{"ID":3049943,"CreatedAt":"2026-06-04T02:13:16.786527022Z","UpdatedAt":"2026-06-06T15:44:26.945507316Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2606.05053","arxiv_id":"2606.05053","title":"Deep Learning Based Multi-Step Channel Prediction for Adaptive Underwater Acoustic OFDM Systems","abstract":"We develop an adaptive OFDM framework for underwater acoustic communications based on PatchCSI-T, a Transformer-based multistep channel prediction model with feature-independent modeling and parameter sharing. Combined with a greedy adaptive modulation and power allocation scheme, the proposed approach enables accurate, low-latency CSI forecasting and improves end-to-end BER and spectral efficiency on real-world UWA channel datasets.","short_abstract":"We develop an adaptive OFDM framework for underwater acoustic communications based on PatchCSI-T, a Transformer-based multistep channel prediction model with feature-independent modeling and parameter sharing. Combined with a greedy adaptive modulation and power allocation scheme, the proposed approach enables accurate...","url_abs":"https://arxiv.org/abs/2606.05053","url_pdf":"https://arxiv.org/pdf/2606.05053v1","authors":"[\"Tian Tian\",\"Ying Zhang\",\"Agastya Raj\",\"Fei-Yun Wu\",\"Marco Ruffini\"]","published":"2026-06-03T16:11:48Z","proceeding":"eess.SP","tasks":"[\"eess.SP\"]","methods":"[\"Transformer\"]","has_code":false}