We present a passive reconfigurable intelligent surface (RIS)-assisted beamforming system operating at 900 MHz, enhanced with vision-based environmental sensing for real-time adaptation of the reflected beam direction. In our setup, the RIS enables signals from a given transmitter to reach a mobile user by setting the appropriate weights for the RIS elements in real time. To handle dynamic conditions, we deploy an 8-bit Integer (INT8) quantized Convolutional Neural Network (CNN) at the receiver smartphone that utilizes images from the camera to infer the optimal RIS beam index given the transient angular difference between the RIS and the smartphone. By using such an out-of-modality image sensor, we avoid an exhaustive search through the entire codebook of beams. We demonstrate the system on Qualcomm's Snapdragon 8 Gen 3 mobile platform hardware with CNN optimization through the open source AI Model Efficiency Toolkit (AIMET). Our demo reveals that carefully optimized machine learning models at the network edge can enable low-latency and computationally efficient RIS beamforming for practical wireless deployments.

Digital twins (DT) offer a low-overhead evaluation platform and the ability to generate rich datasets for training machine learning (ML) models before actual deployment. Specifically, for the scenario of ML-aided millimeter wave (mmWave) links between moving vehicles to roadside units, we show how DT can create an accurate replica of the real world for model training and testing. The contributions of this paper are twofold: First, we propose a framework to create a multimodal Digital Twin (DT), where synthetic images and LiDAR data for the deployment location are generated along with RF propagation measurements obtained via ray-tracing. Second, to ensure effective domain adaptation, we leveragemeta-learning, specificallyModel-Agnostic Meta-Learning(MAML), withtransfer learning(TL) serving as a baseline validation approach. The proposed framework is validated using a comprehensive dataset containing both real and synthetic LiDAR and image data for mmWave V2X beam selection. It also enables the investigation of how each sensor modality impacts domain adaptation, taking into account the unique requirements of mmWave beam selection. Experimental results show that models trained on synthetic data using transfer learning and meta-learning, followed by minimal fine-tuning with real-world data, achieve up to 4.09× and 14.04× improvements in accuracy, respectively. These findings highlight the potential of synthetic data and meta-learning to bridge the domain gap and adapt rapidly to real-world beamforming challenges.