Smart Beam Management for Vehicular Networks Using ML

  1. G. Bharath-Reddy 2
  2. Montero, L 2
  3. Perez-Romero, J 2
  4. Molins-Benlliure, J 3
  5. Ferrando Bataller, M 3
  6. Molina, J 1
  7. J. Romeu 2
  8. L. Jofre-R 2
  1. 1 Universidad Politécnica de Cartagena
    info

    Universidad Politécnica de Cartagena

    Cartagena, España

    ROR https://ror.org/02k5kx966

  2. 2 Universitat Politècnica de Catalunya
    info

    Universitat Politècnica de Catalunya

    Barcelona, España

    ROR https://ror.org/03mb6wj31

  3. 3 Universidad Politécnica de Valencia
    info

    Universidad Politécnica de Valencia

    Valencia, España

    ROR https://ror.org/01460j859

Erakutsi afiliazioak +
Aktak:
XXXVI Simposio Nacional de la Unión Científica Internacional de Radio

Argitalpen urtea: 2021

Mota: Biltzar ekarpena

GOOGLE SCHOLAR lock_openRiuNet editor

Laburpena

The mmWave frequencies will be widely used in future vehicular communications. At these frequencies, the radio channel becomes much more vulnerable to slight changes in the environment like motions of the device, reflections or blockage. In high mobility vehicular communications the rapidly changing vehicle environments and the large overheads due to frequent beam training are the critical disadvantages in developing these systems at mmWave frequencies. Hence, smart beam management procedures are desired to establish and maintain the radio channels. In this paper, we propose that using the positions and respective velocities of the vehicles in the dynamic selection of the beam pair, and then adapting to the changing environments using ML algorithms, can improve both network performance and communication stability in high mobility vehicular communications.