2021 IEEE Global Communications Conference (GLOBECOM)最新文献_第2页

Federated Learning for Air Quality Index Prediction using UAV Swarm Networks 基于无人机群网络的空气质量指数预测的联邦学习

2021 IEEE Global Communications Conference (GLOBECOM) Pub Date : 2021-12-01 DOI: 10.1109/GLOBECOM46510.2021.9685991

P. Chhikara, Rajkumar Tekchandani, Neeraj Kumar, S. Tanwar, J. Rodrigues

{"title":"Federated Learning for Air Quality Index Prediction using UAV Swarm Networks","authors":"P. Chhikara, Rajkumar Tekchandani, Neeraj Kumar, S. Tanwar, J. Rodrigues","doi":"10.1109/GLOBECOM46510.2021.9685991","DOIUrl":"https://doi.org/10.1109/GLOBECOM46510.2021.9685991","url":null,"abstract":"People need to breathe, and so do other living beings, including plants and animals. It is impossible to overlook the impact of air pollution on nature, human well-being, and concerned countries' economies. Monitoring of air pollution and future predictions of air quality have lately displayed a vital concern. There is a need to predict the air quality index with high accuracy; on a real-time basis to prevent people from health issues caused by air pollution. With the help of Unmanned Aerial Vehicle's onboard sensors, we can collect air quality data easily. The paper proposes a distributed and decentralized Federated Learning approach within a UAV swarm. The accumulated data by the sensors are used as an input to the Long Short Term Memory (LSTM) model. Each UAV used its locally gathered data to train a model before transmitting the local model to the central base station. The central base station creates a master model by combining all the UAV's local model weights of the participating UAVs in the FL process and transmits it to all UAV s in the subsequent cycles. The effectiveness of the proposed model is evaluated with other machine learning models using various evaluation metrics using test data from the capital city of India, i.e., Delhi.","PeriodicalId":200641,"journal":{"name":"2021 IEEE Global Communications Conference (GLOBECOM)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117239835","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: 8

MmWave MIMO Hybrid Precoding Design Using Phase Shifters and Switches 使用移相器和开关的毫米波MIMO混合预编码设计

2021 IEEE Global Communications Conference (GLOBECOM) Pub Date : 2021-12-01 DOI: 10.1109/GLOBECOM46510.2021.9685324

Qiang Liu, Chenhao Qi, Xianghao Yu, Geoffrey Y. Li

{"title":"MmWave MIMO Hybrid Precoding Design Using Phase Shifters and Switches","authors":"Qiang Liu, Chenhao Qi, Xianghao Yu, Geoffrey Y. Li","doi":"10.1109/GLOBECOM46510.2021.9685324","DOIUrl":"https://doi.org/10.1109/GLOBECOM46510.2021.9685324","url":null,"abstract":"To reduce the number of phase shifters for analog precoding in millimeter wave massive multiple-input multiple-output communications, we investigate the hybrid use of expensive phase shifters and low-cost switches. Different from the existing fixed phase shifter (FPS) architecture where the phases are fixed and independent of the channel state information, we consider variable phase shifter (VPS) whose phases are variable and subject to the hardware constraint. Based on the VPS architecture, a hybrid precoding design (HPD) scheme named VPS-HPD is proposed to optimize the phases according to the channel state information. Specifically, we alternately optimize the analog precoder and the digital precoder, where the former is converted into several subproblems and each subproblem further includes the alternating optimization of the phase matrix and switch matrix. Simulation results show that the spectral efficiency of the VPS-HPD scheme is very close to that of the fully digital precoding, higher than that of the existing MO-AltMin scheme for the fully-connected architecture with much fewer phase shifters, and substantially higher than that of the existing FPS-AltMin scheme for the FPS architecture with the same number of phase shifters.","PeriodicalId":200641,"journal":{"name":"2021 IEEE Global Communications Conference (GLOBECOM)","volume":"227 14","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120878943","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

