Paper Info
Title
New decoding strategy for underdetermined MIMO transmission using sparse decomposition.
Abstract
In this paper we address the problem of large dimension decoding in MIMO systems. The complexity of the optimal maximum likelihood detection makes it unfeasible in practice when the number of antennas, the channel impulse response length or the source constellation size become too high. We consider a MIMO system with finite constellation and model it as a system with sparse signal sources. We formulate the decoding problem as an underdetermined sparse source recovering problem and apply the l(1)-minimization to solve it. The resulting decoding scheme is applied to large MIMO systems and to frequency selective channel. We also review the computational cost of some l(1)-minimization algorithms. Simulation results show significant improvement compared to other existing receivers.
Year
Venue
Keywords
2013
EUSIPCO
MIMO communication,compressed sensing,decoding,maximum likelihood detection,minimisation,signal sources,ℓ1-minimization algorithms,MIMO systems,channel impulse response length,frequency selective channel,large dimension decoding,optimal maximum likelihood detection,source constellation size,sparse decomposition,sparse signal sources,underdetermined MIMO transmission,underdetermined sparse source recovering problem
Field
DocType
Citations 
Mathematical optimization,Underdetermined system,Sparse approximation,Communication channel,MIMO,Minification,Constellation,Mimo transmission,Decoding methods,Mathematics
Conference
4
PageRank 
References 
Authors
0.45
7
5
Name
Order
Citations
PageRank
Yasser Fadlallah1384.19
Abdeldjalil Aïssa-El-Bey216225.10
Karine Amis37517.77
D. Pastor419323.93
Ramesh Pyndiah57917.12