Effective Channel Order Determination Algorithm for Convolutive Blind Channel Identification
- DOI
- 10.2991/ijcis.d.190819.001How to use a DOI?
- Keywords
- Effective channel order determination; Numerical analysis arguments; Blind channel identification; Higher-order cumulant tensor
- Abstract
Effective channel order determination is an important problem in convolutive blind channel identification. The classical techniques are based on information theoretic criteria, which show a great potentiality to estimate the effective channel order. However, these methods are just effective for the overdetermined case, i.e., the number of sensors is larger than the number of source signals. When the number of sensors is less than or equal to the number of source signals, i.e., in the underdetermined or determined case, it is difficult to detect the effective channel order. In this paper, an improved algorithm is proposed to estimate the effective channel order by integrating numerical analysis arguments and higher-order cumulant tensor. In the proposed algorithm, we exploit the information contained in the higher-order data statistics and rearrange the tensor as a matrix using the unfolding operation, then utilize the eigenvalues of the matrix and combine numerical analysis arguments to detect the effective channel order. Finally, a series of experiment results demonstrate the effectiveness and superiority of the proposed algorithm.
- Copyright
- © 2019 The Authors. Published by Atlantis Press SARL.
- Open Access
- This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).
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TY - JOUR AU - Senquan Yang AU - Haifeng Su AU - Pu Li AU - Songxi Hu AU - Jinru Chen PY - 2019 DA - 2019/09/12 TI - Effective Channel Order Determination Algorithm for Convolutive Blind Channel Identification JO - International Journal of Computational Intelligence Systems SP - 947 EP - 954 VL - 12 IS - 2 SN - 1875-6883 UR - https://doi.org/10.2991/ijcis.d.190819.001 DO - 10.2991/ijcis.d.190819.001 ID - Yang2019 ER -