Noise Variance Estimation in 5G NR Receivers: Bias Analysis and Compensation 5G NR接收机噪声方差估计:偏置分析与补偿

2021 IEEE Global Communications Conference (GLOBECOM) Pub Date : 2021-12-01 DOI: 10.1109/GLOBECOM46510.2021.9685289

F. Penna, H. Kwon, Dongwoon Bai

引用次数: 0

BePOCH: Improving Federated Learning Performance in Resource-Constrained Computing Devices BePOCH:改善资源受限计算设备中的联邦学习性能

2021 IEEE Global Communications Conference (GLOBECOM) Pub Date : 2021-12-01 DOI: 10.1109/GLOBECOM46510.2021.9685095

Lenart Ibraimi, Mennan Selimi, Felix Freitag

{"title":"BePOCH: Improving Federated Learning Performance in Resource-Constrained Computing Devices","authors":"Lenart Ibraimi, Mennan Selimi, Felix Freitag","doi":"10.1109/GLOBECOM46510.2021.9685095","DOIUrl":"https://doi.org/10.1109/GLOBECOM46510.2021.9685095","url":null,"abstract":"Inference with trained machine learning models is now pos-sible with small computing devices while only a few years ago it was run mostly in the cloud only. The recent technique of Federated Learning offers now a way to do also the training of the machine learning models on small devices by distributing the computing effort needed for the training over many distributed machines. But, the training on these low-capacity devices takes a long time and often consumes all the available CPU resource of the device. Therefore, for Federated Learning to be done by low-capacity devices in practical environments, the training process must not only target for the highest accuracy, but also on reducing the training time and the resource consumption. In this paper, we present an approach which uses a dynamic epoch parameter in the model training. We propose the BePOCH (Best Epoch) algorithm to identify what is the best number of epochs per training round in Federated Learning. We show in experiments with medical datasets how with the BePOCH suggested number of epochs, the training time and resource consumption decreases while keeping the level of accuracy. Thus, BePOCH makes machine learning model training on low-capacity devices more feasible and furthermore, decreases the overall resource consumption of the training process, which is an important asnect towards greener machine learning techniques.","PeriodicalId":200641,"journal":{"name":"2021 IEEE Global Communications Conference (GLOBECOM)","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127116508","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

Dynamic Channel Access via Meta-Reinforcement Learning 基于元强化学习的动态通道访问

2021 IEEE Global Communications Conference (GLOBECOM) Pub Date : 2021-12-01 DOI: 10.1109/GLOBECOM46510.2021.9685347

Ziyang Lu, M. C. Gursoy

引用次数: 2

Load Balancing for Communication Networks via Data-Efficient Deep Reinforcement Learning 基于数据高效深度强化学习的通信网络负载平衡

2021 IEEE Global Communications Conference (GLOBECOM) Pub Date : 2021-12-01 DOI: 10.1109/GLOBECOM46510.2021.9685294

Di Wu, Jikun Kang, Yi Tian Xu, Hang Li, Jimmy Li, Xi Chen, D. Rivkin, Michael Jenkin, Taeseop Lee, Intaik Park, Xue Liu, Gregory Dudek

{"title":"Load Balancing for Communication Networks via Data-Efficient Deep Reinforcement Learning","authors":"Di Wu, Jikun Kang, Yi Tian Xu, Hang Li, Jimmy Li, Xi Chen, D. Rivkin, Michael Jenkin, Taeseop Lee, Intaik Park, Xue Liu, Gregory Dudek","doi":"10.1109/GLOBECOM46510.2021.9685294","DOIUrl":"https://doi.org/10.1109/GLOBECOM46510.2021.9685294","url":null,"abstract":"Within a cellular network, load balancing between different cells is of critical importance to network performance and quality of service. Most existing load balancing algorithms are manually designed and tuned rule-based methods where near-optimality is almost impossible to achieve. These rule-based meth-ods are difficult to adapt quickly to traffic changes in real-world environments. Given the success of Reinforcement Learning (RL) algorithms in many application domains, there have been a number of efforts to tackle load balancing for communication systems using RL-based methods. To our knowledge, none of these efforts have addressed the need for data efficiency within the RL framework, which is one of the main obstacles in applying RL to wireless network load balancing. In this paper, we formulate the communication load balancing problem as a Markov Decision Process and propose a data-efficient transfer deep reinforcement learning algorithm to address it. Experimental results show that the proposed method can significantly improve the system performance over other baselines and is more robust to environmental changes.","PeriodicalId":200641,"journal":{"name":"2021 IEEE Global Communications Conference (GLOBECOM)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125926716","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

