## 3 Materials and Methods
### Dataset Description  
Table 1 presents a summary of the five scenarios from DeepSense6G considered in this work (i.e., Scenarios 1, 2, 5, 6, and 7) as well as the combined scenario. Figure 1 then illustrates a sample V2I scenario (i.e., Scenario 6, representing one of the Scenarios). The layout of Scenario 6 comprises four lanes. Other scenarios, such as Scenarios 1, 2, 5, and 7, have similar layouts but with varying numbers of lanes and data samples. The data were collected at various locations and times of the day, as well as in different weather conditions, as shown in Table 1.  
For the data collection setups, the TX with a quasi-omni antenna is attached to a vehicle. It transmits omnidirectionally at the 60 GHz mmWave frequency. The TX unit is also equipped with a GPS sensor. The RX is an AP infrastructure situated on the road sidewalk. It has 16 antennas and operates also on the 60 GHz mmWave frequency. It receives the transmitted signal using an oversampled codebook of 64 predefined beams. The vehicle traverses the lanes many times in both directions and transmits its communication and GPS signals to the AP. For each sample point, the AP selects the beam with the highest received power from its beam codebook. Figure 2 shows the received powers across the 64 beams for some data samples for Scenario 6, where the peak for each sample represents the maximum received power and correspondingly the optimal beam index. The typical distance between the base station (BS)/AP and the vehicle (TX) is around 20 m. Detailed information on the experimental testbed (i.e., Testbed 1) describing RX (i.e., unit 1--the street-level infrastructure) and the TX (i.e., unit 2--vehicle with attached TX module) can be found in [19; 20].  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Scenario \({}^{*}\) Number**} & \multirow{2}{*}{**Time \& Weather**} & \multirow{2}{*}{**Total Samples**} & \multicolumn{6}{c}{**Number of Samples**} \\ \cline{3-8}  & & & **80:20 Split** & & **70:30 Split** & & **60:40 Split** \\ \cline{3-8}  & & & **Train (80\%)** & **Test (20\%)** & **Train (70\%)** & **Test (30\%)** & **Train (60\%)** & **Test (40\%)** \\ \hline
1 & Day, Clear & 2411 & 1929 & 482 & 1688 & 723 & 1447 & 964 \\ \hline
2 & Night, Clear & 2974 & 2380 & 594 & 2082 & 892 & 1784 & 1190 \\ \hline
5 & Night, Rainy & 2300 & 1840 & 460 & 1610 & 690 & 1380 & 920 \\ \hline
6 & Day, Clear & 915 & 732 & 183 & 641 & 274 & 549 & 366 \\ \hline
7 & Day, Clear & 854 & 684 & 170 & 598 & 256 & 512 & 342 \\ \hline Combined & Mixed & 9454 & 7564 & 1890 & 6618 & 2836 & 5672 & 3782 \\ \hline \hline \end{tabular}  
* We have retained the scenario numbering as in DeepSense6G [19].  
\end{table}
Table 1: Scenarios’ summary.  
This work employs the GPS data from the vehicle/TX and the received beam powers at the infrastructure/RX for beam prediction. The ML features are the latitude and longitude (i.e., GPS coordinates), and the target variable is the optimal beam index corresponding to the maximum received power at the AP. The features (i.e., GPS coordinates) are converted into Cartesian coordinates and normalized before the ML operations. The methodology employed for the study is shown in the flowchart of Figure 3.  
Figure 1: Scenario 6 layout showing the vehicle’s (TX) positions on the lanes (i.e., colored circles where the colors represent the beam indices) and the RX/AP’s position and its field of view (Reproduced from [[https://www.wi-lab.net/research/position-aided-prediction-paper-how-useful-gps-positions-actually-are/](https://www.wi-lab.net/research/position-aided-prediction-paper-how-useful-gps-positions-actually-are/)]([https://www.wi-lab.net/research/position-aided-prediction-paper-how-useful-gps-positions-actually-are/](https://www.wi-lab.net/research/position-aided-prediction-paper-how-useful-gps-positions-actually-are/)), accessed on 1 May 2024).  
Figure 2: Received powers vs. Beam Indices for selected Scenario 6 data samples (64-beam codebook).