Research Article Signal Detection Based on Parallel Group Detection Algorithm for High-Load Massive MIMO Systems Thanh-Binh Nguyen , 1 Minh-Tuan Le, 2 and Vu-Duc Ngo 3 1 Le Quy Don Technical University, No. 236 Hoang Quoc Viet Street, Cau Giay Dist., Hanoi, Vietnam 2 MobiFone R&D Center, MobiFone Corporation, VP1 Lot, Yen Hoa Ward, Cau Giay Dist., Hanoi, Vietnam 3 Hanoi University of Science and Technology, No. 01 Dai Co Viet Street, Hai Ba Trung Dist., Hanoi, Vietnam Correspondence should be addressed to anh-Binh Nguyen; nguyenthanhbinhsqtt@gmail.com Received 20 April 2019; Revised 29 July 2019; Accepted 13 September 2019; Published 12 December 2019 Academic Editor: Andr´ e L. F. de Almeida Copyright © 2019 anh-Binh Nguyen et al. is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. In this paper, a parallel group detection (PGD) algorithm is proposed in order to address the degradation in the bit error rate (BER) performance of linear detectors when they are used in high-load massive MIMO systems. e algorithm is constructed by converting the equivalent extended massive MIMO system into two subsystems, which can be simultaneously detected by the classical detection procedures. en, using the PGD and the classical ZF as well as the QR-decomposition- (QRD-) based detectors, we proposed two new detectors, called ZF-based PGD (ZF-PGD) and QRD-based PGD (QRD-PGD). e PGD is further combined with the sorted longest basis (SLB) algorithm to make the signal recovery more accurate, thereby resulting in two new detectors, namely, the ZF-PGD-SLB and the QRD-PGD-SLB. Various complexity evaluations and simulations prove that the proposed detectors can significantly improve the BER performance compared to their classical linear and QRD counterparts with the practical complexity levels. Hence, our proposed detectors can be used as efficient means of estimating the transmitted signals in high-load massive MIMO systems. 1. Introduction In recent years, massive multiple-input-multiple-output (massive MIMO) systems have been proposed to improve the quality of signal transmission in wireless communica- tions. In a massive MIMO system, each cell site is equipped with very large number of antennas. erefore, massive MIMO systems can provide not only high energy efficiency but also very high spectral one [1, 2]. Currently, the system with 128 antennas deployed at the BS, which simultaneously serves 8 single-antenna users, has been built successfully in laboratory [3]. Consequently, massive MIMO is expected to be one of the most important technologies for next gen- eration cellular networks. In massive MIMO, all complex signal processing, in- cluding signal detection for the uplink, precoding for the downlink, and channel estimation for both, should be implemented at the BS due to large dimension of the system [1]. For uplink scenario, all active users transmit their signals to the BS using the same time-frequency resources. ese transmitted signal symbols are recovered at the BS by adopting suitable detectors. e detectors used in massive MIMO systems must satisfy the following requirements: (1) they should provide good BER performance or high spectral efficiency and (2) they should have low com- plexities. Low complexity linear detectors, such as zero- forcing (ZF) [4–7] or minimum mean square error (MMSE), can provide near-optimal bit error rate (BER) performance when they are used in massive MIMO systems [1]. In [8], the authors proved that if the system uses the Bell Laboratory Space Time (BLAST) detector, it will obtain a huge energy efficiency compared to that of the classical MMSE. It is worth noting that the BER performance of the system depends on the so-called load factor β, defined by the ratio of the total number of antennas equipped at the users’ Hindawi Wireless Communications and Mobile Computing Volume 2019, Article ID 5609740, 12 pages https://doi.org/10.1155/2019/5609740