Nonlinear Distortion in Distributed Massive MIMO Systems: An Indoor Channel Measurement Analysis 分布式海量MIMO系统的非线性失真:室内信道测量分析

2021 IEEE Global Communications Conference (GLOBECOM) Pub Date : 2021-12-01 DOI: 10.1109/GLOBECOM46510.2021.9685010

B. Liu, Andrea P. Guevara, L. V. D. Perre, S. Pollin

引用次数: 0

Optimal Deployment of Fog Nodes, Microservices and SDN Controllers in Time-Sensitive IoT Scenarios 雾节点、微服务和SDN控制器在时间敏感物联网场景中的优化部署

2021 IEEE Global Communications Conference (GLOBECOM) Pub Date : 2021-12-01 DOI: 10.1109/GLOBECOM46510.2021.9685995

Juan Luis Herrera, J. Galán-Jiménez, P. Bellavista, L. Foschini, J. G. Alonso, J. M. Murillo, J. Berrocal

{"title":"Optimal Deployment of Fog Nodes, Microservices and SDN Controllers in Time-Sensitive IoT Scenarios","authors":"Juan Luis Herrera, J. Galán-Jiménez, P. Bellavista, L. Foschini, J. G. Alonso, J. M. Murillo, J. Berrocal","doi":"10.1109/GLOBECOM46510.2021.9685995","DOIUrl":"https://doi.org/10.1109/GLOBECOM46510.2021.9685995","url":null,"abstract":"The application of Internet of Things (IoT)-based solutions to intensive domains has enabled the automation of real-world processes. The critical nature of these domains requires for very high Quality of Service (QoS) to work properly. These applications often use computing paradigms such as fog computing and software architectures such as the Microservices Architecture (MSA). Moreover, the need for transparent service discovery in MSAs, combined with the need for network scalability and flexibility, motivates the use of Software-Defined Networking (SDN) in these infrastructures. However, optimizing QoS in these scenarios implies an optimal deployment of microservices, fog nodes, and SDN controllers. Moreover, the deployment of each of the different elements affects the optimality of the others, which calls for a joint solution. In this paper, we motivate the joining of these three optimization problems into a single effort and we present Umizatou, a holistic deployment optimization solution that makes use of Mixed Integer Linear Programming. Finally, we evaluate Umizatou over a healthcare case study, showing its scalability in topologies of different sizes.","PeriodicalId":200641,"journal":{"name":"2021 IEEE Global Communications Conference (GLOBECOM)","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126110205","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: 8

Receiving Colliding LoRa Packets with Hard Information Iterative Decoding 接收硬信息迭代解码的冲突LoRa报文

2021 IEEE Global Communications Conference (GLOBECOM) Pub Date : 2021-12-01 DOI: 10.1109/GLOBECOM46510.2021.9685208

Raejoon Jung, P. Levis

引用次数: 1

Monte Carlo Tree Search for Network Planning for Next Generation Mobile Communication Networks 下一代移动通信网络规划的蒙特卡洛树搜索

2021 IEEE Global Communications Conference (GLOBECOM) Pub Date : 2021-12-01 DOI: 10.1109/GLOBECOM46510.2021.9685526

Linzhi Shen, Shaowei Wang

引用次数: 3