2018 10th International Conference on Wireless Communications and Signal Processing (WCSP)最新文献

Feature Analysis and Optimization of Underwater Target Radiated Noise Based on t-SNE 基于t-SNE的水下目标辐射噪声特征分析与优化

2018 10th International Conference on Wireless Communications and Signal Processing (WCSP) Pub Date : 2018-10-01 DOI: 10.1109/WCSP.2018.8555717

Yuechao Chen, S. Du, H. Quan

{"title":"Feature Analysis and Optimization of Underwater Target Radiated Noise Based on t-SNE","authors":"Yuechao Chen, S. Du, H. Quan","doi":"10.1109/WCSP.2018.8555717","DOIUrl":"https://doi.org/10.1109/WCSP.2018.8555717","url":null,"abstract":"The analysis and optimization for underwater target radiated noise recognition are addressed. The t-SNE (t-distributed stochastic neighbor embedding) algorithm is taken for dimension reduction of the underwater target radiated noise spectrum bands segmented by frequency and the visual results can be obtained. The average distance between classes and coincidence rate are constructed as indicators of the visualization results. By analyzing the separability of each frequency band, the optimal features can be obtained. Then the optimal features are recognized by three classification algorithms which are Random Forest, SVM (Support Vector Machine) and AdaBoost. The processing results of experimental signal spectrum with two types of target are as follows. The separability of the target signal spectrum decreases with the increase of frequency. The spectrum band of 10-150Hz has the best separability. SVM is sensitive to the increase of the data dimension with non optimal separability. Random Forests and AdaBoost can apply separability components in wider frequency bands and have greater tolerance. The overall recognition accuracy of AdaBoost is the highest, but the computation efficiency is the lowest. These analysis results shows that the t-SNE algorithm can be used to optimize the underwater target radiated noise spectrum features for the purpose of improving the accuracy and efficiency of the classification algorithm.","PeriodicalId":423073,"journal":{"name":"2018 10th International Conference on Wireless Communications and Signal Processing (WCSP)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115150036","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: 2

A Space-Time Graph based Minimum Cost Routing Algorithm for the Random Traffic in the Satellite Network 基于空时图的卫星网络随机流量最小代价路由算法

2018 10th International Conference on Wireless Communications and Signal Processing (WCSP) Pub Date : 2018-10-01 DOI: 10.1109/WCSP.2018.8555699

Chengcheng Shi, Peng Yuan, Zhihua Yang

引用次数: 3

High-SNR Capacity Analysis of mmWave Systems under Finite-Dimensional Channel Model 有限维信道模型下毫米波系统的高信噪比容量分析

2018 10th International Conference on Wireless Communications and Signal Processing (WCSP) Pub Date : 2018-10-01 DOI: 10.1109/WCSP.2018.8555706

Xi Yang, Xiao Li, Shengli Zhang, Shi Jin

引用次数: 2

3D Convolutional Generative Adversarial Networks for Missing Traffic Data Completion 缺失交通数据补全的三维卷积生成对抗网络

2018 10th International Conference on Wireless Communications and Signal Processing (WCSP) Pub Date : 2018-10-01 DOI: 10.1109/WCSP.2018.8555917

Zhimin Li, Haifeng Zheng, Xinxin Feng

{"title":"3D Convolutional Generative Adversarial Networks for Missing Traffic Data Completion","authors":"Zhimin Li, Haifeng Zheng, Xinxin Feng","doi":"10.1109/WCSP.2018.8555917","DOIUrl":"https://doi.org/10.1109/WCSP.2018.8555917","url":null,"abstract":"The problem of data missing is a common issue in practical traffic data collection for an Intelligent Transportation System. However, how to efficiently impute the missing entries of the traffic data is still a challenge. Previous works on missing traffic data imputation usually apply matrix or tensor completion based methods which are unable to fully exploit the spatio-temporal features of historical traffic data to achieve a satisfactory performance. In this paper, we propose a 3D convolutional generative adversarial networks to impute missing traffic data. We propose to use a fractionally strided 3D convolutional neural network to construct the generator network since it can upsample efficiently in a deep network and a 3D convolutional neural network to construct the discriminator network to fully capture spatio-temporal features of traffic data. We also present numerical results with real traffic flow dataset to show that the proposed model can significantly improve the performance in terms of recovery accuracy over the other existing tensor completion methods under various data missing patterns. We believe that the proposed approach provides a promising alternative for data imputation in ITS and other applications.","PeriodicalId":423073,"journal":{"name":"2018 10th International Conference on Wireless Communications and Signal Processing (WCSP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124391850","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 Back-tracing Partition based On-path Caching Distribution Strategy over integrated LEO Satellite and Terrestrial networks 基于反向跟踪分区的低轨道卫星与地面综合网络路径缓存分配策略

2018 10th International Conference on Wireless Communications and Signal Processing (WCSP) Pub Date : 2018-10-01 DOI: 10.1109/WCSP.2018.8555886

