Multimodal Sensing-Enabled Digital Twin for 6G RAN in Indoor Environments

Integrating sensing, enabling network intelligence, and improving spectral efficiency are key drivers of the vision for sixth-generation (6G) wireless systems. Conventional pilot-based channel characterization methods incur significant radio-resource overhead, particularly in indoor traffic scenarios, whereas sensing-aided channel characterization offers a viable alternative. Digital twins (DTs) are seen as a promising enabler providing high-fidelity virtual representations of physical environments, which can enhance sensing-aided channel characterization. This paper proposes a DT framework for channel characterization in radio access networks (RANs), leveraging multimodal sensing. Ray tracing is performed on a fused and segmented multimodal point cloud that supports arbitrary placements of both transmitters and receivers, and the DT is validated using a bistatic radio link. In this work, we use received signal strength (RSS) and power delay profile (PDP) as the KPIs. Our results show that multimodal sensing significantly improves environmental awareness, achieving a relative mean absolute error of 2.5 % for RSS and below 15 % for PDP. Therefore, the DT facilitates accurate sensing-aided channel characterization in indoor environments, enabling closed-loop optimization in RAN. © 2026 IEEE.