Deep Learning Based Underwater Acoustic Channel Estimation Technology
李军 1张志晨 1王荣 1何波 2郑文静 1李明明3
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作者信息
1. 齐鲁工业大学(山东省科学院) 信息与自动化学院,济南 250353
2. 山东大学 信息科学与工程学院,山东 青岛 266000
3. 工业和信息化部,北京 100037
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摘要
在水声通信中,传统的正交频分复用(Orthogonal Frequency Division Multiplexing,OFDM)系统需要大量导频数量和循环前缀以维持系统性能,严重浪费有限的频域资源.因此,利用深度学习辅助OFDM恢复失真的传输数据.具体地讲,利用改进的残差块提取和双向记忆接收信号.将自归一化网络(Self-Normalizing Network,SNN)与注意力机制结合,有效分配信道权重,以便系统更有效地利用信道资源,最小化信号失真.使用深度神经网络(Deep Neural Network,DNN)实现接收信号的分类,以准确地恢复接收信号.提出的 ICANet(Im-proved Residual Network,Convolutional Neural Network and Attention Mechanism Self-Normalizing Network)模型可应用于由Bellhop软件生成的水声环境.仿真结果证明,与传统技术中的最小二乘法(Least Square,LS)以及现有的深度学习模型相比,所提出的模型在循环前缀受限的情况下可达到更低的误码率.
Abstract
In underwater acoustic communication,traditional orthogonal frequency division multi-plexing(OFDM)systems require a large number of pilot symbols and cyclic prefixes to maintain system performance,resulting in significant wastage of limited frequency domain resources.Therefore,deep learning is utilized to assist in restoring distorted transmitted data in OFDM.Spe-cifically,an improved residual block is used for feature extraction and bidirectional memory for receiving signals.Self-normalizing network(SNN)is combined with attention mechanism to ef-fectively allocate channel weights,so that the system can more effectively utilize channel re-sources and minimize signal distortion.Deep neural network(DNN)is employed for the classifi-cation of received signals,ensuring accurate signal recovery.The proposed improved residual net-work,convolutional neural network and attention mechanism self-normalizing network(ICANet)model can be applied to underwater acoustic environments generated by Bellhop software.Simu-lation results demonstrate that compared to traditional techniques like least square(LS)and ex-isting deep learning models,the proposed model achieves lower bit error rate,especially under limited cyclic prefixes.
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