Science China Information Sciences

, Volume 59, Issue 2, pp 1–15 | Cite as

Channel capacity investigation of a linear massive MIMO system using spherical wave model in LOS scenarios

  • Liu Liu
  • David W. Matolak
  • Cheng Tao
  • Yongzhi Li
  • Bo Ai
  • Houjin Chen
Research Paper Special Focus on 5G Wireless Communication Networks

Abstract

Massive multiple-input multiple-output (MIMO) is a key technology for the 5th generation (5G) of wireless communication systems. The traditional plane wave channel model (PWM) is often not suitable for the large antenna structure, and in certain cases should be replaced by the more accurate spherical wave model (SWM). By using the spherical wave characterization method, this paper investigates the channel capacity performance of a linear massive MIMO system in line-of-sight (LOS) scenarios. Two types of access settings, the point to point (PTP) system and multi-user (MU) system, are considered. In the PTP setting, a geometrical optimization is performed to obtain configurations that are able to generate a full rank channel matrix for a linear massive MIMO system, which yields full spatial diversity even in LOS scenarios. Compared with the approximate and commonly applied rank-1 PWM, this is very useful for fixed wireless access and radio relay systems requiring high throughput. For the MU case, we compare the eigenvalue distributions of the LOS channels using the plane wave and spherical wave characterization method, and sum rate results are obtained by Monte Carlo simulations. The results show that MU systems using the more realistic and accurate SWM can achieve a higher sum rate than results from the PWM. This is beneficial and informative when designing massive MIMO wireless networks.

Keywords

massive MIMO channel model plane wave model spherical wave model channel capacity 

直射场景下大规模线性多天线系统基于球面波模型的信道容量研究

摘要

摘要

大规模多天线是未来第五代移动通信系统的关键技术之一,当天线尺寸较大时,传统的球面波信道模型并不适合用于准确的描述传播环境。本文使用更为准确的球面波信道模型,研究了直射场景下大规模线性多天线系统(点对点接入和多用户接入两种架构)的信道容量特征。在点对点接入架构中,本文提出了一种基于位置的系统优化方式,相对于平面波信道模型获得的信道秩为1的结果,该优化方式可以让多天线系统在直射场景下获得信道满秩,从而获得全部空间分集增益;在多用户接入架构下,本文比较了球面波和平面波信道模型下的信道的特征值分布特征,通过使用蒙特卡洛仿真方法研究了系统的信道和速率,仿真结果表明,使用球面波信道模型的信道和速率高于平面波信道模型的信道和速率。

创新点

本文使用更为准确的球面波信道模型,研究了直射场景下大规模线性多天线系统(点对点接入和多用户接入两种架构)的信道容量特征。在点对点接入架构中,本文提出了一种基于位置的系统优化方式,相对于平面波信道模型获得的信道秩为1的结果,该优化方式可以让多天线系统在直射场景下获得信道满秩,从而获得全部空间分集增益;在多用户接入架构下,本文比较了球面波和平面波信道模型下的信道的特征值分布特征,通过使用蒙特卡洛仿真方法研究了系统的信道和速率,仿真结果表明,使用球面波信道模型的信道和速率高于平面波信道模型的信道和速率。

