024680","url":null,"abstract":"As space networks become more and more important, the Space-ground Integration network (SGIN) has received unprecedented attention. However, dynamic changes of topology and link status of satellite networks bring many challenges to routing optimization in the SGIN. Traditional routing optimization methods do not perform well, as they do not consider changes of topology and link status, as well as the association between flows. Since the Machine Learning (ML) technologies have shown significant advantages in dynamic routing optimization, we proposed a Machine Learning-based Space-ground Integration Networking (ML-SSGIN) framework that combines Software-Defined Networking (SDN) technologies to solve this challenge. To evaluate the feasibility of the proposed framework, the Deep Deterministic Policy Gradient (DDPG), a Deep Reinforcement Learning (DRL) algorithm, is deployed to perform routing optimization, which can make routing decisions based on real-time link status. In particular, we utilize a neural network that integrates Long Short-Term Memory Network (LSTM) and Dense layers for its actor and critic part to improve perceptual capabilities of contextual correlations between flows. We compared the proposed DDPG neural network with the one only having the Dense layers. The results show that the proposed architecture is feasible and effective. What's more, compared to Open Shortest Path First (OSPF) algorithm, our proposed routing optimization method can adapt to continuously change flows, and link status, which improves end-to-end throughput and latency.","PeriodicalId":210825,"journal":{"name":"2019 IEEE Globecom Workshops (GC Wkshps)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129349180","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: 11

Optimum Aerial Base Station Deployment for UAV Networks: A Reinforcement Learning Approach 无人机网络空中基站优化部署:一种强化学习方法

2019 IEEE Globecom Workshops (GC Wkshps) Pub Date : 2019-12-01 DOI: 10.1109/GCWkshps45667.2019.9024648

Meng-Chun Hou, Der-Jiunn Deng, Chia‐Ling Wu

{"title":"Optimum Aerial Base Station Deployment for UAV Networks: A Reinforcement Learning Approach","authors":"Meng-Chun Hou, Der-Jiunn Deng, Chia‐Ling Wu","doi":"10.1109/GCWkshps45667.2019.9024648","DOIUrl":"https://doi.org/10.1109/GCWkshps45667.2019.9024648","url":null,"abstract":"The boom of unmanned aerial vehicles (UAVs) is projected to fundamentally shift paradigms of transportations, logistics, agricultures, and public safety as a dominating unmanned application in following decades. To optimally process assigned tasks, each UAV requires prompt and ubiquitous information provisioning regarding the varying operation conditions, which renders exploiting base stations (BSs) of existing wireless infrastructures a tractable solution. To receive services from a BS, a UAV should stay within the coverage area of a BS, which however limits the operation range of a UAV. This obstacle thus drives the deployment of a special sort of UAV, known as an aerial base station (ABS), to relay signals between a BS and a UAV. Based on different flight paths of UAVs, an ABS should autonomously decide its own flight trajectory so as to maximize the number of UAVs which can receive wireless services. However, the inherently non-stationary environment renders the optimum autonomous deployment of an ABS a challenging issue. Inspired by the merit of interacting with the environment, we consequently propose a reinforcement learning scheme to optimize the flight trajectory of an ABS. To eliminate the engineering concern in the conventional Q-learning scheme that most state-action pairs may not be fully visited in the deployment of an ABS, in this paper, a state-amount-reduction (SAR) k-step Q-learning scheme is proposed to avoid the issue in the conventional Q-learning, so as to maximize the number of UAVs receiving services from an ABS. Through providing analytical foundations and simulation studies, outstanding performance of the proposed schemes is demonstrated as compared with that of the conventional reinforcement learning based ABS deployment.","PeriodicalId":210825,"journal":{"name":"2019 IEEE Globecom Workshops (GC Wkshps)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127288017","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: 4

