Location-Aware Coordinated Beam Alignment in mmWave Communication

Orikumhi, Igbafe; Kang, Jeongwan; Park, Chan-Sik; Yang, Joon Mo; Kim, Sunwoo

doi:10.1109/allerton.2018.8635826

“…In [6] the authors present a framework that combines matching theory and swarm intelligence to dynamically and efficiently perform user association and beam alignment in a vehicle-tovehicle communication network. Methods aided by location information have been proposed in [7], as have methods which use information from road-side and vehicle sensors [8], [9]. In our own previous work [10], a method leveraging traffic regulating signals was proposed to alleviate the need for realtime beam realignment.…”

Section: Introductionmentioning

confidence: 99%

MmWave Beam Management in Urban Vehicular Networks

Fazliu

¹

,

Malandrino

²

,

Chiasserini

³

et al. 2021

IEEE Systems Journal

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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.

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“…In [15] the authors present a framework that combines matching theory and swarm intelligence to dynamically and efficiently perform user association and beam alignment in a vehicle-to-vehicle communication network. Methods aided by location information have been proposed in [14], as have methods which use information from road-side and vehicle sensors [2,8]. In our own previous work [13], a method leveraging traffic regulating signals was proposed to alleviate the need for real-time beam realignment.…”

Section: Introductionmentioning

confidence: 99%

Graph-based model for beam management in Mmwave vehicular networks

Fazliu

¹

,

Chiasserini

²

,

Malandrino

³

et al. 2020

Proceedings of the Twenty-First International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Netw

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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. CCS CONCEPTS • Networks → Mobile networks.

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