论文标题
通过期望最大化刺激（EM-EP）对大规模MIMO系统进行聚类的稀疏通道估计
Clustered Sparse Channel Estimation for Massive MIMO Systems by Expectation Maximization-Propagation (EM-EP)
论文作者
论文摘要
我们研究多用户大量多重输入多重输出（MIMO）系统中下行链路通道估计的问题。为此，我们考虑了一种贝叶斯压缩传感方法，其中采用了角域中的通道的簇稀疏结构来减少飞行员的头顶。为了捕获群集结构，我们在代表通道的稀疏向量上采用有条件独立的分布式分布的bernoulli-gaussian先验，以及其支持向量的马尔可夫先验。开发了期望传播（EP）算法，以近似稀疏矢量上的棘手的关节分布及其与指数族的分布的支持。然后将近似分布用于直接估计通道。 EP算法假定模型参数已知先验。由于这些参数未知，因此我们使用期望最大化（EM）算法估算这些参数。 EM和EP称为EM-EP算法的组合让人联想到变异EM方法。仿真结果表明，所提出的EM-EP算法的表现优于文献中最近提供的几种算法。
We study the problem of downlink channel estimation in multi-user massive multiple input multiple output (MIMO) systems. To this end, we consider a Bayesian compressive sensing approach in which the clustered sparse structure of the channel in the angular domain is employed to reduce the pilot overhead. To capture the clustered structure, we employ a conditionally independent identically distributed Bernoulli-Gaussian prior on the sparse vector representing the channel, and a Markov prior on its support vector. An expectation propagation (EP) algorithm is developed to approximate the intractable joint distribution on the sparse vector and its support with a distribution from an exponential family. The approximate distribution is then used for direct estimation of the channel. The EP algorithm assumes that the model parameters are known a priori. Since these parameters are unknown, we estimate these parameters using the expectation maximization (EM) algorithm. The combination of EM and EP referred to as EM-EP algorithm is reminiscent of the variational EM approach. Simulation results show that the proposed EM-EP algorithm outperforms several recently-proposed algorithms in the literature.
