Collapsed VBI-DP Based Structured Sparse Channel Estimation Algorithm for Massive MIMO-OFDM

被引:5
作者
Lu, Xinhua [1 ,2 ]
Manchon, Carles Navarro [3 ]
Wang, Zhongyong [1 ]
机构
[1] Zhengzhou Univ, Sch Informat Engn, Zhengzhou 450001, Henan, Peoples R China
[2] Nanyang Inst Technol, Coll Comp & Informat Engn, Nanyang 473004, Peoples R China
[3] Aalborg Univ, Dept Elect Syst, DK-9220 Aalborg, Denmark
来源
IEEE ACCESS | 2019年 / 7卷
关键词
Massive MIMO; structured sparse channel; Dirichlet process; collapsed variational Bayesian inference; WIRELESS;
D O I
10.1109/ACCESS.2019.2896125
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Massive multiple input multiple output (MIMO) technology significantly improves the capacity of wireless communication systems by deploying hundreds of antennas at the base station. However, the large scale of the array implies higher computational complexity and pilot overhead when implementing channel estimation in the uplink. Utilizing the sparse channel structure is a promising approach to improve the channel estimation performance while circumventing such problems. In this paper, we investigate the detailed physical structure in the delay-spatial domain of uplink channels in massive MIMO-orthogonal frequency division multiplexing (MIMO-OFDM) systems and construct a hierarchical probabilistic model based on Dirichlet process (DP) prior to match the channel's structural sparse features. Based on the model, we derive a structured sparse channel estimation algorithm by implementing collapsed variational Bayesian inference (CVBI). The simulation results demonstrate that the proposed CVBI-DP algorithm can improve channel estimation performance significantly compared with the state-of-the-art methods for massive MIMO-OFDM, without increasing the computational complexity and pilot overhead.
引用
收藏
页码:16665 / 16674
页数:10
相关论文
共 19 条
[1]   Variational Inference for Dirichlet Process Mixtures [J].
Blei, David M. ;
Jordan, Michael I. .
BAYESIAN ANALYSIS, 2006, 1 (01) :121-143
[2]  
Gao X., 2015, P JOINT NEWCOM COST, P4558, DOI DOI 10.1109/ICC.2015.7249041
[3]   Structured Compressive Sensing-Based Spatio-Temporal Joint Channel Estimation for FDD Massive MIMO [J].
Gao, Zhen ;
Dai, Linglong ;
Dai, Wei ;
Shim, Byonghyo ;
Wang, Zhaocheng .
IEEE TRANSACTIONS ON COMMUNICATIONS, 2016, 64 (02) :601-617
[4]  
Kurihara K, 2007, 20TH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P2796
[5]   Massive MIMO for Next Generation Wireless Systems [J].
Larsson, Erik G. ;
Edfors, Ove ;
Tufvesson, Fredrik ;
Marzetta, Thomas L. .
IEEE COMMUNICATIONS MAGAZINE, 2014, 52 (02) :186-195
[6]   Estimation of Sparse Massive MIMO-OFDM Channels With Approximately Common Support [J].
Lin, Xincong ;
Wu, Sheng ;
Kuang, Linling ;
Ni, Zuyao ;
Meng, Xiangming ;
Jiang, Chunxiao .
IEEE COMMUNICATIONS LETTERS, 2017, 21 (05) :1179-1182
[7]   Noncooperative Cellular Wireless with Unlimited Numbers of Base Station Antennas [J].
Marzetta, Thomas L. .
IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, 2010, 9 (11) :3590-3600
[8]   Efficient Coordinated Recovery of Sparse Channels in Massive MIMO [J].
Masood, Mudassir ;
Afify, Laila H. ;
Al-Naffouri, Tareq Y. .
IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2015, 63 (01) :104-118
[9]  
Minka T, 2000, Tech. Rep.
[10]   Joint channel estimation algorithm via weighted Homotopy for massive MIMO OFDM system [J].
Nan, Yang ;
Sun, Xin ;
Zhang, Li .
DIGITAL SIGNAL PROCESSING, 2016, 50 :34-42