稀疏信道下基于稀疏贝叶斯学习的精简星座盲均衡算法

针对稀疏信道的盲均衡问题，在精简星座均衡算法框架下建立线性模型，利用稀疏信道下均衡器固有的稀疏特性，引入具有稀疏促进作用的先验分布对均衡器系数加以约束，使用稀疏贝叶斯学习方法迭代求解均衡器系数得到最大后验估计值。该文提出的均衡方法属于数据复用类均衡算法的范畴，能够适用于数据较短的应用场合。与随机梯度方法相比，算法性能受均衡器长度影响较小，收敛后误符号率性能更好，仿真实验验证了算法的有效性。

Reduced Constellation Equalization Algorithm for Sparse Multipath Channels Based on Sparse Bayesian Learning

This paper deals with blind equalization of sparse multipath channels. A linear model is built under the framework of Reduced Constellation Algorithm (RCA). And the inherent sparse nature of the equalizer is exploited by employing a sparse promoting prior distribution. Then, the sparse Bayesian learning iterative inference method is applied to the proposed model in order to obtain the optimal sparse equalizer. The new proposed algorithm, which belongs to data recycling equalization algorithm domain, can be applied to short packet data applications. Compared with traditional Stochastic Gradient Descent (SGD) method, the new proposed algorithm performs more steadily under different equalizer order and has superior steady-state Symbol-Error-Rate (SER) performance. The effectiveness of the proposed algorithm is verified by simulations.