# Context-Aware Beam Selection for IRS-Assisted mmWave V2I Communications
## 5 Results and Discussion  
In the section, we present experimental results for the proposed C-MAB machine learning based beam selection. The scenario considered for experiment is presented in Figure 1, with the system parameters outlined in Table 1. In this scenario, we consider a standalone single mmBS with 12 beams. At any given time, the BS can only activate four beams for service. Vehicles are created and absorbed at certain locations on the map. Those locations are the main entrance point to the city and the exit point from the city. Moreover,we also included several main parking spots in the city as vehicle creation and absorption locations. A pair of vehicle creation and absorption locations is used to create a route simulating a vehicle either passing through the city, entering to or exiting from the city. We use A-STAR path finding algorithm [28] to establish the route for vehicles. We assume that each of these vehicles requires downlink data service when entering the small cell. In our simulation, there are 50 vehicles which continuously travel on the map. We simulate three hours of operation, during which the base station begins with full exploration for learning, and then it switches to full exploitation after the first 50% of the simulation time. As the Explore-First strategy stops triggering exploration after the learning phase, this enables us to focus on the study of learning effectiveness acquired during the learning phase. The key simulation parameters used in our experiments are summarized in Table 1. Experiments are conducted via our own developed Python Mobility Simulation Platform (PyMoSim, version 0.8.10). The detailed documentation of PyMoSim, along with example usage scenarios, is publicly accessible at [[https://github.com/cfoh/pymosim-doc](https://github.com/cfoh/pymosim-doc)]([https://github.com/cfoh/pymosim-doc](https://github.com/cfoh/pymosim-doc)), accessed on 22 June 2025. We plan to release the full source code of PyMoSim and our scenario setup code in the near future). The proposed C-MAB algorithm is selected for its lightweight, model-free nature, which makes it well-suited for real-time decision-making at the network edge. Unlike deep reinforcement learning (DRL) methods, which require large training datasets, extensive tuning, and high computational resources, C-MAB learns directly from interactions with the environment. This allows it to rapidly adapt to evolving contexts (e.g., vehicle movement, traffic light transitions) without prior training or simulation. C-MAB is also advantageous over location prediction or trajectory estimation methods, which typically rely on accurate positioning data and historical mobility patterns. Instead, our approach uses high-level context information--such as proximity and direction relative to mmBS or traffic lights--making it robust even in GPS-challenged environments. However, C-MAB also has limitations. Its performance can degrade in large or continuous context spaces due to the need to estimate a reward for every context-action pair. Additionally, C-MAB does not capture long-term or delayed rewards, which could limit its performance in sequential decision tasks with temporal dependencies. To address such scenarios, future work may explore hierarchical or hybrid learning frameworks capable of modeling temporally extended behavior. The proposed algorithm does not directly estimate or rely on channel coherence time, but rather focuses on link-level stability through the observed beam sojourn time. At mmWave frequencies, where highly directional transmissions are used, the concept of beam coherence time becomes more critical than traditional channel coherence time. Beam coherence time denotes the interval during which a beam remains aligned with a moving user before misalignment occurs and is typically an order of magnitude longer than the channel coherence time.