SIMO系统吉布斯盲迭代均衡算法

针对符号间干扰信道的多天线分集接收问题,提出一种单输入多输出(SIMO)系统盲迭代均衡算法.该算法利用吉布斯样本法处理思路,在SIMO条件下推导了信道冲击响应、发送符号等未知参数的条件后验分布,根据该条件概率逐个参数进行随机采样,通过不断迭代更新来逼近最大后验概率(MAP)估计的结果.该算法的一个显著特点是具有软输入软输出(SISO)结构,因此在编码系统中可以与信道译码结合,通过联合迭代进一步提升均衡的性能.计算机仿真结果表明,在严重符号间干扰信道条件下,SIMO系统肓迭代均衡算法的性能非常接近于已知信道时迭代均衡算法的性能,距离理想无符号间干扰信道分集合成的性能差距只有约1 dB.

Iterative Blind Equalization Based on Gibbs Sampler for SIMO Communication Systems

To solve the problem of symbol detection in intersymbol interference (ISI) channel of spatial diversity system, a Bayesian blind equalization algorithm is proposed for single-input multiple-ouput (SIMO) communication systems based on the Gibbs sampler method. The proposed algorithm draws random samples from the conditional posterior distributions of all unknown quantities, so that joint channel estimation and equalization is accomplished. A salient feature of the equalization algorithm is that it has a soft-input soft-output (SISO) structure. Hence, it is well suited for iterative processing in a coded communication system, which allows the blind equalization to improve its performance. Simulation results show that, the iterative blind equalization algorithm performs closely to the algorithm with channel response perfectly known to the receiver in severe ISI channels. Performance gap between our approach and AWGN channel with no ISI is only 1dB.