Low-Complexity Hybrid Precoding and Combining Scheme Based on Array Response Vectors

被引:0
作者
Bahingayi, Eduard Elias [1 ]
Lee, Kyungchun [1 ]
机构
[1] Seoul Natl Univ Sci & Technol, Dept Elect & Informat Engn, Res Ctr Elect & Informat Technol, Seoul, South Korea
来源
2020 IEEE WIRELESS COMMUNICATIONS AND NETWORKING CONFERENCE (WCNC) | 2020年
基金
新加坡国家研究基金会;
关键词
Millimeter wave; multiple-input multiple-output (MIMO); massive MIMO; combining; precoding; array response vectors; dictionary; subset selection; PHASE SHIFTERS; DESIGN;
D O I
10.1109/wcnc45663.2020.9120599
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The hybrid precoding and combining algorithms for mmWave massive multiple-input multiple-output (MIMO) systems must consider the trade-off between the complexity and performance of the system. Unfortunately, because of the unit-norm constraint imposed by the use of phase shifters, the optimization of the radio frequency (RF) precoder and combiner becomes a non-convex problem. As a consequence, the algorithm for hybrid precoding and combining design often incurs high complexity. This paper proposes a low-complexity algorithm for hybrid precoding and combining design based on array response vectors. The proposed algorithm considers a decoupled optimization scheme between the RF and baseband domains for the spectral efficiency-maximization problem. In the RF domain, we propose an incremental successive selection method to find a subset of array response vectors from a dictionary, which forms the RF precoding/combining matrices. For the digital domain, we employ singular-value decomposition (SVD) of the low-dimensional effective channel matrix to generate the digital baseband precoder and combiner. Through numerical simulation, we show that the proposed algorithm achieves near-optimal performance with 89.9% - 99.4% complexity reduction compared to the conventional state-of-the-art hybrid precoding and combining algorithm.
引用
收藏
页数:6
相关论文
共 17 条
  • [1] A Survey on Hybrid Beamforming Techniques in 5G: Architecture and System Model Perspectives
    Ahmed, Irfan
    Khammari, Hedi
    Shahid, Adnan
    Musa, Ahmed
    Kim, Kwang Soon
    De Poorter, Eli
    Moerman, Ingrid
    [J]. IEEE COMMUNICATIONS SURVEYS AND TUTORIALS, 2018, 20 (04): : 3060 - 3097
  • [2] Bartholdi J. J. III, 1982, Operations Research Letters, V1, P190, DOI 10.1016/0167-6377(82)90038-4
  • [3] Compressive Sensing (CS) Assisted Low-Complexity Beamspace Hybrid Precoding for Millimeter-Wave MIMO Systems
    Chen, Chiang-Hen
    Tsai, Cheng-Rung
    Liu, Yu-Hsin
    Hung, Wei-Lun
    Wu, An-Yeu
    [J]. IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2017, 65 (06) : 1412 - 1424
  • [4] Design considerations for 60 GHz CMOS radios
    Doan, CH
    Emami, S
    Sobel, DA
    Niknejad, AM
    Brodersen, RW
    [J]. IEEE COMMUNICATIONS MAGAZINE, 2004, 42 (12) : 132 - 140
  • [5] Spatially Sparse Precoding in Millimeter Wave MIMO Systems
    El Ayach, Omar
    Rajagopal, Sridhar
    Abu-Surra, Shadi
    Pi, Zhouyue
    Heath, Robert W., Jr.
    [J]. IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, 2014, 13 (03) : 1499 - 1513
  • [6] El Ayach O, 2012, IEEE INT WORK SIGN P, P100, DOI 10.1109/SPAWC.2012.6292865
  • [7] Fast antenna subset selection in MIMO systems
    Gharavi-Alkhansari, M
    Gershman, AB
    [J]. IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2004, 52 (02) : 339 - 347
  • [8] Capacity limits of MIMO channels
    Goldsmith, A
    Jafar, SA
    Jindal, N
    Vishwanath, S
    [J]. IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, 2003, 21 (05) : 684 - 702
  • [9] Golub G.H., 2012, MATRIX COMPUTATIONS
  • [10] Horn R. A., 1991, TOPICS MATRIX ANAL