摘要翻译：
与由eNodeBs集中管理上行/下行数据业务的主流蜂窝网络相反，在车辆到车辆(V2V)广播通信中eNodeBs只参与子信道分配，但最终不介入数据业务控制。因此，车辆利用分配的子信道直接与它们的对应方通信。由于V2V\textIt{mode-3}具有松散控制的one-to-all特性，因此对于时间要求严格的应用程序非常有利。然而，在满足服务质量（QoS）要求的同时，在没有冲突的情况下完成子信道的分配是非常必要的。据我们所知，对于V2V\textIt{mode-3}并不存在一个统一的框架，它既考虑了分配冲突的预防，又考虑了QoS的实现。因此，四种类型的条件对于获得QoS感知的无冲突分配具有强有力的特征:$(i)$确保每辆车的不同QoS，$(ii)$排除簇内子帧冲突，$(ii)$安全分配子信道的最小时间分散和$(iv)$阻止簇间子信道的一跳冲突。这些条件已被系统化并以整体方式合并，允许非复杂操作来执行子信道分配优化。此外，我们提出了一个不影响最优性的问题的替代松弛，只要满足某些条件。
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英文标题：
《Subchannel Allocation for Vehicle-to-Vehicle Broadcast Communications in
  Mode-3》
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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：
  Conversely to mainstream cellular networks where uplink / downlink data traffic is centrally managed by eNodeBs, in vehicle-to-vehicle (V2V) broadcast communications \textit{mode-3} eNodeBs engage solely in subchannel assignment but ultimately do not intervene in data traffic control. Accordingly, vehicles communicate directly with their counterparts utilizing the allotted subchannels. Due to its loosely controlled one-to-all nature, V2V \textit{mode-3} is advantageous for time-critical applications. Nevertheless, it is imperative that the assignment of subchannels is accomplished without conflicts while at the same time satisfying quality of service (QoS) requirements. To the best of our knowledge, there exists no unified framework for V2V \textit{mode-3} that contemplates both prevention of allocation conflicts and fulfillment of QoS. Thus, four types of conditions that are of forceful character for attaining QoS-aware conflict-free allocations have been identified: $(i)$ assure differentiated QoS per vehicle, $(ii)$ preclude intra-cluster subframe conflicts, $(iii)$ secure minimal time dispersion of allotted subchannels and $(iv)$ forestall one-hop inter-cluster subchannel conflicts. Such conditions have been systematized and merged in an holistic manner allowing non-complex manipulation to perform subchannel allocation optimization. In addition, we propose a surrogate relaxation of the problem that does not affect optimality provided that certain requisites are satisfied.
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PDF链接：
https://arxiv.org/pdf/1805.07003