Transformer-Based Physics Prior-Enhanced Residual Learning for OFDM Channel Estimation: The PERL Architecture

Abstract

Accurate channel estimation in millimeter-wave MIMO-OFDM systems is hindered by limited pilot resources and the high sensitivity of physical priors to propagation conditions. This paper proposes PERL, a physics-enhanced residual learning framework that integrates ray-tracing (RT) priors with deep neural networks to refine coarse RT-based estimates. Unlike direct channel reconstruction, PERL learns a residual correction atop the RT baseline, with the correction magnitude adaptively gated according to noise level and RT reliability, thereby relying more on physical priors under low SNR and exploiting pilot observations for refinement at high SNR. The framework leverages a Transformer-based multimodal attention mechanism to deeply fuse sparse pilot observations, path-level propagation features (delay, angle, power, and phase), and scene-level statistical descriptors, enabling physical constraints and data-driven refinement to interact effectively. Experiments on a 28 GHz urban macro-cellular MIMO-OFDM scenario demonstrate that PERL achieves an overall NMSE of −28.82 dB, outperforming the RT baseline by 5.30 dB and the MMSE estimator by over 20 dB, with notably larger gains under non-line-of-sight conditions where the RT prior is less accurate. Link-level evaluations further confirm improved error vector magnitude and maintained bit/block error rates relative to the RT baseline, validating that the proposed physical-data collaborative paradigm not only enhances estimation accuracy but also preserves communication reliability, while offering a promising foundation for future detection-aware and integrated-sensing-and-communication optimizations.