2021 IEEE/CIC International Conference on Communications in China (ICCC)最新文献_第8页

Grant-Free Random Access in Massive MIMO Based LEO Satellite Internet of Things 基于大规模MIMO的LEO卫星物联网无授权随机接入

2021 IEEE/CIC International Conference on Communications in China (ICCC) Pub Date : 2021-07-28 DOI: 10.1109/iccc52777.2021.9580408

Zhen Gao, Keke Ying, Chen He, Zhenvu Xiao, Dezhi Zheng, Jun Zhang

引用次数: 4

Age-aware Communication Strategy in Federated Learning with Energy Harvesting Devices 基于能量收集装置的联邦学习中的年龄感知沟通策略

2021 IEEE/CIC International Conference on Communications in China (ICCC) Pub Date : 2021-07-28 DOI: 10.1109/iccc52777.2021.9580240

Xin Liu, Xiaoqi Qin, Hao Chen, Yiming Liu, Baoling Liu, Ping Zhang

{"title":"Age-aware Communication Strategy in Federated Learning with Energy Harvesting Devices","authors":"Xin Liu, Xiaoqi Qin, Hao Chen, Yiming Liu, Baoling Liu, Ping Zhang","doi":"10.1109/iccc52777.2021.9580240","DOIUrl":"https://doi.org/10.1109/iccc52777.2021.9580240","url":null,"abstract":"Federated learning is considered as a privacy-preserving distributed machine learning framework, where the model training is distributed over end devices by fully exploiting scattered computation capability and training data. Different from centralized machine learning where the convergence time is decided by number of training rounds, under the framework of FL, the convergence time also depends on the communication delay and computation delay for local training in each round. Therefore, we employ total training delay as the performance metric in our strategy design. Note that the training delay per round is prone to the limited wireless resources and system heterogeneity, where end devices have different computational and communication capabilities. To achieve timely parameter aggregation over limited spectrum, we incorporate age of parameter in device scheduling for each training round, which is defined as the number of rounds elapsed since last time of parameter uploading. Moreover, since diversity of uploaded parameters is important for training performance over data with non-IID distributions, we exploit energy harvesting technology to prevent device drop-outs during training process. In this paper, we propose an age-aware communication strategy for federated learning over wireless networks, by jointly considering the staleness of parameters and heterogeneous capabilities at end devices to realize fast and accurate model training. Numerical results demonstrate the effectiveness and accuracy of our proposed strategy.","PeriodicalId":425118,"journal":{"name":"2021 IEEE/CIC International Conference on Communications in China (ICCC)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114454651","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

Unsourced Random Access with a Massive MIMO Receiver: Exploiting Angular Domain Sparsity 大规模MIMO接收机的无源随机访问:利用角域稀疏性

2021 IEEE/CIC International Conference on Communications in China (ICCC) Pub Date : 2021-07-28 DOI: 10.1109/iccc52777.2021.9580441

Xinyu Xie, Yongpeng Wu

引用次数: 1

Deep Learning based Antenna Selection and CSI Extrapolation in Massive MIMO Systems 大规模MIMO系统中基于深度学习的天线选择和CSI外推

2021 IEEE/CIC International Conference on Communications in China (ICCC) Pub Date : 2021-07-28 DOI: 10.1109/iccc52777.2021.9580209

Bo Lin, F. Gao, Shun Zhang, Ting Zhou, A. Alkhateeb

{"title":"Deep Learning based Antenna Selection and CSI Extrapolation in Massive MIMO Systems","authors":"Bo Lin, F. Gao, Shun Zhang, Ting Zhou, A. Alkhateeb","doi":"10.1109/iccc52777.2021.9580209","DOIUrl":"https://doi.org/10.1109/iccc52777.2021.9580209","url":null,"abstract":"A critical bottleneck of massive multiple-input multiple-output (MIMO) system is the huge training overhead caused by downlink transmission, like channel estimation, downlink beamforming and covariance observation. In this paper, we propose to use the channel state information (CSI) of a small number of antennas to extrapolate the CSI of the other antennas and reduce the training overhead. Specifically, we design a deep neural network that we call an antenna domain extrapolation network (ADEN) that can exploit the correlation function among antennas. We then propose a deep learning (DL) based antenna selection network (ASN) that can select a limited antennas for optimizing the extrapolation, which is conventionally a type of combinatorial optimization and is difficult to solve. We trickly designed a constrained degradation algorithm to generate a differentiable approximation of the discrete antenna selection vector such that the back-propagation of the neural network can be guaranteed. Numerical results show that the proposed ADEN outperforms the traditional fully connected one, and the antenna selection scheme learned by ASN is much better than the trivially used uniform selection.","PeriodicalId":425118,"journal":{"name":"2021 IEEE/CIC International Conference on Communications in China (ICCC)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134387302","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: 16

