Distributed Robust Beamforming Based on Low-Rank and Cross-Correlation Techniques: Design and Analysis

被引:8
|
作者
Ruan, Hang [1 ]
de Lamare, Rodrigo C. [2 ,3 ]
机构
[1] Univ York, Dept Elect, York YO10 5DD, N Yorkshire, England
[2] Pontifical Catholic Univ Rio de Janeiro, CETUC, BR-22451900 Rio De Janeiro, Brazil
[3] Univ York, Commun Grp, Dept Elect Engn, York YO10 5DD, N Yorkshire, England
基金
巴西圣保罗研究基金会;
关键词
Robust distributed beamforming; SINR maxi-mization; subspace projection techniques; RELAY NETWORKS;
D O I
10.1109/TSP.2019.2954519
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
In this work, we present a novel robust distributed beamforming (RDB) approach based on low-rank and cross-correlation techniques. The proposed RDB approach mitigates the effects of channel errors in wireless networks equipped with relays based on the exploitation of the cross-correlation between the received data from the relays at the destination and the system output and low-rank techniques. The relay nodes are equipped with an amplify-and-forward (AF) protocol and the channel errors are modeled using an additive matrix perturbation, which results in degradation of the system performance. The proposed method, denoted low-rank and cross-correlation RDB (LRCC-RDB), considers a total relay transmit power constraint in the system and the goal of maximizing the output signal-to-interference-plus-noise ratio (SINR). We carry out a performance analysis of the proposed LRCC-RDB technique along with a computational complexity study. The proposed LRCC-RDB does not require any costly online optimization procedure and simulations show an excellent performance as compared to previously reported algorithms.
引用
收藏
页码:6411 / 6423
页数:13
相关论文
共 50 条
  • [31] Multivariate Singular Spectrum Analysis by Robust Diagonalwise Low-Rank Approximation
    Centofanti, Fabio
    Hubert, Mia
    Palumbo, Biagio
    Rousseeuw, Peter J.
    JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS, 2025, 34 (01) : 360 - 373
  • [32] Low-rank tensor train for tensor robust principal component analysis
    Yang, Jing-Hua
    Zhao, Xi-Le
    Ji, Teng-Yu
    Ma, Tian-Hui
    Huang, Ting-Zhu
    APPLIED MATHEMATICS AND COMPUTATION, 2020, 367
  • [33] AUTO AND CROSS-CORRELATION ANALYSIS TECHNIQUES ON-BOARD SPACECRAFT
    JONES, D
    ANNALES DES TELECOMMUNICATIONS-ANNALS OF TELECOMMUNICATIONS, 1979, 34 (3-4): : 187 - 195
  • [34] Online Robust Low-Rank Tensor Modeling for Streaming Data Analysis
    Li, Ping
    Feng, Jiashi
    Jin, Xiaojie
    Zhang, Luming
    Xu, Xianghua
    Yan, Shuicheng
    IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2019, 30 (04) : 1061 - 1075
  • [35] Robust Low-Rank Latent Feature Analysis for Spatiotemporal Signal Recovery
    Wu, Di
    Li, Zechao
    Yu, Zhikai
    He, Yi
    Luo, Xin
    IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2023, : 1 - 14
  • [36] Low-rank representation based discriminative projection for robust feature extraction
    Zhang, Nan
    Yang, Jian
    NEUROCOMPUTING, 2013, 111 : 13 - 20
  • [37] STATISTICAL INFERENCE BASED ON ROBUST LOW-RANK DATA MATRIX APPROXIMATION
    Feng, Xingdong
    He, Xuming
    ANNALS OF STATISTICS, 2014, 42 (01): : 190 - 210
  • [38] Infrared Target Tracking Based on Robust Low-Rank Sparse Learning
    He, Yujie
    Li, Min
    Zhang, Jinli
    Yao, Junping
    IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2016, 13 (02) : 232 - 236
  • [39] Low-rank reconstruction-based autoencoder for robust fault detection
    Hu, Zhengwei
    Zhao, Haitao
    Peng, Jingchao
    CONTROL ENGINEERING PRACTICE, 2022, 123
  • [40] Point Cloud Reconstruction Based on Robust Low-Rank Collaborative Estimation
    Feng X.
    Du G.
    Zhao Y.
    He M.
    Jisuanji Fuzhu Sheji Yu Tuxingxue Xuebao/Journal of Computer-Aided Design and Computer Graphics, 2023, 35 (09): : 1344 - 1352