Structured Contextual Deep Learning for Channel Estimation in UAV-OFDM Systems Under Beam Squint and Doppler Effects

Abstract

Reliable channel estimation (CE) in unmanned aerial vehicles (UAVs)-assisted orthogonal frequency-division multiplexing (OFDM) systems is fundamentally challenged by mobility-induced Doppler dynamics and frequency-dependent beam squint, which jointly distort pilot observations and reduce channel coherence across subcarriers. These impairments limit the effectiveness of conventional model-based estimators and increase retransmissions, thereby degrading spectral and energy efficiency. This paper develops a structured UAV-assisted OFDM framework that explicitly captures Doppler-induced phase evolution and wideband spatial distortion. Least-squares (LS) estimation and a genie-aided minimum mean square error (MMSE) equalizer are employed as analytical references, where the latter serves as an ideal upper performance bound. Building upon this foundation, we introduce a physics-informed contextual deep learning formulation that refines LS estimates by exploiting cross-subcarrier frequency correlation. The proposed hybrid architecture, termed CRDBA-Net, integrates dilated residual convolution, bidirectional sequential modeling, and attention-based subcarrier weighting to capture multi-scale frequency structure and mobility-driven variation. Extensive bit error rate (BER) evaluations demonstrate that the proposed framework consistently outperforms classical and representative learning-based baselines across a wide signal-to-noise ratio range and under severe Doppler and beam-squint conditions, while approaching genie-aided MMSE performance without requiring prior channel statistics or matrix inversion. The results highlight the potential of structured deep learning to enhance reliability and computational efficiency in high-mobility green UAV communication networks.