首页|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

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

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  • 国家科技期刊平台
  • NETL
  • NSTL
  • 万方数据
针对当前广义频分复用(Generalized Frequency Division Multiplexing,GFDM)系统时变信道估计精度低的问题,提出基于稀疏贝叶斯学习的GFDM系统联合信道估计与符号检测算法.具体地,采用无干扰导频插入的GFDM多重响应信号模型,在稀疏贝叶斯学习框架下,结合期望最大化算法(Expectation-Maximization,EM)和卡尔曼滤波与平滑算法实现块时变信道的最大似然估计;基于信道状态信息的估计值进行GFDM符号检测,并通过信道估计与符号检测的迭代处理逐步提高信道估计与符号检测的精度.仿真结果表明,所提算法能够获得接近完美信道状态信息条件下的误码率性能,且具有收敛速度快、对多普勒频移鲁棒性高等优点.
Iterative Channel Estimation and Symbol Detection for GFDM Systems Based on Sparse Bayesian Learning
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.

generalized frequency division multiplexing(GFDM)time-varying channelsparse Bayesian learningexpectation-maximization(EM)Kalman filtering and smoothing

王莹、于永海、郑毅、林彬

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大连海事大学信息科学技术学院,辽宁大连 116026

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

国家重点研发计划国家自然科学基金国家自然科学基金

2019YFE01116006197108351939001

2024

10.12263/DZXB.20220480

电子学报
中国电子学会

电子学报

CSTPCD北大核心
影响因子：1.237
ISSN：0372-2112
年,卷(期)：2024.52(5)