DeepRT-Cal: Learning to Calibrate Ray-Traced Twin Channel Statistics with Low-Fidelity Environmental Geometry

Abstract

Sixth-generation (6G) wireless systems increasingly rely on digital twin channels (DTCs) to represent and interact with physical radio environments. Ray tracing (RT) is naturally suited to DTC construction because it provides deterministic and physically interpretable channel predictions. However, high-fidelity three-dimensional environment models are expensive to obtain, whereas geometric inaccuracies in low-fidelity models can alter the detected multipath components (MPCs) and consequently distort received-power and delay-domain twin channel statistics. Building on the DeepRT ray-tracing framework, this paper proposes DeepRT-Cal, a geometry-aware cross-fidelity calibration framework that predicts high-fidelity twin channel statistics from low-fidelity geometry and RT features.The network encodes global link descriptors, masked low-fidelity MPC tokens, propagation-corridor geometry, and link-centered environmental objects. During training, detailed geometry is introduced as privileged information with structured masking, while deployment requires only low-fidelity geometry and low-fidelity RT information. A link-conditioned cross-attention module retrieves the environmental context relevant to each Tx--Rx link. The prediction heads estimate high-fidelity twin channel statistics, including total received power, RMS delay spread, mean excess delay, and a normalized power-weighted power-delay profile (PDP). Outdoor evaluation shows that, in an non-line of sight (NLoS) region, the method reduces power MAE from 16.80 to 3.88 dB and RMS delay-spread MAE from 304.89 to 98.19 ns. In a LoS-dominated region, power prediction improves, while RMS delay-spread accuracy remains comparable to direct RT.