Time-sequence channel inference for beam alignment in vehicular networks


SOURCE IEEE GlobalSIP’18, Anaheim, Nov.,26-29,2018

Published Date:2018-11


In this paper, we propose a learning-based low-overhead
beam alignment method for vehicle-to-infrastructure communication
in vehicular networks. The main idea is to remotely
infer the optimal beam directions at a target base station in
future time slots, based on the CSI of a source base station
in previous time slots. The proposed scheme can reduce
channel acquisition and beam training overhead by replacing
pilot-aided beam training with online inference from a
sequence-to-sequence neural network. Simulation results
based on ray-tracing channel data show that our proposed
scheme achieves a 8:86% improvement over location-based
beamforming schemes with a positioning error of 1m, and is
within a 4:93% performance loss compared with the genieaided
optimal beamformer.

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