摘要翻译：
第三代合作伙伴计划(3GPP)已经在REL中引入。14一种新的技术，称为车辆--对--车辆(V2V)\textIt{mode-3}。在此方案下，eNodeB协助资源分配过程，为车辆分配副链路子信道。因此，车辆以广播的方式发送它们的信号，而不需要前者的干预。因此，eNodeBs将在子信道的分配中发挥决定性的作用，因为它们可以有效地管理V2V业务并防止分配冲突。后者是其他车辆可靠接收信号的一个关键方面。为此，我们提出了两种资源分配方案，即基于二分图匹配的连续分配（BGM-SA）和基于二分图匹配的并行分配（BGM-PA）。这两个方案都纳入了限制因素，以防止出现分配冲突。在本研究中，我们只考虑交叉路口或合并道路上形成的重叠簇。仿真结果表明，BGM-SA可以获得接近最优的性能，而BGM-PA虽然性能较差，但复杂度较低。此外，由于BGM-PA是基于簇间车辆预分组的，我们探索了不同的度量方法，可以有效地描述预分组车辆的整体信道状况。当然，就最大化系统容量而言，这不是最优的--因为分配过程将基于简化的代理信息--但它降低了计算复杂性。
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英文标题：
《Parallel and Successive Resource Allocation for V2V Communications in
  Overlapping Clusters》
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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：
  The 3rd Generation Partnership Project (3GPP) has introduced in Rel. 14 a novel technology referred to as vehicle--to--vehicle (V2V) \textit{mode-3}. Under this scheme, the eNodeB assists in the resource allocation process allotting sidelink subchannels to vehicles. Thereupon, vehicles transmit their signals in a broadcast manner without the intervention of the former one. eNodeBs will thereby play a determinative role in the assignment of subchannels as they can effectively manage V2V traffic and prevent allocation conflicts. The latter is a crucial aspect to be enforced in order for the signals to be received reliably by other vehicles. To this purpose, we propose two resource allocation schemes namely bipartite graph matching-based successive allocation (BGM-SA) and bipartite graph matching-based parallel allocation (BGM-PA) which are suboptimal approaches with lesser complexity than exhaustive search. Both schemes incorporate constraints to prevent allocation conflicts from emerging. In this research, we consider overlapping clusters only, which could be formed at intersections or merging highways. We show through simulations that BGM-SA can attain near-optimal performance whereas BGM-PA is subpar but less complex. Additionally, since BGM-PA is based on inter-cluster vehicle pre-grouping, we explore different metrics that could effectively portray the overall channel conditions of pre-grouped vehicles. This is of course not optimal in terms of maximizing the system capacity---since the allocation process would be based on simplified surrogate information---but it reduces the computational complexity.
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PDF链接：
https://arxiv.org/pdf/1805.07012