2023 IEEE/CIC International Conference on Communications in China (ICCC)最新文献_第10页

ADMM-Based Hybrid-Field Uplink Channel Estimation for Extremely Large-Scale MIMO Systems 基于admm的超大规模MIMO系统混合场上行信道估计

2023 IEEE/CIC International Conference on Communications in China (ICCC) Pub Date : 2023-08-10 DOI: 10.1109/ICCC57788.2023.10233516

Yiqing Li, Miao Jiang

引用次数: 0

RIS-Aided Federated Edge Learning Exploiting Statistical CSI 利用统计CSI的ris辅助联邦边缘学习

2023 IEEE/CIC International Conference on Communications in China (ICCC) Pub Date : 2023-08-10 DOI: 10.1109/ICCC57788.2023.10233543

Heju Li, Rui Wang, Jun Wu, Wei Zhang, Ismael Soto

{"title":"RIS-Aided Federated Edge Learning Exploiting Statistical CSI","authors":"Heju Li, Rui Wang, Jun Wu, Wei Zhang, Ismael Soto","doi":"10.1109/ICCC57788.2023.10233543","DOIUrl":"https://doi.org/10.1109/ICCC57788.2023.10233543","url":null,"abstract":"Federated edge learning (FEEL) as an emerging distributed learning paradigm can effectively resolve the resource constraints and privacy issues in the Internet of Things (IoT) by featuring collaborative training at the edge devices under the premise of data localization. Nevertheless, owing to the scarce resources and the inscrutable communication fading, issues such as model damage and signal deviation will critically diminish the convergence performance and learning accuracy of FEEL. With this in mind, reconfigurable intelligent surface (RIS) has recently been integrated to enhance the communication quality of wireless systems by adaptively reconfiguring the signal propagation environment. To utterly release the applied potency of RIS, the configuration of phase shifts is of paramount importance, where accurate channel state information (CSI) is required. Unfortunately, the accurate instantaneous CSI is extremely challenging to be estimated. In this paper, we focus on the realistic scenario assuming only statistical CSI is known, which can be comparatively easily and accurately explored. On the other hand, considering the wireless outage caused by the random non-line-of-sight channel, we rigorously derive an explicit convergence upper bound of the RIS enabled FEEL framework with respect to the outage probability. Based on the convergence theory, a unified resource allocation problem is further established by jointly optimizing bandwidth allocation, and the RIS configuration matrix. Extensive simulations are conducted to demonstrate that the proposed design dramatically promotes the learning performance compared against the baseline solutions.","PeriodicalId":191968,"journal":{"name":"2023 IEEE/CIC International Conference on Communications in China (ICCC)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132184532","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

Dynamic Unmanned Aerial Vehicle Selection via Evolutionary Game 基于进化博弈的动态无人机选择

2023 IEEE/CIC International Conference on Communications in China (ICCC) Pub Date : 2023-08-10 DOI: 10.1109/ICCC57788.2023.10233335

Anqi Meng, Tianshi Ren, Mei Yang, Xiaozheng Gao, Xianchao Zhang, Jinbo Zhang

引用次数: 0

Mobility Aware and Traffic Driven Neighbor Discovery For FANET 基于移动性感知和流量驱动的FANET邻居发现

2023 IEEE/CIC International Conference on Communications in China (ICCC) Pub Date : 2023-08-10 DOI: 10.1109/ICCC57788.2023.10233584

Yilei Shi, Jie Zhang, Lei Ding, Lina Zhu, Tom H. Luan

引用次数: 0

Channel Estimation Based on An Improved Conditional GAN for MIMO-OFDM Systems 基于改进条件GAN的MIMO-OFDM系统信道估计

2023 IEEE/CIC International Conference on Communications in China (ICCC) Pub Date : 2023-08-10 DOI: 10.1109/ICCC57788.2023.10233396

