Vehicle-to-infrastructure (V2I) communication is a critical component of intelligent transportation systems, enabling real-time data exchange between vehicles and roadside units (RSUs) for applications like cooperative perception, traffic management, and autonomous driving. Millimeter wave (mmWave) frequencies offer the high throughput these applications demand, but maintaining a reliable mmWave link with a fast-moving vehicle is exceptionally challenging. Machine learning (ML) is emerging as a powerful tool to address this problem.
The Beam Tracking Problem in V2I
In a V2I scenario, an RSU equipped with a mmWave antenna array communicates with vehicles traveling at speeds of 30-120 km/h (or more on highways). Due to the narrow beamwidths used in mmWave systems (often just a few degrees), even small changes in a vehicle's position can cause the beam to become misaligned, resulting in a sharp drop in received signal strength.
Consider a vehicle traveling at 100 km/h. It covers approximately 28 meters per second. If the RSU's beam has a 3-degree beamwidth and the vehicle is 50 meters away, the beam footprint on the road is roughly 2.6 meters wide. This means the vehicle can traverse the entire beam footprint in under 100 milliseconds. The system must update the beam direction at least this frequently to maintain alignment — a rate that strains conventional beam management procedures.
Traditional beam tracking relies on periodic transmission of reference signals (such as SSB or CSI-RS in 5G NR) and measurement reports from the user. The base station then selects the best beam from a predefined codebook. This process has two fundamental limitations in high-mobility V2I scenarios:
- Latency: The measurement-report-update cycle introduces delays that can exceed the beam coherence time at vehicular speeds.
- Overhead: Frequent beam sweeps consume time and spectral resources that could otherwise carry data, reducing effective throughput.
How Machine Learning Helps
Machine learning approaches reframe beam tracking from a reactive measurement problem to a predictive one. Instead of waiting for signal measurements to indicate misalignment, an ML model anticipates where the vehicle will be and which beam direction will be optimal — before the link degrades.
Several ML paradigms have been explored for this purpose:
1. Supervised Learning for Beam Prediction
Deep neural networks (DNNs) can be trained on datasets of beam measurements paired with contextual features (position, velocity, past beam indices) to predict the optimal beam index for future time steps. Architectures that have shown promise include:
- Recurrent Neural Networks (RNNs) and LSTMs: These sequence models capture temporal patterns in beam index trajectories. Given a history of the last N beam indices, an LSTM can predict the next optimal beam, effectively learning the vehicle's motion dynamics implicitly from the RF measurements alone.
- Convolutional Neural Networks (CNNs): When the input is structured as a spatial representation (e.g., a beam-space power profile across time), CNNs can extract spatial-temporal features for beam prediction.
- Transformer-based models: Self-attention mechanisms allow the model to weigh different time steps differently, capturing both short-term dynamics (sudden lane changes) and longer-term trends (highway curves).
2. Reinforcement Learning for Adaptive Beam Management
Reinforcement learning (RL) formulates beam tracking as a sequential decision-making problem. The RSU (agent) selects beam directions (actions) based on the current channel state (environment), receiving reward signals based on achieved signal quality. Deep Q-Networks (DQN) and policy gradient methods like Proximal Policy Optimization (PPO) have been applied to this setting.
The advantage of RL is its ability to adapt online: the agent can learn to handle new environments (different road geometries, traffic patterns) without requiring pre-collected labeled datasets. It can also learn to balance exploration (trying new beam directions) with exploitation (using known good beams).
3. Sensor-Aided Beam Prediction
One of the most promising directions involves using out-of-band sensory information to assist beam management. Modern vehicles and RSUs may be equipped with GPS receivers, LiDAR sensors, cameras, and radar. These sensors provide rich spatial information about the environment that can be leveraged for beam prediction:
- GPS/Position data: Knowing a vehicle's GPS coordinates and heading allows geometric computation of approximate beam directions, which can narrow the search space significantly.
- LiDAR point clouds: 3D point clouds provide precise spatial awareness of the environment, including vehicle positions and potential blockers. ML models can map point cloud data directly to optimal beam indices.
- Camera images: Computer vision models can detect and track vehicles in camera feeds, estimating their positions and trajectories. This visual information can then be mapped to beam predictions through a learned function.
- Radar: Automotive radar provides velocity and range information, useful for predicting how quickly beam directions need to change.
Sensor fusion — combining multiple modalities — further improves prediction accuracy and robustness, as different sensors compensate for each other's weaknesses (e.g., cameras struggle in poor lighting while LiDAR remains reliable).
Challenges in Practical Deployment
While ML-driven beam tracking shows great promise in simulation and controlled experiments, several challenges must be addressed for real-world deployment:
- Generalization: Models trained on data from one intersection or highway segment may not generalize to different environments with distinct scattering characteristics, road layouts, or traffic densities.
- Dataset availability: Collecting large-scale, real-world mmWave beam measurement datasets paired with sensor data is expensive and time-consuming. Synthetic datasets generated through ray-tracing simulators (such as Wireless InSite or Sionna) help, but the sim-to-real gap remains.
- Computational constraints: ML models must run with low latency at the RSU. The inference time of the model must be significantly shorter than the beam coherence time to be useful.
- Robustness to blockage: Sudden blockage events (a truck passing between the RSU and the target vehicle) are difficult to predict and can cause abrupt beam failures that even learned models struggle to handle gracefully.
The Road Ahead
The convergence of machine learning and mmWave communication represents a paradigm shift in how wireless systems are designed. Rather than relying solely on model-based signal processing, future V2I systems will likely employ hybrid approaches — using physics-based models to constrain the search space and ML to refine predictions within that space. As sensor-equipped intelligent infrastructure becomes more widespread and datasets grow richer, ML-driven beam management will be key to making mmWave V2I communication reliable enough for safety-critical applications.
My ongoing research focuses on developing efficient ML frameworks that integrate multi-modal sensory data for real-time beam prediction in vehicular environments, aiming to bridge the gap between simulation results and practical deployment.