A Hybrid Load Forecasting Method Based on Neural Network in Smart Grid 基于神经网络的智能电网混合负荷预测方法

2021 IEEE/CIC International Conference on Communications in China (ICCC) Pub Date : 2021-07-28 DOI: 10.1109/iccc52777.2021.9580230

Jingyi Zhang, Wenpeng Jing, Zhaoming Lu, Yueting Wang, X. Wen

{"title":"A Hybrid Load Forecasting Method Based on Neural Network in Smart Grid","authors":"Jingyi Zhang, Wenpeng Jing, Zhaoming Lu, Yueting Wang, X. Wen","doi":"10.1109/iccc52777.2021.9580230","DOIUrl":"https://doi.org/10.1109/iccc52777.2021.9580230","url":null,"abstract":"Power load forecasting is of great significance to ensure the smooth operation of smart grid. Because the load generation and consumption are related to the grid internal and environmental factors external, reliable and accurate power load forecasting is undoubtedly challenging in smart grid. Since weather factors are always the leading causes that affecting power generation load in smart grid, especially in distributed photovoltaic power generation, we propose a load forecasting method to realize the forecast of the generated load under different weather conditions in this paper. We firstly investigates the combined effect of various weather factors on power load comprehensively. Specially, the parametric regression models are utilized to analyse the relationship between the power load and weather factors. Secondly, a hybrid forecasting method based on Multilayer Perceptron (MLP) neural network is proposed to achieve reliable and accurate power load forecasting of various weather conditions. Different from the existing works, we not only take into account the weather factors, but also select corresponding parametric models integrated as the additional input of the MLP neural network to predict the power load. More importantly, a modified extreme learning machine (ELM) based hierarchical learning algorithm is introduced to train the formulated model. As a result, the training process of the neutral network can be accelerated in the sense that iteration times are reduced, in which case the learning accuracy can also be guaranteed. The proposed method is evaluated on the real dataset which consist of meteorological factors and corresponding load data. The results show the proposed method outperforms the existing algorithms in prediction accuracy. The prediction error Mean Square Error(MSE) and Root Mean Squared Error(RMSE) can be reduced by 36.28% and 20.18% respectively, which ensure the reliability of the power load forecasting.","PeriodicalId":425118,"journal":{"name":"2021 IEEE/CIC International Conference on Communications in China (ICCC)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129328368","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

SDN Controller Placement in LEO Satellite Networks Based on Dynamic Topology 基于动态拓扑的LEO卫星网络SDN控制器布局

2021 IEEE/CIC International Conference on Communications in China (ICCC) Pub Date : 2021-07-28 DOI: 10.1109/iccc52777.2021.9580367

Jianming Guo, Lei Yang, David Rincón Rivera, S. Sallent, Chengguang Fan, Quan Chen, Xuanran Li

引用次数: 4

Beams Selection for MmWave Multi-Connection Based on Sub-6GHz Predicting and Parallel Transfer Learning 基于Sub-6GHz预测和并行迁移学习的毫米波多连接波束选择

2021 IEEE/CIC International Conference on Communications in China (ICCC) Pub Date : 2021-07-28 DOI: 10.1109/iccc52777.2021.9580346

