Low-Complexity Downlink User Selection for Massive MIMO Systems

被引:18
作者
Liu, Haijing [1 ]
Gao, Hui [1 ]
Yang, Shaoshi [2 ]
Lv, Tiejun [1 ]
机构
[1] Beijing Univ Posts & Telecommun, Beijing 100876, Peoples R China
[2] Univ Southampton, Sch Elect & Comp Sci, Southampton SO17 1BJ, Hants, England
来源
IEEE SYSTEMS JOURNAL | 2017年 / 11卷 / 02期
基金
中国国家自然科学基金;
关键词
Low complexity; massive multiple input multiple output (MIMO); system sum rate; user fairness; user selection; LARGE-SCALE MIMO; MULTIUSER MIMO; CHANNEL ESTIMATION; PERFORMANCE; ANTENNAS; DESIGN;
D O I
10.1109/JSYST.2015.2422475
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
In this paper, we propose a pair of low-complexity user selection schemes with zero-forcing precoding for multi-user massive multiple-input-multiple-output downlink systems, in which the base station is equipped with a large-scale antenna array. First, we derive approximations of the ergodic sum rates of the systems invoking the conventional random user selection (RUS) and the location-dependent user selection (LUS). Then, the optimal number of simultaneously served user equipments (UEs), i.e., K*, is investigated to maximize the sum rate approximations. Upon exploiting K*, we develop two user selection schemes, namely, K*-RUS and K*-LUS, where K* UEs are selected either randomly or based on their locations. Both the proposed schemes are independent of the instantaneous channel state information of small-scale fading, therefore enjoying the same extremely low computational complexity as that of the conventional RUS scheme. Moreover, both of our proposed schemes achieve significant sum rate improvement over the conventional RUS. In addition, it is worth noting that, like the conventional RUS, the K*-RUS achieves good fairness among UEs.
引用
收藏
页码:1072 / 1083
页数:12
相关论文
共 33 条
  • [1] 3GPP, 2010, document TR 36
  • [2] [Anonymous], 2008, 21101 3GPP TR
  • [3] [Anonymous], 2011, CELLULAR NETWORKS PO
  • [4] [Anonymous], 2011, 36817 3GPP TR
  • [5] Bai O., 2014, P IEEE INT C COMM SY, P1
  • [6] Training-based MIMO channel estimation: A study of estimator tradeoffs and optimal training signals
    Biguesh, M
    Gershman, AB
    [J]. IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2006, 54 (03) : 884 - 893
  • [7] Optimal Design of Energy-Efficient Multi-User MIMO Systems: Is Massive MIMO the Answer?
    Bjornson, Emil
    Sanguinetti, Luca
    Hoydis, Jakob
    Debbah, Merouane
    [J]. IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, 2015, 14 (06) : 3059 - 3075
  • [8] Resource Allocation for Cost Minimization in Limited Feedback MU-MIMO Systems With Delay Guarantee
    Chen, Xiaoming
    Zhang, Zhaoyang
    Yuen, Chau
    [J]. IEEE SYSTEMS JOURNAL, 2015, 9 (04): : 1229 - 1236
  • [9] Spectral and Energy Spectral Efficiency Optimization of Joint Transmit and Receive Beamforming Based Multi-Relay MIMO-OFDMA Cellular Networks
    Cheung, Kent Tsz Kan
    Yang, Shaoshi
    Hanzo, Lajos
    [J]. IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, 2014, 13 (11) : 6147 - 6165
  • [10] Couillet R., 2011, Random matrix methods for wireless communications