Time-Sequence Channel Inference for Beam Alignment in Vehicular Networks
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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 genie-aided optimal beamformer.
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