Paper Info
Title
A Rao-Blackwellized particle filter for joint channel/symbol estimation in MC-DS-CDMA systems
Abstract
This paper deals with the joint estimation of Rayleigh fading channels and symbols in a MC-DS-CDMA system. Formerly, particle filtering has been introduced as a set of promising methods to solve communication problems. PF consists in simulating possible values of the unkwnown parameters and selecting the most likely candidates with regard to the received signal. Here, the Rao-Blackwellized particle filter (RBPF) is used to significantly decrease the variance of the channel/symbol estimates. Our contribution is twofold. Firstly, sinusoidal stochastic models have been shown to better represent the statistical properties of Rayleigh channels than classical autoregressive models. Therefore, the proposed RBPF estimator is based on these models which are expressed as the sum of two sinusoids in quadrature at the maximum Doppler frequency with autoregressive processes as amplitudes. The model parameters are unknown and need to be estimated. Since PFs are not well-suited to recover non-varying parameters, we propose to cross-couple the RBPF with a Kalman filter which makes use of the RBPF ouputs to sequentially update the parameters. Secondly, the choice of an efficient proposal distribution to simulate the particles is crucial for PF performance. We suggest using a suboptimal distribution which simulates likely values of the symbols at a reasonable computational cost.
Year
DOI
Venue
2010
10.1109/TCOMM.2010.08.070558
IEEE Transactions on Communications
Keywords
Field
DocType
rayleigh channel,rao-blackwellized particle filter,mc-ds-cdma system,rayleigh fading channel,joint channel,kalman filter,proposed rbpf estimator,pf performance,rbpf ouputs,efficient proposal distribution,classical autoregressive model,symbol estimation,autoregressive process,kalman filters,code division multiple access,autoregressive model,cdma,fading,modeling,computational modeling,monte carlo method,doppler effect,stochastic model,spread spectrum communication,parameter estimation,particle filter,monte carlo methods,gaussian noise,stochastic process,estimation
Autoregressive model,Rayleigh fading,Control theory,Computer science,Fading,Particle filter,Kalman filter,Electronic engineering,Estimation theory,Gaussian noise,Estimator
Journal
Volume
Issue
ISSN
58
8
0090-6778
Citations 
PageRank 
References 
4
0.45
16
Authors
4
Name
Order
Citations
PageRank
Audrey Giremus112920.57
Eric Grivel213633.92
Julie Grolleau351.23
Mohamed Najim414932.29