MIMO channel estimation based on distributed compressed sensing for LTE-advanced

2013 ◽  
Author(s):  
Lijuan Xu ◽  
Kai Niu ◽  
Zhiqiang He ◽  
Wenbo Xu ◽  
Zhen Zheng
Keyword(s):  
Channel Estimation ◽  
Compressed Sensing ◽  
Mimo Channel ◽  
Distributed Compressed Sensing ◽  
Lte Advanced

Distributed Compressed Sensing Based Channel Parameter Estimation for MIMO-FBMC Communications

2021 ◽  
Author(s):  
Han Wang ◽  
Xianpeng Wang
Keyword(s):  
Channel Estimation ◽  
Compressed Sensing ◽  
Signal Reconstruction ◽  
Estimation Method ◽  
Sparse Signal ◽  
Weak Selection ◽  
Sparse Signal Reconstruction ◽  
Distributed Compressed Sensing ◽  
Time Frequency ◽  
Selection Threshold

Abstract For the sparse correlation between channels in multiple input multiple output filter bank multicarrier with offset quadrature amplitude modulation (MIMO-FBMC/OQAM) systems, the distributed compressed sensing (DCS)-based channel estimation approach is studied. A sparse adaptive distributed sparse channel estimation method based on weak selection threshold is proposed. Firstly, the correlation between MIMO channels is utilized to represent a joint sparse model, and channel estimation is transformed into a joint sparse signal reconstruction problem. Then, the number of correlation atoms for inner product operation is optimized by weak selection threshold, and sparse signal reconstruction is realized by sparse adaptation. The experiment results show that proposed DCS-based method not only estimates the multipath channel components accurately but also achieves higher channel estimation performance than classical orthogonal matching pursuit (OMP) method and other traditional DCS methods in the time-frequency dual selective channels.

scholarly journals OFDM-based Massive MIMO Channel Estimation using Gaussian Mixture Learning and Compressed Sensing Methods

2019 ◽  
Vol 9 (2S3) ◽  
pp. 7-13
Keyword(s):  
Channel Estimation ◽  
Compressed Sensing ◽  
Massive Mimo ◽  
Gaussian Mixture ◽  
Symbol Error Rate ◽  
Mimo Channel ◽  
Sparse Channel Estimation ◽  
High Data ◽  
Data Rates ◽  
Vector Distribution

Massive MIMO-OFDM system is proved to be an effective and most sustainable technology to forthcoming applications of 5G wireless communications. It furnished significant gains that facilitate a higher number of user connections at high data rates with improved latency and reliability. To achieve accurate channel knowledge, lessen pilot overhead is necessary. To resolve this problem, one of the favorite approaches is compressed sensing. Sparse channel estimation develops the essential sparsity between the communicating channels that can be improved by the channel estimation efficacy with lower pilot overhead. To achieve this, non-zero vector distribution can be taking into consideration the Gaussian mixture accordingly, learn their characteristics towards the expectation-maximization procedure. The results of simulation have proved the performance of proposed estimation approach of channel keeping with minimum pilot overhead and developed exceptional symbol error rate (SER) performance of the system.

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