Paper Info
Title
A Low-Complexity Linear Precoding Algorithm Based On Jacobi Method For Massive Mimo Systems
Abstract
In massive multiple-input multiple-output (MIMO) systems with the increase of the number of received antennas at base station (BS), linear precoding, as zero-forcing (ZF), is able to achieve near-optimal performance and capacity-approaching due to the asymptotically orthogonal channel property, but it involves matrix inversion with high computational complexity. To avoid the matrix inversion, in this paper, we propose a novel low-complexity linear precoding algorithm based on Jacobi method (JM). The proposed JM-based precoding can achieve the near-optimal performance and capacity-approaching of the ZF precoding in an iterative way, which can reduce the complexity by about one order of magnitude. Furthermore, the convergence rate achieved by JM-based precoding is quantified, which reveals that JM-based precoding converges faster with the increasing number of BS antennas. Simulation results show that JM-based precoding achieves the near-optimal performance and capacity-approaching of ZF precoding with a reduced number of iterations.
Year
Venue
Keywords
2018
2018 IEEE 87TH VEHICULAR TECHNOLOGY CONFERENCE (VTC SPRING)
Massive MIMO, Jacobi method, matrix inversion, zero-forcing, BER, channel capacity
Field
DocType
Citations 
Base station,Jacobi method,Computer science,Matrix (mathematics),Algorithm,MIMO,Rate of convergence,Precoding,Computational complexity theory,Telecommunications link
Conference
0
PageRank 
References 
Authors
0.34
0
2
Name
Order
Citations
PageRank
Juan Minango101.35
Celso Almeida236.47