脉冲干扰下基于变分贝叶斯推断的水声正交频分复用联合估计方法  

Variational Bayesian inference-based joint estimation method for underwater acoustic OFDM under impulsive interference

作　　者：葛威 焦桦坤 佟文涛 生雪莉 韩笑[1,2,3] GE Wei;JIAO Huakun;TONG Wentao;SHENG Xueli;†HAN Xiao(National Key Laboratory of Underwater Acoustic Technology,Harbin Engineering University,Harbin 150001;Key Laboratory for Polar Acoustics and Application of Ministry of Education(Harbin Engineering University),Ministry of Education,Harbin 150001;College of Underwater Acoustic Engineering,Harbin Engineering University,Harbin 150001;State Key Laboratory of Acoustics,Institute of Acoustics,Chinese Academy of Sciences,Beijing 100190)

机构地区：[1]哈尔滨工程大学水声技术全国重点实验室,哈尔滨150001 [2]教育部极地海洋声学与技术应用教育部重点实验室(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]中国科学院声学研究所声场声信息国家重点实验室,北京100190

出　　处：《声学学报》2024年第5期1051-1060,共10页Acta Acustica

基　　金：国家自然科学基金项目(U20A20329,62301181,62127801);黑龙江省优秀青年基金项目(YQ2022F001)资助。

摘　　要：脉冲干扰环境下水声正交频分复用通信性能严重下降,为此提出了基于变分贝叶斯推断的信道估计方法。该方法利用水声信道和脉冲干扰的稀疏特性,基于平均场变分贝叶斯推断,将信道向量和脉冲干扰向量的后验概率分布分别分解为简单概率分布进行拟合,基于导频子载波迭代直至收敛,得到信道和脉冲干扰的最大后验估计。所提方法改进了基于稀疏贝叶斯学习的干扰、信道联合估计方法中信道和干扰构成的联合向量无法分离二者稀疏度的问题,并且显著降低了计算复杂度。在此基础上,进一步提出了基于变分贝叶斯推断的干扰、信道和符号联合估计方法,将未知符号融入变分贝叶斯推断框架,与干扰和信道一起迭代,最终得到更精确的符号估计。仿真和试验结果验证了所提算法的有效性,与现有方法相比,本文所提方法具有更低的误码率和复杂度。To address the severe performance degradation of underwater acoustic orthogonal frequency division multiplexing(OFDM)communication in the presence of impulsive interference,a channel estimation method based on variational Bayesian inference is proposed.This method exploits the sparse characteristics of the underwater acoustic channel and impulsive interference.By utilizing mean-field variational Bayesian inference,this approach decomposes the posterior probability distributions of the channel vector and impulsive interference vector into simple probability distributions for fitting respectively.Iterative estimation is performed based on pilot subcarriers until convergence is achieved,resulting in the maximum a posteriori estimation of the channel and impulsive interference.The proposed method alleviates the problem that one cannot separate the sparsity of channel vector and interference vector in the joint estimation method.Meanwhile,it significantly reduces the computational complexity.Based on this,a joint estimation method of interference,channel,and symbols based on variational Bayesian inference is further proposed,where the unknown symbols are integrated into the variational Bayesian inference framework for iterative estimation with interference and channel,leading to more accurate symbol estimates.Simulation and experimental results demonstrate the effectiveness of the proposed algorithms.Compared to the existing methods,the proposed approach achieves lower error rates and complexity.

关 键 词：正交频分复用 脉冲干扰 变分贝叶斯推断 稀疏贝叶斯学习 联合估计 

分 类 号：TB56[交通运输工程—水声工程] TN929.3[理学—物理]