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A Multiagency Long Short-Term Model Beamforming Prediction Model for Cellular Vehicle to Everything
ISSN: 2574-0741, e-ISSN: 2574-075X
Published May 08, 2023 by SAE International in United States
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):2023, https://doi.org/10.4271/12-06-04-0030.
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.