Paper Info
Title
Low-Complexity Mmse Receivers Based On Weighted Matrix Polynomials In Frequency-Selective Mimo Systems
Abstract
Compared to the Matrix Wiener Filter (MWF), reduced-rank signal processing in the Minimum Mean Square Error (MMSE) sense is a well-known method for the design of low-complexity receivers. In this paper, we reveal the relationship between different reduced-rank receivers based on weighted matrix polynomials approximating the MWF in a Krylov subspace, viz., the MultiStage Matrix WF (MSMWF), the parallel implementation of Multi-Stage Vector WFs (MSVWFs), and Polynomial Expansion (PE). Besides, we present PE where the weights are approximated based on Random Matrix (RM) theory assuming the application to a frequency- selective Multiple-Input Multiple-Output (MIMO) system. Simulation results show that the MSMWF outperforms the considered reduced-rank methods if their order of computational complexity is the same.
Year
DOI
Venue
2005
10.1109/ISSPA.2005.1581034
ISSPA 2005: THE 8TH INTERNATIONAL SYMPOSIUM ON SIGNAL PROCESSING AND ITS APPLICATIONS, VOLS 1 AND 2, PROCEEDINGS
Keywords
Field
DocType
random matrix,mimo,krylov subspace,computational complexity,polynomials,frequency,wiener filter,signal to noise ratio,matrix polynomial,autocorrelation,signal processing
Krylov subspace,Polynomial,Matrix (mathematics),Artificial intelligence,Wiener filter,Mathematical optimization,Pattern recognition,Minimum mean square error,MIMO,Algorithm,Polynomial expansion,Mathematics,Random matrix
Conference
Citations 
PageRank 
References 
0
0.34
6
Authors
3
Name
Order
Citations
PageRank
Guido Dietl112715.21
Ingmar Groh263.58
Wolfgang Utschick31755176.66