Yue Li, Qinyu Zhang, Peng Yuan, Zhihua Yang

{"title":"A Back-tracing Partition based On-path Caching Distribution Strategy over integrated LEO Satellite and Terrestrial networks","authors":"Yue Li, Qinyu Zhang, Peng Yuan, Zhihua Yang","doi":"10.1109/WCSP.2018.8555886","DOIUrl":"https://doi.org/10.1109/WCSP.2018.8555886","url":null,"abstract":"In these years, hybrid low earth orbit (LEO) satellites and terrestrial networks are widely developed for a variety of content distribution services, such as internet of things, satellite sensor networks and tactical distribution networks, due to low deployment cost and broader coverage capability. However, an unreasonable distribution strategy in the integrated LEO satellite and terrestrial user network will incur prolonged content access latency and significant overheads for users, due to obviously time-varying topology and limited on-board resources. In this paper, therefore, we proposed a back-tracing partition directed on-path caching mechanism for the publisher/subscriber distribution over the hybrid LEO constellation and terrestrial network. In the proposed mechanism, a back-tracing algorithm instructed node selection strategy is well-designed as well as a cross-slot graph based Dijkstra routing algorithm. Through avoiding intermittent connectivity as much as possible, the proposed mechanism could efficiently reduce the redundant transmissions of data access for different users, by fetching objective file mainly from a limited group of intermediate caching nodes, instead of directly from the source node. The simulation results verified the proposed method can obviously degrade the overall overheads and access delay of all users compared with the minimum spanning tree (MST) algorithm.","PeriodicalId":423073,"journal":{"name":"2018 10th International Conference on Wireless Communications and Signal Processing (WCSP)","volume":"101 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116672069","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

Polar Coding for Noncoherent Block Fading Channels 非相干块衰落信道的极性编码

2018 10th International Conference on Wireless Communications and Signal Processing (WCSP) Pub Date : 2018-10-01 DOI: 10.1109/WCSP.2018.8555698

Mengfan Zheng, Wen Chen, Cong Ling

引用次数: 5

Big Data Enabled Vehicle Collision Detection Using Linear Discriminant Analysis 基于线性判别分析的大数据车辆碰撞检测

2018 10th International Conference on Wireless Communications and Signal Processing (WCSP) Pub Date : 2018-10-01 DOI: 10.1109/WCSP.2018.8555647

Yiwen Nie, Junhui Zhao, Jin Liu, Rong Ran

引用次数: 0

An Efficient Location based Localized Broadcasting Algorithm for Wireless Ad Hoc and Sensor Networks

2018 10th International Conference on Wireless Communications and Signal Processing (WCSP) Pub Date : 2018-10-01 DOI: 10.1109/WCSP.2018.8555530

Xu Qin, Tao Wang, Baoxian Zhang

引用次数: 2

Fair-Energy Trajectory Plan for Reconnaissance Mission Based on UAVs Cooperation 基于无人机协同的侦察任务公平能量轨迹规划

2018 10th International Conference on Wireless Communications and Signal Processing (WCSP) Pub Date : 2018-10-01 DOI: 10.1109/WCSP.2018.8555684

Zhenjie Qin, Aijing Li, Chao Dong, Haipeng Dai, Ai Xu

引用次数: 6

An Energy Harvesting Chain Model for Wireless-Powered IoT Networks 无线物联网网络的能量收集链模型

2018 10th International Conference on Wireless Communications and Signal Processing (WCSP) Pub Date : 2018-10-01 DOI: 10.1109/WCSP.2018.8555723

Jie Wang, Xin Kang, Ying-Chang Liang, Sumei Sun

{"title":"An Energy Harvesting Chain Model for Wireless-Powered IoT Networks","authors":"Jie Wang, Xin Kang, Ying-Chang Liang, Sumei Sun","doi":"10.1109/WCSP.2018.8555723","DOIUrl":"https://doi.org/10.1109/WCSP.2018.8555723","url":null,"abstract":"Internet of Things (IoT) is an emerging technology that will change our daily life. However, due to the size limitation, most of them cannot be equipped with a large battery to support the long serving time requirement. Thus, the energy harvesting (EH) technology, which can wirelessly provide energy for battery-constrained devices, has become a promising solution for IoT. In this paper, we propose an energy harvesting chain model for a wireless-powered IoT network with one information access point (AP), one radio frequency RF energy source and a set of IoT devices. The energy is transferred from the RF source to the first user, and the subsequent user harvests energy from the previous one while it transmits information to the AP. In this paper, we study two problems under the proposed energy harvesting chain model, which are total-time minimization (TTM) and sum-throughput maximization (STM). By applying convex optimization techniques, both problems can be solved in closed-form. Then, a suboptimal time allocation scheme is studied. It is shown by simulation results that the proposed chain model is useful and can be applied to specific practical application scenarios.","PeriodicalId":423073,"journal":{"name":"2018 10th International Conference on Wireless Communications and Signal Processing (WCSP)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125310502","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