摘要翻译：
在Release 14中，3GPP完成了蜂窝交通工具到一切(C-V2X)通信的第一个版本，其中引入了两种模式。其中一种称为\textIt{mode-3}的方案需要eNodeBs的支持才能实现子信道调度。本文讨论了一种半持久调度(SPS)的图论方法，该方法利用了一种感知机制，使车辆能够监测跨旁路子信道的信噪比(SINR)水平。eNodeBs从车辆请求这样的测量，并利用它们来完成合适的子信道分配。然而，由于SINR值--这里也称为边信息--跨度很大，所以需要量化。我们得出结论，每辆车每100毫秒3比特可以提供足够的粒度，以保持适当的性能而不会严重退化。此外，将该算法与伪随机和贪婪SPS算法进行了比较。
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英文标题：
《Impact of Quantized Side Information on Subchannel Scheduling for
  Cellular V2X》
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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, 3GPP completed a first version of cellular vehicle--to--everything (C-V2X) communications wherein two modalities were introduced. One of these schemes, known as \textit{mode-3}, requires support from eNodeBs in order to realize subchannel scheduling. This paper discusses a graph theoretical approach for semi-persistent scheduling (SPS) in \textit{mode-3} harnessing a sensing mechanism whereby vehicles can monitor signal--to--interference--plus--noise ratio (SINR) levels across sidelink subchannels. eNodeBs request such measurements from vehicles and utilize them to accomplish suitable subchannel assignments. However, since SINR values---herein also referred to as side information---span a wide range, quantization is required. We conclude that 3 bits per vehicle every 100 ms can provide sufficient granularity to maintain appropriate performance without severe degradation. Furthermore, the proposed algorithm is compared against pseudo-random and greedy SPS algorithms.
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PDF链接：
https://arxiv.org/pdf/1807.0483