Paper Info
Title
Deterministic and Monte Carlo approaches for joint iterative data detection and channel estimation
Abstract
This paper deals with joint data detection and channel estimation for single input single output systems in presence of inter symbol interference. Therefore, deterministic methods, the Gibbs-sampler and combinations between determin- istic and Monte Carlo approaches are compared. The examined methods belong to the class of block by block iterative algorithms alternating between channel estimation and data detection. It will be shown that the deterministic method might get trapped in a local maximum of the likelihood function, whereas the Monte Carlo methods theoretically almost converge to a global maximum. Based on simulation results it will be shown that a performance gain can be achieved at the expense of slower convergence speed or an increased computational effort. I. INTRODUCTION The requirements of spectral efficiency for wireless com- munication systems are still growing. In order to get reli- able transmission, the receiver of a wireless communication link requires channel state information. Usually, a pilot data sequence embedded in the data block enables the receiver to estimate the channel. Since this pilot sequence bears no information, the waste of bandwidth caused by pilot symbols should be kept as low as possible. It has been shown that the quality of channel estimates can be improved dramatically by feeding back the decided data as pseudo reference signal for the channel estimation. On the other hand the data detection becomes more reliable using the improved channel estimates. Thus, iterative equalizer structures alternating between data detection and channel estimation promise good performance gains, since the number of required pilot symbols can be decreased and thus more bandwidth can be utilized to transmit information.
Year
DOI
Venue
2004
10.1109/WSA.2004.1407641
WSA
Keywords
Field
DocType
Monte Carlo methods,channel estimation,intersymbol interference,iterative methods,maximum likelihood detection,maximum likelihood estimation,Gibbs-sampler,block iterative algorithm,channel estimation,deterministic Monte Carlo approach,inter symbol interference,joint iterative data detection,local maximum likelihood function,single input single output system
Mathematical optimization,Monte Carlo method,Likelihood function,Monte Carlo algorithm,Iterative method,Computer science,Algorithm,Hybrid Monte Carlo,Monte Carlo integration,Monte Carlo molecular modeling,Gibbs sampling
Conference
ISBN
Citations 
PageRank 
0-7803-8327-3
0
0.34
References 
Authors
6
3
Name
Order
Citations
PageRank
Ansgar Scherb1293.58
Kiihn, V.200.34
Kammeyer, K.-D.319421.42