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.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    A Low-Complexity Linear Precoding Algorithm Based on Jacobi Method for Massive MIMO Systems


    Contributors:


    Publication date :

    2018-06-01


    Size :

    160152 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Low-Complexity LSQR-Based Linear Precoding for Massive MIMO Systems

    Xie, Tian / Lu, Zhaohua / Han, Qian et al. | IEEE | 2015


    Approximate Partially Decentralized Linear EZF Precoding for Massive MU-MIMO Systems

    Kaziu, Brikena / Shanin, Nikita / Spano, Danilo et al. | IEEE | 2024


    Large System Analysis of Linear Precoding in Massive MIMO Relay Systems

    Liu, Yang / Ding, Zhiguo / Shi, Jia et al. | IEEE | 2018