Fast Computation of Zero-Forcing Precoding for Massive MIMO-OFDM Systems

被引:2
作者
Liu, Junkai [1 ]
Zhang, Wei [2 ,3 ]
Jiang, Yi [1 ]
机构
[1] Fudan Univ, Sch Informat Sci & Technol, Key Lab Informat Sci Electromagnet Waves, MoE, Shanghai 200433, Peoples R China
[2] Fudan Univ, Engn Sch Informat Sci & Technol, Dept Commun Sci, Shanghai 200438, Peoples R China
[3] Chinese Acad Sci, Shanghai Inst Microsyst & Informat Technol, Shanghai 200050, Peoples R China
基金
中国国家自然科学基金;
关键词
Massive MIMO-OFDM; zero-forcing precoding; low computational complexity; time domain precoding; conjugate gradient; QR decomposition; PERFORMANCE ANALYSIS; WIRELESS; QR;
D O I
10.1109/TSP.2024.3355742
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
As a simple and popular transmission scheme, zero-forcing (ZF) precoding can effectively reap the great benefits of a multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) wireless system. But as the wireless technology evolves to massive MIMO-OFDM, even the ZF precoding may incur too cumbersome computational complexity. In this paper, we first derive the ZF precoder in the time domain. By exploiting the block-Toeplitzness of the time-domain channel matrix, we propose a novel approximate solution to the original time-domain ZF precoding problem for fast computation. We then provide an approximation analysis to show the convergence to the exact solution. To compute the approximate scheme efficiently, we propose two novel low-complexity algorithms: a fast Fourier transform (FFT) based conjugate gradient algorithm, which can obtain the MIMO-OFDM ZF precoder as a time-domain MIMO finite impulse response (FIR) filter, and a flexible block Toeplitz QR decomposition algorithm exploiting the special structure of the time-domain channel matrix. We also extend the proposed approximate scheme and two low-complexity algorithms to the regularized ZF precoding scenario. Simulation results and complexity analysis show that our methods can achieve favorable performance but with significantly lower computational complexity compared with the state-of-the-art methods.
引用
收藏
页码:912 / 927
页数:16
相关论文
共 35 条
  • [1] BLOCK TOEPLITZ MATRIX INVERSION
    AKAIKE, H
    [J]. SIAM JOURNAL ON APPLIED MATHEMATICS, 1973, 24 (02) : 234 - 241
  • [2] Massive MIMO: Ten Myths and One Critical Question
    Bjornson, Emil
    Larsson, Erik G.
    Marzetta, Thomas L.
    [J]. IEEE COMMUNICATIONS MAGAZINE, 2016, 54 (02) : 114 - 123
  • [3] Optimal Multiuser Transmit Beamforming: A Difficult Problem with a Simple Solution Structure
    Bjornson, Emil
    Bengtsson, Mats
    Ottersten, Bjorn
    [J]. IEEE SIGNAL PROCESSING MAGAZINE, 2014, 31 (04) : 142 - 148
  • [4] Pareto Characterization of the Multicell MIMO Performance Region With Simple Receivers
    Bjornson, Emil
    Bengtsson, Mats
    Ottersten, Bjorn
    [J]. IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2012, 60 (08) : 4464 - 4469
  • [5] QR FACTORIZATION OF TOEPLITZ MATRICES
    BOJANCZYK, AW
    BRENT, RP
    de Hoog, FR
    [J]. NUMERISCHE MATHEMATIK, 1986, 49 (01) : 81 - 94
  • [6] Conjugate gradient methods for toeplitz systems
    Chan, RH
    Ng, MK
    [J]. SIAM REVIEW, 1996, 38 (03) : 427 - 482
  • [7] Large-Scale Beamforming for Massive MIMO via Randomized Sketching
    Choi, Hayoung
    Jiang, Tao
    Shi, Yuanming
    Liu, Xuan
    Zhou, Yong
    Letaief, Khaled B.
    [J]. IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2021, 70 (05) : 4669 - 4681
  • [8] Chowdhury Agniva, 2018, P MACHINE LEARNING R, P989
  • [9] FAST PARALLEL ALGORITHMS FOR QR AND TRIANGULAR FACTORIZATION
    CHUN, J
    KAILATH, T
    LEVARI, H
    [J]. SIAM JOURNAL ON SCIENTIFIC AND STATISTICAL COMPUTING, 1987, 8 (06): : 899 - 913
  • [10] Massive MIMO Linear Precoding: A Survey
    Fatema, Nusrat
    Hua, Guang
    Xiang, Yong
    Peng, Dezhong
    Natgunanathan, Iynkaran
    [J]. IEEE SYSTEMS JOURNAL, 2018, 12 (04): : 3920 - 3931