Ming Ye, Cunhua Pan, Yinfei Xu, Ming Jiang, Xiao Liang, Chunguo Li

{"title":"Channel Estimation Based on An Improved Conditional GAN for MIMO-OFDM Systems","authors":"Ming Ye, Cunhua Pan, Yinfei Xu, Ming Jiang, Xiao Liang, Chunguo Li","doi":"10.1109/ICCC57788.2023.10233396","DOIUrl":"https://doi.org/10.1109/ICCC57788.2023.10233396","url":null,"abstract":"Convolutional neural network (CNN) based channel estimation approaches have achieved remarkable success in massive multiple-input multiple-output (MIMO) systems. However, the channel estimation performance even needs to be improved. Meanwhile, the loss functions are not well designed in traditional deep learning (DL) based schemes for the problem of channel estimation. To improve the channel estimation accuracy, we propose an improved conditional generative adversarial network (GAN) based channel estimation method in this paper. Specifically, we first design two novel loss functions to better train the proposed conditional GAN model. The training stability of GANs is improved by coupling the mapping from the received signals to the channel matrices with its inverse mapping. Then, by jointly training these two loss functions and the proposed conditional GAN, we develop an improved conditional GAN to estimate channels more accurately. Simulation results demonstrate that the proposed improved conditional GAN based method improves the estimation performance and shows great robustness in MIMO systems.","PeriodicalId":191968,"journal":{"name":"2023 IEEE/CIC International Conference on Communications in China (ICCC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129043131","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

Enhancing Digital Twin Model for Connected Vehicles by Powertrain and Longitudinal Dynamics 基于动力总成和纵向动力学的网联汽车数字孪生模型改进

2023 IEEE/CIC International Conference on Communications in China (ICCC) Pub Date : 2023-08-10 DOI: 10.1109/ICCC57788.2023.10233600

Xiaoxu Wang, Rong Yu, Mingxing Ye, Min Hao

{"title":"Enhancing Digital Twin Model for Connected Vehicles by Powertrain and Longitudinal Dynamics","authors":"Xiaoxu Wang, Rong Yu, Mingxing Ye, Min Hao","doi":"10.1109/ICCC57788.2023.10233600","DOIUrl":"https://doi.org/10.1109/ICCC57788.2023.10233600","url":null,"abstract":"Digital twin for connected vehicles is a key enabling technology to accelerate the full digitization of Intelligent Transportation System (ITS). In a well-designed wireless environment such as 6G network, an accurate digital twin model for connected vehicles will significantly strengthen the efficiency, reliability and security of ITS. For a vehicle system, there are many potential practical factors that affect the accuracy of its digital twin model, including air resistance, tire rolling resistance, carrying loads, transmission shafts, controller and engine performance, etc. To create a realistic digital representation of the vehicle system, this paper elaborately integrates the powertrain and longitudinal dynamics into the digital twin model for car-following scenarios. The Measure-of-Performances (MoPs) and Goodness-of-Fit functions (GoFs) are exploited for model parameter fitting and fitting error measurement, respectively, using real trajectory dataset. The analytical and experimental results indicate that the accuracy of digital twin model increases 13.28%, 14.51% and 12.80% than that of the conventional Full Velocity Difference Model (FVDM) under the GoFs of Mean Absolute Percentage Error (MAPE), Root Mean Square Percentage Error (RMSPE_tilde) and Theil’s Inequality Coefficient U-function, respectively.","PeriodicalId":191968,"journal":{"name":"2023 IEEE/CIC International Conference on Communications in China (ICCC)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116931698","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

AoI Minimization for Multi-user Networks with Delayed Feedback 带延迟反馈的多用户网络AoI最小化

2023 IEEE/CIC International Conference on Communications in China (ICCC) Pub Date : 2023-08-10 DOI: 10.1109/ICCC57788.2023.10233345

