检索结果-内蒙古大学图书馆

Training Sequence Based Doppler Shift Estimation for Vehicular Communication

学校读者我要写书评

暂无评论

Training Sequence Based Doppler Shift Estimation for Vehicul...

IEEE Conference on Wireless communications and Networking

作者： Qingpeng Ma Gang Qiu Qixun Zhang Huiqing Sun Zhiyong Feng Zhu Han Key Laboratory of Universal Wireless Communications Ministry of Education Beijing University of Posts and Telecommunications Beijing P.R.China ZTE Corporation LTE & 5G Wireless Research Institute Shenzhen P.R.China Electrical and Computer Engineering Department University of Houston Houston TX

ISBN: (数字)9781728131061

ISBN: (纸本)9781728131078

To guarantee the safety requirements of autonomous driving vehicles, the efficient and robust sensing information sharing among vehicles is crucial to overcome the environment sensing limitations beyond a single sensor or vehicle. The millimeter wave (mmWave) technology is considered as one of the potential mobile broadband communication solutions to solve high data rate sensing information sharing problem among vehicles with the low latency constraint. However, the Doppler shift effect is much more severe, which will deteriorate the performance of vehicular communication links, especially in a high speed vehicle communication scenario in the mmWave frequency band. Therefore, we propose a training sequence based estimator (TSBE) by designing a hybrid structure combining the fixed section and the repeated sequences, aiming to achieve a fast and accurate Doppler shift estimation (DSE) in the mmWave vehicular scenario. The fixed section provides a fast estimation, and the accuracy of DSE can be effectively improved by the random correlation selection of repeated sequences. Furthermore, the key performances of our proposed DSE algorithm are evaluated by both the link-level simulation and the hardware testbed results. Results prove that the mean-squared error of our proposed TSBE algorithm outperforms other conventional algorithms under various signal to noise ratio values. The hardware testbed shows a remarkable performance improvement on the error vector magnitude and the bit error rate using our proposed TSBE and compensation algorithm.

关键词：

Clustering-correcting codes

学校读者我要写书评

暂无评论

arXiv 2019年

作者： Shinkar, Tal Yaakobi, Eitan Lenz, Andreas Wachter-Zeh, Antonia Department of Computer Science Technion Israel Institute of Technology Haifa3200009 Israel Institute for Communications Engineering Technical University of Munich Munich80333 Germany

A new family of codes, called clustering-correcting codes, is presented in this paper. This family of codes is motivated by the special structure of data that is stored in DNA-based storage systems. The data stored in these systems has the form of unordered sequences, also called strands, and every strand is synthesized thousands to millions of times, where some of these copies are read back during sequencing. Due to the unordered structure of the strands, an important task in the decoding process is to place them in their correct order. This is usually accomplished by allocating a part of the strand for an index. However, in the presence of errors in the index field, important information on the order of the strands may be lost. Clustering-correcting codes ensure that if the distance between the index fields of two strands is small, then there will be a large distance between their data fields. It is shown how this property enables to place the strands together in their correct clusters even in the presence of errors. We present lower and upper bounds on the size of clustering-correcting codes and an explicit construction of these codes which uses only a single bit of redundancy. Copyright © 2019, The Authors. All rights reserved.

关键词： Digital storage

Design of a Long-Short Range Soil Monitoring Wireless Sensor Network for Medium-Scale Deployment

学校读者我要写书评

暂无评论

Design of a Long-Short Range Soil Monitoring Wireless Sensor...

2018 IEEE Region 10 Conference, TENCON 2018

作者： Co, Jerelyn Tiausas, Francis Jerome Aldrin Domer, Prince Guico, Maria Leonora Claro Monje, Jose Oppus, Carlos Department of Information Systems and Computer Science Ateneo de Manila University Quezon City1108 Philippines Department of Electronics Computer and Communications Engineering Ateneo de Manila University Quezon City1108 Philippines

ISBN: (纸本)9781538654576

This paper presents the design and development of the hardware and software components of a soil monitoring Wireless Sensor Network (WSN) for medium-scale deployments. It is composed of multiple remote sensing and transmitting devices for autonomous monitoring of soil and several surrounding environmental conditions. Using commercially-available sensor probes, it can gather soil parameters such as air and soil temperature, sunlight, moisture, humidity, and soil pH. The proposed system, subdivided into three node classes, namely sensor, relay, and aggregator nodes, has wireless communication capabilities enabled by short-range packet radio, and long-range/low-power radio (LoRa) modules which can attend to telemetry-related challenges imposed by large area coverage, vegetation density, and location remoteness. © 2018 IEEE.

