A Scalable Framework for CSI Feedback in FDD Massive MIMO via DL Path Aligning

被引:8
|
作者
Luo, Xiliang [1 ]
Cai, Penghao [1 ]
Zhang, Xiaoyu [1 ]
Hu, Die [2 ]
Shen, Cong [3 ]
机构
[1] ShanghaiTech Univ, Sch Informat Sci & Technol, Shanghai 201210, Peoples R China
[2] Fudan Univ, Sch Informat Sci & Technol, Shanghai 200433, Peoples R China
[3] Univ Sci & Technol China, Hefei 230022, Peoples R China
关键词
Multiple-input multiple-output; MIMO; massive MIMO; frequency-division duplexing; FDD; time-division duplexing; TDD; channel state information; CSI feedback; TDD reciprocity; aligning; pilots; CHANNEL ESTIMATION; ANTENNA SYSTEMS; DESIGN;
D O I
10.1109/TSP.2017.2713768
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
Unlike the time-division duplexing systems, the down-link (DL) and uplink (UL) channels are not reciprocal in the case of frequency-division duplexing (FDD). However, some long-term parameters, e.g., the time delays and angles of arrival of the channel paths, enjoy reciprocity. In this paper, by efficiently exploiting the aforementioned limited reciprocity, we address the DL channel state information (CSI) feedback in a practical wideband massive multiple-input multiple-output system operating in the FDD mode. With orthogonal frequency-division multiplexing waveform and assuming frequency-selective fading channels, we propose a scalable framework for the DL pilots design, DL CSI acquisition, and the corresponding CSI feedback in the UL. In particular, the base station (BS) can transmit the FFT-based pilots with carefully selected phase shifts. Then, the user can rely on the so-called time-domain aggregate channel to derive the feedback of reduced dimensionality according to either its own knowledge about the statistics of the DL channels or the instruction from the serving BS. We demonstrate that each user can just feed back one scalar number per DL channel path for the BS to recover the DL CSIs. Comprehensive numerical results further corroborate our designs.
引用
收藏
页码:4702 / 4716
页数:15
相关论文
共 50 条
  • [31] Modular CSI Quantization for FDD Massive MIMO Communication
    Liao, Jialing
    Vehkalahti, Roope
    Pllaha, Tefjol
    Han, Wei
    Tirkkonen, Olav
    IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, 2023, 22 (12) : 8543 - 8558
  • [32] Scalable user selection in FDD massive MIMO
    Xing Zhang
    Ashutosh Sabharwal
    EURASIP Journal on Wireless Communications and Networking, 2021
  • [33] Scalable user selection in FDD massive MIMO
    Zhang, Xing
    Sabharwal, Ashutosh
    EURASIP JOURNAL ON WIRELESS COMMUNICATIONS AND NETWORKING, 2021, 2021 (01)
  • [34] Intelligent CSI feedback method for fast time-varying FDD massive MIMO system
    Liao Y.
    Wang S.
    Sun N.
    Tongxin Xuebao/Journal on Communications, 2021, 42 (07): : 211 - 219
  • [35] Integrated Deep Implicit CSI Feedback and Beamforming Design for FDD mmWave Massive MIMO Systems
    Xue, Qiulin
    Dong, Chao
    Li, Xiangjun
    Yi, Jianzhong
    Niu, Kai
    IEEE WIRELESS COMMUNICATIONS LETTERS, 2023, 12 (01) : 119 - 123
  • [36] Deep Learning-Based Denoise Network for CSI Feedback in FDD Massive MIMO Systems
    Ye, Hongyuan
    Gao, Feifei
    Qian, Jing
    Wang, Hao
    Li, Geoffrey Ye
    IEEE COMMUNICATIONS LETTERS, 2020, 24 (08) : 1742 - 1746
  • [37] Learning a Common Dictionary for CSI Feedback in FDD Massive MU-MIMO-OFDM Systems
    Gadamsetty, Pavan Kumar
    Hari, K. V. S.
    Hanzo, Lajos
    IEEE OPEN JOURNAL OF VEHICULAR TECHNOLOGY, 2023, 4 : 530 - 544
  • [38] Improve Downlink Rates of FDD Massive MIMO Systems by Exploiting CSI Feedback Waiting Phase
    Tao, Zhihao
    Wangt, Tianyu
    Wang, Shaowei
    2019 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM), 2019,
  • [39] FDD-RT: A Simple CSI Acquisition Technique via Channel Reciprocity for FDD Massive MIMO Downlink
    Liang, Han-Wen
    Chung, Wei-Ho
    Kuo, Sy-Yen
    IEEE SYSTEMS JOURNAL, 2018, 12 (01): : 714 - 724
  • [40] Deep Learning and Compressive Sensing-Based CSI Feedback in FDD Massive MIMO Systems
    Liang, Peizhe
    Fan, Jiancun
    Shen, Wenhan
    Qin, Zhijin
    Li, Geoffrey Ye
    IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2020, 69 (08) : 9217 - 9222