DocumentCode :
2853508
Title :
Bayesian blind PARAFAC receivers for DS-CDMA systems
Author :
De Baynast, Alexandre ; Aazhang, Behnaam ; Declerq, D. ; De Lathauwer, Lieven
Author_Institution :
Dept. of Electr. & Comput. Eng., Rice Univ., Houston, TX, USA
fYear :
2003
fDate :
28 Sept.-1 Oct. 2003
Firstpage :
323
Lastpage :
326
Abstract :
In this paper an original Bayesian approach for blind detection for code division multiple access (CDMA) systems in presence of spatial diversity at the receiver is developed. In the noiseless context, the blind detection/identification problem relies on the canonical decomposition (also referred as parallel factor analysis [Sidiropoulos, IEEE SP´00], PARAFAC). The author in [Bro,INCINC´96] proposes a suboptimal solution in least-squares sense. However, poor performances are obtained in presence of high noise level. The recently emerged Markov chain Monte Carlo (MCMC) signal processing method provides a novel paradigm for tackling this problem. Simulation results are presented to demonstrate the effectiveness of this method.
Keywords :
Markov processes; Monte Carlo methods; code division multiple access; diversity reception; radio receivers; signal detection; signal processing; spread spectrum communication; Bayesian blind PARAFAC receivers; DS-CDMA systems; Markov chain Monte Carlo; blind detection; canonical decomposition; code division multiple access; identification problem; least-squares sense; signal processing method; spatial diversity; Baseband; Bayesian methods; Councils; Data models; Fading; Monte Carlo methods; Multiaccess communication; Noise level; Signal processing; Transmitting antennas;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Statistical Signal Processing, 2003 IEEE Workshop on
Print_ISBN :
0-7803-7997-7
Type :
conf
DOI :
10.1109/SSP.2003.1289410
Filename :
1289410
Link To Document :
بازگشت