The future of 6G networks will evolve beyond high-speed data transmission to include Joint Communication and Sensing (JC&S), where devices actively model and learn from their environments in real-time. This paradigm shift relies on multimodal sensing that fuses data from cameras, LiDAR, and RF transceivers to create five-dimensional world models, enabling systems to simulate scenarios and predict environmental changes before they occur. Central to this vision is the integration of AI-driven simulations, which allow devices to dynamically update their sensing and communication policies based on synthetic data and continuous learning. While this approach promises unprecedented adaptability for autonomous vehicles and smart cities, it faces significant hurdles regarding the massive data requirements, computational complexity, and precise synchronization needed to fuse diverse sensor inputs effectively.
Keywords: multimodal sensing, AI-driven simulations, adaptive policies, autonomous systems, smart city infrastructure