Loading...
Please wait, while we are loading the content...
Similar Documents
Adaptive Sparse Channel Estimation Using Re-Weighted Zero-Attracting Normalized Least Mean Fourth
| Content Provider | Semantic Scholar |
|---|---|
| Author | Weig, Ad |
| Copyright Year | 2013 |
| Abstract | key Sta app is o be by zer alg spa the coe me alg me con K est NL ma sys fad is n On est Fig (LM alg and alg on and sta [3] ver Ad Weig Abstract—Accu y technical is andard norma plied to adapti often describe exploited and adaptive spa ro-attracting gorithm. How arsity efficient e reason why efficients unifo ethod using re gorithm. Simu ethod achieve nventional one Keywords—nor timation (ASC LMF). Broadband ainstream tech stems. Due t ding is unavoi necessary at th ne of effectiv timation (ACE g. 1. It is we MF) algorithm gorithm in ach d steady-stat gorithm is uns n the following d weight initia able normalize ]. Recently, m rified that bro daptive ghted Z urate channel ssues in broa alized least me ive channel est d by sparse ch then estimatio arse channel e normalized l wever, this alg tly. By virtual-norm spa ormly. In this e-weighted zero ulation results es better est e. rmalized LMF CE), re-weighte I. IN signal transm hniques in the to the fact th idable, accurat he receiver for ve approaches E). A typical ell known tha m outperform hieving a goo te performan stable due to t g three factors alization [2]. T ed LMF (NLM many channel oadband chann e Spar Zero-A Guan D {gui, mehbo estimation pr adband wirele an fourth (NL timation (ACE hannel model, on performanc estimation (AS least mean f gorithm cann of geometrical arse constrain paper, we pro o-attracting N show that th imation perfo F (NLMF), adap ted zero-attrac NTRODUCTION mission is bec e next generat hat frequency te channel stat r adaptive coh s is adopting framework of at ACE using ms the least m od balance be nces. Howeve the fact that i : input signal p To improve th MF) algorithm measurement nels often exhi rse Ch Attrac Gui, Abolfazl Department of Graduate T d}@mobile.e roblem is one ess communica LMF) algorithm E). Since the ch such sparsity e could be imp SCE) methods fourth (ZA-N not exploit ch l figures, we e t penalizes ch opose a novel NLMF (RZA-N he proposed ormance than aptive sparse ch cting NLMF coming one o tion communic y-selective ch te information herent detectio g adaptive ch f ACE is sho g least mean f mean square (tween conver er, standard ts stability de power, noise … |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://www.mobile.ecei.tohoku.ac.jp/paper/pdf/inter_2013/03_in_2013_gui.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
