摘要翻译：
在Release 14中，第三代合作伙伴计划(3GPP)引入了蜂窝交通工具(C-V2X)\textIt{mode-4}作为一种新的颠覆性技术，以支持覆盖外场景中的旁路车辆通信。C-V2X\TextIt{mode-4}已经设计成以分布式方式运行，其中车辆在选择一个用于使用之前自动监控跨旁链子信道的接收功率。通过这样的策略，车辆试图$(i)$发现和$(i)$保留具有低干扰的子信道，这些子信道可能具有最大化它们自己广播的安全消息的接收可能性的潜力。然而，由于车辆环境的动态性，子通道的最优性可能会随着时间的推移而迅速波动。因此，车辆需要每几百毫秒进行一次新的选择。与3GPP一致，子信道选择阶段依赖于监视窗口期间每个子信道上的感知功率强度的线性平均值。然而，在本文中，我们提出了一个非线性功率平均阶段，其中最最新的测量被分配更高的优先级通过指数加权。我们通过仿真表明，系统的整体性能可以在城市和高速公路场景中发挥作用。此外，线性平均可以被认为是指数加权移动平均的一个特例，保证了与标准化方法的向后相容性。最后详细描述了3GPP\textIt{mode-4}调度方法。
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英文标题：
《Enhanced C-V2X Mode-4 Subchannel Selection》
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作者：
Luis F. Abanto-Leon, Arie Koppelaar, Sonia Heemstra de Groot
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最新提交年份：
2018
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分类信息：

一级分类：Electrical Engineering and Systems Science        电气工程与系统科学
二级分类：Signal Processing        信号处理
分类描述：Theory, algorithms, performance analysis and applications of signal and data analysis, including physical modeling, processing, detection and parameter estimation, learning, mining, retrieval, and information extraction. The term "signal" includes speech, audio, sonar, radar, geophysical, physiological, (bio-) medical, image, video, and multimodal natural and man-made signals, including communication signals and data. Topics of interest include: statistical signal processing, spectral estimation and system identification; filter design, adaptive filtering / stochastic learning; (compressive) sampling, sensing, and transform-domain methods including fast algorithms; signal processing for machine learning and machine learning for signal processing applications; in-network and graph signal processing; convex and nonconvex optimization methods for signal processing applications; radar, sonar, and sensor array beamforming and direction finding; communications signal processing; low power, multi-core and system-on-chip signal processing; sensing, communication, analysis and optimization for cyber-physical systems such as power grids and the Internet of Things.
信号和数据分析的理论、算法、性能分析和应用，包括物理建模、处理、检测和参数估计、学习、挖掘、检索和信息提取。“信号”一词包括语音、音频、声纳、雷达、地球物理、生理、（生物）医学、图像、视频和多模态自然和人为信号，包括通信信号和数据。感兴趣的主题包括：统计信号处理、谱估计和系统辨识；滤波器设计；自适应滤波/随机学习；（压缩）采样、传感和变换域方法，包括快速算法；用于机器学习的信号处理和用于信号处理应用的机器学习；网络与图形信号处理；信号处理中的凸和非凸优化方法；雷达、声纳和传感器阵列波束形成和测向；通信信号处理；低功耗、多核、片上系统信号处理；信息物理系统的传感、通信、分析和优化，如电网和物联网。
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英文摘要：
  In Release 14, the 3rd Generation Partnership Project (3GPP) introduced Cellular Vehicle--to--Everything (C-V2X) \textit{mode-4} as a novel disruptive technology to support sidelink vehicular communications in out--of--coverage scenarios. C-V2X \textit{mode-4} has been engineered to operate in a distributed manner, wherein vehicles autonomously monitor the received power across sidelink subchannels before selecting one for utilization. By means of such an strategy, vehicles attempt to $(i)$ discover and $(ii)$ reserve subchannels with low interference that may have the potential to maximize the reception likelihood of their own broadcasted safety messages. However, due to dynamicity of the vehicular environment, the subchannels optimality may fluctuate rapidly over time. As a consequence, vehicles are required to make a new selection every few hundreds of milliseconds. In consonance with 3GPP, the subchannel selection phase relies on the linear average of the perceived power intensities on each of the subchannels during a monitoring window. However, in this paper we propose a nonlinear power averaging phase, where the most up--to--date measurements are assigned higher priority via exponential weighting. We show through simulations that the overall system performance can be leveraged in both urban and freeway scenarios. Furthermore, the linear averaging can be considered as a special case of the exponentially-weighted moving average, ensuring backward compatibility with the standardized method. Finally, the 3GPP \textit{mode-4} scheduling approach is described in detail.
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PDF链接：
https://arxiv.org/pdf/1807.04819