2019 11th International Conference on Wireless Communications and Signal Processing (WCSP)最新文献_第6页

Wireless Content Caching in Sliced Cellular Networks with Multicast Beamforming 多播波束成形切片蜂窝网络中的无线内容缓存

2019 11th International Conference on Wireless Communications and Signal Processing (WCSP) Pub Date : 2019-10-01 DOI: 10.1109/WCSP.2019.8928095

Kan Wang, Wenchao Ji, Junhuai Li, Huaijun Wang, Ting Cao

{"title":"Wireless Content Caching in Sliced Cellular Networks with Multicast Beamforming","authors":"Kan Wang, Wenchao Ji, Junhuai Li, Huaijun Wang, Ting Cao","doi":"10.1109/WCSP.2019.8928095","DOIUrl":"https://doi.org/10.1109/WCSP.2019.8928095","url":null,"abstract":"In this paper, we consider the joint caching and multicast beamforming optimization in cellular networks with caching functions equipped on each base station (BS), to facilitate the network-wide utility improvement by means of content caching among multiple enhanced Mobile BroadBand (eMBB) slices. Firstly, by presenting the two-timescale setup and sample average approximation (SAA) technique, we decouple the intractable and stochastic joint clustering, caching and multicast beamforming optimization problem into two different timescales, the basic principle behind which is to iteratively solve the large- and small-timescale one on the basis of SAA and semidefinite relaxation (SDR), respectively. Then, we leverage the linear programming (LP) theory to solve the formulated joint clustering and caching problem at the large timescale. In particular, the integer linear programming (ILP)-based large-timescale problem is reformulated and relaxed as an LP one, along with a verification that its optimal solution is exactly equivalent to that of its relaxed LP counterpart. Finally, simulations are conducted to demonstrate the effectiveness of proposed algorithm, revealing that the network-wide utility could benefit from both the multicast beamforming and content sharing among multiple slices.","PeriodicalId":108635,"journal":{"name":"2019 11th International Conference on Wireless Communications and Signal Processing (WCSP)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126291290","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

Discriminatory Channel Estimation in MIMO Relay Systems against Active Eavesdropper 针对有源窃听的MIMO中继系统歧视性信道估计

2019 11th International Conference on Wireless Communications and Signal Processing (WCSP) Pub Date : 2019-10-01 DOI: 10.1109/WCSP.2019.8927873

Xingbo Lu, Weiwei Yang, Yueming Cai, Xinrong Guan, Lei Wang

引用次数: 0

Regularized Training Framework for Combining Pruning and Quantization to Compress Neural Networks 结合剪枝和量化压缩神经网络的正则化训练框架

2019 11th International Conference on Wireless Communications and Signal Processing (WCSP) Pub Date : 2019-10-01 DOI: 10.1109/WCSP.2019.8928083

Qimin Ding, Ruonan Zhang, Yi Jiang, D. Zhai, Bin Li

{"title":"Regularized Training Framework for Combining Pruning and Quantization to Compress Neural Networks","authors":"Qimin Ding, Ruonan Zhang, Yi Jiang, D. Zhai, Bin Li","doi":"10.1109/WCSP.2019.8928083","DOIUrl":"https://doi.org/10.1109/WCSP.2019.8928083","url":null,"abstract":"Many convolutional neural networks(CNNs) have been proposed to solve computer vision tasks such as image classification and image segmentation. However the CNNs usually contain a large number of parameters to determine which consumes very high computation and power resources. Thus, it is difficult to deploy the CNNs on resource-limited devices. Network pruning and network quantization are two main methods to compress the CNNs, researchers often apply these methods individually without considering the relationship between them. In this paper, we explore the coupling relationship between network pruning and quantization, as well as the limits of the current network compression training method. Then we propose a new regularized training method that can combine pruning and quantization within a simple training framework. Experiments show that by using the proposed training framework, the finetune process is not needed anymore and hence we can reduce much time for training a network. The simulation results also show that the performance of the network can over-perform the traditional methods. The proposed framework is suitable for the CNNs deployed in portable devices with limited computational resources and power supply.","PeriodicalId":108635,"journal":{"name":"2019 11th International Conference on Wireless Communications and Signal Processing (WCSP)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121808272","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

Error Resilience of Haptic Data in Interactive Systems 交互系统中触觉数据的错误恢复能力

2019 11th International Conference on Wireless Communications and Signal Processing (WCSP) Pub Date : 2019-10-01 DOI: 10.1109/WCSP.2019.8927919

Yiwen Xu, Yaping Huang, Weiling Chen, Hong Xue, Tiesong Zhao

引用次数: 4

Optimal Beam Power Control for Co-Existing Multibeam GEO and LEO Satellite System 多波束同步轨道和低轨道卫星共存系统的最优波束功率控制

2019 11th International Conference on Wireless Communications and Signal Processing (WCSP) Pub Date : 2019-10-01 DOI: 10.1109/WCSP.2019.8928079

