Received March 1, 2021, accepted March 10, 2021, date of publication March 15, 2021, date of current version March 29, 2021. Digital Object Identifier 10.1109/ACCESS.2021.3065923 Low Complexity Linear Detectors for Massive MIMO: A Comparative Study MAHMOUD A. ALBREEM 1 , (Senior Member, IEEE), WAEL A. SALAH 2 , (Senior Member, IEEE), ARUN KUMAR 3 , (Member, IEEE), MOHAMMED H. ALSHARIF 4 , ALI HANAFIAH RAMBE 5 , (Member, IEEE), MUZAMMIL JUSOH 6 , (Member, IEEE), AND ANTHONY NGOZICHUKWUKA UWAECHIA 7 1 Department of Electronics and Communication Engineering, A’Sharqiyah University, Ibra 400, Oman 2 Department of Electrical Engineering, College of Engineering and Technology, Palestine Technical University - Kadoorie, Tulkarm, Palestine 3 Department of Electronics and Communication Engineering, JECRC University, Jaipur 303905, India 4 Department of Electrical Engineering, College of Electronics and Information Engineering, Sejong University, Seoul 05006, South Korea 5 Department of Electrical Engineering, Universitas Sumatera Utara, Medan 20155, Indonesia 6 Advanced Communication Engineering, Centre of Excellence, Faculty of Electronic Engineering Technology, Universiti Malaysia Perlis, Kangar 02600, Malaysia 7 Department of Electronics and Computer Engineering, Veritas University, Abuja, Nigeria Corresponding authors: Mahmoud A. Albreem (mahmoud.albreem@asu.edu.om) and Muzammil Jusoh (muzammil@unimap.edu.my) This research has been financially supported in part by the Research Council (TRC) of the Sultanate of Oman (agreement No. TRC/BFP/ASU/01/2019) and in part by the Universiti Malaysia Perlis (UniMAP). ABSTRACT Massive multiple-input multiple-output (M-MIMO) is a significant pillar in fifth generation (5G) networks where a large number of antennas is deployed. It provides massive advantages to modern communication systems in data rate, spectral efficiency, number of users serviced simultaneously, energy efficiency, and quality of service (QoS). However, it requires advanced signal processing for data detection. The growing MIMO size leads to complicated scenarios, which makes the detector design a knotty problem. The problem is also becoming more complicated when high-order modulation schemes are exploited and more users are multiplexed. Therefore, it is not practical to employ the maximum likelihood (ML) detector despite the excellent performance. Linear detectors are alternative solutions and relatively simple. Unfortu- nately, they still need an exact matrix inversion computation, which bears to a significant high complexity. Therefore, several iterative methods are utilized to approximate or evade the matrix inversion rather than computing it. This paper studies the pros and cons of iterative matrix inversion methods where the number of computations and bit-error-rate (BER) are considered to compare between the methods. The comparison is conducted in several scenarios such as different ratio between the number of base station (BS) antennas and user terminal (UT) antennas (β ), the number of iterations (n), and the relaxation parameter (ω). This paper also studies the impact of ω in the performance of Richardson (RI) and the successive over-relaxation (SOR) methods. Numerical results show that the conjugate gradient (CG) and optimized coordinate descent (OCD) methods exhibit the lowest complexity with an acceptable performance. In addition, the Gauss-Seidel (GS) method outperforms all other detectors with a trivial complexity increment. It is also noticed that the performance is not improved with every iteration. It is also shown that ω has a great impact and a significant role in achieving a satisfactory performance in both RI and SOR based detectors. From implementation point of view, detectors based on RI, OCD, and CG methods have achieved the highest hardware efficiency (HE) while Jacobi (JA) based detector has obtained the lowest HE. Recent research advances of detection methods are also presented in the open research direction with a potential impact of linear detection methods in initialization and pre-processing. INDEX TERMS 5G, M-MIMO, detection, relaxation parameter, hardware efficiency, performance, Jacobi, conjugate gradient, Gaus-Seidel, optimized coordinate descent, Richardson, Neumann series. I. INTRODUCTION The number of mobile devices and mobile data traffic are tremendously growing year over year. It is anticipated that The associate editor coordinating the review of this manuscript and approving it for publication was Barbara Masini . the number of mobile devices will approach 12.3 billion in 2022 while it was 7.6 billion in 2015 [1]. Sequen- tially, the mobile data rate will grow more than twenty-fold between 2015 and 2023 [17]–[19]. However, mobile carri- ers are requested to provide higher data rates, better spec- tral efficiency, and larger network capacity. Fifth generation 45740 This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/ VOLUME 9, 2021