2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring)最新文献_第3页

IoT via Satellite: Asynchronous Random Access for the Maritime Channel 通过卫星的物联网:海事通道的异步随机访问

2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring) Pub Date : 2020-05-01 DOI: 10.1109/VTC2020-Spring48590.2020.9129580

Federico Clazzer, A. Munari

引用次数: 0

How to Associate Users with Access Points in a Small Cell Network? 如何在小型蜂窝网络中将用户与接入点联系起来?

2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring) Pub Date : 2020-05-01 DOI: 10.1109/VTC2020-Spring48590.2020.9129021

Hong Yang

引用次数: 1

LDPC Coded Non-Recursive GMSK System with Quasi-Coherent Demodulation 准相干解调LDPC编码非递归GMSK系统

2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring) Pub Date : 2020-05-01 DOI: 10.1109/VTC2020-Spring48590.2020.9129629

Mengmeng Liu, Zhongyang Yu, Qingya Lu, B. Bai, Min Zhu

引用次数: 1

Parameter Optimization for Energy Efficient Indoor Massive MIMO Small Cell Networks 节能室内大规模MIMO小蜂窝网络参数优化

2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring) Pub Date : 2020-05-01 DOI: 10.1109/VTC2020-Spring48590.2020.9129437

Chen Chen, Yan Jiang, Jiliang Zhang, Xiaoli Chu, J. Zhang

引用次数: 0

Convolutional Neural Network Aided Signal Modulation Recognition in OFDM Systems 卷积神经网络辅助OFDM系统信号调制识别

2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring) Pub Date : 2020-05-01 DOI: 10.1109/VTC2020-Spring48590.2020.9128455

Sheng Hong, Yu Wang, Yuwen Pan, Hao Gu, Miao Liu, Jie Yang, Guan Gui

{"title":"Convolutional Neural Network Aided Signal Modulation Recognition in OFDM Systems","authors":"Sheng Hong, Yu Wang, Yuwen Pan, Hao Gu, Miao Liu, Jie Yang, Guan Gui","doi":"10.1109/VTC2020-Spring48590.2020.9128455","DOIUrl":"https://doi.org/10.1109/VTC2020-Spring48590.2020.9128455","url":null,"abstract":"Signa1 modulation recognition (SMR) is an essential and challenging topic in orthogonal frequency-division multiplexing (OFDM) systems, and also it is the fundamental technique for signal detection and recovery. However, traditional feature extraction based SMR methods cannot effectively acquire the characteristics of the OFDM signals. Hence, the modulated OFD-M signal cannot be reliably identified. In this paper, we propose a deep learning (DL) based SMR method for recognizing OFDM signals, which is combined with a convolutional neural network (CNN) trained on in-phase and quadrature (IQ) samples. In the network model, the batch normalization (BN) layer and dropout layer are used to speed up model training and prevent overfitting, respectively. Three convolution layers with different convolution kernels perform well than traditional feature extraction methods in obtaining intrinsic properties of OFDM signals. The same number of multiple modulated signals are mixed and sent to the trained model for identification. Experiments are conducted to show that the method we proposed performs better than the traditional methods, mainly reflected in a higher probability of correct classification (PCC) and better consistency.","PeriodicalId":348099,"journal":{"name":"2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128995871","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: 13

Ergodic Energy Efficiency of mmWave System Considering Insertion Loss Under Dynamic Subarray Architecture 动态子阵列结构下考虑插入损耗的毫米波系统遍历能量效率

2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring) Pub Date : 2020-05-01 DOI: 10.1109/VTC2020-Spring48590.2020.9129395

Ji-Chong Guo, Qiyue Yu, Wei-Xiao Meng, W. Xiang

{"title":"Ergodic Energy Efficiency of mmWave System Considering Insertion Loss Under Dynamic Subarray Architecture","authors":"Ji-Chong Guo, Qiyue Yu, Wei-Xiao Meng, W. Xiang","doi":"10.1109/VTC2020-Spring48590.2020.9129395","DOIUrl":"https://doi.org/10.1109/VTC2020-Spring48590.2020.9129395","url":null,"abstract":"Energy efficiency (EE) performance of millimeter-wave (mmWave) large-array systems attracts a lot of attention. And insertion loss is an inherent characteristic of hybrid precoding systems, which greatly reduces the system performance. EE analysis considering the insertion loss based on the dynamic array architecture is still an open issue. This paper researches the ergodic EE of mmWave hybrid pre-coding system with the insertion loss, based on the adaptive overlapped subarray (OSA) architecture. The ergodic EE is a more valuable indicator than the instantaneous EE for the system under dynamic architecture. However, it is difficult to directly calculate the precise ergodic EE due to the intractable relationship between EE and the channel matrix. Instead, we simplify the precise ergodic EE to get a lower bound, and analyze the ergodic EE performance based upon the lower bound. By this way, we analyze the ergodic EE performance of two typical hybrid pre-coding schemes taking the dynamic architectures. Simulation results verify the effectiveness of analyses.","PeriodicalId":348099,"journal":{"name":"2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130398596","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

Achievable Rate of Multi-Cell Downlink Massive MIMO Systems with D2D Underly 基于D2D的多小区下行链路大规模MIMO系统的可实现速率

2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring) Pub Date : 2020-05-01 DOI: 10.1109/VTC2020-Spring48590.2020.9129647

Ashraf Al-Rimawi, Laith Ibrahim, W. Ajib

引用次数: 3

Intelligent Transport Systems – Road weather information and forecast system for vehicles 智能运输系统。车辆用道路天气信息和预报系统

