2021 38th National Radio Science Conference (NRSC)最新文献_第2页

Radiofrequency Ablation of the Renal Tumors 肾肿瘤的射频消融术

2021 38th National Radio Science Conference (NRSC) Pub Date : 2021-07-27 DOI: 10.1109/NRSC52299.2021.9509810

Rawhia S. Hamouda, Mohamed A. Eldosokyl, Ahmed S. Elhggar

引用次数: 0

Detection and Correction of Faulty Patterns in Four-Dimensional Antenna Linear Array Using Particle Swarm Optimization 基于粒子群算法的四维天线线阵故障方向图检测与校正

2021 38th National Radio Science Conference (NRSC) Pub Date : 2021-07-27 DOI: 10.1109/NRSC52299.2021.9509817

A. S. Zainud-Deen, H. Malhat, M. Badway, M. Rihan

{"title":"Detection and Correction of Faulty Patterns in Four-Dimensional Antenna Linear Array Using Particle Swarm Optimization","authors":"A. S. Zainud-Deen, H. Malhat, M. Badway, M. Rihan","doi":"10.1109/NRSC52299.2021.9509817","DOIUrl":"https://doi.org/10.1109/NRSC52299.2021.9509817","url":null,"abstract":"This paper investigates the using of particle swarm optimization technique (PSO) in detecting the number and locations of failed antenna elements in a time modulated linear array (TMLA) from their damaged radiation patterns. A systematic process for radiation pattern correction is investigated. The TMLA operates by sampling the input for array elements in a switched time sequence rather than phase shift in phased antenna arrays. The failure of the array elements damages the radiation patterns which appears in different sidelobe levels (SLLs) and the locations of nulls. The TMLA elements are connected to RF switches with their on-time instants and the on-time durations sequences reconfigured to compensate the damage in the radiated pattern. The correction method involves the calculation of damaged array radiation pattern. Then, the PSO compares the damaged pattern with an optimized pattern through the minimization of the cost function. The periodic on-off sequences for array excitation are estimated using the PSO for optimum pattern. The performance of this technique is applied on 32-elements TMLA with Dolph-Chebyshev excitation. Single and multiple faults are detected and the radiation pattern are corrected using the PSO.","PeriodicalId":231431,"journal":{"name":"2021 38th National Radio Science Conference (NRSC)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127872586","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

A Dual-Band (38 – 60 GHz) Antenna Designed for Millimeter Wave Wireless Communication Systems 一种用于毫米波无线通信系统的双频(38 - 60ghz)天线

2021 38th National Radio Science Conference (NRSC) Pub Date : 2021-07-27 DOI: 10.1109/NRSC52299.2021.9509830

Mirehan A. Aboulila, Radwa K. Hamad, A. Zaki, M. Aly

引用次数: 0

Quantification of Throughput Enhancement using Cooperative Communication in Cognitive Radio Networks 认知无线电网络中使用协作通信提高吞吐量的量化研究

2021 38th National Radio Science Conference (NRSC) Pub Date : 2021-07-27 DOI: 10.1109/NRSC52299.2021.9509827

Mohamed Abdelraheem, Reham M. Al-Hashmy, U. S. Mohamed

{"title":"Quantification of Throughput Enhancement using Cooperative Communication in Cognitive Radio Networks","authors":"Mohamed Abdelraheem, Reham M. Al-Hashmy, U. S. Mohamed","doi":"10.1109/NRSC52299.2021.9509827","DOIUrl":"https://doi.org/10.1109/NRSC52299.2021.9509827","url":null,"abstract":"In this paper, we study the throughput enhancement achieved by cooperative communication technique in cognitive radio networks for different global objectives and constraints. The secondary network coordinator distributes the nodes among the available channels and pairs them in a cooperative collation to enhance their transmission characteristics. The coordinator objective is to maximize the total throughput or to achieve a fair distribution of the available resources under some minimum throughput demand constraints. We formulate the optimization problems for different throughput objectives in the cooperative and non-cooperative modes. The results show a noticeable enhancement in the total throughput achieved by the network as well as the slow node in the cooperative mode compared to the non-cooperative one. Also, the proportional fairness, and maximizing the minimum user throughput objectives gives a fair distribution for the available resources which significantly enhances the throughput of the slow nodes compared to the case when the objective is to maximize the total throughput.","PeriodicalId":231431,"journal":{"name":"2021 38th National Radio Science Conference (NRSC)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130295322","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

Full-wave Simulation of Time-modulated Parabolic Linear Array Using the Method of Moments 基于矩量法的时调制抛物线阵全波仿真

2021 38th National Radio Science Conference (NRSC) Pub Date : 2021-07-27 DOI: 10.1109/NRSC52299.2021.9509819

A. S. Zainud-Deen, H. Malhat, M. Badway, M. Rihan

引用次数: 4

A Deep Learning Approach for Channel Estimation in 5G Wireless Communications 5G无线通信中信道估计的深度学习方法

