基于模型驱动的深度展开均衡器用于水声OFDM通信
Model-Driven Based Deep Unfolding Equalizer for Underwater Acoustic OFDM Communications
AI总结：
本文提出基于深度展开的水声OFDM均衡器UDNet，结合MMSE结构、KL散度四分类和带状滑动结构，在实测水声信道下以低复杂度实现比MMSE低近一个数量级的符号错误率。
摘要：
为复杂的时频双选择性信道设计均衡器具有挑战性。本文采用深度展开方法为水声（UWA）正交频分复用（OFDM）系统建立均衡器，称为UDNet。UDNet的每一层均根据经典的最小均方误差（MMSE）均衡器设计。此外，我们将QPSK均衡视为四分类任务，并采用最小Kullback-Leibler（KL）散度，以独热编码代替MMSE准则，从而获得更低的符号错误率（SER）。另外，我们引入基于信道矩阵带状近似的滑动结构，以减小网络规模，并帮助UDNet在不改变网络结构的情况下对不同长度的信号表现良好。进一步地，我们应用实测的海上双选择性UWA信道和近海背景噪声来评估所提出的均衡器。实验结果表明，所提出的UDNet在低计算复杂度下性能更优。具体而言，UDNet的SER比MMSE低近一个数量级。
英文摘要：
It is challenging to design an equalizer for the complex time-frequency doubly-selective channel. In this paper, we employ the deep unfolding approach to establish an equalizer for the underwater acoustic (UWA) orthogonal frequency division multiplexing (OFDM) system, namely UDNet. Each layer of UDNet is designed according to the classical minimum mean square error (MMSE) equalizer. Moreover, we consider the QPSK equalization as a four-classification task and adopt minimum Kullback-Leibler (KL) to achieve a smaller symbol error rate (SER) with the one-hot coding instead of the MMSE criterion. In addition, we introduce a sliding structure based on the banded approximation of the channel matrix to reduce the network size and aid UDNet to perform well for different-length signals without changing the network structure. Furthermore, we apply the measured at-sea doubly-selective UWA channel and offshore background noise to evaluate the proposed equalizer. Experimental results show that the proposed UDNet performs better with low computational complexity. Concretely, the SER of UDNet is nearly an order of magnitude lower than that of MMSE.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
