scholarly journals Learning to Be Green: Robust Energy Efficiency Maximization in Dynamic MIMO–OFDM Systems

2016 ◽  
Vol 34 (4) ◽  
pp. 743-757 ◽  
Author(s):  
Panayotis Mertikopoulos ◽  
E. Veronica Belmega
2019 ◽  
Vol 2019 ◽  
pp. 1-16 ◽  
Author(s):  
Wenjie Zhang ◽  
Hui Li ◽  
Rong Jin ◽  
Shanlin Wei ◽  
Wei Cheng ◽  
...  

In massive multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) systems, accurate channel state information (CSI) is essential to realize system performance gains such as high spectrum and energy efficiency. However, high-dimensional CSI acquisition requires prohibitively high pilot overhead, which leads to a significant reduction in spectrum efficiency and energy efficiency. In this paper, we propose a more efficient time-frequency joint channel estimation scheme for massive MIMO-OFDM systems to resolve those problems. First, partial channel common support (PCCS) is obtained by using time-domain training. Second, utilizing the spatiotemporal common sparse property of the MIMO channels and the obtained PCCS information, we propose the priori-information aided distributed structured sparsity adaptive matching pursuit (PA-DS-SAMP) algorithm to achieve accurate channel estimation in frequency domain. Third, through performance analysis of the proposed algorithm, two signal power reference thresholds are given, which can ensure that the signal can be recovered accurately under power-limited noise and accurately recovered according to probability under Gaussian noise. Finally, pilot design, computational complexity, spectrum efficiency, and energy efficiency are discussed as well. Simulation results show that the proposed method achieves higher channel estimation accuracy while requiring lower pilot sequence overhead compared with other methods.


ETRI Journal ◽  
2014 ◽  
Vol 36 (4) ◽  
pp. 686-689 ◽  
Author(s):  
Jiakuo Zuo ◽  
Van Phuong Dao ◽  
Yongqiang Bao ◽  
Shiliang Fang ◽  
Li Zhao ◽  
...  

2013 ◽  
Vol E96.B (3) ◽  
pp. 830-835 ◽  
Author(s):  
Wen ZHONG ◽  
Anan LU ◽  
Xiqi GAO
Keyword(s):  

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