Low-complexity signal detection for massive MIMO systems via trace iterative method

doi:10.23919/JSEE.2024.000061

Journal of Systems Engineering and Electronics ›› 2024, Vol. 35 ›› Issue (3): 549-557.doi: 10.23919/JSEE.2024.000061

• HIGH-DIMENSIONAL SIGNAL PROCESSING • Previous Articles

Low-complexity signal detection for massive MIMO systems via trace iterative method

A. Khoso IMRAN¹^,²(), Xiaofei ZHANG¹^,*(), Hayee Shaikh ABDUL¹(), A. Khoso IHSAN³(), Ahmed Dayo ZAHEER⁴()

¹ College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
² School of Electrical Engineering, Korea University, Seoul 02841, Korea
³ School of Mathematics, South China University of Technology, Guangzhou 510641, China
⁴ Department of Computer Science, Huanggang Normal University, Huanggang 438000, China

Received:2022-09-15 Online:2024-06-18 Published:2024-06-19
Contact: Xiaofei ZHANG E-mail:imrankhoso2@gmail.com;zhangxiaofei@nuaa.edu.cn;shaikhhayee@yahoo.com;ihsankhoso@gmail.com;hjxnd88@126.com
About author:
IMRAN A. Khoso was born in 1991. He received his M.S. degree in information and communication engineering from the University of Science and Technology Beijing, China, and Ph.D. degree in communication and information systems from Nanjing University of Aeronautics and Astronautics, Nanjing, China in 2019. He is currently with the School of Electrical Engineering, Korea University, Seoul, Korea. His research interests include random matrix theory, signal processing, and wireless communications. E-mail: imrankhoso2@gmail.com

ZHANG Xiaofei was born in 1977. He received his M.S degree from Wuhan University, Wuhan, China, in 2001, and Ph.D. degree in communication and information systems from Nanjing University of Aeronautics and Astronautics in 2005. He is a full professor with the Electronic Engineering Department, Nanjing University of Aeronautics and Astronautics, Nanjing, China. His research interest include array signal processing and communication signal processing. E-mail: zhangxiaofei@nuaa.edu.cn

ABDUL Hayee Shaikh was born in 1989. He received his M.E. degree in computer and information Engineering from Mehran University in 2016. He is currently working toward his Ph.D. degree in communication and information systems at Nanjing University of Aeronautics and Astronautics. His research interests include array signal processing and wireless communications technology. E-mail: shaikhhayee@yahoo.com

IHSAN A. Khoso was born in 1990. He received his B.S. degree in mathematics, the Institute of Mathematics and Computer Science, the University of Sindh, Sindh, Pakistan, in 2012, and M.S. degree in mathematics with the South China University of Technology, Guangzhou, China in 2016. His research interests include nonlinear partial differential equations and random matrix theory. E-mail: ihsankhoso@gmail.com

ZAHEER Ahmed Dayo was born in 1989. He received his M.E. degree in telecommunication engineering and management from the Mehran University of Engineering and Technology, Jamshoro, Pakistan, in 2014, and Ph.D. degree in communication and information systems from Nanjing University of Aeronautics and Astronautics, Nanjing, China, in 2021. He is currently an associate professor with the Department of Computer Science, Huanggang Normal University, Hubei, China. His research interests include multiple-input multiple-output techniques, designing and manufacturing of compact, broadband, high-gain antennas, array topology and optimization schemes, active and passive frequency selective surfaces, multi-band and slot antennas, and reconfigurable and meta-material inspired antennas. E-mail: hjxnd88@126.com
Supported by:
This work was supported by National Natural Science Foundation of China (62371225;62371227).

Abstract

Abstract:

Linear minimum mean square error (MMSE) detection has been shown to achieve near-optimal performance for massive multiple-input multiple-output (MIMO) systems but inevitably involves complicated matrix inversion, which entails high complexity. To avoid the exact matrix inversion, a considerable number of implicit and explicit approximate matrix inversion based detection methods is proposed. By combining the advantages of both the explicit and the implicit matrix inversion, this paper introduces a new low-complexity signal detection algorithm. Firstly, the relationship between implicit and explicit techniques is analyzed. Then, an enhanced Newton iteration method is introduced to realize an approximate MMSE detection for massive MIMO uplink systems. The proposed improved Newton iteration significantly reduces the complexity of conventional Newton iteration. However, its complexity is still high for higher iterations. Thus, it is applied only for first two iterations. For subsequent iterations, we propose a novel trace iterative method (TIM) based low-complexity algorithm, which has significantly lower complexity than higher Newton iterations. Convergence guarantees of the proposed detector are also provided. Numerical simulations verify that the proposed detector exhibits significant performance enhancement over recently reported iterative detectors and achieves close-to-MMSE performance while retaining the low-complexity advantage for systems with hundreds of antennas.

Key words: signal detection, low-complexity, linear minimum mean square error (MMSE), massive multiple-input multiple-output (MIMO), trace iterative method (TIM)

A. Khoso IMRAN, Xiaofei ZHANG, Hayee Shaikh ABDUL, A. Khoso IHSAN, Ahmed Dayo ZAHEER. Low-complexity signal detection for massive MIMO systems via trace iterative method[J]. Journal of Systems Engineering and Electronics, 2024, 35(3): 549-557.

Figures/Tables 7

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Step	Complexity
Initialization matrix	$ U+7 $
Improved Newton ($ i=2 $)	$ {2U}^{2}+2U $
Improved Newton ($ i=3 $)	$ {6U}^{2}+7U $
TIM iteration	$ {U}^{2}+U+1 $

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Low-complexity signal detection for massive MIMO systems via trace iterative method

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