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Your search keyword '"Li, Youming"' showing total 143 results
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143 results on '"Li, Youming"'

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

Author

LI Youming, MA Chongya, WU Yonghong, and GUO Qiang

Abstract

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. [ABSTRACT FROM AUTHOR]

Published
2024
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25. Impulsive Noise Suppression Based on ℓq-norm and Graph Laplacian Regularization for NOMA System

Author

Cai, Wanyuan, Li, Youming, Zhang, Xiaolong, Jiang, Tao, and Qi, Qinke

Abstract

This letter proposes a method for impulsive noise (IN) suppression in non-orthogonal multiple access (NOMA) downlink system by leveraging the graph signal processing (GSP) and $\ell _{q}$ -norm-based sparse signal recovery theories. Within GSP framework, we propose a graph-based representation to characterize the underlying structure of the sub-carriers. Accordingly, the graph Laplacian regularization (GLR) is defined to capture the similarities between sub-carriers. Then, the IN suppression problem is formulated as a constraint optimization problem, incorporating the $\ell _{q}$ -norm and GLR, which can efficiently characterize the sparsity of IN and the underlying structure of the sub-carriers. Finally, an augment Lagrange multiplier (ALM) framework is introduced to solve the optimization problem. Numerical experiment results illustrate the effectiveness of the proposed method in comparison with existing methods.

Published
2024
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