OTFS channel estimation method based on IBO-dynamic gated Bi-GRU

This paper introduces a novel channel estimation method for Orthogonal Time Frequency Space (OTFS) systems affected by nonlinear distortion from High-Power Amplifiers (HPA). The method integrates a Bidirectional Gated Recurrent Unit (Bi-GRU) with a dynamic gating mechanism driven by the Input Back-Off (IBO) parameter of the HPA, combined with a multi-head attention network. The dynamic gating mechanism adaptively adjusts the update gate of the Gated Recurrent Unit (GRU) based on real-time IBO values, optimizing the trade-off between historical memory and current input during training. The multi-head attention module further captures long-range dependencies in the channel response. Theoretical analysis indicates that the proposed IBO-driven dynamically gated Bi-GRU achieves a computational complexity reduction of 20–46.7% compared to a Bi-GRU architecture. Simulation results demonstrate the superior performance of the proposed method across both bit error rate (BER) and normalized mean square error (NMSE) metrics under high mobility and nonlinear distortion. It achieves up to 22.6 quantified in decibels (dB) lower NMSE and, at a signal-to-noise ratio (SNR) of 30 dB, a 15.2 dB reduction in logarithmic BER compared to conventional methods, along with a 3–4 dB improvement over deep learning baselines at the same SNR. It also provides over 7 dB peak-to-average power ratio (PAPR) reduction over traditional methods, confirming strong robustness and accuracy in challenging communication scenarios.