OFDM系统中基于贝叶斯学习的联合稀疏信道估计与数据检测

Joint Sparse Channel Estimation and Data Detection Based on Bayesian Learning in OFDM System

众所周知,一个宽带无线信道的冲击响应是近似稀疏的,从某种意义上,相对于时延扩展来讲,它仅有一小部分重要的组成成分。针对正交频分复用系统,基于稀疏贝叶斯学习方法,提出两种稀疏信道估计算法:稀疏贝叶斯算法和联合稀疏贝叶斯算法。在信道测量矩阵未知的情况下,所提算法仍能够有效地估计出信道抽头。蒙特卡洛仿真显示,与经典正交匹配追踪算法和变分消息传递算法相比,所提算法在均方误差和误码率相同的情况下,信噪比有3~5 dB的提升。

It is well known that the impulse response of a wide band wireless channel is approximately sparse,in the sense that it has a small number of significant components relative to the channel delay spread.In this paper,two sparse channel estimation algorithms based on spare bayesian learning(SBL)method are proposed for orthogonal frequency division multiplexing(OFDM)system,which we call SBL algorithm and J-SBL algorithm.In the case of unknown channel measurement matrix,the proposed algorithms can still estimate channel taps effectively.Compared with the classical algorithms:orthogonal matching pursuing(OMP)algorithm and variational messaging(VMP)algorithm,montecarlo simulation shows that the proposed algorithms perform better than classical algorithms in terms of the same mean square error and bit error rate and their SNR is improved by 3~5 dB.