Channel Estimation for FDD Multi-User Massive MIMO: A Variational Bayesian Inference-Based Approach
Channel Estimation for FDD Multi-User Massive MIMO: A Variational Bayesian Inference-Based Approach
复制标题
FDD 多用户大规模 MIMO 的信道估计：基于变分贝叶斯推理的方法
DOI：
10.1109/twc.2017.2751046
复制
发表时间：
2017-11-01
影响因子：
10.4
通讯作者：
Li, Shaoqian
中科院分区：
文献类型：
--
作者：
Cheng, Xiantao;Sun, Jingjing;Li, Shaoqian
研究了频分双工多用户海量多输入多输出系统下行链路的信道估计问题。假设一个基站与K个移动用户通信，则任务是估计K个信道矩阵，每个信道矩阵对应于一个用户。由于物理传播中的有限散射，每个信道矩阵在虚拟角域中是稀疏的。此外，不同的用户链接往往共享一些共同的散点。因此，不同的信道矩阵可以具有部分公共的稀疏模式。这些观察结果促使我们采用基于变分贝叶斯推理的信道估计方法。具体地，我们设计了一种高斯混合先验模型，该模型能够有效地捕捉每个通道矩阵中的个体稀疏性和不同通道矩阵共享的部分联合稀疏性。此外，我们还发展了一种变分期望最大化策略来估计与先验模型和信道矩阵相关的超参数。与已有的同类方法相比，该方法在保持较低计算复杂度的同时，在信道估计精度方面取得了更好的性能。
This paper addresses downlink channel estimation for frequency division duplex multi-user massive multiple-input multiple-output systems. Suppose that a base station communicates with K mobile users, then the task is to estimate K channel matrices, each corresponding to one user. Due to the limited scattering in physical propagation, each channel matrix is sparse in the virtual angular domain. Besides, different user links tend to share some common scatterers. As such, different channel matrices may have a partially common sparsity pattern. These observations motivate us to take a variational Bayesian inference based approach for channel estimation. Specifically, we design a Gaussian mixture prior model, which can efficiently capture the individual sparsity in each channel matrix and the partially joint sparsity shared by different channel matrices. Furthermore, we develop a variational expectation maximization strategy to estimate the hyperparameters associated with the prior model and the channel matrices. Compared with the existing counterparts, the proposed approach achieves much better performance in terms of the channel estimation accuracy, while maintaining a low computational complexity.