Delay-Doppler Domain Underwater Acoustic Channel Prediction Using Multi-Scale Convolutional LSTM

Adaptive modulation and coding (AMC) is an effective technique for mitigating the severe dynamics of underwater acoustic (UWA) channels. However, its performance strongly relies on accurate channel state prediction, and prediction errors can cause significant performance degradation. Most existing approaches primarily focus on time-domain (TD) prediction, but the rapid variability of UWA channels makes long-term prediction highly challenging and limits their effectiveness. To overcome this limitation, we exploit the relative stability and sparsity of channel parameters in the delay–Doppler (DD) domain and propose a multi-scale convolutional long short-term memory (ConvLSTM) prediction framework. The DD-domain formulation reduces model complexity and supports multi-step forecasting, while the multi-scale architecture captures channel dynamics across different temporal scales and structural levels. The effectiveness of our method is validated through extensive experiments on real-world UWA measurements. The proposed framework achieves a normalized mean square error (NMSE) of 0.0818 in the DD-domain, significantly lower than the 0.2196 NMSE of direct TD prediction. Compared to conventional predictors, the proposed framework consistently reduces NMSE by approximately 70% across multiple datasets, thereby enhancing the robustness and feasibility of long-horizon channel forecasting for AMC systems.