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A Multiagency Long Short-Term Model Beamforming Prediction Model for Cellular Vehicle to Everything
Journal Article
12-06-04-0030
ISSN: 2574-0741, e-ISSN: 2574-075X
Sector:
Citation:
Elangovan, V., Xiang, W., and Liu, S., "A Multiagency Long Short-Term Model Beamforming Prediction Model for Cellular Vehicle to Everything," SAE Intl. J CAV 6(4):459-472, 2023, https://doi.org/10.4271/12-06-04-0030.
Language:
English
Abstract:
Machine learning (ML) for predicting wireless channels of vehicular
communications networks has attracted interest in recent years. Beamforming is a
technique used to selectively transmit and receive data in a desired direction.
The receiver should be capable of choosing the right beam at the right time. The
usage of adaptive antenna scanning, i.e., scanning all the beams and choosing
the best beam will result only in 30% accuracy, which means 70% of the data will
be lost. This article studied a multiagency long short-term memory (LSTM)
beamforming prediction model based on signal strength to forecast optimum beams
within each beacon interval (BI) for cellular vehicle-to-everything (C-V2X)
systems. The model combines the outputs of several parallel prediction models
resulting in an enhanced accuracy of prediction. Simulation data validated the
effectiveness of the proposed prediction model on the university campus,
resulting in a 24% improvement in prediction accuracy.