AI-Aided Delay-Doppler Estimation for Bistatic OFDM ISAC Systems

This letter introduces a novel artificial intelligence (AI)-aided delay-Doppler estimation method for bistatic integrated sensing and communications systems employing orthogonal frequency division multiplexing (OFDM) under dense clutter and inter-cell interference. An attention-based recurrent neural network is designed to extract features from individual OFDM symbols using Long Short-Term Memory layers followed by an attention layer to determine the relative importance, aiming to improve sensing accuracy and robustness. We demonstrate by numerical results that the proposed AI-aided approach can outperform conventional baseline methods and is robust against the degrading effects of heavy clutter and colored noise.