Converge challenge: multimodal learning for 6G wireless communications

High-frequency mmWave and sub-THz systems enable ultra-high data rates but suffer from severe path loss and blockage sensitivity. Visual sensing can enhance reliability by providing environmental awareness for proactive beam management, yet progress has been limited by the lack of synchronized real-world multimodal datasets. This CONVERGE challenge addresses this gap with a novel indoor mmWave dataset and three tasks: Blockage Prediction, UE Localiza-tion, and Channel Prediction. These tasks are designed to benchmark cross-modal learning and promote collaboration between the wireless and computer vision communities.