2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring)最新文献_第8页

Real-time Recursive Risk Assessment Framework for Autonomous Vehicle Operations 自动驾驶车辆运行的实时递归风险评估框架

2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring) Pub Date : 2021-04-01 DOI: 10.1109/VTC2021-Spring51267.2021.9448759

Wei Ming Dan Chia, S. Keoh, A. L. Michala, Cindy Goh

{"title":"Real-time Recursive Risk Assessment Framework for Autonomous Vehicle Operations","authors":"Wei Ming Dan Chia, S. Keoh, A. L. Michala, Cindy Goh","doi":"10.1109/VTC2021-Spring51267.2021.9448759","DOIUrl":"https://doi.org/10.1109/VTC2021-Spring51267.2021.9448759","url":null,"abstract":"Existing risk assessment (RA) methodology used for autonomous vehicle (AV) development and validation is insufficient for future AV operations. Existing frameworks operate based on processes such as hazard analysis and risk assessment (HARA) where risk is defined based on functional hazardous event severity and the likelihood of occurrence. This is a static process performed during the development stage and relies on prior lessons learnt and know-how. A drawback of this is the omission of potential complex environments that could occur during real-time – especially with more stringent safety requirements for AV operating at higher automation levels. Therefore, there is a need for an additional framework to further enhance the safety levels of the AV, focusing on real-time instead of static risk assessment during development. In this paper, a novel real-time recursive RA framework (ReRAF) addresses the gap by creating a novel risk representation, predictive risk number (PRN), and eventual safety levels (SLs) in the temporal and spatial domain. This approach focuses on risk assessment based on AV collision to the detected hazardous object and controllability of the AV. A dynamic recursive RA continuously captures potentially hazardous events in real-time and compares them with past occurrences to predict future safety actions. ReRAF provides a continuous improvement on the RA and acts as an additional safety layer for AV operations.","PeriodicalId":194840,"journal":{"name":"2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring)","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126293718","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

Drone-Assisted Cellular Networks: Optimal Positioning and Load Management 无人机辅助蜂窝网络:最佳定位和负载管理

2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring) Pub Date : 2021-04-01 DOI: 10.1109/VTC2021-Spring51267.2021.9448643

T. Pijnappel, H. V. D. Berg, S. Borst, R. Litjens

引用次数: 3

Spectrum Access Management of Multi-class Secondary Users in Hybrid Cognitive Radio Networks 混合认知无线网络中多类辅助用户的频谱接入管理

2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring) Pub Date : 2021-04-01 DOI: 10.1109/VTC2021-Spring51267.2021.9448748

Ahmed F. Tayel, S. Rabia, A. El-Malek, A. Abdelrazek

引用次数: 1

Experiment on MIMO Communications in Seawater by RF Signals 基于射频信号的海水中MIMO通信实验

2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring) Pub Date : 2021-04-01 DOI: 10.1109/VTC2021-Spring51267.2021.9449016

K. Takizawa, Ryotaro Suga, T. Matsuda, F. Kojima

引用次数: 4

Scenario Detection in Unlabeled Real Driving Data with a Rule-Based State Machine Supported by a Recurrent Neural Network 基于规则的递归神经网络状态机在未标记真实驾驶数据中的场景检测

2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring) Pub Date : 2021-04-01 DOI: 10.1109/VTC2021-Spring51267.2021.9449032

Francesco Montanari, Haoyu Ren, Anatoli Djanatliev

引用次数: 1

Turbo-AI, Part I: Iterative Machine Learning Based Channel Estimation for 2D Massive Arrays Turbo-AI，第一部分:基于迭代机器学习的二维海量阵列信道估计

2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring) Pub Date : 2021-04-01 DOI: 10.1109/VTC2021-Spring51267.2021.9449026

Yejian Chen, Jafar Mohammadi, Stefan Wesemann, T. Wild

{"title":"Turbo-AI, Part I: Iterative Machine Learning Based Channel Estimation for 2D Massive Arrays","authors":"Yejian Chen, Jafar Mohammadi, Stefan Wesemann, T. Wild","doi":"10.1109/VTC2021-Spring51267.2021.9449026","DOIUrl":"https://doi.org/10.1109/VTC2021-Spring51267.2021.9449026","url":null,"abstract":"Channel estimation belongs to one of the potential applications, that can exploit Artificial Intelligence (AI) and Machine Learning (ML) to enhance Physical Layer (PHY) performance in the context of 5th Generation (5G) and Beyond 5G (B5G) wireless communication systems. In this paper, we focus on the ML-based channel estimation for 2-Dimensional (2D) antenna arrays. Due to the extremely high computational requirement for 2D massive arrays with Conventional Training, we exploit the 2D Kronecker covariance model to perform Subspace Training for the vertical and horizontal spatial domains independently, which achieves a complexity cost saving factor $mathcal{O}left( {{M^4}{N^4}} right)/mathcal{O}left( {M{N^4} + N{M^4}} right)$ for an M × N 2D-array. Furthermore, we propose an iterative training approach, referred to as Turbo-AI. Along with Subspace Training, the new approach can monotonically reduce the effective variance of additive noise of the observation, by updating the Neural Network (NN) models with re-training. Furthermore, we propose a concept, named Universal Training. It allows to use one NN for a wide range of Signal-to-Noise-Ratio (SNR) operation points and spatial angles, which can greatly simplify Turbo-AI usage. Numerical results exhibit that Turbo-AI can tightly approach the genie-aided channel estimation bound, especially at low SNR.","PeriodicalId":194840,"journal":{"name":"2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring)","volume":"208 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121196615","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

