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

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.

In order to improve the accuracy of time-varying channel estimation in generalized frequency division multiplexing(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 estimation 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.