Huajiao Chen, Changyin Sun, Fan Jiang, Jing Jiang

{"title":"Beams Selection for MmWave Multi-Connection Based on Sub-6GHz Predicting and Parallel Transfer Learning","authors":"Huajiao Chen, Changyin Sun, Fan Jiang, Jing Jiang","doi":"10.1109/iccc52777.2021.9580346","DOIUrl":"https://doi.org/10.1109/iccc52777.2021.9580346","url":null,"abstract":"To meet the increasing wireless data demands, leveraging millimeter wave(mmWave) frequency band has become imperative for 5G systems due to the rich spectrum resources and greater bandwidth. In mmWave communication systems, multi-connection is an indispensable key technology, where the coordinated service of multiple links will enable users to get more wireless resources and ensure mobile robustness. However, mmWave multi-connections face challenges in beams selection process: (i) The time of multi-link serial search is long relative to single link, and the search overhead is large and the hardware complexity is high; (ii) In the case of multi-connection parallel transmission, the mutual interference between beams results in low multiplexing gain; (iii) The conventional codebook produces non-standard (non-pencil-shaped) beam shapes, which makes it difficult to reduce inter-beam interference only by relying on different codebooks. In response to the above problems, this paper uses sub-6GHz channel and deep neural network (DNN) to enhance beam search for mmWave multi-connection. Specifically, the spatial correlation between the low frequency band and the mmWave frequency band is exploited to map the sub-6GHz channel information to the mmWave beam index. To speed beams search process, a parallel deep neural network with transfer learning is proposed to predict the best beams for multi-links of a user. Simulation results show that the sub-6G Hz channel information can be used to effectively predict the optimal mmWave beams for multi-connected user, and the parallel transfer learning structure can facilitate in reducing interference and training overhead. As a result, near-optimal system sum-rate can be achieved.","PeriodicalId":425118,"journal":{"name":"2021 IEEE/CIC International Conference on Communications in China (ICCC)","volume":"153 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122068240","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

Robust Resource Scheduling for Air-Ground Cooperative Mobile Edge Computing 基于地空协同移动边缘计算的鲁棒资源调度

2021 IEEE/CIC International Conference on Communications in China (ICCC) Pub Date : 2021-07-28 DOI: 10.1109/iccc52777.2021.9580344

Yiwei Lu, Yang Huang, Tianyu Hu

{"title":"Robust Resource Scheduling for Air-Ground Cooperative Mobile Edge Computing","authors":"Yiwei Lu, Yang Huang, Tianyu Hu","doi":"10.1109/iccc52777.2021.9580344","DOIUrl":"https://doi.org/10.1109/iccc52777.2021.9580344","url":null,"abstract":"Mobile edge computing (MEC) is a novel technology for enhancing the computation capacity of user equipment (UEs), by offloading the computation-intensive tasks at UEs to a base station. In the context of UAV-mounted MEC, state of the art only addresses the optimization of offloading and wireless/computing resource allocation in the presence of air-ground channels. In contrast, this paper addresses the optimization, considering both the time-varying/random terrestrial channels and the line-of-sight air-ground channels, where a robust optimization problem is formulated so as to minimize the energy consumption of the UAV and the UEs. In order to develop a resource scheduling scheme which enables energy-efficient air-ground cooperative MEC, we propose a joint iterative optimization algorithm by exploiting the weighted mean square error approach and S-procedure. Numerical results demonstrate that, compared to various baseline schemes, the proposed algorithm can effectively reduce the energy consumption in the presence of a large number of input tasks. Compared with the non-robust schemes, the proposed algorithm can reduce the energy consumption more stably.","PeriodicalId":425118,"journal":{"name":"2021 IEEE/CIC International Conference on Communications in China (ICCC)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123638147","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

Scalable Network Coding over Embedded Fields 嵌入式字段的可扩展网络编码

2021 IEEE/CIC International Conference on Communications in China (ICCC) Pub Date : 2021-07-28 DOI: 10.1109/iccc52777.2021.9580416

Hanqi Tang, Ruobin Zheng, Zongpeng Li, Q. T. Sun

引用次数: 3

Optimal Configuration of Intelligent Walls for Interference Management in Smart Buildings 面向智能建筑干扰管理的智能墙优化配置

2021 IEEE/CIC International Conference on Communications in China (ICCC) Pub Date : 2021-07-28 DOI: 10.1109/iccc52777.2021.9580397

Jun Zong, Fuqian Yang, Yong Zhou, H. Qian, Xiliang Luo

引用次数: 0