What question did this study set out to answer?

This work aims to evaluate the performance of machine learning algorithms for beam selection in mmWave V2I communications using geolocation data.

October 25, 2025Annals of Emerging Technologies in ComputingOpen Access

Beam Prediction for mmWave V2I Communication aided by Geolocation and Machine Learning

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Implication

Randomized trial evaluates beam selection accuracy in mmWave V2I using geolocation and ML algorithms, highlighting the need for multi-modal approaches.

Key Points

This work aims to evaluate the performance of machine learning algorithms for beam selection in mmWave V2I communications using geolocation data.
Leveraged real-world datasets based on mmWave measurements for analysis.
Compared three ML algorithms: k-nearest neighbours, support vector machine, and decision tree.
Evaluated performance under ten different scenarios and measured the impact of codebook sizes on accuracy.
Average accuracy varied significantly, with some scenarios below 30% and others exceeding 90%.
Highlighting the limitations of sole reliance on geolocation for beam prediction.
Indicated the need for using multi-modal approaches for better performance in mmWave V2I scenarios.

Cite This Study

A 2025 study studied this question.

synapsesocial.com/papers/6a7e8a116cdd7a4db3026200 https://doi.org/10.33166/aetic.2025.05.001

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Implication

Randomized trial evaluates beam selection accuracy in mmWave V2I using geolocation and ML algorithms, highlighting the need for multi-modal approaches.

Beam Prediction for mmWave V2I Communication aided by Geolocation and Machine Learning

Discussion

Member takes

Implication

Key Points

Cite This Study

Beam Prediction for mmWave V2I Communication aided by Geolocation and Machine Learning

Discussion

Member takes

Implication

Key Points

Cite This Study

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