## 2 Related Works
Most of these prior works employed synthetic or simulated datasets and not real-world measurement-based datasets, whereas synthetic datasets do not accurately represent the real world. As a result, the ML algorithms that perform well with simulated datasets do not perform as well with datasets from field measurements. Therefore, different from earlier approaches, the authors of [16] proposed position-aided beam prediction frameworks that use global positioning system (GPS) coordinates for V2I beam selection and evaluated the frameworks using the DeepSense6 datasets. The authors considered three approaches for performance evaluation: (i) look-up table, (ii) \(k\)-nearest neighbors (KNN), and (iii) fully-connected neural network. The results across nine different scenarios show average beam prediction accuracy less than 40% for a 64-beam codebook and 80% for the downsampled 8-beam codebook. These results are less than the typical over 95% accuracy from synthetic datasets, and this underscores how the proposed ML-based and GPS-aided solutions perform on real-world datasets. The solutions in [16] leave room to test other ML algorithms. Similarly, in [17], the authors presented an innovative approach that utilizes visual and positional sensory data for beam prediction. Their proposed ML solution is based on neural networks and integrates both position-based and vision-based predictions. To validate the effectiveness of their approach, they used two scenarios from the deepSense6G datasets. The scenarios include positional data, visual data from cameras, and mmWave beam training data, facilitating robust evaluation of the neural network's performance in diverse vehicular environments. There are many studies that have explored how to improve wireless communication systems by utilizing user positions. However, only a few studies have examined real-world scenarios [18]. To the best knowledge of the authors, none of the earlier works have considered the scenarios, algorithms, and metric combinations employed in this work for real-world datasets. In this paper, thus, we utilize measurement-based data obtained from the DeepSense6G dataset [19], which is a multi-modal communication and sensing dataset derived from extensive field experiments [16; 20]. The dataset is composed of several scenarios, each featuring a diverse range of sensor data, including LiDAR, radar, received mmWave power, and GPS location coordinates [20]. The dataset [19] currently consists of more than forty different scenarios representing different use cases. For example, Scenarios 1-9, 13-15, and 31-35 are for V2I use cases. Scenarios 10-12 are for pedestrian communications, Scenario 16 is for indoor communications, Scenario 23 is for drone communications, Scenarios 36-39 are for V2V communication, and Scenarios 42-44 are for integrated sensing and communication use cases. For the numbering of scenarios throughout this manuscript, we have retained the scenario numbering as used in the DeepSense6G dataset [19]. In this study, we focus on selected V2I scenarios from [19] to investigate and analyze the influence of beam codebook and dataset sizes on the evaluation metrics (i.e., beam accuracy, precision, recall, specificity, and F1-score) of beam prediction in GPS-aided mmWave V2I communication scenarios. We examine the performance of four ML algorithms--KNN, SVM, decision tree (DT), and naive Bayes (NB)--in predicting beams. We also present the results using confusion matrices to visualize the (mis)classification statistics. To the best of our knowledge, no prior research has compared the performance of these four algorithms using real-world datasets in the context of beam prediction for mmWave V2I communication systems. Our study aims to fill this gap by providing insights into the comparative performance of these ML algorithms for beam prediction.