摘要翻译：
第三代合作伙伴计划(3GPP)最近在REL成立。14一种用于车载广播通信的网络辅助资源分配方案。这种新的模式被称为Vehicle-to-Vehicle(V2V)\textIt{mode-3}，它由eNodeBs组成，只参与覆盖范围内车辆之间的旁路子信道的分配。因此，在没有前者进一步干预的情况下，车辆将直接向对应车辆广播各自的信号。由于子信道的分配是断断续续地发生以减少信令，因此它必须主要是无冲突的，以便不危及信号的接收。我们确定了必须保证的四种关键类型的分配要求：一种服务质量（QoS）要求和三种必须排除的冲突条件，以保持接收可靠性。潜在的问题被描述为一个系统和容量的最大化，其中包含四种必须执行的约束。此外，我们提出了一个三阶段次优方法，将其转化为多个独立背包问题(MIKPs)。我们通过仿真比较了这两种方法，表明后一种方法可以在较小的复杂度下获得可接受的性能。
---
英文标题：
《Network-Assisted Resource Allocation with Quality and Conflict
  Constraints for V2V Communications》
---
作者：
Luis F. Abanto-Leon, Arie Koppelaar, Sonia Heemstra de Groot
---
最新提交年份：
2018
---
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。
--

---
英文摘要：
  The 3rd Generation Partnership Project (3GPP) has recently established in Rel. 14 a network-assisted resource allocation scheme for vehicular broadcast communications. Such novel paradigm is known as vehicle--to--vehicle (V2V) \textit{mode-3} and consists in eNodeBs engaging only in the distribution of sidelink subchannels among vehicles in coverage. Thereupon, without further intervention of the former, vehicles will broadcast their respective signals directly to their counterparts. Because the allotment of subchannels takes place intermittently to reduce signaling, it must primarily be conflict-free in order not to jeopardize the reception of signals. We have identified four pivotal types of allocation requirements that must be guaranteed: one quality of service (QoS) requirement and three conflict conditions which must be precluded in order to preserve reception reliability. The underlying problem is formulated as a maximization of the system sum-capacity with four types of constraints that must be enforced. In addition, we propose a three-stage suboptimal approach that is cast as multiple independent knapsack problems (MIKPs). We compare the two approaches through simulations and show that the latter formulation can attain acceptable performance at lesser complexity.
---
PDF链接：
https://arxiv.org/pdf/1807.04829