The Reinforcement Learning based Interference Whitening Scheme for 5G 基于强化学习的5G干扰美白方案

2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring) Pub Date : 2021-04-01 DOI: 10.1109/VTC2021-Spring51267.2021.9448740

Kwonyeol Park, Hyungjong Kim, Daecheol Kwon, Haejoon Kim, H. Kang, Min-Ho Shin, Jonghan Kim, Woonhaing Hur

{"title":"The Reinforcement Learning based Interference Whitening Scheme for 5G","authors":"Kwonyeol Park, Hyungjong Kim, Daecheol Kwon, Haejoon Kim, H. Kang, Min-Ho Shin, Jonghan Kim, Woonhaing Hur","doi":"10.1109/VTC2021-Spring51267.2021.9448740","DOIUrl":"https://doi.org/10.1109/VTC2021-Spring51267.2021.9448740","url":null,"abstract":"To achieve high spectral efficiency, a modern cellular network such as LTE or 5G New Radio (NR) aims to operate with full frequency reuse. This deployment will significantly increase the level of Co-Channel Interference (CCI) for cell-edge User Equipments (UEs), and the CCI has become a major throughput-limiting factor. Thus, the suppression of CCI in the 5G network is the most important feature to increase downlink throughput. In order to mitigate CCI, Interference Whitening (IW) is an effective low-complexity linear method to suppress colored interference in a MIMO-OFDM system. However, conventional IW can degrade the performance when the noise-dominant environment due to limited samples, e.g., DMRS (De-Modulation Reference Signal). To address that, we propose a Reinforcement Learning based Interference Whitening (RL-IW) that adaptively controls the IW mode by learning algorithm. The experimental results show that RL-IW has performance gain in terms of both BLER (Block Error Rate) and downlink throughput than conventional IW.","PeriodicalId":194840,"journal":{"name":"2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123831238","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 Novel Intelligent SIC Detector for NOMA Systems Based on Deep Learning 一种基于深度学习的新型NOMA系统智能SIC检测器

2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring) Pub Date : 2021-04-01 DOI: 10.1109/VTC2021-Spring51267.2021.9449008

Jialiang Fu, Yue Xiao, Haoran Liu, Ping Yang, Bo Zhang

引用次数: 2

Passive Filter Design Algorithm for Transient Stabilization of Automotive Power Systems 汽车电力系统暂态稳定的无源滤波器设计算法

2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring) Pub Date : 2021-04-01 DOI: 10.1109/VTC2021-Spring51267.2021.9449066

M. Baumann, Ali Shoar Abouzari, Christoph Weissinger, B. Gustavsen, H. Herzog

引用次数: 3

Remez Exchange Algorithm for Approximating Powers of the Q-Function by Exponential Sums 用指数和逼近q函数幂的Remez交换算法

2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring) Pub Date : 2021-04-01 DOI: 10.1109/VTC2021-Spring51267.2021.9448807

Islam M. Tanash, T. Riihonen

{"title":"Remez Exchange Algorithm for Approximating Powers of the Q-Function by Exponential Sums","authors":"Islam M. Tanash, T. Riihonen","doi":"10.1109/VTC2021-Spring51267.2021.9448807","DOIUrl":"https://doi.org/10.1109/VTC2021-Spring51267.2021.9448807","url":null,"abstract":"In this paper, we present simple and tight approximations for the integer powers of the Gaussian Q-function, in the form of exponential sums. They are based on optimizing the corresponding coefficients in the minimax sense using the Remez exchange algorithm. In particular, the best exponential approximation is characterized by the alternation of its absolute error function, which results in extrema that alternate in sign and have the same magnitude of error. The extrema are described by a system of nonlinear equations that are solved using Newton– Raphson method in every iteration of the Remez algorithm, which eventually leads to a uniform error function. This approximation can be employed in the evaluation of average symbol error probability (ASEP) under additive white Gaussian noise and various fading models. Especially, we present several application examples on evaluating ASEP in closed forms with Nakagami-m, Fisher–Snedecor $mathcal{F}$, η − µ, and κ − µ channels. The numerical results show that our approximations outperform the existing ones with the same form in terms of the global error. In addition, they achieve high accuracy for the whole range of the argument with and without fading, and it can even be improved further by increasing the number of exponential terms.","PeriodicalId":194840,"journal":{"name":"2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring)","volume":"23 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132623937","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