Graph-based model for beam management in Mmwave vehicular networks

Zana Limani Fazliu, C. Chiasserini, F. Malandrino, A. Nordio
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引用次数: 5

Abstract

Mmwave bands are being widely touted as a very promising option for future 5G networks, especially in enabling such networks to meet highly demanding rate requirements. Accordingly, the usage of these bands is also receiving an increasing interest in the context of 5G vehicular networks, where it is expected that connected cars will soon need to transmit and receive large amounts of data. Mmwave communications, however, require the link to be established using narrow directed beams, to overcome harsh propagation conditions. The advanced antenna systems enabling this also allow for a complex beam design at the base station, where multiple beams of different widths can be set up. In this work, we focus on beam management in an urban vehicular network, using a graph-based approach to model the system characteristics and the existing constraints. In particular, unlike previous work, we formulate the beam design problem as a maximum-weight matching problem on a bipartite graph with conflicts, and then we solve it using an efficient heuristic algorithm. Our results show that our approach easily outperforms advanced methods based on clustering algorithms.
基于图的毫米波车载网络波束管理模型
毫米波频段被广泛吹捧为未来5G网络的一个非常有前途的选择,特别是在使此类网络能够满足高要求的速率要求方面。因此,在5G车载网络的背景下,这些频段的使用也受到越来越多的关注,预计联网汽车将很快需要传输和接收大量数据。然而,毫米波通信需要使用狭窄的定向波束来建立链路,以克服恶劣的传播条件。先进的天线系统也允许在基站进行复杂的波束设计,其中可以设置多个不同宽度的波束。在这项工作中,我们专注于城市车辆网络中的光束管理,使用基于图的方法对系统特征和现有约束进行建模。特别地,与以往的工作不同,我们将梁设计问题表述为具有冲突的二部图上的最大权匹配问题,然后使用有效的启发式算法进行求解。我们的结果表明,我们的方法很容易优于基于聚类算法的高级方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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