基于近似消息传递的NOMA系统信道和脉冲噪声联合估计方法  

Joint channel and impulsive noise estimation method based on approximate message passing for NOMA systems

作　　者：李有明[1] 马冲亚 吴永宏 国强[3] Youming;MA Chongya;WU Yonghong;GUO Qiang(Faculty of Electrical Engineering and Computer Science,Ningbo University,Ningbo 315211,China;China Research Institute of Radio Wave Propagation,Qingdao 266075,China;College of Information and Communication Engineering,Harbin Engineering University,Harbin 150001,China)

机构地区：[1]宁波大学信息科学与工程学院,浙江宁波315211 [2]中国电波传播研究所,山东青岛266075 [3]哈尔滨工程大学信息与通信工程学院,黑龙江哈尔滨150001

出　　处：《电信科学》2024年第9期44-53,共10页Telecommunications Science

基　　金：国家自然科学基金资助项目(No.61571250);浙江省自然科学基金资助项目(No.LY22F010018);科技部战略性国际科技创新合作项目重点专项基金资助项目(No.2018YFE0206500)。

摘　　要：针对非高斯脉冲噪声背景下的非正交多址接入(non-orthogonal multiple access,NOMA)系统的信道估计问题,利用信道和脉冲噪声的稀疏特性,提出一种基于近似消息传递的信道和脉冲噪声联合估计方法。首先构建全子载波的压缩感知方程,然后基于稀疏贝叶斯学习理论提出一种信道、脉冲噪声和数据符号的联合估计优化问题。为解决这一超参量非线性非凸问题,设计了一种基于高斯广义近似消息传递和稀疏贝叶斯学习理论的期望最大化实现算法。仿真结果表明,与基于期望最大化的稀疏贝叶斯学习方法相比,所提算法在信道和脉冲噪声估计的均方误差、误码率等方面性能虽略有下降,但算法复杂度降低了1个数量级。To address the channel estimation problem for non-orthogonal multiple access(NOMA)systems under non-Gaussian impulsive noise,a joint channel and impulsive noise estimation method based on approximate message passing was proposed,by exploiting the joint sparsity of the channel and impulsive noise.Firstly,based on sparse Bayesian learning theory,a compressed sensing equation was constructed by using all subcarriers,and then a joint estimation optimization problem for the channel,impulsive noise,and data symbols was proposed.To address this hyperparameter nonlinear non-convex problem,an expectation maximization(EM)implementation algorithm based on Gaussian generalized approximation message passing and sparse Bayesian learning(SBL)theory was designed.Simulation results show that compared to the SBL method based on EM,the proposed algorithm exhibited a slight degradation in terms of mean square error(MSE)for channel and impulsive noise estimation,bit error rate(BER).However,the complexity of the proposed algorithm was reduced by one order of magnitude.

关 键 词：非正交多址接入 信道估计 脉冲噪声估计 稀疏贝叶斯学习 近似消息传递 

分 类 号：TN929[电子电信—通信与信息系统]