2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring) Pub Date : 2020-05-01 DOI: 10.1109/VTC2020-Spring48590.2020.9129368

D. Stepanova, T. Sukuvaara, V. Karsisto

{"title":"Intelligent Transport Systems – Road weather information and forecast system for vehicles","authors":"D. Stepanova, T. Sukuvaara, V. Karsisto","doi":"10.1109/VTC2020-Spring48590.2020.9129368","DOIUrl":"https://doi.org/10.1109/VTC2020-Spring48590.2020.9129368","url":null,"abstract":"The modern vehicular communication methodologies allow the creation of the very perspective road weather services, delivered directly to vehicles, and enhanced with near real-time vehicular observations. Intelligent Transport Systems (ITS) initiative along with related standardization presents the technological approaches for a vehicular communication and an autonomous driving. Vehicular local area networking (VANET) utilizing Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication tools with the IEEE 802.11p protocol and the European ITS-G5 amendment, along with a 4G/5G cellular networking and hybrid models of them are the key technological approaches for this communication entity. Road weather services are based on a weather information generated either for the large geographical areas or at the specific spots like city centers. However, regular and exact weather forecasts and services, tailored for the specific road stretches, would be more beneficial for the road users, thus also increasing a traffic fluency and a safety. To provide such services, one would need to have access to the latest observations originating from the mobile vehicles and the local road weather stations as well as the weather forecasts from experts. One of the objectives for the Finnish Meteorological Institute (FMI) is developing a system to collect, deliver and display a road weather data. In this paper, we present our approach for this ideology. FMI is developing a service to help driving in the different weather conditions, allowing increase the traffic safety by delivering near real-time road weather and hazard information directly to the vehicular application.","PeriodicalId":348099,"journal":{"name":"2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring)","volume":"146 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130618975","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

A Scheme on Pedestrian Detection using Multi-Sensor Data Fusion for Smart Roads 基于多传感器数据融合的智能道路行人检测方案

2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring) Pub Date : 2020-05-01 DOI: 10.1109/VTC2020-Spring48590.2020.9128855

Haibo Wang, Changle Li, Yao Zhang, Zhao Liu, Yilong Hui, Guoqiang Mao

{"title":"A Scheme on Pedestrian Detection using Multi-Sensor Data Fusion for Smart Roads","authors":"Haibo Wang, Changle Li, Yao Zhang, Zhao Liu, Yilong Hui, Guoqiang Mao","doi":"10.1109/VTC2020-Spring48590.2020.9128855","DOIUrl":"https://doi.org/10.1109/VTC2020-Spring48590.2020.9128855","url":null,"abstract":"Transforming our roads into smart roads is an indispensable step towards future self-driving systems, and therefore has drawn increasing attention from both academia and industry. To this end, this paper develops a novel cost-effective IoT-based target detection system utilizing the multi-sensor data fusion technology with a particular focus on pedestrian detection, as an important component of smart road system. Particularly, the developed intelligent pedestrian detection module (${i}$PDM) consists of three major sensors, i.e., Doppler microwave radar sensor, passive infrared (PIR), and geomagnetic sensor. A multi-sensor data fusion algorithm is developed to fuse the sensor data and achieves reliable target detection. After that, ${i}$PDM sends the relevant warning signal wirelessly to nearby base station and vehicles. Experiments are conducted on real traffic environment to evaluate the performance of ${i}$PDM. The results validate the high reliability of ${i}$PDM with an average 91.7% detection accuracy. Moreover, to our best knowledge, ${i}$PDM is the first IoT-based implementation for pedestrian detection of smart roads. It is necessary to highlight that ${i}$PDM is a low-cost, low-power, wide-coverage pedestrian detection system where the cost of a single ${i}$PDM is only US $ 30, which makes it suitable to large-scale deployment.","PeriodicalId":348099,"journal":{"name":"2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130648757","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

User Association to Overcome Human Blockage at mmWave Cellular Networks 用户协会克服毫米波蜂窝网络的人为障碍

2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring) Pub Date : 2020-05-01 DOI: 10.1109/VTC2020-Spring48590.2020.9129407

Yuva Kumar, T. Ohtsuki

{"title":"User Association to Overcome Human Blockage at mmWave Cellular Networks","authors":"Yuva Kumar, T. Ohtsuki","doi":"10.1109/VTC2020-Spring48590.2020.9129407","DOIUrl":"https://doi.org/10.1109/VTC2020-Spring48590.2020.9129407","url":null,"abstract":"The large spectral bandwidth at millimeter-wave (mmWave) frequencies provides a mean to achieve very high data rates in wireless communication systems. A unique characteristic of mmWave is that mmWave links are very sensitive to blockage and have large propagation path loss, which exhibits low line-of-sight (LoS) probability, unstable connectivity and unreliable communication. To overcome such challenges, one of the existing solution is to associate the user equipment (UE) with other available Base Stations (BSs) by handover (HO) if the serving BS is blocked. In this paper, for a pedestrian scenario, we propose two reinforcement learning (RL) based user association algorithms, which accounts for the past experience of the blockage on the position of the UE. One focuses on the reward to increase the sum LoS probability and is named as Blockage-Aware User Association (BAUA). The other focuses on the reward to balance the tradeoff between the throughput and the LoS probability and is named as modified BAUA. Simulation results show that the BAUA algorithm increased sum LoS probability and the modified BAUA algorithm show better trade-off between the throughput and the LoS probability than the maximum Signal-to-Interference-plus-Noise Ratio (SINR) based and maximum-throughput based user association algorithms.","PeriodicalId":348099,"journal":{"name":"2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123477098","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