AngularWM: Wireless World Modeling for AoA Prediction from Multi-Antenna I/Q

Abstract

Tracking angle-of-arrival (AoA) under mobility enables proactive beam alignment in multi-antenna wireless systems. However, learning this evolution directly from raw I/Q data is challenging because multipath, channel-gain variations, hardware impairments, and noise mix with angular information. This paper proposes \emph{AngularWM}, a self-supervised wireless world-modeling approach that learns a predictive latent angular state from ordered multi-antenna I/Q observations. A joint-embedding predictive architecture is combined with a label-free trajectory-level geometric regularizer that encourages an approximately affine, non-collapsed latent evolution motivated by the antenna-array response. No AoA, motion, or displacement labels are used during pretraining. The resulting structured world state supports direct multi-step encoder-space extrapolation and generalizes to angular step sizes not seen during training. On large-scale over-the-air measurements, AngularWM reduces the mean predictor-free AoA error over eight steps from $29.97^\circ$ to $0.49^\circ$. After pretraining, only three or four labeled reference angles are required to calibrate the complete 225-point AoA grid, achieving $0.48^\circ$ and $0.43^\circ$ MAE, respectively. AngularWM also maintains robust beam alignment and spectral efficiency under increasing angular displacement, while reducing the deployed model size from 1.42M to 0.30M parameters and forecast latency.