电子学报2024，Vol.52Issue(5)：1496-1505,10.DOI:10.12263/DZXB.20220480
基于稀疏贝叶斯学习的GFDM系统联合迭代信道估计与符号检测
Iterative Channel Estimation and Symbol Detection for GFDM Systems Based on Sparse Bayesian Learning
摘要
Abstract
In order to improve the accuracy of time-varying channel estimation in generalized frequency division mul-tiplexing(GFDM)systems,a joint iterative channel estimation and symbol detection algorithm for GFDM systems using sparse Bayesian learning is proposed.Specifically,we use a GFDM multi-response signal model with non-interfering pilot insertion.Under the sparse Bayesian learning framework,we combine the expectation-maximization(EM)algorithm and the Kalman filter and smoothing algorithm to realize the maximum likelihood estimation of the block time-varying channel.Consequently,GFDM symbols are detected based on the estimated channel state information(CSI),and the accuracy of the channel estimation and symbol detection is progressively improved through the iterative processing of the channel estima-tion and symbol detection.Simulation results demonstrate that the proposed algorithm can achieve better bit error rate(BER)performance close to that under perfect CSI conditions,and it has the advantages of fast convergence speed and high robustness to Doppler frequency shift.关键词
广义频分复用/时变信道估计/稀疏贝叶斯学习/期望最大化/卡尔曼滤波与平滑Key words
generalized frequency division multiplexing(GFDM)/time-varying channel/sparse Bayesian learning/expectation-maximization(EM)/Kalman filtering and smoothing分类
信息技术与安全科学引用本文复制引用
王莹,于永海,郑毅,林彬..基于稀疏贝叶斯学习的GFDM系统联合迭代信道估计与符号检测[J].电子学报,2024,52(5):1496-1505,10.基金项目
国家重点研发计划(No.2019YFE0111600) （No.2019YFE0111600）
国家自然科学基金(No.61971083,No.51939001) National Key Research and Development Program of China(No.2019YFE0111600) （No.61971083,No.51939001）
National Natural Science Foundation of China(No.61971083,No.51939001) （No.61971083,No.51939001）
