Work

Deep Learning Based Multi-Step Channel Prediction for Adaptive Underwater Acoustic OFDM Systems

Year: 2026

Type: conference-paper

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.

Source: MTS/IEEE OCEANS Conference (OCEANS)

DOI: 10.1109/oceans66983.2026.11616845

Authors: Tian Tian, Ying Zhang, Agastya Raj, Fei-Yun Wu, Marco Ruffini

Institutions: Trinity College, Hohai University, Jimei University

Language: English

Topic: Underwater Vehicles and Communication Systems

Subfield: Ocean Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: hybrid

Funders: National Natural Science Foundation of China, Natural Science Foundation of Fujian Province, National Key Research and Development Program of China

Awards: G5784101358, G595402589, 融合稀疏优化的水声信道时延-多普勒自适应均衡研究