Joint Symbol Detection and Channel Estimation for MIMO-OFDM Systems via the Variational Bayesian EM Algorithm

2008 ◽  
Author(s):  
Xiao-Ying Zhang ◽  
De-Gang Wang ◽  
Ji-Bo Wei
Keyword(s):  
Channel Estimation ◽  
Em Algorithm ◽  
Ofdm Systems ◽  
Variational Bayesian ◽  
Symbol Detection ◽  
Mimo Ofdm

SAGE Algorithm for Semi-Blind Channel Estimation and Symbol Detection for STBC MIMO OFDM Systems

2012 ◽  
Vol 71 (2) ◽  
pp. 1541-1555 ◽  
Author(s):  
Mohsen Keshavarz Hedayati ◽  
Hamidreza Bakhshi ◽  
Mehdi Cheraghi
Keyword(s):  
Channel Estimation ◽  
Blind Channel Estimation ◽  
Ofdm Systems ◽  
Symbol Detection ◽  
Blind Channel ◽  
Mimo Ofdm

Optimal SP Training and Precoding Designs for Channel Estimation and Symbol Detection in MIMO-OFDM Systems

2015 ◽  
Vol 1 ◽  
pp. 34
Author(s):  
Nguyen N. Tran ◽  
Dang L. Khoa ◽  
Vo T. Tri ◽  
Ha X. Nguyen
Keyword(s):  
Channel Estimation ◽  
Fading Channel ◽  
Wireless Channels ◽  
Training Sequence ◽  
Ofdm Systems ◽  
Frequency Selective Fading ◽  
Symbol Detection ◽  
Source Data ◽  
Mimo Ofdm ◽  
Wireless Fading Channel

Based on convex programming for optimization, a closed-from solution of superimposed (SP) training on linearly precoded data for jointly optimal channel estimation and symbol detection is proposed in this paper for MIMO-OFDM systems. The newly designed method not only efficiently identifies the frequency-selective fading channel but also effectively enhances the symbol detection. Although linearly precoding technique is employed and protects the transmitted data over the multi-path MIMO wireless channels, the precoded data is always arithmetically added to the training sequence to prevail transmission bandwidth. Analytical and numerical results confirm that the proposed design efficiently estimates the wireless fading channel and effectively recovers the source data symbols.

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