Moving Receiver Signal Prediction via Wavefield–Trajectory Decoupling

Predicting future electromagnetic signals at moving receivers is an important task in mobile wireless systems, supporting beam tracking, Doppler compensation, and resource allocation. The received signal varies both as the wavefield evolves over time and as receiver motion changes the sampled field amplitude and phase. In particular, motion-induced Doppler shifts can produce rapid phase variations, making accurate prediction challenging. Existing methods tend to jointly learn temporal wavefield evolution and spatial trajectory sampling to predict future signals. Encoding these two factors with different properties into a shared representation can cause different receiver velocities to induce conflicting gradient directions during optimization, thereby impairing the learned wavefield representation. To focus the network on wavefield evolution while avoiding neural approximation of trajectory sampling, we propose Wavefield–Trajectory Decoupling (WTD), comprising a velocity-agnostic complex neural operator and a parameter-free geometric projector. The operator predicts Eulerian wavefields from past wavefields, and the projector samples the predicted field along known trajectories to obtain future received signals. A phase-preserving complex activation and full-field Sobolev supervision constrain high-frequency wavefield learning. WTD outperforms baselines on in-distribution and out-of-distribution velocity, with up to $13.9\times$ lower relative $L_2$ signal error and $11.5\times$ lower absolute Doppler error.