# Underwater-Acoustic-OFDM Channel Estimation Based on Deep Learning and Data Augmentation
###### Abstract  
In UnderWater-Acoustic-Orthogonal-Frequency-Division-Multiplexing-(UWA-OFDM) communication, the traditional interpolated channel estimation method produces error codes, due to the small number of user pilots, uneven distribution, and complex channel characteristics. In this paper, we propose a novel UWA-channel-estimation method based on Deep Learning (DL). First, based on a small number of channel samples, we used the CWGAN-GP model to generate enhanced classified underwater-acoustic channel samples to have semantic similarity to the real samples and also to present the diversity of the samples. After obtaining the channel sample, the pilot estimation matrix was processed in a similar image way. Here, we extracted the channel features by constructing a convolutional network structure similar to U-Net, weakening the impact of feature information loss. A Channel-Attention-Denoising-(CAD) module was also designed, to further optimize the reconstructed channel information. The simulation results verified the superiority of the proposed algorithm, in terms of Mean Square Error (MSE) and Bit-Error Rate (BER) compared to the existing Least-Squares-(LS), Deep-Neural-Network-(DNN), and ChannelNet algorithms. underwater acoustic; OFDM; DL; channel estimation; channel attention Article Underwater-Acoustic-OFDM Channel Estimation Based on Deep Learning and Data Augmentation [PERSON] _et al._ (2020); [PERSON] _et al. receiving end, so as to realize the recovery of the data signal. Common channel-estimation and equalization methods include the Least-Squares-(LS) algorithm [9; 10], the Minimum-Mean-Square-Error-(MMSE) [11] algorithm, and the Compressed-Sensing-(CS) method [12]. Although the LS algorithm is simple to implement, its overall performance is poor. While the MMSE algorithm can approximate the optimal solution in a statistical sense, it requires a prior channel state as the premise, so its operability in a practical application is not strong. In addition, this kind of algorithm does not have the characteristics of adaptation. The CS algorithm can utilize the sparsity of the UWA channel to reconstruct the original signal at a sampling rate lower than the Nyquist sampling rate. Keeping this in consideration, the authors in [12] formulated an iterative-UWA-channel-estimation approach, assuming that the UWA channel undergoes Rayleigh fading. In recent years, the design of a UWA communication system based on machine learning has been an emerging research topic, and machine-learning methods have great potential to promote the further development of UWA communication technology. In [13], the authors established a robust five-layer-neural-network model designed for water-acoustic channel estimation and equilibrium. They applied this model to simulate the channel, using BELLHOP for offline training and online testing. The outcomes revealed that the Deep-Neural-Network-(DNN)-based network model exhibited significant advantages over traditional algorithms, particularly in scenarios with limited OFDM-communication pilot numbers. In [14], the authors also proposed a joint UWA-OFDM demodulation, channel-estimation, and balanced-neural-network structure, to realize the integration of the receiving device. The authors in [15] proposed a UWA receiving system based on a Deep Belief Network (DBN), which effectively alleviated the performance decline caused by the Doppler effect and multiple-path transmission in communication. The simulation and offshore test results verified the effectiveness of this method. Unlike traditional methods, two different framework models based on DNN were proposed in [16], to address the UWA-OFDM-channel-estimation problem. Extensive experiments were conducted on the proposed methods, to compare them to traditional methods, such as LS, MMSE, and the back propagation algorithm.