Article (Scientific journals)
Semi-blind Data-Selective and Multiple Threshold Volterra Adaptive Filtering
Barboza da Silva, Felipe; Alves Martins, Wallace
2019In Circuits, Systems, and Signal Processing, p. 1-24
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Abstract :
[en] This work proposes the use of data-selective semi-blind schemes in order to decrease the amount of data used to train the adaptive filters that employ Volterra series, while reducing its computational complexity. It is also proposed a data-selective technique that exploits the structure of Volterra series, employing a different filter for each of its kernels. The parameter vector of these filters grows as the order of the kernel increases. Therefore, by assigning larger error thresholds to higher-order filters, it is possible to decrease their update rates, thus reducing the overall computational complexity. Results in an equalization setup indicate that both proposals are capable of achieving promising results in terms of mean square error and bit error rate at low computational complexity, and in the case of semi-blind algorithms, using much less training data.
Disciplines :
Electrical & electronics engineering
Author, co-author :
Barboza da Silva, Felipe;  Federal University of Rio de Janeiro (UFRJ)
Alves Martins, Wallace ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)
External co-authors :
yes
Language :
English
Title :
Semi-blind Data-Selective and Multiple Threshold Volterra Adaptive Filtering
Publication date :
25 July 2019
Journal title :
Circuits, Systems, and Signal Processing
ISSN :
0278-081X
Publisher :
Birkhaeuser, United States
Pages :
1-24
Peer reviewed :
Peer Reviewed verified by ORBi
Focus Area :
Security, Reliability and Trust
Available on ORBilu :
since 16 August 2019

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