基于排序码本的水声自适应OFDM通信中信道状态信息反馈研究

刘凇佐; 韩雪; 马璐; 徐金颉; 杨洋

doi:10.11999/JEIT230878

基于排序码本的水声自适应OFDM通信中信道状态信息反馈研究

doi: 10.11999/JEIT230878

刘凇佐^{1, 2, 3, 4},
韩雪^{1, 2, 3},
马璐^{1, 2, 3, 4, ,},
徐金颉^{1, 2, 3},
杨洋^{1, 2, 3}

1.
哈尔滨工程大学水声技术全国重点实验室哈尔滨 150001
2.
工业和信息化部海洋信息获取与安全工信部重点实验室(哈尔滨工程大学) 哈尔滨 150001
3.
哈尔滨工程大学水声工程学院哈尔滨 150001
4.
哈尔滨工程大学三亚南海创新发展基地三亚 572024

基金项目: 国家自然科学基金(6227116)，国家重点研发计划(2021YFC***1101)，山东省重点研发计划(2022CXGC020409)，水声技术全国重点实验室基金(2023-JCJQ-LB-072-08)

详细信息

作者简介:
刘凇佐：男，教授，研究方向为水声通信技术、水声侦察技术、嵌入式开发等

韩雪：女，硕士生，研究方向为水声高速通信等

马璐：女，教授，研究方向为水声高速通信、水声多用户通信、水声通信网络、嵌入式开发等

徐金颉：男，研究方向为水声高速通信等

杨洋：女，博士生，研究方向为水声高速通信等

通讯作者:
马璐　malu@hrbeu.edu.cn

中图分类号: TN929.3
计量
- 文章访问数: 22
- HTML全文浏览量: 9
- PDF下载量: 5
- 被引次数: 0
出版历程
- 收稿日期: 2023-08-10
- 修回日期: 2024-03-14
- 网络出版日期: 2024-04-16

Research on Channel State Information Feedback in Underwater Acoustic Adaptive OFDM Communication Based on Sequenced Codebook

LIU Songzuo^{1, 2, 3, 4},
HAN Xue^{1, 2, 3},
MA Lu^{1, 2, 3, 4
, ,},
XU Jinjie^{1, 2, 3},
YANG Yang^{1, 2, 3}

1.
National Key Laboratory of Underwater Acoustic Technology, Harbin Engineering University, Harbin 150001, China
2.
Key Laboratory of Marine Information Acquisition and Security(Harbin Engineering University), Ministry of Industry and Information Technology, Harbin 150001, China
3.
College of Underwater Acoustic Engineering, Harbin Engineering University, Harbin 150001, China
4.
Sanya Nanhai Innovation and Development Base of Harbin Engineering University, Sanya 572024, China

Funds: The National Natural Science Foundation of China (62271161), The National Key R&D Plan (2021YFC***1101), The Key Research and Development Program of ShanDong Province (2022CXGC020409), The National Key Laboratory Foundation of Underwater Acoustic Technology (2023-JCJQ-LB-072-08)

摘要

摘要: 水声(UWA)信道时延扩展大等特点导致信道频响(CFR)快衰落，水声通信(UWAC)技术发展受到挑战。发射端获取有效可靠的信道状态信息(CSI)是自适应通信的前提，针对水声自适应正交频分复用(OFDM)通信的需求，该文提出基于排序码本的信道状态信息分组排序拟合反馈算法(CSI-GSFF)，包括分组、排序、数据拟合3个步骤。该算法首先将相邻导频子载波分组，以组为反馈单元；然后对各组内的导频子载波按照信道增益值进行排序，以减轻水声信道频响快衰落造成的反馈开销大等不利影响；最后进行多项式拟合，排序操作有效地降低了拟合阶数。通过实测海试时变信道数据仿真，结果表明，该文提出的信道状态信息反馈算法能够基本达到完美信道状态信息情形下的水声自适应OFDM通信系统误码率性能，同时可以有效地减少反馈开销。
- 水声通信 /
- 正交频分复用 /
- 自适应调制 /
- 信道状态信息 /
- 有限反馈
Abstract: As a result of the characteristics of UnderWater Acoustic (UWA) channel, such as rapid fading of Channel Frequency Response (CFR) due to large delay spreading, the development of UnderWater Acoustic Communication (UWAC) technology is challenged. The acquisition of effective and reliable Channel State Information (CSI) at the transmitter is a prerequisite for adaptive communication. To meet the needs of UWA adaptive OFDM communication, a CSI-Grouping-Sequencing-Fitting-Feedback (CSI-GSFF) based on sequenced codebook algorithm is proposed, which consists of three steps, including grouping, sequencing, and data fitting. Firstly, adjacent pilot subcarriers are divided into several groups and each group is seen as a feedback cell. Then, the pilot subcarriers within each group are sorted according to the channel gains to mitigate adverse effects such as high feedback overhead caused by the rapid fading of CFR. Finally, polynomial fitting is performed, and the sorting operation effectively reduces the fitting order. Through the simulation of time-varying channel data in sea trials, the results show that the CSI-GSFF algorithm can achieve the Bit Error Rate (BER) performance of the UWA adaptive OFDM communication system under the perfect CSI, while the CSI-GSFF algorithm can effectively reduce the feedback overhead.
- UnderWater Acoustic Communication (UWAC) /
- Orthogonal Frequency Division Multiplexing (OFDM) /
- Adaptive modulation /
- Channel State Information (CSI) /
- Limited feedback

HTML全文

图 1 基于有限反馈的水声自适应OFDM通信系统原理框图

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图 2 排序前后导频位置处信道增益曲线对比图

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图 3 分组排序拟合反馈算法示意图(以32组为例)

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图 4 试验设置图

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图 5 实测时变信道冲击响应图

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图 6 不同反馈算法间信道增益拟合曲线对比图

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图 7 Hughes-Hartogs算法下不同反馈算法自适应通信系统SNR-BER图

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表 1 仿真参数设置表

参数名称	参数值
调制方式	BPSK/QPSK/8PSK/16QAM
采样频率(kHz)	48
中心频率(kHz)	9
信号带宽(kHz)	6
总子载波数目	1024
数据子载波数目	704
导频子载波数目	256
OFDM符号周期(ms)	171
循环前缀(ms)	50

下载: 导出CSV

表 2 不同反馈算法间归一化均方误差对比

反馈算法	归一化均方误差(×10^–5)
多项式拟合(20阶)	21.4
多项式拟合(10阶)	23.9
压缩感知(压缩比为0.1)	8.2
压缩感知(压缩比为0.05)	26.8
分组排序拟合(32组2阶)	1.1
分组排序拟合(32组1阶)	2.5

下载: 导出CSV

表 3 反馈量对比

反馈算法	反馈量(Bytes)
多项式拟合(20阶)	42
多项式拟合(10阶)	22
压缩感知(压缩比为0.1)	408
压缩感知(压缩比为0.05)	204
分组排序拟合(32组2阶)	256
分组排序拟合(32组1阶)	19

下载: 导出CSV

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