Uplink-aided Downlink Channel Estimation in FDD Massive MIMO by Variational Bayesian Inference

被引：0

作者：

Lu, Wei ^{[1
]}

Wang, Yongliang ^{[1
]}

Hua, Xiaoqiang ^{[2
]}

Zhang, Wei ^{[1
]}

Peng, Shixin ^{[3
]}

Zhong, Liang ^{[4
]}

机构：

[1] Air Force Early Warning Acad, Wuhan, Peoples R China

[2] Natl Univ Def Technol, Changsha, Peoples R China

[3] Cent China Normal Univ, Wuhan, Peoples R China

[4] China Univ Geosci, Wuhan, Peoples R China

来源：

PROCEEDINGS OF THE 4TH INTERNATIONAL CONFERENCE ON COMMUNICATION AND INFORMATION PROCESSING (ICCIP 2018) | 2018年

基金：

美国国家科学基金会; 中国博士后科学基金;

关键词：

Massive MIMO; channel estimation; Bayesian inference; uplink support; Cramer-Rao bound;

D O I：

10.1145/3290420.3290448

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

In this paper, we discuss the downlink channel estimation in frequency division duplex (FDD) massive MIMO system. Based on the angular reciprocity between uplink and downlink, we combined the uplink support prior information into the downlink channel estimation. A downlink channel estimation method based on variational Bayesian inference(VBI) is proposed, which is by taking the support prior information into consideration. Meanwhile the VBI is discussed for complex number in our system model, and the structural sparsity is utilized in the Bayesian inference. The Bayesian Cramer-Rao bound for the channel estimation MSE is also given out. Compared with Bayesian compressed sensing and other algorithms, the proposed algorithm achieves much better performance in terms of channel estimation accuracy by simulations.

引用

页码：268 / 272

页数：5

共 15 条

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