Low-PAPR Pilot-Aided Deep Channel Estimation for OTFS Systems With HPA Nonlinearity

Abstract

High-power amplifier (HPA) nonlinearity distorts orthogonal time frequency space (OTFS) signals and degrades pilot observations, thereby posing significant challenges to channel estimation, particularly under severe Doppler shifts in high-mobility scenarios. This letter proposes a robust OTFS channel estimation method combining a Zadoff–Chu (ZC)-based embedded pilot with a low peak-to-average power ratio (PAPR), correlation-based feature enhancement, and a long short-term memory (LSTM) estimator with self-attention. The low-PAPR pilot mitigates nonlinear distortion, while cross-correlation with a locally generated ZC reference enhances delay–Doppler path responses and suppresses interference. Simulations show that the proposed method achieves lower bit error rate (BER) than practical benchmarks across a wide range of signal-to-noise ratios (SNRs) under different mobile speeds, input back-off levels, and modulation schemes.