TY - GEN
T1 - Sparsity analysis using a mixed approach with greedy and LS algorithms on channel estimation
AU - De Paiva, Nilson Maciel
AU - Marques, Elaine Crespo
AU - De Barros Naviner, Lirida Alves
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/11/3
Y1 - 2017/11/3
N2 - Various channels can be denoted by sparse channels and many algorithms have been proposed to exploit their sparsity. In this paper, we propose a mixed algorithm based on Greedy and LS algorithms for sparse channel estimation. Analyses of the proposed and commonly used algorithms in terms of performance and complexity are performed considering the channel's sparsity, the length of training sequence and the stopping criterion. Our results show that a suitable trade-off can be found and effective channel estimations can be obtained with a low-cost algorithm.
AB - Various channels can be denoted by sparse channels and many algorithms have been proposed to exploit their sparsity. In this paper, we propose a mixed algorithm based on Greedy and LS algorithms for sparse channel estimation. Analyses of the proposed and commonly used algorithms in terms of performance and complexity are performed considering the channel's sparsity, the length of training sequence and the stopping criterion. Our results show that a suitable trade-off can be found and effective channel estimations can be obtained with a low-cost algorithm.
KW - compressive sensing
KW - greedy algorithms
KW - sparse channel estimation
U2 - 10.1109/ICFSP.2017.8097148
DO - 10.1109/ICFSP.2017.8097148
M3 - Conference contribution
AN - SCOPUS:85039916087
T3 - 2017 3rd International Conference on Frontiers of Signal Processing, ICFSP 2017
SP - 91
EP - 95
BT - 2017 3rd International Conference on Frontiers of Signal Processing, ICFSP 2017
A2 - Szczypiorski, Krzysztof
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 3rd International Conference on Frontiers of Signal Processing, ICFSP 2017
Y2 - 6 September 2017 through 8 September 2017
ER -