Blind source separation of QAM signals by using a novel optimality of the constant modulus algorithm

Author(s):  
H Zheng ◽  
B Yan ◽  
H Su ◽  
Y Liu ◽  
Z Abderrahmen
2021 ◽  
Author(s):  
Renan Brotto ◽  
Kenji Nose-Filho ◽  
João M. T. Romano

<div>This letter introduces the concept of antisparse Blind Source Separation (BSS), proposing a suitable criterion based on the $\ell_\infty$ norm to explore the antisparsity feature. </div><div>The effectiveness of the criterion is theoretically demonstrated and it is also evaluated by computational simulations, which consider up to ten distinct sources with different correlation levels. Moreover, we simulated a scenario in wireless communication with binary sources, comparing our approach to the Constant Modulus algorithm. Both the theoretical and the simulation results highlight the potentiality of using antisparsity as a prior in BSS.</div>


2021 ◽  
Author(s):  
Renan Brotto ◽  
Kenji Nose-Filho ◽  
João M. T. Romano

<div>This letter introduces the concept of antisparse Blind Source Separation (BSS), proposing a suitable criterion based on the $\ell_\infty$ norm to explore the antisparsity feature. </div><div>The effectiveness of the criterion is theoretically demonstrated and it is also evaluated by computational simulations, which consider up to ten distinct sources with different correlation levels. Moreover, we simulated a scenario in wireless communication with binary sources, comparing our approach to the Constant Modulus algorithm. Both the theoretical and the simulation results highlight the potentiality of using antisparsity as a prior in BSS.</div>


2016 ◽  
Vol 20 (3) ◽  
pp. 486-489 ◽  
Author(s):  
Chuanlong Wu ◽  
Zheng Liu ◽  
Xiang Wang ◽  
Wenli Jiang ◽  
Xiaohu Ru

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