关键词： Soils

State-aware rate adaptation for UAVs by incorporating on-board sensors

学校读者我要写书评

暂无评论

arXiv 2019年

作者： He, Shiyue Wang, Wei Yang, Hang Cao, Yang Jiang, Tao Zhang, Qian School of Electronic Information and Communications Huazhong University of Science and Technology Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong

Nowadays unmanned aerial vehicles (UAVs) are being widely applied to a wealth of civil and military applications. Robust and high-throughput wireless communication is the crux of these UAV applications. Yet, air-to-ground links suffer from time-varying channels induced by the agile mobility and dynamic environments. Rate adaptation algorithms are generally used to choose the optimal data rate based on the current channel conditions. State-of-the-art approaches leverage physical layer information for rate adaptation, and they work well under certain conditions. However, the above protocols still have limitation under constantly changing flight states and environments for air-to-ground links. To solve this problem, we propose StateRate, a state-optimized rate adaptation algorithm that fully exploits the characteristics of UAV systems using a hybrid deep learning model. The key observation is that the rate adaptation strategy needs to be adjusted according to motion-dependent channel models, which can be reflected by flight states. In this work, the rate adaptation protocol is enhanced with the help of the on-board sensors in UAVs. To make full use of the sensor data, we introduce a learning-based prediction module by leveraging the internal state to dynamically store temporal features under variable flight states. We also present an online learning algorithm by employing the pre-trained model that adapts the rate adaptation algorithm to different environments. We implement our algorithm on a commercial UAV platform and evaluate it in various environments. The results demonstrate that our system outperforms the best-known rate adaptation algorithm up to 53% in terms of throughput when the velocity is 2-6 m/s. Copyright © 2019, The Authors. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

Receding horizon optimization for energy-efficient UAV communication

学校读者我要写书评

暂无评论

arXiv 2019年

作者： Zhang, Jingwei Zeng, Yong Zhang, Rui Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore National Mobile Communications Research Laboratory School of Information Science and Engineering Southeast University China

In this letter, we study a wireless communication system with a fixed-wing unmanned aerial vehicle (UAV) employed to collect information from a group of ground nodes (GNs). Our objective is to maximize the UAV's energy efficiency (EE), which is defined as the achievable rate among all GNs per unit propulsion energy consumption of the UAV. To efficiently solve this problem with continuous-time functions, we propose a new method based on receding horizon optimization (RHO), which significantly reduces the computational complexity compared to the conventional time discretization method. Specifically, we sequentially solve the EE maximization problem over a moving time-window of finite duration, for each of which the number of optimization variables is greatly reduced. Simulation results are provided to show the effectiveness of the proposed method. Copyright © 2019, The Authors. All rights reserved.

关键词： Energy efficiency

Optimal server selection for straggler mitigation

学校读者我要写书评

暂无评论

arXiv 2019年

作者： Badita, Ajay Parag, Parimal Aggarwal, Vaneet Department of Electrical and Communications Engineering Indian Institute of Science Bangalore Karnataka560012 India School of Industrial Engineering School of Electrical and Computer Engineering Purdue University West LafayetteIN United States

The performance of large-scale distributed compute systems is adversely impacted by stragglers when the execution time of a job is uncertain. To manage stragglers, we consider a multi-fork approach for job scheduling, where additional parallel servers are added at forking instants. In terms of the forking instants and the number of additional servers, we compute the job completion time and the cost of server utilization when the task processing times are assumed to have a shifted exponential distribution. We use this study to provide insights into the scheduling design of the forking instants and the associated number of additional servers to be started. Numerical results demonstrate orders of magnitude improvement in cost in the regime of low completion times as compared to the prior works. Copyright © 2019, The Authors. All rights reserved.

关键词： Scheduling

Artificial intelligence enabled radio propagation for communications - Part I: Channel characterization and antenna-channel optimization

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Huang, Chen He, Ruisi Ai, Bo Molisch, Andreas F. Lau, Buon Kiong Haneda, Katsuyuki Liu, Bo Wang, Cheng-Xiang Yang, Mi Oestges, Claude Zhong, Zhangdui The Purple Mountain Laboratories Nanjing211111 China The National Mobile Communications Research Laboratory School of Information Science and Engineering Southeast University Nanjing210096 China The State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China The Key Laboratory of Railway Industry of Broadband Mobile Information Communications Beijing Jiaotong University Beijing100044 China The School of Information Engineering Zhengzhou University Zhengzhou450001 China Peng Cheng Laboratory Shenzhen518055 China The Ming Hsieh Department of Electrical and Computer Engineering University of Southern California Los AngelesCA90089 United States The Department of Electrical and Information Technology Lund University Lund22100 Sweden The Department of Radio Science and Engineering Aalto University Aalto00076 Finland The School of Engineering University of Glasgow GlasgowG12 8QQ United Kingdom The Purple Mountain Laboratories Nanjing211111 China The Institute of Information and Communication Technologies Electronics and Applied Mathematics Universite Catholique de Louvain Louvain-la-Neuve 1348 Belgium

