2021 IEEE Wireless Communications and Networking Conference Workshops (WCNCW)最新文献_第3页

NOMA-based Proactive Content Caching in Hybrid Satellite-Aerial-Terrestrial Networks 星-航-地混合网络中基于noma的主动内容缓存

2021 IEEE Wireless Communications and Networking Conference Workshops (WCNCW) Pub Date : 2021-03-29 DOI: 10.1109/WCNCW49093.2021.9420015

Xiaokai Zhang, Bangning Zhang, K. An, Gang Wu, Yuewei Jia, Shuai Qi, D. Guo

引用次数: 5

Information Embedding Design for the Joint MIMO Radar and Communications via FH Code Selection 联合MIMO雷达与跳频选码通信的信息嵌入设计

2021 IEEE Wireless Communications and Networking Conference Workshops (WCNCW) Pub Date : 2021-03-29 DOI: 10.1109/WCNCW49093.2021.9420000

Xiaonan Xu, Yongzhe Li, R. Tao

引用次数: 3

Differential Modulation for Grant-free Short-packet Transmission in Flat Fading Channels 平坦衰落信道中无授权短包传输的差分调制

2021 IEEE Wireless Communications and Networking Conference Workshops (WCNCW) Pub Date : 2021-03-29 DOI: 10.1109/WCNCW49093.2021.9420023

Wenfei Cui, Huiying Niu, Donghao Liu, Zhongwei Si

引用次数: 0

Federated Channel-Beam Mapping: from sub-6GHz to mmWave 联邦信道波束映射:从6ghz以下到毫米波

2021 IEEE Wireless Communications and Networking Conference Workshops (WCNCW) Pub Date : 2021-03-29 DOI: 10.1109/WCNCW49093.2021.9420006

Irched Chafaa, Romain Negrel, E. Belmega, M. Debbah

{"title":"Federated Channel-Beam Mapping: from sub-6GHz to mmWave","authors":"Irched Chafaa, Romain Negrel, E. Belmega, M. Debbah","doi":"10.1109/WCNCW49093.2021.9420006","DOIUrl":"https://doi.org/10.1109/WCNCW49093.2021.9420006","url":null,"abstract":"Accurate beamforming is a critical challenge for mmWave communications. Because of the large training overhead of beam training at high frequencies, it becomes relevant to exploit the available knowledge at sub-6GHz to predict the mmWave beamforming vectors using deep learning tools. In addition, fully centralized learning (CL) approaches require training over all the users data, rising major issues in terms of signaling and computational cost. To address these issues, we propose a federated learning (FL) scheme in a wireless network composed of multiple communicating links (access points – users) to predict directly the downlink mmWave beamforming vectors from the uplink sub-6GHz channels. The access points train their local deep neural networks using local data and only share their model parameters to obtain an average global one, which improves the quality of their prediction in terms of data rate. Our experiments demonstrate the potential and robustness of our proposed scheme especially under difficult conditions, performing close to the fully centralized one. When the training data is scarce, the relative gain of our scheme can reach up to 50% compared to a fully distributed one. Remarkably, our scheme can even outperform the fully centralized one when the quality of the training data is poor, enjoying a relative gain of up to 14%.","PeriodicalId":262379,"journal":{"name":"2021 IEEE Wireless Communications and Networking Conference Workshops (WCNCW)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130758730","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: 7

Proactive Eavesdropping in UAV Systems Via Trajectory Planning and Power Optimization 基于轨迹规划和功率优化的无人机系统主动窃听

2021 IEEE Wireless Communications and Networking Conference Workshops (WCNCW) Pub Date : 2021-03-29 DOI: 10.1109/WCNCW49093.2021.9419975

Mengya Huang, Yu Chen, Xiaofeng Tao

引用次数: 1

Content-based Vehicle Selection and Resource Allocation for Federated Learning in IoV 基于内容的车联网联邦学习车辆选择与资源分配

2021 IEEE Wireless Communications and Networking Conference Workshops (WCNCW) Pub Date : 2021-03-29 DOI: 10.1109/WCNCW49093.2021.9419986

Siyu Wang, Fangfang Liu, Hailun Xia

{"title":"Content-based Vehicle Selection and Resource Allocation for Federated Learning in IoV","authors":"Siyu Wang, Fangfang Liu, Hailun Xia","doi":"10.1109/WCNCW49093.2021.9419986","DOIUrl":"https://doi.org/10.1109/WCNCW49093.2021.9419986","url":null,"abstract":"In order to use datasets collected from multiple vehicles to train a machine learning model while ensuring vehicle user privacy, federal learning framework was introduced into the Internet of Vehicles. Federated learning is a distributed learning framework. Under the federated learning framework, the packet error rate and wireless bandwidth have a great influence on the global model training process because the vehicle exchanges model parameters with the central server through the wireless channel. With limited bandwidth, the central server needs to select a more appropriate subset of vehicles candidates to participate in federated learning. In this paper, image classification is taken as a typical application in the Internet of vehicles. The dataset contents of different vehicles are different, and the selection of different subsets of vehicles will affect the accuracy and convergence rate of the global model. Therefore, an algorithm of vehicle selection and wireless resource allocation based on dataset content is proposed. Vehicles selection and wireless resource allocation are designed as an optimization problem by joint considerations of vehicle computing resource, datasets, and wireless resources with the goal of maximizing loss function decay of the global model. Finally, simulation with the CIFAR-10 dataset verifies that the vehicle selection and resource allocation algorithm based on the dataset content is superior to the baseline methods in terms of model accuracy and convergence rate.","PeriodicalId":262379,"journal":{"name":"2021 IEEE Wireless Communications and Networking Conference Workshops (WCNCW)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116550587","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: 23

