2019 UK/ China Emerging Technologies (UCET)最新文献

A Miniaturized Wide Band Implantable Antenna for Biomedical Application 生物医学应用的小型宽带植入式天线

2019 UK/ China Emerging Technologies (UCET) Pub Date : 2019-10-24 DOI: 10.1109/UCET.2019.8881849

Zubair Bashir, Muhammad Zahid, Naeem Abbas, M. Yousaf, S. Shoaib, Muhammad Adeel Asghar, Y. Amin

引用次数: 8

Design and Implementation of a 3D Printed Sensory Ball for Wireless Water Flow Monitoring 用于无线水流监测的3D打印传感球的设计与实现

2019 UK/ China Emerging Technologies (UCET) Pub Date : 2019-10-24 DOI: 10.1109/UCET.2019.8881861

Yi Zhang, R. Ghannam, M. Valyrakis, H. Heidari

引用次数: 2

Intrusion Detection through Leaky Wave Cable in Conjunction with Channel State Information 结合信道状态信息的漏波电缆入侵检测

2019 UK/ China Emerging Technologies (UCET) Pub Date : 2019-10-24 DOI: 10.1109/UCET.2019.8881845

Syed Shah, Syed Yaseen Shah, Syed Aziz Shah

引用次数: 10

ECG-based affective computing for difficulty level prediction in Intelligent Tutoring Systems 基于脑电图的情感计算在智能辅导系统中的难度预测

2019 UK/ China Emerging Technologies (UCET) Pub Date : 2019-10-24 DOI: 10.1109/UCET.2019.8881872

Fehaid Alqahtani, Stamos Katsigiannis, N. Ramzan

引用次数: 3

Design of an LCP-based Antenna Array for 5G/B5G Wearable Applications 面向5G/B5G可穿戴应用的基于lcp的天线阵列设计

2019 UK/ China Emerging Technologies (UCET) Pub Date : 2019-08-21 DOI: 10.1109/UCET.2019.8881850

M. Saeed, M. Rehman

引用次数: 7

Position Paper: Prototyping Autonomous Vehicles Applications with Heterogeneous Multi-FpgaSystems 意见书:基于异构多fpga系统的自动驾驶汽车原型应用

2019 UK/ China Emerging Technologies (UCET) Pub Date : 2019-08-21 DOI: 10.1109/UCET.2019.8881834

Tarek Elouaret, Stéphane Zuckerman, L. Kessal, Yoan Espada, N. Cuperlier, Guillaume Bresson, F. Ouezdou, Olivier Romain

引用次数: 3

Monitoring Body Motions Related To Huntington Disease by Exploiting the 5G Paradigm 利用5G模式监测与亨廷顿病相关的身体运动

2019 UK/ China Emerging Technologies (UCET) Pub Date : 2019-08-01 DOI: 10.1109/UCET.2019.8881867

Daniyal Haider, O. Romain, J. Kernec, Syed Yaseen Shah, Malik Muhammad Umer Farooq, Zunaira Qadus

引用次数: 6

Reinforcement Learning Method for Beam Management in Millimeter-Wave Networks 毫米波网络波束管理的强化学习方法

2019 UK/ China Emerging Technologies (UCET) Pub Date : 2019-08-01 DOI: 10.1109/UCET.2019.8881841

Ruiyu Wang, Oluwakayode Onireti, Lei Zhang, M. Imran, Guangmei Ren, Jing Qiu, Tingjian Tian

{"title":"Reinforcement Learning Method for Beam Management in Millimeter-Wave Networks","authors":"Ruiyu Wang, Oluwakayode Onireti, Lei Zhang, M. Imran, Guangmei Ren, Jing Qiu, Tingjian Tian","doi":"10.1109/UCET.2019.8881841","DOIUrl":"https://doi.org/10.1109/UCET.2019.8881841","url":null,"abstract":"With the rapid growth of mobile data demand, the fifth generation (5G) mobile network must exploit the large amount of spectrum in the millimeter wave (mmWave) band to increase the network capacity. Due to the limitation of propagation distance, line-of-sight (LOS) link is highly desirable for mmWave systems. However, LOS channel is not feasible all the time and mmWave is also impacted significantly by the surrounding environment. The LOS signal can be easily blocked by surrounding buildings. Based on this issue, in this paper, we propose to use reinforcement learning to manage the non line of sight (NLOS) scenario. Specifically, we build a model simulating blocked LOS signal for the user equipment (UE) with only NLOS channel available for the UE. Q-Learning is used to select the NLOS beam that meets the UE's quality of service requirements. Simulation results show that Q-Learning can be used to manage the beam selection. In particular, at initial training stage the Q-Learning explores in the environment. However, with the training process, Q-Learning learns from experience and the received power increases significantly and converges to an excellent level.","PeriodicalId":169373,"journal":{"name":"2019 UK/ China Emerging Technologies (UCET)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117032695","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: 9

Non-intrusive Electricity Sub-metering in Selected Households in Qatar 卡塔尔选定住户的非侵入式分计量

2019 UK/ China Emerging Technologies (UCET) Pub Date : 2019-08-01 DOI: 10.1109/UCET.2019.8881848

I. S. Bayram

引用次数: 4

Deep Learning for Signal Detection in Non-Orthogonal Multiple Access Wireless Systems 基于深度学习的非正交多址无线系统信号检测

2019 UK/ China Emerging Technologies (UCET) Pub Date : 2019-08-01 DOI: 10.1109/UCET.2019.8881888

Narengerile, J. Thompson

{"title":"Deep Learning for Signal Detection in Non-Orthogonal Multiple Access Wireless Systems","authors":"Narengerile, J. Thompson","doi":"10.1109/UCET.2019.8881888","DOIUrl":"https://doi.org/10.1109/UCET.2019.8881888","url":null,"abstract":"This paper presents an initial investigation of deep learning (DL) for multi-user detection in non-orthogonal multiple access (NOMA) wireless systems. In NOMA systems, the successive interference cancellation (SIC) process is usually performed at the receiver, where multiple users are decoded in a sequential fashion. Due to error propagation effects, the detection accuracy will largely depend on the correct detection of previous users. A DL-based NOMA receiver is designed to decode messages for multiple users in a one-shot process, without estimating channels explicitly. The DL-based NOMA receiver is represented by a deep neural network (DNN), which performs channel estimation and signal detection in a joint manner. The DNN is first trained offline using simulation data based on channel statistics and then used to recover the transmitted symbols directly in the online deployment stage. Initial results show that the DL approach can outperform the conventional pilot-based channel estimation methods and is more robust to the number of pilot symbols. The DNN is shown to be capable of mitigating the potential error propagation effects that occur in the SIC detector. Furthermore, when the inter-symbol interference is severe, the DL approach can achieve better performance than a maximum likelihood detector that does not account for interference effects.","PeriodicalId":169373,"journal":{"name":"2019 UK/ China Emerging Technologies (UCET)","volume":"249 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123681481","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: 31