To provide higher data rates, as well as better coverage, cost efficiency, security, adaptability, and scalability, the 5G and beyond 5G networks are developed with various artificial intelligence techniques. In this two-part paper, we investigate the application of artificial intelligence (AI) and in particular machine learning (ML) to the study of wireless propagation channels. It firstly provides a comprehensive overview of ML for channel characterization and ML-based antenna-channel optimization in this first part, and then it gives a state-of-the-art literature review of channel scenario identification and channel modeling in Part II. Fundamental results and key concepts of ML for communication networks are presented, and widely used ML methods for channel data processing, propagation channel estimation, and characterization are analyzed and compared. A discussion of challenges and future research directions for ML-enabled next generation networks of the topics covered in this part rounds off the paper. © 2021, CC BY-NC-ND.

关键词： Machine learning

Joint Transmit Power and Trajectory Optimization for Two-Way Multi-Hop UAV Relaying Networks

学校读者我要写书评

暂无评论

Joint Transmit Power and Trajectory Optimization for Two-Way...

IEEE International Conference on communications Workshops, ICC

作者： Bing Li Shengjie Zhao Rongqing Zhang Hao Zhang Hanli Wang Liuqing Yang School of Software Engineering Tongji University Shanghai China National Mobile Communications Research Laboratory Southeast University China Ministry of Education Key Laboratory of Embedded System and Service Computing Tongji University Shanghai China School of Electronic and Information Engineer Tongji University Shanghai China Department of Electrical & Computer Engineering Colorado State University CO USA

ISBN: (数字)9781728174402

ISBN: (纸本)9781728174419

Unmanned aerial vehicle (UAV) has been regarded as a promising means to supplement ground communications. As flying relays, UAVs can be rapidly and flexibly deployed to assist data transmissions in many practical scenarios. In this paper, we investigate a two-way multi-hop UAV relaying network, where there are two ground users as sources and multiple UAVs as relays to help the two ground sources exchange information. We first provide an efficient two-way multi-hop UAV relaying pattern, which can achieve a data rate of 2/4 data packets per time slot with decode-and-forward (DF) protocol. Then, we further formulate a joint transmit power and trajectory optimization problem for the UAVs in this two-way multi-hop relaying scenario. The formulated problem is non-convex which makes it difficult to solve directly, hence we propose an iterative algorithm to obtain an approximate optimal solution. Numerical results demonstrate that our proposed network achieves significant throughput gains.

关键词： Relays Protocols Throughput Trajectory optimization Data communication

High-Resolution Synchronous Digital Ballistocardiography Setup

学校读者我要写书评

暂无评论

High-Resolution Synchronous Digital Ballistocardiography Set...

computers in Cardiology (CinC)

作者： Nico Jähne-Raden Ulf Kulau Henrike Gütschleg Thiemo Clausen Tobias Jura Stephan Sigg Lars Wolf Peter L. Reichertz Institute for Medical Informatics Germany Institute of Computer Engineering TU Braunschweig Germany Department of Communications and Networking Aalto University Finland Institute of Operating Systems and Computer Networks TU Braunschweig Germany

ISBN: (数字)9781728169361

ISBN: (纸本)9781728159423

Ballistocardiography (BCG) describes the measurement and analysis of ballistic forces that are evoked by the heart's movement and propagated to the surface of the body. Although there are several research groups focusing on BCG, the understanding on general BCG signals is still limited, as existing studies only consider a small number of subjects and insufficient measurement setups. This paper presents first results of our ongoing study to collect BCG ground-truth data. We present a custom hardware implementation for both, high data quality and simultaneous ECG reference measurement. In a large-scale study we currently collected more than 10 hours of BCG data from 34 subjects including BCG in resting position as well as under physical stress. In addition, related biometric data are collected to enable comprehensive investigations. We plan to provide access to the data for interested researchers.

关键词： Electrocardiography Accelerometers Heart Medical diagnostic imaging Acceleration Databases Data integrity