Yuxiao Lu, Xiaoli Xu, Xinmei Huang

{"title":"AoI Minimization for Multi-user Networks with Delayed Feedback","authors":"Yuxiao Lu, Xiaoli Xu, Xinmei Huang","doi":"10.1109/ICCC57788.2023.10233345","DOIUrl":"https://doi.org/10.1109/ICCC57788.2023.10233345","url":null,"abstract":"This paper considers the packet scheduling for minimizing the age of information (AoI) in multi-user networks under delayed feedback, and investigates the impact of feedback delay on the AoI performance. With delayed feedback, the transmitter is not aware of the real-time receiving status at the moment of making the scheduling decisions. Hence, this problem is modelled as a partially-observable Markov decision process (POMDP), which includes a belief vector describing the probability distribution of the receiving status for all the users. The belief vector is updated based on the delayed feedback and the historical actions. Solving the POMDP optimally is rather challenging due to the large state space. We further propose a low-complexity policy, which selects the action that maximizes the expected immediate reward at each time slot. Numerical results show that the proposed policy significantly outperforms the stationary random policy. By comparing with the scheduling algorithm under instantaneous feedback, we show that the performance degradation caused by feedback delay increases with the packet arrival rate, channel erasure probability and the number of users.","PeriodicalId":191968,"journal":{"name":"2023 IEEE/CIC International Conference on Communications in China (ICCC)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116932889","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

An Offshore Maritime Edge Computing Network: Design and Experiment 海上边缘计算网络的设计与实验

2023 IEEE/CIC International Conference on Communications in China (ICCC) Pub Date : 2023-08-10 DOI: 10.1109/ICCC57788.2023.10233377

Lue Yang, Xinyu Dou, Xiaoyu Zhang, Xiao Han, Chaoyue Zhang, Bin Lin

引用次数: 0

Enhancing Water Pollutant Detection with few Training Samples using Feature Image and Acoustic Signals 基于特征图像和声学信号的少样本水污染物检测

2023 IEEE/CIC International Conference on Communications in China (ICCC) Pub Date : 2023-08-10 DOI: 10.1109/ICCC57788.2023.10233348

Jie Zhang, KeXin Zhou, Zhongmin Wang, Lei Xie, Lei Tang

{"title":"Enhancing Water Pollutant Detection with few Training Samples using Feature Image and Acoustic Signals","authors":"Jie Zhang, KeXin Zhou, Zhongmin Wang, Lei Xie, Lei Tang","doi":"10.1109/ICCC57788.2023.10233348","DOIUrl":"https://doi.org/10.1109/ICCC57788.2023.10233348","url":null,"abstract":"Water pollution is a major global environmental issue with severe consequences, highlighting the importance of accurate pollutant detection. While traditional chemical analysis methods are commonly employed to detect water quality, which involves collecting water samples for analysis to detect water quality, they are time-consuming and resource-intensive. In recent years, computer-based detection methods have emerged, but those typically rely on the experience of researchers to manual feature extraction and can be limited in their ability to generalize. This paper proposes a novel approach to water pollutant detection using acoustic signals and Convolutional Neural Network (CNN) technology. By leveraging the microphone and speaker embedded in smartphones to collect and process acoustic data, this method extracts time-frequency domain features and converts them into images for analysis by the CNN. The experimental results demonstrate high accuracy about 98% even in scenarios with limited data, indicating that this method could be an effective and efficient tool for detecting water pollutants.","PeriodicalId":191968,"journal":{"name":"2023 IEEE/CIC International Conference on Communications in China (ICCC)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117239650","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

Covert Communication for Intelligent Reflecting Surface based Symbiotic Radio Systems 基于智能反射面共生无线电系统的隐蔽通信

2023 IEEE/CIC International Conference on Communications in China (ICCC) Pub Date : 2023-08-10 DOI: 10.1109/ICCC57788.2023.10233549

Zhen Xu, Manlin Wang, Bin Xia, Jiangzhou Wang

引用次数: 0