基于快速信息收集和虚拟训练序列的低复杂度双向水声信道精准估计

陈建军, 曲亚东, 梁俊燕, 孙冬雪, 李森

doi:10.16411/j.cnki.issn1006-7736.2024.03.002

大连海事大学学报 >

2024 , Vol. 50 >Issue 3: 13 - 22

DOI: https://doi.org/10.16411/j.cnki.issn1006-7736.2024.03.002

基于快速信息收集和虚拟训练序列的低复杂度双向水声信道精准估计

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(1.青岛理工大学信息与控制工程学院，山东青岛 266525；2.大连海事大学信息科学技术学院，辽宁大连116026)

陈建军（1992 — ），男，博士，讲师，E-mail：chenjianjunplus@163.com，数据处理。曲亚东（1999 — ），男，硕士生，研究方向：水声通信。梁俊燕（1999 — ），女，硕士生，研究方向：水声通信。孙冬雪（1997 — ），女，研究生，研究方向：水声通信。李森（1973 — ），女，博士，教授，博士生导师，研究方向：通信信号处理。

网络出版日期: 2024-03-28

基金资助

山东省自然科学基金资助项目（ZR2021QF113）；国防科技创新特区项目

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Accurate estimation of low-complexity bidirectional underwater acoustic channels based on fast information collection and virtual training sequence

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(1.Information and Control Engineering College, Qingdao University of Technology, Qingdao 266525, China;2.Information Science and Technology College, Dalian Maritime University, Dalian 116026, China)

Online published: 2024-03-28

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摘要

针对运动水声通信难以低复杂度精准获取时变信道状态信息的问题，提出基于快速信息收集和虚拟训练序列（FIC-VT）的低复杂度双向水声信道精准估计算法。采用叠加训练（ST）方案，将符号序列和训练序列线性叠加，使得训练序列持续传输，提高时变信道跟踪能力。基于置信传播，提出FIC-VT算法，将一块数据分成多个短块，每个短块又分成多个子段，通过快速信息收集算法，将多个子段的信道信息进行融合，从而获得每个短块的低复杂度局部信道估计。利用短块间的信道相关性，对多个短块的信道信息进行双向信息融合，从而获得当前短块的低复杂度全局信道估计。基于Turbo均衡，将估计的符号序列虚拟成训练序列（virtual training, VT），通过迭代计算实现时变水声信道低复杂度精准估计。本文算法由快速傅里叶变换（FFT）实现，每个抽头计算复杂度仅为对数级。计算机仿真、水池运动通信试验和胶州湾运动通信试验验证了本文算法的有效性。

关键词： 时变水声信道; 低复杂度双向信道估计; 快速信息收集; 虚拟训练序列; 叠加训练; 快速傅里叶变换（FFT）

本文引用格式

陈建军, 曲亚东, 梁俊燕, 孙冬雪, 李森 . 基于快速信息收集和虚拟训练序列的低复杂度双向水声信道精准估计[J]. 大连海事大学学报, 2024 , 50(3) : 13 -22 . DOI: 10.16411/j.cnki.issn1006-7736.2024.03.002

Abstract

Aiming at the difficulty of accurately obtaining time-varying channel state information in moving underwater acoustic communication with low complexity, a low-complexity bidirectional underwater acoustic channel estimation algorithm based on fast information collection and virtual training (FIC-VT) was proposed. A superimposed training (ST) scheme was adopted to linearly superimpose a symbol sequence to ensure continuous transmission of the training sequence and improve the time-varying channel tracking capability. Based on belief propagation, the FIC-VT algorithm was proposed,which divided a block of data into multiple short blocks, each of which was divided into multiple sub-segments, and by a fast information collection algorithm, the channel information of multiple sub-segments was fused to obtain low-complexity local channel estimates for each short block. Turbo equalization was employed to virtualize the estimated symbol sequence into a virtual training sequence (VT), and through iterative calculation, the proposed algorithm eventually achieves low-complexity and accurate estimation of time-varying underwater acoustic channels. The proposed algorithm is implemented by using fast Fourier transform(FFT), with computational complexity per tap at a logarithmic level. The effectiveness of the proposed algorithm is verified through computer simulation, pool motion communication experiments, and Jiaozhou Bay motion communication experiments.

Key words：

time-varying underwater acoustic channel

; low complexity bidirectional channel estimation; fast information collection; virtual training sequence;superimposed training;fast Fourier transform（FFT）

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