arXiv cs.AI· Mingjie Cao, Gewei Zhang, Deqing Wang, Lizhao You, Fei-Yun Wu, Liqun Fu·· 1 天前AI 评分18
LLM4UACP：双域结构保持与预训练 Transformer 实现多步水声信道预测
Multi-Step Underwater Acoustic Channel Prediction via Dual-Domain Structure Preservation and Pretrained Transformer
AI 导读
LLM4UACP 通过双域结构保持框架改造预训练 Transformer，实现少样本多步水声信道预测。该方法融合 CIR 与 CFR 特征并以延迟-时间 token 编码，经 Butterfly Orthogonal Fine-tuning 单次前向预测多个未来 CIR，避免递归误差累积。
正文 · 原文
Abstract:Accurate future channel state information (CSI) is essential for underwater acoustic (UWA) communication because delays associated with signal propagation, feedback, scheduling, and transceiver reconfiguration can render currently available CSI outdated. However, most existing predictors estimate only the next channel state and obtain multi-step forecasts through recursive rollout, which causes prediction errors to accumulate as the multipath structure evolves. The scarcity of sea-trial measurements further limits reliable model training. To address these challenges, we propose LLM4UACP, a dual-domain, structure-preserving framework that adapts a pretrained Transformer for few-shot multi-step UWA channel prediction. LLM4UACP extracts complementary features from the channel impulse response (CIR) and channel frequency response (CFR), aligns them through Fourier-consistent fusion, and encodes the fused representation as delay-time tokens. A pretrained Transformer with a frozen backbone is adapted through Butterfly Orthogonal Fine-tuning to jointly predict multiple future CIRs in a single forward pass, thereby avoiding the error accumulation associated with recursive prediction. Experiments on the measured SO-06 and SO-07 channels show that LLM4UACP achieves normalized mean squared error (NMSE) values of 0.0411 and 0.0399, corresponding to reductions of 88.53% and 85.15%, respectively, relative to the most recent baseline. With only 0.11M trainable parameters, LLM4UACP maintains robust performance across varying prediction horizons and observation-window lengths, limited-data settings, and cross-dataset transfer scenarios.
| Subjects: | Sound (cs.SD); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Signal Processing (eess.SP) |
| Cite as: | arXiv:2611.14592 [cs.SD] |
| (or arXiv:2611.14592v1 [cs.SD] for this version) | |
| https://doi.org/10.48550/arXiv.2611.14592 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Deqing Wang [view email]
[v1]
Tue, 10 Nov 2026 08:38:49 UTC (3,960 KB)
来源：arXiv cs.AI · arxiv.org