In underwater acoustic (UWA) adaptive communication system, due to time-varying channel, the transmitter often has outdated channel state information (CSI), which results in low efficiency. UWA channels are much more difficult to estimate and predict than terrestrial wireless channels, given the more severe multipath environments with varying propagation speeds in different locations, non-linear propagation paths, several-order higher propagation latency, mobile transceiver and obstacles in the sea, etc. To handle the complexity, this paper proposes an efficient online CSI prediction model for UWA CSI prediction considering the complicated correlationship of UWA channels in both the time and frequency domains. This paper designs a learning model called CsiPreNet, which is an integration of a one-dimensional convolutional neural network (CNN) and a long short term memory (LSTM) network. The performance is compared with the widely used recursive least squares (RLS) predictor, the approximate linear dependency recursive kernel least-squares (ALD-KRLS), and two common conventional deep neural networks (DNN) predictors, i.e., back propagation neural network (BPNN) and LSTM network using the measured data recorded in the South China Sea. To validate the efficacy of prediction, we investigate the prediction of CSI in simulated downlink UWA orthogonal frequency division multiple access (OFDMA) systems. Specifically, the measured UWA channel is used in the OFDMA system. A joint subcarrier-bit-power adaptive allocation scheme is used for resource allocation. To further improve the performance, we develop an offline-online prediction scheme, enabling the prediction results to be more stable. Simulation and experimental results show that the CsiPreNet has superior performance than the existing solutions, thanks to its capability in capturing both the temporal and frequency correlation of the UWA CSIs.

中文翻译：

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自适应水下声学下行 OFDMA 系统的信道状态信息预测：基于深度神经网络的方法


在水声（UWA）自适应通信系统中，由于信道时变，发射机往往具有过时的信道状态信息（CSI），导致效率低下。考虑到更严峻的多径环境，不同位置的传播速度不同、非线性传播路径、高几个数量级的传播延迟、移动收发器和海上障碍物，UWA 信道比地面无线信道更难以估计和预测，为了处理复杂性，考虑到UWA信道在时域和频域上的复杂相关性，本文提出了一种用于UWA CSI预测的高效在线CSI预测模型。本文设计了一种名为 CsiPreNet 的学习模型，它是一维卷积神经网络（CNN）和长短期记忆（LSTM）网络的集成。其性能与广泛使用的递归最小二乘（RLS）预测器、近似线性依赖递归核最小二乘（ALD-KRLS）以及两种常见的传统深度神经网络（DNN）预测器（即反向传播神经网络）进行了比较。 BPNN）和LSTM网络使用南海记录的测量数据。为了验证预测的有效性，我们研究了模拟下行链路 UWA 正交频分多址 (OFDMA) 系统中的 CSI 预测。具体地，测量的UWA信道用于OFDMA系统中。联合子载波比特功率自适应分配方案用于资源分配。为了进一步提高性能，我们开发了离线在线预测方案，使预测结果更加稳定。 仿真和实验结果表明，由于 CsiPreNet 能够捕获 UWA CSI 的时间和频率相关性，因此其性能优于现有解决方案。