Paper Info
Title
Near-optimal signal detection with low complexity based on Gauss-Seidel method for uplink large-scale MIMO systems
Abstract
Minimum mean square error (MMSE) algorithm is near-optimal for uplink large-scale MIMO systems, but involves high-complexity matrix inversion. In this paper, based on a special property of uplink large-scale MIMO systems that the filtering matrix of the MMSE algorithm is symmetric positive definite as we will prove, we propose to exploit the Gauss-Seidel method to iteratively realize the MMSE algorithm without the complicated matrix inversion. The proposed signal detection algorithm can reduce the complexity by one order of magnitude. Simulation results verify that the proposed algorithm outperforms the recently proposed Neumann series approximation algorithm, and achieves the near-optimal performance of the classical MMSE algorithm with a small number of iterations.
Year
DOI
Venue
2014
10.1109/BMSB.2014.6873569
BMSB
Keywords
Field
DocType
uplink large-scale mimo systems,mmse algorithm,complicated matrix inversion,near-optimal signal detection algorithm,gauss-seidel method,minimum mean square error algorithm,neumann series approximation algorithm,matrix algebra,iterative method,mimo communication,filtering theory,signal detection,mean square error methods,high-complexity matrix inversion,uplink,symmetric matrices,mimo,approximation algorithms,vectors,gauss seidel method
Mimo systems,Detection theory,Computer science,Algorithm,Real-time computing,Theoretical computer science,Gauss–Seidel method,Telecommunications link
Conference
ISSN
Citations 
PageRank 
2155-5044
0
0.34
References 
Authors
14
4
Name
Order
Citations
PageRank
Xinyu Gao138717.68
Zhao-Hua Lu26612.54
Yanjun Han341.09
Jiaqi Ning400.34