2019 IEEE Globecom Workshops (GC Wkshps) Pub Date : 2019-12-01 DOI: 10.1109/GCWkshps45667.2019.9024486

Zhijuan Hu, Danyang Wang, Qifan Fu, Zan Li

{"title":"Similar Data Detection for Cooperative Spectrum Monitoring in Space-Ground Integrated Networks","authors":"Zhijuan Hu, Danyang Wang, Qifan Fu, Zan Li","doi":"10.1109/GCWkshps45667.2019.9024486","DOIUrl":"https://doi.org/10.1109/GCWkshps45667.2019.9024486","url":null,"abstract":"Space-ground aided cooperative spectrum monitoring, which combines the benefits of satellite components and terrestrial components for improving monitoring accuracy and enlarging monitoring area, has been becoming an emerging application of the space-ground integrated networks (SGIN). However, a short transmission window is usually provided for satellite components to connect with ground gateway, which means only a limited transmission time is allowed for the satellite component to upload the collected spectrum data. On the other hand, lots of redundancy may exist among the spectrum data collected by a single sensor during one collection period, which may further reduce the data uploading efficiency. In this paper, we investigate the similar data detection which is a matching problem for comparing two data, and it is important to the following data compression for improving data uploading efficiency. Firstly, the definition of the sharing fragment set is given. Then a metric method is presented to measure the redundancy of one data with respect to another data. We propose a Sharing Fragment Set (SFS) algorithm that can select a good sharing fragment set. Theoretical analysis proves that the proposed SFS algorithm is well suited to determine the redundancy between datas. In addition, we conduct an experiment based on the randomly produced synthetic dataset. Numerical results shows that the SFS algorithm performs better in selecting sharing fragment set compared with the Greedy-String-Tiling (GST) and simple greedy algorithm.","PeriodicalId":210825,"journal":{"name":"2019 IEEE Globecom Workshops (GC Wkshps)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122367519","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

Multipath Protections and Dynamic Link Recoveryin Softwarized 5G Networks Using Segment Routing 基于网段路由的软化5G网络多路径保护与动态链路恢复

2019 IEEE Globecom Workshops (GC Wkshps) Pub Date : 2019-12-01 DOI: 10.1109/GCWkshps45667.2019.9024556

A. Barakabitze, Lingfen Sun, I. Mkwawa, E. Ifeachor

{"title":"Multipath Protections and Dynamic Link Recoveryin Softwarized 5G Networks Using Segment Routing","authors":"A. Barakabitze, Lingfen Sun, I. Mkwawa, E. Ifeachor","doi":"10.1109/GCWkshps45667.2019.9024556","DOIUrl":"https://doi.org/10.1109/GCWkshps45667.2019.9024556","url":null,"abstract":"Future softwarized 5G networks have to be robust enough so as to ensure high network reliability and services availability. 5G network architecture should make sure that any failed parts in the network are detected, restored and recovered within a permissible period of time and at the lowest achievable cost. In this paper, we propose a Multipath Protection and Link-Failure free MPTCP/SR-based SDN/NFV architecture that increases survivability, resilience, availability of services in 5G networks. We present our system model and a multiPath protection and dynamic Link- Failure free algorithm called \"PathReLief\" that greatly reduces the failure recovery time and avoids link congestion in MPTCP/SR SDN/NFV 5G networks. To demonstrate the effectiveness of our proposal, we compare the performance of the proposed algorithm and the conventional topology discovery mechanisms for link/node failures in POX and OpenDaylight controllers. Preliminary results show that, our approach outperforms others used in the commonly used controllers (i.e., POX and OpenDaylight), in terms of reduced failure recovery time and localization time.","PeriodicalId":210825,"journal":{"name":"2019 IEEE Globecom Workshops (GC Wkshps)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132419303","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: 6

A Joint Mechanism for Fog-Relay Networks Based on NOMA and Network Coding 基于NOMA和网络编码的雾中继网络联合机制

2019 IEEE Globecom Workshops (GC Wkshps) Pub Date : 2019-12-01 DOI: 10.1109/GCWkshps45667.2019.9024638

Fanbo Wei, Ting Zhou, Tianheng Xu, Honglin Hu, Xiaoming Tao

引用次数: 2