稀疏贝叶斯学习水声OFDM系统信道与脉冲噪声联合估计

doi:10.12305/j.issn.1001-506X.2025.10.31

摘要/Abstract

摘要：

水声信道中时常存在脉冲噪声（impulsive noise，IN），严重降低水声正交频分复用（orthogonal frequency division multiplexing，OFDM）系统的性能。针对这一问题，提出利用全部子载波的基于广义近似消息传递稀疏贝叶斯学习（generalized approximate message passing-based sparse Bayesian learning，GAMP-SBL）的信道与IN联合估计（joint channel and IN estimation，JCIE）算法及其改进算法。首先，基于信道与IN的联合稀疏性，将数据符号视作未知参数，利用全部子载波构建压缩感知（compressed sensing，CS）模型。然后，使用GAMP-SBL算法恢复联合稀疏向量，并将符号估计引入GAMP-SBL框架中，实现信道冲激响应、IN与数据符号的联合估计。此外，利用初始化数据符号过程中获取的信道与IN先验信息，剔除全部子载波CS模型字典矩阵中的冗余原子，进一步降低计算复杂度。仿真结果表明：所提算法与现有的基于GAMP-SBL的信道与IN估计算法相比，能够有效提高信道估计、IN估计和系统误码性能；与基于SBL的信道与IN估计算法相比，能够在低复杂度情况下保持相近的系统性能。

关键词: 水声通信, 脉冲噪声, 正交频分复用, 信道估计, 稀疏贝叶斯学习

Abstract:

Impulsive noise （IN） often occurs in underwater acoustic channels and severely degrades the performance of underwater acoustic orthogonal frequency division multiplexing （OFDM） systems. To address this issue, a joint channel and IN estimation （JCIE） algorithm based on generalized approximate message passing-based sparse Bayesian learning （GAMP-SBL） using all subcarriers and its improved version are proposed. Firstly, utilizing the joint sparsity of the channel and IN, and regarding the data symbols as unknown parameters, a compressed sensing （CS） model is conceived using all subcarriers. Subsequently, the GAMP-SBL algorithm is applied to recover the joint sparse vector, and the symbol estimation is incorporated into the GAMP-SBL framework to jointly estimate the channel impulse response, IN and data symbols. Moreover, exploiting the prior information of the channel and IN attained during the data symbol initialization, redundant atoms are pruned from the dictionary matrix of the all subcarrier-based CS model to further decrease the computational complexity. Simulation results demonstrate that the proposed algorithms can effectively enhance the performance in terms of the channel estimation, IN estimation, and system bit error rate in comparison with the existing GAMP-SBL-based channel and IN estimation algorithms. And compared with the corresponding SBL-based counterparts, the proposed methods can maintain comparable system performance with lower complexity.

Key words: underwater acoustic communications, impulsive noise （IN）, orthogonal frequency division multiplexing （OFDM）, channel estimation, sparse Bayesian learning （SBL）

中图分类号:

TN 929.3

谭钢, 鄢社锋, 毛琳琳, 杨基睿. 稀疏贝叶斯学习水声OFDM系统信道与脉冲噪声联合估计[J]. 系统工程与电子技术, 2025, 47(10): 3482-3491.

Gang TAN, Shefeng YAN, Linlin MAO, Jirui YANG. Joint channel and impulsive noise estimation for underwater acoustic OFDM systems based on sparse Bayesian learning[J]. Systems Engineering and Electronics, 2025, 47(10): 3482-3491.

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估计算法		复杂度	文献
SBL类（-S）	SCIE-S	O（UK²+PL²）	[22]
	JCIE-S	O（PN²）	[20]
	JCIEA-S	O（PN²+KN²）	[19]
GAMP-SBL类（-GS）	SCIE-GS	O（UK+PL）	[22]
	JCIE-GS	O（PN）	[23]
	JCIEA-GS	O（PN+KN）	本文
	JCIEA-GSP	O（PN+KM）	本文

参数	值
带宽B/kHz	4
中心频率f_c/kHz	12
采样频率f_s/kHz	96
子载波数K	512
子载波间隔Δf/Hz	7.8
OFDM符号周期T/ms	128
循环前缀T_cp/ms	25

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