Beam Prediction for mmWave V2I Communication Using ML-Based Multiclass Classification Algorithms

Beam management is a key functionality in establishing and maintaining reliable communication in cellular and vehicular networks, and it becomes more critical at millimeter-wave (mmWave) frequencies and for high-mobility scenarios. Traditional approaches consume wireless resources and incur high beam training overheads in finding the best beam pairings, thus necessitating alternative approaches such as position-aided, vision-aided, or, more generally, sensing-aided beam prediction approaches. Current systems are also leveraging artificial intelligence/machine learning (ML) to optimize the beam management procedures; however, the majority of the proposed ML frameworks have been applied to synthetic datasets, leading to overestimated performances. In this work, in the context of vehicle-to-infrastructure (V2I) communication and using the real-world DeepSense6G experimental datasets, we investigate the performance of four ML algorithms on beam prediction accuracy for mmWave V2I scenarios. We compare the performance of K-nearest neighbour (KNN), support vector machine (SVM), decision tree (DT), and na & iuml;ve Bayes (NB) algorithms on position-aided beam prediction accuracy and related metrics such as precision, recall, specificity, and F1-score. The impacts of different beam codebook sizes and dataset split ratios on five different scenarios' datasets were investigated, independently and collectively. Confusion matrices and area under the receiver operating characteristic curves were also employed to visualize the (mis)classification statistics of the considered ML algorithms. The results show that SVM outperforms the other three algorithms, for the most part, on the scenario-per-scenario cases. However, for the combined scenario with larger data samples, DT outperforms the other three algorithms for both the different codebook sizes and data split ratios. The results also show comparable performance for the different data split ratios considered for the different algorithms. However, with respect to the codebook sizes, the results show that the higher the codebook size, the lower the beam prediction accuracy. With the best accuracy results around 70% for the combined scenario in this study, multi-modal sensing-aided approaches can be explored to increase the beam prediction performance, although at the expense of higher system complexity when compared to the position-aided approach considered in this study.