丁寒雪1,,,
郭庆华2,
颜琪1,
王辛杰1
1.青岛理工大学信息与控制工程学院 青岛 266520
2.伍伦贡大学电气计算机与电信工程学院 伍伦贡 2522
基金项目:国家自然科学基金(61771271)，山东省自然科学基金面上项目(ZR2020MF010, ZR2020MF001)，青岛市源头创新计划-青年专项(19-6-2-4-cg)，山东省高等学校科学技术(J18KA315)
详细信息
作者简介:杨光：男，1981年生，博士，讲师，研究方向为水声通信
丁寒雪：女，1998年生，硕士生，研究方向为水声通信
郭庆华：男，1978年生，博士，副教授，IEEE高级会员，研究方向为水声通信和无线通信
颜琪：女，1996年生，硕士生，研究方向为水声通信
王辛杰：男，1980年生，博士，讲师，研究方向为水声通信
通讯作者:丁寒雪 edit231@163.com
中图分类号:TN929.3计量
文章访问数:500
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被引次数:0
出版历程
收稿日期:2020-04-28
修回日期:2020-12-09
网络出版日期:2021-01-04
刊出日期:2021-03-22
Estimation and Equalization of Time-varying Underwater Acoustic Channel Based on Superimposed Training and Low-complexity Turbo Equalization in Frequency Domain
Guang YANG1,Hanxue DING1,,,
Qinghua GUO2,
Qi YAN1,
Xinjie WANG1
1. School of Information and Control Engineering, Qingdao University of Technology, Qingdao 266520, China
2. School of Electrical, Computer and Telecommunications Engineering, University of Wollongong, Wollongong 2522, Australia
Funds:The National Natural Science Foundation of China (61771271), The General Project of Natural Science Foundation of Shandong Province (ZR2020MF010, ZR2020MF001), Qingdao Source Innovation Program - Special Project for Young Scholars (19-6-2-4-cg), Shandong Province Higher Educational Science and Technology Program (J18KA315)
摘要
摘要:针对时变水声信道估计和均衡问题，该文提出基于叠加训练序列(ST)和低复杂度频域Turbo均衡(LTE)的时变水声信道估计和均衡(ST-LTE)算法。基于叠加训练序列方案，将训练序列和符号线性叠加，使得训练序列和符号信道信息一致；基于最小二乘算法，进行信道估计。基于频域训练序列干扰消除技术，在频域消除训练序列对符号的干扰；基于频域线性最小均方误差(LMMSE)均衡算法，通过先验、后验、外均值和方差的计算，实现低复杂度信道均衡(符号估计)；基于Turbo均衡算法，软重构叠加训练序列和更新信道估计，进行均衡器和译码器的信息交换，利用编码冗余信息，大幅度提升信道均衡性能。进行仿真、水池静态通信试验(通信频率12 kHz，带宽6 kHz，采样频率96 kHz，符号传输速率4.8 ksym/s，训练序列和符号的功率比为0.25:1)和胶州湾运动通信试验(通信频率12 kHz，带宽6 kHz，采样频率96 kHz，符号传输速率3 ksym/s，训练序列和符号的功率比为0.25:1)，仿真和试验结果验证了所提算法的有效性。
关键词:运动水声通信/
时变水声信道/
叠加训练序列/
低复杂度频域均衡/
Turbo均衡
Abstract:To solve the problems of time-varying underwater acoustic channel estimation and equalization, an estimation and equalization algorithm of time-varying underwater acoustic channel based on Superimposed Training (ST) and Low-complexity Turbo Equalization (LTE) in frequency domain (ST-LTE) is proposed. Based on the ST scheme, the training sequence and symbols are linearly superimposed to make the channel information of the training sequence and symbols consistent; Based on the least square algorithm, channel estimation is performed. Based on the interference elimination technique of training sequence in frequency domain, the interference of training sequence on symbols is eliminated in frequency domain; Based on the Linear Minimum Mean Square Error (LMMSE) equalization algorithm in frequency domain, the low-complexity channel equalization (symbol estimation) is realized by the calculation of prior, posterior, extrinsic mean and variance; Based on the Turbo equalization algorithm, soft reconstruction of superimposed training and update of channel estimation are conducted, the information exchange between equalizer and decoder is also carried out and the performance of channel equalization is extremely improved by using coding redundancy information. Simulation, static communication experiment in a pool (communication frequency is 12 kHz, bandwidth 6 kHz, the sampling frequency 96 kHz, the transmission rate of symbols 4.8 ksym/s and the power ratio of the training sequence on symbols 0.25:1) and moving communication experiment in Jiaozhou Bay (communication frequency is 12 kHz, bandwidth 6 kHz, the sampling frequency 96 kHz, the transmission rate of symbols 3 ksym/s and the power ratio of the training sequence on symbols 0.25:1) are carried out and simulation and experimental results verify the effectiveness of the proposed algorithm.
Key words:Moving underwater acoustic communication/
Time-varying underwater acoustic channel/
Superimposed Training (ST)/
Low-complexity equalization in frequency domain/
Turbo equalization
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