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Low-Complexity MMSE Signal Detection Based on the AOR Iterative Algorithm for Uplink Massive MIMO Systems

  • Zhenyu Zhang
  • , Yuanyuan Dong
  • , Zhongshan Zhang
  • , Xiyuan Wang
  • , Xiaoming Dai*
  • , Linglong Dai
  • , Haijun Zhang
  • *此作品的通讯作者
  • University of Science and Technology Beijing
  • Tsinghua University

科研成果: 书/报告/会议事项章节会议稿件同行评审

摘要

Massive multiple-input multiple-output (MIMO) systems can substantially improve the spectral efficiency and system capacity by equipping a large number of antennas at the base station and it is envisaged to be one of the critical technologies in the next generation of wireless communication systems. However, the computational complexity of the signal detection in massive MIMO systems presents a significant challenge for practical hardware implementations. This work proposed a novel minimum mean square error (MMSE) signal detection method based on the accelerated overrelaxation (AOR) iterative algorithm. The proposed AOR-based method can reduce the overall complexity of the classical MMSE signal detection by an order of magnitude from O(K3) to O(K2), where K is the number of users. Numerical results illustrate that the proposed AOR-based algorithm can outperform the performance of the recently proposed Neumann series approximation-based algorithm and approach the conventional MMSE signal detection involving exact matrix inversion with significantly reduced complexity.

源语言英语
主期刊名5G for Future Wireless Networks - 1st International Conference, 5GWN 2017, Proceedings
编辑Zhiyong Feng, Yonghui Li, Victor C.M. Leung, Keping Long, Haijun Zhang, Zhongshan Zhang
出版商Springer Verlag
385-394
页数10
ISBN(印刷版)9783319728223
DOI
出版状态已出版 - 2018
已对外发布
活动1st International Conference on 5G for Future Wireless Networks, 5GWN 2017 - Beijing, 中国
期限: 21 4月 201723 4月 2017

丛书

姓名Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
211
ISSN(印刷版)1867-8211

会议

会议1st International Conference on 5G for Future Wireless Networks, 5GWN 2017
国家/地区中国
Beijing
时期21/04/1723/04/17

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