Adaptive Sparse Channel Estimation for Time-Variant MISO Communication Systems

tec (M lea cha des exp ada LM two sig To a n (AS AS alg cha me sim est tha K nor (AS (M tec ma wa mu MI im effi rec to fre cha eac em MI Tim Abstract—Cha chnical issues MSIO) commun ast mean squ annel estimati scribed by sp ploited and th aptive sparse c MS algorithms o main drawb gnal and 2) uns o overcome the novel ASCE m SCE-NLMS). SCE method gorithm (ASCE annel sparsity ethods is conf mulation resul timation metho an conventiona Keywords—lea rmalized LMF SCE), multiple-Signal transm MIMO) chann chniques in the ajor motivation ay of using m ultiple streams IMO in cellula mproved data ficiency, and ceiver requires the fact equency-select annel estimati ch antenna at mploying very IMO ") at ba Adap me-Va {g annel estimatio in time-vari nication system are (LMS) a ion (ACE). Si parse channe en estimation channel estima s. However, co backs: 1) sens stable in low s se two harmfu ethod using no In addition, using normali E-NLMF). Two y effectively. firmed by ma lts show tha ods can achiev al methods. ast mean square F (NLMF), ad-input single-ou I. IN mission over nel is becom e next generat n is due to the multiple anten s of data in w ar systems bri rate, improve reduced inte s accurate cha that wirel tive fading cha ion problem is receiving side large number se station an ptive S on problem iant multiple-ms. To estimate lgorithm is a ince the MIS l model, suc performance c ation (ASCE) m onventional A itive to rando signal-to-noise ul factors, in thi ormalized LMS we also prop ized least me o proposed met Also, stability athematical de t the propos ve better estim e (LMS), least daptive sparse utput (MISO). NTRODUCTION multiple-inp ming one o tion communic e fact that MIM nnas to simul wireless comm ings improvem ed reliability, erference. Ho annel state inf ess signal annel. In these s to estimate th e. One of the r of antenna (nd only one Sparse MISO Wei Peng, Abo Department of Graduate T bod}@mobile is one of th-input single-o e the MISO ch applied to ad O channel is h sparsity ca can be improv methods using s ASCE methods om scale of tr ratio (SNR) re is paper, we pr S (NLMS) algo posed an imp ean …