Improved adaptive sparse channel estimation using mixed square/fourth error criterion

@article{Gui2015ImprovedAS,
  title={Improved adaptive sparse channel estimation using mixed square/fourth error criterion},
  author={Guan Gui and Li Xu and Shin-ya Matsushita},
  journal={J. Franklin Institute},
  year={2015},
  volume={352},
  pages={4579-4594}
}
Sparse channel estimation problem is one of challenge technical issues in stable broadband wireless communications. Based on square error criterion (SEC), adaptive sparse channel estimation (ASCE) methods, e.g., zero-attracting least mean square error (ZA-LMS) algorithm and reweighted ZA-LMS (RZA-LMS) algorithm, have been proposed to mitigate noise interferences as well as to exploit the inherent channel sparsity. However, the conventional SEC-ASCE methods are vulnerable to 1) random scaling of… CONTINUE READING
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