基于变分贝叶斯推断的半盲信道估计

现有MIMO中继通信系统中,基于张量分解的半盲信道估计不能有效地将信道先验信息引入估计过程中,为此提出一种基于变分贝叶斯推断的信道估计算法.该算法首先利用NP(Nested PARAFAC)张量模型,引入有效精度、噪声精度等隐性超参数,建立信道估计概率图模型;由于所求信道参数后验概率分布较为复杂,传统最大似然和最大后验等点估计方法难以实现,算法采用变分贝叶斯推断,推导出信道矩阵、有效精度及噪声精度的递推公式,使具有因子分解形式的q分布逼近所求信道参数的后验分布;并分析了模型证据的下界、模型的初始化及算法复杂度等.该算法能利用信道先验信息以提高信道估计性能,有效精度和噪声精度等参数可自动调节,且计算复杂度与数据的维度呈线性关系.仿真结果表明:在平稳瑞利衰落信道条件下,与基于交替最小二乘(Alternating Least Square,ALS)的半盲估计算法相比,算法的计算复杂度较低,收敛速度较快;与带监督序列的双线性最小二乘(Bilinear Alternating Least Square,BALS)非盲估计算法,基于ALS及非线性最小二乘(Nolinear Least Square,NLS)的半盲估计算法相比,算法具有较高的估计精度.

Semi-blind channel estimation based on variational bayesian inference

The prior information of channel can''t be induced in the process of channel estimation in MIMO relay communication system. To solve the problem, a novel semi-blind channel estimation based on variational inference is proposed. In this algorithm, some latent hyper-parameters such as factor precision, noise precision are introduced into the algorithm, and channel estimation probability model is built based on nested PARAFAC tensor decomposition. Since the posterior probability distribution of the channel parameters is complex, some point estimation methods, such as traditional maximum likelihood and maximum posteriori algorithm, are difficult to implement. The iteration formulas of factor matrix, factor precision and noise precision are deduced by the idea of variational inference principle, making the q distribution, which has the factor decomposition form, approach the unknown parameter posterior distribution. In addition, low bound of model evidence, model initiation and algorithm complexity are also analyzed. The algorithm can utilize the prior information of channel to improve channel estimation performance. The parameters can be tuned automatically, and complexity is linear with the dimension of observed data. Simulations show that the proposed algorithm has better estimation performance under flat Rayleigh channel condition, compared with No-blind algorithm, Alternating Least Square (ALS) based algorithm and No-linear Least Square (NLS) based algorithm, and has lower complexity and faster convergence speed, compared with alternating least square algorithm.