Channel Magnifying Limited Feedback Technique for FDD Massive MIMO Systems

被引:0
|
作者
Hoondong Noh
Younsun Kim
Chungyong Lee
机构
[1] Digital Media and Communications R&D Center,Department of Electrical and Electronic Engineering
[2] Yonsei University,undefined
来源
关键词
Massive MIMO; FDD system; Limited feedback;
D O I
暂无
中图分类号
学科分类号
摘要
A limited feedback system, so-called, channel magnifying (CM) is proposed for a downlink (DL) frequency-division duplex (FDD) massive multiple-input multiple-output (MIMO) system. Although massive MIMO system has received significant research interest as a key technology for beyond 4G wireless communications systems, it has a number of issues that needs to be technically addressed. Among such issues is the difficulty of acquiring channel state information at transmitter for an FDD massive MIMO system which cannot exploit channel reciprocity as in a time-division duplex system. The proposed CM technique makes it possible to support a few user equipments in DL FDD massive MIMO system by finding a balance between spatial resources and channel quantization error (CQE). By choosing a subchannel with low CQE, CM can secure multiplexing gain at high SNR based on a fixed size codebook. Two types of subchannel indicator alignment (SIA) schemes are introduced for efficient interference nulling for the proposed CM technique. Specifically, we discuss how to maximize the sumrate of CM through genie added SIA and minimum CQE based SIA. Simulation results show that the sum rate of the proposed CM has a higher multiplexing gain than that of random vector quantization, especially when the number of transmit antennas is sufficiently large.
引用
收藏
页码:953 / 962
页数:9
相关论文
共 50 条
  • [1] Channel Magnifying Limited Feedback Technique for FDD Massive MIMO Systems
    Noh, Hoondong
    Kim, Younsun
    Lee, Chungyong
    [J]. WIRELESS PERSONAL COMMUNICATIONS, 2016, 88 (04) : 953 - 962
  • [2] Downlink Channel Covariance Matrix Reconstruction for FDD Massive MIMO Systems With Limited Feedback
    Li, Kai
    Li, Ying
    Cheng, Lei
    Shi, Qingjiang
    Luo, Zhi-Quan
    [J]. IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2024, 72 : 1032 - 1048
  • [3] Joint Channel Training and Feedback for FDD Massive MIMO Systems
    Shen, Wenqian
    Dai, Linglong
    Shi, Yi
    Shim, Byonghyo
    Wang, Zhaocheng
    [J]. IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2016, 65 (10) : 8762 - +
  • [4] Limited Feedback Scheme for Massive MIMO in Mobile Multiuser FDD Systems
    Kurniawan, Ernest
    Joung, Jingon
    Sun, Sumei
    [J]. 2015 IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS (ICC), 2015, : 1710 - 1715
  • [5] FDD Multiuser Massive MIMO Systems with Adaptive Channel Covariance Feedback
    Bazzi, Samer
    Xu, Wen
    [J]. 2019 IEEE 30TH ANNUAL INTERNATIONAL SYMPOSIUM ON PERSONAL, INDOOR AND MOBILE RADIO COMMUNICATIONS (PIMRC), 2019, : 620 - 625
  • [6] A Covariance-Based Hybrid Channel Feedback in FDD Massive MIMO Systems
    Qiu, Shuang
    Gesbert, David
    Chen, Da
    Jiang, Tao
    [J]. IEEE TRANSACTIONS ON COMMUNICATIONS, 2019, 67 (12) : 8365 - 8377
  • [7] Joint Channel Estimation and Feedback with Low Overhead for FDD Massive MIMO Systems
    Dai, Linglong
    Gao, Zhen
    Wang, Zhaocheng
    [J]. 2015 IEEE/CIC INTERNATIONAL CONFERENCE ON COMMUNICATIONS IN CHINA (ICCC), 2015,
  • [8] Block Compressive Channel Estimation and Feedback for FDD Massive MIMO
    Gao, Zhen
    Dai, Linglong
    Dai, Wei
    Wang, Zhaocheng
    [J]. 2015 IEEE CONFERENCE ON COMPUTER COMMUNICATIONS WORKSHOPS (INFOCOM WKSHPS), 2015, : 49 - 50
  • [9] Downlink Channel Estimation with Limited Feedback for FDD Multi-User Massive MIMO with Spatial Channel Correlation
    Almosa, Hayder
    Mosleh, Susanna
    Perrins, Erik
    Liu, Lingjia
    [J]. 2018 IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS (ICC), 2018,
  • [10] AoD-Adaptive Subspace Codebook for Channel Feedback in FDD Massive MIMO Systems
    Shen, Wenqian
    Dai, Linglong
    Gui, Guan
    Wang, Zhaocheng
    Heath, Robert W., Jr.
    Adachi, Fumiyuki
    [J]. 2017 IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS (ICC), 2017,