RIS-Assisted mmWave Channel Estimation Using Convolutional Neural Networks 基于卷积神经网络的ris辅助毫米波信道估计

2021 IEEE Wireless Communications and Networking Conference Workshops (WCNCW) Pub Date : 2021-03-29 DOI: 10.1109/WCNCW49093.2021.9419974

Eyad Shtaiwi, Hongliang Zhang, A. Abdel-Hadi, Zhu Han

{"title":"RIS-Assisted mmWave Channel Estimation Using Convolutional Neural Networks","authors":"Eyad Shtaiwi, Hongliang Zhang, A. Abdel-Hadi, Zhu Han","doi":"10.1109/WCNCW49093.2021.9419974","DOIUrl":"https://doi.org/10.1109/WCNCW49093.2021.9419974","url":null,"abstract":"Reconfigurable intelligent surface (RIS) has recently been proposed as a smart reflector that significantly provides an energy and spectrum efficient solution for beyond 5G communications. The RIS is a planar surface made of a large number of reflecting elements. More specifically, an array of phase shifters. RIS provides an additional degree of freedom (DoF) by smartly reconfiguring the wireless environment. However, channel estimation (CE) is a challenging issue to benefit from RIS. To obtain the accurate channel state information (CSI) at the base station (BS), the number of channel coefficients is proportional to the number of reflecting elements and the number of users. Therefore, the training overhead prohibitively increases as the number of reflecting elements and/or users increases. In this paper, we propose a two-stage CE approach in mmWave communications to address this issue. In the first stage, we reduce the number of active users in the training period. Then we exploit the sparsity of the mmWave channel of the active users to divide the estimation process into three simple subproblems. In the second stage, we use the partial CSI collected in the first stage, exploit the spatial-temporal correlation between the channels for nearby users to estimate the channels for the remaining users. We deploy the spatial-temporal-spectral (STS) framework based on deep convolutional neural networks (CNNs) to estimate the channel coefficients for inactive users in the training period. Simulation results demonstrate that the performance of the proposed approach outperforms a benchmark scheme.","PeriodicalId":262379,"journal":{"name":"2021 IEEE Wireless Communications and Networking Conference Workshops (WCNCW)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124715843","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: 10

Bypassing Channel Estimation for OTFS Transmission: An Integrated Sensing and Communication Solution OTFS传输的旁路信道估计:一种集成的传感和通信解决方案

2021 IEEE Wireless Communications and Networking Conference Workshops (WCNCW) Pub Date : 2021-03-29 DOI: 10.1109/WCNCW49093.2021.9420020

W. Yuan, Shuangyang Li, Zhiqiang Wei, Jinhong Yuan, D. W. K. Ng

引用次数: 6

Blockchain Empowered Reliable Federated Learning by Worker Selection: A Trustworthy Reputation Evaluation Method 区块链通过工人选择授权可靠的联邦学习:一种值得信赖的声誉评估方法

2021 IEEE Wireless Communications and Networking Conference Workshops (WCNCW) Pub Date : 2021-03-29 DOI: 10.1109/WCNCW49093.2021.9420026

Qinnan Zhang, Qingyang Ding, Jian-ming Zhu, Dandan Li

{"title":"Blockchain Empowered Reliable Federated Learning by Worker Selection: A Trustworthy Reputation Evaluation Method","authors":"Qinnan Zhang, Qingyang Ding, Jian-ming Zhu, Dandan Li","doi":"10.1109/WCNCW49093.2021.9420026","DOIUrl":"https://doi.org/10.1109/WCNCW49093.2021.9420026","url":null,"abstract":"Federated learning is a distributed machine learning framework that enables distributed model training with local datasets, which can effectively protect the data privacy of workers (i.e., intelligent edge nodes). The majority of federated learning algorithms assume that the workers are trusted and voluntarily participate in the cooperative model training process. However, the situation in practical application is not consistent with this. There are many challenges such as worker selection schemes for participating workers, which hamper the widespread adoption of federated learning. The existing research about worker selection scheme focused on multi-weight subjective logic model to calculate reputation value and adopted contract theory to motivate workers, which may exist subjective judgmental factors and unfair profit distribution. To address above challenges, we calculate the reputation value by model quality parameters to evaluate the reliability of workers. Blockchain is designed to store historical reputation value that realized tamperresistance and non-repudiation. Numerical results indicate that the worker selection scheme can improve the accuracy of the model and accelerate the model convergence.","PeriodicalId":262379,"journal":{"name":"2021 IEEE Wireless Communications and Networking Conference Workshops (WCNCW)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124350985","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: 12

Dual-band OFDM Signal Excitation based RFID Channel Estimation and Single-station Ranging 基于OFDM信号激励的RFID信道估计与单站测距

2021 IEEE Wireless Communications and Networking Conference Workshops (WCNCW) Pub Date : 2021-03-29 DOI: 10.1109/WCNCW49093.2021.9420025

Y. Zhengwu, Changyou Fu, Z. Tian, Likun Yang

引用次数: 0