TY - GEN
T1 - Adaptive sparse channel estimation using re-weighted zero-attracting normalized least mean fourth
AU - Gui, Guan
AU - Mehbodniya, Abolfazl
AU - Adachi, Fumiyuki
PY - 2013
Y1 - 2013
N2 - Accurate channel estimation problem is one of the key technical issues in broadband wireless communications. Standard normalized least mean fourth (NLMF) algorithm was applied to adaptive channel estimation (ACE). Since the channel is often described by sparse channel model, such sparsity could be exploited and then estimation performance could be improved by adaptive sparse channel estimation (ASCE) methods using zero-attracting normalized least mean fourth (ZA-NLMF) algorithm. However, this algorithm cannot exploit channel sparsity efficiently. By virtual of geometrical figures, we explain the reason why ℓ1-norm sparse constraint penalizes channel coefficients uniformly. In this paper, we propose a novel ASCE method using re-weighted zero-attracting NLMF (RZA-NLMF) algorithm. Simulation results show that the proposed ASCE method achieves better estimation performance than the conventional one.
AB - Accurate channel estimation problem is one of the key technical issues in broadband wireless communications. Standard normalized least mean fourth (NLMF) algorithm was applied to adaptive channel estimation (ACE). Since the channel is often described by sparse channel model, such sparsity could be exploited and then estimation performance could be improved by adaptive sparse channel estimation (ASCE) methods using zero-attracting normalized least mean fourth (ZA-NLMF) algorithm. However, this algorithm cannot exploit channel sparsity efficiently. By virtual of geometrical figures, we explain the reason why ℓ1-norm sparse constraint penalizes channel coefficients uniformly. In this paper, we propose a novel ASCE method using re-weighted zero-attracting NLMF (RZA-NLMF) algorithm. Simulation results show that the proposed ASCE method achieves better estimation performance than the conventional one.
KW - adaptive sparse channel estimation (ASCE)
KW - normalized LMF (NLMF)
KW - re-weighted zero-attracting NLMF (RZA-NLMF)
UR - http://www.scopus.com/inward/record.url?scp=84893322135&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84893322135&partnerID=8YFLogxK
U2 - 10.1109/ICCChina.2013.6671144
DO - 10.1109/ICCChina.2013.6671144
M3 - Conference contribution
AN - SCOPUS:84893322135
SN - 9781479910335
T3 - 2013 IEEE/CIC International Conference on Communications in China, ICCC 2013
SP - 368
EP - 373
BT - 2013 IEEE/CIC International Conference on Communications in China, ICCC 2013
T2 - 2013 IEEE/CIC International Conference on Communications in China, ICCC 2013
Y2 - 12 August 2013 through 14 August 2013
ER -