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Millimeter-Wave V2V Communications: Distributed Association and Beam Alignment

  • Cristina Perfecto*
  • , Javier Del Ser
  • , Mehdi Bennis
  • *Autor correspondiente de este trabajo
  • Basque Center for Applied Mathematics
  • University of Oulu
  • Kyung Hee University

Producción científica: Contribución a una revistaArtículorevisión exhaustiva

161 Citas (Scopus)

Resumen

Recently, millimeter-wave (mmWave) bands have been postulated as a means to accommodate the foreseen extreme bandwidth demands in vehicular communications, which result from the dissemination of sensory data to nearby vehicles for enhanced environmental awareness and improved safety level. However, the literature is particularly scarce in regards to principled resource allocation schemes that deal with the challenging radio conditions posed by the high mobility of vehicular scenarios. In this paper, we propose a novel framework that blends together matching theory and swarm intelligence to dynamically and efficiently pair vehicles and optimize both transmission and reception beamwidths. This is done by jointly considering channel state information and queue state information when establishing vehicle-to-vehicle (V2V) links. To validate the proposed framework, simulation results are presented and discussed, where the throughput performance as well as the latency/reliability tradeoffs of the proposed approach are assessed and compared with several baseline approaches recently proposed in the literature. The results obtained in this paper show performance gains of 25% in reliability and delay for ultra-dense vehicular scenarios with 50% more active V2V links than the baselines. These results shed light on the operational limits and practical feasibility of mmWave bands, as a viable radio access solution for future high-rate V2V communications.

Idioma originalInglés
Número de artículo7959165
Páginas (desde-hasta)2148-2162
Número de páginas15
PublicaciónIEEE Journal on Selected Areas in Communications
Volumen35
N.º9
DOI
EstadoPublicada - sept 2017

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