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