Gram-Schmidt based hybrid beamforming for mmWave MIMO systems

doi:10.1016/S1005-8885(16)60070-5

JOURNAL OF CHINA UNIVERSITIES OF POSTS AND TELECOM ›› 2016, Vol. 23 ›› Issue (6): 53-59.doi: 10.1016/S1005-8885(16)60070-5

• Wireless • Previous Articles Next Articles

Gram-Schmidt based hybrid beamforming for mmWave MIMO systems

Received:2016-08-15 Revised:2016-12-19 Online:2016-12-31 Published:2016-12-30
Contact: Xiao-Hui LI E-mail:xhli@mail.xidian.edu.cn
Supported by:
National Natural Science Foundation of China;State 863 Project;State 111 Project

Abstract

Abstract: Due to the high cost and power consumption of the radio frequency (RF) chains, it is difficult to implement the full digital beamforming in millimeter-wave (mmWave) multiple-input multiple-output (MIMO) systems. Fortunately, the hybrid beamforming (HBF) is proposed to overcome these limitations by splitting the beamforming process between the analog and digital domains. In recent works, most HBF schemes improve the spectral efficiency based on greedy algorithms. However, the iterative process in greedy algorithms leads to high computational complexity. In this paper, a new method is proposed to achieve a reasonable compromise between complexity and performance. The novel algorithm utilizes the low-complexity Gram-Schmidt method to orthogonalize the candidate vectors. With the orthogonal candidate matrix, the slow greedy algorithm is avoided. Thus, the RF vectors are found simultaneously without any iteration. Additionally, the phase extraction is applied to satisfy the element-wise constant-magnitude constraint on the RF matrix. Simulation results demonstrate that the new HBF algorithm can make substantial improvements in complexity while maintaining good performance.

Key words: MIMO, hybrid beamforming, mmWave

References

1. Andrews J G, Buzzi S, Choi W, et al. What will 5G be? IEEE Journal on Selected Areas in Communications, 2014, 32(6): 1065-1082

2. Boccardi F, Heath R W, Lozano A, et al. Five disruptive technology directions for 5G. IEEE Communications Magazine, 2014, 52(2): 74-80

3. Rusek F, Persson D, Lau B K, et al. Scaling up MIMO: opportunities and challenges with very large arrays. IEEE Signal Processing Magazine, 2013, 30(1): 40-60

4. Rangan S, Rappaport T S, Erkip E. Millimeter-wave cellular wireless networks: potentials and challenges. Proceedings of the IEEE, 2014, 102(3): 366-385

5. Kim T, Park J, Seol J Y, et al. Tens of Gbps support with mmWave beamforming systems for next generation communications. Proceedings of the 2013 IEEE Global Communications Conference (GLOBECOM’13), Dec 9-13, 2013, Atlanta, GA, USA. Piscataway, NJ, USA: IEEE, 2013: 3685-3690

6. Pi Z, Khan F. An introduction to millimeter-wave mobile broadband systems. IEEE Communications Magazine, 2011, 49(6): 101-107

7. Roh W, Seol J Y, Park J, et al. Millimeter-wave beamforming as an enabling technology for 5G cellular communications: theoretical feasibility and prototype results. IEEE Communications Magazine, 2014, 52(2): 106-113

8. El Ayach O, Heath R W, Abu-Surra S, et al. Low complexity precoding for large millimeter wave MIMO systems. Proceedings of the 2012 IEEE International Conference on Communications (ICC’12), Jun 10-15, 2012, Ottawa, Canada. Piscataway, NJ, USA: IEEE, 2012: 3724-3729

9. El Ayach O, Rajagopal S, Abu-Surra S, et al. Spatially sparse precoding in millimeter wave MIMO systems. IEEE Transactions on Wireless Communications, 2014, 13(3): 1499-1513

10. Chen C E. An iterative hybrid transceiver design algorithm for millimeter wave MIMO systems. IEEE Wireless Communications Letters, 2015, 4(3): 285-288

11. Yu X H, Shen J C, Zhang J, et al. Alternating minimization algorithms for hybrid precoding in millimeter wave MIMO systems. IEEE Journal of Selected Topics in Signal Processing, 2016, 10(3): 485-500

12. Lee Y Y, Wang C H, Huang Y H. A hybrid RF/baseband precoding processor based on parallel-index-selection matrix-inversion-bypass simultaneous orthogonal matching pursuit for millimeter wave MIMO systems. IEEE Transactions on Signal Processing, 2015, 63(2): 305-317

13. Rusu C, Méndez-Rial R, González-Prelcicy N, et al. Low complexity hybrid sparse precoding and combining in millimeter wave MIMO systems. Proceedings of the 2015 IEEE International Conference on Communications (ICC’15), Jun 8-12, 2015, London, UK. Piscataway, NJ, USA: IEEE, 2015: 1340-1345

14. Gao X, Dai L, Han S, et al. Energy-efficient hybrid analog and digital precoding for mmWave MIMO systems with large antenna arrays. IEEE Journal on Selected Areas in Communications, 2016, 34(4): 998-1009

15. Hung W L, Chen C H, Liao C C, et al. Low-complexity hybrid precoding algorithm based on orthogonal beamforming codebook. Proceedings of the 2015 IEEE Workshop on Signal Processing Systems (SiPS’15), Oct 14-16, 2015, Hangzhou, China. Piscataway, NJ, USA: IEEE, 2015: 5p

16. Björck Å. Numerics of Gram-Schmidt orthogonalization. Linear Algebra and Its Applications, 1994, 197-198: 297-316

Metrics

Comments

Copyright © 2020 The Journal of China Universities of Posts and Telecommunications
　 Adress: P.O. Box 231,Beijing University of Posts and Telecommunications,10 Xi Tucheng Road,Beijing 100876,P.R.China　Post Code: 100081
Tel：86-010-62282493　Fax： 86-010-62283461　E-mail: jchupt@bupt.edu.cn
Support by: Beijing Magtech Co.Ltd

Gram-Schmidt based hybrid beamforming for mmWave MIMO systems

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 0

Recommended Articles

Metrics

Comments