## 2 Related Works  
The \"AI/ML for wireless\" communication paradigm has seen a gradual and continuous rise in addressing different communication challenges, such as beam management in mmWave cellular and vehicular networks. In such networks, AI/ML techniques are used to improve network functions across different layers of the protocol stack to improve system performance [7]. Several studies have, therefore, investigated the application of AI/ML to beam management operations for different network scenarios. In 2020, the 3 GPP started AI/ML-based beam management schemes such as: (i) spatial-domain prediction use case where the AI/ML model predicts one or a few best beams pointing in certain directions instead of measurements of all beams; (ii) time-domain prediction use case where the AI/ML model predicts one or a few best beams to be used in the next time instant based on the past time instances of selected beam sequences [7]. The position-aided beam prediction investigated in this manuscript aligns with the spatial domain use of the 3 GPP for 5G-Advanced, where the position data of TX can be used to narrow down the candidate beams to a few best beams, such as 3 to 5 candidate beams, instead of making measurements on a large set of beams (e.g., 64 beams). By leveraging these ML algorithms, the networks can reduce signaling/measurement overheads, optimize network resources, and enhance system performance. In [4], the authors introduced an online learning strategy for beam pairings. They proposed a strategy that incorporates risk awareness in mitigating the occurrence of severe beam misalignment during the learning phase. The employed methodology involves the design of algorithms that selectively choose high-risk beam pairings less frequently, thereby managing the risk associated with beam misalignment. The objective is to strike a balance between the learning burden on users in the early stages and the pace at which they acquire knowledge. The proposed solution was further characterized by regret evaluations of the algorithms, providing insights into the cost of learning incurred due to the integration of risk awareness. Regret evaluations typically quantify the cumulative difference between the performance of the algorithm and an idealized, optimal approach. In a similar context, the authors of [3] introduced a beam alignment technique augmented by ML. Their methodology leverages ML models trained to predict the optimal access point (AP) and beam for a user equipment (UE) based solely on the UE's location information. The approach streamlines the beam alignment process by utilizing predictive models to determine the most suitable AP and beam configuration for maximizing signal quality and network performance. The proposed solution is a promising solution for enhancing the efficiency and accuracy of beam alignment processes in wireless communication systems. However, to validate their approach, the authors trained and evaluated their ML models using datasets generated from commercial-grade ray-tracing simulations. In [12], the authors introduced an ML-based approach that utilizes historical beam training data along with contextual information such as vehicle locations, receiver type, and proximity to neighboring vehicles. By incorporating these factors into the ML model, the system learns to identify the optimal beam pair index for maximizing communication performance. Along the same line, the authors of [13] utilized the support vector machine (SVM) model to solve the beam management problem in a 5G New Radio (NR) mmWave system. This solution uses geolocation data to improve beam management operations. Further, the authors of [14] leveraged the light detection and ranging (LiDAR) sensory data for predicting current and future beams and evaluated their approach in a V2I communication scenario involving highly mobile vehicles. Similarly, the authors of [15] proposed a deep learning-based model that enhances efficiency, reduces overhead, and increases the probability of selecting the optimal beam for V2I scenarios.