2021 38th National Radio Science Conference (NRSC) Pub Date : 2021-07-27 DOI: 10.1109/NRSC52299.2021.9509813

Karam M. Ebrahiem, H. Soliman, S. Abuelenin, H. El-Badawy

{"title":"A Deep Learning Approach for Channel Estimation in 5G Wireless Communications","authors":"Karam M. Ebrahiem, H. Soliman, S. Abuelenin, H. El-Badawy","doi":"10.1109/NRSC52299.2021.9509813","DOIUrl":"https://doi.org/10.1109/NRSC52299.2021.9509813","url":null,"abstract":"This paper studies the applications of Deep Learning (branch of Machine Learning) in 5G wireless communication systems. Deep Learning requires having large sets of data for training and testing purposes. Obtaining such large datasets through measurement campaigns is a challenging and costly task. Therefore, it is a common practice to use synthesized data. The aim of the current paper is to survey different applications of deep learning in 5G systems, and more specifically, that implementing the massive multiple input multiple output (mMIMO) using deep learning, for estimating the wideband mMIMO channel, and for beamforming co-operative transmission. Then, methods for dataset generation for such systems are covered and reviewed. The required datasets for deep-learning may be constructed via ray-tracing simulators. Illustration of the methodology with some deployment scenarios relying on Remcom wireless InSite® ray-tracing simulator are introduced. These scenarios are used to provide the required datasets to be used for deep learning. Then, several directions are proposed for future work that are based on sensing the environment type, (Indoor/Outdoor), and jointly estimating the channel vector, Direction-of-Arrival (DoA), Direction of Departure (DoD), and Rx point power allocation. In a manner similar to adaptive beamforming, the RF radiation pattern can be adapted according to the time-varying DoA and DoD, and estimated channel.","PeriodicalId":231431,"journal":{"name":"2021 38th National Radio Science Conference (NRSC)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131528921","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

Compact Reconfigurable Polarization Plasma Square Microstrip Patch MIMO Antenna for 5G Wireless Applications 用于5G无线应用的紧凑型可重构极化等离子体方形微带贴片MIMO天线

2021 38th National Radio Science Conference (NRSC) Pub Date : 2021-07-27 DOI: 10.1109/NRSC52299.2021.9509808

Sarah. Elgiddawy, H. Malhat, S. Zainud-Deen, A. Ibrahim, Hisham Hamed

引用次数: 6

A Comparative Study of Different Human Skin Impedance Models 不同人体皮肤阻抗模型的比较研究

2021 38th National Radio Science Conference (NRSC) Pub Date : 2021-07-27 DOI: 10.1109/NRSC52299.2021.9509823

Mohamed S. Ghoneim, Amr Mohammaden, L. Said, A. Madian, A. Radwan, A. Eltawil

引用次数: 1

Going Shallower with MobileNets: On the Impact of Wavelet Pooling 用mobilenet变浅:小波池的影响

2021 38th National Radio Science Conference (NRSC) Pub Date : 2021-07-27 DOI: 10.1109/NRSC52299.2021.9509825

S. El-Khamy, A. Al-Kabbany, Shimaa El-bana

{"title":"Going Shallower with MobileNets: On the Impact of Wavelet Pooling","authors":"S. El-Khamy, A. Al-Kabbany, Shimaa El-bana","doi":"10.1109/NRSC52299.2021.9509825","DOIUrl":"https://doi.org/10.1109/NRSC52299.2021.9509825","url":null,"abstract":"MobileNet is a light-weight neural network model that has facilitated harnessing the power of deep learning on mobile devices. The advances in pervasive computing and the ever-increasing interest in deep learning has resulted in a growing research attention on the enhancement of the MobileNet architecture. Beside the enhancement in convolution layers, recent literature has featured new directions for implementing the pooling layers. In this work, we propose a new model based on the MobileNet-V1 architecture, and we investigate the impact of wavelet pooling on the performance of the proposed model. While traditional neighborhood pooling can result in information loss, which negatively impacts any succeeding feature extraction, wavelet pooling allows us to utilize spectral information which is useful in most image processing tasks. On two widely adopted datasets, we evaluated the performance of the proposed model, and compared to the baseline MobileNet, we attained a 10% and a 16% increase in classification accuracy on CIFAR-10 and CIFAR-100 respectively. We also evaluated a shallow version of the proposed architecture with wavelet pooling, and we showed that it maintained the classification accuracy either higher than, or <1% less than, the deep versions of MobileNet while decreasing the number of model parameters by almost 40%.","PeriodicalId":231431,"journal":{"name":"2021 38th National Radio Science Conference (NRSC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131045119","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

Dual-Band Cavity-Backed Ka-Band Antenna for Satellite Communication 用于卫星通信的双频空腔ka波段天线

2021 38th National Radio Science Conference (NRSC) Pub Date : 2021-07-27 DOI: 10.1109/NRSC52299.2021.9509811

S. I. Abd El-Rahman, A. Attiya

引用次数: 2