DocumentCode :
3539540
Title :
A Novel Blind Adaptive Prediction&Subspace Code-Aided AR Narrowband Interference Suppression for CDMA Systems
Author :
Fulian Yin ; Ya Gao ; Yao Qin
Author_Institution :
Inf. Eng. Sch., Commun. Univ. of China, Beijing, China
fYear :
2012
fDate :
21-23 Sept. 2012
Firstpage :
1
Lastpage :
4
Abstract :
In this paper, a projection approximation subspace tracking with deflation (PASTd) algorithm based on subspace technique for narrowband interference (NBI) suppression of code division multiple access (CDMA) is proposed to improve the performance of recursive least square (RLS) algorithm based on direct matrix inverse (DMI) code-aided technique firstly. Specifically, this technique is against the type of NBI, namely, autoregressive (AR) stochastic process. Then a recursive least square (RLS) prediction&PASTd code-aide algorithm is proposed to solve the problem of difficult determination on low order with above PASTd algorithm. In this paper, its signal to noise and interference rate (SINR) performance against NBI and MAI is compared with DMI RLS algorithm. It is seen that this method outperforms current DMI RLS technique.
Keywords :
autoregressive processes; code division multiple access; interference suppression; least squares approximations; stochastic processes; CDMA systems; PASTd algorithm; SINR performance; autoregressive stochastic process; blind adaptive prediction; code division multiple access; code-aided technique; direct matrix inverse; narrowband interference suppression; projection approximation subspace tracking with deflation; recursive least square algorithm; signal to noise and interference rate; subspace code-aided AR narrowband interference suppression; Interference; Multiaccess communication; Narrowband; Prediction algorithms; Signal to noise ratio; Stochastic processes; Vectors;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Wireless Communications, Networking and Mobile Computing (WiCOM), 2012 8th International Conference on
Conference_Location :
Shanghai
ISSN :
2161-9646
Print_ISBN :
978-1-61284-684-2
Type :
conf
DOI :
10.1109/WiCOM.2012.6478322
Filename :
6478322
Link To Document :
بازگشت