Rui Li, Pengwenlong Gu, Cunqing Hua

引用次数: 14

Performance Analysis for D2D-Enabled Cellular Networks With Mobile Edge Computing 基于移动边缘计算的d2d蜂窝网络性能分析

2019 11th International Conference on Wireless Communications and Signal Processing (WCSP) Pub Date : 2019-10-01 DOI: 10.1109/WCSP.2019.8928027

Han Hu, Pengfei Zong, Hong Wang, Hongbo Zhu

{"title":"Performance Analysis for D2D-Enabled Cellular Networks With Mobile Edge Computing","authors":"Han Hu, Pengfei Zong, Hong Wang, Hongbo Zhu","doi":"10.1109/WCSP.2019.8928027","DOIUrl":"https://doi.org/10.1109/WCSP.2019.8928027","url":null,"abstract":"In this paper, we propose an analysis framework for the D2D-enabled cellular networks with mobile edge computing, which is composed of two types of computing nodes, i.e., access point (AP) nodes and device-to-device nodes (DNs). The mobile users (MUs), whose locations are modeled as a Poisson Point Process (PPP) distribution, make an independent selection to offload their computation tasks to either the nearby AP or the D2D nodes according to the assumed task offloading modes. Besides, in order to alleviate the intra-tier interference both in uplink and downlink, a prioritized channel access policy (PCAP) is explored. By using the stochastic geometry and queuing theory, the performance of the proposed system model is evaluated in terms of the total successful edge computing probability, which is influenced by both the communication and computation performance of the two offloading modes. We obtain interesting tradeoffs that depend on the effects of D2D node densities and distance thresholds of the assumed offloading mode selection on the above performance metrics. When compared to the scheme without PCAP, the PCAP can contribute more to the total successful edge computing probability of a MU. The derived results could provide an useful guideline for the provisioning and planning in employing D2D-enabled cellular network with edge computing units.","PeriodicalId":108635,"journal":{"name":"2019 11th International Conference on Wireless Communications and Signal Processing (WCSP)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114255201","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

Sparse Active User Detection and Channel Estimation Using ADMM in Uplink C-RAN 基于ADMM的上行C-RAN稀疏主动用户检测与信道估计

2019 11th International Conference on Wireless Communications and Signal Processing (WCSP) Pub Date : 2019-10-01 DOI: 10.1109/WCSP.2019.8927903

Zhenjun Dong, Ronghua Ji, Jian Zhao

引用次数: 0

Performance Analysis of Superposition Modulation in Physical Layer Security 物理层安全中叠加调制的性能分析

2019 11th International Conference on Wireless Communications and Signal Processing (WCSP) Pub Date : 2019-10-01 DOI: 10.1109/WCSP.2019.8928058

Shubo Sun, N. Li, Haowei Wang, Xiaofeng Tao

引用次数: 0

Two-Stage Power Allocation for Cooperative NOMA in D2D Communications with Imperfect CSI 不完全CSI下D2D通信协同NOMA的两级功率分配

2019 11th International Conference on Wireless Communications and Signal Processing (WCSP) Pub Date : 2019-10-01 DOI: 10.1109/WCSP.2019.8927904

Tianqi Xing, Nan Ma, Ping Zhang

引用次数: 4

CapsHash: Deep Supervised Hashing with Capsule Network CapsHash:基于胶囊网络的深度监督哈希

2019 11th International Conference on Wireless Communications and Signal Processing (WCSP) Pub Date : 2019-10-01 DOI: 10.1109/WCSP.2019.8927934

Yang Li, Rui Zhang, Zhuang Miao, Jiabao Wang

{"title":"CapsHash: Deep Supervised Hashing with Capsule Network","authors":"Yang Li, Rui Zhang, Zhuang Miao, Jiabao Wang","doi":"10.1109/WCSP.2019.8927934","DOIUrl":"https://doi.org/10.1109/WCSP.2019.8927934","url":null,"abstract":"To better deal with large-scale image retrieval problem, deep hashing models based on convolutional neural network (CNN) have been widely used as effective methods, which can map similar images to compact binary hash codes with smaller hamming distance. Despite their positive results, CNN-based methods have few limitations, which are unable to understand the spatial relationship between features. To overcome this challenge, in this paper, a novel deep supervised hashing method was proposed based on capsule networks. Our method, referred to as CapsHash, can learn discriminative hash codes and capsule vectors at the same time. Moreover, we introduce a novel compound loss function that has two parts: classification hashing loss and margin loss. This compound loss function can greatly improve the discriminative ability of binary codes and further improve the image retrieval performance. Extensive experiments under different scenarios demonstrate that our CapsHash method can preserve the instance-level similarity and outperform previous state-of-the-art hashing approaches. To the best of our knowledge, CapsHash is the first method about the application of capsule networks in the deep supervised hashing domain.","PeriodicalId":108635,"journal":{"name":"2019 11th International Conference on Wireless Communications and Signal Processing (WCSP)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131105638","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