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景连友, 薛哲凯, 何成兵, 史文涛, 殷洪玺. 正交时频空间调制移动水声通信方法[J]. 声学学报, 2024, 49(2): 308-317. DOI: 10.12395/0371-0025.2022190
引用本文: 景连友, 薛哲凯, 何成兵, 史文涛, 殷洪玺. 正交时频空间调制移动水声通信方法[J]. 声学学报, 2024, 49(2): 308-317. DOI: 10.12395/0371-0025.2022190
JING Lianyou, XUE Zhekai, HE Chengbing, SHI Wentao, YIN Hongxi. A mobile underwater acoustic communication method based on orthogonal time frequency space modulation[J]. ACTA ACUSTICA, 2024, 49(2): 308-317. DOI: 10.12395/0371-0025.2022190
Citation: JING Lianyou, XUE Zhekai, HE Chengbing, SHI Wentao, YIN Hongxi. A mobile underwater acoustic communication method based on orthogonal time frequency space modulation[J]. ACTA ACUSTICA, 2024, 49(2): 308-317. DOI: 10.12395/0371-0025.2022190

正交时频空间调制移动水声通信方法

A mobile underwater acoustic communication method based on orthogonal time frequency space modulation

  • 摘要: 为提高移动水声通信可靠性, 提出了一种基于正交时频空间(OTFS)调制的移动水声通信方法。针对水声信道长时延扩展的特点, 设计了一种低复杂度交叉域Turbo迭代均衡算法, 先在时域内对接收信号进行最小均方误差(MMSE)均衡, 将均衡后的外部信息传递到时延−多普勒域进行软译码; 再将译码后的软信息反馈回时域, 作为下一次时域均衡的先验信息, 实现均衡与译码的迭代检测。此外, 针对OTFS水声通信中OTFS符号维度过大导致MMSE均衡复杂度过高的问题, 采用了一种低复杂度MMSE算法, 先利用矩阵对角线元素得到一组初始估计值, 再将矩阵求逆问题转换为求解初始估计值与准确求解值之间的误差, 继而通过对误差向量的不断迭代估计来实现低复杂度计算。湖试数据处理结果表明, 所提方法可在最大移动速度4 kn的情况下实现OTFS信号的可靠检测。

     

    Abstract: To improve the reliability of mobile underwater acoustic (UWA) communication systems, a mobile underwater acoustic communication method based on orthogonal time frequency space (OTFS) modulation is proposed. Firstly, a low complexity cross domain Turbo iterative equalization algorithm is designed to deal with long delay spread of UWA channel, which performs minimum mean square error (MMSE) equalization on the received signal in the time domain, and transfers the external information to the delay Doppler domain for decode. Next, the decoded soft information of the delay Doppler domain is fed back to the time domain as a priori information for the next time domain equalization. From this, the iterative detection of equalization and decoding is implemented. In addition, in order to solve the problem of excessive complexity, a low complexity computation algorithm is adopted. The algorithm first uses diagonal elements of matrix to obtain initial estimation, then converts the matrix inversion problem into solving the error between the initial estimation and the accurate value, so that low complexity calculations can be achieved through iterative estimation of the error vector. The results of lake experiments show that the proposed method can achieve reliable performance at a maximum moving speed of 4 knots.

     

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