In this paper, by exploiting the powerful ability of deep learning, we devote to designing a well-performing and pilot-saving neural network for the channel estimation in underwater acoustic (UWA) orthogonal frequency division multiplexing (OFDM) communications. By considering the channel estimation problem as a matrix completion problem, we interestingly find it mathematically equivalent to the image super-resolution problem arising in the field of image processing. Hence, we attempt to make use of the very deep super-resolution neural network (VDSR), one of the most typical neural networks to solve the image super-resolution problem, to handle our problem. However, there still exist significant differences between these two problems, we thus elegantly modify the basic framework of the VDSR to design our channel estimation neural network, referred to as the channel super-resolution neural network (CSRNet). Moreover, instead of training an individual network for each considered signal-to-noise ratio (SNR), we obtain an unified network that works well for all SNRs with the help of transfer learning, thus substantially increasing the practicality of the CSRNet. Simulation results validate the superiority of the CSRNet against the existing least square (LS) and deep neural network (DNN) based algorithms in terms of the mean square error (MSE) and the bit error rate (BER). Specifically, compared with the LS algorithm, the CSRNet can reduce the BER by 44.74% even using 50% fewer pilots.
Underwater Cooperative MIMO Communications using Hybrid Acoustic and Magnetic Induction Technique
未来的智能海洋应用需要长距离和可靠的通信，将水下传感器/机器人与远程地面基站连接起来。由于水声信道的恶劣性和动态性，实现这一目标具有挑战性。多输入多输出（Multiple-Input and Multiple-Output，MIMO）技术可以提高可靠性和传输范围，但由于波长较大，很难在单个水下设备上放置多个换能器。协作MIMO技术虽然可以解决这一问题，但由于声波的传播时延极大且动态变化，使得分布式的水下设备之间无法实现同步。为此，本文提出了一种基于声磁混合技术的水下协作MIMO通信机制。利用多波束融合技术可以很好地解决节点间的同步问题，使分布式声换能器能够协同形成窄波束，实现远距离水下通信。由于MI具有可忽略的信号传播延迟，同步时间和误差显著减少。为了定量地分析改进，严格推导出所提出的系统的同步误差、信噪比（SNR）、有效通信时间和吞吐量的封闭形式的表达式。所提出的混杂系统实现在一个软件定义的测试床下的波束成形和空时编码方案。通过数值分析和实际实验表明，与传统的水声系统相比，本文提出的混合协作MIMO机制具有更低的误比特率和同步误差。