关键词

大规模多天线 信道模型 平面波模型 球面波模型 信道容量 

Keywords

022303 

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References

  1. 1.
    Rusek F, Persson D, Lau B K, et al. Scaling up MIMO: opportunities and challenges with very large arrays. IEEE Signal Process Mag, 2013, 30: 40–60CrossRefGoogle Scholar
  2. 2.
    Larsson E, Edfors O, Tufvesson F, et al. Massive MIMO for next generation wireless systems. IEEE Commun Mag, 2014, 52: 186–195CrossRefGoogle Scholar
  3. 3.
    Ngo H Q, Larsson E, Marzetta T. Energy and spectral efficiency of very large multiuser MIMO Systems. IEEE Trans Commun, 2013, 61: 1436–1449CrossRefGoogle Scholar
  4. 4.
    Ma Z, Zhang Z Q, Ding Z G, et al. Key techniques for 5G wireless communications: network architecture, physical layer, and MAC layer perspectives. Sci China Inf Sci, 2015, 57: 041301Google Scholar
  5. 5.
    Wu S, Wang C X, Haas H, et al. A non-stationary wideband channel model for massive MIMO communication systems. IEEE Trans Wirel Commun, 2015, 14: 1434–1446CrossRefGoogle Scholar
  6. 6.
    Wu S, Wang C X, Aggoune E-H, et al. A non-stationary 3-D wideband twin-cluster model for 5G massive MIMO channels. IEEE J Sel Area Commun, 2014, 32: 1207–1218CrossRefGoogle Scholar
  7. 7.
    Ngo H Q, Larsson E, Marzetta T. Aspects of favorable propagation in massive MIMO. In: Proceedings of the 22nd European Signal Processing Conference (EUSIPCO), Bristol, 2014. 76–80Google Scholar
  8. 8.
    Xing C W, Ma S D, Fei Z S, et al. A general robust linear transceiver design for multi-hop amplify-and-forward MIMO relaying systems. IEEE Trans Signal Process, 2013, 61: 1196–1209MathSciNetCrossRefGoogle Scholar
  9. 9.
    Bohagen F, Orten P, Oien G. Construction and capacity analysis of high-rank line-of-sight MIMO channels. In: Proceedings of IEEE Wireless Communications and Networking Conference (WCNC), New Orleans, 2005. 432–437Google Scholar
  10. 10.
    Bohagen F, Orten P, Oien G. Design of optimal high-rank line-of-sight MIMO channels. IEEE Trans Wirel Commun, 2007, 6: 1420–1425CrossRefGoogle Scholar
  11. 11.
    Bohagen F, Orten P, Oien G. On spherical vs. plane wave modeling of line-of-sight MIMO channels. IEEE Trans Commun, 2009, 57: 841–849Google Scholar
  12. 12.
    Popovski P, Braun V, Ren Z. Deliverable D1.1 Scenarios, requirements and KPIs for 5G mobile and wireless system. Mobile and wireless communications Enablers for the Twenty-twenty Information Society. Technical Report. 2013Google Scholar
  13. 13.
    Rappaport T, Sun S, Mayzus R, et al. Millimeter wave mobile communications for 5g cellular: It will work! IEEE Access, 2013, 1: 335–349CrossRefGoogle Scholar
  14. 14.
    Hoydis J, Hosseini K, Brink S T, et al. Making smart use of excess antennas: massive MIMO, small cells, and TDD. BELL Labs Tech J, 2013, 18: 5–21CrossRefGoogle Scholar
  15. 15.
    Rade L, Westergren B. Mathematics Handbook for Science and Engineering. Berlin/New York: Springer Lund (Sweden), 2004CrossRefMATHGoogle Scholar
  16. 16.
    Haustein T, Kruger U. Smart geometrical antenna design exploiting the los component to enhance a MIMO system based on rayleigh-fading in indoor scenarios. In: Proceedings of the IEEE 14th International Symposium on Personal, Indoor and Mobile Radio Communications, Beijing, 2003. 1144–1148Google Scholar
  17. 17.
    Tse D, Viswanath P. Fundamentals of Wireless Communication. New York: Cambridge University Press, 2005CrossRefMATHGoogle Scholar
  18. 18.
    Capps C. Near field or far field? EDN, 2001. 95–101Google Scholar
  19. 19.
    Liu L, Matolak D W, Tao C, et al. Far region boundary definition of linear massive MIMO antenna arrays. In: Proceedings of the 82nd IEEE Vehicular Technology Conference (VTC2015-Fall), Boston, 2015. 1–6Google Scholar
  20. 20.
    Chen S Z, Zhao J. The requirements, challenges, and technologies for 5G of terrestrial mobile telecommunication. IEEE Commun Mag, 2014, 52: 36–43CrossRefGoogle Scholar
  21. 21.
    Tulino A M, Verdú S. Random matrix theory and wireless communications. Found Trends Commun Inf Theory, 2004, 1: 1–182. http://dx.doi.org/10.1561/0100000001CrossRefMATHGoogle Scholar

Copyright information

© Science China Press and Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Liu Liu
    • 1
    • 2
    • 3
  • David W. Matolak
    • 2
  • Cheng Tao
    • 1
    • 3
  • Yongzhi Li
    • 1
  • Bo Ai
    • 1
  • Houjin Chen
    • 1
  1. 1.Institute of Broadband Wireless Mobile CommunicationsBeijing Jiaotong UniversityBeijingChina
  2. 2.Department of Electrical Engineering University of South CarolinaolumbiaUSA
  3. 3.National Mobile Communications Research LaboratorySoutheast UniversityNanjingChina

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