TY - GEN
T1 - Beam Training and Data Transmission Optimization in Millimeter-Wave Vehicular Networks
AU - Scalabrin, Maria
AU - Michelusi, Nicolo
AU - Rossi, Michele
N1 - Funding Information:
†Dept. of Information Engineering, University of Padova, email: {scalabri, rossi}@dei.unipd.it ‡School of Electrical and Computer Engineering, Purdue University, email: michelus@purdue.edu This research has been funded by NSF under grant CNS-1642982 and by a grant from the Fondazione Ing. Aldo Gini.
Funding Information:
This research has been funded by NSF under grant CNS-1642982 and by a grant from the Fondazione Ing. Aldo Gini.
Publisher Copyright:
© 2018 IEEE.
PY - 2018
Y1 - 2018
N2 - Future vehicular communication networks call for new solutions to support their capacity demands, by leveraging the potential of the millimeter-wave (mm-wave) spectrum. Mobility, in particular, poses severe challenges in their design, and as such shall be accounted for. A key question in mm-wave vehicular networks is how to optimize the trade-off between directive Data Transmission (DT) and directional Beam Training (BT), which enables it. In this paper, learning tools are investigated to optimize this trade-off. In the proposed scenario, a Base Station (BS) uses BT to establish a mm-wave directive link towards a Mobile User (MU) moving along a road. To control the BT/DT trade-off, a Partially Observable (PO) Markov Decision Process (MDP) is formulated, where the system state corresponds to the position of the MU within the road link. The goal is to maximize the number of bits delivered by the BS to the MU over the communication session, under a power constraint. The resulting optimal policies reveal that adaptive BT/DT procedures significantly outperform common-sense heuristic schemes, and that specific mobility features, such as user position estimates, can be effectively used to enhance the overall system performance and optimize the available system resources.
AB - Future vehicular communication networks call for new solutions to support their capacity demands, by leveraging the potential of the millimeter-wave (mm-wave) spectrum. Mobility, in particular, poses severe challenges in their design, and as such shall be accounted for. A key question in mm-wave vehicular networks is how to optimize the trade-off between directive Data Transmission (DT) and directional Beam Training (BT), which enables it. In this paper, learning tools are investigated to optimize this trade-off. In the proposed scenario, a Base Station (BS) uses BT to establish a mm-wave directive link towards a Mobile User (MU) moving along a road. To control the BT/DT trade-off, a Partially Observable (PO) Markov Decision Process (MDP) is formulated, where the system state corresponds to the position of the MU within the road link. The goal is to maximize the number of bits delivered by the BS to the MU over the communication session, under a power constraint. The resulting optimal policies reveal that adaptive BT/DT procedures significantly outperform common-sense heuristic schemes, and that specific mobility features, such as user position estimates, can be effectively used to enhance the overall system performance and optimize the available system resources.
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U2 - 10.1109/GLOCOM.2018.8647890
DO - 10.1109/GLOCOM.2018.8647890
M3 - Conference contribution
AN - SCOPUS:85063521507
T3 - 2018 IEEE Global Communications Conference, GLOBECOM 2018 - Proceedings
BT - 2018 IEEE Global Communications Conference, GLOBECOM 2018 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 IEEE Global Communications Conference, GLOBECOM 2018
Y2 - 9 December 2018 through 13 December 2018
ER -
